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Liu Yameng, Zhao Youquan, Sun Zhentao, Chen Chen
2024,38(5):1-9,
Abstract:
Circular ripples are surface defects in the manufacturing process of contact lenses, resulting from uneven distribution of hydrogel materials, causing a concentric contraction along the edge of the lens. These defects are challenging to detect in projection inspections, leading to poor product quality. Detecting circular ripple defects poses a technical challenge in the production of contact lenses. In this study, a circular ring illumination imaging system was constructed based on the characteristics of this defect. An image model database of circular ripple defects was collected, and a lightweight detection algorithm for contact lens circular ripple defects based on an improved RT-DETR was introduced. Initially, the original ResNet18 backbone of RT-DETR was modified by replacing the BasicBlock with a lightweight FasterNetBlock. Subsequently, the SimAM three-channel attention mechanism was integrated into the Neck part of RT-DETR to enhance the model’s accuracy. Finally, the GIoU loss function was replaced with the MPDIoU loss function to accelerate convergence and improve detection accuracy. Experimental results demonstrate that the improved RT-DETR algorithm achieved a mAP@0.5 of 94% on the contact lens circular ripple database, a 3.1% improvement over the original RT-DETR algorithm. Params and FLOPs were reduced by 15.6% and 13%, respectively, compared to the original algorithm. This algorithm effectively reduces computational complexity, enhancing the mean average precision of contact lens circular ripple defect detection. It holds promise for overcoming technical challenges in online detection of circular ripple defects in contact lenses.
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Tang Zhi, Bo Lin, Bai Hao, Wu Guo, Wang Zhangxu
2024,38(5):10-18,
Abstract:
The aerospace engine test bench is a key equipment for verifying engine reliability, and its health status assessment is of great significance for ensuring the safe operation of the engine. The gas circuit system of the engine test bench has the characteristics of complex and variable fault modes, strong correlation between multi-point and multimodal sensing information, etc. Moreover, there are issues such as uneven distribution of collected health status samples, high signal noise, human resource waste caused by manual monitoring of the operating status of the gas pipeline system, and high false alarm rates. To this end, a health assessment model for test benches based on adaptive reconstruction of phase space and support for high-order tensor machines is proposed. This method first involves designing stability criterion for E1(m) to achieve adaptive phase space reconstruction of the gas path system. Secondly, tensors are used to characterize the multi-point and multimodal data of the pneumatic system. Then, a high-order tensor machine is used to mine the multi-source sensor correlation information and fault modes in tensor samples, achieving a health status assessment of the test bench pneumatic system. Finally, the proposed method is compared with the support vector machine, decision tree and plain Bayesian algorithms based on the actual test data from the engine test bench of a China National Aviation Corporation (CNAC). The results show that the proposed method has a good evaluation capability in a weak data environment, with an overall evaluation accuracy of 89.7%, and the accuracy drop is kept within 8% in an extremely weak data environment.
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Li Peifeng, Liu Li, Wang Yu, Liu Xin, Bai Qing, Jin Baoquan
2024,38(5):19-28,
Abstract:
Phase-sensitive optical time domain reflectometer (Φ-OTDR) usually uses coherent detection to achieve long-distance, distributed and high-sensitivity vibration detection. To accurately obtain the position and phase information of the vibration signal, the quadrature demodulation algorithm is an important technology widely used at present, but the algorithm has the limitation of time-consuming. In order to solve this problem, a fast demodulation scheme of Rayleigh scattering signal based on field programmable gate array (FPGA) is proposed. The pipeline structure is used to realize the synchronization of sensor data acquisition and data demodulation. Two orthogonal signals are obtained by digital quadrature mixing technology. The finite impulse response low-pass filter is used to remove the high-frequency component, and the coordinate rotation digital algorithm (CORDIC) vector mode is used to realize the hardware demodulation of the vibration phase, which can improve the overall real-time performance of the coherent detection Φ-OTDR system. The experimental results show that the scheme can successfully realize the positioning and phase reduction of the vibration signal under the condition of a detection distance of 40 kilometer. When the detection distance unchanged and the pieces of data acquisition increased to 4 000, the FPGA demodulation scheme only takes 1.60 seconds, which is 145.61 seconds shorter than the traditional host computer CPU demodulation scheme, thus providing a reference for the real-time demodulation of Φ-OTDR vibration sensing data.
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Ma Chao, Zheng Xinhui, Wang Shaohong, Xu Xiaoli
2024,38(5):29-37,
Abstract:
The operating conditions of planetary transmission are mostly non-stationary operating conditions. During the operation, the gear meshing vibration signals are coupled with each other, which leads to the aliasing of test signals, and the difficulty of hidden fault diagnosis increases. At the same time, when applying complex neural network models for fault diagnosis and prediction, most of them will be limited by the hardware of industrial field edge computing equipment. Aiming at the related problems, an intelligent recognition model based on smooth and pseudo Wigner-Vile distribution (SPWVD) and knowledge distillation is proposed to reduce the parameters of the network model while ensuring the accuracy of planetary transmission fault diagnosis. First, the ensemble empirical mode decomposition (EEMD) method is used to decompose the multi-component vibration signal, then the single-component signal is selected for SPWVD calculation and linearly superimposed to obtain a two-dimensional time-frequency diagram as input. The ResNet101 is used as the teacher model to guide the student model MobileNet for training. The complex teacher model imparts the knowledge in the data to the student model, which improves the accuracy of the student model.The method is compared with similar methods. The results show that the storage cost of the model is reduced to 24.55% of the teacher model at the expense of 2.43% accuracy, which is 9.61% higher than that of MobileNet without knowledge distillation. This research method provides an effective and feasible solution to improve the practical application of deep learning model in engineering and reduce the deployment cost of edge computing equipment.
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Wang Nan, Wei Yujie, Zhang Nan, Wang Dandan, Wang Mingwu, Zhang Changming
2024,38(5):38-46,
Abstract:
Existing test water lubrication bearing characteristics of the important characterisation parameter water film pressure of the many methods, because the sensor distance from the real measurement point is far away, or trauma to the shaft system is large and other reasons, the accurate water film pressure empirical data is difficult to obtain, restricting the bearings of further research and development. In response to these challenges, a new monitoring method is proposed in which thin film sensors are embedded in bearing shingles, while pressure data are transmitted via wireless sensing. Firstly, the physical model of axial tile grooved bearing is established, and the location, structure and number of grooves are determined by finite element analysis of axial tile deformation near the grooves; A physical model of the bearing fluid domain and solid domain is established to simulate and analyse the water film pressure distribution; Then, a thin film sensor calibration method is proposed to calibrate it accurately; Finally, a multi-operating condition bearing water film pressure test experiment is carried out, it is compared and analysed with the simulation results and existing methods. The results of the study show that it is feasible to embed a thin film sensor in the groove of the bearing shaft tile and transmit the data wirelessly, and the measured data of water film pressure deviates less than 10% from the simulation results, which is more accurate than the measured data of existing methods. The water film pressure decreases along the axial direction, and there exists a part of lubrication film inside the bearing in a mixed lubrication state.
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Wei Xinyuan, Zhou Jinghuan, Qian Muyun, Li Dan, Huang San′ao
2024,38(5):47-55,
Abstract:
Ultrasonic detection is a common method of steel defect detection. The classification model established by machine learning algorithm can realize effective defect identification. Neural network is the most commonly used algorithm at present, but it has the problem of complex model structure and large amount of training data. In this paper, an ultrasonic defect recognition method based on random forest is proposed, which can realize intelligent and accurate identification of defect types to solve the problems of complex model structure and large training data requirements. Firstly, ultrasonic detection experiments were carried out for defects of different shapes, sizes and depths in the specimen. Based on the experimental data, an ultrasonic defect recognition model was established using random forest algorithm. Then, the defect recognition effect of the model is analyzed, and compared with support vector machine, K-nearest neighbor classification algorithm, AdaBoosting algorithm and convolutional neural network. Then the defect identification verification experiment is carried out with the verification specimen to further verify the validity of the established defect identification model. The results show that the proposed method has the highest accuracy compared with other algorithms, and the accuracy of defect classification reaches 94.6% in the verification experiment.
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Shan Zebiao, Guo Jinghao, Liu Xiaosong, Sun Yuqi, Bai Yu
2024,38(5):56-63,
Abstract:
Addressing the challenges of low measurement accuracy and restricted applicability range in existing passive sound source localization algorithms, this paper proposes a passive sound source localization estimation method based on an optimal quadruple-array. This method constructs an optimal quadruple-array structure to enable multi-element point sharing, aiming to achieve fusion localization estimation of the target sound source with a reduced total number of elements, thereby enhancing localization accuracy. Spatial target localization equations are derived from the array model, transforming the problem of solving position coordinates into that of determining time delay differences between array elements. Subsequently, a second-order fractional low-order covariance algorithm is employed to resolve the corresponding time delay differences between array elements in an impulse noise environment. After obtaining the self-fractional low-order covariance of array signals and the mutual-fractional low-order covariance between two array elements, the mutual-fractional low-order covariance of both is recalculated to further mitigate the impact of impulse noise and improve time delay estimation accuracy. Finally, the obtained time delay estimation information is incorporated back into the localization equation set to achieve localization estimation of spatial sound sources. The feasibility of the proposed method and the superiority of the array structure are validated through numerical simulations and field experiments. In the field experiments, the estimation error of sound source localization is only 0.085 1 meters, demonstrating the method’s capability to achieve high accuracy in sound source localization under impulse noise environments. This work extends the application scenarios of passive sound source localization algorithms and holds practical application value.
