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Enhanced Condition Monitoring of Low Voltage Motors in Harsh Environments with Ladybug Beetle Optimization and Context-Aware Graph Convolutional Networks 4

Enhanced Condition Monitoring of Low Voltage Motors in Harsh...

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4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Rajasekar, P. Tiwari, Praveen Prakash Sharmila, L. Garg, Akriti Srinivas, Rayudu Tallapragada, V. V. Satyanarayana Srm Institute of Science and Technology Department of Data Science and Business System Tamil Nadu Kattankulathur603203 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India Mahatma Jyotiba Phule Rohilkhand University Department of Electrical Engineering Uttar Pradesh Bareilly243006 India Aditya College of Engineering & Technology Department of Electrical and Electronics Engineering Andhra Pradesh Surampalem533437 India School of Engineering Mohan Babu University Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India

ISBN: (纸本)9798331540364

Low voltage motors are essential in many applications in contemporary industrial settings, as they power machines and guarantee efficient operations. However, these motors often operate in harsh environments where they are exposed to factors such as extreme temperatures, moisture, dust, and vibrations, which can lead to premature wear and failure. This paper proposes a hybrid method for low voltage motor condition monitoring in challenging industrial environments. The proposed method is the combined execution of Ladybug Beetle Optimization algorithm (LBOA) and Heterogeneous Context-Aware Graph Convolutional Network (HCAGCN). Hence it is named as LBOA-HCAGCN technique. Initially, the input data is collected from Triboelectric vibration sensors. Then, the collected data is fed into preprocessing utilizing Generalized Multi-kernel Maximum Correntropy Kalman Filter (GMKMCKF). This filter normalizes and cleans the data by effectively removing noise. Recursive Hilbert Transform (RHT) is used to extract time domain features such as mean, Root Mean Square (RMS), Peak-to-Peak Value, Skewness, and Kurtosis. Then the extracted features are given to HCAGCN for classify the motor health conditions such as normal, broken-bar, bowed-bar, bowed-rotor, faulty bearing, and voltage imbalance. In general, HCAGCN does not express adapting optimization strategies to determine optimal parameters to classify motor conditions. Hence, the LBOA is used to optimize the weight parameter of the HCAGCN which accurately classify motor conditions. The proposed LBOA-HCAGCN is implemented in MATLAB. Performance indicators such as Mean Absolute Error (MAE), Precision, and Accuracy were used to analyze the effectiveness of the proposed approach. Comparing the proposed LBOA-HCAGCN methodology to other existing techniques like Deep Residual Neural Network (DRNN), Convolutional Neural Network (CNN), and Recursive Neural Network (RNN), it achieves 25.8%, 26.4%, and 24.7% greater accuracy, 26.7%, 29.4%, and

关键词： Mean square error

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A Scheme for Authentication and Traceability of 5G Mobile Terminal Based on IPv6 and Blockchain 16

A Scheme for Authentication and Traceability of 5G Mobile Te...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Ji, Xinsheng Zhao, Ying Liao, Xingxing Yang, Jie Wang, Chunxiao Tsinghua University Department of Computer Science and Technology Beijing100084 China National Digital Switching System Engineering & Technological R&d Center Zhengzhou450002 China Purple Mountain Laboratories Nanjing211111 China School of Cyber Science and Engineering Southeast University Nanjing211102 China

ISBN: (纸本)9798350390643

To satisfy the service-side requirements for network layer authentication and traceability of users, this paper proposes a method for mobile terminal authentication and identity tracing based on IPv6 and blockchain. Our method introduces a new verification function (VEF) to verify the interface identification between the terminal and the 5G core network (5GC), thereby effectively enhancing the security of terminal access. Furthermore, considering the privacy protection of user identity information to prevent attackers from deducing encrypted content, it is necessary to periodically update encryption keys. Focusing on the practical deployment of VEFs on a network-wide scale and server-proximal for authentication and traceability, a time-varying key sharing scheme based on blockchain is proposed. The experimental section analyzes the communication performance and security of the proposed scheme, showing that our scheme does not significantly degrade the original network performance while providing substantial security gains. © 2024 IEEE.

