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Variable petri nets for mobility

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arXiv 2021年

作者： Ding, Zhijun Yang, Ru Cui, Puwen Zhou, MengChu Jiang, Changjun Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China Department of Computer Science and Technology Tongji University Shanghai201804 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Mobile computing systems, service-based systems and some other systems with mobile interacting components have recently received much attention. However, because of their characteristics such as mobility and disconnection, it is difficult to model and analyze them by using a structure-fixed model. This work proposes a new Petri net model called Variable Petri Net (VPN) for modeling and analyzing these systems. The definition, firing rule, and related analysis technology of VPN are introduced in detail. In a VPN, the possible interaction interfaces are abstracted as a new kind of places called virtual places, and the occurrences of (dis)connections are described by new functions, which makes it appropriate to describe the component collaboration in systems and realize the scalability and pluggability of systems. Moreover, to overcome the shortcoming that markings cannot reflect link capability of a system, VPNs add a constraint function along with a marking to represent a complete system configuration. Several examples are used to demonstrate the newly proposed model and method. Copyright © 2021, The Authors. All rights reserved.

关键词： Embedded systems

Group-wise inhibition based feature regularization for robust classification

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arXiv 2021年

作者： Liu, Haozhe Wu, Haoqian Xie, Weicheng Liu, Feng Shen, Linlin 1Computer Vision Institute College of Computer Science and Software Engineering 2SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society 3National Engineering Laboratory for Big Data System Computing Technology 4Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen 518060 China

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most discriminative regions, but ignores the auxiliary features when learning, leading to the lack of feature diversity for final judgment. In our method, we propose to dynamically suppress significant activation values of CNN by group-wise inhibition, but not fixedly or randomly handle them when training. The feature maps with different activation distribution are then processed separately to take the feature independence into account. CNN is finally guided to learn richer discriminative features hierarchically for robust classification according to the proposed regularization. Our method is comprehensively evaluated under multiple settings, including classification against corruptions, adversarial attacks and low data regime. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both robustness and generalization performances, when compared with the state-of-the-art methods. Code is available at https://***/LinusWu/TENET_Training. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

Petri net-based real-time scheduling of time-constrained single-arm cluster tools with activity time variation

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Petri net-based real-time scheduling of time-constrained sin...

IEEE International Conference on Robotics and Automation (ICRA)

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

It is challenging to schedule time-constrained cluster tools subject to activity time variation. With the help of their Petri net model, a real-time control policy is used to offset the activity time variation. Based on it, the schedulability conditions and scheduling algorithms are presented for single-arm cluster tools. The schedulability conditions can be analytically checked. Algorithms are developed based on analytical expressions such that it is also computationally efficient. The schedule obtained by the scheduling algorithms together with a real-time control policy forms the real-time schedule. It is optimal in terms of cycle time.

关键词： Robots Semiconductor device modeling Schedules Load modeling Real time systems Manganese Bismuth

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

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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

Modeling and analysis of dual-arm cluster tools for wafer fabrication with revisiting

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Modeling and analysis of dual-arm cluster tools for wafer fa...

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

Scaling Laws of Multicast Capacity for Power-Constrained Wireless Networks under Gaussian Channel Model

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IEEE Transactions on computers 2012年第5期61卷 713-725页

作者： Cheng Wang Changjun Jiang Xiang-Yang Li Shaojie Tang Yuan He Xufei Mao Yunhao Liu Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science and Engineering Building of Electronics and Information Engineering University of Tongji Shanghai China Department of Computer Science Illinois Institute of Technology Chicago IL USA Tsinghua National Laboratory for Information Science and Technology (TNLIST) Tsinghua University Beijing China Department of Computer Science and Engineering University of Tongji China Department of Computer Science Hong Kong University of Science and Technology Kowloon Hong Kong China Tsinghua National Laboratory for Information Science and Technology (TNLIST) School of Software Tsinghua University Beijing China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China

We study the asymptotic networking-theoretic multicast capacity bounds for random extended networks (REN) under Gaussian channel model, in which all wireless nodes are individually power-constrained. During the transmission, the power decays along path with attenuation exponent α >; 2. In REN, n nodes are randomly distributed in the square region of side length √n. There are n s randomly and independently chosen multicast sessions. Each multicast session has n d + 1 randomly chosen terminals, including one source and n d destinations. By effectively combining two types of routing and scheduling strategies, we analyze the asymptotic achievable throughput for all n s = ω(1) and nd. As a special case of our results, we show that for n s = Θ(n), the per-session multicast capacity for REN is of order Θ(1/√n d n) when nd = O(n/(log n) a+1 ) and is of order Θ(1/n d · (log n) -n/2 ) when n d = Ω(n/log n).

