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Liu Yuting, Zheng Xiujuan, Zhang Xuegang, Xu Zhihui, Zhou Xiangcai, Tu Haiyan
2026,40(7):1-12,
Abstract:
The long-term accumulation of mental stress significantly impacts an individual’s physical and mental health as well as workplace safety. Identifying mental stress naturally and non-intrusively in daily work and life holds substantial research significance and practical value. Addressing the limitations of traditional mental stress identification methods—which rely on subjective questionnaires or contact-based physiological sensors and struggle to meet non-intrusive application requirements—this paper proposes an auxiliary mental stress identification method based on implicit information from facial videos. This method utilizes remote photoplethysmography (rPPG) as its foundation. It extracts subtle color variation signals caused by skin reflection from facial video sequences to reconstruct rPPG signals and extract key physiological features. Additionally, it incorporates facial action unit (AU) features through a dual-branch feature fusion approach to achieve mental stress recognition. Furthermore, a facial video dataset under various stress-inducing conditions was constructed based on the Montreal imaging stress task (MIST) paradigm to validate the proposed method’s effectiveness. Experimental results demonstrate that the proposed method achieves a mental stress recognition accuracy of 95.64%. Furthermore, the findings reveal consistent patterns of change in pulse wave modulation and facial movement expression corresponding to mental stress states at the feature level, providing an effective technical approach for non-intrusive and interpretable mental stress recognition.
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Dual-branch spatiotemporal disentanglement and reconstruction for robust remote photoplethysmography
Zhang Jianhai, Jiang Feng, Cheng Shichao, Zhao Changchen
2026,40(7):13-23,
Abstract:
Remote photoplethysmography (rPPG) can recover pulse-related physiological signals from facial videos captured by ordinary cameras. However, the cardiac-induced color variations are extremely subtle and are easily corrupted by head motion, pose variation, illumination fluctuation, shadow occlusion, and imaging noise. Most existing deep learning methods formulate rPPG estimation as an end-to-end regression problem, but they usually lack explicit modeling of the generation mechanisms of pulse and noise components, making the intermediate representations difficult to interpret. To address this problem, this paper proposes PNRNet, a physics-guided dual-branch spatiotemporal disentanglement and reconstruction network for robust rPPG. The proposed method models facial RGB temporal variations as a superposition of physiological pulse components and non-physiological noise components. Two independent 3D convolutional branches are used to learn pulse-related and interference-related dynamics, and a feature orthogonality constraint is introduced to reduce redundant coupling between the two latent representations. Inspired by the dichromatic reflection model, an RGB temporal reconstruction module projects the predicted pulse and noise signals back to the RGB observation domain, enabling the network to be jointly supervised by reference PPG waveforms and video-domain reconstruction. Experimental results show that PNRNet achieves MAE of 3.02, 1.18, and 1.78 bpm on the iBVP, UBFC-rPPG, and PURE datasets, respectively, with an 8.8% MAE reduction over the best competing method on iBVP. These results indicate that the proposed pulse-noise disentanglement and RGB observation-domain reconstruction mechanism helps improve the robustness and interpretability of rPPG models under complex interference conditions.
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Li Yangjie, He Xingyan, Fu Changhong, Hong Hong
2026,40(7):24-33,
Abstract:
Conventional remote photoplethysmography (rPPG) methods suffer from limited robustness in complex scenarios, while deep learning approaches often involve high computational cost and poor interpretability. To address these issues, a lightweight solution that integrates the advantages of machine learning and linear modeling is developed.The proposed method first partitions the facial region into multiple regions of interest (ROI) to extract multi-channel (RGB) temporal signals, followed by correlation-based filtering to achieve temporal alignment between video signals and reference pulse signals. A lightweight network is then employed to learn the optimal linear fusion weights across channels, and pulse signal reconstruction is performed under joint constraints in both the time and frequency domains. In this process, the model preserves the interpretability of traditional linear combination methods while introducing a data-driven weight learning mechanism, enabling adaptive fusion of multi-channel signals. Finally, high-resolution spectral analysis and ridge-based time-frequency tracking are applied for heart rate estimation and dynamic tracking. Experimental results on multiple public datasets, including VitalVideo, MMPD, UBFC-Phys, iBVP, and PhyRec, demonstrate consistent performance across different scenarios. On the VitalVideo dataset, the proposed method achieves a signal-to-noise ratio (SNR) of 6.15 dB and a mean absolute error (MAE) of 1.45 min-1. On the PhyRec dataset, the MAE reaches 0.29 min-1. In the overall evaluation, the method achieves an MAE of 8.45 min-1, a root mean square error (RMSE) of 10.43 min-1, an SNR of -0.55 dB, and an accuracy (ACC) of 0.84. These results indicate that the proposed multi-channel linear fusion strategy improves signal reconstruction quality and heart rate estimation stability while maintaining model interpretability. The method effectively balances interpretability and performance under complex conditions, making it suitable for non-contact physiological signal measurement.
