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30th International Conference on Nuclear engineering, ICONE 2023

作者： Ma, Yugao Bao, Hui Lu, Jiahao Ding, Shuhua Zhang, Meng Yang, Xiaolong Deng, Jian Zhang, Muhao Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu610059 China

ISBN: (纸本)9784888982566

The Helium-Xenon (He-Xe) gas-cooled reactor uses a He-Xe gas mixture to remove the heat from the reactor core. It has the characteristics of a long life-cycle, simple operation, and high power-mass ratio, which has an excellent application prospect in the field of Space Reactors Power systems. The high operation temperature distinguishes the He-Xe gas-cooled reactor from the traditional reactor in reactivity feedback characteristics. Except for the Doppler effect of the fuel and monolith, the thermal expansion also introduces reactivity feedback during reactor operation. In this work, reactivity feedback characteristics, steady-state characteristics, and transient characteristics of a hundred-kilowatt-level He-Xe gas-cooled reactor (Submersion-Subcritical Safe Space reactor, S4) were analyzed. The Doppler feedback characteristics and thermal expansion feedback coefficients of fuel, matrix, and reflector from the cold to the hot state of the S4 reactor core were calculated based on the reactor Monte Carlo code (RMC). The results show that the S4 reactor core is mainly affected by the fuel Doppler effect and the matrix thermal expansion effect. Furthermore, the temperature distributions of fuel and He-Xe gas of the S4 reactor were calculated using the He-Xe reactor transient analysis code (HXRTRAN). The transient characteristics of the S4 reactor core when different reactivity was introduced were analyzed. This study may provide a reference for the operation state of the He-Xe gas-cooled reactor. © 2023 by JSME.

关键词： Helium

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Design and Control of Continuous Jumping Gaits for Humanoid Robots Based on Motion Function and Reinforcement Learning

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Procedia Computer Science 2024年 250卷 51-57页

作者： Zida Zhao Shilong Sun Haodong Huang Qingbin Gao Wenfu Xu State Key Laboratory of Robotics and System (HIT) Harbin Institute of Technology Harbin Heilongjiang China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen 518055 China

Continuous jumping of humanoid robots is a challenging problem that requires appropriate gait and force control for the robot to achieve. Reinforcement learning (RL) has been applied to the motion control of humanoid robots and has achieved significant progress. However, traditional RL control methods primarily rely on designing reward signals to generate human-like movements, and it requires developing complex reward signals for continuous jumping, resulting in limited actions and suboptimal jumping performance. To address this issue, we have designed motion functions for humanoid continuous jumping and incorporated them into the RL framework to obtain desired human-like continuous jumping actions. Furthermore, two different jumping gaits are designed to investigate the jumping effects produced by different jumping functions: bent-knee jumping (BJ) and straight-knee jumping (SJ). BJ is a typical jumping style where the legs flex during the jumping process to accumulate energy, enabling higher explosive power. SJ is more suitable for tasks requiring quick responses, as the legs remain upright during the jump, providing faster reaction speed. The performance in continuous jumping tasks has been evaluated by comparing the effects of training with different jumping functions. This research has particular guidance for improving the jumping ability of robots and enhancing the stability and efficiency of humanoid continuous jumping.

关键词： Continuous jumping gaits Humanoid robots Motion function Reinforcement learning

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Unified-IoU: For High-Quality Object Detection

arXiv

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arXiv 2024年

作者： Luo, Xiangjie Cai, Zhihao Shao, Bo Wang, Yingxun School of Automation Science and Electrical Engineering Beihang University China Institute of Unmanned System Beihang University China

Object detection is an important part in the field of computer vision, and the effect of object detection is directly determined by the regression accuracy of the prediction box. As the key to model training, IoU (Intersection over Union) greatly shows the difference between the current prediction box and the Ground Truth box. Subsequent researchers have continuously added more considerations to IoU, such as center distance, aspect ratio, and so on. However, there is an upper limit to just refining the geometric differences;And there is a potential connection between the new consideration index and the IoU itself, and the direct addition or subtraction between the two may lead to the problem of"over-consideration". Based on this, we propose a new IoU loss function, called Unified-IoU (UIoU), which is more concerned with the weight assignment between different quality prediction boxes. Specifically, the loss function dynamically shifts the model’s attention from low-quality prediction boxes to high-quality prediction boxes in a novel way to enhance the model’s detection performance on high-precision or intensive datasets and achieve a balance in training speed. Our proposed method achieves better performance on multiple datasets, especially at a high IoU threshold, UIoU has a more significant improvement effect compared with other improved IoU losses. Our code is publicly available at: https://***/lxjdrifter/UIOU files. Copyright © 2024, The Authors. All rights reserved.

