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14th International Conference on Power, Energy and Electrical engineering, CPEEE 2024

作者： Vongkoon, Pokkrong Benjanarasut, Jirawut Liutanakul, Pisit Division of Instrumentation and Automation Engineering Technology Faculty of Engineering and Technology King Mongkut's University of Technology North Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Thailand

ISBN: (纸本)9781643685199

The use of dc-dc boost converters is common for increasing output voltage and supplying power. These converters, being time-varying nonlinear circuits, encounter uncertainties in load and variations in input voltage and current over a wide range. Nonlinear sliding mode control (SMC) has emerged as a promising strategy, offering advantages in terms of stability and robustness against parameter and load fluctuations. To address switching frequency variations in conventional hysteresis-based SMC, the indirect SMC or pulse-width modulation (PWM)-based SMC has been adopted. However, the adoption of PWM-based SMC raised concerns regarding steady-state error in power converters. Despite incorporating the integral of the state variable into the sliding surface of PWM-based SMC, the steady-state error still exists. Focusing on the current control loop of a dc-dc boost converter with the goal of reducing steady-state error, this study proposes double integral sliding mode control (DISMC), which employs a sliding surface that includes the double integral term of the current error. Simulation and experimental results demonstrate that the proposed approach effectively mitigates the steady-state error of the inductor current. © 2024 The Authors.

关键词： Pulse width modulation

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Machine Learning for Fog Computing: Perceiving QoE for IoT Applications

Machine Learning for Fog Computing: Perceiving QoE for IoT A...

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作者： Ahmed, Eman Nashaat, Heba Rizk, Rawya Computer and Control Department Faculty of Engineering Port-Said University Port-Said Egypt

Since the Internet of Things (IoT) is a rapidly growing industry, so does the range of IoT applications;IoT transforms every aspect of our lives, from smart homes and cities to healthcare and agriculture. With the increasing popularity of IoT applications in many different areas, the round-trip delay of data processing in the cloud affects the user’s quality perception. A portable operating system distribution, high environmental consistency, and resource isolation are all features of the lightweight application virtualization technology known as containerization. Mainstream cloud service providers for automated application administration have widely incorporated container technologies into distributed system architectures. However, the distributed nature of cloud and edge computing environments and workloads significantly increases the complexity of orchestration systems. Therefore, the Quality of Experience (QoE) is required for Internet of Things (IoT) applications. Machine Learning (ML) algorithms are employed by container orchestration systems for modeling, predicting the multi-dimensional performance metrics, and improving the overall QoE. The main objective of this paper is to provide a comprehensive review of existing machine learning enablement of container orchestration. Moreover, we discuss and classify the ML implementation challenges in IoT applications and enlist the applications of machine learning algorithms with IoT. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Containers

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Simultaneous Placement of Tie-lines and Distributed Generations to Optimize Distribution System Post-outage Operations and Minimize Energy Losses

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CSEE Journal of Power and Energy Systems 2021年第2期7卷 318-328页

作者： Farzaneh Shojaei Mohammad Rastegar Morteza Dabbaghjamanesh Department of Power and Control Engineering School of Electrical and Computer EngineeringShiraz UniversityShirazIran Electrical and Computer Engineering Department The University of Texas at DallasUSA.

This study proposes a new method which aims to optimally install tie-lines and distributed generations *** is done to optimize the post-outage reconfiguration and minimize energy losses and energy not supplied of distribution *** number and location of tie-lines,as well as the number,size,and location of DGs,are pinpointed through teaching the learning-based optimization(TLBO)*** objective function in the current research is to minimize the costs pertaining to the investment,operation,energy losses,and energies not *** addition to the normal operational condition,fault operational condition is also ***,the optimal post-fault reconfigurations for fault occurrences in all lines are ***,the operational constraints such as the voltage and line current limits are taken into account in both normal and post-fault operational ***,the modified IEEE 33-bus and 69-bus distribution test systems are selected and tested to demonstrate the effectiveness of the simultaneous placement of DGs and tie-line technique proposed in this paper.

关键词： Distribution system reliability distributed generation energy loss post-outage reconfiguration tie-lines

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Proximity Estimation with Position Adjustment for Autonomous Industrial Inspection

Proximity Estimation with Position Adjustment for Autonomous...

