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1,777 results on '"Zhang, Guohui"'

1. World Models for Autonomous Driving: An Initial Survey

Author

Guan, Yanchen, Liao, Haicheng, Li, Zhenning, Hu, Jia, Yuan, Runze, Li, Yunjian, Zhang, Guohui, and Xu, Chengzhong

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Robotics
Abstract

In the rapidly evolving landscape of autonomous driving, the capability to accurately predict future events and assess their implications is paramount for both safety and efficiency, critically aiding the decision-making process. World models have emerged as a transformative approach, enabling autonomous driving systems to synthesize and interpret vast amounts of sensor data, thereby predicting potential future scenarios and compensating for information gaps. This paper provides an initial review of the current state and prospective advancements of world models in autonomous driving, spanning their theoretical underpinnings, practical applications, and the ongoing research efforts aimed at overcoming existing limitations. Highlighting the significant role of world models in advancing autonomous driving technologies, this survey aspires to serve as a foundational reference for the research community, facilitating swift access to and comprehension of this burgeoning field, and inspiring continued innovation and exploration.

Published
2024

8. SFEMM: A cotton binocular matching method based on YOLOv7x

Author

Zhang Guohui, Gulbahar Tohti, Chen Ping, Mamtimin Geni, and Fan Yixuan

Subjects
deep learning, binocular ranging, binocular stereo matching, cotton picking robot, cotton centroid positioning, Biotechnology, TP248.13-248.65, Mathematics, QA1-939
Abstract

The cotton-picking robot needs to locate the target object in space in the process of picking in the field and other outdoor strong light complex environments. The difficulty of this process was binocular matching. Therefore, this paper proposes an accurate and fast binocular matching method. This method used the deep learning model to obtain the position and shape of the target object, and then used the matching equation proposed in this paper to match the target object. Matching precision of this method for cotton matching was much higher than that of similar algorithms. It was 54.11, 45.37, 6.15, and 12.21% higher than block matching (BM), semi global block matching (SGBM), pyramid stereo matching network (PSMNet), and geometry and context for deep stereo regression (GC-net) respectively, and its speed was also the fastest. Using this new matching method, the cotton was matched and located in space. Experimental results show the effectiveness and feasibility of the algorithm.

Published
2024
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9. Microstructure and properties of K403 nickel-base superalloy guide vane repaired by tungsten inert gas welding and laser cladding process

Author

ZHANG Guohui, QIN Renyao, ZHOU Biao, ZHAO Zijun, GUO Shaoqing, HUANG Shuai, WANG Yuexin, and AO Bin

Subjects
k403 superalloy, blade repairing, laser cladding repairing, tungsten inert gas welding repairing, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

K403 nickel-base superalloy is widely used in the manufacture of aero-engine turbine blades because of its excellent properties at room temperature and high temperature. In order to solve the problem of turbine blade crack defects caused by long-term service in complex working conditions，in this work，two different processes of （tungsten inert gas，TIG） welding and laser cladding were used to repair the blade cracks，and the microstructure and properties of the repaired region were studied. The influence of TIG welding and laser cladding repairing on microstructure，mechanical properties and failure behavior was analyzed. The results show that the microcracks tend to occur near the repair interface using the TIG welding repairing process，which are mainly caused by carbides and low melting point eutectic structure. The grain and structure of the repaired area by laser cladding repair technology are more uniform，and the microcrack defects can be easier to control. The comprehensive mechanical properties of the samples repaired by laser cladding are obviously higher than those repaired by TIG welding repairing process，and the samples repaired by laser cladding have better process stability. The tensile strengths of the samples using the laser cladding repair process and the TIG welding repair process at room temperature have reached 87.44% and 69.22% of the strength of K403 base material，respectively. According to the failure analysis results，the tensile fracture at room temperature in the repaired region presents mixed fracture characteristics，and the tensile fracture at high temperature presents intergranular fracture characteristics. Microcracks in the repaired area，local liquid phase deficiency defects and carbide structure are the main reasons of failure. The laser cladding technology has the advantages of heat source concentration and smaller heat affected zone，which can effectively restrain the defects and refine the microstructure. Therefore，the laser cladding repair process is used to repair the edge plate crack damage generated during the blade test run. After fluorescence and kerosene-chalk detection，the repairing process meets the relevant reuse requirements.

