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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Yan Qiao NaiQi Wu MengChu Zhou Department of Industrial Engineering School of Mechatronics Engineering Guangdong University of Technology Guangzhou China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Some wafer fabrication processes are repeated processes, e.g. atomic layer deposition (ALD) process. For such processes, the wafers need to visit some processing modules for a number of times, which complicates the cycle time analysis. This paper studies the cycle time analysis problem for such processes. With a Petri net model, it is found that such processes contain local cycles involving only the revisiting PMs and global cycles involving both revisiting and non-revisiting PMs. The process switches between these two types of cycles such that the process never reaches a steady state. Based on this finding, the mechanism underlying such processes is revealed and analytical expressions are given for the calculation of their cycle time. Illustrative examples are presented to show the application of the proposed approach.

关键词： Robots Semiconductor device modeling Fabrication Schedules Analytical models system recovery Load modeling

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Classification of facial diagnosis gloss in Chinese medicine based on different algorithms

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Chinese journal of integrative medicine 2016年 2016 Apr 4页

作者： Fu-Feng Li Rui Zhou Guo-Zheng Li Rui-Wei Zhao Four Diagnosis Laboratory of Traditional Chinese Medicine Shanghai University of Traditional Chinese Medicine Shanghai 201203 China. li_fufeng@***. Four Diagnosis Laboratory of Traditional Chinese Medicine Shanghai University of Traditional Chinese Medicine Shanghai 201203 China. The Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Control Science & Engineering Tongji University Shanghai 201804 China.

OBJECTIVE:To design a face gloss classification model and to provide an automatic and quantitative approach for the diagnosis of Chinese medicine (CM) based on the face images. METHODS:To classify the face gloss images into two groups (gloss and non-gloss), feature extraction methods were applied to the original images. The original images were supposed to obtain a more ideal representation in which gloss information was better revealed in four color spaces [including red, green, blue (RGB), hue, saturation, value (HSV), Gray and Lab]. Principal component analysis (PCA), 2-dimensional PCA (2DPCA), 2-directional 2-dimensional PCA [(2D)PCA], linear discriminant analysis (LDA), 2-dimensional LDA (2DLDA), and partial least squares (PLS) were used as the feature extraction methods of face gloss. k nearest neighbor was used as the classifification method. RESULTS:All the six feature extraction methods were useful in extracting information of face gloss, especially LDA, which had the best prediction accuracy in the 4 color spaces. The average accuracy of LDA in the Lab was 7%-10% higher than that of PCA, 2DPCA, (2D)PCA and 2DLDA P<0.05). The prediction accuracy of LDA reached 98% in the Lab color space and showed practical usage in clinical diagnosis. The consistent rate between the CM experts and the facial diagnosis system was 81%. CONCLUSION:A computer-assisted classifification model was designed to provide an automatic and quantitative approach for the gloss diagnosis of CM based on the face images.

关键词： Chinese medicine computer-assisted face gloss diagnosis feature extraction image processing

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Distributed Computation Offloading using Deep Reinforcement Learning in Internet of Vehicles

Distributed Computation Offloading using Deep Reinforcement ...

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IEEE International Conference on Communications in China (ICCC)

作者： Chen Chen Zheng Wang Qingqi Pei Ci He Zhibin Dou State Key Laboratory of Integrated Services Networks Xidian University Xi'an P.R.China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing China The Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China

ISBN: (数字)9781728173276

ISBN: (纸本)9781728173283

In this paper, we first take the moving vehicles as a RP (resource pool), by which we proposed a distributed computation offloading scheme to fully utilize the available resources and reduce task execution time in I0V (Internet of Vehicles). After that, we divide a complex task into many sub-tasks and indicate that how to assign these small tasks to satisfy the task execution time in RP is a NP problem. The executing time of a task is modeled as the longest calculation time among all small tasks, which is actually a min-max problem. For a dynamically vehicular environment, a distributed computing offloading strategy based on deep reinforcement learning is proposed to find the best offloading scheme to minimize the execution time of a task. Numerical results demonstrate that our scheme is better than Partial Flooding Algorithm and can make full use of the available computing resources of surrounding vehicles by considering the mobility of vehicles, the delay of communication transmission, and the separability of the tasks, thus greatly reducing the execution time of the computing tasks.

