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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Xinwen Liu Jing Wang S. Kevin Zhou Craig Engstrom Shekhar S. Chandra School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia The Commonwealth Scientific and Industrial Research Organisation Canberra Australia Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) School of Biomedical Engineering & Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Human Movement and Nutrition Sciences The University of Queensland Brisbane Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Deep neural networks have been widely studied to predict a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables, and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of various modalities may not always be of high quality, and each modality may have only partial information of medical condition. Thus, predictions from different modalities can be in conflict, and the final prediction may fail in the presence of such a conflict. Therefore, it is important to account for the reliability of each source data and the prediction output when making a final decision. In this paper, we propose an evidence-aware multimodal data fusion framework based on the Dempster-Shafer theory (DST). The backbone models contain an image branch, a non-image branch and a fusion branch. For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch. The output probabilities along with the evidence scores from multiple branches are combined with the Dempster's combination rule to make a final prediction. Experimental results on the public OA initiative (OAI) dataset for the TKR prediction task show that the proposed method has better performance by accounting for conflicts from various modalities.

关键词： Medical conditions Soft sensors Digital images Data integration Imaging Artificial neural networks Reliability theory Feature extraction Reliability engineering Knee replacement

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Speed Planning and Interpolation Algorithm of High-Speed Nurbs Curve Based on an Elliptical Arc Fitting

SSRN

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SSRN 2022年

作者： Liu, Qingjian Zhang, Xu Dong, Xiaoyu Guan, Zenghan Liu, Zhigang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China

Taking advantage of the gradual change characteristic of the curvature of the conventional conic ellipse, this paper proposes a method of fitting a non-uniform rational basis spline (NURBS) curve with an elliptical arc and, thereafter, performing speed planning and interpolation of the elliptical arc itself. First, the method of elliptical arc fitting is introduced and then proven by simulation with comparative previous linear and arc fittings to obtain better fitting accuracy with fewer segments. Subsequently, the tangent vector of any point of an ellipse is used to complete the speed planning and interpolation of that ellipse. In terms of speed planning, the maximum acceleration of any point in the elliptical arc can be obtained by using a tangent vector, and the time speed curve in this work is completed based on this phenomenon. In the interpolation stage, the longest axis of the current point is determined by using the tangent vector of any point of the elliptical arc, according to which the pulse is sent, thereby completing the interpolation operation via this iterative cycle. This method unifies the processing model of the interpolation algorithm, the simplicity and efficiency of which is verified by subsequent simulation data. This method can also be universally applied to most other types of curves processing. © 2022, The Authors. All rights reserved.

关键词： Interpolation

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Simulation of Nuclear Reactor Accident Scenarios using Physics-Informed Neural Networks and Transfer-Learning

Simulation of Nuclear Reactor Accident Scenarios using Physi...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International Conference on

作者： Yang Yu Yufei Xie Wenlin Wang Guohua Wu Haitao Lan Enbo Lin Ping An Zibin Sun Haichuan Zhang Yixian Wu National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan China Nuclear Power Institute of China Chengdu China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the power of Monte Carlo Sampling and Physics-Informed Neural Networks (PINNs) to simulate and effectively address the challenging Loss of Coolant Accidents (LOCA) in nuclear reactors. In the event of a LOCA, reactor core temperatures can soar rapidly, posing a significant threat to fuel integrity and potentially leading to the release of radioactive materials. By leveraging the strengths of both Monte Carlo Sampling and PINNs, this approach aims to provide a comprehensive and accurate simulation framework for assessing and mitigating the consequences of such accidents. The method yields high prediction accuracy (MAE: 0.033, RMSE: 0.098, R2: 0.814) and demonstrates robustness through transfer learning, maintaining strong performance (MAE: 0.064, RMSE: 0.163, R 2 : 0.735).

关键词： Radioactive materials Accuracy Monte Carlo methods Transfer learning Neural networks Robustness Data models Fuels Inductors Accidents

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Road Damage Detection and Classification Based on Multi-Scale Contextual Features

Road Damage Detection and Classification Based on Multi-Scal...

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IEEE International Conference on Big Data

作者： Lu Yang Hualin He Tao Liu Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin China

ISBN: (纸本)9781665480468

Road damage detection is particularly essential for improving road traffic safety. Existing methods do not perform well due to complex road conditions and sparse features contained in road damage. Therefore, we propose a new road damage detection network - YPLNet, which could obtain more refined multi-scale contextual features. Specifically, the network proposed in this paper is based on YOLOv5, by adding the PSA module to the backbone network, the spatial information of the feature map at multi-scale is obtained to enrich the feature space, and the long-term information dependence between channels is further constructed. In addition, in the feature fusion stage, the LCFI module is utilized to obtain contextual features under the multi-scale receptive fields, and these contextual features include rich low-level and high-level information. Our proposed network achieves a Mean-F1 Score of 0.635 in the competition. The source code and model are publicly available at https://***/hehualin-tut/YPLNet

关键词： Roads Source coding Big Data Feature extraction Data models Safety Context modeling

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Digital Mahjong System: Towards Precise Cognitive Assessment with IoT Technologies 1

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24th International Conference on Human-Computer Interaction, HCII 2022

作者： An, Ning Hu, Enze Guo, Yanrui Yang, Jiaoyun Au, Rhoda Ding, Huitong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shenzhen Corecloud Innovation Technology Co. Ltd. Shenzhen China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Department of Anatomy and Neurobiology Neurology and Framingham Heart Study Boston University School of Medicine Boston United States Department of Epidemiology Boston University School of Public Health Boston United States

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

To the best of our knowledge, this paper is the first to apply IoT technologies to transform the popular Mahjong game into a Digital Mahjong System (DMS) for digitally performing cognitive assessments. People have started exploring digital cognitive assessment tools for better objectivity and simplification. However, most of these tools are not friendly for older adults. This paper aims to address this issue by integrating IoT technologies with Mahjong to make cognitive assessment simpler and more engaging for older adults. DMS has the following features: 1) integrating motion tracking devices into Mahjong tiles and transferring them into Digital Mahjong Tiles (DMTs), which can precisely capture their moving trajectory during the assessment, 2) using a Near Field Communication reader to configure DMT’s primary data, 3) supporting Over-The-Air Device Firmware Update that can be done remotely via Bluetooth, and 4) developing a charging base that can charge 16 DMTs simultaneously using Qi wireless charging standard. With further testing, the DMS has the potential to become a digital platform to implement and test various cognitive assessment tools integrating Mahjong elements. © 2022, Springer Nature Switzerland AG.

关键词： Internet of things

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Non-contact Physiological Parameters Detection Based on MTCNN and EVM 5th

Non-contact Physiological Parameters Detection Based on MTCN...

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5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020

作者： Zhao, Jinfa Zhang, Wenchang Chai, Rongxuan Wu, Hang Chen, Wei Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin China Institute of Medical Support Technology Academy of Military Sciences of Chinese PLA Tianjin China

ISBN: (纸本)9789811623356

Non-contact physiological parameters detection has become an important but challenging task. This paper proposes a novel framework for Non-contact physiological parameters detection, which is based on MTCNN (Multi-task convolutional neural network) and EVM (Eulerian Video Magnification). The MTCNN is applied to compensate negative effects of the pseudo motion for improving the efficiency of face detection. The Laplacian pyramid of EVM is added to our framework to perform motion amplification to achieve simultaneous extraction of heart rate and respiration rate, which improves the accuracy of non-contact physiological parameter detection. Then we use FFT to analyze the relevant frequency bands in time domain and calculate the heart rate and breathing rate. In order to improve efficiency, GPU acceleration is implemented under the TensorRT framework. The experimental results based on data sets DUSS (detection under slight shaking) and DUO (detection under occlusion) show the effectiveness of this method. The average error of the heart rate and respiration rate are −0.1 and 0.6, respectively. Our method achieves comparable performance to finger clip oximeter with a good consistency, even in the case of wearing a mask. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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Optimization of experiment teaching in the course of mechanical principle 3

Optimization of experiment teaching in the course of mechani...

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3rd International Conference on Education Technology Management, ICETM 2020

作者： Hao, Pujun Liu, Jingna Ma, Wenpeng Wu, Ruiqin Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China Tianjin Key LABORATORY for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9781450388757

Based on experience of former teaching reformation and philosophy of "Emerging Engineering Education", an experimental teaching reformation program that includes restructured knowledge system, modularization of experimental teaching process and diversified evaluation system is proposed. It is found that students have deeper and comprehensive understanding on knowledge and more enthusiasm in experimental process. Finally students have definite self-cognition about their division of labor in teamwork. Through gathering statistics, the effectiveness of reformation program is verified and this program can enrich practical experience of experimental teaching reformation. © 2020 ACM.

关键词： Optimization

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An accurate stop control method of high-speed rolling machine for automobile production line using laser ranging and two positon close loop

An accurate stop control method of high-speed rolling machin...

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2020 International Conference on Applied Mechanics and Mechanical Engineering, ICAMME 2020

作者： Qiao, Zhifeng Ge, Weimin Zhang, Haikang Wei, Huiyong Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology China NO.591Changjiang Road Nankai DistrictTianjin China

The high-speed rolling machine is a key equipment in the automatic production line of automobile body-in-white welding. This article focuses on analyzing a method for accurately stopping the high-speed rolling machine using laser distance measurement. Firstly, through the analysis of the movement process, the calculation method of the length of each period in the 7-stage S-type acceleration and deceleration in the movement of the high-speed rolling machine is given;secondly, the system control theory model of the measurement and control system is established to analyze the dual-position closed-loop strategy The method of realizing the accurate stopping of the high-speed rolling machine;finally, the accurate stopping control method of the high-speed rolling machine proposed in this paper was verified by building an experimental system and developing the corresponding control program. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Speed

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A Dual-band Monopole Antenna with EBG for Wearable Wireless Body Area Networks

引用

Applied Computational Electromagnetics Society Journal 2021年第1期36卷 48-54页

作者： Wang, Chao Zhang, Liang Wu, Shenbing Huang, Shijie Liu, Changqing Wu, Xianliang Key Lab of Ministry of Education of Intelligent Computing & Signal Processing Anhui University Hefei City China AnHui Province Key Laboratory of Simulation and Design Electronic Information System Hefei Normal University Hefei City China

This paper proposes a dual-band wearable monopole antenna adopting an electromagnetic band-gap (EBG) structure, which operates at 2.45 and 5.8 GHz ISM bands and is suitable for wearable applications. Both the monopole antenna and the EBG structure are fabricated on an F4B semi-flexible substrate having a dielectric constant of 2.2. The EBG structure effectively isolates the human body from the radiation of the antenna and reduces the specific absorption rate (SAR) of it by more than 97.5%. This improves the antenna gain and the peak gain reaches 9.1 dBi at 5.8 GHz. The wearable performance of the antenna showed that it can sustain good performance even under realistic human body loading. Besides, the antenna has a small size, which makes it ideal for wearable applications. © ACES

关键词： Monopole antennas

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

arXiv

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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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