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Node-Decomposition Privacy-Preserving for Finite-Time Secondary control of DC Microgrids

IEEE Transactions on Smart Grid 2025年

作者： Wu, Han Zhu, Si-Min Chai, Li Zhu, Zhen-Hua Tian, Yu-Chu Wuhan University of Science and Technology Engineering Research Center of Metallurgical Automation and Measurement Technology School of Artificial Intelligence and Automation Wuhan430081 China Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Industrial Control Technology Huzhou China Queensland University of Technology School of Computer Science BrisbaneQLD4001 Australia

In the distributed secondary control of DC microgrids, achieving both rapid convergence and robust information security is critical for enhancing control performance and maintaining power quality. However, current privacy-preserving approaches often compromise either control performance or privacy preservation performance. To address this challenge, this paper presents a node-decomposition privacy-preserving (NDPP) method for fully distributed, finite-time secondary control in DC microgrids. By splitting each node in the communication graph into multiple sub-nodes, the method enhances both convergence speed and privacy preservation without sacrificing control accuracy. Sub-nodes communicate within groups, and the topology of the generated graph is concealed from malicious nodes and eavesdroppers. Theoretical analysis is provided to prove finite-time voltage regulation and current sharing with privacy preservation against malicious nodes and external eavesdroppers. Simulations are conducted to verify the effectiveness of the method in maintaining control performance and power quality. © 2010-2012 IEEE.

关键词： Privacy by design

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Acoustic feature processing strategy for leak degree identification in non-metallic pipelines

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Applied Acoustics 2025年 238卷

作者： Yu, Yuebo Cui, Xiwang Gao, Yan Han, Xiaojuan Song, Lijiang Lu, Fangcheng School of Instrument Science and Opto-Electronics Engineering Beijing Information Science and Technology University Beijing100192 China State Key Laboratory of Acoustics Institute of Acoustics Chinese Academy of Sciences Beijing100190 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China

In response to the challenge of accurately identifying the degree of leaks in non-metallic pipelines, this study proposes acoustic feature processing strategy based on the Principal Component Analysis (PCA) and Bidirectional Long Short-Term Memory Networks (Bi-LSTM). The hydrophone sensors are deployed to collect acoustic pressure signals generated by different degrees of pipeline leak. Acoustic signal features of varying leak degrees are extracted from the perspectives of the time domain, the spectral domain, and waveform shape. Subsequently, PCA is employed to streamline and reduce the dimensionality of the extracted features. The optimal number of principal components is ascertained based on the Cumulative Variance Contribution Rate (CVCR), which eliminates redundant features and preserves principal components. Furthermore, the PCA-Bi-LSTM model is designed to identify the degree of pipeline leak. The principal components are organized chronologically into cells for input into the Bi-LSTM. Comparative analysis is performed using different statistical features and neural network architectures, with additional experiments conducted under varying pressures and leak locations to validate the generalizability of the proposed method. The results indicate that the PCA-Bi-LSTM approach achieves a leak identification accuracy of 98.6 %, which is significantly higher than the 91. 5 % accuracy of the unidirectional LSTM network and the 93.2 % accuracy of the traditional Bi-LSTM network. The results demonstrate that the designed strategy effectively improves the accuracy of identifying the degree of non-metallic pipeline leaks and demonstrates a certain level of generalizability. © 2025 Elsevier Ltd

关键词： Leak detection

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Evolutionary Algorithm Based on Surrogate and Inverse Surrogate Models for Expensive Multiobjective Optimization

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IEEE/CAA Journal of automatica Sinica 2025年第5期12卷 961-973页

作者： Deng, Qi Kang, Qi Zhou, Mengchu Wang, Xiaoling Zhao, Shibing Wu, Siqi Ghahramani, Mohammadhossein Tongji University Department of Control Science and Engineering Shanghai 201804 China Macau University of Science and Technology Macao Institute of Systems Engineering Collaborative Laboratory for Intelligent Science and Systems 999078 Macao New Jersey Institute of Technology Helen and John C. Hartmann Department of Electrical and Computer Engineering Newark 07102 NJ United States Birmingham City University Birmingham B4 7RQ United Kingdom

