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SFANet: Efficient Detection of Vehicle Targets in SAR Images Based on SAR-Specialized Feature Aggregation

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhou, Peng Wang, Peng Zhu, Daiyin Zhou, Bilian Lv, Jiming Ye, Zheng Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China Technology Innovation Center for Collaborative Applications of Natural Resources Data in GBA Ministry of Natural Resources Guangdong Guangzhou510062 China Shenzhen Research Institute Nanjing University of Aeronautics and Astronautics Shenzhen518057 China Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Zhengzhou450052 China Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural Resources Zhengzhou450052 China Land and Resources Technical Center of Guangdong Province Guangdong Guangzhou510062 China

With the swift progress of deep learning technologies, the detection of ground vehicles in synthetic aperture radar (SAR) images has emerged as a prominent research focus. In this article, research is conducted on the detection of ground vehicles using MiniSAR mounted on unmanned aerial vehicles. However, the substantial background interference and high similarity between ground vehicles make it challenging for object detection. To address these issues, an efficient object detection network based on SAR-specialized feature attention (SFA) and lightweight global detector (LGD) is proposed in this article. First, the SFA is proposed, which introduces proportional attention to different detection layers. By analyzing and focusing on spatial and channel information, it alleviates the classification challenges when dealing with highly similar ground vehicles and limited information. Second, a novel detector LGD is proposed, which combines Swin Transformer and lightweight convolutions. LGD captures deeper semantic information through global attention, enabling the network to detect vehicles in complex scenes. Finally, transfer learning is utilized to load pre-classification weights of the backbone. To verify the effectiveness of the proposed algorithm, comprehensive experiments were performed across multiple datasets. The results indicate that the proposed method offers substantial performance enhancements. © 2025 IEEE. All rights reserved.

关键词： Vehicle detection

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Remote Online Firmware Upgrade System for Embedded Devices based on ZigBee

Remote Online Firmware Upgrade System for Embedded Devices b...

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Electronic information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Xian Zhang Pengju Tang Taiguo Qu Yiwen Liu School of Computer Science and Engineering Huaihua University Huaihua China College of Computer Science and Electronic Engineering Hunan University Changsha China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application Hunan Province Universities Huaihua China

ISBN: (纸本)9781665413015

To reduce the upgrade and maintenance cost of embedded devices effectively, according to the requirement of a remote firmware update of embedded devices, this paper proposed a remote online firmware upgrade technology based on ZigBee. Combined with the requirement of remote online firmware upgrade application of the tower top amplifier, this paper analyzes and designs the system requirements, ZigBee module selection, and design the software, fault analysis, and other aspects. Designed Experiments to test the ZigBee connection success rate under the device startup state and normal operation state, the relationship between transmission firmware size and transmission success rate, system CPU resource consumption, transmission distance, transmission rate, and other performance indicators. The experimental results show that the remote online firmware upgrade technology of embedded devices based on ZigBee can better meet the performance requirements of all aspects of the system. The results show that this scheme has the advantages of simplicity, low cost, and high reliability and has a broad application prospect.

关键词： Performance evaluation Costs Codes Poles and towers Zigbee Maintenance engineering Software

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Research on Crop Disaster Stress Risk Mapping System Based on Agriculture Big data

Research on Crop Disaster Stress Risk Mapping System Based o...

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Electronic information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Chunqiao Mi Xiaoning Peng Ling Peng Changhua Zhao Xiaowu Deng School of Computer Science and Engineering Huaihua University Huaihua Hunan China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture Huaihua University Huaihua Hunan China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application Hunan Province Universities Huaihua University Huaihua Hunan China

ISBN: (纸本)9781665413015

At present, the research on crop disaster risk is mostly carried out from the perspective of numerical simulation in one factor aspect, and there is a lack of information system practice that can integrate disaster spatio-temporal analysis technologies such as environment characteristic information extraction, geographic data scale transformation, spatial analysis and map visualization. Based on agriculture big data and highlighting the spatial characteristics of crop disasters, in this study a method of crop disaster risk mapping is designed and a prototype system is implemented, which function is composed of agriculture big data cleaning, scale conversion, multi-source data fusion, feature information extraction, risk probability prediction and map visualization. A specific application of the system is also demonstrated by taking the production of thematic map of wind-induced lodging risk probability distribution during maize jointing and tasseling stage as an example, which shows great help for avoiding maize lodging risk in agriculture practice. The research results have a good role in improving the technical level of agriculture disaster mapping and analysis, and improving the efficiency and effect of disaster prevention and reduction in agriculture production. It has important academic significance for promoting the application of big data in agriculture disaster management and establishing a new perspective of crop disaster mapping, and has important practice application value for optimizing the fine zoning of crop planting area and strengthening the accurate management of agriculture disaster risk.