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Zhao Kaihui, Qiao Mengjie, Lyu Yuying, You Xin, Zhang Changfan, Zheng Jian
2024,38(5):64-74,
Abstract:
To address the decline in drive system efficacy due to uncertainties in the model, variations in parameters, and interruptions from external sources affecting permanent magnet synchronous motors (PMSMs), a novel control strategy is proposed. First, to reduce the reliance on the system’s mathematical model, a new super-twisting algorithm is formulated for the speed loop of the PMSM. Secondly, based on the new super-twisting model of the speed loop, a novel model-free super-twisting fast integral terminal sliding mode controller (MFSTFITSMC) is designed by integrating a new type of integral terminal sliding surface and an improved super-twisting control law, achieving precise control of the motor speed. Furthermore, a non-singular fast terminal sliding surface and a dual-power approaching law are used to devise an improved extended nonsingular terminal sliding mode disturbance observer (IENTSMDO). This observer accurately detects and provides feedforward compensation for unknown disturbances, effectively suppressing parameter perturbations and external disturbances, thus enhancing the system’s robustness and improving both dynamic and steady-state performances. Finally, through simulation and experimental comparison with traditional control methods, the proposed algorithm has been verified to improve speed overshoot resistance by 0.412 5% and enhance the torque response speed by 0.013 s. The results indicate that the proposed method possesses strong robustness and good interference rejection capabilities in the presence of unknown disturbances.
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Zhou Xianchun, Shi Zhenting, Wang Ziwei, Li Ting, Zhang Ying
2024,38(5):75-89,
Abstract:
Currently, most image denoising models based on convolutional neural networks cannot fully utilize the redundancy of image data, which limits the expressive power of the models. Moreover, edge information is often used as a priori knowledge for effective denoising, while texture information is usually ignored. To address these issues, a new image denoising network is proposed, which firstly uses the attentional similarity module to extract global similarity features of the image, and smooths and suppresses the noise in the attentional similarity module through average pooling to further improve the network performance; secondly, the dilated residual module is used to extract both local and global features of the image; finally, a global residual learning is utilized to enhance the denoising performance from shallow to deep layers. In addition, a texture extraction network is designed to extract local binary patterns from noisy images to obtain texture information, which can be utilized as a priori knowledge to preserve the details in the evolved images during the denoising process. The experimental results show that compared with some advanced denoising networks, the newly proposed denoising network has a great improvement in image vision, higher efficiency and peak signal-to-noise ratio by about 2 dB, and structural similarity by about 3%, which is more conducive to practical applications.
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2024,38(5):90-97,
Abstract:
In the existing algorithm for fault diagnosis in analog circuits, artificial intelligence-based fault diagnosis algorithms require a large amount of training data and long training time, making it difficult to achieve parameter identification. Traditional circuit analysis methods require multiple test points and involve complex calculations. Based on this, a fault diagnosis algorithm for analog circuits based on optimized matrix perturbation analysis is proposed. Firstly, the Laplace operator is used to convolve the output response matrix of the tested circuit, thereby enhancing the perturbation pattern between matrix elements and circuit component parameters. Secondly, the trace and spectral radius of the matrix are selected as fault characteristics, and a matrix model is established using this perturbation pattern. Then, an improved diagnostic algorithm is used to verify examples in Sallen_Key bandpass filter circuits and leapfrog low-pass filter circuits. The results show that with only one test point, the proposed method can achieve parameter identification of faulty components. The fault diagnosis rate reaches 100%, with parameter identification error controlled within 1%, and computation time controlled at millisecond level. Therefore, this method is easy to implement for online testing and suitable for situations requiring high accuracy in fault localization and precise parameter identification.
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He Lifang, Xu Jiaqi, Huang Xiaoxiao
2024,38(5):98-111,
Abstract:
In order to solve the problems of output saturation and signal amplification difference of the traditional two-dimensional tri-stable stochastic resonance system driven by dual input signals (DTDTSR), a novel system, coupled piecewise symmetric tri-stable stochastic resonance system (coupled piecewise symmetric tri-stable stochastic resonance system) driven by dual-input signals, is ingeniously proposed. A novel system is proposed: coupled piecewise symmetric tri-stable stochastic resonance system driven by dual-input signals (DCPSTSR). Firstly, the problem of output saturation of the system is studied in depth, which provides a key theoretical foundation for the optimization of the system performance. Secondly, the output spectral amplification (SA) function of the system is derived within the framework of the adiabatic approximation theory. The influence of system parameters on it is analyzed in detail, which provides theoretical support for deeper understanding. Further, a comprehensive comparison of the DCPSTSR, coupled piecewise symmetric tri-stable stochastic resonance system (CPSTSR) and DTDTSR systems is carried out through numerical simulations, and the results clearly indicate that the DCPSTSR system is significantly superior to the other systems in terms of output spectral amplification function. Finally, the system parameters are precisely optimized by genetic algorithm and successfully applied to bearing fault detection. The experimental results verify the excellent performance of the DCPSTSR system and provide strong theoretical support and feasibility verification for future theoretical research and engineering applications. This design and its successful application in bearing fault detection provide a new direction and example for further research and practical application in the field of resonance systems, which has important scientific and engineering value.
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2024,38(5):112-118,
Abstract:
The current pedestrian detection algorithm is a research hotspot in the field of driverless driving, but the pedestrian occlusion problem has not been well solved due to factors such as relatively small sample size, diverse occlusion situations, and reduced visual features. Aiming at the problem of missed detection caused by pedestrians blocking each other or pedestrians being blocked by other objects, a pedestrian detection method based on inter-frame directional gradient histogram feature correlation is proposed. First, a tracking method is added based on the YOLOv7 baseline network model to discover missed pedestrians and estimate their location information; the nearest local image containing missed pedestrians is used as the new information, using directional gradient histogram features and support vectors, a machine-based method is used to detect pedestrians at the estimated position of the missed target to improve the missed detection phenomenon caused by partial occlusion. Experimental results compared with the baseline network, the precision (P) value of this method increased by 6.25%, and the average precision (AP) of occluded pedestrians increased from 26.67% to 53.42%. Experiments show that the pedestrian detection method based on inter-frame directional gradient histogram feature correlation can improve pedestrian detection accuracy, has low computational complexity, does not significantly increase the computational overhead of the original method, and has certain application value.
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Wang Zhen, Ye Wenhua, Chen Yuhao, Liang Ruijun
2024,38(5):119-129,
Abstract:
In response to the complex imaging environment, irregular deformation of batteries, and uneven diffuse reflection of metal surfaces encountered during the automated disassembly process of retired cylindrical power lithium batteries, existing visual recognition methods are unable to accurately extract contour and pose information. We propose an accurate contour extraction method based on the Fr-chet distance similarity function and a pose detection method based on rectangles and edge morphology features. By establishing a Lambert diffuse reflection model for cylindrical lithium batteries and using morphological operation methods to obtain the rough localization contour of lithium batteries, as well as based on the similarity function defined by the Fr-chet distance, the contour is accurately extracted by classifying each pixel band in the rough localization image; Subsequently, utilizing the positive and negative terminal features of cylindrical lithium batteries, feature contours of the positive and negative terminal ROI regions are extracted employing an adaptive threshold segmentation algorithm. Finally, by comparing the rectangular values of the two end regions, the pose information of the lithium battery can be calculated. The experimental results show that in the Self-built retired cylindrical lithium battery image dataset that includes deformation, corrosion rust spots, and uneven lighting conditions, the proposed method has high accuracy in identifying lithium batteries of different models and poses. The diameter length detection error is less than 3%, and the pose detection accuracy is higher than 94%, which can meet the actual needs of automated disassembly and detection.
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Chen Yuanmei, Wang Fengsui, Wang Luyao
2024,38(5):130-138,
Abstract:
Unsupervised person re-identification aims to identify the same person from non-overlapping cameras under unsupervised settings. Aiming at the problem that the existing unsupervised person re-identification network cannot fully extract pedestrian features and the difference between cameras leads to pedestrian retrieval errors, we propose an unsupervised person re-identification of adversarial disentangling learning guided by refined features. A feature refinement information fusion module is designed and embedded into different layers of ResNet50 network to enhance the ability of the network to extract key information. A disentangled feature learning method is designed to minimize the mutual information between pedestrian features and camera features, and reduce the negative impact of camera differences on the network. At the same time, the adversarial disentangling loss function is designed for unsupervised joint learning. Using the Market-1501 and DukeMTMC-reID public datasets, we tested the proposed method. The mean average precision increased by 4.6% and 3.1% respectively. Compared with the baseline algorithm, it has strong robustness and meets the needs of pedestrian recognition in unsupervised background.