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Zhu Haijun, Li Wenyang, Xu Lisheng, Liu Xiuling, Zhao Changchen, Du Shuo
2026,40(7):34-43,
Abstract:
Non-invasive and contactless physiological parameter measurement is a vital research direction in health monitoring, and remote photoplethysmography (rPPG) has become a mainstream technique in this field. Existing rPPG algorithms struggle to balance detection accuracy and lightweight deployment, and suffer from low efficiency in modeling long-range temporal dependencies. To solve these problems, this paper proposes an rPPG-Mamba model. The model adopts depthwise separable convolutions to reduce parameters and computational overhead, and leverages bidirectional Mamba to perform efficient long-range temporal modeling. Temporal difference convolution and 3D channel attention are further integrated to improve feature extraction capability in complex scenarios, enabling accurate rPPG signal extraction from facial videos. Extensive experiments were conducted on three public datasets: PURE, UBFC-rPPG and MMPD. In intra-dataset evaluations, the proposed model achieved a mean absolute error (MAE) of 0.18 and a root mean square error (RMSE) of 0.31 on the PURE dataset, as well as an MAE of 0.43 and an RMSE of 0.68 on the UBFC-rPPG dataset, outperforming all comparison methods. Cross-dataset tests also verified its satisfactory generalization ability. On the MMPD dataset with severe illumination and motion interference, its performance was only slightly worse than that of PhysMamba. Compared with EfficientPhys, the proposed model reduced the parameter count by approximately 89.9%, while its computational complexity was 61.5% lower than that of DeepPhys. The experimental results demonstrate that rPPG-Mamba achieves an excellent trade-off between detection accuracy and lightweight design, and possesses great application potential for non-contact remote physiological measurement.
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Li Yihang, Cheng Shichao, Zhao Changchen, Zhang Jianhai
2026,40(7):44-52,
Abstract:
Remote photoplethysmography (rPPG), as a non-contact physiological monitoring technique, is of great value in clinical monitoring and human-computer interaction. However, the performance of deep learning models in rPPG tasks heavily relies on large-scale and diverse annotated data. Existing benchmark datasets, such as PURE and UBFC-rPPG, are limited by subject scale and relatively simple motion scenarios, which leads to insufficient generalization when models face complex nonlinear motion interference. Therefore, this paper proposes a facial video synthesis framework based on generative animation and biophysical embedding. The framework adopts a two-stage strategy of motion generation and signal embedding. First, the generative framework LivePortrait is used to transfer large-scale motions and facial expressions from driving videos to static images, constructing high-fidelity dynamic facial videos and effectively simulating macroscopic displacement noise in real scenes. Then, a layer-separated biophysical embedding model is proposed to implant physiological signals into videos. This model uses a large-kernel Gaussian operator to decompose the image into a base layer and a detail layer, and introduces multiplicative modulation into the low-frequency component, which better conforms to skin optical characteristics and blood-flow variations. While preserving detailed skin texture, it ensures the physical accuracy of weak physiological signals in both spectral and temporal dimensions. Experimental results on the UBFC-rPPG and PURE datasets show that, under sample-limited conditions, after introducing the synthetic data generated by this paper, the MAE of PhysNet on the UBFC-rPPG dataset decreases from 1.11 bpm to 0.78 bpm, and the SNR increases from 5.50 dB to 5.99 dB; on the PURE dataset, the MAE decreases from 1.16 bpm to 0.99 bpm, and the SNR increases from 8.17 dB to 8.36 dB. The results demonstrate that the synthetic data generated by this paper can effectively improve the heart-rate estimation accuracy and signal quality of rPPG models. This study provides a feasible approach with both physical determinacy and motion realism for addressing data scarcity in the rPPG field.
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Wang Jiaxiong, Li Jiajie, Fan Wei, Song Rencheng, Liu Yu, Cheng Juan
2026,40(7):53-66,
Abstract:
Remote photoplethysmography (rPPG), as a contactless heart rate measurement technique, has been widely applied in health monitoring, affective computing, and driving safety. Existing supervised rPPG methods rely heavily on ground truth physiological signal labels, yet such reference labels are difficult to acquire and costly to annotate, which limits their practical deployment. To address this issue, this paper proposes a self-supervised learning method that integrates masked autoencoder (MAE) with contrastive learning (CL), termed PhysCMAE, to enable accurate video-based heart rate measurement when only limited reference physiological signal labels are available. First, multi-scale spatiotemporal maps are constructed from the temporal variations of pixel values in facial skin regions of interest. Next, an asymmetric encoder-decoder architecture based on a Vision Transformer is designed to perform masked autoencoder pretraining on the multi-scale spatiotemporal maps, thereby learning fine-grained physiological feature representations. Then, in the later stage of pretraining, a contrastive learning module is introduced, and a frequency-domain contrastive strategy is adopted to enhance the instance discriminability of physiological features. Finally, an rPPG prediction head is added, and the pretrained encoder is finetuned to map video physiological features to high-precision blood volume pulse (BVP) signals and perform heart rate estimation. In within-dataset experiments on two public datasets, UBFC-RPPG and PURE, the proposed method achieves mean absolute errors of 0.71 bpm and 0.59 bpm, respectively. In cross-dataset experiments, the mean absolute errors are 3.80 bpm and 4.88 bpm, representing error reductions of about 68.0% and 15.2%, respectively, compared with baseline methods. In cross-dataset experiments on the self-collected BSIPL-RPPG dataset, the mean absolute errors are 3.96 bpm and 6.69 bpm, respectively, achieving the second-best results among existing supervised learning methods. Experimental results demonstrate that, compared with existing self-supervised learning approaches, PhysCMAE can achieve higher heart rate estimation accuracy and stronger cross-dataset generalization under conditions of scarce physiological signal labels, providing an effective technical solution for the practical application of video-based rPPG technology.
