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Upper bounds on lifetime of ordinary clustering ultra wide band sensor networks

Ruan Jian Xue Bao/Journal of Software 2011年第2期22卷 313-322页

作者： Xu, Juan Hong, Yong-Fa Wang, Cheng Bai, Xing-Zhen College of Electronics and Information Engineering Tongji University Shanghai 200092 China Key Laboratory of Embedded System and Service Computing of the Ministry of Education Tongji University Shanghai 200092 China College of Information Science and Engineering Shandong University of Science and Technology Qingdao 266510 China

This paper considers the time hopping impulse radio ultra wide band (TH-IR UWB) sensor networks, with n sensor nodes uniformly and independently located on a square and a Sink. The upper bounds on the lifetime of ordinary clustering dense and extended TH-IR UWB sensor networks are derived respectively. For ordinary clustering networks, the cluster head aggregates data from the nodes within its own cluster, and relays data to other cluster heads. The results indicate that the upper bounds on the lifetime of ordinary clustering dense TH-IR UWB are in proportion with the node density (or the number of nodes), while the upper bounds on the lifetime of extended networks are inversely proportional to the size of the deployment area (or the number of nodes). The results also reveal that the upper bound on the lifetime of dense network in the ideal case are longer than those of the static network by a factor of (n/log n)1/2, and the upper bound on the lifetime of extended network in the ideal case are longer than those of the static network by a factor of (λn/log(λn))1/2. Therefore, sensor nodes, or the Sink moving randomly in the square, can improve the lifetime of ordinary clustering TH-IR UWB sensor network. © 2011 ISCAS.

关键词： Energy dissipation

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An Improved Adaptive Exponential Smoothing Model for Short-term Travel Time Forecasting of Urban Arterial Street

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自动化学报 2008年第11期34卷 1404-1409页

作者： LI Zhi-Peng YU Hong LIU Yun-Cai LIU Fu-Qiang Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 P.R. China Intelligent Transportation System Department Shanghai Electrical Apparatus Research Institute Shanghai 200063 P. R. China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 P. R. China

旅行时间的短期的预报为聪明的交通系统的成功是必要的。在这份报纸，我们考察预报模型的短期的交通的 state-of-art 并且构画出他们每个模型的基本想法，相关工作，优点和劣势。一改进适应指数的变光滑(IAES ) 模型也被建议克服以前的... 详细信息

旅行时间的短期的预报为聪明的交通系统的成功是必要的。在这份报纸，我们考察预报模型的短期的交通的 state-of-art 并且构画出他们每个模型的基本想法，相关工作，优点和劣势。一改进适应指数的变光滑(IAES ) 模型也被建议克服以前的适应指数的变光滑模型的缺点。然后，比较实验在状况和反常交通调节评估在牌照匹配获得的直接旅行时间数据(每分钟行数) 上预报模型的四个主要分支的性能的正常交通下面被执行。实验的结果证明每个模型似乎有它的自己的力量和软弱。IASE 的预报表演比在更突然预报地平线(预报的和二步) 的另外的模型优异， IASE 能够处理各种交通条件。

关键词：自适应指数平滑模型短期旅行时间预测预测方法信息处理技术城市街道设计方案

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Facial StO2-based Stress Recognition using Automatic Graph Generation and Dual-Stream GNN

Facial StO2-based Stress Recognition using Automatic Graph G...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jia, Yingjuan Wang, Hanpu Liu, Xinyu Chen, Tong Southwest University College of Electronic and Information Engineering 400715 China Southwest University Chongqing Key Laboratory of Generic Technology and System of Service Robots China Southwest University Institute of Legal Psychology and Intelligent Computing 400715 China

ISBN: (纸本)9798350386226

The recognition of stress responses in health, resilience, and psychopathology holds significant scientific importance. The biological information carried by the facial tissue oxygen saturation (StO2) is a useful indicator for stress recognition. Although graph-based methods have achieved state-of-the-art (SOTA), there is room for improvement. This study proposes an automatic graph generation method for facial StO2, replacing manual methods and enhancing the information of graphs. To further enhance the capability of graphs' representation, a dimensionality reduction strategy for nodes is proposed. The strategy is implemented based on an novel objective function that can increase the inter-class distance and reduce the intra-class distance. After obtaining highly representative graphs, a dual-stream network integrating GAT and GCN is designed to extract stress-related features from the graphs, ultimately achieving the SOTA recognition accuracy. © 2024 IEEE.

