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Wang Zhixin, Zou Yihan, Wang Jian, Chen Hui, Qian Jin, Gong Chunyang
2026,40(5):1-14,
Abstract:
With the widespread deployment of artificial intelligence (AI) applications and the rapid expansion of digital infrastructure, data centers have experienced continuous growth in scale and computing demand, making energy consumption management a critical issue in the energy and power engineering domain. Distinct from conventional electricity consumers, data center loads exhibit unique operational characteristics, with total energy consumption dominated by information technology (IT) equipment and cooling systems. The strong non-linearity, multi-source coupling, and dynamic evolution of these components pose significant challenges to make accurate energy consumption forecasting and power system dispatch. The paper systematically reviews the research progress in data center power supply configurations, load modelling, and energy consumption forecasting. Particular emphasis is focused on a comparative analysis of statistical models, machine learning, and deep learning, which are applied to IT load and cooling energy prediction, applicable conditions, feature construction strategies, and modeling performance, etc. Based on extensive literature analysis, key feature selection principles and model adaptation patterns under multi-source monitoring data are summarized, highlight the role of workload characteristics, thermal environmental variables, and operational control parameters to improving prediction. Furthermore, data center participation mechanisms in power system operation, dispatch based on load and energy consumption forecasting are reviewed also. From both temporal and spatial perspectives, the influence of forecasting on demand response implementation, flexible regulation capability extraction, and coordinated operation between power system and computing resource is analyzed. The results indicate that high-resolution, reliable load forecasting is a prerequisite for unlocking the flexibility potential of data center and enhancing power-computing synergy in modern power system. Finally, considering practical deployment requirements, the paper identifies key challenges faced by current research, including insufficient multi-source data fusion, limited model interpretability, and constrains in real-time forecasting and decision-making. Cutting-edge and future research directions and implementation strategies are proposed to support intelligent energy management and coordinated power-computing operation of data centers, providing systematic references and methodological insights for researchers and practitioners in this field.
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Ren Jiahao, Li Jian, Li Jin, Zeng Zhoumo, Chen Shili, Liu Yang
2026,40(5):15-29,
Abstract:
Ultrasound computed tomography has great application potential in musculoskeletal imaging owing to its advantages of low cost, real-time capability, and portability. However, the pronounced acoustic impedance contrast between soft tissue and bone in musculoskeletal structures gives rise to strong reflections, multiple scattering, and significant attenuation, resulting in a highly complex ultrasound wavefield and thereby limiting the spatial resolution and quantitative accuracy of conventional ultrasound imaging methods. To address the above issue, a quantitative ultrasound imaging method integrating contrast source inversion and full waveform inversion is proposed. This method first introduces multiplicative-regularized contrast source inversion to rapidly reconstruct the large-scale sound speed distribution of tissues under strong scattering conditions, yielding a physically consistent and robust initial model; subsequently, frequency-domain dual-parameter full waveform inversion is performed on a fine grid to achieve joint high-resolution reconstruction of sound speed and attenuation parameters, with computational efficiency enhanced through GPU-based parallel acceleration. Numerical simulations and ex vivo bovine musculoskeletal experiments demonstrate that, with a spatial grid spacing of 0.3 mm, the proposed method can effectively suppress nonlinear effects and imaging artifacts induced by high-impedance interfaces, enabling refined quantitative imaging of sound speed and attenuation distributions in musculoskeletal tissues, with a total inversion time of 16.53 min. The structural similarity indices of the reconstructed sound speed and attenuation images reach 0.954 0 and 0.902 3, respectively. The results indicate that the proposed method achieves a favorable balance between imaging accuracy and computational efficiency, providing an efficient and robust technical solution for high-resolution quantitative ultrasound imaging of musculoskeletal tissues.
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Cheng Wangfeng, Zheng Xiaoliang, Xue Sheng
2026,40(5):30-41,
Abstract:
For the low-concentration gas direct combustion system in coal mines, traditional sensors such as temperature probes and ionization rods are insufficient to accurately reflect the subtle fluctuations in the combustion process. To address this problem, a collaborative monitoring strategy for multi-window low-concentration gas combustion states is proposed. Firstly, a low-concentration gas combustion state dataset is constructed using high-speed cameras (HSC) and high-definition cameras (HDC) with different viewing windows. Data enhancement analysis, such as Gaussian blur, is performed on the dataset to expand its diversity to improve the adaptability of the model. Further, a low-concentration gas combustion state monitoring model is constructed, and backbone, neck, and head models are established, respectively. The backbone model improves the ability to extract local features; the neck model enhances the sensitivity to the dynamic change trend; the head model optimizes the efficiency of gas combustion state feature extraction and reduces the extraction of redundant features. Finally, on the premise that the model parameter quantity meets the deployment requirements, the low-concentration gas combustion state monitoring model is deployed to the embedded platform to monitor the complex combustion state changes of the gas in real-time. The experimental results show that the precision of the low-concentration gas combustion state monitoring model is 96.11%, the accuracy is 95.80%, the monitoring rate is 87.5 fps, the number of model parameters is 40.1×106, the average error is no more than 0.14, the average precision of the model deployed on the embedded platform is 96.10%, and the average monitoring speed is 30.10 fps. It meets the precision, real-time, and adaptability requirements of the gas combustion state monitoring of the low-concentration gas direct combustion system, and has certain industrial application value.
