Abstract:Electronic measurement technology is fundamental and pioneering for equipment development and scientific and technological innovation. Its level of development significantly impacts the enhancement of the national scientific and technological level as well as national defense scientific and technological strength. In light of the rapid development of digital, intelligent, and networked high-tech, test demands and tasks continue to evolve and put forward higher requirements for test indexes and functions. Therefore, traditional test technology must urgently integrate and innovate with advanced technology to overcome technical bottlenecks and make positive contributions to the national strategic scientific and technological strength. This paper firstly analyzes the development background and technical engines of measuring instruments. Then, the characteristics of the artificial intelligence that are of great value to the measuring instruments are discussed. Finally, the trend of combining artificial intelligence technology with measuring instruments is forecasted, and the research and development directions of measuring instruments under the background of artificial intelligence are put forward. Provide reference for how to realize the intelligentization of test instruments and how to realize the independent innovation and self-reliance development of China‘s high-end instrumentation under the background of the rapid development of artificial intelligence.

Abstract:The stable operation of multiport energy complementary equipment under multiple operating conditions and the smooth switching between operating conditions are important supports to ensure the smooth operation of the power grid. To address the volatility of distributed generation within the device, based on the hierarchical control strategy, the paper firstly proposes a method for dividing the multiple operating conditions in the grid-connected and off-grid modes, which maintains the power balance of the device in the proposed multiple operating conditions through the coordination of the port parameters and control commands between the local control layer and the central management layer. Secondly, a smooth switching method is proposed to improve the parallel and off-grid mode, based on which voltage monitoring is added to avoid frequent switching of operating conditions caused by normal voltage fluctuations. Finally, simulation models of three typical operation scenarios are constructed in MATLAB to verify their feasibility. The simulation results show that the DC bus voltage can be quickly restored to a stable state in the switching process of the typical scenarios, and the fluctuation is within 0.5%; meanwhile, the improved phase-locked-loop-less smooth switching method can realize the pre-synchronization within 30ms, which can meet the requirements of stable and fast operation.

Abstract:During the process of indoor positioning, the map matching algorithm based on particle filtering can effectively fuse the indoor positioning results with the map data. Currently, this method mainly faces two problems: the calculation complexity of the wall-crossing detection algorithm during map matching and issue of the positioning results being in unreachable areas. To address the issue of high calculation complexity in the wall-crossing detection algorithm, we propose a wall-crossing detection algorithm based on a map information matrix on a grid map, which reduces the calculation complexity while ensuring the correct detection of wall-crossing particles. To address the issue of positioning results being in unreachable areas, we propose a map matching algorithm based on particle filtering with multiple weight updates to correct positioning results in unreachable areas. Theoretical analysis and experimental results demonstrate the rationality and effectiveness of the improved algorithm.

Abstract:The electric-drive-reutilized onboard charger (EDROC) system, which reuses the electric drive system to propel the motor winding, large-rated inverter device, and control circuit, can both implement charging and electric drive and has significant advantages in cost, power level, and power density. In this paper, virtual synchronous machine (VSM) control is introduced to address the issue of instability of three-phase AC interface under the condition of power network fluctuation, which ensures the safe and stable operation of EDROC system. Firstly, the circuit topology and basic working principle of the six-phase EDROC are introduced. Secondly, the torque generation component of the fundamental plane is mapped to the x-y harmonic subplane using the multi-control degrees of freedom of the six-phase motor, achieving no electromagnetic torque during charging. Then, a mathematical model of VSM control is established for the three-phase AC/DC interface to simulate synchronous motor operation. Finally, a prototype is built and tested. The result is that the total harmonic distortion (THD) is 2.91%, which meets the national standard and the system can make corresponding adjustments to the frequency change in time. The results show that the proposed method has ideal power adjustment and zero electromagnetic torque performance under the condition of frequency fluctuation.

