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Fault detection and fault tole rant control of interturn short circuit of double redundancy motor 3

Fault detection and fault tole rant control of interturn sho...

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2023 3rd international conference on Electrical, Electronics, and Computing technology, EECT 2023

作者： Jin, Tianbao Tian, Wei Zhao, Wenxiang Jiangsu University Zhenjiang China

This paper presents the robust detection of interturn short circuit fault of double redundant permanent magnet synchronous motor. In the static coordinate system and double dq-axis system, the mathematical model of the fault state is established. Further, the residual signal of the dq-axis current caused by the turn-to-turn short circuit fault is predicted using the Luenberger observer. Next, the fault marker and the fault phase marker are obtained by processing the current residual signal. Finally, the processor performs the excision of the faulty phases and hardware reconfiguration, thus allowing fault-tolerant operation of the motor. The simulation results show the effectiveness of the detection and fault tolerance of double redundant permanent magnet synchronous motor. © Published under licence by IOP Publishing Ltd.

关键词： Fault detection

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Investigating the Synergies between Decentralized Protocols and Edge intelligence for a Resilient Web3.0 Infrastructure 3

Investigating the Synergies between Decentralized Protocols ...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Kaliappan, Seeniappan Niveditha, V.R. Maranan, Ramya Irfan, M.M. Kcg College of Technology Dept. of Mechatronics Engineering TN Chennai India Sathyabama Institute of Science and Technology Dept. of Cse TN Chennai India Saveetha School of Engineering Simats Dept. of Research and Innovation TN Chennai India Sr University Dept. of Eee Telangana Warangal India

ISBN: (纸本)9798350343274

This paper presents the results of an in-depth investigation of how edge intelligence and decentralized protocols might work together to create a robust Web3.0 system. An exhaustive literature study is conducted, covering topics such as decentralized protocols, edge computing, and their significance to Web3.0, to provide a solid groundwork for this inquiry. This first stage of the investigation involves a comprehensive review of previous research, methods, and best practices in the field, with the goal of identifying areas where the envisioned synergy may fill a need. Empirical data culled from preexisting Web3.0 infrastructure, real-world use cases, and simulated data are all considered throughout the data collection process. Expert interviews and surveys are also used to collect qualitative information, with the goal of eliciting relevant feedback from specialists in the area. The research approach itself includes the design and execution of a series of experiments and simulations. These exploratory projects are meant to analyze how Web3.0 apps may best make use of decentralized protocols and edge intelligence. These factors comprise measurements relating to latency, throughput, data availability, scalability, and security/privacy. At the end of the study, a comparison is made between the suggested integrated solution and the status quo of centralized systems and other popular approaches to Web3.0 infrastructure. This in-depth research illuminates the benefits and drawbacks of the integration process. © 2023 IEEE.

关键词： Data privacy

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Streamlining Information Capture and Retrieval with AI-Enabled systems 3

Streamlining Information Capture and Retrieval with AI-Enabl...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sudhamsu, Gadug Agarwal, Ankita Dhivrani, Rekha Bhanu, K.S. Jeyalaxmi, M. Nawadkar, Ashwini R. Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Atlas SkillTech University Department of UGDX Maharastra Mumbai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

artificial intelligence (AI) is an emerging era that has proven to have a fantastic capacity to revolutionize how facts are captured and retrieved. AI-enabled systems offer an expansion of abilities to streamline the system of accumulating, managing, storing, and retrieving data. AI-enabled structures provide several benefits, such as using predictive analytics and herbal Language Processing (NLP) to extract records more effectively and correctly and automated pass-referencing competencies to simplify locating records. AI-enabled systems make indexing and saving statistics easier, allowing users to look for the relevant data they need quickly. AI-enabled structures offer higher security measures and encryption techniques for defensive touchy facts. Functions like facial recognition, gadget mastering algorithms, and get admission to hold data safe and far from malicious actors. Such systems may control and store statistics securely, correctly, and managed. AI-enabled systems provide an excellent suite of features for streamlining the manner of seizure and retrieval of statistics. AI answers, including predictive analytics, natural language processing, and automated cross-referencing, allow groups to quickly get the right of entry to the relevant information they need, even by providing higher safety features for included facts. In destiny, this technology might also grow to be even greater advanced, assisting businesses in gaining facts to electricity predictive analytics, helping in advertising efforts, and enhancing the organizational operation. © 2023 IEEE.

