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16th Brazilian Power Electronics Conference, COBEP 2021

作者： Da Silva, Daniel Abreu Macedo Do Nascimento, Andre Cavalcante De Barros Araujo, Rejane De Souza Melo, Rogerio Jose Federal University of Pará Electrical Engineering Graduate Program Belém Brazil Federal Institute of Pará Department of Education Control and Industrial Processes Information and Communication Belém Brazil Undergraduate Electrical Engineering Estácio College Belém Brazil

ISBN: (纸本)9781665421294

This work proposes the project of the Generalized Predictive Controller (GPC), this is applied to a bidirectional converter, whose topology is the non-isolated Buck and Boost derived bidirectional DC-DC, present on a DC Nanogrid. The system consists of a primary bus, represented by Vdc, with high voltage, which is feeded by a renewable energy source that generates 175 V and a secondary source, represented by VBatt, with low voltage from a 48 V battery. The objective is to develop robustness and performance analysis, and ensure reference tracking and rejection of disturbance of this predictive controller when used in a bidirectional converter present in the aforementioned situation. In addition to analyzing how the control weighting factor interferes in the robustness and performance of this controller in this situation. © 2021 IEEE.

关键词： Controllers

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Guest Editorial AI-Generated Content Empowered Healthcare Electronics

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1319-1321页

作者： Gwanggil Jeon Joel Rodrigues Shiping Wen Junxin Chen Nan Ji Abdellah Chehri Department of Embedded Systems Engineering Incheon National University Incheon South Korea Post-Graduation Program in Electrical Engineering Federal University of Piauí Teresina Brazil Instituto de Telecomunicações Universidade de Aveiro Aveiro Portugal Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW Australia School of Software Dalian University of Technology Dalian China Hong Kong Centre for Cerebro-Cardiovascular Health Engineering City University of Hong Kong Hong Kong China Department of Mathematics and Computer Science Military College of Canada Kingston ON Canada

The rapid advancements in AI-Generated Content (AIGC) have revolutionized various domains, including consumer electronics and healthcare technologies. AIGC’s ability to generate high-quality text, images, and videos within seconds has reshaped human-computer interactions, from intelligent customer service to immersive virtual experiences. More importantly, its application in Healthcare Electronics (HE) has opened new frontiers, facilitating automated diagnostics, medical data synthesis, and intelligent healthcare predictions.

关键词： Special issues and sections Generative AI Medical services Artificial intelligence Medical diagnostic imaging Brain modeling Knowledge graphs Electroencephalography Wearable devices Electronic medical records

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ExplSched: Maximizing Deep Learning Cluster Efficiency for Exploratory Jobs

ExplSched: Maximizing Deep Learning Cluster Efficiency for E...

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IEEE International Conference on Cluster Computing

作者： Hongliang Li Hairui Zhao Zhewen Xu Xiang Li Haixiao Xu College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Changchun China High Performance Computing Center Jilin University China

Resource management for Deep Learning (DL) clusters is essential for system efficiency and model training quality. Existing schedulers provided by DL frameworks are mostly adaptations from traditional HPC clusters and usually work on jobs’ makespan, assuming that DL training jobs finish completely. Unfortunately, it is reported that a fair amount of training jobs are exploratory jobs and often finish unsuccessfully (over 30%) in production clusters. This is due to the distinct characteristic of Deep Neural Network (DNN) training that it is an exploratory process of frequent user interventions, such as adjusting model structures, tuning hyperparameters, and exploring feature validity. Existing DL cluster schedulers using offline algorithms are not suitable for exploratory jobs when unexpected early terminations can cause noticeable resource waste. Moreover, DL training jobs are iterative and usually yield diminishing returns as they progress. Equally allocating resource among training iterations is not efficient, especially when dealing with exploratory jobs where it can worsen the degradation of system efficiency. The fundamental goal of a DL training job is to gain model quality improvement, usually indicated by the loss reduction (job profit) of a DNN model. This paper introduces a novel scheduling problem for exploratory jobs that seeks to maximize the overall training profit of a DL cluster. We propose ExplSched, an online scheduling solution based on the primal-dual framework, resulting in a competitive ratio of 2α that belongs to O(ln n). It uses a resource price function that emphasizes the importance of job profit to resource consumption ratio to make quick resource allocation decisions. Experimental results show that ExplSched achieved an average system utility improvement of 87.28% compared with other related work.

