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International Bhurban Conference on Applied Sciences & Technology, IBCAST

作者： Muhammad Latif Hira Ambreen Muhammad Imran Samra Zafar Sajid Bashir Department of Electrical Engineering Military College of Signals (MCS) National University of Sciences & Technology (NUST) Islamabad Pakistan Department of Electrical Engineering Sir Syed Center for Advanced Studies in Engineering Institute of Technology (SS-CASE-IT) Islamabad Pakistan Henan University School of Computer and Information Engineering Kaifeng China College of Electrical & Mechanical Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

The analytical and modelling precision required to build modern wind turbines makes them stable, controlled, and observable. This article presents a state-of-the-art method for lowering the model order of a wind turbine driven by a double-fed induction generator, where the rotational speed varies over time. The suggested approach uses state-space representations to set up a comprehensive and systematic framework for analysing wind turbine performance and checking for compliance with grid standards. A time-constrained Gramians-based novel balanced realization approach is employed to reduce the dimensionality of the large-scale wind turbine model. Stability and reduced approximation error are two benefits of the lower-order system. The proposed method also gives researchers an a priori formula for the error-bound, making computations faster and more efficient. Using substantial simulation data, we show that our method improves upon previous methods in optimising models that include wind turbines with changing rotational speeds, taking into account both stability and error computation. To enhance the efficacy and accuracy of their modeling procedures, academics and researchers analyzing wind turbines should incorporate this new methodology.

关键词： Analytical models Accuracy Uncertainty Computational modeling Stability criteria Velocity control Power system stability Approximation error Wind turbines Computational efficiency

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Prediction and Electricity Forecasting on the Individual Household Level based on PSO-LSSVM Approach 7

Prediction and Electricity Forecasting on the Individual Hou...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Padmaja, Voleti Tamizhselvi, A. Kathir, I. Mohan, Arvind Pal, Subharun Rastogi, Ravi Geethanjali College of Engineering and Technology Electrical and Electronics Engineering Hyderabad India St. Joseph's College of Engineering Department of Information Technology Chennai India V.S.B. Engineering College Department of Electrical & Electronics Engineering Karur India Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun India Indian Institute of Technology Jammu Department of Computer Science and Engineering Jammu & Kashmir Jagti India NIELIT Electronics Division Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350340600

When smart metering systems are used to encourage energy conservation at the residential level, new research challenges arise in the areas of monitoring usage and providing accurate load forecasts. Intelligent smart meters rely on accurate predictions of future electricity use. Many energy and operational improvements, such as more efficient appliances, renewable energy sources, and advanced metering systems, are included into micro-grids. Here is an alternative approach to estimating annual energy use in single-family homes. Incorporating the effects of residents' daily activities and appliance usages on overall home power consumption improves the accuracy of the forecasting model. Data preparation, feature selection, and model training are the three phases of the suggested approach. Data preprocessing usually entails cleaning the data and preparing the features to be used. When it comes to feature selection, the K-means clustering approach is frequently utilized. After using information gain for feature selection, the models are trained using PSO-LSSVM. The proposed method outperforms both GRU and LSSVM, two well-established competitors. © 2023 IEEE.

关键词： K-means clustering

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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A Novel Deep Learning Assisted Lung Nodule Identification Mechanism with Intelligent Image Processing Principles

A Novel Deep Learning Assisted Lung Nodule Identification Me...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Senthil, P. Pandi, V.Samuthira Shobana, D. Jayashankari, J. Priya, J. Lakshmi Kaushik, G. Department of Computer Science and Engineering Cmr College of Engineering & Technology India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350300857

Lung cancer has the greatest fatality rate of all cancers. Due to its efficacy, chest CT has become standard practice for lung cancer screening and diagnosis. Radiologists have a significant challenge when trying to detect nodules in 3D volume imaging due to the tiny size of their shadows. This research study has created a novel computer-Aided Design (CAD) system that can automatically identify tumors in the lungs in CT scans to help with this problem. When it comes to cancers that can be fatal even if treated late, lung cancer is by far the most frequent. At now, CT can be utilized to aid in the early detection of lung cancer. The identification of lung cancer frequently relies on the expertise of medical professionals, who may overlook some patients and so exacerbate the situation. In several fields of medical imaging diagnostics, deep learning has shown to be an effective and widely used technique. This research work proposes an Intelligent Learning based Nodule Detection (ILND), a revolutionary deep learning-based approach to detecting lung cancer. This research study adapts those networks for use in classifying CT images of both malignant and benign lung nodules. The CT imaging dataset was used to assess the performance of the networks. The results of the experiments demonstrate that the suggested Intelligent Learning based Nodule Detection model outperformed the other network models, with an accuracy of 95.67%, a sensitivity of 93.65% and a specificity of 94.82%. © 2023 IEEE.

