检索结果-内蒙古大学图书馆

Parallel Planning:A New Motion Planning Framework for Autonomous Driving

IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

来源：评论

学校读者我要写书评

暂无评论

Optimal Reordering Trace Files for Improving Software Testing Suitcase

引用

Computers, Materials & Continua 2021年第4期67卷 1225-1239页

作者： Yingfu Cai Sultan Noman Qasem Harish Garg Hamïd Parvïn Kim-Hung Pho Zulkefli Mansor School of Measurement and Communication Harbin University of Science&TechnologyHarbinChina Computer Science Department College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia Department of Computer Science Faculty of Applied ScienceTaiz UniversityTaizYemen School of Mathematics Thapar Institute of Engineering and TechnologyDeemed UniversityPatiala147004India Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science Nourabad Mamasani BranchIslamic Azad UniversityMamasaniIran Fractional Calculus Optimization and Algebra Research GroupFaculty of Mathematics and StatisticsTon Duc Thang UniversityHo Chi Minh CityVietnam Fakulti Teknologi dan Sains Maklumat Universiti Kebangsan MalaysiaUKM BangiSelangor43600Malaysia

An invariant can be described as an essential relationship between program *** invariants are very useful in software checking and *** tools that are used to detect invariants are invariant *** are two types of invariant detectors:dynamic invariant detectors and static invariant *** software is an available computer program that implements a special case of a dynamic invariant detection *** proposes a dynamic invariant detection algorithm based on several runs of the tested program;then,it gathers the values of its variables,and finally,it detects relationships between the variables based on a simple statistical *** method has some *** of its biggest drawbacks is its overwhelming time *** is observed that the runtime for the Daikon invariant detection tool is dependent on the ordering of traces in the trace file.A mechanism is proposed in order to reduce differences in adjacent trace *** is done by applying some special techniques of mutation/crossover in genetic algorithm(GA).An experiment is run to assess the benefits of this *** findings reveal that the runtime of the proposed dynamic invariant detection algorithm is superior to the main approach with respect to these improvements.

关键词： Dynamic invariant detection software testing genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Opportunity and shift of nitrogen use in China

引用

Geography and Sustainability 2024年第1期5卷 33-40页

作者： Wangzheng Shen Jing He Sisi Li Yanhua Zhuang Hongyuan Wang Hongbin Liu Liang Zhang Andreas Kappler Hubei Provincial Engineering Research Center of Non-Point Source Pollution Control Innovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhan 430077China University of Chinese Academy of Sciences Beijing 100049China School of Environmental Studies China University of Geosciences(Wuhan)Wuhan 430078China State Environmental Protection Key Laboratory of Source Apportionment and Control of Aquatic Pollution Ministry of Ecology and Environment of the People's Republic of ChinaWuhan 430078China Hubei Key Laboratory of Yangtze River Basin Environmental Aquatic Science Wuhan 430078China Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural SciencesBeijing 100081China Geomicrobiology Group Center for Applied Geosciences(ZAG)University of TuebingenTuebingen D-72076Germany

It is never an easy task for China to feed 1.4 billion people with only 7%of the world's arable *** nearly 30%of the world's nitrogen(N)fertilizer applied,China achieves high crop yields while facing N pollution result-ing from excessive N ***,we calculate the farmland N budget on the national and regional *** N use efficiency(NUE)in China increased by 28.0%during *** improvement is due to the reduction in fertilization and the improvement of crop *** fragmented farmland is changing to large-scale farmland with the increase in cultivated land area per rural population and the development of agricultural *** opportunity brings more possibilities for precision farmland management,thus further improving *** goal of an NUE of 0.6 may be achieved in the 2040s based on the current development *** striking N use shift in China has important implications for other developing countries.

