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A Deep Neural Network Approach for Fault Detection in Industrial Motors: Enhancing Reliability and Efficiency in Industry 4.0 21

A Deep Neural Network Approach for Fault Detection in Indust...

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21st IEEE International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT, HONET 2024

作者： Ali, Hassan Ahyen, Mostafa Yaqub, Raziq Badawi, Ahmed Shifa, Noora Khandakar, Amith University of Doha for Science and Technology Department of Electrical Engineering Doha Qatar Alabama A and M University Department of Electrical Engineering and Computer Science Huntsville United States Qatar University Department of Electrical Engineering Doha Qatar

ISBN: (纸本)9798350378078

In the era of Industry 4.0, the integration of sophisticated monitoring and control technologies is essential for the reliability and efficiency of industrial equipment. This paper presents a deep neural network (DNN)-based approach for fault detection and classification in industrial motors. The approach identifies common motor faults, including bent shaft, loose vibration, loosely mounted components, electrical faults, and bearing vibration. The proposed DNN model demonstrated robust performance, achieving an overall training accuracy of 83.49% ± 0.43% and a test accuracy of 81.91 % ± 0.82%. The model also achieved a mean precision of 82.05% ± 0.77%, mean recall of 81.91 % ± 0.82%, and mean F1-Score of81.86%± 0.78%. The 95% confidence intervals for accuracy, precision, recall, and F1-Score were (80.77%, 83.04%), (80.98%, 83.11 %), (80.77%, 83.04%), and (80.78%,82.95%), respectively. These results demonstrate the DNN model's ability to precisely classify various motor faults with minimal variability, as evidenced by its narrow confidence intervals and low standard deviations. The model's reliability and efficacy for real-world applications in proactive maintenance and fault detection are confirmed by the consistent performance that is observed across cross-validation folds. © 2024 IEEE.

关键词： Predictive maintenance

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V2G-Auth: Lightweight Authentication and Key Agreement Protocol for V2G Environment Leveraging Physically Unclonable Functions

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2023年 1卷 66-78页

作者： Reddy, Alavalapati Goutham Babu, Ponnuru Raveendra Odelu, Vanga Wang, Li Kumar, Sathish A.P. Department of Mathematics and Computer Science Fontbonne University St. LouisMO63105 United States Department of Computer Science and Engineering National Institute of Technology Andhra Pradesh 534101 India Department of Electrical Engineering and Computer Science Cleveland Sate University ClevelandOH44115 United States Department of Computer Science and Engineering Indian Institute of Information Technology Andhra Pradesh Sri City517646 India

Electric vehicle (EV) is a fast-growing technology that could help reduce greenhouse gas emissions in the energy and transportation sectors. The characteristics of EVs allow for rapid smart city expansion and serve as a key weapon in the emergence of vehicle-to-grid (V2G) technology. The entities, such as EVs and charging stations, connect with one another in order to gather data about the status of the EV and the environment in which it is traveling. The exchange of information among entities in V2G over a public channel may be exposed to security threats. The user’s devices are also vulnerable to physical attacks since they are left unprotected at their end. Hence, we suggest a lightweight protocol for key agreement and mutual authentication between entities operating in a V2G environment. Additionally, we assess the proposed scheme using the Scyther tool to conduct formal and informal security analyses in order to demonstrate how effectively it can counteract known security attacks. Finally, the performance study reveals that the suggested protocol provides a communication and computational benefit when compared to relevant protocols. © 2023 Institute of electrical and Electronics Engineers. All rights reserved.

关键词： Vehicle-to-grid

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Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

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computers, Materials & Continua 2023年第3期74卷 5757-5776页

作者： Nebras M.Sobahi Ahteshamul Haque V S Bharath Kurukuru Md.Mottahir Alam Asif Irshad Khan Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah 21589Saudi Arabia Advance Power Electronics Research Lab Department of Electrical EngineeringJamia Millia IslamiaNew DelhiIndia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic(PV)*** light of this requirement,this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated *** achieve this,different types of faults in grid-connected PV systems(GCPVs)and their impact on the energy loss associated with the electrical network are analyzed.A data-driven approach using neural networks(NNs)is proposed to achieve root cause analysis and localize the fault to the component level in the *** localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units(ANFIUs)to develop a power mismatch-based control unit(PMCU)for improving the power output of the *** develop the proposed framework,a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation ***,the developed algorithm is combined with the PMCU implemented with the experimental setup of *** results identified 98.2%training accuracy and 43000 observations/sec prediction speed for the trained classifier,and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.