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Yang Fuhui, Li Yunsheng, Yin Xiaozhong, Zhang Fangrui, Du Chengzhu
2026,40(5):42-49,
Abstract:
Aiming at the issues that current ultra-wideband (UWB) multiple-input multiple-output (MIMO) antennas are prone to signal interference across multiple frequency bands and suffer from poor isolation between ports, a compact high-isolation ultra-wideband four-port MIMO slot antenna with dual-notched characteristics is proposed. The antenna has a dimension of 65 mm×65 mm×0.8 mm. Employing coplanar waveguide (CPW) feeding and integrated with a cross-shaped isolation structure to enhance the isolation between ports, this antenna generates notches in the 3.25~3.85 GHz and 4.32~5.69 GHz frequency bands by etching two C-shaped slots on the radiating patch. These notches can effectively suppress interference signals from the WiMAX (3.3~3.7 GHz) and WLAN (5.15~5.825 GHz) frequency bands. In-depth analyses were carried out on various performance parameters of the antenna, including peak gain, envelope correlation coefficient (ECC), diversity gain (DG), and total active reflection coefficient (TARC). Experimental measurements show that the proposed MIMO antenna operates within the frequency band of 2.48~11.2 GHz, with an isolation of over 20 dB, an ECC of less than 0.07, and a DG of more than 9.97, demonstrating excellent diversity performance. Both simulation and measurement results indicate that the antenna exhibits superior performance in signal transmission and anti-interference capability, with minimal interference between ports. It holds promising application prospects in ultra-wideband MIMO communication systems, particularly in the WiMAX and WLAN notched frequency bands, and can meet the requirements of wireless communication systems.
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Chen Xiaolei, Li Zhengcheng, Shen Xingyang, Ma Pingchuan, Yang Fulong
2026,40(5):50-59,
Abstract:
Wafer defect classification is a critical task in the chip manufacturing process, as accurately identifying various types of defect patterns is essential for promptly locating and resolving manufacturing issues. To overcome the limitations of insufficient multi-scale feature representation and low efficiency in cross-level information fusion, this paper proposes a novel multi-level feature fusion network based on convolutional neural networks (CNN) and Transformers, named CTM-Net. The network adopts a dual-branch parallel architecture: the CNN branch focuses on extracting local morphological structures and fine-grained defect details, while the Transformer branch employs self-attention mechanisms to capture global contextual dependencies and structural information. Based on these complementary representations, a multi-level feature interaction module is further designed to adaptively integrate and enhance cross-scale features, effectively compensating for the limitations of single-branch representations and improving the model’s discriminative capability. Experiments conducted on the publicly available WM-811K dataset show that CTM-Net achieves an accuracy of 99.31%, outperforming five representative state-of-the-art wafer defect classification approaches. Notably, the proposed model demonstrates superior recognition accuracy and stability in critical defect categories such as Edge-Loc, Edge-Ring, and Loc, highlighting the effectiveness and advantages of combining CNN and Transformer architectures for multi-level feature fusion in wafer defect classification tasks.
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Wang Anni, Shan Zebiao, Liu Xiaosong, Yu Yanxin, Su Chengzhi
2026,40(5):60-68,
Abstract:
To address the problem of direction-of-arrival (DOA) estimation in environments with mixed Alpha-stable distributed noise and Gaussian colored noise, a forward prediction and backtracking orthogonal matching pursuit algorithm based on fractional-order cumulants (FOC-LABOMP) is proposed under a coprime array framework. First, a signal reception model is constructed using the coprime array, where the difference co-array formed by sensor spacings is utilized to generate a virtual array. By introducing a hole-filling interpolation technique, the effective aperture of the physical array is extended to the continuous region of the virtual array, thereby enhancing the degrees of freedom and improving angular resolution. Second, the semi-invariant property of fractional-order cumulants is leveraged to effectively suppress the interference from both Gaussian colored noise and Alpha-stable noise. Furthermore, the proposed method incorporates a forward prediction and backtracking orthogonal matching pursuit algorithm, which evaluates the correlation of atoms via inner products and predicts their performance in future iterations to select the optimal atom. A backtracking strategy is employed to improve the accuracy of sparse recovery, ultimately yielding the estimated DOA values. The effectiveness of the proposed algorithm was validated through computer simulation experiments. Under mixed noise conditions consisting of Alpha-stable distribution and colored Gaussian noise, when the mixed signal-to-noise ratio (SNR) is 0 dB, the root mean square error of the proposed algorithm for DOA estimation is 0.536 8 °, which improves the accuracy by 41.45% compared to the PFLOM-MUSIC algorithm. The simulation results fully demonstrate that the proposed algorithm can achieve high-accuracy DOA estimation under mixed noise conditions.
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Li Mu, Ma Chao, Yao Jie, Su Peng, Wang Mengdi, Zhang Haiyang, Xu Haowen
2026,40(5):69-77,
Abstract:
To address the problem of single-modal signals being prone to misclassification in dynamic tasks such as forward, backward, and turning, this paper proposes a multi-stream neural network model based on the fusion of motion posture signals and surface electromyography (sEMG) signals. Lower-limb motion posture and sEMG signals are collected and processed using a gait cycle segmentation and alignment strategy. Based on the heterogeneity of data characteristics in terms of temporal structure, signal type, and expression dimensions, feature information is constructed, including dynamic temporal features of motion posture, frequency-domain octave spectrum of sEMG, and time-domain histogram statistics. A Transformer is used to capture the dynamic evolution of motion posture signals, and a two-way multilayer perceptron (MLP) is used to extract local response characteristics of sEMG in the time and frequency domains, forming a multi-stream neural network structure with complementary feature representations to achieve the fused expression of motion posture and local muscle fiber activation information. Results show that the proposed model improves recognition accuracy by 8%, 6%, and 24% in forward, backward, and turning tasks, respectively, significantly reducing misclassification caused by blurred motion boundaries and demonstrating superior accuracy and generalization performance. This method provides research reference and technical support for the application of multimodal fusion in complex gait recognition, and provides a reference for the design of high-precision gait recognition systems.