Abstract:In order to meet the requirements of high precision, high efficiency and high stability of the precision measure and test appliances, the switching power supply with 0~80 V output voltage, 0~100 A output current and 6 kW output power is designed. The single-phase bridge semi-controlled rectifier filter is adopted as the former stage of the power supply, and the phase-shifted full-bridge converter is applied in the latter stage. The structure of the switching power supply and the working principles are introduced, and the calculation and selection of the key components in the main circuit are given. A prototype is developed, the experiments are carried out and the experiment results show that, the actual output voltage can track the reference voltage and meet the accuracy of 0.5% when the loads change, and the working efficiency can reach up to 87.4%.

Abstract:Aiming at the disadvantages of slow convergence of standard grey wolf optimizer (GWO) and easy to fall into local optimality, an improved grey wolf optimizer (IGWO) with improved iterative local search is proposed. First, the uniformity and diversity of the initial population were enhanced by the strategy of the best point set. Secondly, the dual convergence factor is used, which is nonlinear and adaptive updating based on population location, to balance the global exploration and local development ability in the whole period of population optimization. Thirdly, European dynamic weight and Levy flight strategy were introduced into the population position updating formula to improve the optimization accuracy and help the population jump out of the local optimal value. Finally, the improved iterative local search is introduced to make the search ability of the algorithm more flexible and help the algorithm accelerate convergence. Through the simulation analysis of 10 benchmark test functions and the comparison of population optimization balance, it is proved that IGWO has better optimization accuracy, stability and convergence speed. Then IGWO was applied to the engineering optimization problem. Compared with GWO, GJO, WOA, HSSAHHO, SCHOA, NCPGWO, DSFGWO 7 algorithms, the fitness was optimized by 3.25%, 27.2%, 28.9%, 3.15%, 3.04%, 2.33%, 0.07%, respectively. The feasibility and effectiveness in engineering application are proved.

Abstract:In order to solve the shortcomings of the original RRT in the field of path planning, such as long planning time, uneven path and high path cost, this paper first added dynamic step strategy and embedded Dijkstra algorithm on the basis of the original RRT algorithm to improve efficiency. Then, lower sample smoothing, upper sample smoothing and key point smoothing are added to the obtained path to improve the path smoothness and path cost. MATLAB experiments show that the proposed algorithm improves the planning time by about 45% compared with the traditional RRT algorithm, and is 30% to 70% ahead of Astar, RRTstar and GA algorithms respectively. In terms of path length, the proposed algorithm has a nearly 40% improvement over the traditional RRT, and it also has different degrees of lead compared with other algorithms. It can be concluded that the method proposed in this paper can be well applied to path planning.

Abstract:The image retrieval methods based on deep hashing often use convolution and pooling techniques to extract local information from images and require deepening the network layers to obtain global long-range dependencies. These methods generally have high complexity and computational requirements. This paper proposes a vision Transformer-based image retrieval algorithm enhanced with attention, which uses a pre-trained vision Transformer as a benchmark model to improves model convergence speed and achieves efficient image retrieval through improvements to the backbone network and hash function design. On the one hand, the algorithm designs an attention enhancement module to capture local salient information and visual details of the input feature map, learns corresponding weights to highlight important features, enhances the representativeness of image features input to the Transformer encoder. On the other hand, to generate discriminative hash codes, a contrastive hash loss is designed to further ensure the accuracy of image retrieval. Experimental results on the CIFAR-10 and NUS-WIDE datasets show that the proposed method achieves an average precision of 96.8% and 86.8%, respectively, using different hash code lengths on two different datasets, outperforming various classic deep hashing algorithms and two other Transformer-based image retrieval algorithms.

Abstract:The existing evaluation methods based on large-dimensional random matrices cannot reliably evaluate transformers due to the influence of primary voltage fluctuations, this paper proposes a method for evaluating the error condition of voltage transformers based on measurement consistency of same phase. The paper use kernel principal component analysis (KPCA) and reconstruction algorithm to establish evaluating indicators with higher sensitivity. Considering the similarity of the evaluating indicators of same phase, a difference evaluating indicator of voltage transformers based on measuring same phase is established to diminish the influence of stationary fluctuations and judge the polarity of errors by judging whether the difference evaluating indicators are positive or negative. The simulation experiments and field application results suggest that 0.2% error of voltage transformers can be estimated using the improved evaluation method based on large-dimensional random matrices.