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Jiang Bing, Li Xiang, Chao Yifan, Yu Ziyu, Tao Kai
2024,38(5):139-147,
Abstract:
To address the problems posed by redundant features in transformer fault recognition and the low accuracy of traditional methods, a transformer fault recognition method leveraging kernel principal component analysis (KPCA) in conjunction with chaotic sparrow search algorithm (CGSSA) is introduced. Initially, KPCA is employed to preprocess the transformer fault data, aiming to mitigate the correlations among features. Subsequently, CGSSA is improved by incorporating the improved Tent map and Gaussian mutation to increase the search accuracy and convergence speed of the algorithm. Comparing the results involving CGSSA, SSA, GWO and WOA. Utilizing the data extracted through the KPCA as the model input, CGSSA is then used to select the kernel function parameters and regularization coefficient of KELM, thereby establishing the KPCA-CGSSA-KELM transformer fault recognition model. The experimental results demonstrate that, with the identical input data, CGSSA has the best results in terms of convergence speed and optimization accuracy. In addition, the proposed method shows the fault recognition accuracy of 95.7%, which is 18.6%, 10%, and 15.7% higher than WOA-KELM, GWO-KELM, and SSA-KELM, respectively. These findings suggest that the proposed method effectively manages the impact of redundant features and enhances the precision of transformer fault recognition, thus verifying the validity and feasibility of the proposed method for transformer fault recognition under the feature redundancy.
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Li Guoyan, Tian Mingda, Dong Chunhua, Hao Zhipeng
2024,38(5):148-157,
Abstract:
To address the limitation of standard attention mechanisms that can only generate coarse-grained attention regions, failing to capture the geographical relationships between remote sensing objects and underutilize the semantic content of remote sensing images, a structured image description network named GRSRC (geo-object relational segmentation for remote sensing image captioning) is proposed. Firstly, considering the highly structured nature of remote sensing image features, a feature extraction method based on structured semantic segmentation of remote sensing images is introduced, enhancing the encoder’s feature extraction capability for more accurate representation. Simultaneously, an attention mechanism is incorporated to weight the segmented regions, enabling the model to focus more on crucial semantic information. Secondly, taking advantage of the well-defined spatial relationships among objects in remote sensing images, geographical spatial relations are integrated into the attention mechanism, ensuring more accurate and spatially consistent descriptions. Finally, experimental evaluations are conducted on three publicly available remote sensing datasets, RSICD, UCM, and Sydney. On the UCM dataset, BLEU-1 achieved 84.06, METEOR reached 44.35, and ROUGE_L attained 77.01, demonstrating improvements of 2.32%, 1.15%, and 1.88%, respectively, compared to classical models. The experimental results indicate that the model can better leverage the semantic content of remote sensing images, demonstrating its superior performance in remote sensing image captioning tasks.
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2024,38(5):158-168,
Abstract:
A mathematical method is proposed to evaluate the delay performance of RF-Mesh-Networks that can realize data classification transmission and ensure real-time data requirements, for deficiency in evaluating and analyzing the delay performance of different types of transmission data when the large-scale wireless advanced measurement instruments are applied in smart distribution grid. Based on the analysis of the WMNs architecture of the smart distribution grid, the functional relationship between the initiation stages of the two consecutive time slots is established using Markov chain modulation techniques. In order to avoid the difficulty of solving high-order differential equations during the process of obtaining steady-state solutions, the solution method based on error iteration to obtain steady-state operating points is proposed, and the detailed solution process is also provided. On the basis of obtaining the steady-state working point, the analytical formula to evaluate the average delay performance of real-time data and non-real-time data for uplink and downlink transmission. To verify the effectiveness of the proposed delay performance evaluation method for WMNs applied in smart distribution grid, the delay performance of real-time and non-real-time data is simulated and tested，where they are set up three different transmission rates. The experimental simulation and test results show that the proposed method can achieve performance evaluation and analysis of transmission delay for different types of communication data in intelligent distribution networks, and can improve the transmission performance of WMNs.
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Chen Jian, Jiang Tao, Chen Pin
2024,38(5):169-177,
Abstract:
When the preferred measurement signal for equipment condition monitoring in industrial field is acoustic signal for various reasons, it is especially necessary to propose an equipment condition monitoring method based on acoustic signal. In this paper, a certain type of centrifugal pump is taken as the basis object, and the Mel-scale frequency cepstral coefficients（MFCC）are extracted from the acoustic signals collected in the field as the initial features of the signals, then the dispersion entropy（DE）values of these MFCC initial features are calculated, and the matrix is downscaled by principal component analysis（PCA）, so as to construct the feature matrix. The penalty coefficients and kernel function parameters of the support vector machine（SVM）are optimized by using the bat algorithm（BA）to carry out diagnosis of various fault conditions of centrifugal pumps and compared with various diagnostic methods. The experimental results show that the model optimized by BA improves the diagnostic accuracy by 21.7%; on the basis of this model, the deep mining of the signals extracted by MFCC using DE improves the diagnostic accuracy of the model by 2.05%.
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Xu Hao, Bao Jun, Huang Guoyong, Deng Weiquan, Zhao Chengjun
2024,38(5):178-187,
Abstract:
With the increase of aircraft service time and the extreme service environment, fatigue cracks and other defects may occur in the multi-layer metal riveting structure of aircraft. It is of great significance for damage assessment and maintenance to find defects in time and obtain information such as defect depth and direction. However, due to the concealment of defects caused by multi-layer structure, the detection signal characteristics of conventional eddy current probes are indistinct, and conventional eddy current probes are not sensitive to defects in certain directions, making it difficult to determine the direction of fatigue cracks. To address these problems, a cross-runway-type differential eddy current probe is designed, which is mainly composed of a cross-runway-type excitation coil and two sets of differential detection coils. The feasibility of the new eddy current probe is investigated by establishing a three-dimensional finite element model for defect detection of aircraft multi-layer metal riveting structures, including the optimization of the structure of the probe, and simulations are conducted to analyze the different directions of defects, the buried depths and the lift-off heights, respectively. The results indicate that the new probe can effectively detect deep defects with a buried depth of 6 mm and dimensions of 10 mm×1 mm×1 mm, and it can obtain the direction information of the defects. Compared to traditional probes, designed probes have advantages such as no missing defects in all directions, resistance to lift off effects, and high resolution. The research results can provide some reference for the design of eddy current probes for aircraft multi-layer metal riveting structure detection.
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Niu Jing, Shen Chuanyan, Zhang Lipeng, Li Qijun, Liu Shifeng
2024,38(5):188-200,
Abstract:
Large scale plant protection machinery in non-standard orchards in mountainous areas has poor accessibility, and small wheeled plant protection robots have broad application prospects. A path planning algorithm for wheeled plant protection robots based on improved ACO-DWA algorithm is proposed to solve the problems of visual information misjudgment caused by closed orchard branches and leaves, as well as delayed obstacle avoidance caused by complex working terrain. Firstly, the orchard environment information is obtained through LiDAR, and the voxel grid method is applied to simplify the point cloud density. The grid method is used to segment the ground point cloud, and the K-means algorithm is used to extract the robot’s inter row passage area. Combined with the kinematic model and job specification constraints of the plant protection robot, a series of candidate trajectory sets are generated using the model based prediction algorithm (SBMPO). Then, using the improved ACO-DWA algorithm, the robot’s travel cost is integrated into the objective function of the search node, and path planning is carried out online based on the environmental map. Finally, simulation validation and real-world deployment experiments were conducted using MATLAB R2021 simulation platform and robot ROS operating system, respectively. The experimental results show that this method can significantly improve the traffic capacity of robots in complex orchard scenes, and the path planning effect and operational efficiency are significantly improved.
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Gao Gang, Wei Lisheng, Zhu Shengbo
2024,38(5):201-209,
Abstract:
Aiming at the problem of irregular and random distribution of defects on the surface of texture images, such as scratches and cracks, which leads to low accuracy of defect detection, a self-supervised defect detection method based on the bi-radial fusion of positive and negative sample difference features is proposed. Firstly, Otsu threshold segmentation is used to extract image foreground information, and Perlin noise is superimposed on the data-enhanced positive samples or the texture images, from the DTD dataset, to simulate defects on the positive sample images and synthesize the negative samples. Then, the mean-square error is calculated for feature matching using the intermediate features output from the encoder, while the coordinate attention (CA) and path aggregation network (PANet) are combined to enhance the information fusion of the matched features. Finally, the fused features are input into the decoder together with the low-level and high-level features output from the encoder, and the weights of Focal, L1, and Dice loss functions are optimized and adjusted to realize the prediction of the defective masks more accurately. Experiments show that the average image level and pixel-level AUROC of the proposed model on the texture category of the MVTec AD dataset reaches 0.995 and 0.968, respectively, which improves the classification and segmentation accuracies compared with the other defect detection models, demonstrating the effectiveness of the proposed method in texture defect detection.
