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Monitoring mechanism to users' posting behavior based on Hash chain in wireless Mesh network

Beijing Gongye Daxue Xuebao/Journal of Beijing University of technology 2010年第SUPPL. 2期36卷 119-122页

作者： Pan, Shao-Ming Yang, Ya-Tao Cai, Qi-Zhong Han, Jun-Feng Department of Electronic Information and Control Engineering Guangxi University of Technology Liuzhou 545006 Guangxi China Department of Communication Engineering Beijing Electronic Science and Technology Institute Beijing 100070 China

In order to solve the issue of users' posting behavior via wireless Mesh network, a monitoring mechanism to manage users' continuous posting behavior based on Hash chain is designed. When users access to the Mesh network and write a post, mutual authentication between users and gateway in Mesh network will be implemented firstly, after getting authorization from gateway, users can write new posts on the web server via Mesh network, the gateway in Mesh network forwards users' real and valid information to the web server. When the dissension about posting behavior occurs, web server can execute the non-repudiation tracing to users' posting behavior with the help of users' registrar server. Users' authorization keys can be created continuously by using of Hash chain in our scheme, which can achieve the real-time authorization during users' accessing the network for a long time. Based on this method, users' posting behavior via wireless Mesh network can be monitored and traced, the proposed scheme possesses better referential and practical application value.

关键词： Web services

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Robust distributed routing in dynamical networks with cascading failures

Robust distributed routing in dynamical networks with cascad...

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IEEE Conference on Decision and control

作者： Giacomo Como Ketan Savla Daron Acemoglu Munther A. Dahleh Emilio Frazzoli Department of Automatic Control Lund University Lund Sweden Sonny Astani Department of Civil and Environmental Engineering University of Southern California Los Angeles CA USA Department of Economics Massachusetts Institute of Technology Cambridge MA USA Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

ISBN: (纸本)9781467320658

We consider a dynamical formulation of network flows, whereby the network is modeled as a switched system of ordinary differential equations derived from mass conservation laws on directed graphs with a single origin-destination pair and a constant inflow at the origin. The rate of change of the density on each link of the network equals the difference between the inflow and the outflow on that link. The inflow to a link is determined by the total flow arriving to the tail node of that link and the routing policy at that tail node. The outflow from a link is modeled to depend on the current density on that link through a flow function. Every link is assumed to have finite capacity for density and the flow function is modeled to be strictly increasing up to the maximum density. A link becomes inactive when the density on it reaches the capacity. A node fails if all its outgoing links become inactive, and such node failures can propagate through the network due to rerouting of flow. We prove some properties of these dynamical networks and study the resilience of such networks under distributed routing policies with respect to perturbations that reduce link-wise flow functions. In particular, we propose an algorithm to compute upper bounds on the maximum resilience over all distributed routing policies, and discuss examples that highlight the role of cascading failures on the resilience of the network.

关键词： Resilience Routing Vectors Upper bound Power system faults Power system protection Mathematical model

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Elusion-Based UAV-Assisted Covert Data Collection Under Uncertain Eavesdropper Mobility

Elusion-Based UAV-Assisted Covert Data Collection Under Unce...

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IEEE International Conference on Communications in China (ICCC)

作者： Renge Wang Haichuan Ding Yiqin Deng Ying Ma Jianping An Yuguang Fang School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China School of Control Science and Engineering Shandong University Jinan China School of Information and Electronics Beijing Institute of Technology Beijing China Department of Computer Science City University of Hong Kong Hong Kong China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

This paper studies how to enable efficient covert data collection in a UAV-assisted network under the surveillance of an aerial eavesdropper. Unlike existing work, we consider a mobile eavesdropping UAV which patrols the area of interest with an unknown mobility pattern. By exploiting the legitimate UAV’s mobility, we propose an elusion-based covert data collection scheme by optimizing the trajectory of the legitimate UAV so that it always performs data collection at the locations far away from the eavesdropper to improve the covert throughput. Given the uncertain mobility of the eavesdropper, we formulate the design of the proposed data collection scheme as a reinforcement learning problem and develop a covertness-aware learning algorithm to facilitate efficient solution search. Simulation results demonstrate that, in comparison to existing proposals, our proposed scheme can effectively improve the performance of covert data collection.

关键词： Surveillance Simulation Reinforcement learning Data collection Autonomous aerial vehicles Throughput Search problems

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Online transmission policy in wireless powered networks with urgency-aware age of information 15

Online transmission policy in wireless powered networks with...

