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29th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2024

作者： Gill, Milapji Singh Westermann, Tom Steindl, Gernot Gehlhoff, Felix Fay, Alexander Institute of Automation Technology Helmut Schmidt University Hamburg Germany Institute of Computer Engineering Tu Wien Austria Ruhr University Automation Bochum Germany

ISBN: (纸本)9798350361230

In the following contribution, a method is introduced that integrates domain expert-centric ontology design with the Cross-Industry Standard Process for Data Mining (CRISP-DM). This approach aims to efficiently build an application-specific ontology tailored to the corrective maintenance of Cyber- Physical Systems (CPS). The proposed method is divided into three phases. In phase one, ontology requirements are systematically specified, defining the relevant knowledge scope. Accordingly, CPS life cycle data is contextualized in phase two using domain-specific ontological artifacts. This formalized domain knowledge is then utilized in the CRISP-DM to efficiently extract new insights from the data. Finally, the newly developed data-driven model is employed to populate and expand the ontology. Thus, information extracted from this model is semantically annotated and aligned with the existing ontology in phase three. The applicability of this method has been evaluated in an anomaly detection case study for a modular process plant. © 2024 IEEE.

关键词： Cybersecurity

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Image Dehazing Algorithm Based on Cell Vibration Energy Model 8th

Image Dehazing Algorithm Based on Cell Vibration Energy Mod...

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Lei, Xiaozhou Fei, Zixiang Fei, Minrui School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (纸本)9789819601875

Image dehazing is a challenging task in image enhancement. Due to the widespread presence of haze, it obscures scenes leading to loss of image features, and consequently, failure of visual tasks. To address this issue, this paper proposes a fast and effective image dehizing algorithm based on the cell vibration energy model and image enhancement ideas. Specifically, first, an optimized global enhancement model based on cell vibration is proposed to further improve the computational efficiency of the enhancement process. Then, inspired by image enhancement ideas, a two-round enhancement dehazing framework using stacked modules is proposed. Following that, a negative lightness difference image estimation method combining mean filtering, fast guided filtering, and linear transformation is proposed to solve the core parameter estimation problem within the framework. At the same time, a value range constraint strategy is proposed to limit the value range of the negative lightness map and enhance the adaptability of the algorithm. Lastly, a proportion adjustment method is used to further adjust the brightness of the dehazed image. Experimental results show that the proposed dehazing algorithm outperforms nine state-of-the-art methods in terms of dehazing efficiency and detail restoration. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wiener filtering

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Spatial-Temporal Spiking Feature Pruning in Spiking Transformer

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IEEE Transactions on Cognitive and Developmental Systems 2024年第3期17卷 644-658页

作者： Zhou, Zhaokun Che, Kaiwei Niu, Jun Yao, Man Li, Guoqi Yuan, Li Luo, Guibo Zhu, Yuesheng Peking University School of Electronic and Computer Engineering Shenzhen Graduate School China PengCheng Laboratory China Chinese Academy of Sciences Institute of Automation Beijing100089 China

Spiking Neural Networks (SNNs) are known for brain-inspired architecture and low power consumption. Leveraging bio-compatibility and self-attention mechanism, Spiking Transformers become the most promising SNN architecture with high accuracy. However, Spiking Transformers still face the challenge of high training costs, such as a 51M network requiring 181 training hours on ImageNet. In this work, we explore feature pruning to reduce training costs and overcome two challenges: high pruning ratio and lightweight pruning methods. We first analyze the spiking features and find the potential for a high pruning ratio. The majority of information is concentrated on a part of the spiking features in Spiking Transformer, which suggests that we can keep this part of the tokens and prune the others. To achieve lightweight, a parameter-free Spatial-Temporal Spiking Feature Pruning method is proposed, which uses only a simple addition-sorting operation. The spiking features/tokens with high spike accumulation values are selected for training. The others are pruned and merged through a compensation module called Softmatch. Experimental results demonstrate that our method reduces training costs without compromising image classification accuracy. On ImageNet, our approach reduces the training time from 181 hours to 128 hours while achieving comparable accuracy (83.13% vs. 83.07%). © 2016 IEEE.