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Li Zhongbing, Liu Yajie, Liang Haibo, Ni Pengbo, Yan Bi
2024,38(5):210-218,
Abstract:
The effective monitoring of hydrocarbon gas content is an important aspect of safety assurance in oil and gas exploration and production processes. Infrared spectroscopy, as a safe and efficient detection method, has attracted the attention of on-site engineers. However, it mainly uses offline models for measurement, which cannot cope with the complex working conditions and various nonlinear influencing factors on site, making it difficult for this non updated model to maintain high prediction accuracy. A weighted kernel partial least squares method based on fusion of similarity measurement criteria in just-in-time learning for quantitative analysis of alkane gases is proposed in this paper. Firstly, a similarity criterion based on fusion of multiple similarity measurement criteria is designed to effectively select historical samples for online modeling. Secondly, nonlinear kernel functions are introduced into local PLS models to effectively extract nonlinear features and compensate for the nonlinear processing ability of linear partial least squares models. The experimental results on the multi-component mixed gas infrared spectral data have verified the effectiveness of this method, with a goodness of R2 of 0.994 1. Compared with that of the PLS model, the RMSE and MRE of the proposed model have improved by 43.6% and 85.8%, respectively. It can be effectively used for online updating and high-precision prediction of infrared spectral quantitative analysis models for hydrocarbon gas.
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Liu Xiaoqian, Cui Huanyong, Liu Haining, Fu Yu, Zeng Wensheng, Li Fajia
2024,38(5):219-228,
Abstract:
The process of proton exchange membrane fuel cells (PEMFC) involves strong coupling of multiple physical fields, components, and factors, inevitably leading to prolonged performance degradation and local performance fluctuations during operation. However, effectively identifying key features from the multitude of parameters under the multiple couplings and capturing the overall performance degradation trend becomes exceptionally challenging. In response to these issues, a PEMFC degradation prediction model based on XGBoost and Self-Atten-LSTM is developed. First, a wavelet threshold denoising method is employed to remove noise interference from the original PEMFC data. Then, the XGBoost algorithm is used to select the main features significantly affecting PEMFC performance from the numerous parameters, achieving precise feature selection. Finally, the introduction of the self-attention mechanism in LSTM addresses its limitations in global modeling and complex interaction among multi-dimensional vectors when dealing with long sequences. Through adaptive weighting, it more effectively utilizes PEMFC degradation information. Compared to traditional LSTM, Bi-LSTM, and GRU models, the developed model can more accurately predict fuel cell degradation under both steady-state and dynamic conditions. The model exhibits a reduction in the average mean absolute error by 56.34% to 77.04%, with a predictive accuracy of up to 99.09%. This approach can find broad applications in developing vehicle operation and maintenance strategies and enhancing system reliability.
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2024,38(5):229-237,
Abstract:
The non-contact voltage measurement method is not in direct contact with the metal conductor of the line and can adapt to the voltage monitoring in a variety of application scenarios. This paper designs a system which uses the improved non-contact voltage measurement technology to measure the line voltage and applies the measured voltage waveform to the line fault voltage diagnosis. Based on the topology analysis of the traditional non-contact voltage measurement technology and the improvement of the measurement circuit topology, the voltage on the line can be measured accurately without being affected by the coupling capacitance. Because of the limitation of the current single fault feature extraction method, in order to accurately identify and diagnose the line fault voltage by using the voltage waveform measured by the non-contact voltage measurement technology, in this paper, a fault voltage state identification system based on integrated learning is proposed, and a variety of feature extraction methods are used to extract the voltage waveform features obtained from non-contact voltage measurement. The identification results are used for early warning and processing of line faults. In this paper, aiming at the voltage monitoring system, the measurement accuracy and fault identification test are designed, and the steady-state average error is 0.9%, and the highest fault identification accuracy is 93%, which shows that the voltage monitoring system has high accuracy and fault identification accuracy.
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2024,38(5):238-248,
Abstract:
The improved Harris Hawks optimization algorithm (IHHO) is proposed to solve the problem that analog circuit fault diagnosis is difficult due to multiple fault types, unstable fault states and redundant fault data. IHHO optimized back propagation (BP) neural network to realize fault feature selection and diagnosis of analog circuits. Firstly, the nonlinear adaptive factor, Cauchy variation and stochastic difference perturbation are introduced into the Harris Hawks optimization algorithm to improve the convergence speed and accuracy. Secondly, IHHO is used to select the characteristics of the single fault and the combined fault simulation data of the analog circuit to complete the data preprocessing. Finally, IHHO-BP algorithm is used to train and test the preprocessed fault data to realize the fault diagnosis of analog circuits. The diagnostic results show that the proposed method improves the diagnostic accuracy by 5.5% compared with other algorithms.
Volume 38,2024 Issue 5
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System-level multi-objective optimization design of switched reluctance motor considering MPTC
Abstract:
Aiming at the problems of large torque ripple in Switched Reluctance Motor and traditional optimization design that only starts from the motor without considering the drive control strategy, a system-level multi-objective optimization design strategy for SRM considering Model Predictive Torque Control is proposed by simultaneously considering the motor structure parameters and control parameters. Firstly, the structural parameters of SRM were designed according to the design requirements and MPTC was adopted as the control method to determine the initial values and variation ranges of the motor structure and control parameters; Secondly, an SRM design model considering MPTC was established, and the relationship between structural parameters and prediction models was determined through magnetic circuit analysis. The optimization process of the motor was determined with torque ripple, average torque current ratio, and copper loss as optimization objectives. Sensitivity analysis of structural and control parameters was conducted through orthogonal experiments, and decision variables were selected based on the analysis results. Taguchi algorithm was used for multi-objective optimization of decision variables; Finally, in order to verify the effectiveness of the method, simulation verification was conducted, and a prototype was trial produced based on the optimization results. The experimental results showed that compared with the conventional design, the optimization results reduced the peak motor phase current by 33%, increased the average torque ampere ratio by 33.3%, and reduced torque ripple by 26.3%. The rationality and effectiveness of the optimization method were verified through experiments.
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Bearing fault diagnosis based on time-frequency filter and offset attention neural network
Abstract:
To address the inconsistent bearing fault data distribution that leads to the difficulty of feature offset and distinctive feature extraction, a bearing fault diagnosis method based on time-frequency filter and offset attention neural network is proposed, which processes the fault signal from offline and online parts. In the offline part, a time-frequency filter is proposed to extract the distinctive features from time domain and frequency domain; A spatial sampling method considering both global and local features is proposed. In the online part, an offset attention neural network is proposed. Compared with self attention, offset attention is more conducive to the extraction of offset features, so as to reduce the impact caused by inconsistent data distribution. Experiments on the bearing datasets of Xi'an Jiaotong University (XJTU) and Case Western Reserve University (CWRU) have achieved 100% accuracy, which proves that the proposed method can efficiently extract the distinctive features of fault signals, and effectively suppress the influence of feature offset. The comparative experiment on the bearing dataset of CWRU proves the superiority of the proposed method. In addition, experiments are also carried out on the dataset of gas turbine main bearing collected in the industrial field, and the results show that the proposed method has practical significance.
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The classification of pneumonia based on Raman spectroscopy of respiratory mucus
白 雪, 李晨曦, 翟 嘉, 于粤雯, 邹映雪, 刘 蓉, 陈文亮
Abstract:
Pneumonia and respiratory tract infection could be induced by many types of pathogens, which is also related to a variety of diseases. The accurate classification of pneumonia is a key issue in the diagnosis and treatment. In this study, we studied the classification and diagnosis of pneumonia based on Raman spectroscopy of respiratory mucus. The Raman spectra of respiratory mucus samples from normal person and patients with common pneumonia and pneumonia complicated with plastic bronchitis were examined respectively. Then, the positions and intensities of the characteristic peak in Raman spectrum were analyzed to access the composition and molecular bonding changes corresponding to the glycosylation and fibrosis process of mucin e. The classification model of pneumonia was studied by combining principal component analysis and chemometrics methods. The experimental results show that the accuracy of the method proposed in this paper can reach 99.08% for the classification of pneumonia. According to the confusion matrix, the accuracy of the classification for common pneumonia and pneumonia complicated with plastic bronchitis is higher, reaching 100% and 97.4%, respectively. The classification method of pneumonia based on molecular spectroscopy also provides a reference for the application of Raman spectroscopy technology in the diagnosis of infectious diseases.
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Fault Diagnosis of S700K switch machinebased on DRSN-BiLSTM hybrid model
Abstract:
In the railway system, the turnout machine is a key device to ensure the safe and smooth operation of trains. The fault diagnosis of the S700K turnout machine is crucial for preventing accidents and maintaining railway operations. To solve the shortcomings of traditional diagnostic methods in speed and accuracy, a diagnostic model that integrates deep residual shrinkage networks (DRSN) and bi-directional long short-term memory (BiLSTM) is proposed. First, the power curve of the turnout machine is preprocessed; then, DRSN is used to automatically learn features from the preprocessed data and compress the data length, improving the speed of diagnosis. Its attention mechanism and soft thresholding reduce the impact of noise features, and the DRSN network structure helps overcome network degradation and overfitting problems; subsequently, the bidirectional structure of BiLSTM is used to capture complex relationships in time series data; finally, the Softmax classifier is used for fault classification. Simulation results show that the accuracy, precision, and recall of the DRSN-BiLSTM model all exceed 98.3%. While ensuring the efficiency of the training process, the accuracy of fault diagnosis of the DRSN-BiLSTM model is at least 1.47% higher than that of DRSN, DNN and other models, especially showing excellent robustness in a noisy environment.
