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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Tamezheneal, R. Kajendran, K. Vinitha, J.C. Hemalatha, S. Aruna, K.B. Pandi, V. Samuthira Sri Sai Ram Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India S.A.Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350313987

This research study addresses the need for renewable energy in residence by discussing the development and building of a solar assisted automation system for homes equipped with a wireless Internet of Things (IoT) enabled technology. The photovoltaic array powers the intelligent home using a highly efficient Maximum Power Point Tracking (MPPT) control system. The research's design, analysis, and methodology are all presented here, along with the study's findings on the market. The primary building blocks of a solar UPS are a solar panel and a custom-designed inverter circuit. The inverter circuit was designed in accordance with solar panel specs and requirements. Many other circuits served as inspiration for the present circuit's redesign. The development experts of currently available backup power supplies have also been consulted in order to plan for prospective replacements should a component fail or become unavailable. In this post, we'll look at some of the several ways a solar-powered UPS may be built and how its features might work. This suggests that solar UPSs may soon be able to compete with and even outperform their electrical counterparts. The key components of the design are an outside solar panel equipped with solar cells to convert solar energy towards electrical power and a power conversion circuit to convert the DC current into AC current for usage in household appliances. Two Energy throw electrical parts and one Arduino UNO controller with sensors were used in the development of this prototype automation system. In this paper, we propose developing and implementing a residence energy control system to help individuals and families keep tabs on their energy consumption and adjust their habits accordingly. The purpose of this project is to design, develop, and implement a residential energy optimization system that enables sensing, control, and intelligent algorithms to help clients learn more about and control their consumption of energy. © 2023 IE

关键词： Battery Power Source Home Automation Internet of Things Inverter IoT Photovoltaic PV Array Renewable Energy Solar Power

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Optimizing VNF Migration in B5G Core Networks: A Machine Learning Approach

Optimizing VNF Migration in B5G Core Networks: A Machine Lea...

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International Conference on Smart Communications and Networking (SmartNets)

作者： Brahma Reddy Tanuboddi Gad Gad Zubair Md Fadlullah Mostafa M. Fouda Department of Computer Science Western University London ON Canada Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy-Studies- (CAES) Idaho Falls ID USA

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

Software-Defined Networking (SDN) is an innovative networking paradigm that fundamentally changes the way network management and operation are approached. Network Functions Virtualization (NFV) is a network architecture concept that leverages standard IT virtualization technology to virtualize entire classes of network node functions into building blocks that can be connected or chained together to create communication services. NFV is part of the broader trend towards the virtualization of IT services and infrastructure. When combined with SDN, it provides a complete solution for a fully virtualized network, offering unprecedented levels of agility and efficiency. The concept of Virtual Network Function (VNF) migration has introduced the need for optimized algorithms to minimize migration time and costs, addressing a critical aspect of resource utilization. This paper explores the utilization of machine learning methods, especially neural networks to enhance the migration of VNFs, and presents a framework leveraging Convolution Neural Networks (CNN) and Artificial Neural Networks (ANN) to predict optimal migration paths for VNFs, aiming to minimize migration time and cost. The proposed solution analyzes network conditions, workload patterns, and resource availability, enabling dynamic and efficient VNF re-Iocations. This approach significantly improves network performance and reliability, making it a vital contribution to the field of network function virtualization.

关键词： Technological innovation Costs Machine learning algorithms Convolution Artificial neural networks Machine learning Network function virtualization

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Retraction Note: APO-AN feature selection based Glorot Init Optimal TransCNN landslide detection from multi source satellite imagery

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Multimedia Tools and Applications 2024年第41期83卷 89599-89600页

作者： Kumar, Abhijit Misra, Rajiv Singh, T. N. Dhiman, Gaurav Department of Computer Science and Engineering Indian Institute of Technology Patna Dayalpur Daulatpur India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Earth Science Indian Institute of Technology Bombay Mumbai India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Computer Science and Engineering University Centre for Research and Development Chandigarh University Gharuan India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India

