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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Felix Gliese Matthew G. S. Pearce Aaron Wadsworth Mohammad Siamaki James Storey Grant Lumsden Duleepa J. Thrimawithana Rod Badcock Joerg Roth-Stielow Martin Neuburger Faculty of Mobility and Technology Esslingen University Germany Dept. of Electrical Computer and Software Engineering The University of Auckland New Zealand OpenStar Technologies New Zealand Robinson Research Institute Victoria University of Wellington New Zealand Institute for Power Electronics and Electrical Drives University of Stuttgart Germany

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

In the quest for a zero-emissions future, the aviation sector is facing the challenge of reducing its carbon footprint. This paper presents a characterization of a superconducting homopolar motor designed to meet the stringent power and efficiency requirements of all-electric aircraft. The control system, which features field-oriented control and a multi-layer architecture, ensures precise operation of the motor under various load conditions. The motor uses a stationary cryogenic superconducting DC coil to generate the rotor’s magnetic field. Utilizing superconducting materials helps produce the high power densities critical for aviation. The characterization of the key parameters of the motor, such as inductances, stator resistance and flux, are presented. A description of the cryogenic GaN power electronic inverter power stage immersed in liquid nitrogen is provided. This design enables future integration next to the motor, and improved power density and efficiency. This paper outlines the design, implementation, and initial testing of the superconducting homopolar motor and its power electronics, highlighting its potential for electric propulsion in aviation by providing a viable solution for long-range flights.

关键词： Resistance power system measurements Electric potential Torque Density measurement Cryogenics Motors Control systems Inverters Mathematical models

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A Deployment Scheme of YOLOv5 with Inference Optimizations Based on the Triton Inference Server

A Deployment Scheme of YOLOv5 with Inference Optimizations B...

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IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Jiacong Fang Qiong Liu Jingzheng Li School of Software Engineering South China University of Technology Guangzhou China Zhuhai Unitech Power Technology Company Ltd

Object detection constitutes a large part of computer vision applications. You Only Look Once (YOLO) v5 is a salient object detection algorithm that provides high accuracy and real-time performance. This paper illustrates a deployment scheme of YOLOv5 with inference optimizations on Nvidia graphics cards using an open-source deep-learning deployment framework named Triton Inference Server. Moreover, we developed a non-maximum suppression (NMS) operator with dynamic-batch-size support in TensorRT to accelerate inference. The experimental results show that both throughput and latency are improved significantly through our deployment scheme.

关键词： Graphics Heuristic algorithms Conferences Object detection Throughput Real-time systems Inference algorithms

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SWT-UNet: Medical Image Segmentation Based on Multi-modality UNet with Sliding Window Transformer Block

SWT-UNet: Medical Image Segmentation Based on Multi-modality...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yangqianhui Zhang Dong Han Xiaoming Gang Jiajun Ma Xu Yuan School of Mechanical Engineering Zhejiang University Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Anshan Municipal Central Hospital Anshan China Shenhua Hollysys Information Technology Co. Ltd Beijing China School of Software Technology Dalian University of Technology Dalian China

Medical images are information carriers that visually reflect and record the anatomical structure of the human body, and play an important role in clinical diagnosis, teaching and research, etc. Modern medicine has become increasingly inseparable from the intelligent processing of medical images. In recent years, there have been more and more attempts to apply deep learning theory to medical image segmentation tasks, and it is imperative to explore a simple and efficient deep learning algorithm for medical image segmentation. In this paper, we conduct research on multi-modal medical image segmentation algorithms with a hybrid architecture of Convolutional Neural Networks and Vision Transformer. This paper proposes a multi-modal medical image segmentation model SWT-UNet based on the CNN-ViT hybrid framework. The self-attention mechanism and sliding window design of the visual Transformer are used to capture the global feature association and break the limitation of the receptive field caused by the inductive bias of the convolution operation. At the same time, a widened self-attentive vector is used to streamline the number of modules and compress the model size, so as to fit the characteristics of a small amount of medical data, which makes the model easy to be overfitted. Experiments on two multi-modal medical image datasets validate that the algorithm can achieve efficient medical image segmentation at a lightweight scale.