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EEMD-SpEn-WL denoising method for microwave signal of solid fertilizer flow*
Abstract:
When using Doppler microwave sensors to measure the flow of granular fertilizer, the vibration generated by the operation of the fertilizer applicator and various external disturbances can cause the collected signals to be distorted. Based on the analysis of the denoising effects of wavelet analysis and Kalman filtering, this paper proposes a denoising algorithm based on the integration of empirical mode decomposition (EEMD) and sample entropy (SpEn) combined with wavelet analysis(EEMD-SpEn-WL). Using Stanley 15-15-15 granular fertilizer as the experimental subject, the detection system including the Doppler microwave sensor is deployed on the fertilizer applicator to collect signals related to the mass flow rate of granular fertilizer. The experimental results indicate that, compared to the original signal, the average signal-to-noise ratio (SNR) of the Kalman filtering algorithm improved by 3.548 dB after optimizing the gain coefficient. After optimizing the wavelet denoising parameters, the average SNR of the wavelet analysis algorithm increased by 7.184 dB. When combining the optimized wavelet analysis with the denoising algorithm of integrated empirical mode decomposition and sample entropy, the average SNR of the denoised signal increased by 7.899 dB, while the average root mean square error (RMSE) decreased by 0.184, this algorithm demonstrates significant advantages in denoising the mass flow rate signals of granular fertilizers.
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Research on the key technology of new miniaturised magnetic sensing ball velocity test
Abstract:
Aiming at the problems of small effective area, fixed position and cumbersome arrangement of the traditional velocimetry device in the field of area-intercept velocimetry, based on the principle of electromagnetic induction, a new type of electromagnetic induction sensing unit is proposed and verified for the accurate measurement of the initial velocity of the projectile. Compared with the traditional magnetic induction coil, this structure adopts an induction coil wrapped with a permanent magnet, so that the projectile does not need to be magnetised to generate an induced electromotive force, which improves the sensitivity and measurement accuracy of the velocimetry target. In addition, the sensing unit is independently arranged coaxially with the trajectory, which effectively solves the problem of the relative position between the direction of the ballistic trajectory and the stable position of the test device, increases the effective area of the magnetic induction, and strengthens the portability of the measurement device, which can make it Flexible use in a variety of projectile velocity measurement occasions. The solution uses COMSOL software to model the sensing unit, and conducts detailed simulation analysis of the permanent magnet model and the dynamic process of the projectile passing through the magnetic field under different conditions. Based on the simulation data to create a coil sensing unit, and the simulation results of a number of experimental verification, test results show that the sensing unit sensing voltage increases with the speed of the projectile, and the two are linear within a certain range, consistent with the results obtained from the simulation. This study not only provides theoretical basis and data support for the optimisation of electromagnetic induction velocity target, but also outlines an effective solution for the measurement of in-bore and out-of-bore ballistic muzzle velocity of electromagnetic artillery and other high-speed launch systems.
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Review of research on micro-nano structure and preparation technology of scintillator
Abstract:
Scintillators are widely used in many fields such as nuclear medicine imaging, industrial non-destructive testing, and high-energy physical radioactivity measurement, which greatly promotes scientific and technological progress and innovation in the fields of basic science, medical science, and industrial technology. With the continuous improvement of application requirements, the performance requirements of scintillators are getting higher and higher, especially higher light output yield. Combining scintillators with micro-nano photonics technology research, by preparing micro-nano structures on the surface of scintillators and using their regulation of electromagnetic waves to change the critical angle of photon emission, the technical problem of low light output yield of scintillators due to total internal reflection effect can be effectively solved. In order to achieve the same radiation effect at a smaller dose. This paper describes the research progress of scintillator micro-nano structure preparation technology in recent years, comprehensively reviews the mechanism of micro-nano structure regulating scintillator light output enhancement, summarizes the existing micro-nano structure preparation technology methods, analyzes the influence of different types of micro-nano structures on scintillator light output yield, and summarizes the preparation technology according to the size of scintillator micro-nano structure, and discusses the research and application prospects of various types of scintillator micro-nano structure preparation technology.
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Batch process quality prediction based on CNN-STA-DLSTM model
Abstract:
For the difficulty in extracting deep features of batch process variables, as well as low quality prediction accuracy caused by the temporal, nonlinear, and dynamic characteristics of variables, this article proposes a quality prediction model for batch processes based on Convolutional Neural Networks Spatial and Temporal Attention with Double Long Short Term Memory Networks (CNN-STA-DLSTM). Firstly, the three-dimensional data of the batch process are expanded into a two-dimensional matrix along the direction of the variables, and the two-dimensional data are normalized by the Max-Min method. Then, the partial least squares (PLS) method is used to reduce the dimension of the original data, and the variables with strong correlation with the quality variables are retained. The convolutional neural network (CNN) is used to mine the potential features of the process data and improve the attention of the quality-related feature information. Secondly, the temporal attention mechanism and the spatial attention mechanism are introduced to construct the encoder-decoder structure network of the double-layer LSTM, and the attention mechanism is used to adaptively learn the relevant historical information of the time step, so as to improve the long-term memory ability of the model and strengthen the spatio-temporal correlation between the process variables and the quality variables. Then, the random-grid search method is used to optimize the hyperparameters of the prediction model, and the prediction model is constructed. Finally, the penicillin fermentation simulation platform and the hot strip rolling production process data are used for experimental verification. The results show that the proposed model has more accurate prediction effect.
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Unsupervised monocular depth estimation based on stable photometric loss
Abstract:
The photometric loss has been playing an important role in the training of video-based unsupervised monocular depth estimation models. However, there are large errors in special areas such as texture-less regions and edge regions, and a more robust photometric loss function is proposed to solve this problem. The photometric loss on the image gradient is calculated to eliminate the unreasonable supervision caused by local brightness changes. At the same time, the difference between successive pixels is used to define the blurry pixels, and then the false supervision caused by the blurred pixels on the target frame and the reconstructed target frame is eliminated based on the binary mask. In the test results of the KITTI dataset, multiple indicators such as the average relative error, the square relative error and the root mean square error have improved, the average relative error and the squared relative error are reduced to 0.075 and 0.548 respectively. The experimental results show that the proposed method further improves the performance of the existing models.
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Research on two-input improved VIT recognition for ECG rainbow codes
Abstract:
Leveraging extensive ECG data, intelligent ECG recognition represents a pivotal research focus aimed at supporting physicians in conducting thorough data analysis and diagnosis, thereby enhancing efficiency and mitigating medical resource consumption. In order to solve the problem of feature loss and limited performance of single image and single deep learning algorithm in ECG intelligent recognition, a two-input improved VIT recognition method for ECG rainbow code is proposed. Firstly, a mathematical model is proposed to predict the standard period of ECG, and the potential features of ECG are mined by pumping method to generate ECG rainbow code. Then, a dual input feature extraction module is constructed with convolutional neural network to extract local features of multiple ECG images for fusion to achieve multi-dimensional ECG feature representation and fusion. A VIT coding module is used to pay global attention to fusion features to realize ECG recognition based on multi-feature images as input. The ECG recognition method in MIT-BIH database is used for experiments, and the average accuracy of the proposed ECG recognition method is 99.41%, and the accuracy of the N-type ECG collected in the field is 100%. The experimental results show that the proposed image transformation method can effectively visualize ECG features, and the effect is better than the traditional method. The proposed ECG recognition method can realize ECG recognition effectively and has better performance than other similar methods.
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Deep flux weakening of IPMSM based on feedback super-twisting non-singular fast terminal sliding mode control
Abstract:
For flux weakening control of the internal permanent magnet synchronous motors, when the degree of flux weakening is deeper, the motor parameter perturbation and external disturbances will cause the voltage loop output, torque and current pulsation to increase, and the speed convergence is too slow. A speed-voltage loop feedback super-twisting non-singular fast terminal sliding mode controller (FST-NFTSMC) is proposed for deep flux weakening control. To reduce the dependence of flux weakening control on the system model, the voltage-loop hyperlocal model is constructed according to the mathematical model of the built-in permanent magnet synchronous motor during parameter perturbation. And it is combined with the speed loop hyperlocal model to establish the speed-voltage loop hyperlocal model. Based on this hyperlocal model, the speed-voltage loop FST-NFTSMC is designed by combining the feedback super-twisting algorithm and the non-singular fast terminal switching function. At the same time, an improved sliding mode disturbance observer is built to estimate the unknown part of the system and compensate for the estimated value feedforward to FST-NFTSMC, which further improves the robustness and control accuracy of the system. Simulation and experiment show that compared with the traditional PI control, the convergence speed of the proposed method in the no flux weakening region, shallow flux weakening region, and deep flux weakening region is improved by 66%, 40.6%, and 28.6% respectively. It has better stability and fewer pulsations of the torque and current, proving that the method in the flux weakening control is effective in suppressing the output jitter after the voltage loop is perturbed as well as improving the speed response.
