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3rd International Conference on Power, Energy, control and Transmission Systems, ICPECTS 2022

作者： Basha, Amjath Ali Jakul Saravanan, K. Mangayarkarasi, T. Balarengadurai, C. University of Technology and Applied Sciences Ibra Sultanate of Oman RCC - ENGG Department Computer Engineering Electrical and Electronic Section Department of Engineering Oman R.M.D Engineering College Department of Information Technology Tamil Nadu Chennai India Sri Sairam Engineering College Department of Instrumentation and Control Engineering Tamil Nadu Chennai India Vidyavardhaka College of Engineering Department of Computer Science and Engineering Karnataka Mysuru India

ISBN: (纸本)9781665462754

COVID-19 has lately infected a big number of people worldwide. Medical service frameworks are strained as a result of the infection. The emergency unit, which is part of the medical services area, has experienced several challenges as a result of the low data quality offered by existing ICU clinical equipment. The Internet of Things has enhanced the capability for essential information mobility in medical services in the twenty-first century. Nonetheless, many of today's ideal models use IoT innovation to assess patients' well-being. As a result, executives lack understanding regarding the most effective method to apply such innovation to ICU clinical equipment. The IoT Based Paradigm for Medical Equipment Management Systems, a breakthrough IoT-based paradigm for successfully administering clinical hardware in ICUs, is introduced in this study. During the COVID-19 episode, IoT technology is used to boost the data stream between clinical hardware, executive frameworks, and ICUs, enabling the maximum level of openness and reasonableness in clinical equipment redistribution. IoT MEMS conceptual and functional features were painstakingly drawn. Using IoT MEMS expands the capacity and limits of emergency clinics, effectively easing COVID-19. It will also have a substantial impact on the nature of the data and will improve the partners' trust and transparency. © 2022 IEEE.

关键词： COVID-19

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Design and Analysis of a High-Speed Slotless Permanent Magnet Synchronous Motor Considering Air-Gap Airflow

Design and Analysis of a High-Speed Slotless Permanent Magne...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Ming-Hong Guo Guang-Zhong Cao Hong-Jin Hu Su-Dan Huang Hao-Tian Wang Jiang-biao He Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots College of Mechatronics and Control Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering University of Kentucky Lexington KY USA

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

The air-gap airflow will affect the performance of the high-speed slotless permanent magnet synchronous motor (HSSPMSM). However, the influence of airflow is often neglected in motor design, leading to discrepancies between the designed and actual performance. This paper conducts the design and analysis of the HSSPMSM considering the influence of air-gap airflow. The coupling relationships among airflow, electromagnetic, thermal, and mechanical are established. The dimensions design and performance analysis of the HSSPMSM are performed. Based on the designed dimensions, a lOkW/40krpm HSSPMSM prototype is developed. The experimental results indicate that the developed HSSPMSM matches the design requirements.

关键词： Couplings Air gaps Prototypes Permanent magnet motors Performance analysis Magnetic analysis Electromagnetics

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Machine learning insights in predicting heavy metals interaction with biochar

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Biochar 2024年第1期6卷 157-167页

作者： Xin Wei Yang Liu Lin Shen Zhanhui Lu Yuejie Ai Xiangke Wang School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206People’s Republic of China. School of Mathematics and Physics North China Electric Power UniversityBeijing 102206People’s Republic of China. MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environment and Chemical EngineeringNorth China Electric Power UniversityBeijing 102206People’s Republic of China. Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of ChemistryBeijing Normal UniversityBeijing 100875People’s Republic of China.

The use of machine learning(ML)in the field of predicting heavy metals interaction with biochar is a promising field of research,mainly because of the growing understanding of how removal efficiency is affected by characteristic variables,reaction conditions and biochar *** practical application in biochar still faces large challenges,such as difficulties in data collection,inadequate algorithm development,and insufficient ***,the quantity,quality,and representation of data have a large impact on the accuracy,efficiency,and generalizability of machine learning *** this perspective,the present data descriptors,the efficiency of machine learning-aided property and performance prediction,the interpretation of underlying mechanisms and complicated relationships,and some potential ways to augment the data are discussed regarding the interactions of heavy metals with ***,future perspectives and challenges are discussed,and an enhanced model performance is proposed to reinforce the feasibility of a particular perspective.

关键词： Biochar Heavy metals Interaction mechanism Machine learning

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Nonlinear Deep Subspace Network for Micro-expression Recognition 3

Nonlinear Deep Subspace Network for Micro-expression Recogni...