关键词： computerized tomography

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Performance Analysis Of Insulation Resistance Test And Cable Laying Installation In Library Block

Performance Analysis Of Insulation Resistance Test And Cable...

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Global Power, Energy and Communication Conference (GPECOM)

作者： B. Rubini R. Karthick Kalaiarasi G S. Saranya S. Pradeep Kumar M. Suganiya Department of Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Electrical and Electronics Engineering C.K.College of Engineering and Technology Cuddalore India Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology & Research (Deemed to be University) Guntur India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy India Department of Computing Technologies SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350351088

ISBN: (纸本)9798350351095

The process of installing electrical cables physically in a specific location inside a library block is widely considered as an electrically equipped place. It involves planning the particular route, securing the cables and ensuring technical support and proper protection. In a library block, the process of installing electrical wires can provide power and electrical connectivity to various devices and systems. An insulation resistance tests were conducted a high voltage flow to the insulation and measures the resistance ratio to the actual current flow after cable installation. Testing of measurements given in table. Algorithms help to identify all kinds of weaknesses and faults in the insulation that could lead to any electrical leakages or short circuits. The insulation resistance test is primarily important to ensure the safety and reliability of electrical systems. It is particularly needed in an environment like a library. Libraries are filled with books made of papers that are easily flammable in nature. So, we should give top priority to electrical safety in libraries. Minimum constrained non-linear minimization solver is used to identify the iteration levels and verified with best function values and first order optimality values. This paper shows performance analysis of Insulation resistance test and Cable laying installation in a Library block.

关键词： Resistance Insulation Current measurement Wires Libraries Performance analysis electrical resistance measurement

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Deep Generative Adversarial Network for Occlusion Removal from a Single Image

arXiv

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arXiv 2024年

作者： Jonna, Sanakaraganesh Medhi, Moushumi Sahay, Rajiv Ranjan The department of Computer Science and Engineering Indian Institute of Technology Kharagpur721302 India The Advanced Technology Development Center Indian Institute of Technology Kharagpur721302 India Department of Electrical Engineering Indian Institute of Technology Kharagpur721302 India

Nowadays, the enhanced capabilities of in-expensive imaging devices have led to a tremendous increase in the acquisition and sharing of multimedia content over the Internet. Despite advances in imaging sensor technology, annoying conditions like occlusions hamper photography and may deteriorate the performance of applications such as surveillance, detection, and recognition. Occlusion segmentation is difficult because of scale variations, illumination changes, and so on. Similarly, recovering a scene from foreground occlusions also poses significant challenges due to the complexity of accurately estimating the occluded regions and maintaining coherence with the surrounding context. In particular, image de-fencing presents its own set of challenges because of the diverse variations in shape, texture, color, patterns, and the often cluttered environment. This study focuses on the automatic detection and removal of occlusions from a single image. We propose a fully automatic, two-stage convolutional neural network for fence segmentation and occlusion completion. We leverage generative adversarial networks (GANs) to synthesize realistic content, including both structure and texture, in a single shot for inpainting. To assess zero-shot generalization, we evaluated our trained occlusion detection model on our proposed fence-like occlusion segmentation dataset. The dataset can be found on GitHub. © 2024, CC BY.

关键词： Generative adversarial networks

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Deep Learning-Based Image Analysis to Track Vehicles

Deep Learning-Based Image Analysis to Track Vehicles

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Disruptive Technologies (ICDT), International Conference on

作者： P Prabakaran R. Jayasudha Mukta Sandhu Sandeep Gupta Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Skill Department of Computer Science and Engineering Shri Vishwakarma Skill University Palwal Haryana India Department of Electrical Engineering Graphic Era (Deemed to be University) Dehradun India

There are many ways to track the traffic conditions on roads. With the rise of AI-based image processing technology, there has been a surge in interest in developing traffic monitoring systems that rely on camera vision data. An improved road monitoring system may be derived from the data collected in this research by placing a camera at a junction. The technique detects and classifies vehicles using a deep-learning-based methodology (YOLOv4). By comparing the positions of the vehicle with the map HD, lane-by-lane trajectories of the vehicle may be approximated. The predicted vehicle trajectories are used to calculate each lane's wait lengths and traffic volumes, broken down by travel direction. This technique was evaluated using five distinct metrics: vehicle detection rates, trajectory prediction, type of vehicle categorization, traffic volume estimate, and queue length assessment. Thousands of samples were used to assess its performance. Based on the findings, it is acceptable to assume that the system can identify vehicles with a 98% success rate and an error rate of less than 25% regarding lane-by-lane trip volume estimation and vehicle type classification. Therefore, this study's approach reveals the viability of collecting comprehensive traffic information from a camera set up at a junction. Current traffic monitoring schemes have a reasonable likelihood of being developed by this study's significant contribution, which is a strategy that combines AI with HD map approaches.