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MEMS gyroscope random error compensation based on CPSO-optimized BP network
Abstract:
Aiming at the problem of low measurement accuracy due to the existence of random error in micro-electromechanical system (MEMS) gyroscope, a compensation method based on chaotic particle swarm algorithm (CPSO) optimized back propagation (BP) neural network is proposed to deal with the random error. Firstly, The MEMS gyroscope data are collected, the reconstruction parameters are determined and the phase space is reconstructed using the C-C method, and the chaotic properties are analyzed and verified based on the Lyapunov exponent. Then, the reconstructed data are used as the training samples for the BP neural network model. The BP neural network model is trained, and the weights and thresholds of BP neural network are optimized by using the CPSO algorithm, then the optimized model for error compensation is obtained. Finally, ADXRS624 is used to validate the compensation effect of the optimized model in static experiment, and the compensation results are compared with BP model and particle swarm optimization (PSO) model.Experimental analysis results show that the mean and standard deviation of the gyroscope output errors are -5.76*10-4°/s and 5.19*10-4°/s, which are decreased by 68.6% and 98.4% compared with the BP model, and 52.1% and 93.5% compared with the particle swarm optimization model, respectively. By comparing the error coefficients after compensation for each method using Allan variance identification, the quantization noise, angle random walk and zero bias instability after being compensated by CPSO-BP method are reduced to 0.00059μrad, 0.00151(°)·h-1/2 and 2.82(°)·h-1, respectively. The new method has obvious effect in suppressing the random error and can improve the measurement accuracy of MEMS gyroscope.
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Research progress in visible light communication uplink
Abstract:
A complete communication system must encompass a full two-way communication link, which includes both uplink and downlink. The uplink has always been a challenge for two-way communication systems. In recent years, the rapid development of visible light communication technology, with its advantages of no electromagnetic radiation, large communication capacity, and environmental friendliness, can serve as a supplement to traditional uplink solutions. The article first introduces the application scenarios and system composition of visible light communication, and then provides a review of the current research status of visible light uplink at home and abroad in recent years. In addition, it presents various schemes for visible light uplink, such as visible light with radio frequency, visible light with visible light, visible light with power line carrier, and single-source reverse modulation technology. Finally, it summarizes the current issues faced by the visible light communication uplink and summarizes the advantages and disadvantages of various schemes, as well as prospects for future development trends.
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Underwater mobile node location algorithm based on CNN-LSTM sound velocity prediction
Abstract:
In view of the influence of long delay on information propagation between mobile sensor nodes caused by the complexity and dynamics of underwater environment in underwater wireless sensor networks, and the large node positioning error caused by this problem, this paper proposes an underwater mobile node positioning algorithm based on CNN-LSTM sound velocity prediction. First, the sound velocity data set is divided by K-fold cross-validation method, and then the CNN-LSTM hybrid model is constructed and trained by using the feature extraction capability of CNN and the sequence modeling capability of LSTM. This model can capture both spatial and temporal information of sound velocity data set, thus improving the prediction accuracy of sound velocity data set. Secondly, in the process of mobile node positioning, the sound velocity value predicted by CNN-LSTM model is used for TDOA ranging, and the TDOA ranging value is corrected. Finally, the modified ranging values are used to adaptively select different ranging positioning methods for unknown nodes under different node densities according to the number of reference nodes, so as to achieve accurate positioning of underwater mobile nodes. Experimental results show that under the same beacon node, the mean positioning errors of MCLS proposed in this paper are reduced by 46.96%, 39.93%, 27.64% and 15.24%, respectively, compared with SLMP, DMP, NDSMP and BLSM.
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Online compensation of acceleration on error while drilling based on MICOA
Abstract:
（研究的问题）To improve the measurement accuracy of the downhole accelerometer, a method for online compensation of accelerometer errors based on a magnetic-inertial coati optimization algorithm is designed. （研究的过程和方法）Firstly, an error compensation model is established based on the sources of error; the constraint conditions of the gravity angle and the magnetic-gravity angle are established using a gyroscope and a magnetometer; the difference between the true value of the acceleration and the modulus of the theoretical value is set as the objective function. Secondly, based on the coati optimization algorithm, the initial search boundary for error parameters is determined according to the recursive gravity acceleration, and the boundary is narrowed based on the relative distance among the current error parameters, the optimal error parameters, and the boundary values; a boundary point selection is designed to screen the initial error parameters, enabling the algorithm to initially search in the direction of high-quality solutions while retaining some inferior solutions to increase the diversity of error parameters; in the global exploration stage of the algorithm, parameters are designed to adjust the search range of accelerometer error parameters based on the error between the current error parameters and the average error parameters. Finally, the ratio of the modulus of gravity is set as the threshold for deep development, and a Gaussian mutation vector is constructed to enable the accelerometer error parameters to break out of local optima. （研究结果）Experimental results show that after MICOA compensation, the accelerometer error decreases, and the range of inclination angle decreases by approximately 62.5%; at different drilling angles, the root mean square error and standard deviation of the inclination angle can be maintained below 1°.
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Evaluation Method of Measurement Uncertainty of TransducerBased on Convolution
Abstract:
As the first part of the whole testing system, the measurement uncertainty of transducer influences greatly on the uncertainty of measurement results. For this reason, the main sources of transducer uncertainty have been analyzed, and the evaluation methods have been discussed about their properties; proposes a new method to evaluate the measurement uncertainty of a transducer has been proposed based on convolution of probability density function of sources of measurement uncertainty; the method has been realized via MATLAB .Finally, the method has been successfully applied to evaluate the measurement uncertainty of a load cell, which reveals the effectiveness of the method.
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On-line fault detection method of hydraulic turbine combining PCA and adaptive K-Means clustering
Abstract:
During the operation of the bulb tubular hydropower unit, due to hydraulic factors, machinery, working conditions and other factors, it is easy to cause the runner blades and runner chamber to malfunction, which seriously affects the safe operation of the hydropower unit. Based on the analysis of the fault signal characteristics of the runner blades and runner chamber of the bulb tubular hydropower unit, an online fault detection method for hydropower units based on K-Means and Wright"s criterion is proposed. This method uses principal component analysis (PCA) to reduce the dimensionality of the vibration and noise signal characteristics of the hydropower unit, and integrates the Wright criterion to improve the traditional K-means algorithm to realize the adaptive selection of the K value, and perform online clustering of the features, which can quickly and accurately identify .The variable load state of the turbine and the failure of the metal sweeping chamber. The method proposed in this paper is applied to the fault detection of the bulb tubular unit of Wuling Electric Power’s Jinweizhou Hydropower Station. The experimental results show that the accuracy of the online fault detection using this method is 100% and the accuracy of the variable load online detection is 96.7. %, there has been no fault false positives and false negatives in the past 10 months of operation, indicating the effectiveness of the method.
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Research on positioning of mobile robot based on Laser Information
Abstract:
Aiming at the problems of slower particle convergence and poor positioning accuracy when using traditional Monte Carlo positioning algorithms in the navigation and positioning process of mobile robots, as well as low relocation efficiency after artificial kidnapping, this article gives an improved Particle filter positioning method to improve the navigation and positioning efficiency of mobile robots. First of all, it is improved on the basis of the Monte Carlo positioning algorithm and integrated into the method of adaptive region division to ensure that the region contains more effective information, reduce the convergence time of particles, and complete the preliminary coarse positioning of the robot. Then, in the particle sampling and resampling stage, the normal distribution probability model is used to update the particle weights to achieve faster and more efficient global positioning. Through experimental comparison and analysis, compared with the Monte Carlo positioning algorithm, the given method has shortened the time consumption by 4s, and the adaptive Monte Carlo positioning method in this paper can keep the positioning error at about 6cm, thus verifying the given method Effectiveness and stability.
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Gaussian process enhanced robust cubature Kalman filter and application in integrated navigation
Abstract:
The observable degree of navigation state has a significant effect on the state estimation of GNSS/INS. In order to improve the accuracy of heading of land vehicle, an improved robust cubature Kalman filter (RCKF) method is proposed. First, the resampling-free sigma-point update framework is employed to separate the cubature point update from the Gaussian information limitation, and thus improving the propagation efficiency of the information contained in instantiated points in the iteratively filtering period. Secondly, in order to improve the heading of land vehicle when it travels along a straight-line, the Gaussian process (GP) is introduced into the uncertainty calibration of moment approximation of system model based on state observability analysis. Simulation results indicate that GP-RCKF improves the heading angle obviously when the state observability is weak, and compared with RCKF the heading is improved by 28.9%.
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Research on Traffic Sign Recognition Technology Based on YOLOv5 Algorithm
Abstract:
Aiming at the low detection accuracy of traditional traffic sign recognition algorithms,a traffic sign recognition method with improved YOLOv5 algorithm is proposed.First,improve the loss function of the YOLOv5 algorithm,use the EIOU loss function instead of the GIOU loss function used by the YOLOv5 algorithm to optimize the training model,improve the accuracy of the algorithm, and achieve faster identification of the target,then use the weighted Cluster NMS to improve the YOLOv5 itself.The weighted NMS algorithm improves the accuracy of generating the detection frame.The experimental results show that the mAP value of the model trained on the CCTSDB traffic sign dataset produced by Changsha University of Science and Technology by the improved YOLOv5 algorithm reaches 84.35%,which is 6.23% higher than the original YOLOv5 algorithm.Therefore,the improved YOLOv5 algorithm has higher accuracy in traffic sign recognition and can be better applied to practice.