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3rd Workshop on Facial Micro-Expression: Advanced Techniques for Multi-Modal Facial Expression Analysis, FME 2023

作者： Feng, Weijia Xu, Manlu Chen, Yuanxu Wang, Xiaofeng Guo, Jia Dai, Lei Wang, Nan Zuo, Xinyu Li, Xiaoxue College of Computer and Information Engineering Tianjin Normal University Tianjin China Ping An Technology Shenzhen China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin University of Technology Tianjin China

ISBN: (纸本)9798400702853

Deep learning (DL) models have been widely studied in the field of micro-expression recognition (MER). However, micro-expressions (MEs) suffer from small number of samples and difficulty in extracting subtle and transient features, resulting in limited improvement in the recognition performance. In addition, DL models are prone to overfitting problems and are difficult to extract discriminative features of facial actions from ME images or sequences. To address these issues, we propose a MER method that combines a nonlinear deep subspace network and optical flow features. Firstly, facial motion features are captured using optical flow computation, and then the optical flow features are input into a kernel principal component analysis network (KPCANet) to further learn deeper spatio-temporal features. Finally, a linear support vector machine (SVM) is used for ME classification. Experiments conducted on four public spontaneous ME datasets including SMIC, CASME, CASME II and SAMM validate the effectiveness of the proposed method. The experimental results demonstrate that the proposed method achieves even better recognition performance compared to existing state-of-the-art MER methods. © 2023 ACM.

关键词： Principal component analysis

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Automatic Liver Segmentation from Multiphase CT Using Modified SegNet and ASPP Module

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SN computer Science 2024年第4期5卷 377页

作者： Nayantara, P. Vaidehi Kamath, Surekha Kadavigere, Rajagopal Manjunath, Kanabagatte Nanjundappa Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Radiodiagnosis and Imaging Kasturba Medical College Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal 576104 India

Liver cancer is one of the dominant causes of cancer death worldwide. Computed Tomography (CT) is the commonly used imaging modality for diagnosing it. computer-based liver cancer diagnosis systems can assist radiologists in image interpretation and improve diagnosis speed and accuracy. Since liver segmentation is crucial to such systems, researchers are relentlessly pursuing various segmentation approaches. A clinically viable computer-aided system requires examining multiphase CT images. However, most of the research focuses only on the portal venous phase. In this work, we developed an automatic and efficient Deep Learning (DL) method using SegNet, atrous spatial pyramid pooling module and leaky ReLU layers for liver segmentation from quadriphasic abdominal CT volumes. The proposed method was validated on two datasets, an internal institutional dataset consisting of multiphase CT and a public dataset of portal venous phase CT volumes. The Dice Coefficients (DC) obtained were greater than 96% for the latter dataset and the portal venous phase of the former. For arterial, delayed and plain CT phases of the former dataset, the DC achieved were 94.61%, 95.01% and 93.23%, respectively. Experiments showed that our model performed better than the other state-of-the-art DL models. Ablation studies have revealed that the proposed model leverages the strengths of all the three components that make it up. The promising performance of the proposed method suggests that it is appropriate for incorporation in hepatic cancer diagnosis systems. © The Author(s) 2024.

关键词： Atrous spatial pyramid pooling Computed tomography computer-aided diagnosis Liver cancer Liver segmentation SegNet

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An Unsupervised Leak Detection Method via Assembling Twin Attention-based Prediction Models for Natural Gas Gathering Pipelines

An Unsupervised Leak Detection Method via Assembling Twin At...

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Chinese Automation Congress (CAC)

作者： Hao Zhang Zhonglin Zuo Li Zheng Shan Liang Qingguo Lü College of Automation Chongqing University Chongqing China College of Control Science and Engineering Zhejiang University Hangzhou China College of Computer Science Chongqing University Chongqing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Accurately detecting leaks in natural gas gathering pipelines cannot only help enterprises and governments timely cope with the safety management problems, but also maintain the reliable operation of pipelines. The data-driven approaches for leak detection have become a preferred solution with the widely installation of sensors in pipeline network. However, these methods face challenges in achieving high identification accuracy due to the lack of labeled leak data and insufficient representation learning. To overcome this difficulty, an unsupervised leak detection method that leverages twin attention-based prediction models is proposed for these gathering pipelines in this article. First, attention-based point and sequence prediction approaches are designed by joint utilization of attention mechanism (AM) and the cascade of one-dimensional convolutional network (1D-CNN) and long short-term memory (LSTM) network, where the sequence one also employs the operation of sequence repetition and flipping for better prediction. Based on the designed attention-based point and sequence prediction approaches, an unsupervised twin attention-based prediction structure is then introduced to jointly establish the normal pipeline models. Specifically, the point prediction model is mainly used to learn the short-term dependency patterns whereas the sequence prediction model for the long-term ones from the multivariate time series. Next, a fusion strategy is presented to fuse the prediction errors generated from the twin attention-based models for the computing of overall leak scores as well as exploiting their complementary detection sensibility. To obtain clear status of pipelines without affected by abnormal points resulting from device anomalies or noises, the minimum covariance determinant (MCD) approach is adopted to attain the reliable leak scores of the fused errors. The experimental results on datasets derived from real-world gathering pipelines validate the effectiveness of

关键词： Representation learning Computational modeling Pipelines Time series analysis Predictive models Data models Reliability Leak detection Long short term memory Natural gas

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Adoption of Extended Reality as a Teaching Tool 1

Adoption of Extended Reality as a Teaching Tool

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1st International Conference on Computational Science and Technology, ICCST 2022

作者： Mangay Arkarasi, T. Madhumita, S. Suretha, S. Harini, M. Ananthi, M. Hariharan, V. Sri Sairam Engineering College Department of Instrumentation and Control Chennai India Sri Sairam Engineering College Department of Computer Science and Business Systems Chennai India Founder Space Zee Technologies Private Limited Chennai India

ISBN: (纸本)9781665476553

Extended reality (XR) facets aid pupils in understanding difficult topics. Regular education has been reliant on 2D technology for a very long time. It no longer draws in pupils due to its lack of immersion and engagement assistance. The suggested remedy involves using AR to develop 3D models and educational components. The augmented reality (AR) application is more appealing to pupils than other approaches and can be utilized as an interactive tool to clarify difficult concepts in the classroom. The functions of the following human anatomy parts-the brain, heart, and pancreas-were supplied in a piece of note, and audio was added predominately. The 3d models were created using the unity3d application as a support system, and Sketchfab was used to import them. © 2022 IEEE.

