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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Saber, Ahmad Mohammad Youssef, Amr Svetinovic, Davor Zeineldin, Hatem El-Saadany, Ehab Khalifa University Electrical Engineering and Computer Science Department Abu Dhabi United Arab Emirates Concordia Institute for Information Systems Engineering MontrealQC Canada

ISBN: (纸本)9798350331820

Distributed Volt-Var Control (VVC) is a widely used control mode of smart inverters. However, necessary VVC curve parameters are remotely communicated to the smart inverter, which opens doors for cyberattacks. If VVC curves of an inverter are maliciously manipulated, the attacked inverter's reactive power injection will oscillate, causing undesirable voltage oscillations to manifest in the distribution system, which, in turn, threatens the system's stability. In contrast with previous works which proposed methods to mitigate the oscillations after they are already present in the system, this paper presents an intrusion detection method to detect malicious VVC curves once they are communicated to the inverter. The proposed method utilizes a Multi-Layer Perceptron (MLP) that is trained on features extracted from only the local measurements of the inverter. After a smart inverter is equipped with the proposed method, any communicated VVC curve will be verified by the MLP once received. If the curve is found to be malicious, it will be rejected, thus preventing unwanted oscillations beforehand. Otherwise, legitimate curves will be permitted. The performance of the proposed scheme is verified using the 9-bus Canadian urban benchmark distribution system simulated in PSCAD/EMTDC environment. Our results show that the proposed solution can accurately detect malicious VVC curves. © 2023 IEEE.

关键词： Smart power grids

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A Novel Coupling Control Method for 6-DOF AUVs based on Multi-Motor Cooperation 26

A Novel Coupling Control Method for 6-DOF AUVs based on Mult...

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26th International Conference on Electrical Machines and systems, ICEMS 2023

作者： Lu, Pei Yin, Zhenxiao Zhao, Hang Robotics and Autonomous Systems Thrust Guangzhou China The Hong Kong University of Science and Technology Department of Electronic & Computer Engineering Hong Kong

ISBN: (纸本)9798350317589

The speed difference among underwater motors can induce either strong or weak coupling effects among different control loops within six-degree-of-freedom (6-DOF) autonomous underwater vehicles (AUVs). However, this issue is rarely accorded specialized attention. This paper utilizes the impact of the yaw dimension on roll angle control as an example, and proposes a novel coupling control method. This method hinges on collaborative action among multiple motors to mitigate the amplitude of speed difference fluctuations while introducing a coupling compensator in the feedforward loop. The efficacy of this method in enhancing the stability and robustness of motion control, as well as its superiority over conventional decoupling control methods, are substantiated through both simulation and experimentation. © 2023 IEEE.

关键词： Autonomous underwater vehicles

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Autonomous Search and Rescue Agent Using Deep Reinforcement Learning

Autonomous Search and Rescue Agent Using Deep Reinforcement ...

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2023 International Conference on Recent Advances in science and engineering Technology, ICRASET 2023

作者： Rampriya, R.S. Alam, Mohammed Junaid Jayan, Sri Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamilnadu Vellore India Madras Institute of Technology Department of Computer Technology Tamilnadu Chennai India

ISBN: (纸本)9798350306927

Disaster management is one of the areas of paramount importance for any government or organization. This process gets very tedious and tricky for some specific type of disasters. Deploying manpower for mitigating the disaster and saving the victims is a risky decision because the life of the people in the disaster management team is put to risk, which is a very unfortunate thing. So instead of using manpower, we can deploy intelligent machine agents, which are equivalent or sometimes more efficient than human beings for such highly risky tasks and avert the loss of lives. In our case, we have specifically considered the disaster wherein fire breaks out in a building and people get trapped inside due to the dangerous situation. In this case, where deploying manpower to rescue them requires a high quality protective gear and can even put their lives on risk, we have proposed to deploy intelligent machine agents that are trained to figure out the path on their own and find their way to the victims after which a rescue operation can be performed by safely escorting the victims to a nearby exit in the building. The agent in the proposed application has been evaluated by the metrics such as the time taken to reach the victims and come back to the exit, the total rewards gathered by the agent during its exploration in the environment and the number of correct decisions it made during its journey. The agent takes an average time of 145.6 seconds to complete its operation in the real-time environment. It gathered an average peak cumulative reward value of -0.2 in the real time environment. The agent on an average, records 67.3% chance of making a correct decision (that gets a positive reward) given a state vector. © 2023 IEEE.