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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Zhou, Ziyi Huang, Ping Zheng, Song Xu, Zhezhuang Zhuang, Zixiong College of Electrical Engineering and Automation Fuzhou Univ. and Key Lab. of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798331507886

Due to the similarity in appearance and dense deployment of devices in industrial environments, relying solely on machine vision makes it challenging for inspection robots to accurately identify similar devices. Although wireless signals from industrial internet of things (IoT) can serve as identification features, signal fluctuations impact the accuracy and efficiency of recognition. To address this issue, this paper proposes a proximity estimation algorithm with position adjustment for autonomous industrial inspection. The algorithm considers the spatial relationships between nodes and the topological relationship between the signal strength and variations among the nodes. By analyzing the topological relationship between the signal strength and variations among the nodes, the robot autonomously adjusts its position and selects the proximal node based on the spatial topology relationship between them. We have built an inspection platform using quadruped robots to evaluate the effectiveness of the experiments. The experimental results demonstrate that the algorithm further improves the efficiency of identifying proximal devices while ensuring the estimation accuracy of the algorithm. © 2024 IEEE.

关键词： Topology

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Analysis of CNN and Feed-Forward ANN Model for the Evaluation of ECG Signal

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Current Signal Transduction Therapy 2023年第1期18卷 1-8页

作者： Mathur, Prerak Sharma, Tanu Veer, Karan Department of Instrumentation and Control Engineering Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Punjab Jalandhar India

Background: Heart disease is considered one of the complex diseases that has affected a large number of people around the world. It is important to detect and identify cardiac diseases at early stages. Objective: A large number of methods are already present that detect various heart diseases;how-ever, there are some limitations to these methods, that have degraded their overall performance. Methods: In this paper, an effective and efficient method based on a convolutional neural network (CNN) and a feed-forward artificial neural network (FFANN) is proposed that can effectively detect cardiac diseases after analysing the electrocardiogram (ECG) signals. In this ongoing study, the transformed signals are used to extract the information from the processed data. The extracted features are then passed to the proposed CNN-FFANN classifiers for training and testing purposes. Results: The performance of the proposed CNN-FFANN model is evaluated in the MATLAB software in terms of performance matrices. Conclusion: The simulated outcomes have proved the proposed CNN-FFANN model to be more accurate and efficient in detecting heart diseases from ECG signals and can be adopted for future biomedical applications. © 2023 Bentham Science Publishers.

关键词： artificial intelligence artificial neural network biomedical applications cardiac diseases ECG signal health monitoring system

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Novel Framework of Segmentation 3D MRI of Brain Tumors

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computers, Materials & Continua 2023年第2期74卷 3489-3502页

作者： Ibrahim Mahmoud El-Henawy Mostafa Elbaz Zainab H.Ali Noha Sakr Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt Department of Computer Science Faculty of Computers and InformaticsKafrelsheikh UniversityKafrelsheikhEgypt Department of Embedded Network Sys.and Tec. Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikhEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Medical image segmentation is a crucial process for computer-aided diagnosis and *** image segmentation refers to portioning the images into small,disjointed parts for simplifying the processes of analysis and *** and speckle noise are different types of noise in magnetic resonance imaging(MRI)that affect the accuracy of the segmentation process ***,image enhancement has a significant role in MRI *** paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free *** proposed techniques consider the values of Peak Signal to Noise Ratio(PSNR)and the level of noise as inputs to the attention-U-Net model for segmentation of the *** framework has been divided into three stages:removing speckle and Rician noise,the segmentation stage,and the feature extraction *** framework presents solutions for each problem at a different stage of the *** the first stage,the framework uses Vibrational Mode Decomposition(VMD)along with Block-matching and 3D filtering(Bm3D)algorithms to remove the ***,the most significant Rician noise-free images are passed to the three different methods:Deep Residual Network(DeRNet),Dilated Convolution Auto-encoder Denoising Network(Di-Conv-AE-Net),andDenoising Generative Adversarial Network(DGAN-Net)for removing the speckle *** Bm3D have achieved PSNR values for levels of noise(0,0.25,0.5,0.75)for reducing the Rician noise by(35.243,32.135,28.214,24.124)and(36.11,31.212,26.215,24.123)*** framework also achieved PSNR values for removing the speckle noise process for each level as follows:(34.146,30.313,28.125,24.001),(33.112,29.103,27.110,24.194),and(32.113,28.017,26.193,23.121)forDeRNet,Di-Conv-AE-Net,and DGAN-Net,*** experiments that have been conducted have proved the efficiency of the proposed framework a