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Non-Hermitian chiral degeneracy of gated graphene metasurfaces

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Light(Science & Applications) 2023年第5期12卷 766-775页

作者： Soojeong Baek Sang Hyun Park Donghak Oh Kanghee Lee Sangha Lee Hosub Lim Taewoo Ha Hyun Sung Park Shuang Zhang Lan Yang Bumki Min Teun-Teun Kim Department of Mechanical Engineering Korea Advanced Institute of Science and Technology(KAIST)Daehak-ro 291Daejeon 34141Republic of Korea Department of Electrical and Computer Engineering University of Minnesota200 Union street SEMinneapolisMN 55455USA Harvard Institute of Medicine Harvard Medical SchoolHarvard UniversityBrigham and Women's Hospital25 Shattuck StreetBostonMA 02215USA Center for Integrated Nanostructure Physics Institute for Basic Science(IBS)Sungkyunkwan UniversitySeobu-ro 2066Suwon 16419Republic of Korea Samsung Advanced Institute of Technology Samsung ElectronicsSamsung-roSuwon 16678Republic of Korea Department of Physics The University of Hong KongPokfulam RoadHong Kong Special Administrative RegionChina Department of Electrical and Systems Engineering Washington University1 Brookings DriveSaint LouisMO 63130USA Department of Physics Korea Advanced Institute of Science and Technology(KAIST)Daehak-ro 291Daejeon 34141Republic of Korea Department of Physics University of UlsanDaehakroUlsan 44610Republic of Korea Korea Research Institute of Standards and Science(KRISS) Gajeong-ro 267Daejeon 34113Republic of Korea

Non-Hermitian degeneracies,also known as exceptional points(EPs),have been the focus of much attention due to their singular eigenvalue surface ***,as pertaining to a non-Hermitian metasurface platform,the reduction of an eigenspace dimensionality at the EP has been investigated mostly in a passive repetitive ***,we propose an electrical and spectral way of resolving chiral EPs and clarifying the consequences of chiral mode collapsing of a non-Hermitian gated graphene *** specifically,the measured non-Hermitian Jones matrix in parameter space enables the quantification of nonorthogonality of polarisation eigenstates and half-integer topological charges associated with a chiral ***,the output polarisation state can be made orthogonal to the coalesced polarisation eigenstate of the metasurface,revealing the missing dimension at the chiral *** addition,the maximal nonorthogonality at the chiral EP leads to a blocking of one of the cross-polarised transmission pathways and,consequently,the observation of enhanced asymmetric polarisation *** anticipate that electrically controllable non-Hermitian metasurface platforms can serve as an interesting framework for the investigation of rich non-Hermitian polarisation dynamics around chiral EPs.

关键词： structure chiral integer

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An Improved Bearing Fault Investigation Scheme Using 1D CNN with PCA and SVM

An Improved Bearing Fault Investigation Scheme Using 1D CNN ...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Rajeev Kumar R S Anand Muhammad Nameer Akhtar Rintu Khanna Department of Electrical Engineering Punjab Engineering College Deemed-to-be-University Chandigarh India Department of Electrical Engineering Indian Institute of Technology Roorkee India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

This study presents a novel approach for accurate motor bearing fault detection through a sequential process involving one-dimensional convolutional neural network (1D CNN) for feature extraction, Principal Component Analysis (PCA) for dimensionality reduction, and Support Vector Machine (SVM) for classification. The main objective of this approach is to improve the model performance for complex fault combinations by achieving high precision and recall scores. This study has been carried out using online available datasets of bearings faults provided by Bearing Data Center, Case Western Reserve University (CWRU), USA. In this study, bearing inner race, outer race and rolling ball defects are analysed under variable load operations using the proposed 1D-CNN scheme. This research highlights the potential of the proposed model to advance motor condition monitoring systems, offering a reliable and efficient method for fault detection across diverse operational scenarios.