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Liu Xiaosong, Chen Yueqi, Shan Zebiao, Wang Anni, Yu Yanxin, Su Chengzhi
2026,40(5):78-86,
Abstract:
To address the limitation of existing direction-of-arrival (DOA) estimation algorithms in simultaneously suppressing alpha-stable distribution noise and Gaussian colored noise, a DOA estimation method based on fractional-order cumulants and a weighted hyperbolic composite-function smoothed l0-norm is proposed. First, by exploiting the semi-invariant property of fractional-order cumulants and their insensitivity to both alpha-stable and Gaussian distributions, the adverse effects of alpha-stable noise and Gaussian colored noise are effectively suppressed. Then, the fractional-order cumulant matrix is vectorized, and a sparse DOA reconstruction model based on fractional-order cumulants is formulated. Subsequently, a hyperbolic composite function is constructed to provide a smooth approximation of the l0-norm, where the approximation parameter is adaptively adjusted to balance smoothness and steepness. Meanwhile, a weighting matrix is introduced to enhance the coefficients corresponding to the true source locations while suppressing the remaining entries, thereby further emphasizing the sparse structure of the solution. Based on this formulation, a two-layer iterative optimization strategy combining gradient descent and projection is employed to solve the weighted smoothed l0 optimization problem, leading to accurate DOA estimation. The simulation results demonstrate that, even under conditions where the Alpha stable distribution is mixed with Gaussian colored noise and the mixed signal-to-noise ratio is as low as 0, the proposed algorithm achieves a root mean square error of 0.516 4°in DOA estimation, fully illustrating its effectiveness and high accuracy in complex noise backgrounds.
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Li Yucheng, Zhang Yang, Shi Junjie, Wu Yifeng, Cheng Ming
2026,40(5):87-97,
Abstract:
To address the challenges in the precise identification and quantitative concentration retrieval of atmospheric aerosols, this study proposes an aerosol identification method based on Mueller matrix pattern analysis, focusing on polarization characteristics. Using optical depth as a concentration-equivalent parameter, the evolution of polarization scattering and depolarization characteristics for two typical particle types—smoke (irregular, strongly absorbing) and water mist (spherical, weakly absorbing)—is systematically investigated through combined simulation and experiment at optical depths of 0.77, 2.27, and 4. The results indicate that optical depth is the key parameter governing the macroscopic evolution of polarization and depolarization properties. As it increases from 0.77 to 4.00, the total scattering intensity (M11) and circular polarization preservation capability (M44) increase significantly, with the scattered depolarization intensity rising by more than 75%. The microphysical properties of the particles are the fundamental factor determining differences in their polarization response. At the same optical depth, the M44 value for water mist particles is approximately 76% higher than that for smoke particles, and their scattering images exhibit a unique “fragmented” texture due to discrete droplet distribution. Furthermore, the angular distribution features of key matrix elements (e.g., the peaks of M11 and M44 at 180°) demonstrate good robustness and can serve as effective indicators for identification. Experimental validation shows that the relative error between measured and simulated key parameters is within 6%, confirming the reliability of the method. This study demonstrates that Mueller matrix analysis can simultaneously and sensitively extract polarization information modulated by both particle microstructure and medium concentration, providing a new and effective approach for the high-precision identification and retrieval of atmospheric aerosols based on polarimetric remote sensing.
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Yu Long, Hu Yue, Fan Jianhua, Guo Hui, Hu Jinchao
2026,40(5):98-107,
Abstract:
An automatic modulation recognition methodology based on multi-scale features cross fusion is proposed to address the problem of limited recognition accuracy caused by insufficient feature extraction and utilization in existing deep learning-based modulation recognition methods. The automatic modulation recognition methodology proposed in this paper is based on deep learning model. Firstly, the IQ modulation signal is split into I signal and Q signal to form two inputs for the model. Subsequently, a multi-scale features extraction module is designed to effectively suppress signal noise, and on this basis, multi-scale features of the signal were extracted for modulation recognition. Then, a feature fusion module based on cross attention was designed to deeply fuse the multi-scale features extracted from the I signal and Q signal using the cross attention mechanism. Finally, the modulation recognition results were output. The experiments were conducted based on the benchmark dataset RadioML2016.10A and RadioML2016.10B in the field of modulation recognition. Compared to existing mainstream deep learning-based recognition models, the average recognition accuracy of the proposed methodology in the full signal-to-noise ratio (SNR) range is 57.17% and 62.18% respectively on RadioML2016.10A and RadioML2016.10B dataset, representing an improvement of more than 4.92% and 4.16%. The performance under low SNR conditions is also better than existing mainstream recognition models, and the recognition accuracy under high SNR conditions has been improved by 2.74% and 1.43% respectively. The experimental results show that the proposed methodology effectively improves the accuracy of signal automatic modulation recognition and overcomes the shortcomings of existing methods, such as sensitivity to noise and insufficient feature utilization.
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Zhang Xiaoyu, Lyu Yarui, Guo Runxia, Wu Jun
2026,40(5):108-118,
Abstract:
Civil aircraft flight control systems adopt a multi-redundancy architecture to ensure safety. However, this design, while enhancing reliability, introduces coupling and nonlinear causal dependencies among components, leading to the propagation of single faults across components. Accurately identifying the causal relationships among components is crucial for analyzing the root causes of faults and identifying their propagation paths. Therefore, a method for identifying fault propagation paths in flight control systems based on causal graphs is proposed. Firstly, a dual attention-based multi-channel depthwise separable temporal convolutional network (Da-MDSTCN) is constructed. Through the attention fusion mechanism, the causal association features among components are analyzed, and a system structure causal graph is built in combination with graph theory. Secondly, a multi-channel weighted temporal convolutional network (MC-WTCN) is designed for feature fusion. Through a feature modulation strategy, the collaborative prediction of multi-dimensional state feature parameters is achieved, and a diagnostic framework based on prediction residuals and relative deviation analysis is established to realize the diagnosis of fault origins and the identification of propagation paths. Finally, experiments are conducted based on the A320 flight control system. The fault diagnosis accuracy reaches 94.8%, and the identification of fault propagation paths is more accurate, verifying the accuracy and effectiveness of this method in fault diagnosis and propagation path identification.
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Zheng Jiali, Dong Ling, Li Heng, Xue Xiaojun, Liu Hui
2026,40(5):119-132,
Abstract:
To address the issues of heavy computational burden and difficulty in meeting real-time lightweight requirements of existing PCB defect detection methods, this study designs a lightweight printed circuit board defect detection model named PMAC-YOLO. To achieve an efficient balance between accuracy and lightweight requirements, the model is designed with three core optimized modules: first, a lightweight C3k2_PMLCA module is constructed by fusing partial convolution with the mixed local channel attention mechanism, enhancing the model′s ability to extract and perceive small-target features in complex backgrounds. Second, an ADSdown adaptive depthwise separable downsampling module is proposed to achieve lightweight downsampling while dynamically adjusting the degree of information retention. Additionally, a cross-scale feature fusion module is introduced to fuse multi-scale features, thereby improving the model’s adaptability to scale variations and the detection accuracy of small-target defects. Experiments are conducted on the DeepPCB dataset and the HRIPCB enhanced dataset. The results demonstrate that PMAC-YOLO achieves mAP@05 of 98.8% and 99.1% respectively, representing an improvement of 0.3% and 0.4% compared to the baseline model YOLOv11. Meanwhile, the number of parameters is reduced to 1.22×10-6, the computational complexity is decreased to 3.4 GFLOPs, and the model weight file size is only 2.8 MB, which are 52.64%, 46.03%, and 49.09% lower than those of the baseline, respectively. These results indicate that PMAC-YOLO significantly reduces model complexity and resource consumption while maintaining excellent or even superior detection accuracy, successfully achieving an effective balance between accuracy and lightweight performance. This study provides a feasible lightweight solution for real-time PCB defect detection.
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Li Cuicui, Jin Xi, Xia Changqing, Duan Yong, Xu Chi, Sun Yiming
2026,40(5):133-144,
Abstract:
With the in-depth development of global digital transformation, industrial IoT has put stringent requirements on the transmission delay, reliability, and wide coverage of network communication. TSN, with its deterministic low-delay transmission characteristics, complements the wide coverage advantage of 5G. The TSN-5G heterogeneous network, formed by the deep integration of the two, has become a cutting-edge solution to meet the real-time communication needs of industrial IoT. However, currently there is a lack of optimization methods for fine-grained use of transmission resources in this solution, resulting in the underutilization of limited resources, which in turn affects the success rate of end-to-end transmission. Therefore, based on the TSN and 5G fusion industrial network architecture, this paper designs a whale optimization algorithm based on dual Q-learning. The algorithm uses DDQN to dynamically optimize the execution action selection of “search” or “encirclement” in the whale optimization algorithm, which can accelerate the optimization process and effectively avoid the whale optimization algorithm falling into local optimum. In terms of performance comparison, the algorithm also shows significant advantages. Compared with the traditional whale optimization algorithm and genetic algorithm, The improvement of scheduling success rate ranges from 6.1% to 74%. Thus, the combination of DDQN and whale optimization algorithm can effectively solve the fine-grained resource allocation problem in TSN-5G networks.
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Jiang Xuzhong, Leng Kaimao, Fan Wei
2026,40(5):145-155,
Abstract:
Millimeter-scale close-range displacement measurement is widely used in fields such as precision manufacturing, medical monitoring, and aerospace. Owing to its non-contact operation, strong immunity to interference, and moderate spatial resolution, microwave sensing has become an effective technique for such tasks. Conventional loop probe antennas, however, suffer from multi-mode resonances, and achieving a desirable compromise between antenna size and performance remains challenging. To tackle these issues, this paper proposes a Four-Element sector truncated structure based on mode suppression, together with a nested-configuration transceiver antenna. This design effectively suppresses multi-frequency resonances, enabling efficient radiation at a single target frequency of 24.01 GHz while enhancing receive-signal purity and anti-interference capability. Simulations demonstrate that, while maintaining high gain, the antenna size is reduced by 65.385% typical loop probe antennas reported in the literature; the operational bandwidth is below 0.1%; the reflection coefficient at the target frequency reaches -64.001 dB; and the center-frequency gain attains 11.028 dBi. Experimental measurements closely align with the simulations: the fabricated prototype exhibits a reflection coefficient of -42.807 dB at 24.14 GHz, an absolute bandwidth of 0.027 GHz, and the relative bandwidth of 0.004% is far below the narrowband requirement of 1%. Therefore, the developed antenna is not only compact and structurally simple, but also exhibits robust performance, meeting the requirements of millimeter-scale close-range microwave displacement measurement.