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Extraction of Winter Wheat Planting Area Based on Fused Active and Passive Remote Sensing Images 5

Extraction of Winter Wheat Planting Area Based on Fused Acti...

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5th International Conference on Computer Vision and Data Mining, ICCVDM 2024

作者： Zhang, Yichi Zhou, Qiongbo Cui, Tengfei Jiang, Chuande Wu, Wei Zhang, Keqian Zhang, Ziqian College of Artificial Intelligence and Automation Hohai University No.1915 Hahai Avenue Jintan District Changzhou213000 China University of Southern California Gould School of Law CA90089 United States Boston University Metropolitan College BostonMA02215 United States School of Water Conservancy North China University of Water Resources and Electric Power Zhengzhou450046 China College of Geomatics and Geoinfornation Guilin University of Technology Guilin541000 China College of Surveying and Land Information Engineering Henan Polytechnic University Jiaozuo454003 China School of Software Henan University of Science and Technology Luoyang471003 China

ISBN: (纸本)9781510682863

To obtain fast and accurate crop distribution information, the feasibility of using active remote sensing imagery (Sentinel-1A) and passive remote sensing image(Sentinel-2) to extract the winter wheat spatial distribution was analyzed. Firstly, Sentinel-1A winter wheat image at the whole growth stage were synthesized according to the phenology of winter wheat, and high quality Sentinel-2 images of winter wheat after overwintering were synthesized according to the normalized vegetation index(NDVI) time series curves of various types of features. Three classification schemes, Sentinel-1A images, Sentinl-2 images and fused Sentinel-1A and Sentinl-2 active-passive remote sensing images, were designed, and then winter wheat was classified according to the random forest algorithm on the Google Earth Engine (GEE) cloud platform. The results showed that, accuracy of the user and winter wheat producer accuracy based on Sentinel-1A images at the whole growth stage were 83.15% and 86.44% respectively, and there was more "pepper" noise in the extraction results;the user accuracy and winter wheat producer accuracy based onSentinl-2 images after overwintering was 87.98% and 84.75% respectively, and improved extraction accuracy compared with that of Sentinel-1A image at the whole growth stage, but the classification results were influenced by the "same spectrum of foreign matter", resulting in many misclassifications;the user accuracy and winter wheat producer accuracy with fused active and passive remote sensing images were 96.57% and 95.48%, respectively, compared with that of using only a single source of data, the accuracy of classification of winter wheat was improved to different degrees. © 2024 SPIE.

关键词： Optical remote sensing

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Research and development of two-factor authentication methods using neural networks

Research and development of two-factor authentication method...

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Temirkhan Darbek Assel Mukasheva Alibek Bissembayev Sergiy Gnatyuk Dinargul Mukammejanova Department of Information Systems and Cybersecurity Almaty University of Power Engineering and Telecommunications Almaty Kazakhstan School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan Faculty of Cybersecurity Computer and Software Engineering National Aviation University National Aviation University Kyiv Ukraine Department of Artificial Intelligence and Big Data Kazakh National University named after Al-Farabi Almaty Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

This article is devoted to the research and development of innovative two-factor authentication (2FA) methods using neural networks. 2FA plays a key role in ensuring the security of data and accounts in the modern digital world, and the use of neural networks opens up new prospects in strengthening protection and improving the user experience. The article presents an analysis of existing 2FA methods, identifies their limitations, and discusses the advantages of using neural networks in this area. The main focus is on the development of new authentication algorithms based on deep learning, providing effective anomaly detection and support for a dynamic adaptive approach. In the context of the article, the issues of privacy and data security are also considered, as well as the possibilities of applying the developed methods in various fields, including finance, medicine and the Internet of Things. The results of the study represent a significant contribution to the field of digital

关键词： Codes Face recognition Heuristic algorithms Data security Neural networks Authentication Chatbots

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A Pupil-Positioning Method Based on the Starburst Model

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Computers, Materials & Continua 2020年第8期64卷 1199-1217页

作者： Pingping Yu Wenjie Duan Yi Sun Ning Cao Zhenzhou Wang Guojun Lu School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Hebei Electric Power Research Institute Shijiazhuang050022China School of Internet of Things and Software Technology Wuxi Vocational College of Science and TechnologyWuxi214028China School of Science Engineering and ITFederation University AustraliaGippsland CampusChurchillAustralia.

