检索结果-内蒙古大学图书馆

$\varepsilon$-Constraint and Fuzzy Logic-Based Optimization of Hazardous Material Transportation via Lane Reservation

引用

IEEE Transactions on Intelligent Transportation systems 2013年第2期14卷 847-857页

作者： Zhen Zhou Feng Chu Ada Che MengChu Zhou Computing Integrative Biology and Complex Systems Laboratory University of Evry-Val dEssonne Evry France School of Management Northwestern Polytechnical University Xian China Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

With economic development, a great amount of hazardous material is shipped in the transport network every day. Hazardous material transportation is well known for its high potential risk. An accident can cause very serious economic damage and will have a negative impact on public health and the environment over the long term. Transporting hazardous materials on special lanes can reduce the risk. However, a lane reservation strategy may worsen traffic conditions for other vehicles. This paper investigates a hazardous material transportation problem with lane reservation. The problem lies in how to choose lanes to be reserved in the network and select the path for each hazardous material shipment from the reserved lanes. The goal is to obtain the best compromise between the impact on normal traffic and the transportation risk. A multiobjective integer programming model is presented for the new problem. Then, an algorithm is developed based on the ε -constraint method and a fuzzy-logic-based approach. Pareto optimal solutions are obtained by the former, and a preferred solution is selected by the fuzzy-logic-based approach. Computational results demonstrate the efficiency of the proposed algorithm using an instance based on a real network topology and randomly generated instances.

关键词： Hazardous materials Accidents Roads Linear programming Vectors Optimization

来源：评论

学校读者我要写书评

暂无评论

Trust reasoning and optimization based on associate petri net

引用

ICIC Express Letters 2011年第3期5卷 791-796页

作者： Fang, Xianwen Yan, Chungang Fan, Xiaoqin Yin, Zhixiang Department of Information and Computing Science Anhui University of Science and Technology No. 168 Shungengzhong Road Huainan 232001 China Key Lab of Embedded System and Service Computing Ministry of Education Tongji University No. 1239 Siping Road Shanghai 201804 China

Trust issue is an important problem both in social field and information field. At present, there are few methods considering the interaction behavior relativity between models which can judge the interaction influence, so the obtained trust value is imprecise and unreliable. In the paper, behavior-aware trust computation and reasoning methods based on Associate Petri net (APN) are presented, which use the APN reasoning and take the interaction relativity into account. Using Genetic Algorithm (GA) combined with the properties of APN, a trust optimization method is obtained. Theoretical analysis and experimental results indicate that the method owns both lower computation cost and lower error rate. © 2011 ISSN 1881-803X.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Learning How to Avoiding Obstacles for End-to-End Driving with Conditional Imitation Learning 19

Learning How to Avoiding Obstacles for End-to-End Driving wi...

引用

Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning

作者： Enwei Zhang Hongtu Zhou Yongchao Ding Junqiao Zhao Chen Ye The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University School of Electronics and Information Engineering Shanghai P. R. China Tongji University School of Electronics and Information Engineering Shanghai P. R. China Tongji University School of Electronics and Information Engineering P. R. China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University School of Electronics and Information Engineering P. R. China

ISBN: (纸本)9781450372213

Obstacle avoiding is one of the most complex tasks for autonomous driving systems, which was also ignored by many cutting-edge end-to-end learning-based methods. The difficulties stem from the integrated process of detection and interpretation of environment and obstacles and generation of proper behaviors. We make the use of CARLA, a simulator for autonomous driving research, and collect massive human drivers' reactions to obstacles on road subjecting to given driving commands, i.e. follow, go straight, turn left and turn right for about 6 hours. A behavior-Cloning neural network architecture is proposed with the modified loss that enlarge the effects of errors for steer, which indicates the benefit to high an accuracy. We found the data augmentation of the image is crucial to the training of the proposed network. And a reasonable limit allows avoiding unexpected stop. The experiments demonstrate 3 obstacle avoidance cases: for the same type as the training dataset, other automobile and two-wheeled vehicles. Finally, the CARLA benchmark is also tested.

关键词： imitation learning obstacle avoidance end-to-end autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Symptom selection of inquiry diagnosis data for coronary heart disease in Traditional Chinese Medicine by using social network techniques

Symptom selection of inquiry diagnosis data for coronary hea...