关键词： Crops data visualization Prototypes Production Big data information retrieval Agriculture

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Coordinated operation of electricity and hydrogen blended gas networks via correlation-enhanced joint chance-constrained approach

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International Journal of Hydrogen Energy 2025年 149卷

作者： Sicheng Liu Bo Yang Lun Yang Xu Yang Xin Li Zhaojian Wang Kai Ma Xinping Guan State Key Laboratory of Submarine Geoscience School of Automation and Intelligent Sensing Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory for System Control and Information Processing Ministry of Education of China Shanghai 200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an 710049 China Key Lab of Industrial Computer Control Engineering of Hebei Province and the School of Electrical Engineering Yanshan University Qinhuangdao 066000 China

Blending green hydrogen into natural gas can effectively promote hydrogen consumption and operational flexibility of electricity-gas coupled networks. However, the volatility of renewable energy sources threatens the operation of green hydrogen production and blending, while existing system-level risk management methods either exhibit over-conservatism or are computationally inefficient when constraints are numerous. Therefore, this paper proposes a correlation-enhanced distributionally robust joint chance-constrained (DRJCC) method for the joint scheduling of electricity and hydrogen-enriched compressed natural gas (E-HCNG) networks, aiming to minimize operating cost while limiting joint violation risk under renewable energy uncertainty. Specifically, before DRJCC, an improved hydrogen fraction model is developed for hydrogen blending, refining the representation of fraction variations by incorporating the buffer effect of line packs. Then, for DRJCC solving, the commonly used Bonferroni approximations are overly conservative in allocating joint violation risks to individual chance constraints, especially when constraints are numerous. To mitigate this, by leveraging strongly violation correlations of operational constraints under affine policies, a grouping-based risk allocation strategy is proposed to reduce the over-conservatism. Theoretical derivations confirm the guarantee of joint violation risk via the proposed method. Additionally, customized transformations are developed to address the non-convexities introduced by variable hydrogen fractions. Case studies demonstrate the effectiveness and superiority of the proposed method.

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Statistical Movement Classification based on Gram-Schmidt Transform

Statistical Movement Classification based on Gram-Schmidt Tr...

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Electronic information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Changhua Zhao Chunqiao Mi Yuanquan Shi School of Computer Science and Engineering Huaihua University Huaihua Hunan P. R. China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application Hunan Province Universities Huaihua Hunan P. R. China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan P. R. China

ISBN: (纸本)9781665413015

In this work, a technique for automatic classification of finger motion signals was introduced by using Gram-Schmidt orthonormalization. Because the motion signal has nonlinear and non-stationary processing characteristics, the Gram-Schmidt orthonormalization method is used to analyze the motion signal. By batch processing the high correlation between variables, the classification results are obtained. At the same time, combined with the idea of partial least squares, a new identity measure is proposed to select the best parameters. Finally, SVM is used to classify the movement types of the research objects. According to the test sample records of two finger motion signals (thumb and pinky), the average accuracy is 96.65%. The average accuracy of data activation (thumb and pinky) was 100%. Experiments show that the operation speed of Gram-Schmidt classification method is significantly improved, and it has better interpretability and prediction ability.

关键词： Support vector machines Dimensionality reduction Thumb Transforms Predictive models Time measurement Stability analysis

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

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IEEE Transactions on Consumer Electronics 2025年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Xue, Xingsi Zhang, Fuquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University New Engineering Industry College Putian China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Minjiang University College of Computer and Control Engineering Fuzhou China Sichuan Conservatory of Music Digital Media Art Key Laboratory of Sichuan Province Chengdu China Minjiang University Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Fuzhou China Fuzhou China

Driver drowsiness electroencephalogram (EEG) signal monitoring can promptly alert drivers of their drowsiness in edge-end Consumer Electronics (CE), thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end Lightweight Dual Graph Convolutional Network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. To further enhance the application of CE, we apply Adaptive Pruning Optimization (APO) to the channels and neurons of LDGCN, which nearly halves the inference latency. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization, outperforming existing state-of-the-art algorithms. © 1975-2011 IEEE.

关键词： Neurons

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Automatic Fault Diagnosis of Smart Water Meter Based on BP Neural Network

Automatic Fault Diagnosis of Smart Water Meter Based on BP N...

