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arXiv 2023年

作者： Zhang, Zhenyong Liu, Mengxiang Sun, Mingyang Deng, Ruilong Cheng, Peng Niyato, Dusit Chow, Mo-Yuen Chen, Jiming The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom The State Key Laboratory of Industrial Control Technology the College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The UM SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing the adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from both the aspects of the power system and ML model. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) power system applications. Overall, we encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Kernel-based Class-specific Broad Learning System for software defect prediction

Kernel-based Class-specific Broad Learning System for softwa...

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2021 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2021

作者： Chen, Wuxing Yang, Kaixiang Shi, Yifan Feng, Qiying Zhang, Chengxi Yu, Zhiwen Guangdong University of Technology School of Computer Science and Technology Guangzhou China Zhejiang University College of Control Science and Engineering Hangzhou China Huaqiao University School of Engineering Institute Quanzhou China South China University of Technology Computer Science and Engineering Guangzhou China Harbin Institute of Technology Shenzhen Electronic Information Engineering Shenzhen China

ISBN: (纸本)9781665402453

With the continuous expansion of the software industry, the problem of software defects is receiving more and more attention. There has been a series of machine learning methods applied to the field of software defect prediction (SDP) as a way to ensure the stability of software. However, SDP suffers from the imbalance problem. To solve this problem, we first propose a class-specific broad learning system (CSBLS), which assigns a specific penalty factor to each class in accordance with the data distribution. Then we design a class-specific kernel-based broad learning system (CSKBLS), which adopts kernel mapping instead of random projection. This additive kernel scheme takes into account both outliers and noise in the data set. Extensive experiments on the real-world NASA datasets show that CSKBLS outperforms the comparison methods on the tasks of software defect prediction. © 2021 IEEE.

关键词： Learning systems

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Predicting Off-Design Performance of Axial Compressors Using Multilayer Neural Network Model for Surge control

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SSRN 2023年

作者： Elmelegy, Mohamed Ali Elhosseini, Mostafa A. Ali, Hesham Arafat Haikal, Amira Y. Instrument maintenance support engineer DLNG Damietta LNG Egypt College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Department of Computers and Control Systems Engineering Faculty of Engineering Mansoura University Mansoura Egypt

The incidence of surge within axial compressors profoundly influences the efficacy and reliability of aero-engines. Conventional methodologies in engine design have predominantly concentrated on the precise and efficient forecasting of critical characteristics during such occurrences. This paper presents a novel approach for predicting the surge phenomenon in axial compressors. Surge is a major issue in the operation of axial compressors, causing reduced performance, increased wear and tear, and in some cases, complete machine failure. The proposed approach utilizes a neural network model based on the compressor's design data to forecast the behavior of the compressor's working map under off-design conditions. The model is verified using real data collected over five years during compressor operation, including the opening angle of the Inlet Guided Van (IGV), an important parameter often ignored or assumed to be fixed in previous studies. The results show that the neural network-based approach effectively predicts the compressor's behavior under different operating conditions, providing valuable insights into the onset of surge and offering a real-time surge prediction and control tool. The ability to predict the compressor's working map enables optimizing its performance and controlling instability. This model was validated by comparing its predictions with real operating conditions observed over five years across various compressor operating modes. As a result, this predictive model enables the safe operation of axial compressors by mitigating instability risks during operation. This study's findings highlight the model's effectiveness, as evidenced by the minimal error observed between the predicted and actual data collected across various compressor operating modes. The model exhibits a strong performance, characterized by a high regression factor of 0.92667 and a low mean squared error of 3.04465 x 10^-3. These numerical values underscore the reliability and pr

关键词： Forecasting

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A Formation control for Multi-Agent Systems for Event-Trigger-Based Variable Gain controllers

A Formation Control for Multi-Agent Systems for Event-Trigge...