引用

IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)

作者： Ya-Lei Wang Guo-Zheng Li Wei-Sheng Xu Guo-Ping Liu Yi-Qin Wang The Key Laboratory of Embedded System and Service Computing Department of Control Science & Engineering Ministry of Education University of Tongji Shanghai China Laboratory of Information Access and Synthesis of TCM Four Diagnosis Shanghai University of Traditional Chinese Medicine Shanghai China

Coronary heart disease (CHD) is a common cardiovascular disease in the elderly, which causes high death rate and low cure rate. Therefore, the TCM diagnosis objectification is important. This paper proposes the Relative Associated Density (RAD) method to analyze the data set. RAD results of the symptoms to syndromes are used in performing feature selection, and the prediction results with different classification machines show significant improvements. Compared to other traditional feature selection methods, RAD provides higher Interpretability in the TCM field.

关键词： Heart Medical diagnostic imaging Diseases Blood Social network services Hospitals Lungs

来源：评论

学校读者我要写书评

暂无评论

A New Class of Learning Automata for Selecting an Optimal Subset

A New Class of Learning Automata for Selecting an Optimal Su...

引用

IEEE International Conference on systems, Man, and Cybernetics

作者： JunQi Zhang ZeZhou Li Qi Kang MengChu Zhou Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University Department of Control Science and Engineering Tongji University Department of Electrical and Computer Engineering New Jersy Institute of Technology

ISBN: (纸本)9781479938414

Interacting with a random environment, Learning Automata (LAs) are automata that, generally, have the task of learning the optimal action based on responses from the environment. Distinct from the traditional goal of Learning Automata to select only the optimal action out of a set of actions, this paper considers a multiple-action selection problem and proposes a novel class of Learning Automata for selecting an optimal subset of actions. Their objective is to identify the optimal subset: the top k out of r actions. Based on conventional continuous pursuit and discretized pursuit learning schemes, this paper introduces four pursuit learning schemes for selecting the optimal subset, called continuous equal pursuit, discretized equal pursuit, continuous unequal pursuit and discretized unequal pursuit learning schemes, respectively. In conjunction with a reward-inaction learning paradigm, the above four schemes lead to four versions of pursuit Learning Automata for selecting the optimal subset. The simulation results present a quantitative comparison between them.

关键词： Learning Automata Optimal subset Pursuit algorithm

来源：评论

学校读者我要写书评

暂无评论

Object rigidity and reflectivity identification based on motion analysis

Object rigidity and reflectivity identification based on mot...

引用

IEEE International Conference on Image Processing

作者： Di Zang Paul R. Schrater Katja Doerschner Department of Computer Science and Engineering University of Minnesota USA Key Laboratory of Embedded System and Service Computing Ministry of Education University of Tongji China National Research Center of Magnetic Resonance and Department of Psychology Bilkent University Turkey

Rigidity and reflectivity are important properties of objects, identifying these properties is a fundamental problem for many computer vision applications like motion and tracking. In this paper, we extend our previous work to propose a motion analysis based approach for detecting the object's rigidity and reflectivity. This approach consists of two steps. The first step aims to identify object rigidity based on motion estimation and optic flow matching. The second step is to classify specular rigid and diffuse rigid objects using structure from motion and Procrustes analysis. We show how rigid bodies can be detected without knowing any prior motion information by using a mutual information based matching method. In addition, we use a statistic way to set thresholds for rigidity classification. Presented results demonstrate that our approach can efficiently classify the rigidity and reflectivity of an object.

关键词： Optical imaging Adaptive optics Mutual information Shape Computer vision Integrated optics Object recognition

来源：评论

学校读者我要写书评

暂无评论

Predictive Resilience Assessment Featuring Diffusion Reconstruction for Road Networks Under Rainfall Disturbances

SSRN

引用

SSRN 2024年

作者： Zang, Di Wei, Chenguang Zhao, Jiayi Zhang, Junqi Tang, Keshuang Zhu, Hong Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University No. 4800 Cao’an Road Shanghai200092 China Key Laboratory of Road and Traffic Engineering Ministry of Education College of Transportation Engineering Tongji University No. 4800 Cao’an Road Shanghai200092 China

The ability of road networks to withstand external disturbances is a crucial measure of transportation system performance, where resilience distinctly emerges as an effective perspective for its unique insights into the system's resistance and recovery capabilities. In the face of unforeseen resilience disturbance events, predictive and accurate assessment of road network resilience is essential for better traffic regulation and emergency response management. However, existing resilience assessment methods of road networks are insufficient: they lack reliable real-time big-data analysis, do not possess predictive capabilities for guiding decision-making, and have a narrow view with single-dimensional resilience indicators. To address these issues, focusing on rainfall disturbance scenarios, this work introduces a novel resilience assessment method, which is predictive and real-time, consisting of two components: a traffic indicator prediction model and a comprehensive resilience assessment model. Firstly, we propose a two-stage traffic indicator prediction model, namely the Conditional Diffusion-Reconstruction-based Graph Neural Network (CDRGNN), which particularly enhances disturbance-scenario prediction accuracy, thereby providing reliable foresight in aid of the following assessments. Subsequently, we develop a resilience assessment model featuring structural-functional comprehensive resilience indicators established on the shortest-path aggregation, and the overall resilience assessment is performed through comparative analysis using indicators obtained in real time with historical non-disruptive resilience benchmarks. In a case study focusing on heavy rainfall disturbances on a road network in California, USA, abundant experiments and visualizations are conducted to demonstrate the rationality of our proposed comprehensive resilience indicators as well as the precision and reliability of these predictive resilience assessment outcomes. © 2024, The Authors. All

