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Innovative Smart Network Enabled Charging Station for Future Electric Vehicle

Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 147-156页

作者： Paramasivam, Alagumariyappan Vijayalakshmi, Sankaran Swamynathan, Kudiyarasan Mahalingam, N. Gughan, N.M. Department of Biomedical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Electrical Group Bharatiya Nabhikiya Vidyut Nigam Limited Tamilnadu Kalpakkam603102 India School of Mathematics University of Leeds Leeds United Kingdom

renewable energy has become a major resource in the twenty-first century due to the excess usage of oil. The world is being transitioned to develop technology which uses renewable energy as its core element. The common renewable energy includes solar, wind, wave, geothermal. As technology develops, the Electric Vehicle became a demanding factor in the automobile sector. Increase in Electric vehicle production leads to the creation of the charging station. In this paper, a charging station is designed by utilizing the solar energy as a primary source. The primary focus of the charging station is the dC–dC converter which extracts energy from the PV Array. Further, the charging station detects the vehicle using YOLO v3 model and charge its battery. Additionally, the battery energy information in the charging station can be shared between other charging stations. © 2023, The Institution of Engineers (India).

关键词： Electric vehicles

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refined Trademark Sticker detection Method Using Minimum Pooling Features

Refined Trademark Sticker Detection Method Using Minimum Poo...

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2024 IEEE region 10 Conference, TENCON 2024

作者： Huang, Junchu Song, Yan Research Institute of China Telecom Corporation Ltd Industrial Internet Technology R&d Department Guangzhou China

ISBN: (纸本)9798350350821

Trademark sticker detection stands as a crucial visual task within industrial quality inspection scenarios, de-manding precise determination of the accurate positioning of adhered trademarks on products. despite the remarkable suc-cess achieved by current deep learning networks, their training requirements often involve extensive datasets, posing significant challenges for direct application in trademark sticker detection systems. A major obstacle lies in the vast variety of trademarks encountered in real-world industrial settings, coupled with the rapid speed of trademark updates and iterations. This renders it impractical to gather samples of every trademark type for training purposes. Furthermore, the process of collecting and annotating industrial data samples can be both time-consuming and costly. To overcome these limitations, this paper introduces a novel approach that utilizes image subtraction to analyze the differences between the image with adhered trademarks and the image without adhered trademarks. This approach circumvents the need for vast sample sizes and extensive data annotations. By integrating minimum pooling techniques into the analysis of these difference images, this paper introduces an efficient approach that effectively filters out noise and alleviates the generalization challenges arising from frequent trademark replacements. Experimental findings underscore the robust flexibility of our method in precisely and adaptively detecting and recognizing packaging box labels. Consequently, this approach significantly enhances the quality of product packaging and trademark sticker detection, offering a viable solution for addressing the challenges posed by the diverse and rapidly evolving nature of industrial trademarks. © 2024 IEEE.

关键词： Industrial Quality Inspection Trademark Sticker detection

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Heart disease Prediction and diagnostic Analysis 15

Heart Disease Prediction and Diagnostic Analysis

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Malarvizhi, N. Kumari, Sakshi Nayan, Kunal Jupudi, Vamsi Kalyan VelTech Rangarajan Dr. Sagunthala R&D Institue of Science and Technology Department of CSE Chennai India

ISBN: (纸本)9798331300579

A heart disease is a significant health concern worldwide and is responsible for a considerable number of deaths every year. Early detection of heart disease can significantly reduce the risk of morbidity and mortality associated with it. This has led to the development of various machine learning techniques for predicting and analyzing heart disease. This study investigates the pertinence of machine learning techniques to predict long term outcomes in heart disease patient. The study aims to evaluate the performance of different machine learning algorithms such as decision tree, KNN, and SVM for predicting the occurrence of heart disease in patients. The data used in this project is collected from Kaggle Platform. The performance of each algorithm is evaluated using metrics which is accuracy and the results of this study will provide valuable insights into the effectiveness of different Machine learning algorithms for heart disease prediction and analysis, which can be used for early detection and management of heart disease. Varieties of data points that could lead to heart disease are examined. The diagnostic analysis additionally demonstrates how some factors, like Age, Sex, Cholestrol, CP, restbps etc. used in data-sheet are likely to cause heart disease. Tableau has been used for data Visualization. django has been used as web framework. User have to enter required input value and then prediction analysis will result whether patient having heart disease or not. © Grenze Scientific Society, 2024.