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Fan Xiaodong, Zhang Chuhao, Zhou Jing, Hou Limin, Han Ying
2026,40(5):156-165,
Abstract:
To address the insufficient fusion of local texture details and global semantic information in medical image segmentation, which often leads to blurred boundaries and incomplete structures, a named dual fusion branch UNet（DFB-UNet）is proposed with parallel convolution and self-attention. The encoder employs a convolutional neural network（CNN）branch and a Transformer branch in parallel to learn local and global features. Before cross-branch fusion, a deformable convolution network （DCN）is introduced to enhance the modeling of irregular object boundaries. In addition, a dual-fusion module composed of an attention fusion module（AFM）and a convolution fusion module（CFM）is constructed; CFM incorporates depthwise separable convolutions to achieve effective feature interaction and complementary aggregation with lower computational cost. The experiment results are as follows: mDice/mIoU reach 0.89/0.83 on the dataset Kvasir-SEG; 0.810/0.723 on CVC-ClinicDB. In cross-dataset testing, training on Kvasir-SEG and testing on CVC-ClinicDB yield Dice/IoU of 0.751/0.668. On ISIC2017, IoU/Dice/Acc are 0.816/0.889/0.965, and on LUNA16, Dice/IoU are 0.966 0/0.934 2. The results indicate that the proposed dual-fusion parallel encoder improves segmentation accuracy and generalization while maintaining computational efficiency. The ablation studies further verify the contribution of DCN and the dual-fusion module.
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Wei Zihui, Chen Jiangpeng, Wang Zirui, Zhao Xinyue, Dong Peng
2026,40(5):166-178,
Abstract:
In ultra-wideband wireless communication systems, the accurate identification of non-line-of-sight and line-of-sight environments is crucial for enhancing positioning accuracy and system performance. To this end, a feature extraction method based on an attention mechanism structure is proposed to enhance the sparse autoencoder. The SAE learns stable and effective feature representations through sparse coding, while the attention mechanism highlights crucial information via dynamic weight allocation. Their integration enables more precise feature dimensionality reduction. A semi-supervised classification strategy combining Gaussian Mixture Models and self-training Support Vector Machines is employed for LOS/NLOS state recognition. The GMM models the generative probability distribution parameters of the extracted feature space, while the self-training SVM enhances classification accuracy and robustness in marginal regions. Experimental results demonstrate that, when comparing the performance of different dimensionality reduction methods combined with LOS/NLOS classifiers in the NLOS recognition task, the proposed SAE-ATTENTION feature extraction strategy coupled with the GMM-SVM classification model exhibits significant advantages in recognition accuracy and reliability. In static and dynamic experiments, this approach achieved NLOS recognition accuracies of 95.20% and 93.05% respectively, effectively enhanced the model’s generalisation capability under conditions of limited labelled samples.
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2026,40(5):179-190,
Abstract:
Due to the inherent local receptive field limitations of conventional convolutional networks, the representation of complex features in electrical impedance tomography (EIT) is often restricted, leading to inaccurate reconstruction of inclusion conductivity parameters and shape distortion caused by the neglect of global distribution information. To address these challenges, this paper proposes an end-to-end deep learning framework, HiRef-EIT, which integrates attention mechanisms and vision Transformers. Built upon a U-Net backbone, the proposed method employs hierarchical feature stacking to construct a multi-scale latent space representation. A multi-head spatial reduction attention（MHRA） module is designed to reduce computational complexity while preserving global modeling capability. Additionally, a skip bottleneck module combining channel attention and coordinate attention in parallel is introduced to capture the latent representations of conductivity distribution and inclusion shape from both the channel and spatial perspectives. A decoder unit based on cross-attention is employed to achieve global-local multi-scale feature fusion, thereby enhancing the model’s representation capacity. HiRef-EIT is trained using a large volume of simulated data to obtain optimal model parameters, and is experimentally validated on diverse complex-shaped phantoms and lung simulation models. Quantitative evaluation metrics demonstrate that the proposed method achieves a root mean square error (RMSE) of 6.028 and a structural similarity index (SSIM) of 0.956, with visual results showing strong consistency with the ground truth distributions and boundaries. The experimental results reveal that HiRef-EIT exhibits excellent robustness and generalization. Compared with traditional convolutional imaging models, the proposed method offers a high-quality imaging solution for EIT, thus holding significant theoretical and practical value for applications in medical diagnostics and industrial process monitoring.
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Li Haiyu, Yan Fang, Li Xiaona, Feng Jinqiu, Yang Yang
2026,40(5):191-203,
Abstract:
Traditional quality inspection of zirconium alloy plates mainly relies on manual methods, which are time-consuming, labor-intensive, and prone to low accuracy. To improve the detection accuracy and efficiency of surface defects on zirconium alloy plates, the network structure of the YOLOv8n object detection model was enhanced, and a lightweight multi-scale fusion defect detection model (LEF-YOLOv8) was proposed. To address the issue of small defect features being easily lost, a C2f-edge detail enhancement module (C2f-EDEM) was designed to extract defect edge features while incorporating spatial information, thereby enhancing feature extraction capability. To overcome the lack of explicit feature selection and attention mechanisms in the original model, a global-local interactive attention (GLIA) module was introduced, improving the integration of local and global semantic context. Furthermore, to mitigate the weak information fusion and high computational cost of the original detection head, a lightweight multi-scale fusion detection head (LMSFD) was proposed, which enhances feature representation while reducing model complexity. Experiments on a self-collected zirconium alloy plate dataset and the NEU-DET public dataset demonstrate that LEF-YOLOv8 achieves mAP@0.5 of 86.7% and 75.9%, respectively, improving by 3.2% and 2.4% over the baseline YOLOv8n, without introducing significant computational overhead. These results indicate that the proposed model improves detection accuracy and is suitable for resource-constrained inspection systems, providing an effective solution for automated surface defect detection of zirconium alloy plates.
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2026,40(5):204-212,
Abstract:
In response to the problems of low measurement accuracy, non-intuitive display, and lack of cumulative measurement function in traditional float flowmeters used in medical oxygen generators, an intelligent oxygen flow monitoring system based on a strain gauge sensor was designed. The system adopts a single-ended fixed cantilever-beam strain gauge structure, combined with an improved Wheatstone bridge and a high-precision instrumentation amplifier, to achieve sensitive capture and efficient conversion of weak strain signals caused by gas flow. A voltage-flow rate polynomial equation was fitted using MATLAB, and Kalman filtering with a self-correction algorithm was introduced to effectively suppress temperature drift and noise interference, thereby enhancing the measurement stability and reliability of the system in complex environments. The experimental results are as follows: within the measurement range of 0.5~5 L/min, the maximum absolute error of the sensor is less than 0.2 L/min under ambient temperatures ranging from -10 ℃ to 40 ℃, with a response time of less than 2 s; the output remains stable during 168 h of continuous operation, with no significant drift or failure observed; and the measurement stability is well maintained under pressure variations from 86 to 106 kPa. The results indicate that the system is capable of real-time display of instantaneous flow rate and cumulative oxygen consumption, featuring a simple structure, low cost, high accuracy, and strong environmental adaptability. It provides a reliable solution for the intelligent upgrading of medical oxygen generators.
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Hua Shengye, Wang Qiang, Zhou Haiting, Jin Canhua, Jiang Chenghang
2026,40(5):213-221,
Abstract:
To address the difficulty in detecting defects in butt fusion joints of high-density polyethylene (HDPE) gas pipelines, this paper proposes a microwave imaging method based on the molecular chain structure characteristics of cold welding and over-welding defects, as well as microwave detection principles. This method is used to detect and image typical defects (cold welding and over-welding) in HDPE gas pipeline butt fusion samples. The results show that there are differences in dielectric constant between cold welding and over-welding defects, which can be well reflected in the changes of microwave reflection coefficient amplitude. The average microwave reflection coefficient amplitude of the butt fusion joint of a defect-free sample is around -34 dB, while that of the butt fusion joint with a cold welding defect is around -31 dB, and that of the butt fusion joint with an over-welding defect is around -35 dB. The microwave reflection coefficient amplitude increases as the heat absorption time, welding temperature, and welding pressure decrease. By correlating the areas with abnormal microwave reflection coefficient amplitude with the specific locations of sample defects, and comparing the results with digital radiographic (DR) detection, it is confirmed that this microwave imaging method has better detection capability for cold welding and over-welding defects in HDPE gas pipeline butt fusion joints.
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Hu Baohua, Li Tao, Lu Cuiping, Zhang Zhirong, Wang Yong, Mu Jingsong, Mei Jiaming
2026,40(5):222-233,
Abstract:
Accurate and reliable muscle fatigue detection plays an important role in rehabilitation robot and human-machine cooperation. Considering frequency change of surface EMG signal after muscle fatigue, this paper proposes a muscle fatigue detection method based on improved band energy entropy, which uses fractional weighted S-transform energy entropy and fractional weighted wavelet packet decomposition energy entropy to detect muscle fatigue, and compares them with sample entropy, dispersion entropy and fractional fuzzy dispersion entropy. A total of 32 subjects participated in the muscle fatigue experiment. The results show that the improved frequency band energy entropy performs optimally in terms of noise robustness, data stability, and anti-interference performance. The improved S-transform energy entropy achieved the highest sensitivity to static muscle fatigue among all the compared complexity algorithms. The absolute slope of complexity variation with muscle fatigue is the largest, with an average value of -15.946 5×10-3. The improved wavelet packet energy entropy (-3.100 3×10-3) detection algorithm can also detect muscle fatigue well, and achieves comparable performance to fractional fuzzy dispersion entropy (-2.602 6×10-3). Among all algorithms, this algorithm exhibits the lowest time consumption, second only to the dispersion entropy algorithm. In the dynamic muscle fatigue test, the improved S-transform energy entropy also performs optimally. The improved frequency band energy entropy provided an effective new tool for analysis of sEMG signal complexity.
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Wu Jiamin, Wang Peng, Fu Qirui, Sun Ruxue, Zhang Changming, Yang Fan, Dai Yuqiang
2026,40(5):234-245,
Abstract:
To improve the accuracy of force/torque loading and the feasibility of multi-degree-of-freedom combined loading during the calibration of six-axis force sensors, a dynamic calibration system based on impact-hammer excitation was developed by utilizing a six-degree-of-freedom independent loading static calibration apparatus. The first- and third-order modal frequencies of the sensor structure were obtained through modal analysis to evaluate its dynamic response under multi-frequency excitation, thereby avoiding resonance effects and improving dynamic calibration accuracy. To assess the performance of the proposed calibration system, six-dimensional force/torque data were collected under known loading conditions. Three modeling and fitting methods—traditional linear regression, particle swarm optimization-based backpropagation neural network (PSO-BP), and support vector regression (SVR)—were employed to establish the mapping relationship between the sensor outputs and the applied loads. The fitting performance of the three methods was compared across all six channels. The results indicate that the PSO-BP neural network achieved the highest fitting accuracy, with the mean absolute error (MAE) of 17.86%，which reduced by 8.20% compared to linear regression, and significantly more than the SVR method. The proposed calibration system demonstrates excellent static and dynamic performance, validating the superiority and practicality of the PSO-BP neural network in multi-dimensional force/torque sensor calibration.