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2026,40(7):67-79,
Abstract:
In public safety scenarios, extreme individual behaviors are often sudden, harmful, and highly uncertain. Timely identification of relevant abnormal states in a non-intrusive manner is therefore of great significance for risk warning and early intervention. To address the limitations of existing studies, including the lack of dedicated datasets, insufficient unimodal representations, difficulty in jointly modeling short- and long-term dynamics, and unstable fusion caused by missing modalities and signal quality fluctuations, this paper proposes an individual psychological abnormal state detection method based on multi-scale temporal representation and mask-adaptive fusion. First, a transfer learning scheme is designed by pretraining on a public mental health dataset and fine-tuning on a self-built dataset, so as to leverage transferable psychological state knowledge, improve model initialization, alleviate undertraining and overfitting under small-sample conditions, and enhance adaptation to the target task. In model design, multi-source information from video, audio, and rPPG is integrated, and a multi-scale temporal representation method is introduced to better capture the coexistence of short-term fluctuations and long-term evolution in psychological abnormal states, thereby improving collaborative modeling of cross-modal dynamic cues. Furthermore, a mask-adaptive fusion mechanism is proposed to address modality absence, signal quality variation, and unequal modality contributions in real-world scenarios, thus improving model stability and robustness under complex conditions. Experimental results show that the proposed method achieves an MAE of 5.14 and an RMSE of 6.37 on the AVEC2014 dataset, and an accuracy of 0.76 and an F1-score of 0.82 on the self-built dataset, demonstrating its effectiveness.
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Chen Baoliang, Xia Yuqing, Hu Shanshan, Mu Jingsong, Wang Yong
2026,40(7):80-87,
Abstract:
Based on a self-developed partitioned plantar perturbation force plate (PP-PFP), this paper proposes a phase-partitioning method for analyzing the dynamic process of human postural control following support-surface perturbations. Using acceleration and force data from the built-in sensors of the PP-PFP, three key time points were defined based on a threshold method: the onset of perturbation, the onset of postural adjustment, and the end of postural adjustment. Accordingly, the perturbed postural response process was divided into four phases: balanced standing (BS), imbalanced standing (IBS), compensatory standing (CS), and rebalanced standing (RBS). The center of pressure (CoP) variables calculated include: root mean square distance (RDIST), mean velocity (MVELO), 95% confidence ellipse area (AREA-CE), and the ratio of trajectory length to the straight-line distance between the start and end points (length ratio, LR). Paired-sample t-tests were conducted on the CoP variables for the BS vs. RBS phases and the IBS vs. CS phases, respectively. The results showed significant differences in LR, MVELO, and RDIST between the IBS and CS phases (p<0.01), and significant differences in AREA-CE and RDIST between the BS and RBS phases (p<0.01). Combined with the clear transitional features observed in the CoP trajectory plots across the four phases, these findings preliminarily validate the effectiveness of the proposed PP-PFP and its phase-partitioning method, providing a refined analytical framework for in-depth investigation of the dynamic regulatory mechanisms of postural control.
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Cui Lingyun, Zhao Zhibiao, Shi Yan, Ge Changyu, Li Zhen
2026,40(7):88-102,
Abstract:
To address the problem that limited CSI dimensionality in embedded WiFi sensing scenarios leads to insufficient recognition accuracy for fine-grained alphabet gestures, a lightweight CSI-based fine-grained gesture recognition method is proposed. A staged preprocessing pipeline, including Hampel outlier removal, median filtering, wavelet denoising, and cubic spline interpolation, is applied to CSI sequences to improve signal quality and sequence consistency. Subsequently, a dual-branch network, termed DFANet, is designed to fuse time-frequency features by extracting subcarrier correlation features in the frequency domain and temporal dependency features in the time domain, and a dendritic network is employed for efficient classification. Experimental results show that the proposed model achieves average recognition accuracies of 88.51%, 92.64%, and 91.60% in laboratory, office, and classroom environments, respectively, with an overall average accuracy of 91.21%. The model contains 147.880×103 parameters and requires 49.534×106 FLOPs. Compared with ResNet50, under comparable recognition accuracy, the number of parameters is reduced from 21.291×106 to 147.880×103, and the computational complexity is reduced from 835.781×106 to 49.534×106 FLOPs. The results demonstrate that the proposed method achieves effective fine-grained alphabet gesture recognition while maintaining low computational cost and lightweight characteristics, providing methodological support for future deployment in resource constrained embedded WiFi sensing systems.
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Li Meng, Wang Yan, Yang Xiaoqi, Hu Xueqin, Peng Jinjin, Zhao Liangjing
2026,40(7):103-114,
Abstract:
To address the lack of objective and quantitative criteria for the “acupoint stagnation” state in cervical spine palpation in traditional Chinese medicine (TCM), this study proposed a quantitative method for assessing acupoint tissue stiffness based on a flexible sensing array, and evaluated its effectiveness in the TCM pathological classification of cervical spondylosis. A wearable, vertically distributed flexible sensing array made of gallium-indium alloy and polyvinylidene fluoride (GaIn-PVDF) was employed and attached to the cervical acupoint region in a micro-contact and low physiological burden manner. By leveraging the high-sensitivity response of the GaIn strain unit to skin deformation under force and incorporating Hertz contact theory, a quantitative model was established to convert sensor resistance changes into the equivalent stiffness of tissue at the acupoint. The tissue stiffness output by the model showed a strong correlation with clinical Shore hardness values (Pearson correlation coefficient = 0.96), indicating that the proposed model can effectively reflect the true stiffness of acupoint tissue. On this basis, acupoint electrophysiological parameters and pressure sensitivity thresholds were integrated, and a multi-parameter TCM pathological classification model based on principal component analysis (PCA) and K-nearest neighbor (KNN) was constructed, achieving an overall multi-class prediction accuracy of 85.7%. Furthermore, the proposed sensing array was applied to monitor cervical range of motion, and a comprehensive evaluation framework integrating TCM pathological classification with Western medicine cervical range of motion was established. In addition, the piezoelectric effect of the PVDF unit was utilized to map acupoint pressing signals into the actions of a virtual game character, realizing human-computer interaction and enhancing patient engagement in rehabilitation training. This work provides a new technical pathway for the objectification and digitalization of TCM acupoint diagnosis and demonstrated good potential for use in home health management and smart rehabilitation scenarios.