Published
2024
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10. Effect of heat treatment on high temperature tensile properties of selective laser melting GH3536 alloys

Author

HUANG Shuai, QU Zhiqi, ZHANG Guohui, CHEN Bingqing, ZHOU Biao, and ZHANG Xuejun

Subjects
selective laser melting, gh3536 alloy, microstructure, high temperature tensile, fracture mode, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

Selective laser melting（SLM）can achieve nearly net-shape complex parts of GH3536 alloy，and its high temperature mechanical properties are important indicators for safe service. The effect of heat treatment on microstructure and high temperature tensile properties of SLM GH3536 alloy were studied. The heat treatment at 1225 ℃ for 1 h was carried out to explore the regulation mechanism of microstructure and properties. The results show that heat treatment can effectively eliminate the cellular subgrain structure inside the grains，which significantly enhances the dislocation slip ability. The tensile elongations at room temperature，650 ℃ and 815 ℃ are increased by 75%，92% and 683%，respectively. In addition，the decrease of the aspect ratio of the columnar grains significantly reduces the anisotropy in the heat-treated sample. The fracture analysis shows that the fracture mode of the heat-treated sample changes from intergranular fracture to mixed fracture with the increase of tensile environment temperature.

Published
2024
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11. Measurement of the neutron flux of CSNS Back-n ES#1 under small collimators from 0.5 eV to 300 MeV

Author

Chen, Yonghao, Qiu, Yijia, Li, Qiang, Tang, Shengda, Yang, Yiwei, Ren, Zhizhou, Jiang, Wei, Fan, Ruirui, Yi, Han, Liu, Rong, Tang, Jingyu, Jing, Hantao, Lan, Changlin, Li, Yang, Tan, Zhixin, An, Qi, Bai, Jiangbo, Bao, Jie, Bao, Yu, Cao, Ping, Chen, Haolei, Chen, Qiping, Chen, Zhen, Cui, Zengqi, Feng, Changqing, Gao, Keqing, Gao, Xiaolong, Gu, Minhao, Han, Changcai, Han, Zijie, He, Guozhu, He, Yongcheng, Hong, Yang, Hu, Yiwei, Huang, Hanxiong, Huang, Xiru, Jiang, Haoyu, Jiang, Zhijie, Kang, Ling, Li, Bo, Li, Chao, Li, Jiawen, Li, Xiao, Liu, Jie, Liu, Shubin, Liu, Xingyan, Long, Ze, Luan, Guangyuan, Ning, Changjun, Niu, Mengchen, Qi, Binbin, Ren, Jie, Ruan, Xichao, Song, Zhaohui, Sun, Kang, Sun, Zhijia, Tang, Xinyi, Tian, Binbin, Wang, Lijiao, Wang, Pengcheng, Wang, Zhaohui, Wen, Zhongwei, Wu, Xiaoguang, Wu, Xuan, Xie, Likun, Yang, Xiaoyun, Yu, Li, Yu, Tao, Yu, Yongji, Zhang, Guohui, Zhang, Linhao, Zhang, Qiwei, Zhang, Xianpeng, Zhang, Yuliang, Zhang, Zhiyong, Zhou, Luping, Zhou, Zhihao, and Zhu, Kejun

Published
2024
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23. A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization

Author

Li, Zhenning, Xu, Chengzhong, and Zhang, Guohui

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Inefficient traffic signal control methods may cause numerous problems, such as traffic congestion and waste of energy. Reinforcement learning (RL) is a trending data-driven approach for adaptive traffic signal control in complex urban traffic networks. Although the development of deep neural networks (DNN) further enhances its learning capability, there are still some challenges in applying deep RLs to transportation networks with multiple signalized intersections, including non-stationarity environment, exploration-exploitation dilemma, multi-agent training schemes, continuous action spaces, etc. In order to address these issues, this paper first proposes a multi-agent deep deterministic policy gradient (MADDPG) method by extending the actor-critic policy gradient algorithms. MADDPG has a centralized learning and decentralized execution paradigm in which critics use additional information to streamline the training process, while actors act on their own local observations. The model is evaluated via simulation on the Simulation of Urban MObility (SUMO) platform. Model comparison results show the efficiency of the proposed algorithm in controlling traffic lights.

