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Progress in Applied Electrical engineering (PAEE)

作者： Patryk Mendoń Mariusz Pelc Aleksandra Kawala-Sterniuk Mariusz Gola Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Faculty of Mathematics Physics and Computer Science University of Opole Opole Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

In this paper the authors propose a system architecture which allows product scoring based on sentiment analysis and utilising Machine Learning (ML) and Expert systems technologies. This solution presented in this paper allows decoupling the AI-based part, which is very capable in sentiment analysis but is lacking some flexibility to actually put the sentiment analysis results in a wider context to make a better sense as to what the results actually mean. This task in our solution is performed by the Expert system where the expert may provide some decision making rules in order to determine the final system *** results confirm discrepancy between reviews and star ratings.

关键词： Knowledge engineering Fuzzy logic Sentiment analysis Reviews Systems architecture Stars Machine learning Cognition Expert systems System analysis and design

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Optimization of Neural Networks using Swarm Intelligence Techniques for Achieving Energy Efficiency in Smart Building Architecture

Optimization of Neural Networks using Swarm Intelligence Tec...

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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： M. Karthick Raja Makhbuba Shermatova Mohammed Saleh Al Ansari Shokhjakhon Abdufattokhov Vuda Sreenivasa Rao I. Infant Raj Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Civil Engineering and Architecture Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan College of Engineering Department of Chemical Engineering University of Bahrain Bahrain Automatic Control and Computer Engineering Dept Turin Polytechnic University Dept of Information Technologies Tashkent Intl. University of Education Tashkent Uzbekistan Department of Computer Science and Engineering Koneru Lakshmaiah Education Fou. Green Fields Vaddeswaram A.P India Department of Computer Science and Engineering K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

The increasing prevalence of smart building architectures, driven by the integration of Internet of Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores the application of swarm intelligence techniques as an innovative approach to optimize neural networks, aiming to strike a balance between maintaining the desired performance levels and minimizing energy consumption. The study investigates the integration of swarm-based optimization algorithms, such as Particle Swarm Optimization (PSO) into the training and operation of neural networks. These algorithms enable the networks to dynamically adapt and optimize their parameters in response to changing environmental conditions and user requirements. The research focuses on developing a comprehensive framework that considers the specific challenges posed by smart building architectures, including real-time data processing, sensor integration, and adaptive control. The proposed approach aims to achieve optimal neural network configurations that minimize energy consumption while ensuring reliable and responsive operation of smart building systems. The results demonstrate the potential of swarm intelligence to significantly improve the energy efficiency of neural network-enabled smart building architectures, providing a promising avenue for sustainable and intelligent infrastructure. The proposed model has an accuracy of 98.23% which is 7.64% higher than that of the traditional approaches.

关键词： Training Energy consumption Smart buildings Architecture Heuristic algorithms Neural networks Energy efficiency

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Hard Disk Failure Prediction Based on Association Analysis and Long Short-Term Memory Network

SSRN

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SSRN 2023年

作者： Xiaojun, Bai Pan, Zhaofeng Meng, Gong Wang, Shenhang Fu, Yanfang Xi’an Technological University School of Computer Science and Engineering Xi’an China Beijing Aerospace Automatic Control Institution Beijing China

Hard disk is the main storage device for cloud service, and there always contain massive disks deployed in a data center. Disk failure occur frequently in data center, which may lead to data loss and other disasters, so there have urgent needs for a failure prediction method of hard disk so as to ensure service reliability. This paper proposes a temporal prediction model based on LSTM. Firstly, the SMART data of the disk is analyzed, and the Pearson correlation coefficient is used to analyze the correlation between the monitoring time series data of the faulty disk and the normal disk, and the monitoring index with the lowest correlation is selected as the fault feature;secondly, for the problem of serious imbalance of positive and negative samples in the SMART dataset, the SMOTEENN algorithm is introduced for oversampling to obtain a balanced dataset of positive and negative samples. The proposed method improves accuracy by 8.268% and F1-score by 8.657% compared to the conventional method. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Optimal denial-of-service attack scheduling for remote state estimation via sequential observations

Optimal denial-of-service attack scheduling for remote state...