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2020国际计算机前沿大会

作者： Jing Lin Chunqiao Mi School of Computer Science and Engineering Huaihua University Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province

The smart water meter in water supply network can directly affect water production and usage when faults occur. The traditional method of fault detection is inefficient with time lagging, which is not helpful for modernization of water supply system. The capability of automatic fault diagnosis of smart water meter is an important means to improve the service quality of water supply. In this paper, an automatic fault diagnosis method for the smart device is proposed based on BP neural network. And it was applied on Google Tensorflow platform. Fault symptom vectors were constructed using water meter status data and were used to train the neural network model. In order to improve the learning convergence speed and fault classification effect of the network, a method of weighted symptom was also employed. Experimental results show that it has good performance with a general fault diagnosis accuracy of 98.82%.

关键词： Automaticfaultdiagnosis Smartwatermeter BPneuralnetwork Tensorflow

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A hybrid binary harmony search algorithm for solving the winner determination problem

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International Journal of Innovative Computing and Applications 2019年第1期10卷 59-68页

作者： Lin, Geng Li, Zuoyong Collaborative Innovation Center of IoT Industrialization and Intelligent Production Minjiang University Fuzhou China College of Mathematics and Data Science Minjiang University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

The winner determination problem (WDP) in combinatorial auctions is to determine an allocation of items to bidders such that each item is allocated to at most one bidder, and the auctioneer's revenue is maximised. This paper proposes a hybrid binary harmony search algorithm for the WDP. Firstly, to enhance the global search ability of the proposed algorithm, a modified harmony improvisation mechanism is developed with a modified memory consideration rule and an adaptive pitch adjustment scheme. Next, a repair operator is employed to guarantee the feasibility of the new candidate harmonies. Finally, a tabu search procedure is presented to improve the local search ability. These strategies make a good balance between intensification and diversification. In the experiments, the performance of the proposed algorithm is validated on five groups of 500 instances. Experimental results and comparisons show that the proposed algorithm is very efficient, and the tabu search procedure significantly improves the performance of the proposed algorithm. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Combinatorial optimization

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Unsupervised Time Series Anomaly Detection under data Contamination

Unsupervised Time Series Anomaly Detection under Data Contam...

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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Xiaohui Lin Zuoyong Li Xunhua Huang Xinwei Chen Haoyi Fan College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China School of Computer Science and Technology Harbin University of Science and Technology Harbin China Fujian Engineering and Research Center of New Chinese Lacquer Materials Minjiang University Fuzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Unsupervised learning utilizes unlabeled data to alleviate the reliance on large amounts of labeled data, and it has made great progress in time series anomaly detection. However, there are still some thorny issues unsolved, especially the incapability to maintain the robustness of the model when the time series dataset is contaminated. In this work, we propose an unsupervised time series anomaly detection method on data contamination, named distribution alignment autoencoder (DAAE), in which a temporal sliding reward function combines the adversarial distribution alignment to mine the contaminated data. Specifically, the dimensionality reduction and reconstruction of the time series are implemented by an autoencoder. Next, using two discriminators, the latent features obtained by dimensionality reduction are mapped to the Gaussian distribution, and the high-confidence normal time series are generated by sampling noise from the Gaussian distribution. Then, we compute the mean square error between high-confidence normal samples and the training data. Finally, we feed the error values to the temporal sliding reward function to mine the contaminated training samples. Experimental results compared with baselines on public datasets demonstrate the effectiveness of DAAE.

关键词： Training Dimensionality reduction Time series analysis Training data Mean square error methods Gaussian distribution Robustness

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A Class of Elliptic Quasi–Variational–Hemivariational Inequalities with Applications

arXiv

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arXiv 2023年

作者： Migórski, Stanislaw Yao, Jen-Chih Zeng, Shengda College of Applied Mathematics Chengdu University of Information Technology Sichuan Province Chengdu610225 China Jagiellonian University in Krakow Chair of Optimization and Control ul. Lojasiewicza 6 Krakow30348 Poland Center for General Education China Medical University Taichung Taiwan Guangxi Colleges Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Guangxi Yulin537000 China Jagiellonian University in Krakow Faculty of Mathematics and Computer Science ul. Lojasiewicza 6 Krakow30348 Poland

equalities in reflexive Banach spaces. The inequalities contain a convex potential, a locally Lipschitz superpotential, and a solution-dependent set of constraints. Solution existence and compactness of the solution set to the inequality problem are established based on the Kakutani–Ky Fan–Glicksberg fixed point theorem. Two examples of the interior and boundary semipermeability models illustrate the applicability of our *** Codes 35L15, 35L86, 35L87 © 2023, CC BY.

关键词： Banach spaces

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