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Augmenting Data Analysis Through Innovative Visualization Techniques

Augmenting Data Analysis Through Innovative Visualization Te...

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International Conference on Futuristic Technologies (INCOFT)

作者： Uma Rani K Omkaresh S. Kulkarn Ravi Kant Pareek Trapty Agrawal R Murugan N.V. Balaji Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Civil Engineering Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Karpagam Academy of Higher Education Coimbatore

This paper examines new and progressive methods in information visualization for analysis and selectionmaking. It explains various traits of records that might be made less burdensome to apprehend via visuals, which include graphs, pictures, and other visible representations. It highlights the strength of facts visualization to assist people in interpreting, advantage insights, and conclude from information, making them more green and powerful in their evaluation. The paper covers the significance of records visualization modern and growing techniques and examines how facts analysis and choice-making may be augmented using these stylish visuals. Researchers can use statistics visualization techniques to reach broader audiences and help them understand important information more correctly.

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Optical Analysis of the UPQC using PI Controller in Power flow System 7

Optical Analysis of the UPQC using PI Controller in Power fl...

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7th International Conference on Advanced Computing and Communication Systems, ICACCS 2021

作者： Karthik, Mamidala Vijay Chakravarthi, M.Kalyan Yapanto, Lis M Selvapandian, D. Harish, R. Subramani, Karthick Noida International University Department of Electrical Engineering Uttar Pradesh Greater Noida India VIT-AP University School of Electronics Engineering Amaravathi India Universitas Negeri Gorontalo Faculty of Fishery and Marine Science Indonesia Karpagam Academy of Higher Education Department of Computer Science and Engineering Tamil Nadu India Sns College of Engineering Department of Electrical and Electronics and Engineering Tamil Nadu India Srm Institute of Science and Technology Department of Computer Science and Engineering Uttar Pradesh 201204 India

ISBN: (纸本)9781665405201

This examination depends on a three-phase unified control quality conditioner (UPQC) to extend control quality in a three-phase distribution framework. It is nice for day-to-day trade and domestic sectors. Researchers have endeavored and executed different advantageous advances to induce freed of both voltage and current consonant issues, making strides the quality of control given by the control framework. UPQC was made up of a arrangement and a shunt converter that were associated back-to-back employing a common dc-link capacitor. This paper works by actualizing UPQC control quality to compensate for voltage and current sounds issues. UPQC uses the PI controller, which calculates error value differently between the measured and desired set points. The conventional UPQC tool is limited to the PI controller's DC link voltage law. Also, it presents a three-phase transformation using a special multi-winding transformer connection scheme. A MATLAB simulation is utilized in this paper to demonstrate how the UPQC demonstrate can decrease the percent THD in source voltage, source current, and stack voltage waveforms actuated by nonlinear loads. The execution of UPQC is analyzed with a nonlinear load, and simulation studies utilizing MATLAB/Simulink prove the suitable execution of UPQC. © 2021 IEEE.

关键词： Power quality

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Multi-scale fusion clustering algorithm for urban travel features 3

Multi-scale fusion clustering algorithm for urban travel fea...

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2023 3rd International Conference on Intelligent Traffic Systems and Smart City, ITSSC 2023

作者： Zhang, Zixiang Zhao, Zhilong Deng, Jiaming Zhao, Yingying Xu, Tao Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Key Laboratory of Geographical Information Science Ministry of Education East China Normal University Shanghai200241 China School of Computer and Information Engineering Henan University Kaifeng475004 China International Education College Henan University Zhengzhou450046 China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan University Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China

ISBN: (纸本)9781510672963

Urban travel data typically encompass various temporal and spatial scales and features. Nonetheless, existing traffic travel feature analysis often focuses on a single spatial scale, which may overlook crucial information and hinder features at different scales. Consequently, this could reduce the accuracy of travel pattern recognition. To solve this problem, we introduce a multi-scale fusion transformation algorithm. In our approach, we first establish a multi-level taxi travel data grid system. Then, we apply the I-Clique clustering algorithm to perform grid clustering at each level of the system to identify taxi travel feature regions of different scales. Subsequently, a multi-scale fusion pyramid model is constructed utilizing the acquired travel feature regions at various scales. Finally, we determine the transformation relationships between scales in the pyramid model, enabling the identification of travel feature areas across both known and unknown scales. This approach facilitates the recognition of travel features within cities at any scale. Experimental results demonstrate that the multi-scale fusion transformation algorithm enhances our understanding of spatial aggregation patterns and the hierarchical structure of geographical entities, offering a novel approach for multi-scale urban traffic analysis. © 2024 SPIE. All rights reserved.

关键词： Clustering algorithms

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Geochemistry and the optics of geospatial analysis as a preposition of water quality on a macroscale

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Environmental Science and Pollution Research 2024年第22期31卷 32614-32636页

作者： Lozano, Liliana P. Taleb, Manal F. Abou Ibrahim, Mohamed M. Gonçalves, Janaína O. Neckel, Alcindo Schmitz, Guilherme P. Bodah, Brian William Korcelski, Cleiton Maculan, Laércio Stolfo Silva, Luis F. O. Department of Civil and Environmental Engineering Universidad de La Costa CUC Calle 58 #55-66 Atlántico Barranquilla Colombia Postgraduate Doctoral Program in Society Nature and Development Universidade Federal Do Oeste Do Pará UFOPA Paraná68040-255 Brazil Department of Chemistry College of Science and Humanities in Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia Department of Chemistry College of Science Taif University Taif21944 Saudi Arabia ATITUS Educação RS Passo Fundo30499070-220 Brazil Universidade Do Minho UMINHO Porto4710-057 Portugal Thaines and Bodah Center for Education and Development 840 South Meadowlark Lane OthelloWA99344 United States Workforce Education & amp Applied Baccalaureate Programs Yakima Valley College South 16 Avenue & Nob Hill Boulevard YakimaWA98902 United States CDLAC—Data Collection Laboratory and Scientific Analysis LTDA Porto Alegre90670-090 Brazil

The water treatment depends exclusively on the identification of residues containing toxic chemical elements accumulated in NPs (nanoparticles), and ultrafine particles sourced from waste piles located at old, abandoned sulfuric acid factories containing phosphogypsum requires global attention. The general objective of this study is to quantify and analyze the hazardous chemical elements present in the leachate of waste from deactivated sulfuric acid factories, coupled in NPs and ultrafine particles, in the port region of the city of Imbituba, Santa Catarina, Brazil. Samples were collected in 2020, 2021, and 2022. Corresponding images from the Sentinel-3B OLCI satellite, taken in the same general vicinity, detected the levels of absorption coefficient of Detritus and Gelbstoff (ADG443_NN) in 443 m−1, chlorophyll-a (CHL_NN (m−3)), and total suspended matter (TSM_NN (g m−3) at 72 points on the marine coast of the port region. The results of inductively coupled plasma atomic-emission spectrometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS) demonstrate that the leaching occurring in waste piles at the port area of Imbituba was the likely source of hazardous chemical elements (e.g., Mg, Sr, Nd, and Pr) in the environment. These leachates were formed due to the presence of coal pyrite and Fe-acid sulfates in said waste piles. The mobility of hazardous chemical elements potentiates changes in the marine ecosystem, in relation to ADG443_NN (m−1), CHL_NN (m−3), and TSM NN (g m−3), with values greater than 20 g m−3 found in 2021 and 2022. This indicated changes in the natural conditions of the marine ecosystem up to 30 km from the coast in the Atlantic Ocean, justifying public initiatives for water treatment on a global scale. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Nanoparticles

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Streamlining Video Summarization with NLP: Techniques, Implementation, and Future Direction

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SN computer Science 2025年第2期6卷 1-17页

作者： Alqurni, Jehad Saad Alsmadi, Mutasem K. Alfagham, Hayat Alzoubi, Sharaf Ihab, Sohayla Sameh, Ahmed AbdElminaam, Diaa Salama Khalaf, Osamah Ibrahim Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia College of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan Faculty of Engineering Ain Shams University Cairo 19648 Egypt MEU Research Unit Middle East University Amman 11831 Lebanon Jadara Research Center Jadara University Irbid 21110 Jordan Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Baghdad 42115 Iraq