Published
2021

31. Determination of the 232Th(n,γ) cross section from 10 to 200 keV at the Back-n facility at CSNS

Author

Wang, Jincheng, Ren, Jie, Jiang, Wei, Ruan, Xichao, Sun, Qi, Hu, Jifeng, Jiang, Bing, Bao, Jie, Zhang, Qiwei, Luan, Guangyuan, Huang, Hanxiong, Nie, Yangbo, Ge, Zhigang, An, Qi, Bai, Haofan, Bai, Jiangbo, Cao, Ping, Chen, Qiping, Chen, Yonghao, Chen, Zhen, Cui, Zengqi, Fan, Anchuan, Fan, Ruirui, Feng, Changqing, Feng, Fanzhen, Gao, Keqing, Gu, Minhao, Han, Changcai, Han, Zijie, He, Guozhu, He, Yongcheng, Hong, Yang, Hu, Yiwei, Jia, Weihua, Jiang, Haoyu, Jiang, Zhijie, Jin, Zhengyao, Kang, Ling, Li, Bo, Li, Chao, Li, Gong, Li, Jiawen, Li, Qiang, Li, Yang, Liu, Jie, Liu, Rong, Liu, Shubin, Ning, Changjun, Qi, Binbin, Ren, Zhizhou, Song, Zhaohui, Sun, Kang, Tan, Zhixin, Tang, Jingyu, Tang, Shengda, Wang, Lijiao, Wang, Pengcheng, Wang, Zhaohui, Wen, Zhongwei, Wu, Xiaoguang, Wu, Xuan, Xie, Likun, Yang, Yiwei, Yi, Han, Yu, Yongji, Zhang, Guohui, Zhang, Linhao, Zhang, Mohan, Zhang, Xianpeng, Zhang, Yuliang, Zhang, Yue, Zhang, Zhiyong, Zhao, Maoyuan, Zhou, Luping, Zhu, Kejun, and Zhang, Jie

Published
2023
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32. Multi-UAV Aerial Mission Intelligent Assignment Algorithm Based on LightGBM Model

Author

Wang Yongcheng and Zhang Guohui

Subjects
unmanned aircraft, intelligent assignment, lightgbm model, hungarian algorithm, 68w40, Mathematics, QA1-939
Abstract

With the development of UAV technology, UAVs have the advantages of convenience, speed, and small size. However, when faced with a complex task environment, some traditional artificial intelligence algorithms will significantly increase computation time, and the algorithms that perform better in time performance often fail to adapt to the type and number of random tasks to be assigned. In this paper, we combine the Hungarian algorithm with improving the LightGBM model, simulate the implementation in various task scenarios, and verify that the improved LightGBM model completes the intelligent assignment of multiple UAVs without increasing time cost compared to the classical algorithm through comparative analysis. It is shown that the improved LightGBM model has an average time of 26.39s for 10 runs, which is higher than 16.91s for NSGA-II and 14.73s for PESA, but lower than 46.92s for SPEA2 and 50.01s for SPGA2. For a non-real-time assignment system, a better mission intelligence assignment at a certain time cost scheme is more in line with the system’s performance requirements. Therefore, the improved LightGBM model in this paper can obtain better solutions than the other four algorithms in solving the multi-objective multi-UAV task intelligence assignment problem, and the solution set coverage is larger than several other algorithms. This indicates that the algorithm studied in this paper is feasible in solving the multi-objective multi-UAV task intelligence assignment problem, and it also has some reference significance for solving the multi-objective problem and improving the coverage of the multi-objective solution set.