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Yan Yan, Song Yongxian, Ma Zeyu
2026,40(7):115-124,
Abstract:
To address the challenges of low perception accuracy and poor trajectory tracking robustness in lower-limb rehabilitation robots, which are primarily caused by the mutual interference of multi-source sensor signals in complex environments, this paper investigates adaptive fusion methods and modeling mechanisms for multi-source information. A dynamic gating-driven bio-mechanical cross-modal gait prediction model, termed Gate-TACD, is proposed. The model features a dual-branch feature extraction architecture: the physiological branch fuses motion intent from surface electromyography (sEMG) with joint static background features provided by a goniometer to capture the human bio-postural state; meanwhile, the kinematic branch extracts temporal context features from inertial measurement units (IMUs). A dynamic gating mechanism is designed to adaptively regulate the contribution weights of biological and mechanical modalities in real-time, thereby suppressing signal crosstalk and representation distortion under adverse conditions such as sensor slip. Furthermore, a physics-informed loss function based on the minimum jerk principle is constructed to constrain the predicted trajectories within the bounds of human kinematics, ensuring the continuity and smoothness of the output gait. Experimental results on the Camargo dataset across multiple terrains demonstrate that Gate-TACD achieves superior predictive performance, with the coefficient of determination R2 reaching up to 0.99 and the root mean square error (RMSE) consistently staying between 0.3°and 0.7°under normal conditions, significantly outperforming baseline LSTM and GRU models. Notably, in challenging scenarios involving sensor slip noise, the model maintains a mean RMSE of only 1.31°, representing a 16% reduction in prediction error compared to unimodal kinematic models. This research provides a high-precision and robust cross-modal perception framework for the intelligent control and real-time tracking of lower-limb rehabilitation robots.
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Yuan Zijing, Hu Danfeng, Li Haochen, Wang Jiajun
2026,40(7):125-134,
Abstract:
Random body movements during frequency modulated continuous wave (FMCW) radar-based vital sign monitoring can induce abrupt phase variations, range-bin shifts, and spectral peak deviations, thereby degrading the estimation accuracy of respiratory rate (RR) and heart rate (HR). To address this issue, a vital sign detection algorithm for non-stationary subjects is proposed. First, the phase of the radar intermediate-frequency signal is used to construct a motion trend function based on the second-order phase difference, enabling the characterization of abrupt phase changes induced by random body movements. A dynamic threshold is then applied to identify motion distorted segments. These segments are locally reconstructed using a seasonal autoregressive integrated moving average model (SARIMA), whereas segments without significant motion interference are preserved to avoid unnecessary global modeling. Respiratory rate and heart rate are subsequently estimated through signal separation and spectral analysis. Experimental results show that, when the dynamic threshold adjustment coefficient μ is set to 0.20, the heart rate estimation accuracy reaches 96.10%, with an RMSEHR of 1.653 3. Across different SARIMA parameter combinations, the recovered respiratory rate and heart rate remain stable; however, the running time of higher-order model increases from 57.106 1 s to 335.748 4 s. Further experiments involving four subjects demonstrate that the proposed algorithm achieves a respiratory rate detection accuracy of 93.33% and a heart rate detection accuracy of 96.90%, with corresponding RMSERR and RMSEHR values of 3.43 and 11.01, respectively. These results indicate that, under the random-body-motion conditions considered in this study, the proposed method can reduce the influence of motion interference and improve the stability of respiratory-rate and heart-rate estimation.
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Kong Fanxing, Han Meng, Sun Liying, Zhao Yanghao, Li Yanan, Xiong Jin
2026,40(7):135-148,
Abstract:
Non-invasive continuous blood pressure monitoring is a core technology for the prevention, diagnosis, and management of cardiovascular diseases. Traditional cuff-based measurement methods have limitations such as intermittent readings, patient discomfort, and the “white coat effect”. With the rapid advancement of wearable technology and mobile healthcare, non-invasive continuous blood pressure measurement based on multi-modal physiological signal fusion has emerged as a research hotspot. Research progress in multi-modal physiological signal fusion for non-invasive continuous blood pressure measurement encompasses the characteristics and blood pressure correlations of commonly used physiological signals, as well as multi-modal signal acquisition techniques and sensor integration solutions. This paper provides an in-depth analysis of data-level, feature-level, and decision-level approaches, as well as end-to-end fusion methods based on deep learning. It compares the performance and applicability of different fusion strategies, summarizes the challenges currently faced by wearable device applications, and outlines future development directions. Research indicates that multi-modal fusion can effectively integrate complementary information from various signals, significantly improving the accuracy and robustness of blood pressure estimation. However, individual variability, dynamic interference, data standardization, and model interpretability remain core challenges. Future trends will focus on the establishment of high-quality clinical datasets, the development of novel wearable sensing technologies, intelligent multimodal signal processing approaches, and personalized adaptive blood pressure estimation models.These efforts will drive clinical translation and widespread adoption of this technology, providing robust technical support for achieving early hypertension warning and precision management.