关键词： Autonomous agents

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A TimeImageNet Sequence Learning for Remaining Useful Life Estimation of Turbofan Engine in Aircraft systems

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Structural Durability & Health Monitoring 2021年第4期15卷 317-334页

作者： S.Kalyani K.Venkata Rao A.Mary Sowjanya Department of Computer Science&Systems Engineering Andhra University College of Engineering(A)VisakhapatnamIndia

Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under *** data of all possible states of a system are used for building machine learning *** models are further used to predict the possible downtime for proactive action on the system *** engine data from run to failure is used in the current *** run to failure data includes states like new installation,stable operation,first reported issue,erroneous operation,and final *** the present work,the non-linear multivariate sensor data is used to understand the health status and anomalous *** methodology is based on different sampling sizes to obtain optimum results with great *** time series of each sensor is converted to a 2D image with a specific time *** Images would represent the health of a system in higher-dimensional *** created images were fed to Convolutional Neural Network,which includes both time variation and space variation of each sensed *** these created images,a model for estimating the remaining life of the aircraft is ***,the proposed net is also used for predicting the number of engines that would fail in the given time *** current methodology is useful in avoiding the health index generation for predicting the remaining useful life of the industrial *** accuracy in the classification of components is achieved using the TimeImagenet-based approach.

关键词： Multivariate sensor data TimeImageNet Remaining life estimation machine learning 2D image Convolutional Neural Network

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Volt-Var Control for Utility-scale Solar PV Plants to Replace SVCs 4

Volt-Var Control for Utility-scale Solar PV Plants to Replac...

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4th International Conference on High Voltage engineering and Power systems, ICHVEPS 2023

作者： Shimomukai, Ken Maeda, Haruka Fujita, Goro Shibaura Institute of Technology Functional Control Systems Graduate School of Engineering and Science Tokyo Japan Shibaura Institute of Technology Graduate School of Engineering and Science Department of Electrical Engineering and Computer Science Tokyo Japan Shibaura Institute of Technology Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798350318678

One way to increase solar photovoltaic penetration in the grid is management of voltage fluctuations. This is because a photovoltaic plant cannot be interconnected to the grid if it causes voltage violations. Voltage violation is where voltage exceeds the acceptable range. Often, grid operators request photovoltaic plant owners to regulate voltage sufficiently with expensive and space-consuming static Var compensators. Unfortunately, this sometimes makes the project less feasible. This paper argues that there are better ways to regulate voltage. We ran a simulation with a 70 MWp photovoltaic plant as an addition to the grid. Without voltage regulation, voltage violations were found to be significant. This paper found that oversizing the inverter sufficiently would remove all voltage violations without deploying a static Var compensator. This is often a cheaper and space-saving solution for voltage management. This paper argues that economics and spatial efficiency of reactive power compensation devices is key to increasing photovoltaic penetration. © 2023 IEEE.

关键词： Reactive power

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From Data to Diagnosis: Investigating Approaches in Mental Illness Detection 2

From Data to Diagnosis: Investigating Approaches in Mental I...

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2nd International Conference of Intelligent Methods, systems and Applications, IMSA 2024

作者： Montasser, Reem Kadry Mazen, Sherif A. Helal, Iman M. A. October University for Modern Sciences and Arts Faculty of Computer Science Software Engineering Department Giza Egypt Cairo University Faculty of Computers and Artificial Intelligence Information Systems Department Giza Egypt

ISBN: (纸本)9798350362633

Mental illness is a considerable global public health problem, impacting both individual well-being and society's health. The growing popularity of social media and the increase of other data sources led to more research interest in detecting mental illness with a particular focus on utilizing and fusing emotional information. Artificial intelligence techniques like natural language processing and others, have shown encouraging improvements in recognizing complex relationships in a wide range of textual data, including clinical notes, social media posts, and interviews. In this paper, we assess the different branches of artificial intelligence based on previous research in mental illness detection, discussing methods, trends, challenges, and future research directions. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Healthcare Delivery through CloudBased Clinical Decision Support systems

Optimizing Healthcare Delivery through CloudBased Clinical D...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ebenezar, U. Samson Jenila Vennila, G. Balakrishnan, T. Suresh Krishnan, Prabhakar Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Mohan Babu University School of Computing Department of Ai & Ml Andhra Pradesh Tirupati India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-campus India