关键词： Computational modeling Simulation Reinforcement learning User experience Delays Internet Task analysis

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Multicast Capacity Scaling Laws for Multihop Cognitive Networks

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IEEE Transactions on Mobile computing 2012年第11期11卷 1627-1639页

作者： Cheng Wang Shaojie Tang Xiang-Yang Li Changjun Jiang Key Laboratory of Embedded System and Service Computing Building of Electronics and Information Engineering Ministry of Education Shanghai China Department of Computer Science and Engineering University of Tongji China Department of Computer Science Illinois Institute of Technology Chicago IL USA Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China Department of Computer Science and Engineering University of Tongji Shanghai China

In this paper, we study multicast capacity for cognitive networks. We consider the cognitive network model consisting of two overlapping ad hoc networks, called the primary ad hoc network (PaN) and secondary ad hoc network (SaN), respectively. PaN and SaN operate on the same space and spectrum. For PaN (or SaN, respectively), we assume that primary (or secondary, respectively) nodes are placed according to a Poisson point process of intensity n (or m, respectively) over a unit square region. We randomly choose n s (or m s , respectively) nodes as the sources of multicast sessions in PaN (or SaN, respectively), and for each primary source vp (or secondary source v s , respectively), we pick uniformly at random nd primary nodes (or md secondary nodes, respectively) as the destinations of v p (or v s , respectively). Above all, we assume that PaN can adopt the optimal protocol in terms of the throughput. Our main work is to design the multicast strategy for SaN by which the optimal throughput can be achieved, without any negative impact on the throughput for PaN in order sense. Depending on nd and n, we choose the optimal one for PaN from two strategies called percolation strategy and connectivity strategy, respectively. Subsequently, we design the corresponding throughput-optimal strategy for SaN. We derive the regimes in terms of n, n d , m, and m d in which the upper bounds on multicast capacities for PaN and SaN can be achieved simultaneously. Unicast and broadcast capacities for the cognitive network can be derived by our results as the special cases by letting n d = 1 (or m d = 1) and n d = n - 1 (or m d = m - 1), respectively, which enhances the generality of this work.

关键词： Throughput Ad hoc networks Road transportation Slabs Routing Lattices Mobile computing

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Petri net-based scheduling analysis of dual-arm cluster tools with wafer revisiting

Petri net-based scheduling analysis of dual-arm cluster tool...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Yan Qiao NaiQi Wu MengChu Zhou Department of Industrial Engineering School of Electro-Mechanical Engineering Guangdong University of Technology Guangzhou China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

With wafer revisit, it is complicated to schedule cluster tools in semiconductor fabrication. In wafer fabrication processes, such as atomic layer deposition (ALD), the wafers need to visit some process modules for a number of times. The existing swap-based strategy can be used to operate a dual-arm cluster tool for such a process. It results in a 3-wafer cyclic schedule. However, it is not optimal in the sense of cycle time. Thus, to search for a better schedule, a Petri net model is developed for a dual-arm cluster tool with wafer revisit. With it, the properties of the 3-wafer schedule are analyzed. It is found that, to improve the performance, it is necessary to reduce the number of wafers completed in a cycle. Thus, a 1-wafer schedule is developed by using a new swap-based strategy.

关键词： Robots Schedules Semiconductor device modeling Firing Steady-state Load modeling Transient analysis

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Real-time multi-target path prediction and planning for autonomous driving aided by FCN

arXiv

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arXiv 2019年

作者： Zhou, Hongtu Yang, Xinneng Zhang, Enwei Zhao, Junqiao Cai, Lewen Ye, Chen Wu, Yan Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai Institute of Intelligent Vehicle Tongji University Shanghai Department of Computer Science Nanjing University Nanjing China

Real-time multi-target path planning is a key issue in the field of autonomous driving. Although multiple paths can be generated in real-time with polynomial curves, the generated paths are not flexible enough to deal with complex road scenes such as S-shaped road and unstructured scenes such as parking lots. Search and sampling-based methods, such as A* and RRT and their derived methods, are flexible in generating paths for these complex road environments. However, the existing algorithms require significant time to plan to multiple targets, which greatly limits their application in autonomous driving. In this paper, a real-time path planning method for multi-targets is proposed. We train a fully convolutional neural network (FCN) to predict a path region for the target at first. By taking the predicted path region as soft constraints, the A* algorithm is then applied to search the exact path to the target. Experiments show that FCN can make multiple predictions in a very short time (50 times in 40ms), and the predicted path region effectively restrict the searching space for the following A* search. Therefore, the A* can search much faster so that the multi-target path planning can be achieved in real-time (3 targets in less than 100ms). Copyright © 2019, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Polynomial-time hierarchy of computable reals

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Journal of Computational Information systems 2011年第4期7卷 1108-1115页

作者： Chen, Qingliang Su, Kaile Wu, Lijun Department of Computer Science Jinan University Guangzhou 510632 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Key Laboratory of High Confidence Software Technologies Ministry of Education Peking University Beijing China Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia School of Computer Science and Engineering University of Electronic Science and Technology Chengdu 610054 China

In mathematics, various representations of real numbers have been investigated and all these representations are proved to be mathematically equivalent. Furthermore, it is known that all effective versions of these representations lead to the same class of "computable real numbers". However, when subrecursive (such as primitive recursive) is taken into account, these representations can lead to different notions of "computable real numbers". This paper will look into the polynomial-time version of the problem for computable real numbers under different representations. We will summarize the known results to exhibit the comprehensive hierarchy they form. Our goal is to clarify systematically how the polynomial-time computability depends on the representations of the real numbers. Copyright © 2011 Binary Information Press.