When dealing with expensive multiobjective optimization problems, majority of existing surrogate-assisted evolutionary algorithms (SAEAs) generate solutions in decision space and screen candidate solutions mostly by using designed surrogate models. The generated solutions exhibit excessive randomness, which tends to reduce the likelihood of generating good-quality solutions and cause a long evolution to the optima. To improve SAEAs greatly, this work proposes an evolutionary algorithm based on surrogate and inverse surrogate models by 1) Employing a surrogate model in lieu of expensive (true) function evaluations;and 2) Proposing and using an inverse surrogate model to generate new solutions. By using the same training data but with its inputs and outputs being reversed, the latter is simple to train. It is then used to generate new vectors in objective space, which are mapped into decision space to obtain their corresponding solutions. Using a particular example, this work shows its advantages over existing SAEAs. The results of comparing it with state-of-the-art algorithms on expensive optimization problems show that it is highly competitive in both solution performance and efficiency. © 2014 Chinese Association of Automation.

关键词： Expensives multi-objective optimization reverse model surrogate-assisted evolutionary algorithms (SAEAs)

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TRNet: Two-Tier Recursion Network for Co-Salient Object Detection

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Cong, Runmin Yang, Ning Liu, Hongyu Zhang, Dingwen Huang, Qingming Kwong, Sam Zhang, Wei Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Northwestern Polytechnical University Brain and Artificial Intelligence Laboratory School of Automation Xi'an710129 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Lingnan University Hong Kong

Co-salient object detection (CoSOD) is to find the salient and recurring objects from a series of relevant images, where modeling inter-image relationships plays a crucial role. Different from the commonly used direct learning structure that inputs all the intra-image features into some well-designed modules to represent the inter-image relationship, we resort to adopting a recursive structure for inter-image modeling, and propose a two-tier recursion network (TRNet) to achieve CoSOD in this paper. The two-tier recursive structure of the proposed TRNet is embodied in two stages of inter-image extraction and distribution. On the one hand, considering the task adaptability and inter-image correlation, we design an inter-image exploration with recursive reinforcement module to learn the local and global inter-image correspondences, guaranteeing the validity and discriminativeness of the information in the step-by-step propagation. On the other hand, we design a dynamic recursion distribution module to fully exploit the role of inter-image correspondences in a recursive structure, adaptively assigning common attributes to each individual image through an improved semi-dynamic convolution. Experimental results on five prevailing CoSOD benchmarks demonstrate that our TRNet outperforms other competitors in terms of various evaluation metrics. The code and results of our method are available at https://***/rmcong/TRNetTCSVT2025. © 1991-2012 IEEE.

关键词： HTTP

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EK-Net++: Real-time scene text detection with expand kernel distance and Epoch Adaptive Weight

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Expert Systems with Applications 2025年 267卷

作者： Zhu, Boyuan Chen, Xi Tang, Quan Chen, C.L. Philip Liu, Fagui Computer Science and Engineering South China University of Technology Guangzhou510006 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518066 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Recently, scene text detection has received significant attention due to its wide applications. Accurate detection in complex scenes of multiple scales, orientations, and curvature remains a challenge. Component-based methods have emerged as the dominant approach for offering a more flexible way to achieve superior performance. However, Component-based methods employ the Vatti clipping algorithm for multi-instance training to handle the difficulty of arbitrary-shaped text. Yet we identify several bias results from these approaches called the "shrinked kernel" leading to decreased accuracy. Additionally, almost all text detection methods fail to consider the characteristics of different datasets and directly assign fixed loss weights to multi-instances. To address these limitations, we propose a new approach named Expand Kernel Network (EK-Net++), which presents the expand kernel distance map to compensate for the previous deficiency and incorporates three-stage regression to complete integral instance detection. Furthermore, we design the Epoch Adaptive Weight algorithm which dynamically distinguishes the characteristics of different datasets. EK-Net++ not only achieves precise positioning of arbitrary-shaped text but also strikes a balance between performance and speed. Evaluation results show that EK-Net++ attains state-of-the-art or competitive performance compared to other advanced methods, e.g., F-measure of 86.72% at 35.42 FPS on ICDAR 2015, F-measure of 85.83% at 40.13 FPS on CTW1500. © 2024 Elsevier Ltd

关键词： Epoch Adaptive Weight Expand kernel distance Real-time Scene text detection

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SimGate: A deep learning surrogate model for predicting microstructure evolution using the phase-field method

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Computational Materials science 2025年 256卷