关键词： Authentication Protocol

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Analysis of Sleep apnea Considering Electrocardiogram Data Using Deep learning Algorithms 8

Analysis of Sleep apnea Considering Electrocardiogram Data U...

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8th International Virtual Conference on Biosignals, Images, and Instrumentation, ICBSII 2022

作者： Rajabrundha, A. Lakshmisangeetha, A. Balajiganesh, A. Electronic System Design Lab Velammal Engineering College India Department of Computer Science Engineering Velammal Engineering College India Velammal Engineering College Chennai India

Sleep is a vital component of every human being. Adequate restful and restorative sleep reenergizes the body, enhances overall health and psychological well-being. Sleep hygiene, chaotic lifestyles, disorder breathing, stress, and anxiety contribute to poor sleep quality. Obstructive sleep apnea (OSA) sleep respiratory disorder causes temporary lapses of breathing results in gasping, choking, snoring sounds during sleep. The individual does not consciously wake up, but the brain has to start breathing again which disrupts the sleep quality. Polysomnography (PSG) sleep study is employed to diagnose sleep disorders by using either in-home or laboratory-based comprehensive tests. The untreated OSA leads to deterioration in health, performance consequences with severity including daytime sleepiness, motor vehicle accidents, workplace errors, cardiovascular morbidity, and mortality. The pre-processed, interpolated and segmented ECG signal is considered for the examination of OSA. This paper focuses on three types of deep learning classifiers-based prediction models for detection of apnea from the ECG signal. The accuracy value of Long Short Term Memory model (LSTM) is 85 percent and classifier's ability to distinguish between normal and apnea events is *** Gated Recurrent Unit (GRU) classifier and Convolution Neural Network (CNN) model have an f1- score value of 0.80. The proposed LSTM model provides the optimal performance in comparison to other deep learning models used for classification with respect to area under the curve (AUC) and accuracy metrics. © Published under licence by IOP Publishing Ltd.

关键词： Electrocardiograms

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Optimized Design of a Haptic Unit for Vibrotactile Amplitude Modulation

Optimized Design of a Haptic Unit for Vibrotactile Amplitude...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Huang, Jingchen Fang, Yun Guo, Weichao Qiao, Zhijian Sheng, Xinjun School of Mechanical Engineering Meta Robotics Institute Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration Shanghai200240 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong

ISBN: (纸本)9781665481090

Communicating information to users is a crucial aspect of human-machine interaction. Vibrotactile feedback encodes information into spatiotemporal vibrations, enabling users to perceive tactile sensations. It offers advantages such as lightweight, wearability, and high stability, with broad applications in sensory substitution, virtual reality, education, and healthcare. However, existing haptic unit designs lack amplitude modulation capabilities, which limits their applications. This paper proposed an optimized design of the haptic unit from the perspective of vibration amplitude modulation. A modified elastic model was developed to describe the propagation and attenuation mechanisms of vibration in the skin. Based on the model, two types of hierarchical architectural design were proposed. The design incorporated various materials arranged in multiple layers to amplify or attenuate the vibration amplitude as it traveled through the structure. An experimental platform was built to evaluate the performance of the optimized design. © 2024 IEEE.

关键词： Virtual reality

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An adaptive representation model for geoscience knowledge graphs considering complex spatiotemporal features and relationships

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science China Earth sciences 2023年第11期66卷 2563-2578页

作者： Yunqiang ZHU Kai SUN Shu WANG Chenghu ZHOU Feng LU Hairong LV Qinjun QIU Xinbing WANG Yanmin QI State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China Department of Automation Tsinghua UniversityBeijing 100084China School of Computer Science China University of GeosciencesWuhan 430074China Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Computer Science University of Nottingham Ningbo ChinaNingbo 315100China

