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Impacts of Earthing Variations on Overvoltage in Meshed Multi-terminal DC Grid 50

Impacts of Earthing Variations on Overvoltage in Meshed Mult...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Bader, Amir Ar-Rahman Ukil, Abhisek Dept. of Engineering Science The University of Auckland Auckland New Zealand Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand

ISBN: (纸本)9781665464543

This paper investigates the impact of different earthing methods on overvoltage within modular multilevel Voltage Source Converter (MMC VSC) based Direct Current (DC) transmission and distribution grids, particularly in meshed multi-terminal (MTDC) configurations. A bipolar MTDC grid is modelled and simulated in PSCAD/EMTDC to investigate the influence of earthing on midpoint overvoltages. The transient response of the grid under DC fault scenarios is analysed through various case-studies, including solid earthing placement, variations in earthing impedance, and the application of surge arresters. The findings show the critical role of earthing in mitigating overvoltage, with low-resistive earthing options proving to be the most effective trade-off. © 2024 IEEE.

关键词： bipolar dedicated metallic return earthing meshed MTDC overvoltage

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Advancing Image Forgery Detection: A CNN-Based Approach 8

Advancing Image Forgery Detection: A CNN-Based Approach

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Nalini, N. Rakshitha, Sai Nithyashree, H.V. Rakshitha, S.L. Shravan, Motamarri Sai Uttej, K Sai Nitte Meenakshi Institute of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331505462

In the digital age, image forgery is a significant concern due to advanced editing tools. This study addresses the need for reliable forgery detection, focusing on copy-move, splicing, and retouching techniques. Using Convolutional Neural Networks (CNNs) known for learning hierarchical image features, the research developed a robust detection system. The CNN model, trained on a diverse dataset which has over 10,000+ images and it is split into test and train data for better classification results, detected subtle inconsistencies indicative of manipulation, achieving a peak accuracy of 94% over 50 training epochs. Test cases showed the system effectively detected forged images and identified forgery types, though it had shortcomings with invalid inputs. The CNN architecture employed convolutional and max-pooling layers with specific hyperparameters to enhance feature extraction. This paper offers a valuable tool for digital forensics experts, law enforcement, and journalists to ensure the authenticity of visual content. © 2024 IEEE.

关键词： Convolutional neural networks

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A Multi-Routing Paths' Exploration Algorithm for Multi-paired Communicating Vehicles' Data Transmission Offloading using the Multi-access Edge Computing Architecture 4

A Multi-Routing Paths' Exploration Algorithm for Multi-paire...

引用

4th European Conference on Communication Systems, ECCS 2024

作者： Huang, Chung-Ming Tsai, Cheng-Han National Cheng Kung University Dept. of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350377927

This work proposed a novel method for multiple communicating paired vehicles' data transmission offloading using the Multi-access Edge Computing (MEC) technique. When two paired communicating vehicles are communicating with each other through cellular network's Base Stations (BSs), the data transmission can be offloaded to the Cellular Vehicle-to-Vehicle (C-V2V) path that exists between these two vehicles using 5G NR's sidelink communication;these two paired communicating vehicles can switch back to the Cellular Vehicle-to-Infrastructure (C-V2I) communication to communicate with each other when the corresponding C-V2V path is broken. Using the proposed method, the MEC architecture serves as a centralized computing mechanism to determine the availability of k-hop C-V2V paths for paired communicating vehicles' data transmission offloading. Each vehicle periodically reports its context, such as position, speed, driving direction, and C-V2V sidelinks' Channel Busy Ratios (CBRs), to the MEC server to derive feasible k-hop C-V2V data transmission offloading paths. The simulation result demonstrates that the proposed method has (i) better throughput compared to the method of relying solely on C-V2I communication through BSs and (ii) better throughput, higher offloading fraction and lower average data loss rate comparing with the other k-hop-limited CV2V data transmission offloading method. © 2024 IEEE.