关键词： Object recognition

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NONLINEAR STEAM GENERATOR FEEDWATER system IDENTIFICATION BASED ON WAVELET NEURAL NETWORK 10

NONLINEAR STEAM GENERATOR FEEDWATER SYSTEM IDENTIFICATION BA...

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10th International Symposium on Test automation and Instrumentation, ISTAI 2024

作者： Wang, Hongrui Yuan, Mingzhe Zhang, Pengbin Xing, Kunpeng Guangzhou Institute of Industrial Intelligence Guangzhou China Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China China Nuclear Power Engineering Co. Ltd. Shenzhen China

ISBN: (纸本)9781837242672

Steam generator is the key equipment in the large and complex nuclear power plant, and its water supply system has the characteristics of time-varying, large delay, nonlinear. In this paper, a segmented linearized model based on wavelet neural network is developed for a steam generator feedwater system of a nuclear power plant. All the data of the automation stages of the steam generator, which are steam flow rate, feedwater flow rate and liquid level from 20% load to 100% load, are collected and divided into six stages based on the characteristics of the data. For the first four stages of data, the feedwater system was modelled using the Hammerstein-Wiener method based on wavelet neural network, which was used as the nonlinear input and nonlinear output module of Hammerstein-Wiener. And for the remaining two stages of data, the nonlinear autoregressive models based on with exogenous (ARX) inputs model approach was modelled using nonlinear ARX as the underlying framework of wavelet neural network. The proposed model can be used for model predictive control to switch between different models based on load changes. Through the verification of simulation experiments, the built model can accurately reflect the trend of actual data, and the predicted values have high accuracy and reliability. © The Institution of engineering & Technology 2024.

关键词： Nuclear power plants

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Fuzzy-Cycle: Visible to Infrared Ship Image Translation Based on CycleGAN 6

Fuzzy-Cycle: Visible to Infrared Ship Image Translation Base...

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： He, Long Yu, Yue Liu, Chang Sun, Yangyang Jiang, Feng Hu, Xiaoning Pan, Chengwei Dong, Xiwang School of Artificial Intelli-gence Beihang University Beijing China Beijing Institute of Control and Electronic Technology Beijing China School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University School of Artificial Intelligence Institute of Unmanned System Beijing China

ISBN: (纸本)9798350356618

Detecting targets in infrared ship images is crucial for various applications, such as maritime rescue and ship inspection. Training contemporary mainstream detection and segmentation models, however, requires large-scale, high-quality infrared ship image datasets. In open-world maritime confrontation scenarios, the complex and variable environment, scarcity of target ships, and limited acquisition conditions make it challenging to construct high-quality infrared ship image datasets. To address this issue, this paper proposes an unpaired visible-infrared ship image translation model based on CycleGAN, named Fuzzy-Cycle. The model introduces a fuzzy cycle loss to encourage the model to learn feature mapping rules between different domains while minimizing attention to unnecessary features. Additionally, the method employs U-Net as the generator network to retain shallow image features during upsampling and selects PatchGAN as the discriminator network, embedding Haar wavelet transform to enhance the discriminator's ability to analyze image frequency domain features. Experiments on the VAIS dataset demonstrate that the proposed algorithm outperforms mainstream unpaired image transfer models, showing excellent performance in the task of visible-infrared ship image translation. Furthermore, zero-shot image translation experiments on the HRSC2016 dataset prove the generalizability and reliability of the proposed method. © 2024 IEEE.