Geoscience knowledge graph(GKG)can organize various geoscience knowledge into a machine understandable and computable semantic network and is an effective way to organize geoscience knowledge and provide knowledge-related *** a result,it has gained significant attention and become a frontier in *** knowledge is derived from many disciplines and has complex spatiotemporal features and relationships of multiple scales,granularities,and ***,establishing a GKG representation model conforming to the characteristics of geoscience knowledge is the basis and premise for the construction and application of ***,existing knowledge graph representation models leverage fixed tuples that are limited in fully representing complex spatiotemporal features and *** address this issue,this paper first systematically analyzes the categorization and spatiotemporal features and relationships of geoscience *** this basis,an adaptive representation model for GKG is proposed by considering the complex spatiotemporal features and *** the constraint of a unified spatiotemporal ontology,this model adopts different tuples to adaptively represent different types of geoscience knowledge according to their spatiotemporal *** model can efficiently represent geoscience knowledge,thereby avoiding the isolation of the spatiotemporal feature representation and improving the accuracy and efficiency of geoscience knowledge *** can further enable the alignment,transformation,computation,and reasoning of spatiotemporal information through a spatiotemporal ontology.

关键词： Geoscience Knowledge graph Representation model Spatiotemporal features Spatiotemporal relationships

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Bird Swarm Algorithm with Fuzzy Min-Max Neural Network for Financial Crisis Prediction

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computers, Materials & Continua 2022年第10期73卷 1541-1555页

作者： K.Pradeep Mohan Kumar S.Dhanasekaran I.S.Hephzi Punithavathi P.Duraipandy Ashit Kumar Dutta Irina V.Pustokhina Denis A.Pustokhin Department of Computing Technologies Associate ProfessorSRM Institute of Science and TechnologyKattankulathur603203India Department of Information Technology Kalasalingam Academy of Research and Education626126India Department of Computer Science and Engineering Sphoorthy Engineering CollegeHyderabadTelangana501510India Department of Electrical and Electronics Engineering J B Institute of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Financial crisis prediction(FCP)models are used for predicting or forecasting the financial status of a company or financial *** is considered a challenging issue in the financial *** and machine learning(ML)models can be employed for the design of accurate FCP *** numerous works have existed in the literature,it is needed to design effective FCP models adaptable to different *** study designs a new bird swarm algorithm(BSA)with fuzzy min-max neural network(FMM-NN)model,named BSA-FMMNN for *** major intention of the BSA-FMMNN model is to determine the financial status of a firm or *** presented BSA-FMMNN model primarily undergoes minmax normalization to transform the data into uniformity ***,k-medoid clustering approach is employed for the outlier removal ***,the classification process is carried out using the FMMNN model,and the parameters involved in it are tuned by the use of *** utilization of proficient parameter selection process using BSA demonstrate the novelty of the *** experimental result analysis of the BSA-FMMNN model is validated using benchmark dataset and the comparative outcomes highlighted the supremacy of the BSA-FMMNN model over the recent approaches.

关键词： Financial crisis predictive model machine learning outlier removal clustering metaheuristics

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Hybrid Power Generation Forecasting Using an Intellectual Evolutionary Energy-Preserve Rate Clustering Technique 1

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10th International Conference on Innovations in computer science and engineering, ICICSE 2022

作者： Webber, Julian L. Jayagopal, Vellingiri Mehbodniya, Abolfazl Sengan, Sudhakar Velayutham, Priya Rangasamy, Rajasekar Stalin David, D. Doha Kuwait Department of Software & System Engineering School of Information Technology & Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Computer Science and Engineering Paavai Engineering College Tamil Nadu Namakkal637018 India Department of Computer Science and Engineering GITAM School of Technology GITAM University Karnataka Bengaluru561203 India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Tamil Nadu Chennai600072 India

ISBN: (数字)9789811974557

ISBN: (纸本)9789811974540

The significant growth of Wind Power (WP) has significant challenges due to the inconsistency and uncertainty of Wind Power Generation (WPG) to ensure the power system. Solar energy and WP calculation are great ways to overcome the problem. Mainly voltage and frequency fluctuations and harmonics have a high impact on the power generation and distribution system;in any high case, the power quality of grid systems is reduced. This can be primarily solved with proper design, advanced rapid response control facilities and optimization of power systems. A new type of Multi-term Wind Energy (WE) with an IEEE 30 bus forecasting system based on digital weather forecasting and error modification methods is presented. From the Evolutionary Energy- Preserve Rate Clustering (EEPRC) Technique-based technique for the evaluates the probability ratio in Solar and wind error energy prediction, the error assessment model is proposed, which has the advantage of estimating from real wind farms and statistical Weather Forecast (WF) data and has proven to be a nominal method for improving the predictive accuracy of Multi-term WPG. The primary step of the Energy-Preserve Rate Clustering (EPRC) method is to calculate the uncertainty of real-time data with the predicted value. The computed values are helpful for the increases in Solar WPG production. The proposed wind forecasting system is utilized in the Coastal area-based energy generation. Also, it has been demonstrated that wind speed and WPG with its probability ratio have been evaluated with the EEPRC-based forecasting method, especially for its powerful predictive capability for the complex properties of the controller function. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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Preferences for Using Canva Apps as a Presentation Media Creation Tool Among Gen Z Students: SEM Analysis with TAM and UTAUT2 Approaches 25