关键词： Condition monitoring anomaly detection performance evaluation fault classification optimization

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A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems

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Digital Communications and Networks 2023年第5期9卷 1113-1122页

作者： Tejasvi Alladi Varun Kohli Vinay Chamola F.Richard Yu Department of Computer Science and Information Systems BITS-PilaniPilani Campus333031India Department of Electrical and Electronics Engineering&APPCAIR BITS-PilaniPilani Campus333031India Department of Systems and Computer Engineering Carleton UniversityOttawaONK1S 5B6Canada

With the rise of the Internet of Vehicles(IoV)and the number of connected vehicles increasing on the roads,Cooperative Intelligent Transportation Systems(C-ITSs)have become an important area of *** the number of Vehicle to Vehicle(V2V)and Vehicle to Interface(V2I)communication links increases,the amount of data received and processed in the network also *** addition,networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be ***,there is a need to augment them with intelligent network intrusion detection *** machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent ***,given the expected large network size,there is a necessity for extensive data processing for use in such anomaly detection *** learning solutions are lucrative options as they remove the necessity for feature ***,with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario,the need for deep learning-based techniques is all the more *** work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory(LSTM)and Convolutional Neural Networks(CNNs).The proposed Deep Learning Classification Engines(DCLE)comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge *** data received by the Road Side Units(RSUs)is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this *** proposed classifiers identify 18 different vehicular behavior types,the F1-scores ranging from 95.58%to 96.75%,much higher than the existing *** running the classifiers on testbeds emulating edge servers,the prediction performance and prediction time comparison of

关键词： Vehicular Ad-hoc Networks(VANETs) Intelligent Transportation Systems(ITS) Artificial Intelligence(AI) Deep Learning Internet of Things(IoT)

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Farmbot: An IoT-Based Wireless Agricultural Robot for Smart Cultivation 26

Farmbot: An IoT-Based Wireless Agricultural Robot for Smart ...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Kar, Sudipto Mohammad, Ragib Zaman, Meem Tasfia Reza, Sayed Maher Talukder, Ovi Hasan, Mehadi North South University Department of Electrical and Computer Engineering Dhaka Bangladesh Sylhet Engineering College Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350359015

Smart technologies have significantly transformed various sectors, and agriculture is no exception. This paper aims to introduce a robot called "Farmbot"to revolutionize traditional farming techniques and enhance crop production. The construction of a radio frequency remote-controlled robot using Arduino microcontrollers and ultrasonic sensors is programmed to navigate by the farmer on the ground. The Farmbot aims to aid farmers by delivering valuable data through a smartphone app on moisture levels, soil's potential of hydrogen, temperature, humidity, and the presence of flammable gasses across distinct farm zones, all through a single set of sensors. It provides comprehensive insights into the overall land condition, enabling informed decisions regarding irrigation, fertilization, and the crucial steps required for optimal growth. To further enhance its utility and application, features such as fertilizer and insecticide dispersion and optional plowing functionality are incorporated into the robot to enhance agricultural efficiency, ensuring optimal crop health and pest control and saving labor. The experimental results will demonstrate the effectiveness of the Farmbot in assisting farmers with irrigation management, fertilization management, and automated farming tasks. © 2023 IEEE.

关键词： agriculture air quality humidity micro-controller moisture radio frequency robot sensors temperature

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A Real-Time IoT Cryptograph Communication of Chaotic-ADPCM Coding Method for IoT Applications 6

A Real-Time IoT Cryptograph Communication of Chaotic-ADPCM C...

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6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Iman, Khalid Ismail Boby, R.I. Mollik, Md. Sazib Chowdhury, Imran Rahman, Sahidur Ankur, Wobeydul Haq Abdullah, Khaizuran Stamford University Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Dhaka Bangladesh International Islamic University Malaysia Department of Electrical and Computer Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350385779

Business, education, and research have formed a strong connection with the Internet of Things (IoT), an open-access collective communication service offered by Internet-connected devices. Though it seems to be a good deal in terms of cost and functionality, it is still the most dangerous communication channel due to high hacking rates and the ease of high data loss. Even though contemporary software is becoming more sophisticated for data and security, it still needs greater protection in a real-time setting. Utilizing extensive security measures may increase safety, but doing so will jeopardize the real-time environment. This study introduces a highly efficient encryption framework that combines chaotic maps with the Adaptive Differential Pulse Code Modulation (ADPCM) coding technique. The proposed algorithm ensures real-time secure communication for Internet of Things (IoT) applications while complying with ITU-T Recommendation Point G.726 and ANSI T1.303-1989 standards. It supports ADPCM transcoding algorithms at connection speeds of 16, 24, 32, and 40 kbps. The chaotic map technique was selected for its robust encryption capabilities using random variables and ADPCM for its high energy economy in transcoding and is characterized using two parallel encode functions per frame, which may be executed by small memory. The proposed encryption algorithm will perform three main operations. Firstly, it will be able to generate chaotic values from distinct initial conditions using two chaotic logistic maps. Secondly, it will transform these values into binary tables using a random encoding condition. Finally, it will execute the basic proposed ADPCM process to enhance security further. © 2024 IEEE.