关键词： Support vector machines Dimensionality reduction Condition monitoring Fault detection Computational modeling Motors Feature extraction Convolutional neural networks Principal component analysis Load modeling

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WeatherDG: LLM-assisted Diffusion Model for Procedural Weather Generation in Domain-Generalized Semantic Segmentation

arXiv

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arXiv 2024年

作者： Qian, Chenghao Guo, Yuhu Mo, Yuhong Li, Wenjing Transport Studies at Institute University of Leeds United Kingdom Department of Electrical and Computer Engineering Carnegie Mellon University United States

In this work, we propose a novel approach, namely WeatherDG, that can generate realistic, weather-diverse, and driving-screen images based on the cooperation of two foundation models, i.e, Large Language Model (LLM) and Stable Diffusion (SD). Specifically, we first fine-tune the SD with source data, aligning the content and layout of generated samples with real-world driving scenarios. Then, we propose a procedural prompt generation method based on LLM, which can enrich scenario descriptions and help SD automatically generate more diverse, detailed images. In addition, we introduce a balanced generation strategy, which encourages the SD to generate high-quality objects of tailed classes under various weather conditions, such as riders and motorcycles. This segmentation-model-agnostic method can improve the generalization ability of existing models by additionally adapting them with the generated synthetic data. Experiments on three challenging datasets show that our method can significantly improve the segmentation performance of different state-of-the-art models on target domains. Notably, in the setting of"Cityscapes to ACDC", our method improves the baseline HRDA by 13.9% in mIoU. See the project page for more results:***. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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An Effective Personality Recognition Model Design using Generative Artificial Intelligence based Learning Principles 1

An Effective Personality Recognition Model Design using Gene...

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1st IEEE International Conference on Computing and Data Science, ICCDS 2024

作者： Jenifa, G. Padmapriya, K. Sevanthi, P. Karthika, K. Pandi, V. Samuthira Arumugam, Deepak Andhra Pradesh India RMD Engineering College Department of Computer Science and Engineering Chennai India Department of AI & DS Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Tamil Nadu Chennai India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350365337

Personality recognition from textual data plays a crucial role in various applications such as social media analysis, recommendation systems, and personalized marketing. In this study, we propose an effective personality recognition model leveraging Generative Artificial Intelligence based Learning Principles (GAILP), which combines DenseNet feature extraction from user profile images with text-based features extracted using Natural Language Processing (NLP) techniques. The model is implemented in Python, utilizing popular libraries such as TensorFlow, Scikit-learn, and NLTK. We evaluate the performance of the proposed model on a Myers-Briggs Personality Type Dataset obtained from Kaggle, containing self-reported MBTI types and associated textual data. Experimental results demonstrate the effectiveness of the GAILP model, achieving an accuracy of over 97% in personality recognition tasks. Our findings highlight the importance of integrating visual and textual cues for accurate personality recognition and underscore the potential of GAILP-based approaches in this domain. © 2024 IEEE.

关键词： Support vector machines

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Deduplication of Textual Data by NLP Approaches

Deduplication of Textual Data by NLP Approaches

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IEEE Conference on Vehicular Technology (VTC)

作者： Kiana Ghassabi Peyman Pahlevani Daniel E. Lucani Department of Computer Science Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan Iran Department of Electronic Systems Aalborg University Copenhagen Denmark Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark

With the increasing amount of digital data, data deduplication has become an increasingly popular method for reducing data in large-scale storage systems. Generalized deduplication is an alternative technique for reducing the cost of data storage by identifying similar data chunks. This paper proposes TL-GD, a method for improving cloud storage efficiency using generalized deduplication focusing on textual datasets. The core concept of this study is to develop an efficient deduplication system that combines an alternative technique for splitting data into smaller pieces and a new approach for transforming data pieces into bases and deviations. The performance of the system has been validated using two real-world datasets. We also compare the results to state-of-the-art deduplication methods. Our evaluation results show that TL-GD achieves nearly 67% lossless compression for textual navigation instructions datasets, which is a 25% improvement on average compared to existing deduplication techniques.