关键词： Cardiology

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Virtual Car Showroom 15

Virtual Car Showroom

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Malarvizhi, N. Karthikeyan, V. Kumar, Abhay raj, Nirmal Kumar, Abhishek VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Chennai India

ISBN: (纸本)9798331300579

A virtual showroom is a digital representation of a physical showroom or store, where customers can browse and interact with products in a virtual environment. This can include features such as product information, 360-degree views, and the ability to zoom in on products for closer inspection. The showroom will allow users to explore and interact with different car models in a 3d environment, providing a realistic and immersive experience. Some examples of companies that use virtual showrooms include BMW and ralph. The comfort of real-world experience at their fingertips without the need of travel. The virtual showroom will include interactive features that allow users to interact with the car models. This will require programming using C# scripting in Unity, including animations, physics simulations, and user input handling. The car models will also be programmed with interactive features, such as opening doors, adjusting seats, and turning on headlights. © Grenze Scientific Society, 2024.

关键词： Helmet mounted displays

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Implementation of High-Performance Automated Monitoring Collection Based on Kubernetes 3

Implementation of High-Performance Automated Monitoring Coll...

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3rd International Conference on Cloud Computing, Big data Application and Software Engineering, CBASE 2024

作者： Li, Ke Xiao, Xue Gao, Chuanji Yu, Shenke Tang, Xiaodong Tan, Guoge Inspur Cloud Information Technology Co. Ltd Department of Cloud Products R&D Jinan China

ISBN: (纸本)9798331506582

Unified configuration and centralized management of monitoring collection are prerequisites and key elements for achieving automated monitoring in cloud-native platforms. With the rapid development of container technology, Kubernetes has been widely adopted for container orchestration and cluster management. As the number of application-associated monitoring objects increases and the scale of environmental applications and nodes expands, traditional monitoring systems face challenges in achieving high performance and automated collection. This paper introduces a declarative monitoring collection configuration management method based on Kubernetes and provides a detailed explanation of configuration management and related technologies. The management framework fully leverages the advantages of the Kubernetes platform, seamlessly integrating the cloud-native monitoring system Prometheus. It combines technologies such as collection agents, operators, and custom resources to provide a stable and reliable solution for the high-performance operation and automated management of monitoring collections in cloud-native environments. © 2024 IEEE.

关键词： Cloud platforms

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Cyber Threat detection in UrLs using Machine Learning 15

Cyber Threat Detection in URLs using Machine Learning

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Malarvizhi, N. Krishna, C.H. Sai Kumar, J. Kranthi Kumar, P. Venkata Sai Department of CSE VelTech Rangarajan Dr. Sagunthala R&D Institue of Science and Technology Chennai India

ISBN: (纸本)9798331300579

In the modern digital era cyber-attacks are the major concern. The major mode of these cyber-attacks is in the form of the online threats that are embedded in the UrL's. The main reason for these attacks is to gain the information illegally. To overcome this problem, we have developed an interactive platform for the people where they can check their UrL's about whether they are safe to use or not. The main purpose of the proposed digital platform is to help the people in order to find the threats that are hidden inside the UrL's. The platform requires the UrL from the user as input and displays its nature whether it is good or bad. We have trained our system using Machine-Learning (ML) algorithms in order to study the input UrL and compares it with the previous patterns of threats that are used while training the algorithm. The ML algorithms that we used is Support Vector Machines (SVM). This paper outlines a precise approach to threat detection and ensuring cyber safety through an interactive digital platform. This facilitates to mitigate the cyber-attacks and develops a healthy cyber space. © Grenze Scientific Society, 2024.

关键词： Cyber attacks

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Predictive Finance decision Support System using Machine Learning 15

Predictive Finance Decision Support System using Machine Lea...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Malarvizhi, N. reddy, Y. Shiva Yasaswi, Y. Mohith Harshitha, K. Department of CSE VelTech Rangarajan Dr. Sagunthala R&D Institue of Science and Technology Chennai India

ISBN: (纸本)9798331300579

Predictive Finance decision Support System is a key task for banks. Although the banking system offers a wide range of things for sale, its credit system is its main source of revenue. due to the fact that the credit system can profit from the interest on the loans it credits. Banking system always need accurate modelling system for large number of issues. The loan approval prediction system helps in predicting whether the applicant is eligible for loan or not in no time. The main objective of this paper is to predict whether assigning the loan to particular person will be safe or not. Finding out to whom the loan may be issued which will be a safer alternative for the bank is a usual process because of the growth in the banking industry, many people are seeking for bank loans, but the bank has limited assets that it has to grant to limited people only. It's no more a tedious task by using known machine learning algorithms. The performance of system is evaluated using Kaggle datasets. In this work, we split the data into training and testing data set and the testing can be done by using decision Tree and Naive Bayes algorithms. These algorithms predicts whether the applicant is eligible for loan or not. The performance of system shows that decision Tree and Naive Bayes achieves an accuracy of 70.73% and 82.92%. When comparing the performance of the algorithms, the Naive Bayes algorithm has more accuracy than decision Tree algorithm. Using Naïve Bayes helps banking sector to predict easier for processing the loan. © Grenze Scientific Society, 2024.

