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1st International conference on Electrical, Computer, Telecommunication, and Energy Technologies, ECTE-Tech 2024

ISBN: (纸本)9798350388480

The proceedings contain 62 papers. The topics discussed include: hybrid renewable energy system control comprising wind turbine system, photovoltaic panels system, and battery energy storage system;effects of power supply phase imbalance on the electrical and mechanical performance of induction motor;experimental test bench for power quality enhancement using direct current control based on fuzzy PI controller;a decision tree model for inset-fed microstrip patch antenna;RSU deployment to enhance the network connectivity and minimize the incident response time -comparative study;optimizing elliptical probe-fed and edge-fed antennas for radar applications using a two-stage ANN model;and impact of photovoltaic module aging and component degradation on total harmonic distortion in standalone PV systems.

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Adaptive Discrete-Time Sliding Mode control Design for Buck Converters with Disturbances 42

Adaptive Discrete-Time Sliding Mode Control Design for Buck ...

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42nd Chinese control conference, CCC 2023

作者： Liu, Peng Wang, Zuo School of Automation Nanjing210096 China Ministry of Education Key Laboratory of Industrial Internet of Things & Network Control Chongqing400065 China Shenzhen Research Institute Southeast University Shenzhen518063 China

ISBN: (纸本)9789887581543

In this paper, an adaptive discrete-time sliding mode controller (ADSMC) is investigated for buck converter systems with multiple external disturbances and parameter uncertainties. To improve the tracking accuracy and reduce the range of the quasi-sliding mode domain (QSMD), the second-order difference of disturbance estimations are introduced into the controller, which are provided by a discrete disturbance observer. Furthermore, an adaptive reaching law is implemented instead of constant gain to alleviate the control input chattering. Both dynamic and steady-state performance analyses are presented. Finally, simulation results are illustrated to demonstrate the control performance. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： controllers

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Optimization of Neural networks using Swarm Intelligence Techniques for Achieving Energy Efficiency in Smart Building Architecture 1

Optimization of Neural Networks using Swarm Intelligence Tec...

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1st IEEE International conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Raja, M. Karthick Shermatova, Makhbuba Al Ansari, Mohammed Saleh Abdufattokhov, Shokhjakhon Rao, Vuda Sreenivasa Raj, I. Infant Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Turin Polytechnic University in Tashkent Civil Engineering and Architecture Department Tashkent Uzbekistan University of Bahrain College of Engineering Department of Chemical Engineering Bahrain Turin Polytechnic University Automatic Control and Computer Engineering Dept Italy Tashkent Intl. University of Education Dept of Information Technologies Tashkent Uzbekistan Koneru Lakshmaiah Education Fou. Green Fields Department of Computer Science and Engineering A.P Vaddeswaram522302 India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy621 112 India

ISBN: (纸本)9798350372748

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. © 2024 IEEE.

关键词： Energy efficiency

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CTGGAN: Reliable Fetal Heart Rate Signal Generation Using GANs

CTGGAN: Reliable Fetal Heart Rate Signal Generation Using GA...

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International Joint conference on Neural networks (IJCNN)