Preferences for Using Canva Apps as a Presentation Media Cre...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Maulana, Fairuz Iqbal Lestari, Dian Purnomo, Agung Arifuddin, Rahman Widartha, Vandha Pradwiyasma Adi, Puput Dani Prasetyo Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Medicine Airlangga Surabaya60132 Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia Research Center for Telecommunication Bandung Indonesia

ISBN: (纸本)9798350378573

The purpose of this study is to find out what makes Generation Z students accept and use Canva as a tool for making presentation materials. The conceptual framework of this study is the combination of "Technology Acceptance Model (TAM)"and the "Unified Theory of Acceptance and Use of Technology (UTAUT2)". "Structural Equation Modeling (SEM)"used for data analyzation. This research aims to understand the dynamics of Canva acceptance among students. The findings show that enjoyment, fun, and pleasure in using Canva, combined with its creative tools and design-centric approach, significantly resonate with Gen Z's preference for aesthetics. This research highlights that Canva is favored by students for creating engaging presentations. © 2024 IEEE.

关键词： Students

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Distributed safe resource allocation using barrier functions

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Automatica 2023年 153卷

作者： Wu, Xuyang Magnússon, Sindri Johansson, Mikael School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Department of Computer and System Science Stockholm University Sweden

Resource allocation plays a central role in networked systems such as smart grids, communication networks, and urban transportation systems. In these systems, many constraints have physical meaning and infeasible allocations can result in a system breakdown. Hence, algorithms with asymptotic feasibility guarantees can be insufficient since they cannot ensure that an implementable solution is found in finite time. This paper proposes a distributed feasible method (DFM) for resource allocation based on barrier functions. In DFM, every iterate is feasible and safe to implement since it does not violate the physical constraints. We prove that, under mild conditions, DFM converges to an arbitrarily small neighborhood of the optimum. Numerical experiments demonstrate the competitive performance of DFM. © 2023 The Author(s)

关键词： Resource allocation

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LSTM & MACD Approach: Prediction and Analysis of Stock Market

LSTM & MACD Approach: Prediction and Analysis of Stock Marke...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： L. Jerart Julus S.N. Syed Naweed D. Aadhavan S. Sudhakara Sankar R.S. Rampriya T. Kavitha Department of IT School of Computer Science Engineering and Information System Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information System Vellore Institute of Technology Vellore India Department of ECE Vel Tech University Chennai Tamilnadu India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Investing and researchers alike have long been fascinated by the volatile and dynamic nature of financial markets. For the prediction and analysis of stock market movements, we present a hybrid approach that combines Long Short-Term Memory (LSTM) and Moving Average Convergence Divergence (MACD). Leveraging the power of deep learning and technical analysis, this hybrid model offers a comprehensive solution for forecasting and understanding market trends. The LSTM component, a type of recurrent neural network, provides the model with the ability to capture and learn intricate patterns in historical stock price data, thus enabling accurate short and long-term predictions. The MACD indicator, a widely used tool in technical analysis, supplements the LSTM by identifying potential reversal points and providing valuable insights into market momentum. A hybrid approach will provide a stronger and more reliable forecasting framework by combining both LSTM and MACD. To validate the model's efficacy, we apply it to historical stock market data and conduct a thorough analysis of its predictive performance. Our findings suggest that the Hybrid LSTM-MACD approach outperforms traditional methods and offers a promising solution for investors, traders, and financial analysts seeking to navigate the complex world of stock market trading.

关键词： Analytical models Accuracy Predictive models Market research Data models Internet Stock markets Forecasting Long short term memory Convergence

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