Abstract:As a high precision mechanical component, aero-engine has important influence on aircraft performance and reliability. Accurate prediction of remaining useful life can reduce maintenance costs and reduce the occurrence of safety accidents. The existing prediction methods only focus on the temporal relationship between sensor data, ignoring the spatial relationship between sensors. This paper proposes a network model that integrates spatial-temporal features, and uses graph convolutional networks and long short-term memory to extract spatial and temporal features, respectively. The parallel structure is used to integrate the temporal and spatial features.The RMSE and Score of subdataset FD001 are 12.81 and 252.04 respectively.The experimental results show that the proposed method has higher prediction accuracy than other prediction methods.

Abstract:Accurate estimation of the state of health (SOH) of lithium-ion batteries is the critical to ensure the safety of lithium-ion batteries. However, the existing methods for SOH estimation of lithium-ion batteries exist unsatisfactory evaluation accuracy. To solve this problem, this paper proposes a battery SOH estimation method based on the combination of temporal convolutional network (TCN) and bidirectional gated recurrent unit (BiGRU). Firstly, the health factor is extracted from the battery charging data and its correlation with the battery capacity is discussed. Then, the TCN model is used to process the long series dependent data and carry out feature extraction, and also a Dropout layer is added to the model to prevent overfitting and improve the generalization. Finally, the BiGRU model is used to model the historical data features and predict the data degradation trend. In addition, the BiGRU model is used to model the historical data characteristics and estimate the data degradation trend to achieve an accurate assessment of the SOH of lithium-ion batteries. The results show that the proposed method obtains the better average of coefficient of determination (0.990 4), absolute mean error (0.017 1), and root mean square error (0.022 3) than other comparative methods under four batteries.

Abstract:With the rapid development of science and technology, human-computer interaction based on deep learning has found widespread applications. As an important component of human-computer interaction, gesture recognition holds significant research and application value. However, traditional gesture recognition methods utilizing skin color detection algorithms have limited effectiveness in recognizing gestures against complex backgrounds. To address this problem, a novel gesture recognition method based on convolutional neural network that combines skin color and edge features is proposed. Initially, the ellipse skin color model and Otsu threshold skin color recognition algorithm are used to obtain gesture skin color features in the YCrCb color space. Subsequently, the improved Canny edge detection algorithm is used to obtain the edge features of the gesture skin color images. Following this, the edge filling method is used to process the edge images. Finally, the gesture segmentation images are obtained by logical operation and morphological operation, which are as input to the convolutional neural network for training and recognition. Experimental results demonstrate the effectiveness of approach, with an average recognition rate of 98.83% on the NUS hand posture dataset II. The proposed method shows a significant improvement over traditional gesture recognition methods and can effectively recognize gestures against complex backgrounds.

Abstract:Among the varIoUs sensors used in assisted and automatic driving systems, the perception performance of cameras and lidar is greatly affected by the weather, while the automotive millimeter-wave radar is a low-cost and almost weather-free all-weather working device.Moving objects can extract rich Doppler information. With the development of radar technology and open-source labeled datasets, object detection based on underlying radar data has become a very promising field. To address the issue of inaccurate target detection and positioning caused by low angular resolution of onboard millimeter wave radar data, and to improve the performance of millimeter wave radar target detection, this paper proposes a 3D convolutional millimeter wave radar target detection method based on prior box distance constraint to achieve the detection and classification of multiple dynamic targets. In our method, we design a feature extractor for 3D ResNet to characterize target information in Range Azimuth Doppler tensor, solving the problem of insufficient representation in existing models due to ignoring Doppler information from the original 3D radar signal; Secondly, an absolute distance Loss function is added to train the model to overcome the influence of distance on target presentation and improve the accuracy and robustness of target detection; In addition, a method of resetting prior boxes based on distance unit intervals has been proposed to solve the problem of unreasonable prior box design in existing methods.The proposed model is trained and tested on the RADDet dataset. The experimental results show that compared with the current state-of-the-art method, our model achieves the best when the IoU threshold is 0.1, 0.3, 0.5, 0.7, where IoU The improvement is the most significant when it is 0.1 and 0.3, which are increased by 6.6% and 5.1% respectively.

Abstract:The red imported fire ant (solenopsis invicta buren) is one of the major invasive species that has been causing damage in southern China in recent years. Accurately identifying the nests of red imported fire ants is crucial for their prevention and control. In this paper, an improved red fire ant nest identification model based on ResNet34 is proposed to solve the problems of traditional red fire ant prevention and control relying on manual inspection, high risk and low efficiency, so as to realize intelligent inspection. This model uses red imported fire ant nest images collected from different terrain features, combined with data augmentation techniques for training. By adding an SE attention mechanism module after the first convolutional layer of ResNet34 and before the fully connected layer, the model can enhance its adaptive selection and channel weighting adjustment capabilities to extract local nonlinear texture features on the surface of red imported fire ant nests. Through K-fold cross-validation tests and ablation tests to explore hyperparameters, SE-ResNet34 is compared with AlexNet, VGG-16, ResNet18, ResNet34, and ResNet50, and the results show that SE-ResNet34 achieves a peak accuracy of 98.76%, which is 2.17% higher than ResNet34. It has the characteristics of short training time, high recognition accuracy, strong robustness, and stability compared to other tested models. This method provides a convenient and efficient solution for distinguishing red fire ant nests while reducing manual labor and minimizing the use of insecticides.

Abstract:The denoising process of fringe pattern can recover the boundary information of fringe pattern and thus improve the accuracy of fringe pattern 3D measurement results. In order to recover the boundary information of the fringe pattern as much as possible, a fringe pattern denoising method is proposed to improve the SwinIR neural network. First, the Inception module is introduced and the structure of the RSTB module in the network is optimized to improve the local feature extraction capability of the network. Second, multiple residual blocks are introduced to the overall structure of the network to alleviate the problem of gradient disappearance caused by over-deepening of the network. The de-noising performance was tested by using high-density area stripes. When the noise level is 50, the PSNR value of the improved SwinIR algorithm is 31.96, the SSIM value is 0.995 5, and the denoising time is 4.035 s. Moreover, the improved SwinIR algorithm is compared with seven other representative algorithms, and the results show that the denoising performance of this method is optimal at different noise levels.

Abstract:To address the challenges of confusion among defect categories and the difficulty in detecting small defect targets in printed circuit board defect detection, an improved YOLOv5 detection model was proposed. The Swin-Transformer is incorporated into the backbone network to extract multi-scale features, capturing both local and global information. A prediction feature layer is added specifically for small targets, and the new multi-scale feature fusion and detection structure enable the model to learn more comprehensive feature information. The ECIoU_Loss is employed as the loss function, facilitating collaborative optimization between detection speed and accuracy in circuit board defect detection. Experimental results demonstrate that the improved YOLOv5 model achieves a mean average precision of 98.7% on the PCB Defect dataset, with a precision of 99.7% and a recall of 97.4%. These performance metrics outperform current mainstream detection models, showcasing the improved YOLOv5 model's effectiveness in classifying and localizing circuit board defects.

Abstract:Aiming at the problems that different visual stimulation modulations methods will lead to low classification accuracies of some subjects, this paper designed four stimulation waveforms with four frequencies which were used to design the evoking paradigm, and the inverted sawtooth wave evoking paradigm was proposed for the first time. Eight subjects' EEG signals were collected in the experiment. By extracting and classifying frequency energy features, it was found that different stimuli have different influences on the classification accuracy. On this basis, the waveforms with the highest energy were selected to form a customized paradigm; the average classification accuracies among different stimulation waveforms and customized paradigm were compared. The experimental results show that the inverted sawtooth stimulation paradigm is better than any other traditional stimulation paradigm. Meanwhile, the average accuracy of the customized paradigm is 3%~12% higher than that of any other stimulation paradigm. Therefore, the stimulation paradigms of inverted sawtooth and customized waveforms can improve the performance of SSVEP-based BCI system.