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Monolithically Integrated 940 nm VCSELs on Bulk Ge Substrates

Monolithically Integrated 940 nm VCSELs on Bulk Ge Substrate...

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2024 IEEE Silicon Photonics Conference, SiPhotonics 2024

作者： Wan, Zeyu Yang, Yun-Cheng Chen, Wei-Hsin Chiu, Chih-Chuan Zhao, Yunlong Feifel, Markus Chrostowski, Lukas Lackner, David Wu, Chao-Hsin Xia, Guangrui The University of British Columbia Department Of Materials Engineering Vancouver Canada Fraunhofer Institute For Solar Energy Systems Heidenhofstrasse Freiburg Germany The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada National Taiwan University Graduate Institute Of Electronics Engineering Taipei Taiwan National Taiwan University Graduate Institute Of Photonics And Optoelectronics Taipei Taiwan National Taiwan University Graduate School Of Advanced Technology Taipei Taiwan

ISBN: (纸本)9798350394047

This work successfully developed an independent Ge-VCSEL epitaxy and fabrication technology route. This is the second successful Ge-VCSEL technology reported worldwide, and the first Ge-VCSEL technology with key details disclosed, including Ge substrate specification, transition layer structure and composition, and fabrication process etc. Compared with the GaAs counterparts, after epitaxy optimization, the Ge-VCSEL wafer has a 40% lower surface root-mean square roughness and 72% lower average bow-warp. Additionally, the Ge-VCSEL has a 10% lower threshold current density and a 19% higher maximum slope efficiency than the GaAs-VCSEL. © 2024 IEEE.

关键词： Surface emitting lasers

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Early Retinal Degeneration Detection with Deep Faster CNNs 2

Early Retinal Degeneration Detection with Deep Faster CNNs

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Priyadharshini, K. Balraj, Sindhusaranya Kumar, J.V. Rama Vidhate, Deepak A. Thilakavathy, P. Vanathi, P. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering Sona College of Technonolgy Salem India SRKR Engineering College Andhra Pradesh Bhimavaram India Department of Information Technology Dr. Vithalrao Vikhe Patil College of Engineering Maharashtra Ahmednagar India Vels Institute of Science Technology and Advanced Studies Chennai India Department of Information Technology Adhiyamaan College of Engineering Hosur India

ISBN: (纸本)9798350383867

The issue of retinogenesis probably constitutes a major public health threat which gives the reason to carry out its early detection for intervention purposes to be efficient. Throughout this investigation, we suggest a new technique which uses the faster CNNs for early detection of retina degradation. We set up a diverse data collection where OCT(optical coherence tomography), fundus photography and angiography modality were included . This collection covered the various stages of degeneration. The dataset was prepared for instance with resizing, normalizing, and data augmentation were used to improve share quality and diversity. Deeper and faster networks that were developed and trained using the backpropagation technique performed well and further optimized using Adam, with high accuracy of 94 in the test dataset. 7%. The strength of our results can be seen through evaluation metrics in detail like confusion matrices and classification reports. The model can distinguish various cases of retinal degeneration accurately which proves to have high precision and recall for mild and normal degeneration. Interpolation analysis of the model using Grad-CAM and occlusion sensitivity, giving the clue about the model's decision making process, setting a precedent for clinical interpretation. The enhanced cross-validated model on an independent dataset also arrays the model in a representational where it will be able to generalize in a diverse population and in different imaging conditions. The systematic approach which proved to be more accurate and resilient than the other techniques was the one which was compared to the mentioned ones. In conclusion, our study shows the possibility for application of the Deep Faster CNNs in identifying the early signs of retinal degeneration;the tool therefore becomes an important tool for enhancing patient outcomes and prevention of vision loss in the world at large. ©2024 IEEE.

关键词： Ophthalmology

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