关键词： Environmental Kuznets curve Nitrogen surplus Greenhouse gas emission Climate change Non-point source pollution

来源：评论

学校读者我要写书评

暂无评论

A study of the limits of imaging capability due to water scattering effects in underwater ghost imaging

arXiv

引用

arXiv 2023年

作者： Yuliang, L.I. Chen, Mingliang Jinquan, Q.I. Deng, Chenjin Longkun, D.U. Zunwang, B.O. Chang, H.A.N. Zhihua, M.A.O. Yan, H.E. Shao, Xuehui Han, Shensheng Key Laboratory of Quantum Optics Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Institute for Quantum Science and Technology College of Science National University of Defense Technology Changsha410073 China State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography State Oceanic Administration Hangzhou310012 China Key Laboratory of Space Laser Communication and Detection Technology Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China National Laboratory of Aerospace Intelligent Control Technology Beijing100089 China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou310024 China

Underwater ghost imaging is an effective means of underwater detection. In this paper, a theoretical and experimental study of underwater ghost imaging is carried out by combining the description of underwater optical field transmission with the inherent optical parameters of the water body. This paper utilizes the Wells model and the approximate S-S scattering phase function to create a model for optical transmission underwater. The second-order Glauber function of the optical field is then employed to analyze the scattering field's degradation during the transmission process. This analysis is used to evaluate the impact of the water body on ghost imaging. The simulation and experimental results verify that the proposed underwater ghost imaging model can better describe the degradation effect of water bodies on ghost imaging. A series of experiments comparing underwater ghost imaging at different detection distances are also carried out in this paper. In the experiments, cooperative targets can be imaged up to 65.2m (9.3AL, at attenuation coefficient c=0.1426m-1 and the scattering coefficient b=0.052m-1) and non-cooperative targets up to 41.2m (6.4AL, at c=0.1569m-1 and b=0.081m-1) . By equating the experimental maximum imaged attenuation length for cooperative targets to Jerlov-I water (b=0.002m-1 and a=0.046m-1), the system will have a maximum imaging distance of 193m. Underwater ghost imaging is expected to achieve longer-range imaging by optimizing the system emission energy and detection sensitivity. © 2023, CC BY-NC-SA.

关键词： Light transmission

来源：评论

学校读者我要写书评

暂无评论

Miniaturized multistubs loaded rectangular monopole antenna for multiband applications based on theory of characteristics modes

引用

Progress In Electromagnetics Research C 2019年 92卷 177-189页

作者： Kumar, Ashok Deegwal, Jitendra K. Sharma, Mahendra M. Department of Electronics and Communication Engineering Government Mahila Engineering College Ajmer305002 India Department of Electronics Instrumentation & Control Engineering Government Engineering College Ajmer305025 India Department of Electronics and Communication Engineering Malaviya National Institute of Technology Jaipur302017 India

A miniaturized rectangular monopole antenna (RMA) integrated with a T-shaped stub, inverted long and short L-shaped stub resonators based on application of the theory of characteristic modes (CMs) is investigated for multiband operation. CMs of embedded multistubs resonators on the RMA are examined and perceived that the entire structure is able to excite magnetic and electric CMs, in which three valuable CMs at 2.69/3.68/5.35 GHz are attained to cover WiMAX and WLAN bands. Based on CM analysis, the design formulation of multistubs resonators loaded antenna is presented. The proposed multiband antenna has been fabricated, tested, and experimentally characterized. The measured fractional bandwidths (FBWs) are 7.03% (180 MHz, 2.47-2.65 GHz), 10.43% (360 MHz, 3.27- 3.63 GHz), and 11.42% (630 MHz, 5.20-5.83 GHz). The antenna exhibits isolated multiple frequency bands, stable monopole-like radiation patterns, and flat realized gains over the operating resonance bands while maintaining the small antenna size. © 2019 Electromagnetics Academy. All rights reserved.

关键词： Resonators

来源：评论

学校读者我要写书评

暂无评论

Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

arXiv

引用

arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

External Reflection Recognition for Glass Walls Surface

External Reflection Recognition for Glass Walls Surface

引用

Computer engineering and Intelligent control (ICCEIC) International Conference on

作者： Yongbin Wei Shaodan Zhi Shi Liu Institute of Engineering Measurement and Control Technology China Construction Industrial Engineering and Technology Research Institue Co. Ltd Shunyi China

ISBN: (数字)9781728185736

ISBN: (纸本)9781728185743

Most buildings in modern cities contain many glass facades to show the modern flavor of architectural design and street view, gradually increase the intensity of the lighting, which is also the first choice for most green and energy-saving buildings. However, the strong reflectivity of the glass facade allows street view or sky to be reflected on the surface of the building, while the transparency allows the visualization of internal objects and interferes with the monitoring of the glass building itself. Such features increase the difficulty of urban building monitoring. In this paper, a multi-layer-separation algorithm based on semantic segmentation method is proposed to realize non-contact monitoring of glass buildings, which separate the reflection and the real object from the monitoring images.

关键词： Geometry Image segmentation Buildings Semantics Glass Reflection Monitoring

来源：评论

学校读者我要写书评

暂无评论

Emerging contaminants:A One Health perspective

引用

The Innovation 2024年第4期5卷 140-170,139页

作者： Fang Wang Leilei Xiang Kelvin Sze-Yin Leung Martin Elsner Ying Zhang Yuming Guo Bo Pan Hongwen Sun Taicheng An Guangguo Ying Bryan WBrooks Deyi Hou Damian EHelbling Jianqiang Sun Hao Qiu Timothy MVogel Wei Zhang Yanzheng Gao Myrna JSimpson Yi Luo Scott XChang Guanyong Su Bryan MWong Tzung-May Fu Dong Zhu Karl JJobst Chengjun Ge Frederic Coulon Jean Damascene Harindintwali Xiankui Zeng Haijun Wang Yuhao Fu Zhong Wei Rainer Lohmann Changer Chen Yang Song Concepcion Sanchez-Cid Yu Wang Ali El-Naggar Yiming Yao Yanran Huang Japhet Cheuk-Fung Law Chenggang Gu Huizhong Shen Yanpeng Gao Chao Qin Hao Li Tong Zhang Natàlia Corcoll Min Liu Daniel SAlessi Hui Li Kristian KBrandt Yolanda Pico Cheng Gu Jianhua Guo Jianqiang Su Philippe Corvini Mao Ye Teresa Rocha-Santos Huan He Yi Yang Meiping Tong Weina Zhang Fidèle Suanon Ferdi Brahushi Zhenyu Wang Syed AHashsham Marko Virta Qingbin Yuan Gaofei Jiang Louis A.Tremblay Qingwei Bu Jichun Wu Willie Peijnenburg Edward Topp Xinde Cao Xin Jiang Minghui Zheng Taolin Zhang Yongming Luo Lizhong Zhu Xiangdong Li DamiàBarceló Jianmin Chen Baoshan Xing Wulf Amelung Zongwei Cai Ravi Naidu Qirong Shen Janusz Pawliszyn Yong-guan Zhu Andreas Schaeffer Matthias C.Rillig Fengchang Wu Gang Yu James M.Tiedje State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China University of Chinese Academy of Sciences Beijing 100049China Department of Chemistry Hong Kong Baptist UniversityHong KongChina HKBU Institute of Research and Continuing Education Shenzhen Virtual University ParkShenzhenChina Technical University of Munich TUM School of Natural SciencesInstitute of Hydrochemistry85748 GarchingGermany School of Resources&Environment Northeast Agricultural UniversityHarbin 150030China Climate Air Quality Research UnitSchool of Public Health and Preventive MedicineMonash UniversityMelbourneVICAustralia Faculty of Environmental Science&Engineering Kunming University of Science&TechnologyKunming 650500China Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria College of Environmental Science and EngineeringNankai UniversityTianjin 300350China Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control School of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China Ministry of Education Key Laboratory of Environmental Theoretical Chemistry South China Normal UniversityGuangzhouGuangdong 510006China Department of Environmental Science Baylor UniversityWacoTXUSA Center for Reservoir and Aquatic Systems Research(CRASR) Baylor UniversityWacoTXUSA School of Environment Tsinghua UniversityBeijing 100084China School of Civil and Environmental Engineering Cornell UniversityIthacaNY 14853USA Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province Zhejiang University of TechnologyHangzhou 310014China School of Environmental Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Laboratoire d’Ecologie Microbienne Universite Claude Bernard Lyon 1UMR CNRS 5557UMR INRAE 1418VetAgro Sup69622 VilleurbanneFrance Department of Pla

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary *** global efforts to mitigate legacy pollutants,the continuous introduction of new substances remains a major threat to both people and the *** response,global initiatives are focusing on risk assessment and regulation of emerging contaminants,as demonstrated by the ongoing efforts to establish the UN’s Intergovernmental Science-Policy Panel on Chemicals,Waste,and Pollution *** review identifies the sources and impacts of emerging contaminants on planetary health,emphasizing the importance of adopting a One Health *** for monitoring and addressing these pollutants are discussed,underscoring the need for robust and socially equitable environmental policies at both regional and international *** actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

关键词： pollution planet contaminants

来源：评论

学校读者我要写书评

暂无评论

RGB-D Instance Segmentation-based Suction Point Detection for Grasping

RGB-D Instance Segmentation-based Suction Point Detection fo...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Yeyu Fu Ting Sun Li Wang Shijie Yu Lei Deng Baohua Chen Lin Li Yusen Xie Shuixin Deng Hanxi Yin Joint International Research Laboratory of Advanced Photonics and Elextronics Beijing Information Science & Technology University Beijing China Department of Perception zhong-guancun Open Lab. Space Optoelectronic Measurement and Perception Lab. Beijing Institute of Control Engineering Beijing China Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9781665481106

Suction is a common way for robots to pick objects in industry, which has higher stability and reliability. However, it is necessary to evaluate the suction position on the object's surface when dealing with objects of different shapes. The previous method decouples it into a two-stage suction point detection and objects classification process, making the task more complex and time-consuming. On the other hand, there are significant challenges in acquiring training data. To address that, we propose an object instance segmentation-based suction grasping point detection method. Instance segmentation, as an object pixel-level perception method, can classify objects and find the orientation of objects in the plane, which is more efficient than simple classification. Specifically, our method generate a suction heatmap and object instance segmentation from RGB-D inputs and find the suction grasping position of each object after candidate points screening. To generate the training data, a heatmap mask based on 2D Gaussian-encoded is used to annotate the suction grasping ground truth. We evaluate each object's predicted suction grasping point results and conduct grasping experiments in real environments. The suction grasping success rate of 94% in dense scenes demonstrates the remarkable performance of the proposed method.

关键词： Heating systems Shape Service robots Design methodology Training data Grasping Stability analysis

来源：评论

学校读者我要写书评

暂无评论

Multi-scale pseudo-labels filtering and key pixels adversarial alignment for domain adaptive object detection

引用

engineering Applications of Artificial Intelligence 2025年 157卷

作者： Wei, Xing Xia, Jiong Yang, Fan Zhao, Chong Liu, Cang Wang, Ge Chen, Yanke Lu, Yang School of Computer and Information Hefei University of Technology China Intelligent Manufacturing Technology Research Institute of Hefei University of Technology China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education China Anhui Mine IOT and Security Monitoring Technology Key Laboratory China

Domain Adaptive Object Detection (DAOD) aims to adapt a detector trained on the labeled source domain to perform effectively on the unlabeled target domain. Most existing DAOD methods commonly employ self-training strategy and adversarial learning. However, the methods based on the self-training strategy often generate unreliable pseudo-labels (e.g., missing detections and false positives) due to the absence of target domain semantics, resulting in suboptimal models. Meanwhile, aligning a large number of unimportant pixels hampers adversarial learning's ability to capture domain-invariant semantic information. Therefore, we propose the Multi-scale Adversarial Teacher Framework (MATF) based on the common self-training framework (teacher–student framework). Specifically, to mitigate the impact of unreliable pseudo-labels, we propose the Multi-scale Negatives Filtering (MNF) module in the teacher model, which prevents missing detections from damaging the model by filtering out unreliable negative samples at multiple feature scales. In addition, we propose the Multi-scale Key-Pixel Discriminator (MKPD), which predicts the distributions of key pixels at multiple feature scales emphasizing the adversarial alignment of key pixels rather than unimportant pixels. Finally, to generate higher-quality pseudo-labels, we introduce the discriminator into the student model to reduce the model's bias towards the source domain. Unlike most methods, we construct our method on a computationally efficient but less work-intensive one-stage detector. Extensive experiments conducted on DAOD benchmark datasets such as Cityscapes, FoggyCityscapes, KITTI, and Sim10k demonstrate the strong adaptability and effectiveness of MATF. © 2025 Elsevier Ltd

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：