Abstract:Addressing the issue of low detection accuracy and high false positive and false negative rates caused by the small size of insulator targets, a transmission line insulator detection model based on the improved YOLOv7 is proposed. Firstly, the dual-branch fused channel attention mechanism is integrated with the main ELAN (encoder-local aggregation network) module to emphasize crucial channel information and suppress interference from noise and irrelevant data. Secondly, a locally self-attentive mechanism is introduced in the feature fusion section to enhance the focus on local tiny regions. Additionally, the BiFPN (Bi-directional feature pyramid network) cross-layer connection is incorporated in the Neck section to preserve edge information better and improve the detection of small targets while slightly increasing computational load. Lastly, using evaluation metrics such as precision, recall, and mean average precision, ablation experiments and comparative experiments are conducted on collected datasets. Experimental results indicate that the improved network model achieves a detection accuracy of 92.1% for transmission line insulator detection, a 3% enhancement over the traditional YOLOv7 network model. The average detection mean, recall rate, are also improved by 3.1% and 3.6% respectively. Furthermore, the improved model demonstrates significant advantages over YOLOv5-ECA and Faster R-CNN in various evaluation metrics, proving its effectiveness in detecting transmission line insulators.

Abstract:When the CNC servo press presses the positioning nut, it is necessary to determine its quality based on the collected data signals, which are easily affected by composite noise and can lead to misjudgment of the nut. To address the difficulty in extracting signal envelope features and determining parameters in VMD under complex noise interference, a method is proposed that combines the capuchin search algorithm to optimize VMD parameters and effectively reconstruct data signals. Firstly, select MCCI as the objective optimization function. Secondly, adaptive modal decomposition is performed on the composite signal, and low noise components are filtered out using the permutation entropy algorithm and correlation coefficient for signal reconstruction. Then, using simulated and measured signals as samples, objective comparisons were made using specific values of RMSE and SNR, and reconstructed signals were visually compared using EMD, CEEMDAN, and CapSA-VMD methods, respectively. The results show that CapSA-VMD decomposition does not contain false components, and the denoising effect is significantly better than the other two. The accuracy of nut quality detection is as high as 97.8%. The research results can provide useful reference for denoising composite signals of pressed positioning nuts and improving the accuracy of envelope threshold judgment.

Abstract:Currently, research on data acquisition hardware systems for human activity recognition is limited, and there is a problem of a lack of diverse and generalized reference datasets. In this paper, we design a low-power data acquisition system that supports real-time data transmission and propose a data acquisition method with randomness and crossover. Firstly, a low-power acquisition platform is built for data acquisition, wireless transmitting and receiving, and pre-processing; secondly, a comprehensive and accurate data acquisition scheme is developed to improve the generalization of the new dataset; and finally, a 2D-CNN neural network is used to train a model for the acquired dataset in different modes. The experimental results demonstrate that the designed data acquisition system has a reasonable structure and low power consumption, ensuring real-time data transmission. The application of this system greatly improves the quality of the dataset. The obtained dataset achieves an accuracy of 92.54% on deep learning models. Compared to traditional datasets, the new dataset shows significantly better performance in human activity recognition tasks. The development of this data acquisition system and dataset provides convenience for neural network applications.

Abstract:A gas flow monitoring system based on NB-IoT technology was developed to address the issues of low measurement accuracy, limited functionality, and poor safety performance in traditional gas meters. The hardware aspect of the system employed an STM32 microcontroller as the main control chip and incorporated a specially designed thermal gas flow meter to accurately measure gas flow. Additionally, multiple sensors were integrated to enable real-time monitoring of temperature, humidity, gas concentration, and pressure in the gas environment. The software design utilized the FreeRTOS operating system. Leveraging NB-IoT technology, the system transmitted monitoring data to the OneNET cloud platform, allowing users to monitor gas usage in real-time through a dedicated mobile application. Experimental results demonstrated that the system achieved gas flow measurement ranging from 0.016 to 6 m3/h with a measurement error within ±1% and a repeatability of less than 2%. Furthermore, the system's wireless data transmission remained stable, effectively ensuring the accuracy and safety of gas monitoring.