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Annual Conference of Industrial Electronics Society

作者： Daiki Asada Takuya Nakagawa Yoshikatsu Hoshi Hidetoshi Oya Shunya Nagai Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan The Department of Computer Science Tokyo City University Tokyo Japan Department of Electronics and Control Engineering National Institute of Technology (KOSEN) Niihama College Ehime Japan

This paper deals with a design problem for a new event-trigger-based variable gain controller which achieves consensus for multi-agent systems (MASs) with the leader-follower structure. The proposed variable gain controller consists of stabilizing state feedback laws, state feedback inputs for consensus, and compensation inputs with adjustable parameters, and is designed by taking relative positions into account explicitly. In this paper, we show that sufficient conditions for the existence of the proposed formation control system are given in terms of Linear Matrix Inequalities (LMIs).

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Passivity-Based Distributed Event-Triggered Flocking control of Port-Hamiltonian Systems

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IEEE control Systems Letters 2025年 9卷 342-347页

作者： Aranda-Escolástico, Ernesto Abdelrahim, Mahmoud Fernández-Amorós, David Guinaldo, María Colombo, Leonardo Department of Software and Systems Engineering Universidad Nacional de Educación a Distancia (UNED) Juan del Rosal 16 Madrid 28040 Spain Prince Sultan University Renewable Energy Laboratory College of Engineering Riyadh 11586 Saudi Arabia Computer Science and Automatic Control Department Universidad Nacional de Educación a Distancia (UNED) Juan del Rosal 16 Madrid 28040 Spain Centre for Automation and Robotics (CSIC-UPM) Ctra. M300 Campo Real Km 0200 Madrid Arganda del Rey 28500 Spain

This letter addresses the problem of flocking motion in the context of port-Hamiltonian systems. The port-Hamiltonian framework offers advantages in modeling networks, dissipation, and heterogeneous agents. In this regard, designing flocking controllers in port-Hamiltonian form significantly increases their practical applications. Additionally, event-triggered control has proven effective in reducing bandwidth consumption. However, challenges remain in combining flocking requirements with energy-dissipating terms. Therefore, we propose a novel event-triggered flocking controller suitable for port-Hamiltonian systems. The method is validated through numerical simulations of a group of hexarotors. © 2017 IEEE.

关键词： flocking passivity Port-Hamiltonian systems event-triggered control multi-agent systems

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Hierarchical Clustering-Based State Grouping Reinforcement Learning for Switching Decision of Autonomous Vehicles

Hierarchical Clustering-Based State Grouping Reinforcement L...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Wenjie Ouyang Yiwen Jiao Yang Liu Yang Li Manjiang Hu Hongmao Qin College of Mechanical and Vehicle Engineering Hunan University Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electrical Mechanical and Computer Science Delft University of Technology Delft The Netherlands State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle Engineering Hunan University Changsha China Wuxi Intelligent Control Research Institute of Hunan University Wuxi China

Reinforcement learning (RL) has gained wide attention, but its implementation in autonomous vehicles is still limited by insufficient sample efficiency and heavy training costs. The training efficiency of RL agents is influenced by the dimension of the state space, which can be partitioned to reduce the complexity of sampling and computation. This study proposes a hierarchical clustering-based state grouping reinforcement learning (HCSG-RL) method for the switching decision of autonomous vehicles. First, we partition the base state space into groups and generate a hierarchical tree of state space groups. Then, we train multiple sub-agents for each node in the hierarchical tree. Finally, we add these trained-well sub-model into master policy. This method allows us to fully explore all state spaces and improve the training efficiency of individual agents, which handles the “long-tail” issue and the curse of dimensionality issue. We conduct experiments in a simulation environment and results show that the proposed method has 16–72% reward improvement compared to the tree model in different road length.

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Multi-Attention Gate Based U-net For Retinal Vessel Segmentation

Multi-Attention Gate Based U-net For Retinal Vessel Segmenta...

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

作者： Yu Zhu Rui Li Shenghua Teng Xinrong Cao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

Retinal blood vessel segmentation images can be used to detect and evaluate various cardiovascular and ophthalmic diseases. However, due to the intricate vessel structures and blurred boundaries of vessels, it is a huge challenge to efficiently and accurately segment blood vessels. To deal with the above problems, this paper improves on the U-net by firstly using multi-scale feature convolution with kernels of varying size for feature extraction. Second, a non-local attention mechanism is applied to obtain richer global semantic information. Then multi-attention gate is used in the skip connection part by inputting feature maps of various scales and dimensions and selectively learning the interrelated regions, which improves the segmentation ability of the network model for the tiny structure of blood vessels. Quantitative and qualitative experimental results on two public datasets, DRIVE and CHASE_DB1, demonstrate the effectiveness of the proposed method.