关键词： Edge computing

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Robotics Process Automation Technologies for Data Collection and Analysis 15

Robotics Process Automation Technologies for Data Collection...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Usman, K. Hayath, T.M. Shafiulla, Mohammed Dadapeer Ballari Institute of Technology and Management Dept. of Computer Science and Engineering Karnataka Ballari India

ISBN: (纸本)9798350370249

Data collection and analysis are critical aspects of various business processes. However, these tasks can be time-consuming, prone to errors, and delays employee productivity when done manually, especially when we have to deal with huge data. Robotic Process Automation (RPA) technologies offer a promising solution in form of a BOT, by automating tedious tasks related with data handling. A survey by The Economist corporation believe that automation kick launches digital transformation, robotic process automation (RPA) has evolved from an emerging technology to a must-have digital transformation tool for enterprises to compete globally. An increasing number of companies are now using RPA to automate normal business processes in a non-intrusive and cost-effective way. On the other hand, decision-making, pattern recognition, and many other business-related tasks benefit greatly from the use of big data and analytics. This paper explores the application of RPA in data collection and analysis, examining its functionalities, benefits, and limitations. In practical, Bot produces error-free work and takes 94.44% less time for data collection compared to a manual process, which encourages all firms to adopt to RPA technologies. © 2024 IEEE.

关键词： Automation Data collection Process Robotics RPA

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Software Fault Prediction Using Optimal Classifier Selection: An Ensemble Approach 2

Software Fault Prediction Using Optimal Classifier Selection...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Agrawalla, Bikash Reddy, B. Ramachandra Dept. of Computer Science and Engineering National Institute of Technology Jharkhand Jamshedpur831014 India

Fault prediction is the process of using data analysis and machine learning models to anticipate potential defects or faults in the software system. Using only the base machine learning models for software fault prediction leads to limited performance, difficulty in handling non-linear relationships and imbalanced data, inadequate feature representation, and limited complexity handling. Hence, in order to overcome these challenges, this paper proposes a new technique for the selection of classifiers that forms a heterogeneous ensemble. The main goal is to remove or trim out the classifiers that show poor performance compared to the other base classifiers, which can result into a more effective ensemble and can produce better results. The algorithm proposed in this paper finds a set of classifiers that can perform better than using all the classifiers. The challenge that was faced was how to identify the poor-performing classifiers. This challenge is dealt with by performing an experiment using different threshold values to choose the trimmed set of classifiers. For evaluation of the proposed model, 8 different benchmark software fault datasets were used, which are taken from PROMISE and the Apache repository, and AUC is used as the performance measure. The results obtained after the experimental analysis demonstrate the effectiveness of our algorithm compared to the traditional approaches, which used all the base classifiers. There is a significant increase in the AUC values for 6 datasets out of 8, while using the average of probabilities and majority voting, it was seen that there is improvement in 7 out of 8 datasets used. The best-performing dataset by using the average of probabilities is ARC, where the AUC values increase from 0.6505 to 0.694, and while using majority voting, the best-performing dataset is XALAN, where the AUC values increase from 0.5455 to 0.679. From this, it can be seen that the proposed ensemble approach achieved higher AUC values for the

关键词： Decision trees

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Strategies for Helping Anchor-Based Trackers Learn re-ID Features for Smart City Surveillance

Strategies for Helping Anchor-Based Trackers Learn re-ID Fea...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Chen, Xiu-Zhi Li, Mu-Chuan Chen, Yen-Lin National Taipei University of Technology Dept. of Computer Science & Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324136

Re-identification has become a crucial issue in computer vision today as it allows for tracking objects in both continuous and discontinuous scenarios. Despite achieving perfect detection results, anchor-based trackers encountered difficulties in effectively learning re-identification features, due to various issues. This research proposes strategies aimed at improving the capability of anchor-based trackers to learn high-quality re-identification (re-ID) features. The model developed through our strategies can extract more distinct features and achieve almost 0.57 Multiple Object Tracking Accuracy (MOTA) on MOT20, even under a limited training dataset. This result indicates that our proposed strategies hold potential for improving the performance of anchor-based trackers. © 2024 IEEE.

关键词： Anchor-Based Tracker Re-Identification Smart City Surveillance

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Face Recognition via Thermal Imaging: A Comparative Study of Traditional and CNN-Based Approaches 24

Face Recognition via Thermal Imaging: A Comparative Study of...

