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SSRN 2023年

作者： Zhou, Yu Xie, Wei Yang, Yingkai Sun, Shilong Zhang, Xiao Qin, Jun Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission Wuhan China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Department of Computer Science South-Central Minzu University Wuhan430074 China

Among compressed sensing algorithms, Distributed Compressed Sensing (DCS) stands out for its ability to jointly reconstruct multiple signals, making it a focal point of interest for researchers and engineers , thanks to its outstanding efficiency in signal reconstruction. In contrast to conventional approaches that depend on a priori estimates, like joint sparsity of signals, deep neural networks leverage their robust feature extraction and sequence modeling abilities to automatically capture inter-signal dependencies. This confers them with the benefits of faster solving speed and superior reconstruction accuracy. Most of the current methods, however, do not fully utilize the global dependency information between signals, while also ignoring the detailed features within the signals. Therefore, this paper proposes a Local Transformer Network for DCS (LTN-DCS). It utilizes the self-attention mechanism to extract global contextual information between the reconstructed signal sequences, while also addressing local redundancy partitioning within the signal sequences for finer-grained information capture. This approach generates probabilistic estimates of the non-zero element positions in each signal, forming the foundation for selecting basis atoms from the sampling matrix. Finally, the least-squares method is used to achieve the signal reconstruction. Through extensive experiments, the results show that our proposed method outperforms state-ofthe art DCS methods. © 2023, The Authors. All rights reserved.

关键词： Signal reconstruction

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System for String Monitoring in small Experimental PV Plants

System for String Monitoring in small Experimental PV Plants

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International Conference on Electrical Machines, Drives and Power Systems (ELMA)

作者： Nikolay Valov Boris Evstatiev Tsvetelina Mladenova Vladislav Hinkov Nicolay Mihailov Department of Automation and Mechatronics University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Electronics University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Computer Systems and Technologies University of Ruse “Angel Kanchev” Ruse Bulgaria Center for Computing and Information Services University of Ruse “Angel Kanchev” Ruse Bulgaria Department of Electrical Power Engineering University of Ruse “Angel Kanchev” Ruse Bulgaria

This paper presents a system for real-time measurement of electrical and physical parameters of a small (12kWp) photovoltaic power plant. This experimental photovoltaic installation is located in the urban area of Ruse, on the territory of the University of Ruse. A system was designed that measures the energy produced by each solar array in shaded locations commonly found in urban environments, as well as environment parameters, solar radiation, PV cell temperature and even infrared images of the facility. The collected data will be used to model and forecast energy production from solar panels under urban conditions.

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Enhancing Long-range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-based Sentiment Analysis

arXiv

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arXiv 2024年

作者： Lawan, Adamu Pu, Juhua Yunusa, Haruna Umar, Aliyu Lawan, Muhammad School of Computer Science and Technology Beihang University Beijing China National Research Institute for Teaching Materials in Information Science and Tech. China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computing University of Portsmouth Portsmouth United Kingdom Department of Information and Communication Technology Federal University Gusau Nigeria

Aspect-based Sentiment Analysis (ABSA) evaluates sentiments toward specific aspects of entities within the text. However, attention mechanisms and neural network models struggle with syntactic constraints. The quadratic complexity of attention mechanisms also limits their adoption for capturing long-range dependencies between aspect and opinion words in ABSA. This complexity can lead to the misinterpretation of irrelevant contextual words, restricting their effectiveness to short-range dependencies. To address the above problem, we present a novel approach to enhance long-range dependencies between aspect and opinion words in ABSA (MambaForGCN). This approach incorporates syntax-based Graph Convolutional Network (SynGCN) and MambaFormer (Mamba-Transformer) modules to encode input with dependency relations and semantic information. The Multihead Attention (MHA) and Selective State Space model (Mamba) blocks in the MambaFormer module serve as channels to enhance the model with short and long-range dependencies between aspect and opinion words. We also introduce the Kolmogorov-Arnold Networks (KANs) gated fusion, an adaptive feature representation system that integrates SynGCN and MambaFormer and captures non-linear, complex dependencies. Experimental results on three benchmark datasets demonstrate MambaForGCN’s effectiveness, outperforming state-of-the-art (SOTA) baseline models. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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A Thermal-Aware Scheduling Algorithm for Reducing Thermal Risks in DAG-Based Applications in Cyber-Physical Systems 2nd

A Thermal-Aware Scheduling Algorithm for Reducing Thermal R...

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2nd International Conference on Ubiquitous Security, UbiSec 2022

作者： Ali, Irfan Shehzad, Muhammad Naeem Bashir, Qaisar Elahi, Haroon Awais, Muhammad Naeem Geman, Oana Liu, Pin Department of Computer System Engineering Institute of Business Administration Sukkar Sukkur65200 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Lahore Campus Lahore54000 Pakistan Intel Corporation AustinTX78746 United States Department of Computing Science Umeå University Umeå9036 Sweden Department of Computers Electronics and Automation Stefan Cel Mare University of Suceava Suceava720229 Romania School of Computer Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9789819902712

Directed Acyclic Graph (DAG)-based scheduling applications are critical to resource allocation in the Cloud, Edge, and Fog layers of cyber-physical systems (CPS). However, thermal anomalies in DVFS-enabled homogeneous multiprocessor systems (HMSS) may be exploited by malicious applications posing risks to the availability of the underlying CPS. This can negatively affect the trustworthiness of CPS. This paper proposes an algorithm to address the thermal risks in DVFS-enabled HMSS for periodic DAG-based applications. It also improves the current list scheduling-based Depth-First and Breadth-First techniques without violating the timing constraints of the system. We test the algorithm using standard benchmarks and synthetic applications in a simulation setup. The results show a reduction in the temperature peaks by up to 30%, average temperature by up to 22%, temperature variations up to 3 times, and temperature spatial gradients by up to 4 times as compared to the conventional Depth-First Scheduling algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cyber Physical System

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Fractional Order AGC Design for Power Systems via Artificial Gorilla Troops Optimizer

Fractional Order AGC Design for Power Systems via Artificial...

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International Conference on Power Electronic Drives

作者： Sneha V Sah Vivek Prakash Pawan Kumar Pathak Anil Kumar Yadav School of Automation Banasthali Vidyapith Rajasthan India Department of Technical Education Government of Rajasthan Government Polytechnic Tonk India Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Electrical Engineering National Institute of Technology Hamirpur Hamirpur (H.P.) India

Due to parameter uncertainty, vague loading behavior, and nonlinearities of the interconnected power system (IPS), various controllers come into the picture to deal with automatic generation control (AGC). The critical role of AGC is to maintain a balance among power demands and generations in IPS. This paper deals with the implementation of fractional order PID (FOPID) controller for AGC in two areas reheat thermal type IPS. To obtain the controller parameters, a recently invented metaheuristic technique named Artificial Gorilla Troops optimizer (GTO) is proposed. To replicate the practical scenarios nonlinearities such as generation rate constraints (GRC) and generation dead band (GDB) are considered in the proposed work. A comparative analysis of four state-of-the-art metaheuristic approaches, as well as the performance evaluation of the proposed controller against parametric uncertainties, have been performed. Finally, the obtained results reveal the superior performance of the proposed control scheme.

关键词： Uncertain systems Renewable energy sources Uncertainty Power demand Metaheuristics Automatic generation control Robustness

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Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Control for Unmanned Surface Vehicles with Uncertainties

Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Con...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huarong Zhao Jinjun Shan Xing Li Hongnian Yu School of Internet of Things Engineering Jiangnan University Wuxi P. R. China Department of Earth and Space Science and Engineering York University Toronto ON Canada School of Electrical Engineering and Intelligentization Dong-guan University of Technology Dongguan P. R. China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Liaoning P. R. China School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

This paper investigates a data-driven dynamic event-triggered sliding mode heading control problem for un-manned surface vehicles with uncertain dynamics models. First, a virtual sensor is introduced to establish a compact dynamic linearization model for the unmanned surface vehicle. Then, a dynamic event-triggered scheme is developed to alleviate the communication burden. Moreover, a sliding mode surface is designed, and a data-driven dynamic event-triggered sliding mode heading control approach is formulated. Finally, rigorous mathematical proofs are given, and several simulations demon-strate the effectiveness and superiority of the proposed method compared to existing approaches.

关键词： Uncertainty Event detection Soft sensors Delay effects Mathematical models Data models Steady-state Vehicle dynamics Autonomous vehicles Cybernetics

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TransWorldNG: Traffic Simulation via Foundation Model

TransWorldNG: Traffic Simulation via Foundation Model

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International Conference on Intelligent Transportation

作者： Ding Wang Xuhong Wang Liang Chen Shengyue Yao Ming Jing Honghai Li Shiqiang Bao Li Li Fei-Yue Wang Yilun Lin Urban Computing Lab Shanghai Artificial Intelligence Laboratory Shanghai China 51 WORLD Shanghai China Research and Development Center of Transport Industry of Autonomous Driving Technology RIOH High and Technology Group Department of Automation Tsinghua University Beijing China Institute of Automation Chinese Academy of Sciences Beijing China Macau Institute of Systems Engineering Macau University of Science and Technology Macau China