关键词： Image segmentation Semantics Logic gates Blood vessels Network architecture Feature extraction Retinal vessels

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A Network Combining CNN and Transformer for Blind Image Super-Resolution

A Network Combining CNN and Transformer for Blind Image Supe...

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

作者： Shuhao Zhang Zuoyong Li Shenghua Teng Kun Zeng College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China

Blind super-resolution (SR) requires not only estimating blur kernel, but also super-resolving low-resolution image based on estimated blur kernel. Most blind SR methods use convolutional neural networks (CNNs) for kernel estimation, which cannot exploit long-range dependency within image domain, thus failing to predict blur kernel accurately. To address this issue, we propose a network combining CNN and transformer named NCCT for kernel estimation. By modeling local and non-local image priors simultaneously, NCCT outperforms other blind SR methods in terms of kernel estimation accuracy. Moreover, we design a network module named RRFDB for constructing lightweight blind SR network, which runs faster and achieves comparative SR performance with fewer parameters compared with other state-of-the-art blind SR methods.

关键词： Superresolution Education Estimation Transformers Feature extraction Convolutional neural networks Kernel

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Climate Downscaling Using Neural Operator: Spatiotemporal Multimodal Fusion Operator with State-Query Coupled Kernel

Climate Downscaling Using Neural Operator: Spatiotemporal Mu...

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

作者： Haodi Zhang Yichi Wang Yifan Jian Jiahui Jiang Zhaohai Bai Lin Ma College of Computer Science and Software Engineering Shenzhen University Center for Agricultural Resources Research Institute of Genetic and Developmental Biology Chinese Academy of Sciences State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Climate downscaling is crucial for detailed small- scale analysis and for acquiring climate data in regions without weather stations. Operator learning has proven potential for this task. However, several challenges remain in operator learning, such as multimodal fusion, spatiotemporal fusion and input state and query adaptation. To address these challenges, we propose a Spatiotemporal Multimodal Fusion Operator with a State- Query Coupled Kernel (SMCK). This framework includes a latent space fusion encoder that encodes climate variables using position-wise multihead attention for multimodal fusion and integrates historical information to generate robust and precise representation. Additionally, we introduce a state-query coupled kernel that combines radial basis functions and discrete fourier encoding to enhance query location representation, while also adapting to the state to obtain the coupled kernel. Extensive experiments demonstrate that our method achieves state-of-the-art performance and provides strong support for climate downscaling and the planning of climate-related strategies.

关键词： Couplings Signal processing Encoding Robustness Spatiotemporal phenomena Planning Kernel Speech processing Forecasting Meteorology

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Light focusing in linear arranged symmetric nanoparticle trimer on metal film system

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Chinese Physics B 2022年第1期31卷 540-546页

作者： Yuxia Tang Shuxia Wang Yingzhou Huang Yurui Fang State Key Laboratory of Coal Mine Disaster Dynamics and Control and Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials College of PhysicsChongqing UniversityChongqing 400044China Applied Physics School of Computer Science and Information EngineeringChongqing Technology and Business UniversityChongqing 400067China Key Laboratory of Materials Modification by Laser Electronand Ion Beams(Ministry of Education)School of PhysicsDalian University of TechnologyDalian 116024China

Benefiting from the induced image charge on film surface,the nanoparticle aggregating on metal exhibits interesting optical *** this work,a linear metal nanoparticle trimer on metal film system has been investigated to explore the novel optical *** the electric field and surface charge distributions demonstrate the light is focused on film greatly by the nanoparticles at two sides,which could be strongly modulated by the wavelength of incident *** the influence of nanoparticle in middle on this light focusing ability has also been studied here,which is explained by the plasmon hybridization *** finding about light focusing in nanoparticle aggregating on metal film not only enlarges the novel phenomenon of surface plasmon but also has great application prospect in the field of surface-enhanced spectra,surface catalysis,solar cells,water splitting,etc.

关键词： electric field enhancement light focusing nanoparticle trimer on metal film system plasmonic hybridization

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