关键词： Diffusion

来源：评论

学校读者我要写书评

暂无评论

Structure-Aware Generative Adversarial Network for Text-to-Image Generation

Structure-Aware Generative Adversarial Network for Text-to-I...

引用

IEEE International Conference on Image Processing

作者： Wenjie Chen Zhangkai Ni Hanli Wang Department of Computer Science and Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai P. R. China

Text-to-image generation aims at synthesizing photo-realistic images from textual descriptions. Existing methods typically align images with the corresponding texts in a joint semantic space. However, the presence of the modality gap in the joint semantic space leads to misalignment. Meanwhile, the limited receptive field of the convolutional neural network leads to structural distortions of generated images. In this work, a structure-aware generative adversarial network (SaGAN) is proposed for (1) semantically aligning multimodal features in the joint semantic space in a learnable manner; and (2) improving the structure and contour of generated images by the designed content-invariant negative samples. Experimental results show that SaGAN achieves over 30.1% and 8.2% improvements in terms of FID on the datasets of CUB and COCO when compared with the state-of-the-art approaches.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Predictive Resilience Assessment Featuring Diffusion Reconstruction for Road Networks Under Rainfall Disturbances

SSRN

引用

SSRN 2024年

The ability of road networks to withstand external disturbances is a crucial measure of transportation system performance, where resilience distinctly emerges as an effective perspective for its unique insights into the system's resistance and recovery capabilities. In the face of unforeseen resilience disturbance events, predictive and accurate assessment of road network resilience is essential for better traffic regulation and emergency response management. However, existing resilience assessment methods of road networks are insufficient: they lack reliable real-time big-data analysis, do not possess predictive capabilities for guiding decision-making, and have a narrow view with single-dimensional resilience indicators. To address these issues, focusing on rainfall disturbance scenarios, this work introduces a novel resilience assessment method, which is predictive and real-time, consisting of two components: a deep learning traffic indicator prediction model and a comprehensive resilience assessment model. Firstly, we propose a two-stage traffic indicator prediction model, namely the Conditional Diffusion-Reconstruction-based Graph Neural Network (CDRGNN), which particularly enhances disturbance-scenario prediction accuracy, thereby providing reliable foresight in aid of the following assessments. Subsequently, we develop a resilience assessment model featuring structural-functional comprehensive resilience indicators established through shortest-path aggregation, and the overall resilience assessment is performed through comparative analysis using indicators obtained in real-time with historical non-disruptive resilience benchmarks. In a case study focusing on heavy rainfall disturbances on a road network in California, the United States, abundant experiments and visualizations are conducted to demonstrate the rationality of our proposed comprehensive resilience indicators as well as the precision and reliability of these predictive resilience assessment outcom

关键词： Diffusion

来源：评论

学校读者我要写书评

暂无评论

Petri net modeling and one-wafer scheduling of single-arm multi-cluster tools

Petri net modeling and one-wafer scheduling of single-arm mu...

引用

2013 IEEE International Conference on Automation Science and Engineering, CASE 2013

作者： Zhu, Qinghua Wu, Naiqi Qiao, Yan Zhou, Mengchu School of Computer Guangdong University of Technology Guangzhou 510006 China Macau University of Science and Technology China Department of Industrial Engineering School of Electro-Mechanical Engineering Guangdong University of Technology Guangzhou 510006 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102-1982 United States

ISBN: (纸本)9781479915156

It is very challenging to schedule a multi-cluster tool for it needs to coordinate the activities of multiple robots. This paper studies one-wafer cyclic scheduling for multi-cluster tools whose bottleneck cluster tool is process-bound. A Petri net model is developed to describe the system. Based on the model, it is shown that, for any multi-cluster tool whose bottleneck cluster tool is process-bound, there is always a one-wafer cyclic schedule. Then, a method is presented to find the minimal cycle time and the optimal one-wafer cyclic schedule. The method requires only simple calculation. An example illustrates the application and effectiveness of the proposed method. © 2013 IEEE.

关键词： Scheduling

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：