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Lu Junfeng, Meng Kunying, Zhou Zhihao, Zheng Yadong, Li Liguo
2026,40(7):149-164,
Abstract:
Real-time, continuous, and non-invasive blood pressure (BP) monitoring is of great importance for the early detection and long-term management of hypertension. With the rapid development of wearable technologies, signal acquisition methods based on photoplethysmography (PPG) and micro-cuff oscillometry have gradually emerged as engineering oriented alternatives to conventional cuff-based BP measurement. In recent years, artificial intelligence-based BP estimation algorithms have proliferated, whose performance depends on factors such as signal quality, feature construction, and model architecture. This paper provides a systematic overview of the key technical pipeline for wearable BP estimation, including signal acquisition methods, signal feature extraction, model development, and performance evaluation criteria. It focuses on a comparative analysis of pulse transit time (PTT)-based methods, PPG feature-machine learning methods, PPG signal-deep learning methods, and micro-cuff oscillometry, and further summarizes recent algorithmic advances based on emerging wearable BP measurement technologies, including ultrasound, bioimpedance, and tonometry. Furthermore, this paper summarizes the major challenges in the field, including limited model generalizability, noise and motion artifacts, weak adaptability to individual variability, dependence on initial calibration, and concerns regarding data privacy and ethics. Potential future directions are also discussed, such as multi-feature fusion with mechanism-constrained modeling, few-shot personalized learning, robust temporal modeling for long-term continuous monitoring, unified multimodal modeling with system co-optimization, and standardized evaluation with clinical adaptability. This review aims to provide technical insights and development recommendations for the measurement technology research, algorithm design, and engineering application of wearable BP monitoring systems.
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Zhang Chibin, Huang Qiangxian, Guo Tianwu, Cheng Rongjun, Li Hongli, Wang Chaoqun, Zhang Liansheng
2026,40(7):165-174,
Abstract:
In response to the growing requirements for micro-positioning stages in micro-nano manufacturing and precision metrology, a high-precision motion control system is specially designed based on the structural characteristics, driving mode, and performance requirements of a 6-DOF micro-motion stage with nanometric resolution. Circuit output performance tests and overall drive control effect tests were subsequently conducted. The micro-motion stage features a three-layer structure with hollow interior to facilitate optical paths of 3D laser interferometry. It utilizes flexure hinges in combination with piezoelectric actuators, adopting hybrid serial-parallel driving mode. Its motion control system based on an ARM processor includes main control, signal processing, linear amplification, power supply and protection modules, providing multi-channel output, high voltage stability, low noise, and superior resolution. Experimental characterization demonstrates that the system delivers an output range of 0~170 V, noise of 0.2 mV, linearity error of 0.12%, and an operational bandwidth of 0~100 Hz (with 16 μF load). The stage achieves a displacement stroke exceeding 20 μm, a displacement resolution of 0.5 nm, and a rotational resolution of 0.1″. The micro-motion stage and its motion control system demonstrate strong applicability for nanometer and sub-nanometer level positioning and orientation control in the areas of ultra-precision machining and high precision measurement, such as the measurement triggering and attitude compensation of a cross-scale micro-nano coordinate measuring machine (CMCMM) system.
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Li Xinyuan, Mi Chao, Shen Yang
2026,40(7):175-184,
Abstract:
To address the challenges in automated dry bulk terminal operations of gantry cranes, where strong spatial coupling between wire ropes and the grab bucket in point clouds leads to blurred boundaries, and on-site installation constraints result in sparse and uneven point clouds, making it difficult for traditional segmentation methods to effectively identify wire ropes, a wire rope recognition method based on LiDAR and an improved PointNet++ is proposed. First, point clouds are acquired using LiDAR, and a dynamic pre-segmentation algorithm based on kinematic constraints is employed to extract the operational area, thereby reducing computational redundancy. Subsequently, a spatial-self-attention (SSA) module is embedded into the PointNet++ framework. The self-attention branch establishes long-range dependencies across points to enhance the axial topological continuity of the wire rope, while the spatial attention branch dynamically weights local geometric features to strengthen the boundary response in the coupled region. Their synergistic effect improves segmentation accuracy in sparse and coupled scenarios. Experiments conducted on a self-constructed gantry crane operation point cloud dataset demonstrate that the improved method achieves a wire rope segmentation IoU of 91.2%, an improvement of 9.4% over the baseline PointNet++. Under adverse weather conditions such as rain, fog, and snow, the IoU consistently exceeds 89.8%. Across various real-world operating conditions, including entering and exiting the hatch and boom slewing, the IoU remains stable between 89.4% and 92.7%, significantly outperforming comparative methods. This effectively solves the problem of precise wire rope segmentation in strongly coupled and sparse point cloud scenarios, providing a reliable perceptual foundation for anti-sway control and closed-loop positioning of grabs in automated terminals.
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Wang Yusen, Wang Hongjun, He Shan, Mei Wei, Dou Wei, Jin Zhilei
2026,40(7):185-194,
Abstract:
Rolling bearings are one of the most important components in rotating machinery. During operation, the transient impact signals of their early failures are often masked by strong background noise, and traditional methods are difficult to effectively separate features. This paper proposes a multi-scale sparse maximum correlation kurtosis deconvolution method for extracting weak fault features of rolling bearings in strong noise environments. Firstly, using the Boltzmann Shannon interaction entropy as the optimization criterion for the variational modal decomposition (VMD) algorithm, the penalty factor αand mode number K of VMD are optimized through the GOOSE algorithm (GOOSE) to achieve adaptive decomposition of the signal, and the optimal mode components are selected based on the maximum kurtosis principle of the envelope spectrum. Secondly, in order to enhance the adaptability of signal pulse features, a new feature index KG was constructed, which integrates the sparsity measures of signal envelope spectrum kurtosis and pulse mode decomposition GM2tol, achieving multi-scale fusion of signal impulse components and global pulse distribution. With the feature index KGas the optimization objective, the filter length L, period T, and displacement M of the maximum corresponded kurtosis deconvolution (MCKD) parameter filter were optimized to enhance the features of the optimal component impulse components. Finally, the characteristic frequency of bearing faults is extracted through envelope spectrum analysis. The method was validated using simulated signals and experimental datasets, and the results showed that it clearly extracted fault frequencies and their multiples, with a fault frequency extraction error of 0.54%, achieving the discrimination of fault categories.