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2017,31(1):45-50, DOI: 10.13382/j.jemi.2017.01.007
Abstract:
The concentration of nitrogen oxides (NO2, NO, N2O, etc.) in power plant is an important index of environmental protection. Aiming at the problem that the detection accuracy of nitrogen oxides concentration based on spectral analysis could be interfered by all kinds of factors, such as temperature, moisture content, tar, naphthalene, noise of electric devices, optical lens aging, interference at spectral absorption characteristics of polluting gases etc, it is difficult to improve in a single way. At first, the hardware modification is favorable for gas purification and filter. And then, the self learning and self training ability of RBF neural network can save the traditional model for the study of interference factors, and make the data processing more efficient. On the basis of a large thermal power plant’s real data in 2015, the computer simulation and analysis show that this method can improve the accuracy effectively. The overall average deviation is 0.841%.
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Wang Wen, Zhang Min, Zhu Yewen, Tang Chaofeng
2017,31(1):1-8, DOI: 10.13382/j.jemi.2017.01.001
Abstract:
Spherical joint is a commonly multi degree of freedom mechanical hinge which has many advantages such as compact structure, good flexibility, and high carrying capacity. Realization of its multi dimensional angular displacement measurement is of great significance in the prediction, feedback, and control of the system motion error. Firstly, the application of spherical joint and its structural characteristics were presented in the paper. Then, the motion description of the spherical joint and needed angles for measurement were analyzed. A review of multi dimensional angular displacement measurement method, including structural decoupling detection method, optical based detection method and magnetic field based detection method, at home and abroad was provided, Finally, the development of research on multi dimensional angular displacement measurement method for spherical joint was summarized. The focus and the difficulty of the research were pointed out, and the challenges and the breakthroughs in the key technologies were also stated.
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Liu Kun, Zhao Shuaishuai, Qu Erqing, Zhou Ying
2017,31(1):9-14, DOI: 10.13382/j.jemi.2017.01.002
Abstract:
The complex and various defects of the steel surface bring great difficulty to the feature extraction and selection. Therefore, this paper proposes a new R AdaBoost future selection method with a fusion of feature selection and sample weights updated. The proposed algorithm selects features and reduces the dimension of features via Relief feature selection according to updated samples in each cyle of AdaBoost algorithm, and uses reduced features to remove noise samples by intra class difference among samples, and then update sample library according to dynamic weight of AdaBoost. The weak classifiers are trained by the resulting optimal features, and combined to generate the final AdaBoost strong classifier, and detect and locate strip surface defects by AdaBoost two classifiers. Aiming at a variety of defects such as scratch, wrinkle, mountain, stain, etc. in the actual strip production line, the experimental results show that the proposed R AdaBoost algorithm can effectively extract features with high distinction and independence and reduce the feature dimension, and simultaneously improve the accuracy of defect detection.
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Sun Wei, Wen Jian, Zhang Yuan, Geng Shihan
2017,31(1):15-20, DOI: 10.13382/j.jemi.2017.01.003
Abstract:
Aiming at the random error of MEMS gyroscope is the main factor that restricts its precision and application range, the Kalman filter estimation method based on regression moving average (ARMA) model is proposed in this paper. Firstly, based on the results of Allan variance analysis, the quantization noise, angle random walk and zero bias instability are the main parts of the MEMS gyroscope random noise. Then, the stability of MEMS gyroscope random noise is tested by using time series analysis. Finally, based on the random drift of the auto regressive moving average (ARMA) model, a discrete Kalman filter equation is built to actualize its error estimation and compensation. The results of static vehicle and dynamic environment of digital noise reduction and Kalman filtering compensation experiments show that the Kalman filter estimation method based on the ARMA model has more obvious advantages in MEMS Gyroscope random error compensation.
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Luo Ting, Wang Xiaodong, Ma Jun, Yang Chuangyan
2021,35(12):116-125, DOI:
Abstract:
In view of the nonlinear dynamic characteristics of rolling bearing vibration signal and the low accuracy of reliability evaluation, a rolling bearing health condition assessment method based on improved cross fuzzy entropy (ICFE) and Weibull proportional hazards model (WPHM) was proposed. Firstly, the original vibration signal is decomposed by improved DLMD (Crt- DLMD), and the effective component with the most fault information is selected for reconstruction. Then, the ICFE of the reconstructed signal is calculated by using the sliding mean instead of the original coarse-grained process. Finally, the ICFE is used as the covariate of WPHM for health status assessment. The life cycle data and experiments of rolling bearing from national aeronautics and space administration (NASA) and Xi′an Jiaotong University Changxing Shengyang technology (XJTU-SY) show that the proposed method can accurately and effectively evaluate the health status of rolling bearings.
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He Lifang, Cao Li, Zhang Tianqi
2017,31(1):21-28, DOI: 10.13382/j.jemi.2017.01.004
Abstract:
Empirical mode decomposition（EMD）method attenuates the signals’ energy and generates false signals in decomposing signal noise, which leads to incorrect detection results. In order to solve this problem, a stochastic resonance method under Levy noise after denoised by EMD decomposition is presented in this paper. After decomposed by EMD, the noisy signals are handled by overlaying, averaging and resampling to meet the condition of stochastic resonance. An adaptive algorithm is used to optimize system parameters, and then the processed signal can generate stochastic resonance in bistable system to achieve precise detection. The theoretical analysis and experimental results prove that the method can detect single frequency signal and multi frequency signal under the same characteristic exponent with the Levy noise. The experimental results demonstrate that the SNR of single frequency signal can increase 14 dB in the case of SNR of -28 dB. The spectral amplitude of the 5 Hz spectrum is increased from 311.8 to 724 and 10 Hz spectrum amplitude is increased from 138.9 to 143.2. This method that reduces the residual noise energy and false signal can improve the signal energy in a complex noisy condition. Compared to EMD decomposition which cannot determine the signal components, this method can achieve the detection effect better.
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Yan Fan, Zhang Ying, Gao Ying, Tu Yongtao, Zhang Dongbo
2017,31(1):36-44, DOI: 10.13382/j.jemi.2017.01.006
Abstract:
To solve the time consuming problem of image stitching algorithm based on KAZE, a simple and effective image stitching algorithm based on AKAZE is proposed. Firstly, AKAZE feature points are extracted. Secondly, feature vectors are constructed using the M LDB descriptor and matched by computing the Hamming distance. Thirdly, wrong matches are eliminated by RANSAC and the global homography transform, and then a local projection transform is estimated using moving direct linear transformation in the overlapping regions. The image registration is achieved by combining the two transforms. Finally, the weighted fusion method fuses the images. A performance comparison test can be conducted aiming at KAZE, SIFT, SURF, ORB, BRISK. The experimental results show that the proposed algorithm has better robustness for the various transform, and the processing time is greatly reduced.
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Pan Yuehao, Song Zhihuan, Du Wangze, Wu Legang
2017,31(1):29-35, DOI: 10.13382/j.jemi.2017.01.005
Abstract:
To help nursing staff in senile apartment find the elderly fall and other actions timely, an action recognition method based on video surveillance is proposed. Firstly, the foreground images are extracted by the GMM background modeling method in HS color space. Feature extraction is performed by combining the motion features and morphological features. And action recognition can be achieved by HMM with Gaussian output. The method proposed in this paper can adapt to the changes of illumination. The method also has good robustness to the change of motion direction and motion range, and the recognition accuracy rate reaches 90%. The result shows that the method can meet the basic requirements of action recognition and the method has certain practical value.
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Yin Min, Shen Ye, Jiang Lei, Feng Jing
2017,31(1):76-82, DOI: 10.13382/j.jemi.2017.01.011
Abstract:
In disaster rescue and emergency situations, node energy in sensor network is especially limited. In order to reduce unnecessary forwarding consumption, this paper presents a MANET multicast routing tree algorithm with least forwarding nodes, which is based on shortest routing tree and sub tree deletion. The algorithm is proved and analyzed in detail. Its practical distributed version is also presented. The simulation comparison shows that this distributed algorithm reduces the forwarding transmission in improved ODMRP, especially there are much more receivers in MANET. Minimum forwarding routing tree has the minimum network overhead. It is an effective way to extend the network lifetime.
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Chen Shuo, Luo Tengbin, Liu Feng, Tang Xusheng
2017,31(1):144-149, DOI: 10.13382/j.jemi.2017.01.021
Abstract:
In order to solve the low efficiency and the influence of manual factors and many other problems existed in current water meter verification, the water meter verification system using machine vision technology is proposed. And the research keynote is how to realize the template matching algorithm for rapid location of plum blossom needle and the image morphological algorithm for eliminating the bubble of wet water meter dial. Harris algorithm is used to extract the corner points of the plum blossom needle template beforehand, and the corner points of the on site image are extracted in real time. Then, the fast localization of the plum blossom needle is realized by the partial Hausdorff distance method. Finally, the effect of bubbles is eliminated by using the image morphological algorithm, and the count value of the rotating teeth of the plum blossom needle is completed. The experimental results show that the proposed system can shorten the verification time and improve the verification efficiency while ensuring the verification accuracy. The system solves the adverse effect of the bubble on the dial of the wet water meter, and it’s suitable for the verification of various types of water meters.
