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Fair and Efficient Congestion Management for Low Voltage Distribution Networks 8

Fair and Efficient Congestion Management for Low Voltage Dis...

引用

8th IEEE International Energy Conference, ENERGYCON 2024

作者： Vadavathi, Aswin Ramesh Hoogsteen, Gerwin Hurink, Johann dept. of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (纸本)9798350382150

The widespread increase of photovoltaic panels in the distribution grid often leads to overvoltage. Traditional voltage regulation techniques are unfair for prosumers at the sensitive part of the grid due to larger power curtailment. Consequently, there has been considerable research on implementing fair curtailment strategies for congestion management. However, existing fairness-incorporated curtailment approaches yield varying results based on changes in the grid topology and integration of renewable energy sources. Therefore, this paper introduces proportional voltage fairness that is independent of the above-mentioned network properties and inherently distinguishes regions for fair curtailment. Additionally, there is a need to consider the underutilisation of the grid while adhering to a fair curtailment scheme. Thus this paper proposes a control algorithm incorporating proportional voltage fairness and gives the opportunity to specify the trade-off between fairness and the total power curtailed. Further, a comparative analysis with respect to state-of-the-art notions of fairness is performed. ©2024 IEEE.

关键词： Electric power distribution

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A Survey on Machine and Deep Learning Approaches in Sign Language Recognition: Techniques and Future Trends

A Survey on Machine and Deep Learning Approaches in Sign Lan...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Harshitha, C. Sendil Vadivu, D. Rajagopalan, Narendran National Institute of Technology Puducherry Dept. of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798331540685

Sign language is an essential communication medium for individuals with hearing impairments. It enables them to convey messages, disseminate knowledge, and transfer ideas within the deaf community. However, not everyone understands sign language, making it challenging for individuals with hearing impairments to communicate effectively with other common people. Sign Language Recognition (SLR) is vital for bridging the communication gap between persons with hearing loss and non-sign language speakers. This survey paper aims to explore Machine Learning (ML) and Deep Learning (DL) techniques for recognition of sign languages. Investigate further research scope in this field. © 2024 IEEE.

关键词： Adversarial machine learning

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From Pixels to Diagnosis: Convolutional Neural Networks in Tuberculosis Screening 6

From Pixels to Diagnosis: Convolutional Neural Networks in T...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Zihan, Tanvir Mahtab Zami, Abduz Ahmed, Mohiuddin Islam, Rakibul Ahmed, Fahim Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh Uiu Dept. of Cse Dhaka Bangladesh

ISBN: (纸本)9798350385779

Tuberculosis (TB) is a chronic lung disease that constitutes one of the top 10 global causes of death. Timely and precise identification of TB is critical, as untreated cases can pose life-threatening risks. Traditional radiographic interpretation by trained professionals is subjective, time-consuming, and prone to human error. This study proposes an automated deep learning-based approach for TB detection from chest X-ray images, based on EfficientNet-B0 architecture. The Convolutional Neural Network (CNN) extracts discriminative features, allowing it to differentiate between normal and abnormal lung patterns indicative of TB. Our choice of the EfficientNet-B0 architecture played a pivotal role in the success of our TB detection model. Training and evaluation were conducted on three distinct datasets: Tuberculosis (TB) Chest X-ray database, Shenzhen Hospital CXR Set, and Montgomery County CXR Set. The key breakthrough is attaining an accuracy of 99.29% on the Tuberculosis (TB) Chest X-ray database and 98.50% on the combined dataset. This study presents a promising approach for automated TB screening using readily available chest X-ray images, potentially improving diagnostic accuracy and efficiency, especially in resource-limited settings. We compared our scores with those of other works and found that ours significantly outperforms them. © 2024 IEEE.

关键词： Diagnosis

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Retroflex: Uncovering Visual Equivalences through Reverse Image Recon 18

Retroflex: Uncovering Visual Equivalences through Reverse Im...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Yadav, Sneha M. Joshi, Karik Shailendra Mandavia, Divyah Tarun Puri, Parth Mahendra Vidyavardhini's College of Engineering and Technology Dept. Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9789380544519

This research introduces a comprehensive method for Reverse Image Search (RIS) this can be also called as CBIR that is Content Based Image Retrieval designed for large datasets with more than 50,000 photos in 25 different classes. The approach uses sophisticated deep learning feature extraction methods, such as Auto-encoders and CNNs, to extract complex visual representations for reliable content characterization. We incorporate advanced indexing techniques to address the issues of scalability and retrieval efficiency. The resultant framework exhibits higher accuracy and speed and provides an optimal and sophisticated solution for picture retrieval problems. This effort is a major step forward in improving the capabilities of RIS systems, offering a flexible tool for real-world applications that need for thorough and effective image retrieval across a range of classes. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Auto-encoders CNN Content Based Image Retrieval (CBIR) datasets Framework Reverse Image Search (RIS) Sophisticated Deep Learning Feature Extraction

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Enhancing Network Function Virtualization and Service Function Chaining: Innovative Optimization Strategies and Their Impact 1

Enhancing Network Function Virtualization and Service Functi...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

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

ISBN: (纸本)9798350365016

Throughout this paper, the reader was able to better understand how innovative optimization techniques through NFV and SFC frameworks could and should be applied to address the developing and shifting focus of contemporary network infrastructures. Focusing on performance metrics performance via latency, throughput, resource utilization, and operational cost, the presented algorithms and machine learning models developed and executed due to this research showed innovative and dynamic optimization of network functions. Through further extensive simulations equalling 125 seconds, our findings reported creative results of improving performance in several significant areas: latency was reduced, throughput was enhanced, resource utilization was more efficient, and operational cost was reduced. These results indicated that our optimization strategies can pay off and bring a number of benefits to NFV and SFC that address a clear need for change in the current type of network management models. Therefore, this paper is valuable and serves as a testament of our strategies' power along with opening various doors for following network optimization research. Therefore, one can see how important it is to implement innovation in the face of change. © 2024 IEEE.