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Chen Taifeng, Cheng Yu, Lian Yuanfeng
2026,40(7):195-206,
Abstract:
As a critical piece of equipment in oil and gas stations, the operating condition of centrifugal compressors directly affects the stability and safety of the production system. To address the issues of poor generalization and limited physical interpretability in compressor fault diagnosis models—caused by complex operating conditions and insufficient fault data—this paper proposes a digital twin-driven fault diagnosis method for centrifugal compressors. First, a digital twin framework for centrifugal compressors is constructed, comprising the stages of data acquisition, mapping, fusion and optimization, and model-driven analysis, enabling the storage and integration of operational information within the digital twin entity. Second, variational mode decomposition (VMD) is employed to decompose time-series signals into intrinsic mode functions with limited bandwidth, thereby mapping compressor status features to thermodynamic state variables. Finally, a physics-informed extended long short-term memory network (PX-LSTM) fault diagnosis model is developed, which embeds key physical features from the compressor’s operational process into the extended long short-term memory network (xLSTM) structure for joint modeling. This design guides the model to learn historical fault features while adhering to the underlying thermodynamic behavior of the system. Experimental results demonstrate that the proposed model outperforms mainstream approaches across multiple operating conditions and fault types, validating its feasibility and effectiveness in oil and gas digital twin systems.
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Zhao Yawei, Huo Yiming, Ge Kun, Ran Ning
2026,40(7):207-215,
Abstract:
To address the issues of significant scale differences in targets, sparse distribution of objects, and high similarity between certain classes in remote sensing images, an improved semantic segmentation method DKTU-Net for remote sensing image based on U-Net structure is proposed. Based on the U-Net framework, this method makes targeted improvements to the encoder and feature fusion structure. Firstly, the original convolutional encoder is replaced by a hybrid encoding structure that fuses convolution and Shunted Transformer, which can effectively capture cross-scale and cross-region global semantic dependencies while maintaining local detail sensitivity. Then, the detail-preserving contextual fusion module (DPCF) is adopted to fuse skip connections and upsampled features, and through a learnable fusion strategy, multi-scale features are adaptively combined to improve the problems of significant scale differences and sparse distribution of objects. Finally, the kernel selective fusion attention module (KSFA) is introduced to enhance the features after context fusion. This module can adaptively adjust the receptive field according to the needs of different classes, alleviating the problem of high similarity between easily confused classes. Experiments were conducted on the two high-resolution remote sensing datasets, ISPRS Vaihingen and ISPRS Potsdam. The results show that the proposed DKTU-Net, compared with the original U-Net network model, The MIoU and M-F1 on the ISPRS Vaihingen dataset increased by 6.52% and 4.87% respectively, and the MIoU and M-F1 on the ISPRS Potsdam dataset increased by 3.54% and 2.4% respectively It has effectively improved the problems existing in remote sensing image segmentation, such as large differences in target scale, sparse distribution of objects, and high similarity between easily confused categories.
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Zhang Weifeng, Sun Xingwei, Liu Yin, Pan Fei, Mu Shibo, Dong Haosheng, Zhao Hongxun
2026,40(7):216-231,
Abstract:
Aiming at the low energy efficiency and large profile error of helical surface machining, the optimization research of milling process parameters is carried out considering the high precision and low consumption of helical surface. First, an energy consumption mechanism model for helical surface machining is established based on the energy flow and dissipation mechanisms of machine tools. Then, to achieve high-precision and low-energy-consumption machining of helical surfaces, an optimization model for helical surface milling process parameters is established. This model targets machining energy consumption and profile error values, constrained by machine tool performance and machining economy. The improved multi-objective black-winged kite algorithm (IPMOBKA) is employed to solve the problem of determining optimal process parameters for helical surfaces under conditions of multiple objectives, multiple conflicts, multiple constraints, and high dynamicity. Second, using the machining of a five-head screw rotor on the LXK300X as an example, the uniform test method is employed to collect the baseline operational power consumption of the helical groove CNC milling machine, thereby validating the machining power consumption mechanism model. Using an orthogonal experimental design, machining energy consumption, profile error, and surface roughness during helical surface machining are collected to establish predictive models for all three parameters. Finally, the optimal combination of process parameters balancing machining energy consumption and profile error is determined using a black-winged kite algorithm enhanced with a fusion of multiple mechanisms. Case verification demonstrates that the optimization method proposed in this paper reduces machining energy consumption by 15.95% while decreasing profile error by 13.22%, validating its effectiveness and providing robust support for achieving high-precision and low-energy machining of helical surfaces.
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Jin Min, Wang Jun, Xu Yesong, Liu Wenxue
2026,40(7):232-243,
Abstract:
Aiming at the challenges of large differences in the morphology of multi-category defects in fabric defect detection, the dense distribution of tiny defective targets, and the demand for real-time high-precision detection, FDRT-DETR: a multi-category cloth defect detection algorithm is proposed. Firstly, the inverted residual mobile block (iRMB) module is applied in the backbone network to strengthen multi-scale feature extraction and reduce computational redundancy. Secondly, the token statistics self attention (TSSA) mechanism is invoked to enhance the model’s accuracy in capturing features in defective regions and reduce the interference of background texture. Further, the parallel atrous convolution attention pyramid network (PACAPN) is designed in the neck network to significantly improve the preservation and recognition of small target features. Lastly, for the problem of low matching quality due to the dense distribution of tiny imperfections, a matchability aware loss function (MAL) is introduced to improve the detection performance. Experimental results on Alibaba Tianchi’s fabric defect dataset show that the improved model increases the mAP@0.5 for fabric defect detection by 3.7%, achieving a speed of 64.4 frames/s with only 31.4×106 parameters, thus satisfying the practical needs of industrial production.
