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High Temperature Properties of CO2 Gas Mixture with O2 and HFO-1234yf decompositions: Predominant Molecular Products and Critical Electric Field Strength at 300–3000 K

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IEEJ Transactions on Power and Energy 2021年第11期141卷 700-711页

作者： Yokomizu, Yasunobu Kodama, Naoto Sugita, Takehiro Ishii, Tatsuya Sato, Motohiro Nakamura, Yasunori Department of Electrical Engineering Nagoya University Furo-cho Chikusa-ku Nagoya464-8603 Japan Advanced Technology R&D Center Mitsubishi Electric Corp. 8-1-1 Tsukaguchi-Honmachi Amagasaki661-8661 Japan

The gases used in high voltage electric power equipment are required to withstand against an externally applied electric field strength. It is therefore essential to find out the electrical insulation strength of the gas. However the high temperature gas formed after the current zero through the arc extinction process in a circuit breaker shows different insulation strengths from that at room temperature, because the high temperature gas consists of various molecular products different from the original gas at room temperature. The CF3CFCH2 (called as HFO-1234yf) at room temperature is known to have a remarkable electrical insulation property similar to SF6. After describing the products generated in CO2 gas mixture with O2 and HFO-1234yf decompositions at temperatures below 3000 K, this paper mentions dielectric breakdown properties of the mixtures in view of the molar fractions of the molecular products. discussions are furthermore made to compare dielectric breakdown properties of CO2 gas and CO2/O2 gas mixture at high temperatures. © 2021 The Institute of Electrical Engineers of Japan.

关键词： Sulfur hexafluoride

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Fabrication of Hollow Fiber Nanofiltration Membrane with Poly(vinyl Chloride) Substrate for the removal of Chemicals of Concern

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Industrial and Engineering Chemistry research 2023年第43期62卷 17914-17922页

作者： Liu, Sihua Shi, Le Wang, Xuan Hung, Wei-Song Zhang, Jianhua Lu, Xiaolong Lee, Kueir-rarn Wu, Chunrui Gao, Li State Key Laboratory of Separation Membranes and Membrane Processes School of Material Science and Engineering Tiangong University Tianjin300387 China Advanced Membrane Materials Research Center Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei10607 Taiwan Institute for Sustainable Industries and Liveable Cities Victoria University MelbourneVIC8001 Australia R&D Center for Membrane Technology Department of Chemical Engineering Chung Yuan University Taoyuan32023 Taiwan South East Water Corporation SeafordVIC3198 Australia

A hollow fiber nanofiltration (NF) membrane fabricated on poly(vinyl chloride) (PVC) substrate was studied for the removal of chemicals of concern (CoCs). The functional layer of the hollow fiber NF membrane was fabricated via interfacial polymerization of piperazine (PIP) and trimesoyl chloride (TMC) on the lumen side of the PVC ultrafiltration membrane. A gas sweeping method was employed during the fabrication of the NF functional layer and was assessed based on the separation performance of the NF membrane. It was found that the sweeping gas could increase the performance of the NF membrane, which is also affected by the concentration of PIP and TMC solutions. NF membranes prepared under optimized gas sweeping conditions had a Pb(NO3)2 rejection of 93% and a flux of about 24 L·m-2·h-1. The rejections to Na2SO4, MgSO4, NaCl, glucose, and sucrose were 96.5, 95.3, 53.7, 91.7, and 98.2%, respectively. Moreover, the results of spike tests show that the NF membrane could effectively reject the incident CoCs up to eight times the regulated values. © 2023 American Chemical Society.

关键词： Sodium chloride

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Cloud-Based Intelligent Patient Triage Systems with SVM Classifiers for Emergency departments 1

Cloud-Based Intelligent Patient Triage Systems with SVM Clas...

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1st International Conference on Innovations in Communications, Electrical and Computer Engineering, ICICEC 2024

作者： Shibu, N.V. Sujatha, M. Suresh, M. Xavier Sindhuja, K. raj, T. Saju Srinivasan, C. SRM Institute of Science and Technology Department of Computing Technologies School of Computing Kattankulathur Tamil Nadu Chennai India Easwari Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vit Bhopal University Department of Physics School of Advanced Sciences and Languages Madhya Pradesh Bhopal India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