The rapid growth of digital video content presents significant challenges in information accessibility and consumption, creating a pressing need for efficient video summarization techniques. This paper explores using natural language processing (NLP) to enhance video summarization by leveraging associated textual data such as subtitles, titles, and descriptions. Video summarization can be broadly divided into two main approaches: abstractive and extractive. We focus on extractive summarization, implementing various NLP techniques, including Pure NLTK, TextRank, LexRank, KL-Sum, and Naïve Reduction, each encapsulated in dedicated pipelines to tokenize, rank, and extract essential content. Subsequently, summaries are generated and presented as shortened video formats. Our methodology includes comparing these NLP-based techniques with current state-of-the-art approaches and evaluating them through various quantitative metrics such as Rouge, BLEU, and F1-score to ensure a comprehensive summarization quality and efficiency assessment. The study also addresses computational trade-offs, particularly focusing on optimizing summarization for real-time applications. Recognizing the reliance on textual data, we propose enhancements for handling videos with limited text, such as integrating audio-to-text conversion and visual analysis. Our findings underscore the potential of NLP-driven summarization in improving accessibility across varied video content types and provide a roadmap for future research to enhance scalability, personalization, and real-time applicability. The evaluation results demonstrate the effectiveness and potential of NLP techniques in video summarization regardless of video length, accent, or language. The findings highlight the effect of various NLP techniques on the form of the generated summaries. In addition, a comparison with state-of-the-art methodologies is performed to provide clearer insights into the quality of the summaries, not only regarding a

关键词： Abstractive summarization Audio-to-text conversion Computational efficiency Extractive summarization Multimedia content analysis Natural language processing (NLP) Personalization Real-time processing Scalability Video accessibility Video summarization

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A Battery Capacity Prediction Method Applying to On-Service Vehicles Using Charging Data and Decomposition Framework

SSRN

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SSRN 2024年

作者： Zhao, Xinwei Liu, Yonggui Xiao, Bin Key Laboratory of Autonomous Systems and Network Control Ministry of Education School of Automation Science and Engineering South China University of Technology Guangzhou510641 China School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Information Engineering School Guangzhou Vocational College of Technology & Business Guangzhou511442 China

Lithium-ion batteries are valuable as the primary power source for electric vehicles (EVs). However, their capacity degradation and reliability directly affect driving of on-service EVs, accurately forecasting battery capacity is still a tricky challenge due to complex battery systems and limitations of computing power. To overcome such barriers, a concise battery capacity prediction method is proposed based on charging data and decomposition framework in this paper. First, charging hot-spot intervals are counted to provide a reliable foundation for screening high-quality and low-quantity data. Then, the seasonal-trend decomposition method with independent analysis is designed and applied to extract significant correlation features associated with capacity degradation. Subsequently, a temporal convolution network with the decomposed mode is developed to forecast battery capacity, in conjunction with the use of decoupling mechanism to rectify the analytical interference and misinformation caused by data mixing. Finally, experiment results demonstrate that the proposed method can accurately predict capacity of on-service EVs. Furthermore, this approach alleviates the computational burden without compromising prediction accuracy. © 2024, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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Implementing Automated Reasoning in Natural Language Processing

Implementing Automated Reasoning in Natural Language Process...

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International Conference on Futuristic Technologies (INCOFT)

作者： Kalyan Acharjya Rakshith N N. Thangarasu Hemalatha S Varsha D Adarsh Srivastava Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer ScienceKarpagam Academy of Higher Education Coimbatore Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Science & Engineering Vivekananda Global University Jaipur India

herbal language processing (NLP) is an unexpectedly advancing field state-of-the-art synthetic intelligence and system ultra-modern. It's been able to permit computer systems to apprehend human language and to perform numerous tasks, from textual content type to automated summarization. Automatic reasoning is one of the most advanced and powerful packages modern-day NLP that promises to unlock the electricity cutting-edge computer systems to recognize and answer questions posed in herbal language. Automatic reasoning systems can analyze a fixed ultra-modern statistics after which reason logically and efficaciously based at the information supplied. This paper will speak the demanding situations latest enforcing automated reasoning, and the way improvements in NLP are getting used to enhance the accuracy and pace ultra-modern automatic reasoning. It'll additionally explore the packages contemporary automated reasoning in various domain names, along with question answering and choice making. finally, it'll discuss the capability for automatic reasoning to revolutionize the sector cutting-edge natural language processing by means of advancing the in a ramification modern methods.

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