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15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019

作者： Lu, Yang Xiong, Ke Fan, Pingyi Zhong, Zhangdui Letaief, Khaled Ben School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781538677476

This paper investigates the age of information (AoI) for a radio frequency (RF) energy harvesting (EH) enabled network, where a sensor first scavenges energy from a wireless power station and then transmits the collected status update to a sink node. To capture the thirst for the fresh update becoming more and more urgent as time elapsing, urgency-aware AoI (U-AoI) is defined, which increases exponentially with the increment of time between two received updates. Due to EH, a waiting time is required at the sensor before transmitting the status update. An optimization problem is formulated to minimize the long-term average U-AoI under constraint of energy causality. A two-layer algorithm is presented to solve it, where the outer loop is designed based on Dinklebach's method, and the inner loop presents a semi-closed-form expression of the optimal waiting time policy based on Karush-Kuhn-Tucker (KKT) optimality conditions. Numerical results show that our proposed optimal transmission policy outperforms the zero time waiting policy and equal time waiting policy in terms of long-term average U-AoI, especially when the networks are in slight load. It also shows that the system U-AoI first decreases and then keeps unchanged with the increments of EH circuit's saturation level. © 2019 IEEE.

关键词： Energy harvesting

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Size and composition dependence of melting temperature of binary nanoparticles

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Science China(Physics,Mechanics & Astronomy) 2011年第5期54卷 897-900页

作者： LU YunBin LIAO ShuZhi XIE Bin CHEN Jia PENG HaoJun ZHANG Chun ZHOU HuiYing XIE HaoWen OUYANG YiFang ZHANG BangWei Key Laboratory of Low Dimensional Quantum Structures and Quantum Control of Ministry of Education Department oy rnystcs Hunan Normal University Changsha 410081 China College of Mathematics and Physics University of South China Hengyang 421001 China College of Mathematics and Computer Science Hunan Normal University Changsha 410081 China Computer and Information Engineering School Central South University of Forestry and Technology Changsha 410004 China Department of Physics Guangxi University Nanning 530004 China Department of Applied Physics Hunan University Changsha 410082 China International Centre for Materials Physics Chinese Academy of Sciences Shenyang 110015 China

Based on the ideal solution approximation, the model for size-dependent melting temperature of pure metal nanoparticles is extended to binary alloy systems. The developed model, free of any adjustable parameter, demonstrates that the melting temperature is related to the size and composition of alloy nanoparticles. The melting temperature of CuNi, PbBi and Snln binary alloy nanocrystals is found to be consistent with the experiments and molecular dynamics simulations. The research reveals that alloy nanocrystals have similar melting nature as pure metal.

关键词： nano binary alloy melting temperature size-dependent

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A Complementary Modularized Ramp Metering Approach Based on Iterative Learning control and ALINEA

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IEEE Transactions on Intelligent Transportation Systems 2011年第4期12卷 1305-1318页

作者： Zhongsheng Hou Xin Xu Jingwen Yan Jian-Xin Xu Gang Xiong Advanced Control Systems Laboratory School of Electronic and Information Engineering Beijing Jiaotong University BJTU Beijing China Institute of Automation College of Mechatronics and Automation National University of Defense Technology Changsha China Department of Electrical and Computer Engineering National University of Singapore Singapore Automation Institute Chinese Academy of Sciences (CAS) Beijing China

Ramp metering is an effective tool for traffic management on freeway networks. In this paper, we apply iterative learning control (ILC) to address ramp metering in a macroscopic-level freeway environment. By formulating the original ramp metering problem as an output regulating and disturbance rejection problem, ILC has been applied to control the traffic response. The learning mechanism is further combined with Asservissement Linéaire d'Entrée Autoroutière (ALINEA) in a complementary manner to achieve the desired control performance. The ILC-based ramp metering strategy and the modified modularized ramp metering approach based on ILC and ALINEA in the presence of input constraints are also analyzed to highlight the advantages and the robustness of the proposed methods. Extensive simulations are given to verify the effectiveness of the proposed approaches.

关键词： Traffic control Mathematical model Convergence Learning systems Feedforward neural networks

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Advancing Ultra-Short-Term Wind Power Forecasting with Multi-Channel ML Techniques

Advancing Ultra-Short-Term Wind Power Forecasting with Multi...