关键词： Bioinformatics

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How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization

arXiv

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arXiv 2023年

作者： Zhang, Hai Yu, Hang Zhao, Junqiao Zhang, Di Huang, Chang Zhou, Hongtu Zhang, Xiao Ye, Chen Department of Computer Science Tongji University Shanghai China MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China

Designing and deriving effective model-based reinforcement learning (MBRL) algorithms with a performance improvement guarantee is challenging, mainly attributed to the high coupling between model learning and policy optimization. Many prior methods that rely on return discrepancy to guide model learning ignore the impacts of model shift, which can lead to performance deterioration due to excessive model updates. Other methods use performance difference bound to explicitly consider model shift. However, these methods rely on a fixed threshold to constrain model shift, resulting in a heavy dependence on the threshold and a lack of adaptability during the training process. In this paper, we theoretically derive an optimization objective that can unify model shift and model bias and then formulate a fine-tuning process. This process adaptively adjusts the model updates to get a performance improvement guarantee while avoiding model overfitting. Based on these, we develop a straightforward algorithm USB-PO2 (Unified model Shift and model Bias Policy Optimization). Empirical results show that USB-PO achieves state-of-the-art performance on several challenging benchmark tasks. © 2023, CC BY.

关键词： Benchmarking

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Focus On What Matters: Separated Models For Visual-Based RL Generalization

arXiv

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arXiv 2024年

作者： Zhang, Di Lv, Bowen Zhang, Hai Yang, Feifan Zhao, Junqiao Yu, Hang Huang, Chang Zhou, Hongtu Ye, Chen Jiang, Changjun Department of Computer Science Tongji University Shanghai China MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China

A primary challenge for visual-based Reinforcement Learning (RL) is to generalize effectively across unseen environments. Although previous studies have explored different auxiliary tasks to enhance generalization, few adopt image reconstruction due to concerns about exacerbating overfitting to task-irrelevant features during training. Perceiving the pre-eminence of image reconstruction in representation learning, we propose SMG (Separated Models for Generalization), a novel approach that exploits image reconstruction for generalization. SMG introduces two model branches to extract task-relevant and task-irrelevant representations separately from visual observations via cooperatively reconstruction. Built upon this architecture, we further emphasize the importance of task-relevant features for generalization. Specifically, SMG incorporates two additional consistency losses to guide the agent’s focus toward task-relevant areas across different scenarios, thereby achieving free from overfitting. Extensive experiments in DMC demonstrate the SOTA performance of SMG in generalization, particularly excelling in video-background settings. Evaluations on robotic manipulation tasks further confirm the robustness of SMG in real-world applications. Source code is available at https://***/r/SMG/. © 2024, CC BY.

关键词： 3D reconstruction

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Observer-based controller design for networked systems with multiple packet losses and mixed delay

Observer-based controller design for networked systems with ...

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第27届中国控制与决策会议

作者： Fengfeng Qian Huaicheng Yan Hongbo Shi Hao Zhang Key Laboratory of Advanced Control and Optimization for Chemical Process of Ministry of Education School of Information Science and Engineering East China University of Science and Technology Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University

The paper focuses on a stabilizing controller design problem for networked systems with quantization,mixed delays,and a series of packet losses due to the signal transmission through the unreliable communication *** of both sensor-to-controller and controller-to-actuator are taken into account for the networked systems,and the distributed time delay is also considered in the network *** conditions for designing the controller as well as the system parameters can be obtained by solving certain linear matrix ***,the effectiveness of the designed method is proved by a numerical example.

关键词： quantization,mixed delays,multiple packet losses

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Maximally permissive distributed control of large scale automated manufacturing systems modeled with Petri nets

Maximally permissive distributed control of large scale auto...