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Diabetes Prediction Model for Better Clarification by using Machine Learning 6

Diabetes Prediction Model for Better Clarification by using ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Eben, J. Lysa Jayasudha, R. Ramya, S. Kaliappan, S. Aswal, Shobha Al-Salehi, Khalid Ali Salem Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Division of Research and Development Lovely Professional University Punjab Phagwara144411 India School of Computing Graphic Era Hill University Dehradun India Biomedical Engineering Study Program Faculty of Science and Technology Airlangga University Surabaya Indonesia

ISBN: (纸本)9798350398496

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. The newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy. © 2023 IEEE.

关键词： Forecasting

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High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer

High-Fidelity Document Stain Removal via A Large-Scale Real-...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Mingxian Li Hao Sun Yingtie Lei Xiaofeng Zhang Yihang Dong Yilin Zhou Zimeng Li Xuhang Chen School of Computer Science and Engineering Huizhou University Department of Computer Science and Engineering The Ohio State University School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shenzhen Institute of Advanced Technology Chinese Academy of Sciences School of Electronic and Communication Engineering Shenzhen Polytechnic University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Document images are often degraded by various stains, significantly impacting their readability and hindering downstream applications such as document digitization and analysis. The absence of a comprehensive stained document dataset has limited the effectiveness of existing document enhancement methods in removing stains while preserving fine-grained details. To address this challenge, we construct StainDoc, the first large-scale, high-resolution (2145 x 2245) dataset specifically designed for document stain removal. StainDoc comprises over 5,000 pairs of stained and clean document images across multiple scenes. This dataset encompasses a diverse range of stain types, severities, and document backgrounds, facilitating robust training and evaluation of document stain removal algorithms. Furthermore, we propose StainRestorer, a Transformer-based document stain removal approach. StainRestorer employs a memory-augmented Transformer architecture that captures hierarchical stain representations at part, instance, and semantic levels via the DocMemory module. The Stain Removal Transformer (SRTransformer) leverages these feature representations through a dual attention mechanism: an enhanced spatial attention with an expanded receptive field, and a channel attention captures channel-wise feature importance. This combination enables precise stain removal while preserving document content integrity. Extensive experiments demonstrate StainRestorer's superior performance over state-of-the-art methods on the Stain-Doc dataset and its variants *** and ***, establishing a new benchmark for document stain removal. Our work highlights the potential of memory-augmented Transformers for this task and contributes a valuable dataset to advance future research.

关键词： Training Deep learning computer vision Text analysis Attention mechanisms Semantics Memory architecture Benchmark testing Transformers Image restoration

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Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application

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Acta Pharmaceutica Sinica B 2024年第2期14卷 623-634页

作者： Jiacheng Xiong Rongrong Cui Zhaojun Li Wei Zhang Runze Zhang Zunyun Fu Xiaohong Liu Zhenghao Li Kaixian Chen Mingyue Zheng Drug Discovery and Design Center State Key Laboratory of Drug ResearchShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai 201203China University of Chinese Academy of Sciences Beijing 100049China College of Computer and Information Engineering Dezhou UniversityDezhou 253023China AI Department Suzhou Alphama Biotechnology Co.Ltd.Suzhou 215000China Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and TechnologyShanghaiTech UniversityShanghai 200031China State Key Laboratory of Pharmaceutical Biotechnology Nanjing UniversityNanjing 210023China

Aldehyde oxidase(AOX)is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other ***-mediated metabolism can result in unexpected outcomes,such as the production of toxic metabolites and high metabolic clearance,which can lead to the clinical failure of novel therapeutic *** models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism *** this study,we developed a novel graph neural network called AOMP for predicting AOX-mediated *** integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction,while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both *** significantly outperformed the benchmark methods in both cross-validation and external *** AOMP,we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability,which were validated through in vitro ***,for the convenience of the community,we established the first online service for AOX metabolism prediction based on AOMP,which is freely available at https://***.

关键词： Drugmetabolism Aldehyde oxidase Transferlearning Graph neural network Kinase inhibitor

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