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作者： Alham, Nasullah Khalid Li, Maozhen Liu, Yang Qi, Man Nuffield Division of Clinical Laboratory Sciences Radcliffe Department of Medicine University of Oxford OX3 9DU United Kingdom School of Engineering and Design Brunel University Uxbridge Middlesex UB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Ministry of Education Tongji University China School of Electrical Engineering and Information Systems Sichuan University Chengdu 610065 China Department of Computing Canterbury Christ Church University Canterbury Kent CT1 1QU United Kingdom

A combination of classifiers leads to a substantial reduction of classification errors in a wide range of applications. Among them, support vector machine (SVM) ensembles with bagging have shown better performance in classification than a single SVM. However, the training process of SVM ensembles is notably computationally intensive, especially when the number of replicated training datasets is large. This paper presents MRESVM, a MapReduce-based distributed SVM ensemble algorithm for scalable image annotation which re-samples the training dataset based on bootstrapping and trains an SVM on each dataset in parallel using a cluster of computers. A balanced sampling strategy for bootstrapping is introduced to increase the classification accuracy. MRESVM is evaluated in both experimental and simulation environments, and the results show that the MRESVM algorithm reduces the training time significantly while achieving a high level of accuracy in classifications. © 2013 Elsevier Ltd. All rights reserved.

关键词： Image annotation

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RETRACTED ARTICLE: Stability analysis of dynamic collaboration model under two-lane case

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Nonlinear Dynamics 2014年第1期81卷 1021-1021页

作者： Li, Zhipeng Zhang, Run Xu, Shangzhi Qian, Yeqing The Key Laboratory of Embedded System and Service Computing Supported by Ministry of Education Tongji University Shanghai China

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Unsupervised joint learning of depth, optical flow, ego-motion from video

arXiv

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arXiv 2021年

作者： Li, Jianfeng Zhao, Junqiao Song, Shuangfu Feng, Tiantian The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China School of Surveying and Geoinfomatics Tongji University Shanghai China Insititute of Intelligent Vehicle Tongji University Shanghai China

Estimating geometric elements such as depth, camera motion, and optical flow from images is an important part of the robot's visual perception. We use a joint selfsupervised method to estimate the three geometric elements. Depth network, optical flow network and camera motion network are independent of each other but are jointly optimized during training phase. Compared with independent training, joint training can make full use of the geometric relationship between geometric elements and provide dynamic and static information of the scene. In this paper, we improve the joint self-supervision method from three aspects: network structure, dynamic object segmentation, and geometric constraints. In terms of network structure, we apply the attention mechanism to the camera motion network, which helps to take advantage of the similarity of camera movement between frames. And according to attention mechanism in Transformer, we propose a plug-and-play convolutional attention module. In terms of dynamic object, according to the different influences of dynamic objects in the optical flow self-supervised framework and the depth-pose self-supervised framework, we propose a threshold algorithm to detect dynamic regions, and mask that in the loss function respectively. In terms of geometric constraints, we use traditional methods to estimate the fundamental matrix from the corresponding points to constrain the camera motion network. We demonstrate the effectiveness of our method on the KITTI dataset. Compared with other joint self-supervised methods, our method achieves state-of-the-art performance in the estimation of pose and optical flow, and the depth estimation has also achieved competitive *** Codes 65Dxx © 2021, CC BY-NC-ND.

关键词： Geometry

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Maximally permissive control of flexible manufacturing systems with fewest disjunctive constraints

Maximally permissive control of flexible manufacturing syste...

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International Conference on Advanced Mechatronic systems (ICAMechS)

作者： YuFeng Chen MengChu Zhou XiaoOu Li School of Electro-Mechanical Engineering Xidian University Xi'an China 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 Departamento de Computacion CINVESTAVIPN Mexico Distrito Federal Mexico

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.

关键词： Law Optimal control Vectors Aerospace electronics Petri nets system recovery

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Battery discharge characteristics of wireless sensors in building applications

Battery discharge characteristics of wireless sensors in bui...

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IEEE International Conference on Networking, Sensing and Control

作者： Wenqi Guo William M. Healy Meng Chu Zhou Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA National Institute for Standards and Technology Gaithersburg MD USA Key Laboratory of Embedded System and Service Computing Ministry of Education University of Tongji Shanghai China ECE Department New Jersey Institute of Technology Newark NJ USA

Sensor nodes in wireless networks often use batteries as their source of energy, but replacing or recharging exhausted batteries in a deployed network can be difficult and costly. Therefore, prolonging battery life becomes a principal objective in the design of wireless sensor networks (WSNs). There is little published data that quantitatively analyze a sensor node's lifetime under different operating conditions. This paper presents several experiments to quantify the impact of key wireless sensor network design and environmental parameters on battery performance. Our testbed consists of MicaZ motes, commercial alkaline batteries, and a suite of techniques for measuring battery performance. We evaluate known parameters, such as communication distance, working channel and operating power that play key roles in battery performance. Through extensive real battery discharge measurements, we expect our results to serve as a quantitative basis for future research in designing and implementing battery-efficient sensing applications and protocols.

关键词： Batteries Wireless sensor networks Discharges (electric) Sensor phenomena and characterization Battery charge measurement Voltage measurement

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On iterative liveness-enforcement for a class of generalized Petri nets

On iterative liveness-enforcement for a class of generalized...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： YiFan Hou Ding Liu MengChu Zhou School of Electro-Mechanical Engineering Xidian University Xian China Key Laboratory of Embedded System and Service Computer Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

This work presents an iterative liveness-enforcing method for a class of generalized Petri nets, which can model flexible manufacturing systems. The proposed method checks the liveness of net models using mixed integer programming and controls the token allocations of resource places instead of siphons using a liveness and resource usage ratio-enforcing supervisor. The enumeration of a kind of special structures, which is required in the previous work, is avoided and the number of iterations is bounded by the number of shared resource places in the net model. All strict minimal siphons in the controlled systems are minimally controlled. Several explanatory examples are used to illustrate this method.