关键词： Lattices Throughput Upper bound Road transportation Receivers Interference Routing

DL-SLOT: Dynamic LiDAR SLAM and object tracking based on collaborative graph optimization

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arXiv 2022年

作者： Tian, Xuebo Zhu, Zhongyang Zhao, Junqiao Tian, Gengxuan Ye, Chen Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

Ego-pose estimation and dynamic object tracking are two critical problems for autonomous driving systems. The solutions to these problems are generally based on their respective assumptions, i.e., the static world assumption for simultaneous localization and mapping (SLAM) and the accurate ego-pose assumption for object tracking. However, these assumptions are challenging to hold in dynamic road scenarios, where SLAM and object tracking become closely correlated. Therefore, we propose DL-SLOT, a dynamic LiDAR SLAM and object tracking method, to simultaneously address these two coupled problems. This method integrates the state estimations of both the autonomous vehicle and the stationary and dynamic objects in the environment into a unified optimization framework. First, we used object detection to identify all points belonging to potentially dynamic objects. Subsequently, a LiDAR odometry was conducted using the filtered point cloud. Simultaneously, we proposed a sliding window-based object association method that accurately associates objects according to the historical trajectories of tracked objects. The ego-states and those of the stationary and dynamic objects are integrated into the sliding window-based collaborative graph optimization. The stationary objects are subsequently restored from the potentially dynamic object set. Finally, a global pose-graph is implemented to eliminate the accumulated error. Experiments on KITTI datasets demonstrate that our method achieves better accuracy than SLAM and object tracking baseline methods. This confirms that solving SLAM and object tracking simultaneously is mutually advantageous, dramatically improving the robustness and accuracy of SLAM and object tracking in dynamic road scenarios. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical radar

Maximally permissive control of flexible manufacturing systems with fewest disjunctive constraints

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Maximally permissive control of flexible manufacturing syste...

2012 International Conference onAdvanced Mechatronic systems, ICAMechS 2012

作者： Chen, YuFeng Zhou, MengChu Li, XiaoOu School of Electro-Mechanical Engineering Xidian University Xi'an 710071 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102 United States Departamento de Computación CINVESTAV-IPN Av. IPN 2508 Col. Zacatenco México DF C.P. 07360 Mexico

ISBN: (纸本)9780955529382

For Petri net models whose legal reachability spaces are non-convex, one cannot optimally control them by the conjunctions of linear constraints. This work proposes a method to find a set of linear constraints such that each constraint can forbid all first-met bad markings and every legal marking satisfies at least one constraint. Then, the disjunctions of the obtained constraints can make all legal markings reachable and forbid all first-met bad markings, i.e., the controlled net is live and maximally permissive. The work also develops an integer linear programming model to minimize the number of the constraints. Finally, an example is provided to illustrate the proposed method. © 2012 Intl Jrnal Advanced Mech.

关键词： Flexible manufacturing systems

DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization

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arXiv 2022年

作者： Tian, Xuebo Zhao, Junqiao Ye, Chen Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

Ego-pose estimation and dynamic object tracking are two key issues in an autonomous driving system. Two assumptions are often made for them, i.e. the static world assumption of simultaneous localization and mapping (SLAM) and the exact ego-pose assumption of object tracking, respectively. However, these assumptions are difficult to hold in highly dynamic road scenarios where SLAM and object tracking become correlated and mutually beneficial. In this paper, DL-SLOT, a dynamic Lidar SLAM and object tracking method is proposed. This method integrates the state estimations of both the ego vehicle and the static and dynamic objects in the environment into a unified optimization framework, to realize SLAM and object tracking (SLOT) simultaneously. Firstly, we implement object detection to remove all the points that belong to potential dynamic objects. Then, LiDAR odometry is conducted using the filtered point cloud. At the same time, detected objects are associated with the history object trajectories based on the time-series information in a sliding window. The states of the static and dynamic objects and ego vehicle in the sliding window are integrated into a unified local optimization framework. We perform SLAM and object tracking simultaneously in this framework, which significantly improves the robustness and accuracy of SLAM in highly dynamic road scenarios and the accuracy of objects' states estimation. Experiments on public datasets have shown that our method achieves better accuracy than A-LOAM. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection