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Hyperspectral Target Detection:The Evolution from Conventional to Artificial Intelligence
ZHANG Zhili;SUN Haixiang;DING Yao;LU Zhenhang;LI Huaichun;LI Jian;WU Junhao;To systematically analyze the development trajectory of hyperspectral target detection(HTD)technology from traditional mathematical modeling to deep learning frameworks, the performance of various representative algorithms was quantitatively evaluated. The study performed a horizontal comparison of classic models such as constrained energy minimization and adaptive matched filtering, as well as comprehensively analyzed the core innovations of three cutting-edge intelligent mechanisms: reshaping a convolutional neural network(CNN)into nonlinear relationship measurers based on joint spatial-spectral representation; precisely capturing long-range spectral dependencies across the entire image by combining the multi-head self-attention mechanism of the Transformer with a generative self-supervised paradigm;and achieving deep decoupling of weak targets from complex backgrounds through multi-dimensional physical constraints in the latent space of autoencoders. Based on the Sandiego1, Sandiego2, and Cuprite datasets, a detailed validation of 10 different algorithms using the area under the curve(AUC)as the core evaluation metric was conducted. The results indicate that, constrained by single-pixel feature fitting and high-dimensional redundancy, traditional methods generally struggle to exceed an AUC of 0.81; in contrast, deep learning models achieve a comprehensive AUC exceeding 0.86. The CNN-based SCLHTD model achieves a peak AUC of 0.897 4 on the mixed-background Sandiego2 dataset due to its outstanding long-range correlation capture ability; the CNN-based S2 ADet algorithm achieves an AUC of 0.894 5 on the spatially regular target-distributed Cuprite dataset; and the unsupervised reconstruction-integrated autoencoder model NUN-UTD also achieves the best AUC of 0.8952 on the Sandiego1 dataset. As single network models have reached the bottleneck of feature representation, the future breakthrough in hyperspectral detection technology lies in constructing adaptive hybrid architectures and multi-granularity feature fusion mechanisms, with a shift toward unsupervised and weakly supervised paradigms to address the problems of complex environment generalization and few-shot feature transfer.
Review of Machine Learning-Based Methods for Lithium-ion Battery State of Health Estimation with Limited Sample Data
LI Aihua;WANG Pengxiang;WANG Tao;XU Xiaodong;XIANG Jijun;Accurate state of health(SOH)estimation of lithium-ion batteries is a key technology for ensuring safe battery operation and health management. In practical applications, full-life-cycle battery experiments involve long durations and high labeling costs, whereas real-world operating data are often characterized by scarce labeled samples, sparse short-duration charging samples, and insufficient coverage of operating conditions. Therefore, limited sample data has become a major factor restricting the accuracy and generalization of machine learning-based SOH estimation. This study reviewed the current research status of machine learning-based SOH estimation methods with limited sample data, focusing on the performance differences of four categories of methods, including regression, shallow learning, deep learning, and transfer learning. The methods were compared in terms of their estimation accuracy, computational complexity, deployment requirements, and applicability. The results show that regression methods have advantages in probabilistic inference but suffer from high computational cost; shallow learning methods feature simple structures but are limited in their extrapolation capability; deep learning methods excel in feature extraction but rely heavily on the sample size; while transfer learning methods perform better under limited sample data, they still face problems such as negative transfer and training instability. Future research should focus on unlabeled data mining, physical constraint integration, multimodal sensing, and practical deployment to achieve robust, transferable, and interpretable SOH estimations for lithium-ion batteries.
Review of Control Methods for Permanent Magnetic Synchronous Motors in New Launching Modes
CHEN Guiming;XU Lingliang;CHA Zhengzhong;WANG Baocheng;Against the backdrop of rapid advancements in military technology, new missile launch modes impose higher demands on drive system performance. Permanent magnetic synchronous motors(PMSMs)are widely employed in novel launch systems owing to their high power density, efficiency,and excellent dynamic response characteristics, with their control strategies directly influencing the overall performance and reliability of the system. To address the differential requirements for motor control performance under various launch scenarios, this study provided a systematic review of PMSM control methods from four perspectives: speed outer-loop, current inner-loop, integrated single loop control speed-current,and sensorless control. In the realm of speed loop control, methods such as active disturbance rejection,sliding mode variable structure, and model predictive controls were analyzed, highlighting their significant advantages in enhancing disturbance rejection capability and dynamic response. For current inner-loop control, methods including hysteresis, feedback linearization, model-free, and machine learning-based controls were reviewed, revealing varying trade-offs between control accuracy and implementation complexity.In terms of integrated speed-current control, the application potential of passivity-based, model predictive,and backstepping controls in simplifying control structures and improving dynamic performance was discussed. For sensorless control, a comparative analysis of fundamental wave model-based and high-frequency signal injection methods was conducted, demonstrating their suitability across different speed ranges. Finally, the advantages, disadvantages, and applicable scenarios of different control methods were summarized, and future development trends in PMSM control technology were explored.