Abstract:Aiming at the nonlinear characteristics and uncertain disturbance of the AC servo system of permanent magnet synchronous motor, as well as the linear limitations of traditional PID controllers, a speed control strategy based on the wavelet neural network PID based on whale optimization algorithm is proposed. The wavelet neural network and incremental PID controller are combined to form the speed controller of permanent magnet synchronous motor, and the whale optimization algorithm is used to further optimize the parameters. The experimental results show that the stability time obtained by the proposed method is 0.025 s, the maximum overshoot is 52 r/min, and the speed error after applying torque is 0.002, so the dynamic performance and anti-interference ability of the control strategy in this paper have certain advantages.

Abstract:Aiming at the practical needs of liquid level detection in fermentation tanks during the production of fermentation liquid, a liquid level detection algorithm based on template matching and roughness texture index is proposed. Firstly, the system collects real-time liquid level images from industrial cameras and performs image preprocessing to improve the quality of the collected images in view of the influence of environmental factors; secondly, perspective transformation is performed on the images to achieve the purpose of rectification; then, template matching is performed on the region of interest in the grayscaled image to search for the location of liquid level in a coarse range; finally, the roughness texture index is calculated to precisely search for the location of the liquid level, so as to obtain accurate liquid level height data, and the data is filtered and outputted. The experimental results show that the liquid level detection algorithm based on template matching and roughness texture index has an accuracy of more than 98.2% in practical applications, and has the advantages of good anti-interference effect and strong real-time performance.

Abstract:In order to solve the problem of unstable communication in wireless magnetic induction communication system due to the change of the attitude of the receiving coil, this paper proposes a method of using three mutually orthogonal coils at the receiving end to make up for the weakness of the communication fluctuation of a single coil. Firstly, the mathematical model of single coil and omnidirectional coil is established, and the law of magnetic field size and induced voltage at the receiving end is analyzed. Finally, the feasibility of the method of receiving coil as three-dimensional orthogonal coil is verified by simulation and experiment. The simulation and experimental results show that the three-dimensional orthogonal coil as the receiving coil can effectively improve the induced voltage value in the wireless magnetic induction communication system, improve the stability of the communication channel, and reduce the volatility by 95%.

Abstract:In order to solve the problem that the P-wave vibration field is difficult to reconstruct due to the aliasing of the near-field transverse and longitudinal wave field in underground explosion, a new method of transverse and longitudinal wave separation and decoupling based on adaptive polarization filtering is proposed in this paper. Firstly, the instantaneous polarization distribution of P/S wave in the wave field is obtained by fractional order Hilbert transform to improve the ability of fine analysis of feature information. Secondly, the instantaneous polarization Angle information of P/S wave is extracted by ACM, and P/S wave and P/S wave are recognized by polarization degree. Thirdly, the Angle information is used to construct the spatial polarization filter. Finally, the separation and decoupling effects of different modulation factors and different characteristic combinations are discussed and analyzed by simulation. The experimental simulation results show that the proposed method can effectively realize the separation and decoupling of wave field. When the directional modulation factor is 10 and the amplitude modulation factor is 3, the separation accuracy reaches 96.5%, and the separation and decoupling effect is the best.

Abstract:The intricate internal contour profile and narrow space of the stator make it difficult to accurately measure the cross-sectional profile after the stator processing and use of the all-metal screw drilling tool, and a stator internal contour measurement system of the screw drilling tool was studied. Among them, the mechanical system adopts a parallelogram mechanism to realize the centering function, uses a laser displacement sensor and angular displacement sensor to obtain the radial position coordinates of the stator section, collects data through the ARM processor, and transmits it to the host computer for data processing, and builds a software and hardware platform for the measurement system. The experimental results show that the measurement accuracy of the designed measurement system is ±0.03 mm, and the time to measure one section of the internal contour is within the 60 s, which can adapt to the measurement accuracy requirements of the stator of the screw drilling tool with inner diameters ranging from 98 mm to 180 mm.