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Directional Beam Based LED Deployment for a Short Range Indoor LED Light Communication System

Journal of Optical Communications 2022年第3期43卷 411-420页

作者： Sharma, Ram Charan Kumari, A. Aggarwal, Mona Ahuja, Swaran Department of Electrical Electronics and Communication Engineering NorthCap University Haryana Gurgaon India Department of Electrical Electronics and Communication Engineering School of Engineering NorthCap University Gurgaon India Department of Computer Science and Engineering School of Engineering NorthCap University Gurgaon India

There is now an increasing awareness about the possibility of health hazards due to excessive electromagnetic radiations around us within the indoor environment. Hence, the alternative communication technologies such as indoor mobile communication system based on LED lighting are gaining more attention in the present scenario. The dual use of LEDs i. e. for illumination as well as for setting up of an indoor communication network infrastructure simultaneously makes it an attractive preposition. The LED lighting based communication system is qualified to be part of green technology networks. This paper investigates the optimal LED placement strategy for such a system to optimize the overall communication system parameters including the transmit power optimization. It is observed that the overall optimization of LEDs improve the signal to noise ratio and also a significant reduction in root mean square delay at lower semi-angle values besides improving the mobile coverage area. © 2019 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Light emitting diodes

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Predictive Maintenance Analysis using Multivariate Machine Performance Data for Industrial Downtime Reduction 3

Predictive Maintenance Analysis using Multivariate Machine P...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Saddala, Rajeswara Reddy Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita school of computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita school of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Tool downtime prediction requires efficient monitoring because it helps maximize industrial operation quality while maintaining uninterrupted plant operations. The analysis utilizes machine learning algorithms with Decision Trees to identify machine downtime events through operational measurements which include hydraulic pressure and coolant temperature and spindle speed and vibration levels. Data normalization alongside Forward Selection feature selection methods were used on a balanced dataset to boost model performance for prediction purposes. The Decision Tree model functioned to differentiate machine defects into two discrete categories—failure and no failure—thus proving its competence in such predictive classification activities. The study conducted a comparative evaluation between Decision Trees and Random Forest along with other algorithms which included SVM and Logistic Regression and Adaboost in addition to CatBoost and XGBoost and Naive Bayes and KNN using KD-Tree and Ball-Tree variations. The experimental findings show that Decision Trees provide clear predictions coupled with high precision and fair runtimes to support predictive maintenance implementations. The research delivers fundamental findings that help improve downtime prediction methods to boost operational effectiveness. © 2025 IEEE.

关键词： Predictive maintenance

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Analysis of Electric Arc Furnace Influence on Power Quality in Low and Medium Voltage Networks 24

Analysis of Electric Arc Furnace Influence on Power Quality ...

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24th EEEIC International Conference on Environment and electrical engineering and 8th I and CPS Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2024

作者： Grabowski, Dariusz Klimas, MacIej Lange, Andrzej Silesian University of Technology Faculty of Electrical Engineering Department of Electrical Engineering and Computer Science Gliwice Poland University of Warmia and Mazury Faculty of Technical Sciences Department of Electrical and Power Eng. Electronics and Automation Olsztyn Poland

ISBN: (纸本)9798350355185

This paper focuses on analyzing power quality issues resulting from electric arc furnace operation. It highlights the challenges posed by the stochastic nature of the arc and proposes strategies to mitigate its impact on the power grid. Through measurements carried out on the medium and low voltage side of the furnace, fluctuations in parameters such as voltage, current, and power factor were observed, indicating significant disturbances in power quality. Mitigation strategies, including e.g. installing compensators, are suggested to stabilize the network and improve power quality, enhancing efficiency and reducing energy consumption and electrode usage. © 2024 IEEE.