关键词： Cryptography

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Decision Tree-Based IoT Botnet Attack Detection 2

Decision Tree-Based IoT Botnet Attack Detection

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Najjar, Hazem Al-Rousan, Nadia College Of Information Technology University Of Petra Department Of Computer Science Amman Jordan German Jordanian University Faculty Of Electrical Engineering And Information Systems Department Of Computer Engineering Jordan

ISBN: (纸本)9798350394962

This paper investigated the predictive capabilities of three decision tree models for IoT botnet attack prediction using packet information while minimizing the number of predictors. The study employed three decision tree models (C5, CHAID, and Random Forest) and two additional models (Logistic Regression and Bayesian Network) for comparison purposes. The IoT botnet attack dataset comprised various devices, mainly two types of attacks (Gafgyt and Mirai) and 23 feature engineering variables. Simulation results from all models showed that Accuracy, Precision, Recall, and False Omission Rate (FOR) values are approximately one, with an F1 score of around 0.5. CHAID and C5 models outperform other models in predicting IoT botnet attacks, as they are developed using only four and two variables, respectively. These results demonstrated that decision tree models with fewer variables can perform better than models that utilize all predictors. © 2024 IEEE.

关键词： Forecasting

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In Search for Model-Driven eXplainable Artificial Intelligence 1

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10th IFIP International Workshop on Artificial Intelligence for Knowledge Management, AI4KMES 2023

作者： Ligęza, Antoni Sepiolo, Dominik Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Applied Computer Science AGH University of Krakow al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (数字)9783031610691

ISBN: (纸本)9783031610684

This paper reports on ongoing and innovative research in the area of eXplainable Artificial Intelligence (XAI). A classical XAI task is considered as finding an explanation of the model generated via Machine Learning by identifying the most influential variables for local decision-making. Such an approach suffers from severe limitations. The proposed approach moves the explanatory process to a new, knowledge-level dimension. It is oriented towards Model Discovery, i.e. the internal structure and functions of the components. The concept of Model-Driven XAI is put forward and explained with examples. An experiment on Function Discovery via Grammatical Evolution is reported in brief. © IFIP International Federation for Information Processing 2024.

关键词： Decision making

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An Enhanced Integrated Method for Healthcare Data Classification with Incompleteness

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computers, Materials & Continua 2024年第11期81卷 3125-3145页

作者： Sonia Goel Meena Tushir Jyoti Arora Tripti Sharma Deepali Gupta Ali Nauman Ghulam Muhammad Electrical and Electronics Engineering(EEE)Department Maharaja Surajmal Institute of TechnologyNew-Delhi110058India Information Technology(IT)Department Maharaja Surajmal Institute of TechnologyNew-Delhi110058India Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpuraPunjab140401India Department of Computer Science and Engineering Yeungnam UniversityGyeongsan-si38541Republic of Korea Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11421Saudi Arabia

In numerous real-world healthcare applications,handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification *** approaches often rely on statistical methods for imputation,which may yield suboptimal results and be computationally *** paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved *** classification methods are ill-suited for incomplete medical *** enhance efficiency without compromising accuracy,this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete ***,the linear interpolation imputation method alongside an iterative Fuzzy c-means clustering method is applied and followed by a classification *** effectiveness of the proposed approach is evaluated using multiple performance metrics,including accuracy,precision,specificity,and *** encouraging results demonstrate that our proposed method surpasses classical approaches across various performance criteria.

关键词： Incomplete data nearest neighbor linear interpolation imputation clustering classification

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STaaS: Cost Effective and Scalable Union Mounting in ICP for Aerospace Applications

STaaS: Cost Effective and Scalable Union Mounting in ICP for...

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2024 International Conference on Green Energy, Computing and Sustainable Technology, GECOST 2024

作者： Sakthivel, V. Li, Jueying Rama Abirami, K. Min, Dugki Lee, Jae Woo Choi, Eunmi School of Computer Science and Engineering Vellore Institute of Technology Chennai India Konkuk University Department of Computer Science and Engineering Seoul Korea Republic of Curtin University Department of Electrical and Computer Engineering Malaysia Konkuk Aerospace Design - Airworthiness Institute Konkuk University Seoul Korea Republic of Kookmin University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350357905

Cloud computing widely used in various specific areas leads to the emergence of 'Short-Time as a Service' (STaaS) as a cost-effective and scalable way for enterprises and organizations to access and utilize computing resources. Union mounting, a method that enables multiple file systems to be combined into one virtual file system, has been proposed as a strategy to optimize STaaS by allowing resources to be allocated dynamically based on user requirements. This research paper explores the feasibility and benefits of STaaS with the union mounting method, using a project providing a simulation environment on the Industry Cloud Platform (ICP) for the aerospace organization that requires an environment that can automatically and quickly create cluster groups of Cloud In the Loop Simulation (CILS) for Unmanned Aerial Vehicle (UAV) operations. The results demonstrate that STaaS with a union mounting method can provide significant benefits in terms of CILS cluster group creation, performance optimization, and flexibility. Specifically, our proposed research could reduce instance creation time costs by up to 90% compared to the traditional creation method. Overall, STaaS with union mounting method improves the creation time of the CILS cluster group for the aerospace organization requiring flexibility and cost-effectiveness. © 2024 IEEE.

关键词： Antennas

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