关键词： Cost reduction

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A Review on Cybersecurity Analysis,Attack Detection,and Attack Defense Methods in Cyber-physical Power Systems

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Journal of Modern Power Systems and Clean Energy 2023年第3期11卷 727-743页

作者： Dajun Du Minggao Zhu Xue Li Minrui Fei Siqi Bu Lei Wu Kang Li Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronics Engineering and AutomationShanghai UniversityShanghaiChina Department of Electrical Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA School of Electronic and Electrical Engineering University of LeedsLeeds LS29JTUK IEEE

Potential malicious cyber-attacks to power systems which are connected to a wide range of stakeholders from the top to tail will impose significant societal risks and *** timely detection and defense are of crucial importance for safe and reliable operation of cyber-physical power systems(CPPSs).This paper presents a comprehensive review of some of the latest attack detection and defense ***,the vulnerabilities brought by some new information and communication technologies(ICTs)are analyzed,and their impacts on the security of CPPSs are *** malicious cyber-attacks on cyber and physical layers are then analyzed within CPPSs framework,and their features and negative impacts are ***,two current mainstream attack detection methods including state estimation based and machine learning based methods are analyzed,and their benefits and drawbacks are ***,two current mainstream attack defense methods including active defense and passive defense methods are comprehensively ***,the trends and challenges in attack detection and defense strategies in CPPSs are provided.

关键词： Cyber-physical power systems security threat attack detection attack defense state estimation machine learning

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Wearable IMUs: Advancing Human Motion Analysis with Deep Learning 3rd

Wearable IMUs: Advancing Human Motion Analysis with Deep Le...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Das, Satyesh Das, Divyesh Parashar, Ashana Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Jais India Electronics and Computer Engineering Thapar Institute of Engineering and Technology Patiala India Mobile Automation Vodafone Intelligent Solutions Nashik India

ISBN: (纸本)9789819726707

In recent times, there has been a notable surge in the exploration of studying human body movements through the utilization of inertial measurement units that can be worn. This trend stems from its substantial impact on academic and industrial circles, encompassing applications ranging from portable healthcare solutions to sports and interfacing with computers through human interaction. Developing human activity recognition (HAR) systems that achieve remarkable accuracy in identification while relying on just one sensor remains an ongoing technological hurdle. In this paper, we explore both supervised and partially supervised approaches using convolutional neural networks and denoising autoencoders approaches. The goal of our study revolves around increasing the classification precision pertaining to previous related works and decreasing reliance on human-engineered features. Since the raw IMU data is composed of time series, it is split using a running window approach and segments of roughly one second of data are fed to the neural networks. The above approaches are tested with two different variations of the original dataset, obtained with data augmentation approaches, to balance uneven class distribution. The dataset contains measurements of one single IMU sensor positioned in the belt of different users performing seven actions: running, jumping, walking, falling, sitting, standing, and lying. The score of best 8-layer CNN-based system was 0.972. The worst represented class, falling, with just 2 min of recorded data, has an F1-score of 0.919. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Fast single-image dehazing based filtering and morphology 11

Fast single-image dehazing based filtering and morphology

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11th International Symposium on Precision Mechanical Measurements

ISBN: (纸本)9781510680296

In the process of industrial measurement, the phenomenon of water mist appearing in front of the camera lens due to cold environment can cause fogging of the captured image and greatly interfere with the measurement process, affecting the accuracy of measurement. Correspondingly, solving the problem of image fogging has become particularly important. Therefore, based on the atmospheric scattering model, this paper proposes a fast single-image dehazing method. Specifically, for the estimation of atmospheric light, considering that the distribution of atmospheric light in the image scene is inconsistent, a fast estimation algorithm of atmospheric light map in the scene is constructed by using mean filtering and fast guided filtering methods. Then, dark channels, morphological erosion algorithm and weighted fusion are used to construct an estimation algorithm for the transmission map. Finally, we use linear normalization to readjust the numerical range of the dehazed image to improve the visual effect. Experimental results show that the proposed method successfully restored the image scene after dehazing, and has excellent visual effects. In addition, the algorithm runs very fast and has very low time consumption. © 2024 SPIE.