作者： Yu, Zichang Hu, Yating Lu, Yu Li, Leya Ge, Huilin Fu, Xianghua Shenzhen Univ Coll Appl Sci Shenzhen Peoples R China Shenzhen Technol Univ Coll Big Data & Internet Shenzhen Peoples R China Jiangsu Univ Sci & Technol Coll Automat Zhenjiang Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Ensuring fetal health during pregnancy is critically dependent on precise Fetal Heart Rate (FHR) monitoring. A major challenge in this area is the limited availability of labeled FHR data, which poses a barrier to developing reliable automated analysis systems. To address this gap, our study introduces CTGGAN, a novel method employing Generative Adversarial networks (GANs) to create synthetic, high-quality FHR signals. Specifically, CTGGAN integrates self-attention and residual modules within a Conditional GAN framework, fine-tuned to replicate the complex patterns characteristic of FHR data accurately. A notable feature of CTGGAN is its effective loss function, which combines Wasserstein distance with a gradient penalty to ensure training stability and enhance the authenticity of the generated signals. In performance metrics, Our method demonstrates the highest signal fidelity and distribution similarity, across five key measures: 0.215 maximum mean deviation (MMD), 0.012 sliced Wasserstein distance (SWD), 4.821 percent root mean square difference (PRD), 5.621 relative entropy (RE), and 0.614 Frechet distance (FD). This advancement in generating realistic FHR data with CTGGAN addresses critical issues like data insufficiency and class imbalance, thus advancing the field of prenatal healthcare technology. The code for CTGGAN is available at https://***/ijcnn2024/CTGGAN.

关键词： Generative Adversarial networks data augmentation CTG Fetal Heart Rate Fetal Monitoring systems

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Meta-Verse VICA: AI Counselor Initiating Efficient and Safe Remote Service Support in Meta-Verse 17

Meta-Verse VICA: AI Counselor Initiating Efficient and Safe ...

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17th International conference on Signal-Image Technology and internet-Based systems, SITIS 2023

作者： Taniguchi, Yoshio Knauf, Rainer Fujikawa, Hiroshi Tsuruta, Setsuo Sakaki, Koji Suzuki, Nabuharu Ihara, Hirokazu Hitachi Industry & Control Solutions Ltd. Tokyo Japan School of Computer Science and Automation Ilmenau Germany Maruka Robotics Inc. Tokyo Japan Tokyo Denki University Tokyo Japan Adin-Lab Tokyo Japan

ISBN: (纸本)9798350370911

This paper proposes a framework for efficient remote service support in meta-verse. This work was initiated by a empathy-driven sensitive listening counselor bot enhanced by real and virtual services including digital twins selected by a powerful need/offer matching. This matching is leveraged on ADN (Autonomous Decentralization network) IBN (Intention Based network) for efficiency (high performance) exploiting DLT (Distributed Ledger Technology) for safety. This framework makes it possible to provide highly efficient teleservice including telemedicine in virtual spaces, which enables eternal evolution. Moreover, this paper introduces a concept for an integrated learning and evolutionary self-improvement of our metaverse system. © 2023 IEEE.

关键词： Distributed ledger

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Advanced Convolutional Neural network Approach for Fabric Defect Detection

Advanced Convolutional Neural Network Approach for Fabric De...

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2024 Innovations in Intelligent systems and Applications conference, ASYU 2024

作者： Mohammed, Swash Sami Clarke, Hülya Gökalp Electrical and Electronics Engineering Ondokuz Mayıs University Samsun Turkey

ISBN: (纸本)9798350379433

This paper introduces an innovative method for detecting defects in fabrics, employing a Convolutional Neural network (CNN) optimized for binary classification. The distinctiveness of our approach is rooted in the utilization of a cyclical learning rate scheduler, enhancing the model's learning process over 100 training epochs. Crucially, both training and validation phases were conducted using an original dataset, meticulously collected and comprising three types of fabrics, each characterized by four distinct classes of defects. The CNN architecture, designed for this study, merges a detailed convolutional network for feature extraction with a dense layer, specifically calibrated for accurate defect detection in fabrics. The model's performance was rigorously evaluated against our unique dataset, yielding impressive results. An accuracy of 92% was achieved, signifying the model's effectiveness in defect identification across diverse fabric types. The recall rate stood at 91%, indicative of the model's proficiency in recognizing true defect instances. Precision was remarkably high at 98%, crucial for reducing false positives in quality control processes. The model also achieved an F1 score of 94%, balancing precision and recall effectively. The recorded loss function value of 0.2235 further attests to the model's capability in minimizing training errors. These outcomes underscore the potential of our CNN model, augmented with a cyclical learning rate, in transforming fabric defect detection, especially in settings that require handling varied fabric types and defect classes. The results presented in this study not only demonstrate the robustness of our model in a controlled environment but also suggest significant practical applications in automating quality control in the textile industry, elevating both accuracy and efficiency in defect detection processes. © 2024 IEEE.

关键词： Convolutional neural networks

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TEBDS: A Trusted Execution Environment-and-Blockchain-supported IoT data sharing system

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FUTURE GENERATION COMPUTER systems-THE INTERNATIONAL JOURNAL OF ESCIENCE 2023年 140卷 321-330页

作者： Xie, Hui Zheng, Jun He, Teng Wei, Shengjun Hu, Changzhen Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China

Data sharing services based on massive IoT data have been widely used in various fields such as health monitoring and image recognition, providing users with more reliable, efficient, and flexible data services and significantly improving the user service quality. With the popularization of IoT applications, the usage rate of IoT data is getting higher and higher. Due to the dangerous network environment, the security of IoT data faces challenges. On the one hand, attacks such as data tampering can lead to the failure of IoT tasks, and on the other hand, the intrusion of malicious users can lead to the collapse of the entire IoT network. In recent years, many protection schemes used for IoT data security have been proposed. However, security flaws still exist in these schemes. Therefore, to solve the data security and identity security issues in the IoT data sharing process, in this paper, we propose a TEE-and-Blockchain-supported IoT data sharing architecture(TEBDS), which combines on-chain and off-chain methods to meet the security requirements of the IoT data sharing framework. Therein, the consortium blockchain realizes the protection of on-chain IoT data and the access control of IoT users. An Intel SGX-based Distributed Storage System (SDSS) is proposed to secure off-chain data. Besides, an incentive mechanism is developed to facilitate the whole system. Security analysis shows that TEBDS meets the requirements of data security and identity security. Experimental results show that TEBDS has better performance than the centralized method SPDS.(c) 2022 Published by Elsevier B.V.

关键词： Data sharing internet of things TEE Blockchain IoT security

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12th International conference on High-performance Computing systems and Technologies in Scientific Research, Automation of control and Production, HPCST 2022

12th International Conference on High-Performance Computing ...

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12th International conference on High-performance Computing systems and Technologies in Scientific Research, Automation of control and Production, HPCST 2022

ISBN: (纸本)9783031237430

The proceedings contain 23 papers. The special focus in this conference is on High-performance Computing systems and Technologies in Scientific Research, Automation of control and Production. The topics include: Ontology-Based Data Mining Platform for Diagnosing Sowing quality of Wheat Seeds;algorithm for the Classification of Coronary Heart Disease Based on the Use of Symptom Complexes in the Cardiovascular Environment;business Process Designing of the Institutional Ranking System of Higher Education of the Kyrgyz Republic;evaluation of the Efficiency of the Vaccination Against Chickenpox Based on the Results of Simulation Modeling;hierarchical Volume Mesh Model of Heterogeneous Media Based on Non-Destructive Imaging Data;modeling a Potential Plant Habitat by Ensemble Machine Learning;adaptive Traffic Signal control Based on a Macroscopic Model of the Transport network;optimization and Robustization of Angular Motion control of an Automatic Maneuverable Aerial Vehicle;synthesis of the Rational Analyzing Function for Feature Extraction of Signals from the Electrostatic Location System;Architecture and CAD Software Solutions for PCB Design;application of Computer Numerical control in Eddy-Current Study Methods;Open-Source Solution for Identification and Blocking of Anomalous BGP-4 Routing Information;Simulation of DDoS Attacks on LTE and LoRaWAN Protocols in the NS-3 network Simulator;transaction-Oriented Approach to the Design of Information systems Based on Formal Grammar;frequency-Domain Generalized Phase Transform Method in Pipeline Leaks Locating;inverse Problem Regularization of Parametric Identification of the Particle Temperature Distribution of Gas-Thermal Flow with Optimization of Its Solution;application of the Heterogeneous Multiscale Finite Element Method for Modelling the Compressibility of Porous Media.