关键词： Predictive analytics

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Genetic Algorithm Approach for Smart Industrial Multi-Objective Production Planning 16

Genetic Algorithm Approach for Smart Industrial Multi-Object...

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16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024

作者： Mestre, Antonio Faustino, dinis Silva, Bruno Cruz, Jorge Muvu Technologies R&d Department Lisbon Portugal Nova School of Science and Technology Department of Computer Science Lisbon Portugal

ISBN: (纸本)9798350371154

The fourth industrial revolution has ushered in a transformative era for the industrial sector, marked by the integration of advanced technologies that increase production efficiency and quality. Among these innovations is Smart Scheduling, which aims to optimize production processes while minimizing costs and meeting manufacturing requirements. This article explores the development and application of a Hybrid Genetic Algorithm, optimized by Tabu Search, to meet the complex challenge of multi-objective industrial production planning. The proposed framework introduces a Genetic Algorithm designed for intelligent, multi-objective industrial production planning, demonstrating its effectiveness in generating high-quality planning. It significantly reduces production times while maintaining quality and business needs, as validated in a real-world case study. The algorithm improves on traditional planning methods by dynamically adapting to manufacturing priorities. It also describes potential future adaptations to a wider range of industrial contexts and sectors. This research highlights the fundamental role of genetic algorithms in advancing smart manufacturing, offering a scalable and adaptable solution to the challenges of Industry 4.0. © 2024 IEEE.

关键词： Genetic algorithms

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Human-Machine Cooperative Trajectory Planning for Advanced driving Assistance System in Lane Changing Scenarios 8

Human-Machine Cooperative Trajectory Planning for Advanced D...

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8th CAA International Conference on Vehicular Control and Intelligence, CVCI 2024

作者： Li, Zhenghan Zhang, Ying Hua, Yuanhang Qi, Haoran Fu, Chenxiu Feng, Yun School of Computer Science Northwestern Polytechnical University Xi'an China R&d Institute Northwestern Polytechnical University in Shenzhen Shenzhen China Beijing ConST Instruments Technology Inc R&d Department Beijing China Cas Photoelectric Precision Engineering Co. Ltd R&d Department Xi'an China

ISBN: (纸本)9798331504892

This paper presents a human-machine cooperative trajectory planning system for advanced driving assistance systems in lane-changing scenarios. Firstly, it establishes a vehicle dynamics model and provides evaluation indicators for safe and comfortable driving under lane-changing conditions. Secondly, it constructs a human-machine collaborative driving framework, designs a trajectory pre-generation strategy, and develops a learning-based weight generation model for human-machine collaborative driving. Finally, trajectory planning is implemented based on fuzzy logic theory and PId control. The system is validated through a Simulink/Carsim co-simulation experimental platform with driver-in-the-loop manner. The results demonstrate that the human-machine cooperative driving approach offers better performance in driving stability and safety compared to both autonomous driving and human driving alone. © 2024 IEEE.

关键词： Advanced driver assistance systems

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An optimized convolutional neural network for speech enhancement

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International Journal of Speech technology 2023年第4期26卷 1117-1129页

作者： Karthik, A. Mazher Iqbal, J.L. Department of ECE Veltech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Institute of Aeronautical Engineering Hyderabad500043 India

Speech enhancement is an important property in today’s world because most applications use voice recognition as an important feature for performing operations in it. Perfect recognition of commands is achieved only by recognizing the voice correctly. Hence, the speech signal must be enhanced and free from background noise for the recognition process. In the existing approach, a recurrent convolutional encoder/decoder is used for denoising the speech signal. It utilized the signal-to-noise ratio property for enhancing the speech signal. It removes the noise signal effectively by having a low character error rate. But it does not describe the range of SNr of the noise added to the signal. Hence, in this, optimized deep learning is proposed to enhance the speech signal. AI function deep learning mimics the human brain's ability to analyze data and create patterns for use in making decisions. An optimized convolutional neural network was proposed for enhancing the speech for a different type of signal-to-noise ratio value of noises. Here, the particle swarm optimization process performs tuning the hyper-parameters of the convolutional neural network. The tuning of value is to minimize the character error rate of the signal. The proposed method is realized using MATLAB r2020b software and evaluation takes place by calculating the character error rate, PESQ, and STOI of the signal. Then, the comparison of the proposed and existing method takes place using evaluation metrics with − 5 dB, 0 dB, + 5 dB and + 10 dB. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Signal to noise ratio

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