关键词： Electric arc furnaces

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Machine Learning Aided Spectrum Sensing

Machine Learning Aided Spectrum Sensing

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2023 IEEE International Conference on electrical, electronics, Communication and computers, ELEXCOM 2023

作者： Jabbal, Raunaq Singh Srivastava, Vishwas Prasad, Binod Dept. of Computer Science and Engineering MP Gwalior India Dept. of Electrical and Electronics Engineering MP Gwalior India

ISBN: (纸本)9798350305111

Spectrum sensing (SS) is a key concept that determines the effectiveness of cognitive radio (CR). Although multiple sensing practices have been proposed in the literature, out of them energy detector (ED) have been widely adopted. However, the performance of ED relies on pre-existing knowledge of noise distribution and threshold. Therefore, efficient spectrum sensing remains a challenge in wireless communication technologies. This paper aims to analyze the sensing performance of various machine learning (ML) models in different fading scenarios. We carefully model a spectrum sensing environment and generate a large-scale comprehensive dataset. We then train and test several well-established classical and ML techniques for spectrum sensing. The performance of classical spectrum sensing and ML-based spectrum sensing are compared under various parameters like the number of secondary user (SU), the distance between primary user (PU) and SU;and different fading environments such as Rayleigh, Rician, and Nakagami. The Simulation result shows that machine learning models outperform classical sensing techniques. Furthermore, it is found that support vector machine (SVM) outperformed other ML techniques in predicting channel availability. © 2023 IEEE.

关键词： Cognitive radio

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Energy optimization mechanism using metastable-smart grid (MSSG) 4

Energy optimization mechanism using metastable-smart grid (M...

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4th National Conference on Communication Systems, NCOCS 2022

作者： Kannan, S. Pugazhendiran, P. Rishi Keshav, N. Arul Deva, A. Suresh Kumar, K. Ananth Kumar, T. Electrical and Electronics Engineering IFET College of Engineering Tamilnadu India Electronics and Communication Engineering IFET College of Engineering Tamilnadu India Computer Science and Engineering IFET College of Engineering Tamilnadu India

The Smart Grid model uses energy from renewable sources and utilities to manage energy in a way that ensures uninterrupted power supply for users. In order to operate independently from the main power grid, a DC micro grid is a small power system that generates and uses its own DC electricity. Solar panels, fuel cells, and wind turbines are the most typical power sources for DC micro grids, with batteries used to store any extra energy. The independence of the power grid is one feature of a DC micro grid that promotes increased lifetime. Building owners have more latitude to pursue their sustainability goals since they have control over the production, delivery, and consumption of power. Super-twisting provides a dependable method and an effective instrument for the control of uncertain nonlinear systems by addressing the basic flaws of conventional sliding mode control, notably large control effort and chattering. Fractional order controllers provide greater design freedom than conventional integer order controllers. The purpose of this work was to create a metastable-smart grid(MSSG) user end model that could dynamically manage and optimize energy produced by renewable energy sources and the utility to ensure that customers always have access to electricity. Controlling these many power sources using MSSG and delivering energy in accordance with each user's unique power plan is the responsibility of the central control unit of the distribution grid. The supply is only switched to solar by the central control unit if a user's power consumption falls below a predetermined threshold. Giving consumers with less purchasing power more clout as a result. © Published under licence by IOP Publishing Ltd.

关键词： Smart power grids

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Advanced Machine Learning Models for electrical Fault Detection and Classification in Transmission Lines 3

Advanced Machine Learning Models for Electrical Fault Detect...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Deepika, Dugganapalli Charan, Maridinapalli Madhu Nossam, Sri Chakradhar Manitha, P.V. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

With the fast development of power systems, efficient methods for fault detection and classification are needed to maintain the stability, safety and efficiency of the systems. In particular, this paper investigates advanced machine learning techniques for fault detection and classification in transmission lines using supervised learning algorithms, including Support Vector Machines (SVM), Decision Trees, Random Forests, Naïve Bayes and Linear Discriminant Analysis (LDA). A dataset consisting of three phase currents and voltages from MATLAB simulations of normal and faulty system operations are used to study the problem. These models classify faults to be single line to ground, line to line and the three phase faults. Performance evaluation is done by metrics such as accuracy, precision, recall, F1 score, and AUC with respect to ROC analysis. Decision Trees are of high reliability and have good computational efficiency for fault detection among the models. The results presented in this research point to the importance of machine learning in reducing fault diagnosis circuit in order to achieve a more intelligent and resilient power transmission systems. © 2025 IEEE.