Edge Detection for Multi-Temporal Polarimetric Synthetic Aperture Radar Images Based on Span-Driven Adaptive Neighborhood-Based Three-Dimensional Gaussian-Like Kernel
ZHENG Xiaolong;WANG Yuqing;ZHAO Weiheng;XIAO Zhengju;KANG Shuaizhi;To address the issues of insufficient temporal scattering characterization, covariance estimation bias, and inefficient spatiotemporal information fusion in edge detection for multi-temporal polarimetric synthetic aperture radar(PolSAR)images, an edge detection method based on span-driven adaptive neighborhood(SDAN)-based three-dimensional Gaussian kernel was proposed. First, the spherically invariant random vector model was employed to statistically characterize the PolSAR data, achieving joint modeling of polarimetric covariance matrices and texture components. Subsequently, building upon the two-dimensional Gaussian kernel, an SDAN-based two-dimensional Gaussian kernel was constructed with the SDAN as the spatial support, effectively improving the covariance matrix estimation accuracy in heterogeneous regions. Finally, the SDAN-based two-dimensional Gaussian kernel was weighted and fused with a one-dimensional adaptive convolutional kernel along the temporal dimension to form an SDAN-based three-dimensional Gaussian kernel, which was combined with an adaptive lag threshold to optimize the edge map.The results demonstrate that the proposed method achieves precision rates of 0.84 and 0.94, recall rates of 0.79 and 0.82, respectively. The overall efficiency is superior to that of existing mainstream edge detection methods, exhibiting significant advantages in edge detection for temporally varying scattering regions.
Adaptive Multi-Hop Graph Convolution and Transformer Fusion Network for Hyperspectral Image Classification
ZHOU Zhengyang;LI Wenhao;WANG Nian;ZHU Liangyu;CUI Zhigao;To address the issues of limited receptive fields in conventional graph convolutional networks(GCNs), over-smoothing caused by deep network stacking, and information redundancy in spatial-spectral interactions for hyperspectral image classification, an adaptive multi-hop graph convolution and Transformer fusion network was proposed. An adaptive multi-hop graph attention layer was designed to achieve the adaptive weighted fusion of 0-to K-hop neighborhood features via learnable attention vectors. Concurrently, an enhanced GCN-Transformer fusion interface was constructed by integrating a channel attention mechanism and a one-dimensional neighborhood convolution module, thereby effectively compressing the channel redundancy of the node embeddings and elevating the spectral discriminability. The results on the Indian Pines, Salinas, and Pavia University datasets demonstrate that the proposed method achieves classification accuracies of 96.05%, 96.92%, and 96.28%, outperforming the classic GTFN method by 2.05,0.11, and 1.14 percentage points, respectively. These results confirm that the synergistic effect of multihop spatial topology modeling and enhanced spectral sequence fusion can effectively overcome the “curse of dimensionality” in hyperspectral and significantly improve the delineation accuracy of irregular boundaries.The introduction of spatial graph modeling boosts the overall accuracy by 27.91 percentage points, with adaptive weighting achieving optimal performance at a hop count of K=3, indicating that the adaptive multihop graph attention mechanism can broaden the spatial perspectives with lower computational overhead and reduce over-smoothing risks by utilizing learned weight decay regularities. By incorporating a feature interface into the channel attention module, the model yields the best comprehensive performance, demonstrating that the enhanced interface effectively compresses information redundancy during the interaction phase,which strengthens the discriminative capability for highly similar spectral land covers.