关键词： Network function virtualization

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An Approach of Fog Computing and Edge Computing for Computing Resources Optimization Strategies 1

An Approach of Fog Computing and Edge Computing for Computin...

引用

1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

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

ISBN: (纸本)9798350365016

This paper investigates optimization strategies for fog and edge computing systems, focusing on the key challenges of resource allocation, load balancing, latency minimization, and power efficiency. We analyze the results of the simulation study to determine the impact and potential of our strategies and find that the improvements achieved are small to moderate, varying between 1.75% and 2.95%. This knowledge demonstrates that system optimization requires small, gradual increments and that fog and edge computing are dynamic systems. Our results suggest that optimization based on integration and adaptability can produce a significant difference in the performance and reliability of distributed systems. © 2024 IEEE.

关键词： Fog computing

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Implementation of Pruned and Quantized Semantic Segmentation Neural Network Using Cambridge-Driving Labeled Video database (CamVid) dataset 2

Implementation of Pruned and Quantized Semantic Segmentation...

引用

2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Upadhyay, Deepak Malhotra, Sonal Gupta, Mridul Mishra, Somesh Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350372847

In This research investigates the streamlined implementation of Semantic Segmentation Neural Networks through advanced techniques: pruning and quantization. Leveraging the CamVid dataset, our study achieved remarkable reductions in computational complexity and memory usage. Pruning removed redundant connections, effectively reducing learnable parameters, while quantization significantly minimized memory footprint. Despite these optimizations, the networks maintained high semantic segmentation accuracy. The implications of our findings are pivotal, particularly in resource-constrained applications like autonomous driving and image analysis. By enhancing efficiency without compromising accuracy, this research facilitates the seamless integration of deep learning models into real-time systems. Our study not only advances the field of computer vision but also underscores the practical feasibility of deploying sophisticated neural networks in practical, resource-efficient contexts. © 2024 IEEE.

关键词： Semantic Segmentation

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Lip Reading Into Text Using Deep Learning 14

Lip Reading Into Text Using Deep Learning

引用

14th International Conference on Cloud Computing, data science and engineering, Confluence 2024

作者： Manglani, Krishna Bahuguna, Utkarsh Gupta, Subhash Chand Amity School of Engineering and Technology Dept. of Computer Science Uttar Pradesh India

ISBN: (纸本)9798350344837

An essential part of computer vision, lip reading, has grown significantly and is now used in autonomous driving, public safety, and hearing-impaired communication. This work provides an in-dept. analysis of both conventional and deep learning-based lip reading systems, examining their development over time, difficulties encountered, and critical role in human communication. The suggested LipNet model, which leverages the complementary abilities of Long Short-Term Memory networks (LSTMs) for temporal modelling and Convolutional Neural Networks (CNNs) for spatial feature extraction, is a central focus of this research. When LipNet is tested using the GRID Corpus dataset, it outperforms baseline models in terms of performance. Saliency maps and viseme analysis highlight LipNet's ability to capture phonologically relevant areas in lip motions, providing insight into the network's attention processes. Apart from examining the importance of datasets such as OuluVs2and GRID Corpus, this study presents GhostNet, a lightweight model that tackles the requirement for effective network designs. The testing results highlight LipNet's competitive Character Error Rate (CER) and Word Error Rate (WER) in comparison to baseline models, proving that it is the best method for real-world use. The research ends with a summary of potential future developments in lip reading technology, such as modal integration, realtime applications, personalisation, ethical issues, and the goal of creating a communication environment that is more inclusive. This study highlights the effectiveness of LipNet and paves the way for future developments in the ever-evolving area of lip reading by examining a variety of methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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Survey on Online Learning Applications in Multi-UAV Networks 15

Survey on Online Learning Applications in Multi-UAV Networks

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Kim, Jaemin Kim, Hyosu Paek, Jeongyeup Son, Yongseok Lee, Hyung Tae Cho, Sungrae Chung-Ang University Dept. of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350364637

This paper provides a comprehensive review of the application of online learning techniques in multi-UAV systems, highlighting their role in enhancing autonomy and efficiency in dynamic environments. Key areas of focus include adaptive path planning, collision avoidance, and swarm behavior control, along with the associated technical challenges and future research directions. © 2024 IEEE.

关键词： Federated learning

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Eva: Python-based Desktop Virtual Assistant for Visually Impaired 7

Eva: Python-based Desktop Virtual Assistant for Visually Imp...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Abraham, Anishamol Mathew, Benita Susan Mathew, Dona Lisa Mohammad, Fiaz S Krishnan, Gokul Amal Jyothi College of Engineering Dept. of Computer Science Kottayam686518 India

ISBN: (纸本)9798350372816

Virtual assistants have become essential resources in the digital age, providing users with a wide range of functionalities to expedite work and increase productivity. In this research, we provide a complete examination of Eva, a virtual assistant created using Python, and compare it to other virtual assistants such as Cortana and Copilot. Through a meticulous examination of Eva's features, we demonstrate its superiority in versatility and effectiveness. By embracing Eva's strengths and capitalizing on its competitive advantages, we push for a future in which digital experiences are inclusive and empowering for all users. © 2024 IEEE.

关键词： Python

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