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A Novel Framework for Real-Time Water quality Analysis in Agriculture via ANN and Remote Sensing

A Novel Framework for Real-Time Water Quality Analysis in Ag...

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2024 IEEE International conference on Cognitive Computing and Complex Data

作者： Naeem, Hera Mokhtarzadeh, Amir Ali Naeem, M. Shahzaib Precieux, Kiyindou Michel Shah, Sadaf Huaiyin Inst Technol Dept Comp Sci Huaian Peoples R China Zhejiang Univ Sci & Tchnol Dept Intelligent Control Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798350352900;9798350352894

Human faces a severe water crisis exacerbated by urbanization, industrialization, and population growth. The country is threatened by depleting water resources, poor storage capacity, poor infrastructure, and climate change impacts on glaciers. Water quality monitoring reveals significant contamination, with a large portion of water sources unsafe for consumption. Improved water management is crucial. Irrigation enhances farmers' overall well-being and provides a reliable income. Increasing agricultural productivity through irrigation is a crucial approach to reducing poverty caused by the poor performance of rain-fed systems. Consequently, the study examines the factors contributing to the underutilization of land available for irrigation. Integration of Remote sensing technique and ANN to evaluate inland water quality by examining its images, collected from USGS and Copernicus data space sites. IBM SPSS Modeler used to train and produce ANNs for predictive analytics. TSS (Total Suspended Solids), algal blooms, and Chlorophyll-a are related but distinct. TSS measures suspended particles in water, algal blooms indicate rapid algae growth due to nutrient excess, and Chlorophyll-a measures algae concentration. High TSS and Chlorophyll-a can result from algal blooms, impacting water quality and clarity.

关键词： remote sensing technique artificial neural network (ANN) Water quality Indices

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performance Enhancement of the Healthcare System Using Google Cloud Platform 1

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4th International conference on Advances in Distributed Computing and Machine Learning, ICADCML 2023

作者： Mohanty, Subhadarshini Dash, Alka Mohapatra, Subasish Sahoo, Amlan Mondal, Subrota Kumar Odisha University of Technology and Research Odisha Bhubaneswar India Macau University of Science and Technology Av. Wai Long Contai China

ISBN: (数字)9789819912032

ISBN: (纸本)9789819912025

Recent technology's ongoing evolution is unexpectedly altering many industries. A widely held idea states that the combination of scientific internet of Things (IoT) devices would cause a significant shift, particularly in the healthcare sector. In the implementation of vital medically accepted hardware, software, and connections, the usage of scientific IoT devices has repercussions. This report seeks to identify upcoming standard performance challenges by studying current IT cloud services. As a result, there are now new community performance demands, and there is a need for brand-new community performance standards and recommendations that are focused on network-related, scientific IoT devices, their users, and the associated cloud services in integrated home care. The internet of Things (IoT) produces plenty of statistics. Scientific sensor and actuator statistics can be remotely saved and analyzed using distributed servers thanks to cloud computing. The cloud version for data storage and analysis is becoming increasingly unable to handle the data avalanche, and processing is being pushed out to the edge of the network where the data-producing devices are. Region computing and fog computing are data management architectures that can unload data from the cloud, a method close to the patient, and communicate data from tool to tool or from tool to person in seconds or milliseconds. Fog computing (with nearby nodes) and location computing can be used to process sensor statistics close to the detecting and actuating devices (withinside the sensing devices). Fog computing and location computing, when compared to cloud computing, offer five advantages: faster data transmission, much less reliance on constrained bandwidths, more privacy and security, and greater control over data generated in foreign nations where judicial recommendations may also moreover limit the use or allow unwanted governmental access, and lower costs because more sensor-derived data is used. © 20

关键词： internet of things

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