关键词： Monitoring Petri nets Iterative methods system recovery educational institutions Resource management Vectors

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General capacity scaling of wireless networks

General capacity scaling of wireless networks

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Cheng Wang Changjun Jiang Xiang-Yang Li Shaojie Tang Panlong Yang Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science University of Tongji Shanghai China Department of Computer Science Illinois Institute of Technology Chicago IL USA TNLIST School of Software Tsinghua University China People''s Liberation Army University of Science and Technology China

We study the general scaling laws of the capacity for random wireless networks under the generalized physical model. The generality of this work is embodied in three dimensions denoted by (λ ∈ [1, n], n d ∈ [1, n], n s ∈ (1, n]). It means that: (1) We study the random network of a general node density λ ∈ [1, n], rather than only study either random dense network (RDN, λ = n) or random extended network (REN, λ = 1) as in the literature. (2) We focus on the multicast capacity to unify unicast and broadcast capacities by setting the number of destinations for each session as a general value n d ∈ [1, n]. (3)We allow the number of sessions changing in the range n s ∈ (1, n], rather than assume that n s = Θ(n) as in the *** derive the general lower bounds on the capacity for the arbitrary case of (λ, n d , n s ). Particularly, we show that for the special cases (λ = 1, n d ∈ [1, n], n s = n) and (λ = n, n d ∈ [1, n], n s = n), our schemes achieve the highest multicast throughputs proposed in the existing works.

关键词： Roads Routing Throughput Lattices Schedules Mathematical model

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A cross layer collaborating cache scheme to improve performance of HTTP clients in MANETs

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International Journal of u- and e- service, Science and Technology 2013年第6期6卷 51-57页

作者： Liu, Jin Ren, Hongmin Wang, Jin Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Shanghai International Shipping Institute Shanghai Maritime University Shanghai China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China College of Information Engineering Shanghai Maritime University Shanghai China

This paper proposes a novel cross layer collaborating cache scheme for HTTP clients. It is designed to accelerate the process that an HTTP client retrieves Web content in mobile ad hoc network environment. Unlike other techniques to exploit Web proxy caching or to build P2P protocol overlay to improve HTTP, our scheme incorporates collaboratively working capability into the HTTP client response cache, and thus there is no impact on the compatibility of upper level applications. In addition, as the sharing file information is diffused with routing information, there is no redundant traffic to maintain the cooperating cache mechanism. Using this scheme, cacheable Web content can be distributed among local peers and retrieved later by a client if needed. In our simulation results, this novel cache scheme significantly decreases the latency experienced by a client to download Web content. © 2013 SERSC.

关键词： HTTP

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Memory-Based Contrastive Learning with Optimized Sampling for Incremental Few-Shot Semantic Segmentation

Memory-Based Contrastive Learning with Optimized Sampling fo...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Yuxuan Zhang Miaojing Shi Taiyi Su Hanli Wang Department of Computer Science & Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System & Service Computing Ministry of Education Tongji University Shanghai P. R. China Department of Control Science & Engineering Tongji University Shanghai P. R. China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Incremental few-shot semantic segmentation (IFSS) aims to incrementally expand a semantic segmentation model’s ability to identify new classes based on few samples. However, it grapples with the dual challenges of catastrophic forgetting (due to feature drift in old classes) and overfitting (triggered by inadequate samples in new classes). To address these issues, a novel approach is proposed to integrate pixel-wise and region-wise contrastive learning, complemented by an optimized example and anchor sampling strategy. The proposed method incorporates a region memory and pixel memory designed to explore the high-dimensional embedding space more effectively. The memory, retaining the feature embeddings of known classes, facilitates the calibration and alignment of seen class features during the learning process of new classes. To further mitigate overfitting, the proposed approach implements an optimized example and anchor sampling strategy. Extensive experiments show the competitive performance of the proposed method. The source code of this work can be found in https://***.

关键词： Circuits and systems Semantic segmentation Source coding Semantics Calibration

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Provably Convergent Federated Trilevel Learning

arXiv

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arXiv 2023年

作者： Jiao, Yang Yang, Kai Wu, Tiancheng Jian, Chengtao Huang, Jianwei Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine learning applications, such as robust neural architecture search, hyperparameter optimization, and domain adaptation. Tackling TLO problems has presented a great challenge due to their nested decision-making structure. In addition, existing works on TLO face the following key challenges: 1) they all focus on the non-distributed setting, which may lead to privacy breach;2) they do not offer any non-asymptotic convergence analysis which characterizes how fast an algorithm converges. To address the aforementioned challenges, this paper proposes an asynchronous federated trilevel optimization method to solve TLO problems. The proposed method utilizes µ-cuts to construct a hyper-polyhedral approximation for the TLO problem and solve it in an asynchronous manner. We demonstrate that the proposed µ-cuts are applicable to not only convex functions but also a wide range of non-convex functions that meet the µ-weakly convex assumption. Furthermore, we theoretically analyze the non-asymptotic convergence rate for the proposed method by showing its iteration complexity to obtain ϵ-stationary point is upper bounded by (Formula Presented). Extensive experiments on real-world datasets have been conducted to elucidate the superiority of the proposed method, e.g., it has a faster convergence rate with a maximum acceleration of approximately 80%. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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