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

作者： Hu, Hesuan Zhou, Mengchu Liu, Yang School of Electro-mechanical Engineering Xidian University Xi'an Shaanxi 710071 China School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore 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 United States

ISBN: (纸本)9781479915156

Ensuring nonblockingness remains challenging for automated manufacturing systems (AMS) owing to their discrete event dynamics. Both scalability and maximal permissiveness are essential for the synthesis and implementation of their centralized supervisors. Inspired by the divide and conquer philosophy, this work proposes a partition methodology and distributed control technique for large scale AMS. They are represented as interconnected and overlapping subsystems sharing some common components in terms of buffers. For each subsystem, a local supervisor is designed based on its local behavior and neighboring information only. Generalizing the existing results, we develop a condition under which the control law via decomposition promises the maximal permissiveness. Buffer capacities are well designed for the sake of their decomposition into multiple overlapping subsystems. Theoretical results are developed to characterize the behavior compatibility among local controllers. An experimental study illustrates the effectiveness of the proposed method. © 2013 IEEE.

关键词： Supervisory personnel

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How to fine-tune the model: unified model shift and model bias policy optimization 23

How to fine-tune the model: unified model shift and model bi...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Hai Zhang Hang Yu Junqiao Zhao Di Zhang Chang Huang Hongtu Zhou Xiao Zhang Chen Ye Department of Computer Science Tongji University Shanghai China and MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China

Designing and deriving effective model-based reinforcement learning (MBRL) algorithms with a performance improvement guarantee is challenging, mainly attributed to the high coupling between model learning and policy optimization. Many prior methods that rely on return discrepancy to guide model learning ignore the impacts of model shift, which can lead to performance deterioration due to excessive model updates. Other methods use performance difference bound to explicitly consider model shift. However, these methods rely on a fixed threshold to constrain model shift, resulting in a heavy dependence on the threshold and a lack of adaptability during the training process. In this paper, we theoretically derive an optimization objective that can unify model shift and model bias and then formulate a fine-tuning process. This process adaptively adjusts the model updates to get a performance improvement guarantee while avoiding model over-fitting. Based on these, we develop a straightforward algorithm USB-PO (Unified model Shift and model Bias Policy Optimization). Empirical results show that USB-PO achieves state-of-the-art performance on several challenging benchmark tasks. Code: https://***/betray12138/***

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GradSA: Gradient Sparsification and Accumulation for Communication-Efficient Distributed Deep Learning 15th

GradSA: Gradient Sparsification and Accumulation for Communi...

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15th International Conference on Green, Pervasive, and Cloud computing, GPC 2020

作者： Liu, Bo Jiang, Wenbin Zhao, Shaofeng Jin, Hai He, Bingsheng National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Huazhong University of Science and Technology Wuhan China Library Henan University of Economics and Law Zhengzhou China Department of Computer Science School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9783030642426

Large-scale distributed deep learning is of great importance in various applications. For distributed training, the inter-node gradient communication often becomes the performance bottleneck. Gradient sparsification has been proposed to reduce the communication overhead. However, the sparsification only arbitrarily selects a small fraction of gradients, while the efficiency of selection dimension has been overlooked. Furthermore, gradient staleness is inevitable after applying the sparsification which will ultimately lead to model divergence. In this paper, we propose a staleness-compensated sparse stochastic gradient descent algorithm, GradSA, to improve the training efficiency. Layer-level gradients are sparsified to reduce the communication overhead, which conform to the characteristics of the network structure, and historical accumulation of the approximated gradients is utilized to speed up convergence. We demonstrate the model convergence acceleration and the efficiency of our layer-level selection over existing state-of-the-art works, such as DGC, TernGrad, and 8-Bit quantization. © 2020, Springer Nature Switzerland AG.

关键词： Efficiency

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An efficient algorithm of information recommendation between groups in social networks

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2015年第6期43卷 1090-1093页

作者： Huang, Zhen-Hua Zhang, Bo Fang, Qiang Xiang, Yang Department of Computer and Technology Tongji University Shanghai201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China College of Information Mechanical and Electrical Engineering Shanghai Normal University Shanghai200234 China

Information recommendation between groups is one of the most important ways for information sharing and transmitting in social networks. However, it needs exponential time cost to achieve the exact optimal recommendation solution. Hence this paper proposed an efficient algorithm EAOORS (Efficient Algorithm for Obtaining Optimal Recommendation Solution) which equivalently reduced this exponential time cost problem to extended steiner tree problem, and fast achieved the approximate optimal recommendation solution in the polynomial time complexity. The experimental result shows that the EAOORS algorithm is more efficient than the existing ones. ©, 2015, Chinese Institute of Electronics. All right reserved.

关键词： Approximation algorithms

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