关键词： information organizational predictive processing security

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Non-Invasive Human Body Temperature detection for Online Event Data Recorder 3

Non-Invasive Human Body Temperature Detection for Online Eve...

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3rd international conference on Intelligent Cybernetics technology and Applications, ICICyTA 2023

作者： Sani, Muhammad Ikhsan Ibnugraha, Prajna Deshanta Sari, Marlindia Ike Rizal, Moch Fahru Hanifa, Fanni Husnul Salasa, Salsabila Anza Reksa, Anggi Angga Hartono, Aditya Firmansyah Utama, Nur Cahya Telkom University Dept. of Computer Technology Bandung Indonesia Telkom University Dept. of Digital Marketing Bandung Indonesia Telkom University Dept. of Informatics Bandung Indonesia

ISBN: (纸本)9798350394559

The concept of black-box or Event Data Recorder (EDR) implementation on ground vehicle provides an alternative method for preemptive or prescriptive safety procedure. Utilizing all accessible technologies for accident prevention is crucial for ensuring transportation safety and regulation. Much effort has been devoted to developing thermal imaging as an additional sensor for EDR. However, several factors must be considered when using thermal imaging device on Event Data Recorder. Given that fever is a prevalent indication of several contagious illnesses, it is crucial to emphasize the monitoring of human body temperature for the purpose of fever screening. The goal is to reduce the number of individuals with elevated body temperatures from utilizing public transportation. This work aims to introduce a thermal camera application as a supplementary data source for the Event Data Recorder (EDR) in public transportation. The paper also proposed an alternative solution based on the use of a portable Forward-looking Infrared (FLIR) thermal imaging camera. The FLIR thermal camera is incorporated into the online EDR system. The system has been evaluated and can collect data for future analysis, as demonstrated by both indoor and outdoor tests. It may function in a somewhat limited range within small space area up to 3-meter detection range. Further studies will have to clarify on increasing the accuracy with another algorithm or methodology. © 2023 IEEE.

关键词： Automobile bodies

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A New Design for Protecting Cyber-Attacks and Harmful Threats in IoT Communication Network with Efficient Deep Learning-Based detection system 3

A New Design for Protecting Cyber-Attacks and Harmful Threat...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Dhinakaran, S. Bansal, Vipul Pant, Kumud Joshi, Sanjeev Kumar Maginmani, Umme Habiba Prasad, Ch. Raghava Rathinam College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore India Kurukshetra University Information Technology Haryana India Graphic Era Deemed to be University Department of Biotechnology Uttarakhand Dehradun India Uttaranchal University Uttaranchal Institute of Technology India Secab Institute Of Engineering and Technology Department Of Computer Science & Engineering Karnataka Vijayapur India KoneruLakshmaiah Education Foundation Department of Electronics and Communication Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350399264

Fast progress in both smart devices with limited resources and state-of-the-art communication methods have helped establish the Internet of Things (IoT) as the gold standard for low-power lossy networks. However, because to the low computing, storage, and networking capabilities of the endpoint devices, these networks are susceptible to cyber assaults. IoT network security solutions are often rendered ineffective since many attacks are produced by slightly modifying previously known assaults. With their superior performance, deep learning algorithms have seen rapid adoption in security applications in recent years. In this paper, we introduce the IoT-IDCS-CNN, a brand new, unsupervised deep learning-based method for detecting and categorising cyberattacks in IoT communication networks. The system uses high-performance computing on dependable Nvidia GPUs, and parallel processing on fast Intel CPUs. It is structured into three parts: a traffic classification module, a feature learning module, and a refinement module for engineering features. Each part is developed independently and then tested and combined to create the complete system. © 2023 IEEE.