作者： Wu, Pin Huang, Haiwang Yang, Qingcheng Qian, Bo Gao, Yongxin Yang, Yiguo Zhang, Huiran Zhen, Qiang School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Mechanics in Energy Engineering School of Mechanics and Engineering Science Shanghai Institute of Applied Mathematics and Mechanics Shanghai University Shanghai China Shanghai Institute of Aircraft Mechanics and Control Shanghai China Research Center of Nano Science and Technology College of Sciences Shanghai University China State Key Laboratory of Advanced Refractories Shanghai University Shanghai China

This study introduces SimGate, a novel deep learning surrogate model for predicting microstructure evolution using the phase-field method. Combining the temporal modeling capabilities of "Simpler yet better video prediction (SimVP)" with the multi-order aggregation features of "Multi-order gated aggregation network (MogaNet)", SimGate leverages robust temporal dynamics alongside spatial and channel aggregation modules to ensure precise detail capture and spatial consistency. To demonstrate SimGate's ability to tackle challenging scenarios, high-temperature sintering simulations of polycrystalline cerium dioxide (CeO2) particles were selected as a test case. These simulations, chosen for their complexity, involve both Cahn–Hilliard-type and Allen–Cahn-type phase-field equations along with intricate interfacial dynamics, and they were validated through experimental data. SimGate accurately predicts the sintering process from limited initial time steps and exhibits strong extrapolation capabilities in modeling unseen microstructures over extended time scales. Compared to traditional phase-field simulations, which require hours per case, SimGate reduces computational time to seconds while maintaining a prediction accuracy of around 90%. Additionally, point-wise error analysis shows that the average accuracy is improved by 7.80% and 12.41% compared with the original SimVP and well-known Long Short-Term Memory Networks (LSTM), respectively. An ablation analysis was performed to reveal the contributions of key components in the proposed SimGate framework. By significantly enhancing computational efficiency and accuracy, SimGate demonstrates broad potential as a generalizable microstructure prediction model applicable to diverse material and mechanical processing scenarios beyond sintering. © 2025

关键词： Sintering

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LSU-NET: Lightweight automatic Organs Segmentation Network for Medical Images

LSU-NET: Lightweight Automatic Organs Segmentation Network f...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yujie Ding Shenghua Teng Zuoyong Li Xiao Chen College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.

关键词： Training Image segmentation Convolution Dynamic scheduling Stability analysis Resource management Speech processing Standards Monitoring Biomedical imaging

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Vehicle type recognition based on audio data 58

Vehicle type recognition based on audio data

引用

58th Hawaii International Conference on System sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michał Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Paweł Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk 80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk 80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice 44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE computer Society. All rights reserved.

关键词： acoustics mel-frequency cepstral coefficient mfcc sound spectrogram vehicle type detection vehicle type recognition

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Physics-informed CGAN and multi-scale attention CNN for pipeline leakage diagnosis under imbalanced data

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Advanced engineering Informatics 2025年 66卷

作者： Zhu, Yongqiang Li, Shuaiyong Lang, Xianming Liu, Liang School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China School of Information and Control Engineering Liaoning Petrochemical University Fushun113001 China

In industrial pipeline condition monitoring, class-imbalanced data distributions and multi-physics coupling effects significantly constrain the engineering applicability of intelligent diagnostic models. Class-imbalanced data lead to a scarcity of fault samples, making model training challenging. Meanwhile, complex multi-physics coupling effects complicate the prediction of equipment operating conditions. Generating synthetic data that are consistent in both statistical distribution and physical mechanism has become an effective solution to these issues. In this article, a hybrid framework that integrates a physics-informed data augmentation method with an intelligent fault diagnosis model is proposed. Within this framework, a novel physics-informed conditional generative adversarial network (PI-CGAN) is developed, where a physics-informed network (PIN) converts equipment behavior equations into implicit constraints for the generator, ensuring dual consistency in both statistical distributions and physical mechanisms. Subsequently, a hybrid attention module (HAM) is proposed to enhance data diversity and quality through cross-channel feature correlation and critical temporal segment localization. Furthermore, a multi-scale attention convolutional neural network (MSACNN) is developed to effectively decouple coupled features under complex operating conditions via a multi-scale attention mechanism. Experimental results demonstrate that under extreme data imbalance conditions, the proposed hybrid diagnostic framework can generate industrially viable synthetic data while achieving accurate fault identification. © 2025

关键词： Leak detection

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

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IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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