Traffic simulation is a crucial tool for transportation decision-making and policy development. However, achieving realistic simulations in the face of the high dimensionality and heterogeneity of traffic environments is a longstanding challenge. In this paper, we present TransWordNG, a traffic simulator that uses Data-driven algorithms and Graph computing techniques to learn traffic dynamics from real data. The functionality and structure of TransWorldNG are introduced, which utilize a foundation model for transportation management and control. The results demonstrate that TransWorldNG can generate more realistic traffic patterns compared to traditional simulators. Additionally, TransWorldNG exhibits better scalability, as it shows linear growth in computation time as the scenario scale increases. To the best of our knowledge, this is the first traffic simulator that can automatically learn traffic patterns from real-world data and efficiently generate accurate and realistic traffic environments.

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TransWorldNG: Traffic Simulation via Foundation Model

arXiv

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arXiv 2023年

作者： Wang, Ding Wang, Xuhong Chen, Liang Yao, Shengyue Jing, Ming Li, Honghai Li, Li Bao, Shiqiang Wang, Fei-Yue Lin, Yilun Urban Computing Lab Shanghai Artificial Intelligence Laboratory Shanghai China 51 World Shanghai China Research and Development Center of Transport Industry of Autonomous Driving Technology RIOH High and Technology Group China Department of Automation Tsinghua University Beijing China Institute of Automation Chinese Academy of Sciences Beijing China Macau Institute of Systems Engineering Macau University of Science and Technology China

关键词： Decision making

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Quantum-Inspired Machine Learning for Molecular Docking

arXiv

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arXiv 2024年

作者： Shu, Runqiu Liu, Bowen Xiong, Zhaoping Cui, Xiaopeng Li, Yunting Cui, Wei Yung, Man-Hong Qiao, Nan School of Automation Science and Engineering South China University of Technology Guangzhou510641 China Central Research Institute 2012 Labs Huawei Technologies China Laboratory of AI for Science Huawei Cloud Computing Technologies Co. Ltd Guizhou550025 China State Key Laboratory of Surface Physics Department of Physics Fudan University Shanghai200433 China Shenzhen Institute for Quantum Science and Engineering China

Molecular docking is an important tool for structure-based drug design, accelerating the efficiency of drug development. Complex and dynamic binding processes between proteins and small molecules require searching and sampling over a wide spatial range. Traditional docking by searching for possible binding sites and conformations is computationally complex and results poorly under blind docking. Quantum-inspired algorithms combining quantum properties and annealing show great advantages in solving combinatorial optimization problems. Inspired by this, we achieve an improved in blind docking by using quantum-inspired combined with gradients learned by deep learning in the encoded molecular space. Numerical simulation shows that our method outperforms traditional docking algorithms and deep learning-based algorithms over 10%. Compared to the current state-of-the-art deep learning-based docking algorithm DiffDock, the success rate of Top-1 (RMSD Copyright © 2024, The Authors. All rights reserved.

关键词： Binding sites

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A peer-to-peer blockchain-based architecture for trusted and reliable agricultural product traceability

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Decision Analytics Journal 2023年 9卷

作者： Hasan, A.S.M. Touhidul Sabah, Shabnam Daria, Apubra Haque, Rakib Ul Division of Computing Analytics and Mathematics School of Science and Engineering University of Missouri-Kansas City Kansas City 64110 MO United States Institute of Automation Research and Engineering Dhaka 1205 Bangladesh Department of Electrical and Computer Engineering University of Texas at San Antonio One UTSA Circle San Antonio 78249 TX United States

The lack of new and advanced technologies, such as the Internet of Things (IoT) and Fog computing, makes it challenging to trace the origin of agricultural products in supply chains. In addition, the system's traditional centralized architecture cannot provide a trusted traceability service for farming goods in these supply chains. This paper proposes a peer-to-peer Blockchain-based architecture integrating self-sovereign identity (SSI) and a decentralized key management system (DKMS) for a trusted and reliable traceability service for agricultural food products in the supply chain networks. The public Blockchain network Fantom will provide faster and cheaper transactions. In contrast, SSI will ensure the identity of each entity by adopting DKMS technology on top of Blockchain to facilitate faster authenticity, integrity, and confidentiality service of the supply chain's transaction and verification process. The system is deployed on the different public networks, i.e., Fantom and Ethereum, and shows that the proposed Fantom-based Blockchain network outperforms in terms of transaction and verification timing. © 2023 The Author(s)

关键词： Blockchain Fantom network Self-sovereign identity Supply chain Traceability

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