关键词： Internet of things

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Human-Object Interaction detection Based on Graph Model 3

Human-Object Interaction Detection Based on Graph Model

引用

3rd international conference on Artificial intelligence and Computer Engineering, ICAICE 2022

作者： Ye, Qing Xu, Xiuju School of Information Science and Technology North China University of Technology Beijing100144 China

ISBN: (纸本)9781510663473

Human-Object Interaction (HOI) detection is a fundamental task for understanding real-world scenes. In this paper, a graph model-based human-object interaction detection algorithm is proposed, which aims to make full use of the visual-spatial features and semantic information of human-object instances in the image, thereby improving the accuracy of interaction detection. Aiming at the characteristics of visual-spatial features and semantic information, we take the visual features of human and object instance boxes as nodes, and the corresponding spatial features of interaction relations as edges to construct an initial dense graph, and adaptively update the graph through the spatial and semantic information of instances. The V-COCO dataset is used to evaluate the algorithm, and the final accuracy is significantly improved, which proves the effectiveness of the algorithm. © 2023 SPIE.

关键词： Image enhancement

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An Empirical Evaluation of Machine Learning Algorithms for Groundwater Quality Classification 3

An Empirical Evaluation of Machine Learning Algorithms for G...

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3rd international conference on Innovative Practices in technology and Management, ICIPTM 2023

作者： Domakonda, Vinay Kumar Sasirekha, K. Sangeetha, S. Umasankar, L. Nagendiran, S. Kumar, M Jogendra Vignan's Foundation for Science and Technology Department of Mechanical Engineering Andhra Pradesh India R.M.D Engineering College Department of Computer Science Kavarapettai India Veltech Multitech Department of Information Technology Chennai India R.M.K College of Engineering and Technology Department of Electrical and Electronics Chennai India R.M.K Engineering College Department of Artificial Intelligence Kavaraipettai India KoneruLakshmaiah Education Foundation Department of Computer Science Vaddeswaram India

ISBN: (纸本)9798350336238

Groundwater is an effective monitoring system is essential, one of the most vulnerable resources. The use of spatial data to measure spatial changes in groundwater one of the most key things of soil monitoring. As a result, the most important water constituents based on groundwater characteristics is critical for an effective soil monitoring programmed the development of an efficient reference system that estimates. We evaluated the performance of neural network (NN)-based algorithms and event prediction models (EPM)) to estimate the severity of SS in some Indian regions throughout this study. Using 16 years of We developed a regional and local model remote sensing dataset to estimate the SS of the entire Indian basin and each catchment in the study area. Based on EPM and NN regional models had accuracy and SS of 88%, 96%, 88%, and 87%, The estimation and SS outperformed both the regional and spatial NN by 50-84% and 71-84%, whereas the local model was the empirically derived model, respectively. Consequently, according to the findings, machine learning methods should be used to accurately and continuously monitor groundwater quality parameters. In complex topography of India and other similar land classifications. © 2023 IEEE.

关键词： Landsat

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Hate Speech detection in Audio Using SHAP - An Explainable AI 1

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3rd international conference on Advanced Network Technologies and Intelligent Computing (ANTIC)

作者： Imbwaga, Joan L. Chittaragi, Nagaratna B. Koolagudi, Shashidhar G. Natl Inst Technol Mangaluru Karnataka India Siddaganga Inst Technol Tumkur Karnataka India

ISBN: (数字)9783031640643

ISBN: (纸本)9783031640636;9783031640643

Hate speech detection is a process of recognition of communication media such as text, audio, and/or video, if it contains hatred and/or encourages violence towards a person or a community of people. This is usually based on prejudice against 'protected characteristics' such as their ethnicity, gender, sexual orientation, religion, age and so on. Complex and sophisticated classifiers based hate speech detection systems are available in the literature. However, the characteristics exhibited by explainable artificial intelligence techniques demonstrated versatile capabilities. This potential is due to the complex classifiers presenting themselves as black-box in nature hence limiting the social acceptability and usability of the developed systems. In this study, video datasets for English and Kiswahili languages were manually collected from YouTube, converted to audio, and used to detect hate speech. Ensemble based classification algorithms have been used for implementation of hate speech detection system. Random Forest classifier recorded an accuracy of 95.8% for English language while for Kiswahili language, Extreme Gradient Boosting classifier achieved an accuracy of 91.8%. To explain the results achieved by these classifiers, in terms of how specific audio-based features contributed to the overall detection of hate speech, SHapley Additive exPlanations technique (SHAP) is used.

关键词： XAI Hate Speech SHAP MFCCs Prosodic Kiswahili

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An Enhancing Network Security: A Stacked Ensemble Intrusion detection system for Effective Threat Mitigation 3

An Enhancing Network Security: A Stacked Ensemble Intrusion ...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Vullam, Nagagopiraju Roja, D. Rao, Nagamalleswara Vellela, Sai Srinivas Vuyyuru, Lakshma Reddy Kumar, K Kiran Chalapathi Institute of Engineering and Technology Dept. of CSE AP Guntur India Chalapathi Institute of Technology Dept. of CSE-Data Science AP Guntur India Chalapathi Institute of Technology Dept. of CSE AP Guntur India Koneru Lakshmaiah Education Foundation Dept. of CSE AP Vaddeswaram India

ISBN: (纸本)9798350343632

Modern cyber security relies heavily on intrusion detection in network traffic to quickly identify and mitigate security threats. Conventional intrusion detection systems frequently depend on lone, stand-alone models that could find it difficult to change with the ways that network attacks are evolving. In order to overcome this difficulty, this research study provides an ensemble-based strategy that improves threat detection effectiveness and accuracy by utilizing the strength of several intrusion detection models. Increasing security measures against data breaches and network invasions is essential due to the ever-growing usage of the Internet and networks. As intrusions are frequently hidden within of valid network packets, firewalls have a difficult time identifying and stopping them. Furthermore, the majority of network monitoring systems and algorithms find it increasingly difficult to handle the sheer volume of network traffic. Various intrusion detection strategies have been proposed in response to these issues, with machine learning techniques emerging as a possible route for handling these situations. This paper introduces an Intrusion detection system (IDS) that leverages stacking ensemble learning. The core ensemble comprises three fundamental machine learning models: k-nearest-neighbours, Decision Tree, and Random Forest. To enhance classification performance, the proposed system combines a total of seven machine learning algorithms with preprocessing methods. Stacking ensembles improve system performance greatly by combining the results of these basic models with a meta-model represented by the Logistic Regression algorithm. The UNSW-NB15 dataset is used to assess the IDS's efficacy. With an astounding 96.16% accuracy rate in the training phase and an even greater 97.95% accuracy rate in the testing phase, the suggested IDS performs remarkably well. Additionally, the precision scores are remarkable, with training scores of 97.78% and testing scores of

关键词： Decision tree Intrusion detection system (IDS) k-nearest-neighbour, random forest stacking

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Drowsiness and Lethargy detection Using Machine Learning Techniques 3

Drowsiness and Lethargy Detection Using Machine Learning Tec...

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3rd international conference on Electrical, Computer and Communication Engineering, ECCE 2023

作者： Siddik, Md. Abu Dayan Rahman, Mohammad Shahidur Inst. of Information and Communication Technology Shahjalal University of Science and Technology Sylhet Bangladesh

ISBN: (纸本)9798350345360

Drowsiness has severe effects on the safety of human life. The worldwide death rate due to drowsy driving is quite alarming. As the implementation of artificial intelligence (AI) is growing faster, this paper describes an attempt to implement machine learning (ML) to detect drowsiness. 120 videos of 60 participants are collected from the Real-Life Drowsiness Video Dataset made by a research team of the Vision-Learning-Mining Lab from the University of Texas at Arlington. Then Eye Aspect Ratio, Mouth Aspect Ratio, Pupil Circularity, and Mouth Aspect Ratio Over Eye Aspect Ratio, Nose Length, Chin Length, Nose Length Over Chin Length Ratio are extracted as features of each participant using the 3D Face-Mesh 468 facial landmarks system from those videos. After that, each feature is normalized by its mean and standard deviation. Then the CSV dataset is generated using seven initial and seven normalized features. A total of 30000 instances are there in the dataset. A total of eight classification algorithms are implemented to build the model. The dataset is split such that the individual in the train set will not be in the test set to test the proposed model's ability to predict drowsiness for new faces. 5-fold cross-validation is implemented to measure performance for each algorithm. Convolutional Neural Network (CNN) yields maximum accuracy (91.63%). The state of any individual's eye closing, rapid eye blinking, yawning, putting a hand on the mouth during yawning, and head posing too much up or down can be detected as drowsiness by the proposed model. © 2023 IEEE.

关键词： Aspect ratio

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