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Li Zhen, Zhao Zhibiao, Shi Yan, He Yulang, Zhou Qi
2026,40(5):246-260,
Abstract:
Human activity sensing based on WiFi Channel State Information has emerged as a prominent research focus in recent years. To address the challenges of ineffective activity data extraction and the reliance on handcrafted features in existing methods, this paper proposes a WiFi-based human activity extraction and sensing method that integrates grouped multi-scale attention. First, amplitude and phase features most sensitive to human activity are extracted using antenna selection and conjugate multiplication, followed by wavelet-based denoising for preprocessing. Next, we compute sliding window factors and activity correlation factors through the variance of joint amplitude and phase features, and apply an empirical threshold to extract valid activity sequences. Finally, we design an end-to-end deep learning model (D-GMAFi) based on a grouped multi-scale attention mechanism to capture long-range temporal dependencies, multi-scale spatial information, and dynamically emphasize critical features across different dimensions. Extensive simulation experiments conducted on the self-collected WiACT dataset demonstrate that D-GMAFi achieves average recognition accuracies of 94.63% and 91.06% under single-person single-scene and multi-person multi-scene settings, respectively. The proposed method outperforms the baseline models overall and maintains stable recognition performance across different users and scenarios, indicating a certain degree of robustness and adaptability to application-oriented settings. These results suggest that the proposed approach exhibits strong potential for WiFi-based human activity recognition tasks and can provide a useful reference for the practical application of WiFi sensing technologies in real-world scenarios.
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2026,40(5):261-271,
Abstract:
In response to the pressing demands of current wearable medical devices for wide bandwidth, multi-functional integration, and biocompatibility, this paper presents the design of a hybrid reconfigurable dual-band antenna based on an eco-friendly flexible paper substrate. The proposed antenna employs photo paper as the dielectric substrate, with compact dimensions of 22×25×0.27 mm3, offering excellent flexibility and lightweight characteristics. Dual-band operation is achieved through stub-loading techniques, while integrated PIN diodes function as RF switches. By controlling their ON/OFF states to alter the current paths, the antenna achieves four reconfigurable operating modes in both frequency and radiation pattern: specifically, two dual-band directional radiation modes, one low-frequency omnidirectional radiation mode, and one high-frequency omnidirectional radiation mode. The antenna structure was fabricated on the photo paper substrate using the screen-printing technique with silver paste, and the prototype was completed by integrating PIN diode control units via a bias circuit module. The measured results demonstrate that the antenna operates effectively in either dual-band or single-band modes across the 2.4 and 5.8 GHz bands. Across all modes, the -10 dB impedance bandwidth exceeds 20%, the peak gain remains stable at approximately 3 dBi, and the radiation efficiency is above 60%. Under bending conditions with a curvature radius of 20 mm and when loaded with a human tissue model, the antenna maintains stable impedance matching and radiation performance, demonstrating good structural robustness and wearability. The specific absorption rate (SAR) values are 1.145 W/kg at 2.5 GHz and 1.312 W/kg at 6 GHz, both complying with international safety standards and meeting the electromagnetic safety requirements for wearable devices used on the human body. The combination of eco-friendly materials, a compact structure, and hybrid reconfigurability endows the proposed antenna with significant potential for practical applications in short-term medical monitoring and body area communication wearable systems.
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Zhu Yuan, Dou Yuqi, Xu Shihan, Cai Weimin, Su Dan
2026,40(5):272-285,
Abstract:
To address the challenges of high costs pertaining to physical sensors and the necessity for manual calibration of tire pressure reference values in indirect tire pressure monitoring systems (iTPMS), this paper proposes a hybrid tire pressure monitoring system (hTPMS) based on the degeneration gated recurrent unit (DGRU) network. First, this study conducts a theoretical analysis of the tire torsional vibration model, which serves as the fundamental basis for tire pressure identification. A simplified filtering algorithm is devised to compensate for ring gear errors, and in conjunction with the Ackermann steering model, the wheel speed discrepancy between the inner and outer tires under small-steering conditions is eliminated, thereby extending the effective operating envelope of the radius iteration method. By fusing the signals from a single physical tire pressure sensor and anti lock braking system (ABS) sensors, DGRU neural network estimators are established independently in both the time and frequency domains, enabling real-time and high-precision estimation of tire pressure values. Results of real-vehicle road tests demonstrate that when the tire pressure experiences a sudden drop from 2.6 bar to 1.9 bar, the frequency-domain feature input mode achieves a minimum response time of 73.6 s, with the tire pressure estimation error confined within the tolerance range specified by national standards. Compared with sequence-based input, the time-domain feature input mode enhances the response speed by up to 62.66%, and the algorithm operates on the Aurix TC397 chip with an average CPU utilization rate of merely 0.464%.This system reduces the cost of physical sensors by 75% relative to conventional direct tire pressure monitoring systems (dTPMS), and its accuracy and stability across diverse operating conditions fully comply with the requirements of the national standard GB 26149-2017, offering an innovative approach that integrates mechanical modeling and intelligent algorithms for tire pressure monitoring technology.