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Energy Systems and Electrical Power (ICESEP), International Conference on

作者： Jiapin Ren Chenlin Zhu Zeyu Huang Ruihan Chen Shurui Xu Peng You Department of Information and Electrical Engineering Xuhai College China University of Mining and Technology Xuzhou China Metropolitan College Boston University Boston USA School of Information Engineering China Jiliang University Hangzhou China School of Mathematical Sciences Zhejiang Normal University Jinhua China Cognitive Neuroscience Laboratory Beijing Meishi Cha Technology Co. Ltd. Beijing China National and Local Joint Engineering Laboratory of Internet Applied Technology on Mines China University of Mining and Technology School of Information and Control Engineering China University of Mining and Technology Xuhai College China University of Mining and Technology Xuzhou China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

To address the inherent unpredictability of wind speeds and directions, which significantly affect wind turbine output, this paper presents a multi-channel feature combination strategy for ultra-short term wind power prediction. The research utilises a number of data sources, including meteorological observations, historical power data and geographical inputs, to create a robust feature set for prediction models. By using advanced machine learning techniques, in particular deep learning models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, the study effectively captures the complex dynamics of wind power generation. The novel approach of reclassifying data sets and refining model features through task-specific channels significantly improves prediction accuracy. Experimental results show significant improvements in prediction accuracy, highlighting the potential of the proposed method to optimise grid operation and energy management in real-time scenarios.

关键词： Accuracy Wind power generation Predictive models Feature extraction Data models Wind turbines Convolutional neural networks

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Swarm aggregations of heterogeneous multi-agent systems

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International Journal of control 2014年第12期87卷 2594页

作者： Liang, Haili Su, Housheng Wang, Xiaofan Chen, Michael Z.Q. [a] Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China[b] School of Automation Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 China[c] Department of Mechanical Engineering The University of Hong Kong Hong Kong

This paper investigates the problem of swarm aggregations of heterogeneous multi-agent systems. Comparing with the existing studies on swarm aggregations of homogeneous multi-agent systems, this paper is much more resembling the practical situations, where the agents have different dynamics. We show that the heterogeneous agents will gather with a certain error under some assumptions and conditions. The stability properties have been proven by theoretical analysis and verified via numerical simulation. The stability of the heterogeneous multi-agent systems has been achieved based on matrix theory and the Lyapunov stability theorem. Numerical simulation is given to demonstrate the effectiveness of the theoretical result. [PUBLICATION ABSTRACT]

关键词： swarm multi-agent systems heterogeneous systems stability

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A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction

A Multi-Stage Goal-Driven Network for Pedestrian Trajectory ...

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Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Xiuen Wu Tao Wang Yuanzheng Cai Lingyu Liang George Papageorgiou Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Data Science Fuzhou University Fuzhou China School of Electronic and Information Engineering South China University of Technology Guangzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus

ISBN: (数字)9798350373820

ISBN: (纸本)9798350373837

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians’ future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multi-stage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method.

关键词： Deep learning Pedestrians Computational modeling Predictive models Prediction algorithms Vectors Inference algorithms

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Temporal wheeler’s delayed-choice experiment based on cold atomic quantum memory

arXiv

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arXiv 2019年

作者： Dong, Ming-Xin Ding, Dong-Sheng Yu, Yi-Chen Ye, Ying-Hao Zhang, Wei-Hang Li, En-Ze Zeng, Lei Zhang, Kan Li, Da-Chuang Guo, Guang-Can Shi, Bao-Sen Key Laboratory of Quantum Information University of Science and Technology of China Hefei Anhui230026 China Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China Department of Fundamental Education Anhui Institute of Information Technology Wuhu Anhui241002 China Institute for Quantum Control and Quantum Information and School of Physics and Materials Engineering Hefei Normal University Hefei Anhui230601 China

Nowadays the most intriguing features of wave-particle complementarity of single photon is exemplified by the famous Wheeler’s delayed choice experiment in linear optics, nuclear magnetic resonance and integrated photonic device systems. Studying the wave-particle behavior in light and matter interaction at single photon level is challenging and interesting, which gives how single photons complement in light and matter interaction. Here, we demonstrate a Wheeler’s delayed choice experiment in an interface of light and atomic memory, in which the cold atomic memory makes the heralded single photon divided into a superposition of atomic collective excitation and leaked pulse, thus acting as memory beam-splitters. We observe the morphing behavior between particle and wave of a heralded single photon by changing the relative proportion of quantum random number generator, the second memory efficiency, and the relative storage time of two memories. The reported results exhibit the complementarity behavior of single photon under the interface of light-atom interaction. Copyright © 2019, The Authors. All rights reserved.

关键词： Particle beams

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