Human eye detection has become an area of interest in the field of computer vision with an extensive range of applications in human-computer interaction,disease diagnosis,and psychological and physiological ***-tracking systems are an important research topic in the human-computer interaction *** one of the core modules of the head-mounted gaze-tracking system,pupil positioning affects the accuracy and stability of the *** tracking eye movements to better locate the center of the pupil,this paper proposes a method for pupil positioning based on the starburst *** method uses vertical and horizontal coordinate integral projections in the rectangular region of the human eye for accurate positioning and applies a linear interpolation method that is based on a circular model to the reflections in the human *** this paper,we propose a method for detecting the feature points of the pupil edge based on the starburst model,which clusters feature points and uses the RANdom SAmple Consensus(RANSAC)algorithm to perform ellipse fitting of the pupil edge to accurately locate the pupil *** experimental results show that the algorithm has higher precision,higher efficiency and more robustness than other algorithms and excellent accuracy even when the image of the pupil is incomplete.

关键词： Human eye localization ellipse fitting pupil contour pupil center

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Neutronic calculations of the China dual-functional lithium–lead test blanket module with the parallel discrete ordinates code Hydra

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Nuclear Science and Techniques 2020年第8期31卷 1-12页

作者： Guang-Chun Zhang Jie Liu Liang-Zhi Cao Hong-Chun Wu Xian-Bao Yuan Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China School of Nuclear Science and Technology Xi’an Jiaotong UniversityXi’an 710049China College of Mechanical and Power Engineering China Three Gorges UniversityYichang 443002China

The China dual-functional lithium–lead test blanket module(DFLL-TBM) is a liquid Li Pb blanket concept developed by the Institute of Nuclear Energy Safety technology of the Chinese Academy of Sciences for testing in ITER to validate relevant tritium breeding and shielding technologies. In this study, neutronic calculations of DFLL-TBM were carried out using a massively parallel three-dimensional transport code, Hydra, with the Fusion Evaluated Nuclear Data Library/MG. Hydra was developed by the Nuclear engineering Computational Physics Lab based on the discrete ordinates method and has been devoted to neutronic analysis and shielding evaluation for nuclear facilities. An in-house Monte Carlo code(MCX) was employed to verify the discretized calculation model used by Hydra for the DFLL-TBM calculations. The results showed two key aspects:(1) In most material zones,Hydra solutions are in good agreement with the reference MCX results within 1%, and the maximal relative difference of the neutron flux is merely 3%, demonstrating the correctness of the calculation model;(2) while the current DFLL-TBM design meets the operation shielding requirement of ITER for 4 years, it does not satisfy the tritium self-sufficiency requirement. Compared to the two-step approach, Hydra produces higher accuracies as it does not rely on the homogenization technique during the calculation process. The parallel efficiency tests of Hydra using the DFLL-TBM model also showed that this code maintains a high parallel efficiency on O(100) processors and, as a result, is able to significantly improve computing performance through parallelization. Parameter studies have been carried out by varying the thickness of the beryllium armor layer and the tritium breeding zone to understand the influence of the beryllium layer and breeding zone thickness on tritium breeding performance. This establishes a foundation for further improvement in the tritium production performance of DFLL-TBM.

关键词： Discrete ordinates method DFLL-TBM Neutronic analysis Tritium breeding performance

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Research on the Improved YOLOv5 Algorithm Fall Monitoring System for Home Care for the Elderly

Research on the Improved YOLOv5 Algorithm Fall Monitoring Sy...