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Wang Zhenyu, Zhao Kedong, Sun Yongrong, Yang Jie, Chen Qi
2026,40(5):286-295,
Abstract:
In multi-scenario perception and intelligent control systems, the accurate and efficient recognition of traffic light states is an important support for ensuring stable system operation and the collaborative operation of intelligent devices. In response to the diverse appearances and scarce samples in traffic light detection scenarios, transfer learning has become a mainstream solution. However, traditional full parameter fine-tuning and shallow freezing do not quantify the specific contributions of network layers to the task, leading to overly broad training updates, parameter redundancy, and a tendency to overfit under small sample conditions, which limits model performance. To address this, this paper proposes a layer-adaptive guided freezing strategy generation method based on CNN-LRP. By utilising the forward correlation ratio, spatial focus degree, and layer depth penalty factor, the pixel-level correlation of CNN-LRP is expanded to the convolutional layer level, constructing a layer contribution evaluation system that integrates interpretative analysis with the freezing strategy. To tackle the issue of insufficient task adaptability in traditional strategies, this paper designs a multi-metric fusion convolutional layer adaptability scoring method that automatically generates freezing strategies, and realises task-driven migration by freezing the low-adaptability layer, so as to optimise the model. Finally, verification tests were conducted using scene data with different lighting and distances. The results show that compared with full fine-tuning and other freezing strategies, the model generated by the method proposed in this paper has an improvement of 3.1% in test accuracy, a reduction of 34.49% in parameter tuning volume, and better performance in terms of accuracy, efficiency, generalization, robustness, and the universality of convolutional networks.
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Wu Wenhuan, Chen Jie, Wang Wenshu
2026,40(5):296-307,
Abstract:
Object detection is a core technology in the environmental perception of autonomous driving, and its task is to identify and locate key objects in the surrounding environment of the vehicle. To address the challenges of missed detection of small targets, misjudgment due to occlusion, and insufficient dynamic adaptability in autonomous driving scenarios, an improved object detection algorithm DPM-YOLO based on YOLOv11n is proposed. Firstly, in the Backbone network, a dynamic attention pyramid module is constructed to replace the original rapid spatial pyramid pooling structure. By utilizing parameter-shared dilated convolutions and a decomposed spatial attention mechanism, multi-granularity features are efficiently captured and the context information of each target can be better aggregated. Secondly, in the Neck network, a parallel fusion convolution module is designed. It can achieve channel-wise dynamic decoupling and multi-scale features fusion by heterogeneous multi-branch convolutions. Finally, an improved MPDWIoU loss function is proposed, which integrates a dynamic gradient focusing mechanism with corner constraints and optimizes bounding box localization accuracy by combining a batch statistics adaptive strategy. Experimental results demonstrate that the mAP@0.5 of DPM-YOLO on the KITTI dataset is 92.7%, which is 2.0 percentage points higher than that of YOLOv11n. The mAP@0.5 of DPM-YOLO on the Cityscapes and BDD100K datasets is improved by 1.6 and 0.8 percentage points respectively compared with YOLOv11n. The mAP@0.5 of DPM-YOLO on the extreme weather dataset Foggy-Cityscapes increases by 1.2 percentage points over YOLOv11n. The results indicate that the proposed method effectively balances detection accuracy and real-time performance in complex driving scenarios.
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Chen Wei, He Haoran, Li Xin, Qiu Ya, Chu Biao
2026,40(5):308-323,
Abstract:
To address the challenges of severe outlier interference and temporal modeling difficulties in predicting NOx concentrations within cement denitrification systems, this paper proposes a composite prediction method integrating SSAD-Z-score anomaly detection with LFLSTM deep learning models. First, based on NOx generation and SNCR reaction mechanisms, core input variables-including kiln tail flue gas chamber NOx concentration and SNCR ammonia injection frequency—are selected using correlation coefficients. To handle complex anomalies like backwashing, the SSAD-Z-score algorithm is designed. It combines sliding window slope changes with global Z-score criteria to achieve adaptive detection and correction for multiple anomaly types, including sudden spikes, trends, local disturbances, and backwashing data. Furthermore, multi-step lag features are introduced into the LSTM model to fully leverage historical time-series information, enhancing the model’s ability to capture dynamic changes in NOx concentration. Experimental results demonstrate that the SSAD-Z-score method significantly outperforms traditional Z-score methods, Isolated Forest, LOF, and Wavelet Transform in metrics such as anomaly detection coverage (47.67%) and recall (0.844). Additionally, SSAD-Z-score exhibits exceptional performance in corrected standard deviation, achieving the lowest value among all comparison methods with an average correction magnitude of 8.81. Parameter sensitivity analysis indicates that under moderate parameter settings—sliding window size 10, slope threshold 0.15, and Z-score threshold 1.65-SSAD-Z-score achieves optimal balance in terms of corrected standard deviation (35.65) and average correction magnitude (8.81). Inputting the corrected data into the LFLSTM model for NOx concentration prediction yielded an average single-sample prediction time of just 4.88 milliseconds-significantly below the 1-second data acquisition frequency. The model achieved an R2 of 0.884 5, RMSE of 2.026 5, and MAE of 1.798 5, demonstrating markedly superior predictive performance compared to mainstream models like Transformer, Informer, and GRU. Ultimately, the LFLSTM model combined with SSAD-Z-score correction demonstrated the best overall system performance, achieving an R2 of 0.824 097, an RMSE of 4.305 981, and an MAE of 3.297 620. These metrics significantly outperformed prediction models using either LFLSTM alone or Z-score-LFLSTM. The research results validate the effectiveness of the proposed method in enhancing anomaly detection accuracy and NOx concentration prediction precision, providing a robust data foundation and methodological support for intelligent control of cement denitrification systems.
Volume 40,2026 Issue 5
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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.