关键词： Wiener filtering

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Sawtooth-enhanced bend sensor for gesture recognition

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Science China(Technological Sciences) 2024年第6期67卷 1727-1736页

作者： BAI YanRu ZHANG ZiHang WANG HaoYu GUO Rui LI XiSheng School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China School of Advanced Engineering University of Science and Technology BeijingBeijing100083China School of Precision Instrument and Opto-Electronics Engineering Tianjin UniversityTianjin300072China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China

Gesture recognition has diverse application prospects in the field of human-computer ***,gesture recognition devices based on strain sensors have achieved remarkable results,among which liquid metal materials have considerable advantages due to their high tensile strength and *** improve the detection sensitivity of liquid metal strain sensors,a sawtooth-enhanced bending sensor is proposed in this *** with the results from previous studies,the bending sensor shows enhanced resistance *** addition,combined with machine learning algorithms,a gesture recognition glove based on the sawtooth-enhanced bending sensor is also fabricated in this study,and various gestures are accurately *** the fields of human-computer interaction,wearable sensing,and medical health,the sawtooth-enhanced bending sensor shows great potential and can have wide application prospects.

关键词： liquid metal bending sensor gesture recognition machine learning

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Finite-Time Synchronization of Complex Networks With Intermittent Couplings and Neutral-Type Delays

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 2026-2028页

作者： Engang Tian Yi Zou Hongtian Chen School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Dear Editor, This letter focuses on the finite-time synchronization(FTS) of neutral-type complex networks with intermittent couplings. Different from most of the existing references concerning neutral-type systems,a delay-independent dynamical event-triggering controller is considered, operating the same way as the intermittent coupling and excluding the Zeno behavior naturally.

关键词： neutral letter concerning

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Effective Tool Augmented Multi-Agent Framework for Data Analysis

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Data Intelligence 2024年第4期6卷 923-945页

作者： Xilin Zhang Zhixin Mao Ziwen Chen Shen Gao School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Computer Science and Engineering(School of Cyber Security) University of Electronic Science and Technology of ChinaChengdu 611731China

Data analysis tasks aim to provide insightful analysis for given data by incorporating background knowledge of the represented phenomenon, which in turn supports decision-making. While existing large language models(LLMs) can describe data trends, they still lag behind human data analysts in terms of integrating external knowledge and in-depth data analysis. Therefore, we propose a multi-agent data analysis framework based on LLMs. The framework decomposes the data analysis task into subtasks by employing three different agents. By empowering agents with the ability to utilize data search tools, the framework enables them to search for arbitrary relevant knowledge during the analysis process, leading to more insightful analysis. Moreover, to enhance the quality of the analysis results, we propose a multi-stage iterative optimization method that iteratively performs data analysis to form more in-depth conclusions. To validate the performance of our framework, we apply it to three real-world problems in the research development of higher education in China data. Experimental results demonstrate that our approach can achieve more insightful data analysis results compared to directly using LLMs alone.

关键词： Exploratory data analysis Multi-agent Large language models

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UWB Radar-based Human Indoor Activity Recognition Using StarNet Network 4

UWB Radar-based Human Indoor Activity Recognition Using Star...

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4th International Conference on Control Theory and Applications, ICoCTA 2024

作者： Pan, Keyu Zhu, Wei-Ping Xu, Nan Concordia University Electrical and Computer Engineering Montreal Canada Southeast University School of Automation Nanjing China

ISBN: (纸本)9798331506155

Human Activity Recognition (HAR) using radar sensors offers significant advantages due to their ability to operate in varying lighting conditions and through obstacles, preserving privacy. This paper proposes a novel HAR system that utilizes a multi-radar setup in conjunction with the StarNet model integrating convolutional neural networks (CNNs) and transformer technologies. The multi-radar system enhances the generalizability and fidelity of indoor activity data. Meanwhile, StarNet enhances traditional CNNs by incorporating the star operation, which efficiently maps features into high-dimensional spaces, achieving superior performance with an accuracy of 95.8%, and outperforming other models by at least 5.0%. This system is particularly beneficial in healthcare, eldercare, and smart home environments, providing robust and reliable activity monitoring. © 2024 IEEE.

关键词： Convolutional neural networks

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