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Cao Xinrong, Xue Lanyan, Lin Jiawen, Yu Lun
2017,31(1):51-57, DOI: 10.13382/j.jemi.2017.01.008
Abstract:
A simple, rapid and efficient retinal vessels segmentation method is proposed. After a general analysis on gray value distribution and contrast changes of fundus images, the standardizing fundus images are obtained by using the matched filtering technique to overcome the interference of background and noise. Then, a threshold can be automatically selected to achieve the effective segmentation of blood vessels in the fundus images by estimating the proportion of the background pixels. A lot of tests show that the good performance is achieved in the public fundus images database. The experiment shows that the proposed method based on matched filtering and automatic threshold has strong practicability and high accuracy. It is useful for computer aided diagnosis of ocular diseases．
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Sun Li, Zhang Xiaofeng, Zhang Lifeng, Zhou Wenju
2017,31(1):106-111, DOI: 10.13382/j.jemi.2017.01.015
Abstract:
Velocity smoothing is one problem which is proposed in high speed machining and coal mine safety production, the aim of which is to improve machining accuracy and equipment life. Aiming at this problem, this paper proposes a stage wise model and deduces the closed form expression solution for each stage based on the relationship of acceleration and velocity, and then deduces the general solutions of cubic equation in detail for the model. Finally, the solutions are applied to the velocity smoothing. The proposed schema shows the advantages of easy to program and smoothing in transition curve when being applied for velocity smoothing in coalmine. The result demonstrates that the proposed method adapts the high speed scenarios well and has used in other several projects.
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2017,31(1):83-91, DOI: 10.13382/j.jemi.2017.01.012
Abstract:
A fuzzy perception model is proposed to the directional sensor nodes based on the sensing characteristics of the nodes, and also the fuzzy data fusion rule is built to reduce the network uncertain region. Aiming at the problem of directional sensor network strong barrier coverage, a directional sensor network strong barrier coverage enhancement algorithm based on particle swarm optimization is proposed. The convergence rate of the algorithm is improved through the n dimensional problem be transformed into one dimensional problem. The simulation results show that, under random deployment, the perception direction of sensor nodes can be adjusted continuously. Compared with the existing algorithms, the proposed algorithm can effectively form strong barrier coverage to the target area, has a faster convergence rate, and prolongs the network lifetime.
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Zhang Gang, Bi Lujie, Jiang Zhongjun
2023,37(1):177-190, DOI: 10.13382/j.issn.1000-7105.2023.01.020
Abstract:
For the difficulties of classical bi-stable stochastic resonance (CBSR) system in amplification and detection of weak signals, an underdamped exponential tri-stable stochastic resonance (UETSR) system in a Levy noise background is proposed. The UETSR system is constructed by combining the bi-stable potential and exponential potential function, and using the property that non-Gaussian noise can effectively improve the signal-to-noise ratio. Firstly, the steady-state probability density function of the system is derived. The mean signal-to-noise ratio improvement (MSNRI) is adopted as an index to measure the stochastic resonance performance. The quantum particle swarm algorithm is used on parameters optimization. The effect of each parameter of the system on the output variation pattern of the UETSR system with different parameters α and β of Levy noise is investigated. Finally, the UETSR, CBSR and classical tri-stable stochastic resonance system (CTSR) are applied to the bearing fault diagnosis, and the amplitudes at the inner and outer ring fault frequencies after the system output increased by 197. 58, 1. 153, 18. 81 and 238. 87, 26. 63, 39. 72, respectively, compared to the input signal. The spectral level ratios of the highest peak to the second highest peak were 5. 44, 4. 03, 3. 85 and 5. 10, 3. 79, 5. 05. The experimental results show that SR phenomena can be induced by different system parameters, and the UETSR system outperformed the CBSR system and the CTSR system. The above conclusions prove that the system has excellent performance and strong practical significance
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Wan Yong, Zhang Xiaobin, Ni Weining, Zhang Wei, Sun Weifeng, Dai Yongshou
2017,31(1):99-105, DOI: DOI: 10.13382/j.jemi.2017.01.014
Abstract:
The key point of azimuthal propagation resistivity logging while drilling focuses on the structural design of the coil system. And the detection performance of azimuthal propagation resistivity LWD is mainly affected by the transmission frequency of electromagnetic wave signal, the transmitter receiver spacing, the receiver interval, the coil’s angle and the formation resistivity. The testing method of measurements is determined with different inspection requirements of azimuthal propagation resistivity LWD. According to the various constraints of the coil system under the condition of different testing method, the structure of the coil system for azimuthal propagation resistivity LWD is designed by experimental simulation method. The results provide reference for the structural design of the coil system for azimuthal propagation resistivity LWD.
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Zhou Na, Lu Changhua, Xu Tingjia, Jiang Weiwei, Du Yun
2017,31(1):139-143, DOI: 10.13382/j.jemi.2017.01.020
Abstract:
In order to improve the multi target tracking robustness and enhance the difference between the targets, this paper uses an energy minimization method for multi target tracking. Different to the existing algorithm, the algorithm focuses on the representation of the complex problem in multi target tracking as energy function model, which includes a better target segmentation strategy (similarity model). By assigns every possible solutions a cost (the “energy”), the algorithm transforms the multiple target tracking problem into an energy minimization problem. In the energy minimization optimization method, the algorithm uses the conjugate gradient algorithm and a series of jump moves to find the minimum energy value. The experimental results of open data demonstrate the effectiveness. And the quantitative analysis results show that this algorithm can improve the difference between targets or between target and background so as to obtain better robust performance compared with other algorithms.
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Xia Fei, Luo Zhijiang, Zhang Hao, Peng Daogang, Zhang Qian, Tang Yiwen
2017,31(1):118-124, DOI: 10.13382/j.jemi.2017.01.017
Abstract:
Aiming at the shortcoming of the low accuracy of transformer fault diagnosis, the PSO SOM LVQ（particle swarm optimization,self organizing maps,learning vector quantization） mixed neural network algorithm is presented in this paper. Firstly, the weight of SOM neural network is optimized by the method of PSO algorithm to obtain the more effective topology. Based on that, LVQ neural network is combined to cover the shortage of unsupervised learning SOM neural network. The mixed neural network algorithm combined with PSO, SOM and LVQ can improve the accuracy and reduce the error of transformer fault diagnosis. Through simulation, the three algorithms of SOM, PSO SOM and PSO SOM LVQ are compared. The comparison result show that the PSO SOM LVQ mixed neural network algorithm has the highest accuracy, and the fault diagnosis accuracy rate is 100%. Thus it can be seen, the PSO SOM LVQ mixed neural network algorithm can enhance the performance of transformer fault diagnosis effectively.
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Chen Zhenhai, Yu Zongguang, Wei Jinghe, Su Xiaobo, Wan Shuqin
2017,31(1):132-138, DOI: 10.13382/j.jemi.2017.01.019
Abstract:
A low power, small die size 14 bit 125 MSPS pipelined ADC is presented. Switched capacitor pipelined ADC architecture is chosen for the 14 bit ADC. In order to achieve low power and compact die size, the sample and hold amplifier is removed, the 4.5 bit sub stage circuit is used in the first pipelined stage. The capacitor down scaling technique is introduced, and the current mode serial transmitter is used. A modified miller compensation technique is used in the operation amplifiers in the pipelined sub stage circuits, which offers a large bandwidth without additional current consumption. A 1.75 Gbps transmitter is introduced to drive the digital output code, which only needs 2 output pins. The ADC is fabricated in 0.18 μm 1.8 V 1P5M CMOS technology. The test results show that the 14 bit 125 MSPS ADC achieves the SNR of 72.5 dBFS and SFDR of 83.1 dB, with 10.1 MHz input at full sampling speed, while consumes the power consumption of 241 mW and occupies an area of 1.3 mm×4 mm.
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Cao Shasha, Wu Yongzhong, Cheng Wenjuan
2017,31(1):125-131, DOI: 10.13382/j.jemi.2017.01.018
Abstract:
Musical simulation based on spectrum model is the use of acoustic theory that can achieve musical instrument’s sounds by sum of products of a series of basic functions and time varying amplitude. A new digital piano sound simulation technique is proposed by analyzing piano string vibration and damping characteristics and investigating the resonance effect of resonance box. The simulation model consists of two parts: the excitation system and the resonance system. Based on the vibration equation of the strings, the envelope modification of time domain is carried out to simulate the natural attenuation of the strings, which can make music harmonious between the notes. Then, the filter group is modeled by spectrum envelope in frequency domain to achieve the simulation of resonance system. This new method can more effectively carving voice, has better performance timbre at the same time, therefore, it makes the sound more harmonious.
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Identification method of Dongba pictograph based on topological characteristic and projection method
Xu Xiaoli, Jiang Zhanglei, Wu Guoxin, Wang Hongjun, Wang Ning
2017,31(1):150-154, DOI: 10.13382/j.jemi.2017.01.022
Abstract:
Dongba pictograph has been known as "the only living pictograph in the world".In the aspects of image recognition, content interpretation,the current English and Chinese character recognition system often can not be applied to Dongba pictograph.Concerning the difficulties in the identification of Dongba pictograph, a new character recognition is proposed. Topological features processing and projection methodcompose thefeature extraction method,then, the character recognition method based on template matching is adopted.It is showed that the feature extraction method based on the intrinsic characteristic of the pictograph,and the Dongba character recognition method based on template matching,has high accuracy through the experiment.