Efficient patient triage in emergency departments is crucial for the timely and successful allocation of resources and treatment in the constantly changing healthcare environment. To improve patient triage systems, this research suggests a new strategy that makes use of cloud computing and support vector machine (SVM) classifiers. The proposed solution endeavors to enhance patient outcomes in emergency departments by optimizing resource allocation via the use of cloud infrastructure's scalability and accessibility in conjunction with SVM algorithms' strong classification capabilities. Quickly classifying patients according to severity, the system integrates and analyzes data in real-time, allowing healthcare providers to prioritize high-risk situations and distribute resources appropriately. Healthcare practitioners can work together more efficiently and share patient data with cloud-based architecture, which promotes a more comprehensive approach to treatment. results from the experiments prove that the system works as advertised and can be easily scaled up, highlighting its revolutionary potential to change the way emergency healthcare facilities handle patient triage. The growing body of evidence demonstrates the value of intelligent systems for healthcare delivery optimization and emergency patient care improvement via the use of technology. © 2024 IEEE.

关键词： data-driven triage Emergency medicine Healthcare efficiency Healthcare optimization real-time decision support resource management

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Optimizing dry eye disease diagnosis: Integrating deep learning insights from OSdI questionnaires and ocular blinking videos

Optimizing dry eye disease diagnosis: Integrating deep learn...

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2023 International Conference on Computer Application and Information Security, ICCAIS 2023

作者： Wang, Mini Han Lin, Zhiyuan Zeng, Peijin Fang, Xiaoxiao Yu, Yang Hu, Wenhan Liu, Yunxiao Liu, Haoyang Li, Xinyue Jiang, Xudong Chen, Guangshun Zhou, ruoyu Hou, Guanghui Chong, Kelvin K.L. Fang, Junbin The Faculty of Medicine Chinese University of Hong Kong 999077 Hong Kong City University of Macao The Faculty of Data Sciences 999078 China Guangdong Zhuhai519000 China AI and big Data R&D Department Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Guangdong Zhuhai519000 China AI and Ophthalmology R&D Laboratory Perspective Technology Group Guangdong Zhuhai519000 China Beijing Institute of Technology Guangdong Zhuhai519000 China The Department of Dry Eye Disease Zhuhai Aier Eye Hospital Zhuhai519000 China School of Biomedical Engineering Guangdong Medical University Guangdong Dongguan523000 China Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Guangdong Zhuhai519000 China School of Optometry and Ophthalmology Tianjin Medical University Tianjin300000 China Department of Optoelectronic Engineering Jinan University Guangdong Guangzhou510000 China

ISBN: (纸本)9781510675032

This study introduces a novel and comprehensive diagnostic approach for dry Eye disease (dEd) by combining a dedicated Ocular Surface disease Index (OSdI) questionnaire and a measurement system tailored for Chinese citizens with the implementation of the YOLOv8 deep learning model. The research involves the analysis of 52 real-world ophthalmic videos to detect eye blinking conditions, with the model trained to identify abnormal blinking patterns through feature extraction such as blink frequency, duration, and irregularities. Performance metrics, including mean Average Precision (mAP), specificity, recall, f1-score, and Frame Per Second (FPS), are measured on a PC (CPU, Core i5-10500H) with an input size of 640*640. The integration of these deep learning methods, utilizing both subjective OSdI questionnaires and objective ocular blinking videos, signifies a groundbreaking approach that enhances diagnostic accuracy for dEd. The study anticipates transformative effects on dEd diagnosis and improved patient outcomes as technology advances. Additionally, the research team introduces a user-friendly system for dry eye detection, named the "AI dry Eye Analytic System," accessible at the UrL "***," demonstrating the practical implementation of the developed methodologies. © 2024 SPIE.

关键词： Object detection

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High Speed Neural Network MPPT Algorithm for dFIG Based Wind Energy Conversion System 7

High Speed Neural Network MPPT Algorithm for DFIG Based Wind...

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

作者： Tharwin Kumar, r. Balaji, V. Sakthidhasan, K. Gomathi, S. Sreedhar, r. Janaki, N. Puducherry Technological University Department of Electrical and Electronics Engineering Puducherry India MAI-NEFHI College of Engineering and Technology Faculty of Electrical and Electronics Engineering Asmara Eritrea Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electrical and Electronics Engineering Chennai India St. Joseph's College of Engineering Department of Electrical and Electronics Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India Vels Institute of Science Technology and Advanced Studies Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350372816

For the generation of electrical energy, breeze energy is a trustworthy and talented renewable vitality basis. An innovative proposal with many scientific and economic benefits is using wind source technology to charge electric vehicles. Worldwide, the market for Wind Energy Conversion System (WECS) has been expanding significantly. High-quality output power is commonly produced when wind energy demand grows. WECS has significant modelling, control, and grid integration issues as a result of the intermittent nature of wind energy. A proper controller mechanism has to be created in order to significantly improve WECS. An innovative method for maximizing the power of wind energy founded on radial Basis Function Neural Networks (rBFNN) is proposed in this study to increase the power of double Fed Induction Generators (dFIG). dFIG based wind turbine, and to provide outstanding dynamic performance in response to breeze rapidity fluctuations. MPPT is a strong algorithm that operates around an ideal rotating speed by utilizing rBFNN for mechanical speed control. We evaluated the performance of dFIG founded WECS with MATLAB/Simulink. The rBFNN efficiency is 91% when comparing MPPT controllers. © 2024 IEEE.

关键词： radial basis function networks

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Analysis of tool wear in micromilling Ti6Al4V and its impact on generated surface integrity

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Wear 2025年 570卷

作者： Gonçalves, Maria Clara Coimbra Alsters, rob Curtis, david Bushlya, Volodymyr M'Saoubi, rachid Ghadbeigi, Hassan University of Sheffield Mechanical Engineering Department Mappin Street Sheffield United Kingdom R&D Materials and Technology Development Seco Tools AB Zandterweg 14 Lottum5973 RC Netherlands Advanced Manufacturing Research Centre University of Sheffield RotherhamS60 5TZ United Kingdom Lund University Ole Römers väg 1 Lund221 00 Sweden R&D Materials and Technology Development Seco Tools AB Fagersta737 82 Sweden

Tool wear is one of the dominant factors that impacts the surface integrity of machined materials. Understanding the impact of tool wear on surface integrity is crucial in micromilling, due to the size of the features being machined and the cutting tools. Therefore, this work aims to analyse the impact of tool wear on surface integrity during the micromilling of Ti6Al4V. In this context, microslots were manufactured on a Ti6Al4V workpiece using both coated and uncoated 1 mm flat end mills at constant cutting parameters with varying cutting lengths in order to analyse the progression of tool wear with machined length and its impact on surface integrity. The microtools were investigated using both Scanning Electron Microscope (SEM) and infinite focus optical imaging (Alicona) to determine the tool wear evolution. Surface integrity was assessed by analysing surface roughness areal parameters, surface microscope images, and subsurface microstructure and microhardness perpendicular to the cutting direction. The results show that surface quality was not affected by the evolution of tool wear, with surface topography, including surface roughness parameters, remaining within a similar range until the catastrophic failure of the tool. Analysis of the machined surface revealed small chips adhered to it, which affected the surface texture height measurements, leading to a predominance of atypical peaks on the surface. Subsurface analysis of the machined material showed that the microstructure and microhardness remained consistent with the bulk material characteristics, indicating no evidence of severe plastic deformation in the machined subsurface. However, once the tool failed and began rubbing against the workpiece surface, swept grains due to material dragging and heat-affected zones were observed in the subsurface microstructure. Swept grains, caused by material extrusion, were also observed in the microstructure of the top burr formation regions throughout the experiments

关键词： Titanium alloys

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A New System-level Fault diagnosis Method for Train Transmission Systems Based on Graph Learning 15

A New System-level Fault Diagnosis Method for Train Transmis...

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15th IEEE Global reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Xie, Yuhan Wang, Biao Wang, Chao Qin, Yong Wang, Lianfu Liang, Liang Liu, Xiao School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China Kunming Vehicle Section China Railway Kunming Group Co. Ltd. Kunming650011 China New Technology Research Department of R&D CRRC ChangChun Railway Vehicles Co. Ltd. Changchun China

ISBN: (纸本)9798350354010

Efficient and reliable fault diagnosis of train transmission systems is crucial for ensuring the safety of train operations. With the advancements in big data and deep learning technologies, intelligent fault diagnosis techniques have made many achievements. However, a significant limitation in existing related research is the development of separate diagnostic models for each component, which neglects the mutual coupling relationships among different components and the propagation and interaction of faults. This oversight significantly hampers the overall diagnostic accuracy of these models. To address the aforementioned problem, a novel system-level fault diagnosis method is proposed for train transmission systems. In the proposed method, convolutional neural networks are first employed to obtain low-dimensional node attributes. Then, the graph attention mechanism is introduced for feature fusion by incorporating the spatial relationships between components, enabling the identification of the health status of each node. The efficacy of the proposed method is validated by designing and carrying out fault simulation experimental of the bogie transmission system at Beijing Jiaotong University. Experimental results demonstrate a significant improvement in diagnostic accuracy for each component node, with only a minimal increase in parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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remaining Useful Life Prediction of Bearings Based on Jensen-Shannon divergence and dynamic Multi-Time Scale Aggregation 15

Remaining Useful Life Prediction of Bearings Based on Jensen...