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Zhou Xianchun, Ni Hongjun, Li Hanzhe
2026,40(7):244-256,
Abstract:
In recent years, deep learning has demonstrated excellent performance in the field of change detection. However, there are still shortcomings such as unclear fine-grained boundaries and internal voids caused by the loss of detailed information and insufficient semantic information in terms of false detections and missed detections. To address this issue, this paper proposes a new multi-scale adaptive fusion network（MSAF-Net）, which uses ResNet as the backbone network. Firstly, it presents a feature difference fusion module (FDFM), which can roughly extract boundary information from changed regions through simple operations. Then, to tackle the problem of lost detailed texture information, a multi-scale dual-feature fusion module (MDFM) is proposed to aggregate low-level texture information and high-level semantic information from multiple receptive fields. Furthermore, to solve the problem of insufficient high-level semantic information in complex scenes, a feature synergy enhancement module (FSEM) is put forward to extract deeper high-level semantic information by enhancing the fusion of feature information between branches. The integration of the FDFM, MDFM, and FSEM modules enables efficient and accurate pixel-level change recognition. The network adopts a dual-stream-single-branch architecture and improves performance through the following key technologies: performing difference processing in two stages, combining the shallow FDFM module to capture detailed differences and the deep MDFM module to conduct cross-scale feature fusion; and introducing the convolutional block attention module（CBAM）, in the feature fusion stage to adaptively weight important changed regions. The experimental results show that MSAF-Net achieved F1 values of 90.71%, 91.71%, and 82.71% on the LEVIR-CD, WHU-CD, and SYSU-CD datasets, respectively. Compared with the current mainstream methods, the performance has improved by 0.14, 1.2, and 0.87 percentage points, respectively.
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Xie Chunxue, Chen Xiangyu, Liu Zhixiang
2026,40(7):257-267,
Abstract:
To address the challenges of aligning dual holes during installation of large mining equipment pins—such as difficulty in alignment and non-intuitive adjustment—this solution precisely quantifies the relative positional deviation between the two holes, providing intuitive control parameters for assembly robots. A pose measurement method based on 3D fitting and Euler angle decomposition is proposed. A rotatable laser ranging device is used to collect multi-depth circumferential data, and the geometric parameters of the axis are extracted by fitting a sequence of circle centers using the least squares method. Based on Z-Y-X order Euler angle decomposition, spatial deviations are converted into executable three-axis translation and rotation compensation values in the robotic arm coordinate system. The method was experimentally validated on a 220-ton mining truck pin hole, with results compared against those from a high-precision measuring instrument. Experimental results show that the relative coaxiality error ranges between 4% and 7%. Parameter analysis indicates that computational error decreases as the number of measurement layers (m) and circumferential points (n) increases; accuracy saturates when n≥36 and m>2, making 36 the optimal number of sampling points. The influence of initial angular deviation α on accuracy increases nonlinearly, and large inclination angles cause cross-sectional projection distortion into ellipses, thereby amplifying axis decomposition errors. This method requires no visual hardware and demonstrates strong robustness against dust and occlusion. Its system accuracy meets the requirements for assembly of large-scale mining machinery, and the output pose compensation values can be seamlessly integrated with control commands for heavy-duty robotic arms, significantly improving the assembly efficiency of transmission components and providing a new technical pathway for automated operation and maintenance of large equipment.
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Deng Zhihan, Cai Haoyuan, Liu Xiaodong, Chen Junbin, Zhu Wanhua
2026,40(7):268-279,
Abstract:
In aeromagnetic surveys, the quality of magnetic information requires high-precision data processing and real-time assurance. Aiming at the problems that existing software compensation systems based on general-purpose processors suffer from high calculation latency and unpredictable timing on embedded platforms, making it difficult to meet the strict requirements for real-time performance and determinism of payload-constrained platforms such as unmanned aerial vehicles (UAVs), this paper proposes an FPGA hardware acceleration architecture for the Tolles-Lawson (TL) model. By performing hardware-oriented algebraic reconstruction on the TL model information row vectors, two processing architectures, namely “fully parallel pipelining” and “time division multiplexing,” were designed and compared on the Xilinx Artix-7 platform. Experimental results indicate that the fully parallel architecture compresses the single compensation latency to 540 ns but consumes 132 digital signal processing (DSP) slices; whereas the time-division multiplexing architecture, through floating-point unit sharing, occupies only 9 DSPs and 7273 look-up tables (LUTs), achieving over 90% DSP resource saving rate with a latency of 6.1 μs. Measured data validation based on the Cessna 208 flight platform shows that the compensation improvement ratio of the system reaches up to 14.8, and the standard deviation of magnetic interference signals decreases from 364.5 pT to 24.7 pT. Compared with software implementations on general-purpose processors, this hardware architecture improves calculation efficiency by 1-2 orders of magnitude while maintaining basically equivalent compensation precision. This design effectively solves the deployment problem of high-precision magnetic compensation algorithms on low-power hardware, realizes airborne aeromagnetic online compensation with high precision, strong real-time performance, and strong determinism, and provides a low-cost and engineering-feasible implementation scheme for aeromagnetic systems on platforms such as UAVs and light aircraft.