关键词： MRI Rician noise speckle noise segmentation deep learning

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Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

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Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

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Formation control of unmanned rotorcraft systems with state constraints and inter-agent collision avoidance

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Autonomous Intelligent Systems 2023年第1期3卷 84-95页

作者： Panpan Zhou Shupeng Lai Jinqiang Cui Ben M.Chen Department of Mechanical and Automation Engineering The Chinese University of Hong KongShatinN.T.Hong Kong. Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore117583Singapore. Peng Cheng Laboratory Shenzhen518055China.

We present in this paper a novel framework and distributed control laws for the formation of multiple unmanned rotorcraft systems,be it single-rotor helicopters or multi-copters,with physical constraints and with inter-agent collision avoidance,in cluttered *** proposed technique is composed of an analytical distributed consensus control solution in the free space and an optimization based motion planning algorithm for inter-agent and obstacle collision *** specifically,we design a distributed consensus control law to tackle a series of state constraints that include but not limited to the physical limitations of velocity,acceleration and jerk,and an optimization-based motion planning technique is utilized to generate numerical solutions when the consensus control fails to provide a collision-free ***,a sufficiency condition is given to guarantee the stability of the switching process between the consensus control and motion ***,both simulation and real flight experiments successfully demonstrate the effectiveness of the proposed technique.

关键词： Multi-agent system Unmanned systems Rotorcraft systems Formation control Consensus control

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A Novel Deep Learning Model for Classifying Power Quality Problems in PV-integrated Microgrids using CNN-LSTM 1

A Novel Deep Learning Model for Classifying Power Quality Pr...

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Channa, Irfan Ali Li, Dazi Dahri, Fida Hussain Abro, Ghulam E. Mustafa Zahid, Faizan University of Chemical Technology Institute of Automation Beijing Beijing100069 China Southeast University School of Computer Science and Engineering Nanjing China Saudi Arabia University of Catania Department of Electrical Electronic and Computer Engineering Italy

ISBN: (纸本)9798350348637

Distributed generation based on photo-voltaic (PV) modules play a vital role to provide continuous power supply for consumers. Power quality (PQ) events caused by integration of distributed energy sources in microgrids (MG) deteriorate the operation of distribution and utility of power supply. In this work, a new CNN-LSTM based deep learning approach is proposed to identify power quality disturbances (PQDs) in PV-integrated microgrid. In a proposed framework, full closed-loop neural architecture containing different layers to facilitate automatic feature extraction and long-time short memory (LSTM) network provide temporal feature for intelligent classifier to improve the training speed. Moreover, comparison with other deep neural networks proves that proposed approach can simplify the procedure of PQ problems with the conventional signal analysis and feature selection methods. A typical PV-integrated microgid is simulated on PSCAD software to prove the robustness of classifier for classification of PQDs in renewable energy integrated distribution networks. © 2024 IEEE.

关键词： Microgrids

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Distributed Privacy-Preserving State-of-Charge Balance control for Multiple Energy Storage Systems in Smart Grid

Distributed Privacy-Preserving State-of-Charge Balance Contr...

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2023 China automation Congress, CAC 2023

作者： Liu, Weijia Guo, Fanghong Xing, Lantao Su, Hongye Wang, Lei College of Control Science and Engineering Zhejiang University Hangzhou China Zhejiang University of Technology Department of Automation Hangzhou China School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9798350303759

This paper studies the distributed state of charge (SoC) balance control strategy in battery energy storage systems (BESSs), satisfying privacy-preserving requirement of BESSs privacy-sensitive data, i.e., configurations and SoC, which may be transmitted among BESSs. Considering each BESS communicates with its neighboring BESS, we use a distributed dynamic average consensus algorithm to do parameter estimations. Using obtained parameters, we set proper power output of each BESS and maintains overall maximum capability. Different from common algorithms, this algorithm not only converges to the exact average of reference signals and thus help set control power outputs, but also ensures the desired differential privacy by adding zero mean Laplace noises, thus protecting privacy-sensitive data from possible adversaries. Effectiveness of the presented approach is demonstrated by simulations. © 2023 IEEE.

关键词： Charging (batteries)

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