关键词： Power transmission lines

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An Enhanced Framework for Churn Prediction using Stratifed Bagging and Stacked Multi-Output Random Forests 3

An Enhanced Framework for Churn Prediction using Stratifed B...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Bhardwaj, Anay Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Customer churn prediction is an important task in customer relationship management because it helps businesses know who is at risk of leaving and retain such at-risk *** and time-efficient churn prediction is essential to identify the factors driving churn, enabling businesses to implement timely and effective retention strategies.A framework is proposed which combines stratified bagging with multi-output Random Forest and stacked feature transformation, to enhance prediction accuracy. This approach balances the dataset, trains a custom Random Forest using stratified bagging, and applies stacked learning using Logistic Regression on transformed features. Tested on real-world data, the model demonstrated 99% accuracy, showing improvements over the classic Random Forest in precision, recall, and prediction time. © 2025 IEEE.

关键词： Sales

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Predicting Customer Lifetime Value in E-commerce: A Data-Driven Approach to Enhance Customer Retention Strategies 3

Predicting Customer Lifetime Value in E-commerce: A Data-Dri...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dey, Sonali Radha, D. Kirthika Devi, V.S. Department of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Predicting Customer Lifetime Value (CLV) is one of the most critical tasks that businesses undertake in order to improve customer retention and optimize marketing strategies. The present paper proposes a predictive model for CLV using several machine learning techniques. The features considered include customer demographics, purchasing behavior, and transaction history. All these features were preprocessed using feature engineering and missing value handling. Several regression models, including Linear Regression, Decision Tree, and Random Forest Regressor, were compared in terms of predictive performance. The study includes the decision trees,hash tables as advance data structures such that they can handle large datasets effectively. Of the ones tested, Random Forest Regressor proved to be superior to the others, having the lowest Mean Squared Error (MSE) and highest R2 score, thereby validating its robustness in identifying the complex patterns within the data. The work highlights the time and space complexities of different models. This study contributes to the application of machine learning in customer analytics and provides a foundation for future research to integrate more diverse data sources and enhance model performance through hyperparameter optimization. Future work will also explore real-time prediction capabilities and the integration of multimodal data to further refine CLV forecasting. © 2025 IEEE.

关键词： Sales

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Smart Home Automation with Smart Metering Using ZigBee Technology and Deep Belief Networks - Optimization Using MB-ABCOA Algorithm 2

Smart Home Automation with Smart Metering Using ZigBee Techn...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Navaz, K. Mano Raja Paul, M. Yamuna Bee, J. Kavitha, S. Bhuvanesh, A. Mariappan, R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Nehru Institute of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India PSN College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India Sri Eshwar College of Engineering Department of English Tamil Nadu Coimbatore India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350383652

In the domain of smart homes and smart metering, ZigBee technology plays a pivotal role within the energy efficiency of homes, albeit facing challenges such as packet transmission delays and uneven energy consumption among nodes. To address these issues while emphasizing energy efficiency, we propose the Energy-efficient ZigBee Directionally Opportunistic Short-cut Trees Routings (EP-ZDOSTR), which optimizes network lifetimes and throughput by prioritizing energy-efficient packet transmission. Complemented by a hybrid Monarchy Butterfly and Artificial Bees Colonies Optimizations Algorithms (MB-ABCOA) approach, EP-ZDOSTR demonstrates significant improvements in network performances metric, including higher throughputs, increased packet delivery ratios, reduced transmission delays, and minimized energy consumption. Comparisons with existing methods like ZigBee shortcut tree routing (ZSTR), ZigBee opportunistic shortcut tree routing (ZOSTR), and Energy Efficient shortcut tree routing (EPSTR) validate its superiority in enhancing overall network efficiency and reliability. Furthermore, integrating ZigBee with Deep Belief Networks (DBNs) holds promise for further enhancing network intelligence, enabling predictive analysis of energy usage patterns and optimization of network operations, thereby advancing the capabilities of smart home in the context of smart homes and metering systems within the IoT landscape. © 2024 IEEE.

关键词： Zigbee

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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