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第43届中国控制会议

作者： Xinxin Yang Yuqing Ni Xiaomeng Chen Shan Chen Fei Liu The Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) School of Internet of Things EngineeringJiangnan University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This study investigates the optimal allocation problem of attack energy in multi-system remote state estimation for Cyber-Physical Systems(CPSs). We consider a scenario where there are M independent systems, each equipped with a remote sensor monitoring system. The local estimates from these systems are sent to a remote estimator through a packet loss channel. The objective of the attacker is to degrade the communication channel between the sensors and the remote estimator. Due to capacity limitations, the attacker is restricted to attacking at most one out of M channels. We incorporate interference from other sensors or interference sources into the communication model based on the SINR(Signal-to-Interference-plus-Noise Ratio)model. It should be noted that the same attack power can lead to different packet loss rates in different network *** the attacker's perspective, the goal is to find the optimal attack strategy that maximizes the average estimation error of the remote estimator. Given the uncertainties of wireless network environments, we employ the sequentially-observed Markov decision process(SOMDP) framework to solve the problem. By observing the wireless channels, we gather additional information and incorporate it into the decision-making policy, resulting in better performance than using a standard Markov decision process(MDP). The simulation results support our theoretical findings.

关键词： SOMDP remote state estimation packet loss channel CPS security

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Chinese Nested Entity Recognition Benchmark in the Field of Naval Warfare

Chinese Nested Entity Recognition Benchmark in the Field of ...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Shengjie Zhang Qi Li Yueping Kou Jun Wang Zhengwei Li Baolei Wu School of Computer Science and Technology China University of Mining and Technology Xuzhou China School of Information and Control Engineering China University of Mining and Technology Xuzhou China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

Named entity recognition (NER) is a fundamental task in natural language processing and a key technology for building knowledge graphs. However, the performance of NER is often limited by domain-specific characteristics. For unpopular Chinese entity recognition fields, such as naval warfare, NER typically performs poorly due to the lack of sufficient pre-trained models and labeled data. Furthermore, the naval warfare field includes numerous specialized terms and complex domain knowledge, with nested structures and physical relationships that are uncommon in conventional corpora, posing additional challenges for NER. To address these issues, we manually construct a high-quality naval warfare domain corpus. We also propose a new nested entity recognition framework, which fully leverages entity boundary information, enhances the learning of entity adjacency modeling information, and uses multi-scale convolution to obtain richer span feature representation. Experiments demonstrate the effectiveness of this method in four Chinese NER corpora.

关键词： Convolution Autonomous systems Buildings Named entity recognition Knowledge graphs Benchmark testing Data models Data mining

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The Prediction of Hand, Foot and Mouth Disease (HFMD) with Multi-type Historical Data Based on LSTM

The Prediction of Hand, Foot and Mouth Disease (HFMD) with M...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Zhong Zhang Guanyu Yang Yang Chen Tianjiao Ji Hongtu Liu Wenbo Xu School of Computer Science And Engineering Southeast University Nanjing China National Institute For Viral Desease Control And Prevention Beijing China

Hand-foot-mouth disease (HFMD) has gradually become prevalent in mainland China since the first official report in 2008. The incidence of HFMD is increasing among children mainly aged zero to five. Thus how to monitor and prevent HFMD is becoming an important public health challenge. This study aims to build a forecasting framework of HFMD for two Chinese cities, Chengdu in Sichuan Province and Guangdong in Guangzhou Province based on LSTM, by utilizing clinical data of HFMD, meteorological factors, and Baidu index data. Our method achieves significant results

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Event-triggered leader-following formation control for multi-agent systems under communication faults: application to a fleet of unmanned aerial vehicles

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Journal of Systems engineering and Electronics 2021年第5期32卷 1014-1022页

作者： VAZQUEZ TREJO Juan Antonio GUENARD Adrien ADAM-MEDINA Manuel PONSART Jean-Christophe CIARLETTA Laurent ROTONDO Damiano THEILLIOL Didier Research Center for Automatic Control of Nancy University of LorraineNancy F-54000France Electronic Engineering Department National Institute of Technology of MexicoCuernavaca Morelos 62490Mexico Lorraine Research Laboratory in Computer Science and Its Applications University of LorraineNancy F-54000France University of Stavanger Department of Electrical and Computer EngineeringStavanger 4021Norway

The main contribution of this paper is the design of an event-triggered formation control for leader-following consensus in second-order multi-agent systems(MASs)under communication *** the agents must follow the trajectories of a virtual leader despite communication faults considered as smooth time-varying delays dependent on the distance between the *** matrix inequalities(LMIs)-based conditions are obtained to synthesize a controller gain that guarantees stability of the synchronization *** on the closed-loop system,an event-triggered mechanism is designed to reduce the control law update and information exchange in order to reduce energy *** proposed approach is implemented in a real platform of a fleet of unmanned aerial vehicles(UAVs)under communication faults.A comparison between a state-of-the-art technique and the proposed technique has been provided,demonstrating the performance improvement brought by the proposed approach.

关键词： event-triggered leader-following consensus communication fault formation control unmanned aerial vehicle(UAV) experimental result

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Average Communication Rate for Networked Event-Triggered Stochastic control Systems

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IEEE Transactions on control of Network Systems 2025年

作者： Zhang, Zengjie Liu, Qingchen Mamduhi, Mohammad H. Hirche, Sandra Eindhoven University of Technology Department of Electrical Engineering Netherlands University of Science and Technology of China Department of Automation Hefei China University of Birmingham School of Computer Science United Kingdom Technical University of Munich Chair of Information-Oriented Control Munich Germany

Quantifying the average communication rate (ACR) of a networked event-triggered stochastic control system (NET-SCS) with deterministic thresholds is challenging due to the non-stationary nature of the system's stochastic processes. For a NET-SCS, the propagation of nonlinear statistics of the network communication status brought up by deterministic thresholds makes the precise computation of ACR difficult. Previous work used to over-simplify the computation using a Gaussian distribution without incorporating this nonlinearity, leading to sacrificed precision. This paper proposes a novel approach to calculate the exact ACR for a NET-SCS using a recursive model. We use theoretical analysis and a numerical study to qualitatively evaluate the deviation gap of the conventional approach that ignores the side information. The accuracy of our proposed method, alongside its comparison with the simplified results of the conventional approach, is validated by experimental studies. Our work is promising to benefit the efficient resource planning of networked control systems with limited communication resources by providing accurate ACR computation. © 2025 IEEE.

关键词： Networked control systems

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Fault Diagnosis Research of Laser Gyroscope Based on Optimized-Kernel Extreme Learning Machine

SSRN

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SSRN 2023年

作者： Xiaojun, Bai Ma, Zhenxi Chen, Wei Wang, Shenhang Fu, Yanfang Xi’an Technological University School of Computer Science and Engineering Xi’an China Beijing Aerospace Automatic Control Institution Beijing China

Traditional diagnostic models for laser gyroscopes, widely utilized as high-precision angular velocity sensors in aerospace applications, often suffer from limited reliability and accuracy due to the difficulty of feature extraction, a high computational cost, and training time. In light of these challenges, the present study puts forth a new method for diagnosing faults in laser gyroscopes using the Kernel Extreme Learning Machine (KELM). Specifically, the proposed method leverages Wavelet Packet Decomposition (WPD) to effectively extract features from the laser gyroscope signal as input for our diagnostic model. Furthermore, the KELM model is trained for fault diagnosis, after which we employ the Improved Dung Beetle Optimizer (IDBO) algorithm to optimize its parameters for enhanced optimization performance. According to the experimental results, our proposed IDBO-KELM model demonstrates a 3.68% improvement in diagnostic accuracy compared to traditional approaches, while also offering the advantages of a shorter training time and increased precision. © 2023, The Authors. All rights reserved.

关键词： Failure analysis

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Seven Benefits of Using Series Elastic Actuators in the Design of an Affordable, Simple controlled, and Functional Prosthetic Hand

Seven Benefits of Using Series Elastic Actuators in the Desi...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Erfan Koochakzadeh Alireza Kargar Parsa Sattari Diba Ravanshid Rezvan Nasiri Research Institute for Robotics Artificial Intelligence and Information Science (RAIIS) Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper highlights the benefits of using series elastic actuators (SEA) in designing a cost-efficient, easily controlled, and functional prosthetic hand. The designed 3D-printed hand uses only two motors in an antagonistic configuration, transferring power to the fingers via pulleys, cables, and springs; i.e., the motors are in an SEA configuration with the load/fingers. In the designed underactuated prosthetic hand, the thumb is adjustable for various tasks, and the optimization of pulley diameters ensures synchronized finger movement during hand flexion and extension. Thanks to the SEA configuration of the motors and fingers, simple position control of the motor enables features like hand position control, morphological grasp, force control, impedance control, slippage detection, safe interaction, and efficient grasp. An extensive set of experiments has been conducted to evaluate the designed prosthetic hand’s performance. The experiments confirm the hand’s satisfactory performance while also highlighting the importance of improving the proposed design in different aspects. To attain better position control and morphological grasp, minimizing the cable-body and joint friction is recommended. A higher resolution of the current/torque sensor is needed for the precise force control and slippage detection. Finally, a motor brake system is required to achieve efficient grasping.

关键词： Hands Actuators Pulleys Thumb Position control Motors Synchronization Force control Springs Prosthetic hand

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