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8th International Conference on Machine Learning and Soft Computing, ICMLSC 2024

作者： Lin, Shinfeng Chen, Luming Chen, Wensheng Chang, Boyu Dept. of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan

ISBN: (纸本)9798400716546

In this article, a face recognition via thermal imaging: a comparative study of traditional and CNN-based approaches is proposed. The methodology comprises two distinct components: traditional face recognition and CNN-based face recognition. In the traditional face recognition, we employ Random Forest (RF) and Support Vector Machine Classifier (SVM) techniques. Conversely, CNN-based face recognition leverages Convolutional Neural Networks (CNN) to identify individuals. Our research involves a comprehensive evaluation conducted across different databases, including the PUCV Drunk Thermal Face (PUCV-DTF) and the UCH Thermal Temporal Face (UCH-TTF) datasets. To emulate real-life scenarios, we introduce elements such as glasses, face mask, and noise into the original thermal images during experimentation. The recognition rates of traditional and CNN-based methods for small size and middle size databases achieve 90% and 100%, respectively, under a challenging condition, wearing glasses and mask. Experimental results demonstrate the feasibility and effectiveness of our proposed method, showcasing its robustness in tackling various challenges. © 2024 Owner/Author.

关键词： Infrared imaging

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An Immersive Sound Effects System Based on Dynamic Spatial Perception and Architectural Response 9

An Immersive Sound Effects System Based on Dynamic Spatial P...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Lai, Wen-Hsing Ma, Chia-Hao Wang, Siou-Lin National Kaohsiung University of Science and Technology Dept. of Computer and Communication Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350318241

This paper achieves the conversion of monaural audio into spatially immersive stereo audio through the use of binaural speech synthesis and Pyroomacoustics. It integrates dynamic spatial perception with the acoustic response of architectural spaces to recreate immersive audio experiences in desired environments like concert halls. The system encompasses diverse options for motion trajectory generation and architectural space modeling within its interface, allowing users to customize and merge various environmental spatial effects and surround modes, delivering an unparalleled auditory encounter. © 2024 IEEE.

关键词： Speech synthesis

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Adaptive Strategies for Maximizing Spectral Efficiency in dual branch MRC Systems Facing non-Uniform Fading Patterns 12

Adaptive Strategies for Maximizing Spectral Efficiency in du...

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12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Sharma, Rashmi Upadhyay, Abhay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Nagpur Campus Pune India

ISBN: (纸本)9798350387315

The presented research paper investigates the development and performance of adaptive strategies for nonuniform fading patterns in dual-branch Maximal Ratio Combining systems. The study employs key enabling strategies, including power control, adaptive modulation, and antenna selection, with the purpose of improving the spectral efficiency and reliability of wireless communication systems. Conducting simulations in MATLAB and Python, this paper presents a comprehensive analysis using heatmaps, 3D surface plots, and dynamic simulation outputs to show the effect of the strategies under different Signal-to-Noise Ratios and fading conditions. The outcomes, illustrated in a clear-cut manner in the visuals, reveal that the adaptive strategies lead to a drastic reduction in the Bit Error Rates and a more optimal distribution of power across the branches, ensuring highly effective performance even in the case of the most challenging non-uniform fading patterns. Ultimately, the research findings show sight into the necessity of adaptive strategies to ensure proper coverage and quality of communication in non-uniform fading conditions, which novel research could benefit from. © 2024 IEEE.

关键词： MATLAB

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ResNet-50: The Deep Networks for Automated Breast Cancer Classification using MR Images 2

ResNet-50: The Deep Networks for Automated Breast Cancer Cla...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Das, Tejaswini Nayak, Debasish Swapnesh Kumar Kar, Anindita Jena, Lambodar Swarnkar, Tripti University Dept. of Computer Application FET-ITER Bhubaneswar India University FET-ITER Dept. of Computer Science and Engineering Bhubaneswar India Bhubaneswar India

ISBN: (纸本)9798350370188

One in every four cancer cases in women is breast cancer, which is the most prevalent malignancy in this group globally. In 2020, breast cancer will account for one in every eight new instances of cancer, according to projections of 2.3 million new cases. Breast cancer identification, both early and accurate, is critical to improve patient outcomes and survival rates. Magnetic resonance imaging (MRI) has developed as a valuable method for diagnosing breast cancer. Deep learning algorithms, notably convolutional neural networks (CNNs), have demonstrated exceptional effectiveness in a variety of medical image processing applications, including breast cancer classification. The ResNet-50 architecture has received a lot of interest in this context because of its excellent performance in image recognition tasks. ResNet-50 is a deep residual network that introduces skip connections and residual learning to solve the difficulty of training very deep neural networks. A total number of 1480 cancer MRI samples are collected from the Kaggle database, having two classes, which include healthy and malignant scans. It is observed that our proposed model with the data pre-processing techniques has achieved a classification accuracy of 92.01%. © 2024 IEEE.

关键词： Magnetic resonance imaging

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