关键词： Polynomial approximation

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Multicast Throughput of Hybrid Wireless Networks Under Gaussian Channel Model

Multicast Throughput of Hybrid Wireless Networks Under Gauss...

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IEEE International Conference on Distributed computing systems

作者： Cheng Wang Shaojie Tang Xiang-Yang Li Changjun Jiang Yunhao Liu Department of Computer Science and Technology Tongji University Shanghai China Department of Computer Science Illinois Institute of Technology Chicago IL 60616 Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science and Engineering Hong Kong University of Science and Technology

ISBN: (纸本)9781424444816

We study the multicast capacity for hybrid wireless networks consisting of ordinary wireless nodes and base stations under Gaussian Channel model, which generalizes both the unicast capacity and broadcast capacity for hybrid wireless networks. We simply consider the hybrid extended network, where the ordinary wireless nodes are placed in the square region A(n) with side-length n~(1/2) according to a Poisson point process with unit intensity. In addition, m additional base stations (BSs) serving as the relay gateway are placed regularly in the region A(n) and they are connected by a high-bandwidth wired network. Three broad categories of multicast strategies are proposed in this paper. According to the different scenarios in terms of m, n and n_d, we select the optimal scheme from the three categories of strategies, and derive the achievable multicast throughput based on the optimal decision.

关键词： Wireless networks Multicast Gaussian channels point operation BASE STATION HYBRID Hybrid electric vehicles Throughput

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ASBSO: An improved brain storm optimization with flexible search length and memory-based selection

arXiv

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arXiv 2021年

作者： Yu, Yang Gao, Shangce Wang, Yirui Cheng, Jiujun Todo, Yuki The Faculty of Engineering University of Toyama Toyama930-8555 Japan Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science and Technology Tongji University Shanghai200092 China The School of Electrical and Computer Engineering Kanazawa University Kanazawa-shi920-1192 Japan

Brain storm optimization (BSO) is a newly proposed population-based optimization algorithm which uses a logarithmic sigmoid transfer function to adjust its search range during the convergent process. However, this adjustment only varies with the current iteration number, and lacks of flexibility and variety which makes a poor search efficiency and robustness of BSO. To alleviate this problem, an adaptive step length structure together with a success memory selection strategy are proposed to be incorporated into BSO. This proposed method, adaptive step length based on memory selection BSO, namely ASBSO, applies multiple step lengths to modify the generation process of new solutions, thus supplying a flexible search according to corresponding problems and convergent periods. The novel memory mechanism which is capable of evaluating and storing the degree of improvements of solutions is used to determine the selection possibility of step lengths. A set of 57 benchmark functions are used to test ASBSO's search ability, and four real-world problems are adopted to show its application value. All these test results indicate the remarkable improvement in solution quality, scalability and robustness of ASBSO. Copyright © 2021, The Authors. All rights reserved.

关键词： Swarm intelligence

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Risk Contagion in Interbank Lending Networks: A Multi-Agent-Based Modeling and Simulation Perspective

SSRN

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SSRN 2024年

作者： Ding, Zhijun Yan, Huanlan Chen, Yijun The Key Laboratory of Embedded System and Service Computing Tongji University No.4800 Caoan Gonglu Road Shanghai201804 China The Department of Computer Science and Technology Tongji University No.4800 Caoan Gonglu Road Shanghai201804 China Shanghai Artificial Intelligence Laboratory No.701 Yunjin Road Shanghai200232 China

The interbank lending network, while facilitating transactions, poses risk contagion in the banking system. To address challenges in existing research, this paper models the interbank lending network using a multi-agent approach and generates a dynamic network through a bidirectional matching mechanism(BiMM). Additionally, We divide banks into three categories based on realistic bank balance sheet data. Given the fact that banks of different categories exhibit different degrees of contagion, we propose the weighted DebtRank (W-DebtRank). Then, comprehensive simulation analysis is conducted on the risk contagion of heterogeneous banks in the dynamic network using W-DebtRank. The proposed method addresses four issues: the heterogeneity of banks, the dynamic nature of networks, the practicality of lending strategies, and the comprehensiveness of risk analysis. The model is validated using the SRISK metric and the Monte Carlo-based SYMBOL simulation model. Through multiple perspectives in simulation experiments, the risk contagion process among banks is deeply investigated in our paper. © 2024, The Authors. All rights reserved.

关键词： Computational methods

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