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He Liuquan, Wu Jiakang, Liang Junge, Gu Xiaofeng
2026,40(7):280-288,
Abstract:
A wireless passive flexible pressure sensor integrating ultra-high frequency (UHF) radio frequency identification (RFID) technology with capacitive sensing principles was designed and fabricated to address the sensing requirements of flexible electronics and the Internet of Things. The sensor combines a conventional RFID tag with a capacitive unit to enable real-time pressure monitoring. The response mechanism linking external pressure to the phase variation of the RFID signal was elucidated: under applied load, the flexible dielectric layer undergoes compressive deformation, reducing the spacing between conductive layers and thus altering the capacitance of the sensing element. This capacitance change modifies the reflection boundary condition at the antenna port, manifested as a pronounced phase shift in the reader-received signal. To optimize performance, the influence of a micro-pyramidal structured dielectric layer on sensitivity was experimentally investigated. Performance tests demonstrate that the developed sensor exhibits excellent response characteristics in the 0~5 kPa range, with a sensitivity of 4.011°/kPa and an upper detection limit of 20 kPa, evidencing a broad linear detection range and good stability. This work provides a reference scheme for wireless real-time pressure detection in the UHF band and promotes the potential application of wireless passive pressure sensors in the field of wearable electronic devices.
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Li Ke, Cheng Weibin, Xu Yang, Ye Lichuan, Hu Shaobing, Chen Xushuang, Chen Tingdiao
2026,40(7):289-297,
Abstract:
To improve the extraction accuracy of weak Coriolis acceleration signals in accelerometer-based north-seeking systems, a three-stage collaborative signal processing framework based on CiSSA-GSWOA-VMD is proposed. In the first stage, circulant singular spectrum analysis (CiSSA) is applied to the raw accelerometer output, where effective components with distinct periodic characteristics are extracted through frequency-feature mapping of the cyclic matrix, thereby suppressing broadband noise. In the second stage, a global-search whale optimization algorithm (GSWOA) is employed to adaptively optimize the key parameters of variational mode decomposition (VMD). In the final stage, the optimized VMD is applied to the selected components to further remove residual noise and accurately isolate the target signal. Experimental results demonstrate that, in terms of solution accuracy, the proposed method reduces the overall average MAE by 1.781° and 2.276° compared with EWT and the original CiSSA, respectively. Relative to the CiSSA-VMD method, the overall average MAE is further reduced by 1.271°. With regard to solution stability, the average standard deviation decreases by approximately 1.048° and 0.489° compared with EWT and CiSSA, respectively, and by 0.140° compared with CiSSA-VMD. Overall, the proposed framework outperforms EWT and the original CiSSA in both solution accuracy and robustness, while achieving a small but robust performance improvement compared to the CiSSA-VMD method. It is suitable for high-precision extraction of weak Coriolis components in accelerometer-based north-seeking systems under high-noise environments.
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2026,40(7):298-308,
Abstract:
Wind speed sequence itself has randomness and non-stationary characteristics, which constrain the accuracy of traditional prediction methods. A hybrid prediction framework was constructed for this purpose, which integrates seasonal trend decomposition, optimized variational mode decomposition, sparrow search algorithm, bidirectional gated recurrent unit, and attention mechanism. This method first uses seasonal and trend decomposition (STL) to adaptively decompose the original wind speed data, obtaining trend terms, seasonal terms, and residual terms. Then, the optimized variational mode decomposition (OVMD) algorithm is used to perform secondary decomposition on the residual terms, obtaining several intrinsic mode functions, effectively reducing signal complexity and mode aliasing phenomena. Secondly, for the trend and seasonal components, a bidirectional gated recurrent unit (BiGRU) is used to capture long-term dependencies and periodic features, and the global hidden state is output through bidirectional information fusion of forward and backward GRUs; Introducing self attention mechanism to dynamically weight the local detail features of each intrinsic mode function (IMF) component, generate context vectors focused on key features, and suppress redundant information interference. Next, in response to the significant impact of BiGRU key hyper parameters on model performance, the SSA algorithm is introduced for global optimization to solve the problems of traditional grid search being prone to local optima and low computational efficiency. Finally, the final wind speed prediction value is output by integrating the trend component, seasonal component, and the predicted results of each IMF component. Taking the short-term wind speed dataset of actual wind farms as the research object, the results were compared and verified with five benchmark models. The results showed that the prediction accuracy and trend capture ability were significantly better than other models. Compared with the basis benchmark model, the MSE decreased by 33.5%, RMSE decreased by 18.5%, MAE decreased by 16.9%, and R2 increased by 3.6%, training time decreased by 42.0%, and parameter number decreased by 41.0%, providing an effective solution for high-precision prediction of non-stationary wind speed sequences.
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2026,40(7):309-322,
Abstract:
To achieve high-precision and highly robust path planning for mobile robots in complex scenarios, this paper proposes a hybrid path planning method that integrates an improved Theta* algorithm with the dynamic window approach (DWA). Firstly, the global path planning Theta* algorithm is optimized by introducing safety distance constraints, an adaptive neighborhood expansion strategy, improved line-of-sight checks, and B-spline curve smoothing, thereby enhancing path safety and smoothness. Secondly, the local path planning DWA algorithm is improved by incorporating a fuzzy logic control-based dynamic parameter matching mechanism, velocity change curvature constraints, and first-order low-pass filter smoothing optimization to suppress velocity fluctuations. Finally, the two algorithms are deeply integrated within the robot operating system(ROS) move_base framework to achieve synergy between global and local planning. Simulation results show that, compared to the traditional Theta* algorithm, the optimized version increases the minimum safety distance by 10.36%, reduces the number of path nodes by 27.27%, and optimizes the path length by 2.43%. Compared to the traditional DWA, the improved version enhances path smoothness by 77.78%, reduces high-frequency variance by 90.91%, and decreases planning time by 3.12%. Real-world experiments further verify that the integrated algorithm effectively maintains safety distance and ensures motion smoothness in environments with both static and dynamic obstacles.
Volume 40,2026 Issue 7
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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.