关键词： Wavelet transforms

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A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation system (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

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Patchcore-SAM: Rail Defect Unsupervised Detection and Segmentation 27

Patchcore-SAM: Rail Defect Unsupervised Detection and Segmen...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： He, Shuyi Cao, Yuan Wang, Feng Sun, Yongkui Su, Shuai Yang, Weifeng Wang, Wenkun National Engineering Research Center of Rail Transportation Operation Control System School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Crrc Zhuzhou Institute Co Ltd Zhuzhou412001 China

ISBN: (纸本)9798331505929

The heavy-haul railways plays a crucial role in facilitating the transportation of large-scale goods, yet the challenging operational conditions significantly increase the occurrence of rail defects. Despite the growing importance of computer vision and artificial intelligence in detecting rail defects, current methods face issues of low accuracy and efficiency. The success of the Segment Anything Model (SAM) provides a promising solution to these problems. In this study, a novel two-stage rail defect evaluation methodology called Patchcore-SAM was proposed. Firstly, Patchcore enables the unsupervised detection and localization of defects. Subsequently, the detected anomalies are transformed into anomaly images and prompts, which serve as inputs for the fine-tuned SAM to achieve precise rail defect segmentation. The experimental results demonstrate the superior performance of this method, providing robust support for ensuring the safe operation of heavy-haul railways. © 2024 IEEE.

关键词： Railroads

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Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 27-33页

作者： ZHAO Yanfei XIAO Peng WANG Jingchuan GUO Rui Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240China State Grid Intelligence Technology Co. Ltd.Jinan250013China

Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior *** order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic *** last,comparative experiments were carried out in the real *** show that our method is superior in terms of safety,comfort and docking accuracy.

关键词： semi-autonomous navigation mobile robot semantic map voice interaction

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Rail Fastener Defect Detection of Heavy Haul Railway Based on Improved YOLOv8 27

Rail Fastener Defect Detection of Heavy Haul Railway Based o...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Li, Xinman Cao, Yuan Wang, Feng Sun, Yongkui Su, Shuai Yang, Weifeng Wang, Wenkun School of Automation and Intelligence Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation Control System Beijing100044 China Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing100044 China Crrc Zhuzhou Institute Co Ltd Zhuzhou412001 China

ISBN: (纸本)9798331505929

Rail fasteners play a crucial role in the heavy haul railway lines, serving as vital components of railway infrastructure. However, due to the intricate nature of the heavy haul railway environment, fasteners can often be obscured, leading to misjudgments and inaccuracies for the existing defect detection algorithms. To address this challenge, a novel method to detect rail fastener defects based on an improved YOLOv8 model is proposed. Initially, a new dataset specifically tailored for fastener defect detection in heavy haul railway lines is constructed utilizing onsite data. To enrich feature information and enhance detection accuracy, multiple improvements are introduced, including the addition of a P6 feature layer, the design of a cross-layer connection neck (CCNeck) feature fusion network, and the introduction of the DySample up-sampling operator. Furthermore, the Quality Focal Loss (QFL) function is introduced to address class imbalance problem within the datasets. Finally, experiments are conducted and the results demonstrate significant enhancements in detection mean average precision (mAP) compared to existing state-of-the-art networks. © 2024 IEEE.

关键词： Rails

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Emergency Evacuation Route Planning of Cruise Ship Based on Intelligent Optimization Algorithm 5th

Emergency Evacuation Route Planning of Cruise Ship Based on ...

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5th China Conference on Intelligent Networked Things, CINT 2022

作者： Peng, Cheng Yu, Song Zhang, Lianyi School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Simulation Center Beijing Institute of Electronic System Engineering Beijng100854 China

ISBN: (纸本)9789811989148

When an emergency such as fire happens on a cruise ship, it is of great significance to quickly plan out a reasonable and effective emergency escape route to ensure the life safety of passengers. First, to achieve the safe evacuation of passengers in an emergency, a three-deck diagram is constructed as the simulation evacuation place, which is modeled by the combination of undirected graph and data set. Secondly, combined with the characteristics of passenger ship personnel composition, the comprehensive evaluation index of route is established, the planning principle of combining global static path planning with local dynamic path planning was formulated, and the path planning algorithm is realized by using Dijkstra algorithm and A* algorithm. Finally, taking an emergency such as fire as an example, considering the influence of the changes of situation information such as fire diffusion, passenger flow density and path cost on the result of path planning, the simulation experiment is carried out in a deck fire scene, and the evacuation situation under the conditions of delay time and fire location is analyzed and compared, It provides a reference for the formulation of reasonable emergency evacuation strategies and key prevention areas. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion planning

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