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Computer Information Science and Application technology (CISAT), International Conference on

作者： Wanchao Xu Junchao Zhang Xinyu Zhang Fengxia Li Jinyi Tao Rongli Zhao College of Electrical and Power Engineering Taiyuan University of Technology Taiyuan Shanxi China Shanxi Provincial Engineering Research Center of Electric Drive and Internet of Things Taiyuan Shanxi College of Software Nanjing University Nanjing Jiangsu National Intelligent Lighting and Smart Wearable Technology Innovation Center Taiyuan Shanxi Province Engineering Technology Research Center of Smart City Lighting Data Sharing and Public Service Platform Based on Big DataShanxi Province Taiyuan Shanxi Province Research Institute of New Materials and Chemical Engineering Shanxi Zhejiang University Taiyuan Shanxi

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

In view of the risk of fall for the elderly in the home care environment, a surveillance video display fall detection method of the improved YOLOv5 algorithm is improved, and the Transformer module that increases the human attention mechanism is combined with the CSPDarknet structure of the backbone part of the YOLOv5 algorithm. The system collects video data through the camera, trains and verifies the video data, and then judges the action and posture of the target person. It connects the WIFI module of Orange School to send the action and posture information to the guardian’s mobile phone to remind the guardian to timely treat the person who fell down, so as to improve the rescue efficiency. On the basis of improving the algorithm structure, the loss function was improved and conducted repeated training. The experimental results showed that the improved algorithm mAP improved by 2.8%, the $F 1$ score by 2.2%, the number of processing frames per second by 10% during inference, and achieved the fall detection accuracy of 98.6%.

关键词： YOLO Training Accuracy Attention mechanisms Surveillance Transformers Inference algorithms Older adults Fall detection Wireless fidelity

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Electric Field Distribution Characteristics of Novel Fiber Sensor with Semiconductor Light Source 2

Electric Field Distribution Characteristics of Novel Fiber S...

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2nd IEEE International Conference on Electronic technology, Communication and Information, ICETCI 2022

作者： Jie, Huang Ni, Pengcheng Shuo, Chen Zhao, Xuyang Global Energy Internet Research Institute Changping Beijing China Elec. Pwr. Intelligent Sensing Technology and Application of State Grid Corporation Joint Laboratory Beijing China Anhui Jiyuan Software Co. Ltd Sgitg Hefei China North China Electric Power University School of Electrical and Electronic Engineering Changping Beijing China

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

The light source element is an important part of the optical fiber sensor, and the selection of a suitable and reliable light-emitting element is critical factor for accurately measuring the parameters. The structural optimization of the optical fiber sensor semiconductor light source device is based on obtaining a more accurate field distribution. This paper analyzes the electric field distribution characteristics of a novel optical fiber sensor with semiconductor light source device by finite element method (FEM). The rationality of the electromagnetic shielding design of the model is verified by analyzing electric field distribution of the semiconductor light source device under different electromagnetic radiation conditions. © 2022 IEEE.

关键词： Finite element method

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Two-Layer Smart System to Predict HIV Risk 17

Two-Layer Smart System to Predict HIV Risk

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17th International Conference on Sensing technology, ICST 2024

作者： Nguyen, Trinh Duc Minh Le, Viet Tien Nguyen, Mai Hoang Long Nguyen, Phuc Hau Van Nguyen, Thi Phuoc Hanoi-Amsterdam High School Hanoi Viet Nam Hanoi University of Industry HAUI Faculty of Electronic Engineering Hanoi Viet Nam PT Automotive-AIS FPT Software Ho Chi Minh Viet Nam Electric Power University Faculty of Information Technology Hanoi Viet Nam Thanh Do University Department of Information Technology Hanoi Viet Nam

ISBN: (纸本)9798350374827

In this work, a two-layer privacy protection system was proposed for HIV risk prediction. The system involves various clinic centres, each using its data to build a model to predict HIV risk. The models from each clinic are gathered at a central server/cloud to create a global model. To protect the individual clinic models, they are encrypted before being sent to the server. At the server, the encrypted models are aggregated to create the global model without decryption. The encrypted global model is then distributed to each clinic for use. This method ensures that patient information from each clinic is protected by two layers: modelling and encryption. We used data from Vietnam 2005 to validate the proposed framework. The performance of the proposed method is superior the local model in terms of accuracy and AUC (Area Under The Curve). The enhancement is larger than 10% in comparison with recent works. © 2024 IEEE.

关键词： digital health federated learning HIV prediction homomorphic encryption machine learning

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