关键词： Augmented reality

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Federated Multi-View Multi-Label Classification

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IEEE Transactions on Big Data 2024年

作者： Meng, Hongdao Deng, Yongjian Zhong, Qiyu Wang, Yipeng Yang, Zhen Lyu, Gengyu Beijing University of Technology College of Computer Science China Ministry of Education Engineering Research Center of Intelligence Perception and Autonomous Control Beijing100124 China

Multi-view multi-label classification is a crucial machine learning paradigm aimed at building robust multi-label predictors by integrating heterogeneous features from various sources while addressing multiple correlated labels. However, in real-world applications, concerns over data confidentiality and security often prevent data exchange or fusion across different sources, leading to the challenging issue of data islands. To tackle this problem, we propose a general federated multi-view multi-label classification method, FMVML, which integrates a novel multi-view multi-label classification technique into a federated learning framework. This approach enables cross-view feature fusion and multi-label semantic classification while preserving the data privacy of each independent source. Within this federated framework, we first extract view-specific information from each individual client to capture unique characteristics and then consolidate consensus information from different views on the global server to represent shared features. Unlike previous methods, our approach enhances cross-view fusion and semantic expression by jointly capturing both feature and semantic aspects of specificity and commonality. The final label predictions are generated by combining the view-specific predictions from individual clients and the consensus predictions from the global server. Extensive experiments across various applications demonstrate that FMVML fully leverages multi-view data in a privacy-preserving manner and consistently outperforms state-of-the-art methods. © 2015 IEEE.

关键词： Federated learning

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Internet of things-based automation design and organizational innovation of manufacturing enterprises

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Materials Today: Proceedings 2022年 56卷 1769-1775页

作者： Babu, T. Roopa, H. Shukla, Arvind Kumar Stalin David, D. Jayadatta, S. Rajesh, A.S. Department of Instrumentation and Control Engineering St. Joseph's College of Engineering Chennai India Department of Information Science and Engineering Bangalore Institute of Technology Bangalore India Department of Computer Application IFTM University Moradabad India Department of CSE IFET College of Engineering Villupuram India Department of Management Studies KLE's IMSR BVB Campus Hubli India Department of Mechanical Engineering JSS S&T University Mysore India

The Internet of Things (IoT) foresaw a transparent network between the real and virtual worlds. IoT offers a promising potential to develop strong market and industrial applications. The export-oriented economy of industrial manufacturers relies on product and service creativity to retain a competitive advantage. The IoT-based applications of innovative manufacturing companies (IOTs), where most companies are involved and engaged in many of the case IoT-based applications, is motivated by computing capacity, networking, cost reduction and electronic components. A common motive is the Internet of Things applications and the improvement of after-sales service activities. The suggested Linear Product Design Regression (LPDR) algorithm classifies business product innovation design based on consumer input to support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. IoT offers one of the latest innovations in manufacturing to allow us to meet key challenges. The product design and development engineering are engaged in utilizing the Internet of Things (IoT) to create comprehensive policies and policy creation for manufacturing *** support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. To allow us to meet key challenges and to create comprehensive policies and policy creation for manufacturing companies. © 2021

关键词： Product design

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Secure Semantic Communication for Image Transmission in the Presence of Eavesdroppers

arXiv

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arXiv 2024年

作者： Tang, Shunpu Liu, Chen Yang, Qianqian He, Shibo Niyato, Dusit College of Information Science and Electronic Engineering Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore

Semantic communication (SemCom) has emerged as a key technology for the forthcoming sixth-generation (6G) network, attributed to its enhanced communication efficiency and robustness against channel noise. However, the open nature of wireless channels renders them vulnerable to eavesdropping, posing a serious threat to privacy. To address this issue, we propose a novel secure semantic communication (SemCom) approach for image transmission, which integrates steganography technology to conceal private information within non-private images (host images). Specifically, we propose an invertible neural network (INN)-based signal steganography approach, which embeds channel input signals of a private image into those of a host image before transmission. This ensures that the original private image can be reconstructed from the received signals at the legitimate receiver, while the eavesdropper can only decode the information of the host image. Simulation results demonstrate that the proposed approach maintains comparable reconstruction quality of both host and private images at the legitimate receiver, compared to scenarios without any secure mechanisms. Experiments also show that the eavesdropper is only able to reconstruct host images, showcasing the enhanced security provided by our approach. © 2024, CC BY.

关键词： Steganography

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