Published
2024
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33. Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning

Author

Li, Zhenning, Yu, Hao, Zhang, Guohui, Dong, Shangjia, and Xu, Cheng-Zhong

Subjects
Computer Science - Artificial Intelligence
Abstract

Inefficient traffic control may cause numerous problems such as traffic congestion and energy waste. This paper proposes a novel multi-agent reinforcement learning method, named KS-DDPG (Knowledge Sharing Deep Deterministic Policy Gradient) to achieve optimal control by enhancing the cooperation between traffic signals. By introducing the knowledge-sharing enabled communication protocol, each agent can access to the collective representation of the traffic environment collected by all agents. The proposed method is evaluated through two experiments respectively using synthetic and real-world datasets. The comparison with state-of-the-art reinforcement learning-based and conventional transportation methods demonstrate the proposed KS-DDPG has significant efficiency in controlling large-scale transportation networks and coping with fluctuations in traffic flow. In addition, the introduced communication mechanism has also been proven to speed up the convergence of the model without significantly increasing the computational burden.

Published
2021
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44. Measurements of differential and angle-integrated cross sections for the $^{10}$B($n, {\alpha}$)$^{7}$Li reaction in the neutron energy range from 1.0 eV to 2.5 MeV

Author

Jiang, Haoyu, Jiang, Wei, Bai, Huaiyong, Cui, Zengqi, Zhang, Guohui, Fan, Ruirui, Yi, Han, Ning, Changjun, Zhou, Liang, Tang, Jingyu, An, Qi, Bao, Jie, Bao, Yu, Cao, Ping, Chen, Haolei, Chen, Qiping, Chen, Yonghao, Chen, Yukai, Chen, Zhen, Feng, Changqing, Gao, Keqing, Gu, Minhao, Han, Changcai, Han, Zijie, He, Guozhu, He, Yongcheng, Hong, Yang, Huang, Hanxiong, Huang, Weiling, Huang, Xiru, Ji, Xiaolu, Ji, Xuyang, Jiang, Zhijie, Jing, Hantao, Kang, Ling, Kang, Mingtao, Li, Bo, Li, Chao, Li, Jiawen, Li, Lun, Li, Qiang, Li, Xiao, Li, Yang, Liu, Rong, Liu, Shubin, Liu, Xingyan, Luan, Guangyuan, Mu, Qili, Qi, Binbin, Ren, Jie, Ren, Zhizhou, Ruan, Xichao, Song, Zhaohui, Song, Yingpeng, Sun, Hong, Sun, Kang, Sun, Xiaoyang, Sun, Zhijia, Tan, Zhixin, Tang, Hongqing, Tang, Xinyi, Tian, Binbin, Wang, Lijiao, Wang, Pengcheng, Wang, Qi, Wang, Taofeng, Wang, Zhaohui, Wen, Jie, Wen, Zhongwei, Wu, Qingbiao, Wu, Xiaoguang, Wu, Xuan, Xie, Likun, Yang, Yiwei, Yu, Li, Yu, Tao, Yu, Yongji, Zhang, Linhao, Zhang, Qiwei, Zhang, Xianpeng, Zhang, Yuliang, Zhang, Zhiyong, Zhao, Yubin, Zhou, Luping, Zhou, Zuying, Zhu, Danyang, Zhu, Kejun, and Zhu, Peng