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15th IEEE Global reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Niu, Yingcheng Wang, Biao Wang, Chao Qin, Yong Shao, Junjie Liu, Jing Wang, Jialu School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China Kunming Vehicle Section China Railway Kunming Group Co. Ltd. Kunming650011 China New Technology Research Department of R&D CRRC ChangChun Railway Vehicles Co. Ltd. Changchun China

ISBN: (纸本)9798350354010

remaining useful life (rUL) prediction is a key research task in intelligent maintenance of mechanical equipment. However, there are still shortcomings in bearings rUL prediction: 1) due to the different degradation stages of rolling element bearings, health indicators (HI) also exhibit different degradation characteristics. However, the two-stage nature of the HI is often overlooked in existing research. 2) Model-based methods in current use typically employ a single model to describe the bearing degradation process, making it difficult to comprehensively describe the various potential degradation trends of bearings in the future. To deal with the aforementioned issues, this study constructs a health indicator, named HI-JS, using the self-organizing map (SOM) clustering and Jensen-Shannon (JS) divergence methods. The effectiveness of HI-JS is validated using comprehensive evaluation criterion. A new bearings rUL prediction process is proposed using JS divergence and dynamic multi-time scale aggregation for bearings rUL prediction. The proposed approach is evaluated using XJTU-SY bearing datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving the accuracy of rUL prediction of rolling element bearings. © 2024 IEEE.

关键词： Prediction models

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Macro Modeling of High Frequency dielectric Barrier discharge

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IEEJ Transactions on Fundamentals and Materials 2022年第3期142卷 52-58页

作者： Tamida, Taichiro Iwabuki, Hiroyasu Iwata, Akihiko Department of Electrical and Electronic Engineering Faculty of Engineering Osaka Electro-Communication University 18-8 Hatsu-cho Neyagawa572-8530 Japan Advanced Technology R&D Center Mitsubishi Electric Corp. 8-1-1 Tsukaguchi-Honmachi Amagasaki661-8661 Japan Department of Electronics Information and Communication Engineerng Osaka Sangyo University 3-1-1 Nakagaito Daito574-8530 Japan

We have developed a macro model that focuses only on electrical characteristics to handle the discharge phenomenon as a circuit. We considered the discharge as a kind of non-equilibrium system and modeled the transient changes in the discharge impedance. We also examined how to fix these model parameters from measurable values. We applied it to the high frequency dielectric barrier discharge and obtain sufficient agreement with the measurement results. We think that this model can be applied, not only to discharge phenomena but also as a method for circuit-modeling a general physical phenomenon in which the state changes transiently in a non-equilibrium system. © 2022 The Institute of Electrical Engineers of Japan.

关键词： dielectric barrier discharge

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Mobile robot Tracking Method Based on Improved YOLOv8 Pedestrian detection Algorithm

Mobile Robot Tracking Method Based on Improved YOLOv8 Pedest...

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Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM), International Conference on

作者： Yuecong Zhu Tianjie Zhong Yu Wang Jing Kan Fangyan dong Kewei Chen Faculty of Mechanical Engineering & Mechanics Ningbo University Ningbo China R&D Department China Academy of Safety Science & Technology Beijing China Advanced Institute of Information Technology Peking University Beijing China

Human following of mobile robot is a classical application field in the category of human-computer interaction. To address the problem of unstable following of the target in complex scenes, a method of moving robot target following (YOB-CF) based on YOLOv8CSM-tiny and BKPs-CF (Correlation Filtering of Body Key Points) algorithm is proposed. Firstly, based on the YOLOv8 Network, the backbone network is replaced by Multi-Head Attention under this framework, which focuses on improving the detection accuracy of human target recognition in complex scenes, while lightening the model, the model reasoning speed and parameters are reduced to meet the deployment requirements of mobile robot platform. Then the BKPs-CF algorithm based on kernel correlation filter (KCF) is used to fuse the features of body key points, and the key points are updated by optical flow method under the condition of target occlusion, to ensure that the robot can still track the target stably under the occluded condition. Finally, the quantitative experiments are carried out on the self-built test set and the public dataset, and the target-following test and performance evaluation of the mobile robot are completed in the real scene, experimental results show that the proposed method is robust and real-time.

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