Heat Transfer Characteristics in Triply Periodic Minimal Surface Radiators
ZHONG Sifu;SUN Yasong;WANG Yifan;MA Jing;ZHANG Da;LIN Zican;To address the problem in which conventional heat dissipation structures cannot simultaneously achieve high thermal efficiency and spatial compactness, this study considered two types of triply periodic minimal surface(TPMS)heat sinks: diamond( D-type)and gyroid( G-type). By constructing a thermal-fluid-solid coupling simulation model, the influence mechanisms of the cell size and wall thickness on the internal flow and heat transfer performance of radiators were systematically investigated. Simulation results indicate that within a volumetric flow rate range of 20-120 L/min, the simulated temperature differences at the inlets and outlets of D-type and G-type TPMS radiators are consistent with the experimental data, with relative errors of only 7.68% and 3.34%, respectively, validating the accuracy and reliability of the simulation model. Reducing cell size and increasing wall thickness can significantly enhance the disturbance of cooling medium, thereby improving the absolute heat transfer capability. However, j-factor analysis reveals that configurations with smaller unit cells and thicker walls cause a sharp increase in flow resistance, failing to achieve optimal heat transfer performance at equivalent pumping power. Under identical geometric parameters, the D-type TPMS radiator outperforms the G-type counterpart in comprehensive heat transfer owing to its non-through-hole architecture and larger heat exchange area. The performance evaluation criteria( PEC)are adopted to conduct comprehensive evaluation under multiple conditions, and the results show that the TPMS radiators exhibit the best synergistic performance of flow and heat transfer under conditions of a large unit cell size, thin wall thickness, and low volumetric flow rate.
Remaining Useful Life Prediction Method Incorporating Sample Contribution Difference under Data-Model Linkage Framework
LUO Zhengyang;PEI Hong;ZHENG Jianfei;HAN Qihui;CHEN Xi;SI Xiaosheng;To address the insufficient fitting accuracy of late-stage equipment degradation features and excessive prediction errors of remaining useful life(RUL)under existing data-model linkage frameworks,a novel improved RUL prediction method incorporating sample contribution differences was proposed. Multiple weight functions were embedded to revise the conventional objective function, assigning a higher priority to late-stage prediction performance during model optimization. In the offline training phase, multisensor monitoring data were first integrated into composite health indicators using multidimensional fusion coefficients. Second, given the nonlinear characteristics of equipment degradation, an evolution model of composite health indicators was established, and the analytical formula of the RUL was derived based on the first-passage time theory. Finally, an objective function combining the prediction bias and variance was constructed to obtain the optimal fusion coefficients and failure thresholds. In the online prediction phase,sequential Bayesian estimation was utilized for iterative model parameter updating, therby enabling realtime RUL prediction. Comparative validation experiments conducted on aircraft engine cases demonstrate that, compared to traditional data-model linkage RUL prediction methods, the proposed approach reduces the root mean square error by 32.8% and maintains an average relative error within 30% across the entire life cycle. It achieves higher accuracy in late-stage RUL predictions, providing a valuable reference for fault diagnosis and life prediction of industrial equipment, such as aircraft engines.
Shielding and Lightweight Performance of Carbon Nanotube Film-Functional Nanoparticle Composite Material
WANG Fei;YANG Miao;ZHANG Wei;WANG Xiaobin;A Troop of PLA Rocket Force;To address the dual requirements of shielding performance and lightweight design for gamma external radiation protection equipment, a gradient composite shielding material system based on synergistic enhancement by carbon nanotube films and functional nanoparticles was designed. Based on the highthroughput computation of shielding-performance parameters, Bi, Bi_2O3, and Ta were selected as functional-layer materials. The carbon nanotube film functional layer composite samples were then fabricated and evaluated for their radiation shielding performance. In addition, a substrate functional layer composite shielding simulation model was established to compare carbon nanotube films with conventional substrates,and verify their advantages for lightweight design. The results show that a 1.33 mm-thick carbon nanotube film Bi composite sample achieves shielding efficiencies of 4.13% against 60Co gamma ray, demonstrating excellent protection against gamma-ray. When the functional layer thickness is fixed at 150 nm, a 20μm carbon nanotube film Bi composite exhibits shielding performance comparable to that of 100μmconventional substrates, such as Bi composites based on polyethylene, polyvinyl chloride, and silicone rubber. These findings suggest that carbon nanotube films can enhance the shielding performance of composite materials with reduced substrate thickness, highlighting their potential for light-weight protective equipment against external gamma radiation exposure.