ISBN: (纸本)9798350373783

The experiment aims at evaluating how four machine learning algorithms work: Decision Tree, Random Forest, SVM, and Neural Network, in a task, of binary classification. Models were evaluated using comprehensive metrics such as accuracy, precision, recall, F-score and AUC that revealed a good diagnostic performance. Models selected for the experiment and parameters tested on real datasets show steady changes in performance. The Neural Network model remained on top, achieving accuracy, precision, recall, F-score and AUC-ROC of 91.8%, 92.1%, 91.3%, 91.7% and 0.93 respectively. Random Forest displayed a competitive accordance along with the accuracy of 88.6%, precision of 89.2%, recall of 88.7%, and the F-score of 89.0%, and the AUC of 0.91. From the results, Support Vector Machine and Decision Tree models posted lower accuracy, recall, and fscores compared to the Neural Network and Random Forest models in most cases. These reveal the need for the improvement of the suitable machine learning model that comes from specific task needs as well as the consideration of each one of factors including accuracy, interpretability, and computational efficiency. Extensive and development of the model may result in more comprehensive understanding of how the model leads classifying of real-world applications such as medical diagnoses, fraud detection and target marketing. © 2024 IEEE.

关键词： Random forests

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Comparison of Structural Optimization for PMSMs Based on Various Machine Learning Methods 49

Comparison of Structural Optimization for PMSMs Based on Var...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Li, Fang Zhao, Hang Yin, Zhenxiao Robotics and Autonomous Systems Thrust Guangzhou China The Hong Kong University of Science and Technology Department of Electronic & Computer Engineering Hong Kong

ISBN: (纸本)9798350331820

Existing sensitivity analysis methods suffer from issues such as small differentiation in parameter sensitivity and slow computational speed. To solve these problems, three machine learning methods, namely Ridge regression, Lasso regression, and Elastic Net, are investigated in this paper. First, a PMSM's performance is analyzed using Finite Element Method (FEM) to identify the variation range of the structural parameters that require optimization. Following that, a finite element sample database is established based on this range using the Design of Experiment (DOE). Next, the variable selection results of PMSM optimization are compared using different machine learning methods and traditional machine sensitivity analysis method. Finally, the performances of the PMSM optimized based on the three machine learning methods are evaluated. The results indicates that Lasso Regression proves to be the most effective method. It achieves rapid convergence and effectively identifies the most sensitive parameter, enhancing the optimization efficiency of PMSMs while retaining the global optimum. © 2023 IEEE.

关键词： Sensitivity analysis

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD) 3

Blockchain Integration for Robust Intellectual Property Prot...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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A Novel Gradient Boosted Energy Optimization Model(GBEOM)for MANET

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computer systems science & engineering 2023年第7期46卷 637-657页

作者： Neenavath Veeraiah Youseef Alotaibi Saleh Alghamdi Satish Thatavarti Department of Electronics and Communications DVR&DHS MIC Engineering CollegeKanchikacharlaA.P521180India Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramA.P522502India

Mobile Ad Hoc Network(MANET)is an infrastructure-less network that is comprised of a set of nodes that move *** MANET,the overall performance is improved through multipath multicast routing to achieve the quality of service(quality of service).In this,different nodes are involved in the information data collection and transmission to the destination nodes in the *** different nodes are combined and presented to achieve energy-efficient data transmission and classification of the *** route identification and routing are established based on the data broadcast by the network *** transmitting the data packet,evaluating the data delivery ratio is necessary to achieve optimal data transmission in the ***,energy consumption and overhead are considered essential factors for the effective data transmission rate and better data delivery *** this paper,a Gradient-Based Energy Optimization model(GBEOM)for the route in MANET is proposed to achieve an improved data delivery ***,the Weighted Multi-objective Cluster-based Spider Monkey Load Balancing(WMC-SMLB)technique is utilized for obtaining energy efficiency and load balancing *** WMC algorithm is applied to perform an efficient node clustering process from the considered mobile nodes in *** balancing efficiency is improved with a higher data delivery ratio and minimum routing overhead based on the residual energy and bandwidth ***,the Gradient Boosted Multinomial ID3 Classification algorithm is applied to improve the performance of multipath multicast routing in MANET with minimal energy consumption and higher load balancing *** proposed GBEOM exhibits∼4%improved performance in MANET routing.

关键词： Manet routing load balancing clustering gradient boosting

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