Subjects
Nuclear Experiment
Abstract

Differential and angle-integrated cross sections for the $^{10}$B($n, {\alpha}$)$^{7}$Li, $^{10}$B($n, {\alpha}$$_{0}$)$^{7}$Li and $^{10}$B($n, {\alpha}$$_{1}$)$^{7}$Li$^{*}$ reactions have been measured at CSNS Back-n white neutron source. Two enriched (90%) $^{10}$B samples 5.0 cm in diameter and ~85.0 ${\mu}$g/cm$^{2}$ in thickness each with an aluminum backing were prepared, and back-to-back mounted at the sample holder. The charged particles were detected using the silicon-detector array of the Light-charged Particle Detector Array (LPDA) system. The neutron energy E$_{n}$ was determined by TOF (time-of-flight) method, and the valid ${\alpha}$ events were extracted from the E$_{n}$-Amplitude two-dimensional spectrum. With 15 silicon detectors, the differential cross sections of ${\alpha}$-particles were measured from 19.2{\deg} to 160.8{\deg}. Fitted with the Legendre polynomial series, the ($n, {\alpha}$) cross sections were obtained through integration. The absolute cross sections were normalized using the standard cross sections of the $^{10}$B($n, {\alpha}$)$^{7}$Li reaction in the 0.3 - 0.5 MeV neutron energy region. The measurement neutron energy range for the $^{10}$B($n, {\alpha}$)$^{7}$Li reaction is 1.0 eV $\le$ En < 2.5 MeV (67 energy points), and for the $^{10}$B($n, {\alpha}$$_{0}$)$^{7}$Li and $^{10}$B($n, {\alpha}$$_{1}$)$^{7}$Li$^{*}$ reactions is 1.0 eV $\le$ En < 1.0 MeV (59 energy points). The present results have been analyzed by the resonance reaction mechanism and the level structure of the $^{11}$B compound system, and compared with existing measurements and evaluations., Comment: 19 pages, 34 figures, to be published in Chinese Physics C

Published
2019
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45. Measurement of the neutron beam profile of the Back-n white neutron facility at CSNS with a Micromegas detector

Author

Qi, Binbin, Li, Yang, Zhu, Danyang, Zhang, Zhiyong, Fan, Ruirui, Pan, Jiang, Feng, Jianxin, Liu, Chengming, Feng, Changqing, Liu, Jianbei, Shao, Ming, Zhou, Yi, Wang, Yanfeng, Yi, Han, An, Qi, Bai, Huaiyong, Bao, Jie, Cao, Ping, Chen, Qiping, Chen, Yonghao, Cheng, Pinjing, Cui, Zengqi, Gu, Minhao, Guo, Fengqin, Han, Changcai, Han, Zijie, He, Guozhu, He, Yongcheng, He, Yuefeng, Huang, Hanxiong, Huang, Weiling, Huang, Xiru, Ji, Xiaolu, Ji, Xuyang, Jiang, Haoyu, Jiang, Wei, Jing, Hantao, Kang, Ling, Kang, Mingtao, Li, Bo, Li, Lun, Li, Qiang, Li, Xiao, Liu, Rong, Liu, Shubin, Liu, Xingyan, Luan, Guangyuan, Ma, Yinglin, Ning, Changjun, Ren, Jie, Ruan, Xichao, Song, Zhaohui, Sun, Hong, Sun, Xiaoyang, Sun, Zhijia, Tan, Zhixin, Tang, Hongqing, Tang, Jingyu, Wang, Pengcheng, Wang, Qi, Wang, Taofeng, Wang, Zhaohui, Wang, Zheng, Wen, Jie, Wen, Zhongwei, Wu, Qingbiao, Wu, Xiaoguang, Wu, Xuan, Xie, Likun, Yang, Yiwei, Yu, Li, Yu, Tao, Yu, Yongji, Zhang, Guohui, Zhang, Jing, Zhang, Linhao, Zhang, Liying, Zhang, Qingming, Zhang, Qiwei, Zhang, Xianpeng, Zhang, Yuliang, Zhao, Yingtan, Zhou, Liang, Zhou, Zuying, Zhu, Kejun, and Zhu, Peng

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

The Back-n white neutron beam line, which uses back-streaming white neutrons from the spallation target of the China Spallation Neutron Source, is used for nuclear data measurements. A Micromegas-based neutron detector with two variants was specially developed to measure the beam spot distribution for this beam line. In this article, the design, fabrication, and characterization of the detector are described. The results of the detector performance tests are presented, which include the relative electron transparency, the gain and the gain uniformity, and the neutron beam profile reconstruction capability. The result of the first measurement of the Back-n neutron beam spot distribution is also presented., Comment: 31 pages, 18 figures

Published
2019
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