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Lagrangian generic second order traffic flow models for node

Journal of Traffic and Transportation engineering(English Edition) 2018年第1期5卷 14-27页

作者： Asma Khelifi Habib Haj-Salem Jean-Patrick Lebacque Lotfi Nabli Engineering of Surface Transportation Networks and Advanced Computing Laboratory IFSTTAR/GRETTIA Department of Electrical Engineering Research Laboratory of Control Signal Processing and Imaging National Engineering School of Monastir

This study sheds light on higher order macroscopic traffic flow modeling on road networks, thanks to the generic second order models （GSOM family） which embeds a myriad of traffic models. It has been demonstrated that such higher order models are easily solved in Lagrangian coordinates which are compatible with both microscopic and macroscopic descriptions. The generalized GSOM model is reformulated in the Lagrangian coordinate system to develop a more efficient numerical method. The difficulty in applying this approach on networks basically resides in dealing with node dynamics. Traffic flow characteristics at node are different from that on homogeneous links. Different geometry features can lead to different critical research issues. For instance, discontinuity in traffic stream can be an important issue for traffic signal operations, while capacity drop may be crucial for lane-merges. The current paper aims to establish and analyze a new adapted node model for macroscopic traffic flow models by applying upstream and downstream boundary conditions on the Lagrangian coordinates in order to perform simulations on networks of roads, and accompanying numerical method. The internal node dynamics between upstream and downstream links are taken into account of the node model. Therefore, a numerical example is provided to underscore the efficiency of this approach. Simulations show that the discretized node model yields accurate results. Additional kinematic waves and contact discontinuities are induced by the variation of the driver attribute.

关键词： Macroscopic Traffic flow modeling GSOM family Lagrangian Node

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Data protection and provenance in cloud of things environment: Research challenges

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International Journal of Information and Computer Security 2020年第4期12卷 416-435页

作者： Wang, Chundong Yang, Lei Guo, Hao Wan, Fujin Key Laboratory of Computer Vision and System Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin China Global Energy Internet Research Institute Beijing University of Technology Tianjin China Department of College of Computer and Control Engineering Nankai University Tianjin China

Internet of things are increasingly being deployed over the cloud (also referred to as cloud of things) to provide a broader range of services. However, there are serious challenges of CoT in the data protection and security provenance. This paper proposes a data privacy protection and provenance model (DPSPM) based on CoT. It can protect the privacy data of the users and trace the source of leaked data. In detail, security encryption and watermarking algorithms are proposed. Meanwhile, we use the improved k-anonymity data masking algorithm and pseudo-row watermarking algorithm in this scheme. Those algorithms can carry out security control over the whole process of data publishing, especially in data encryption, data masking and provenance verification. Finally, the experimental results show that our scheme has good efficiency. It is proved that the data masking time is proportional to the parameters k and L, the results also show good robustness to the common database watermarking attacks. © 2020 Inderscience Enterprises Ltd.

关键词： Data privacy

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Disease transmission in dynamic social networks constructed by reinforcement learning-driven preventive game

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Chaos, Solitons & Fractals 2025年 199卷

作者： Yinuo Qian Dawei Zhao Chengyi Xia School of Control Science and Engineering Tiangong University Tianjin 300387 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan 250014 China School of Artificial Intelligence Tiangong University Tianjin 300387 China

Reinforcement learning delves into the intrinsic behaviors of multi-agents, while complex networks reveal their interactive relationships. In real life, an individual can be considered as a sophisticated multi-agent, whose behavioral choices are influenced by a myriad of factors. When contagious diseases penetrate a population, the adoption of preventive measures represents a manifestation of behavioral selection. This paper introduces reinforcement learning into the multi-agent decision within the network, leveraging the Q-learning algorithm to train individual decision-making. At each time step, individuals select the next preventive strategy based on their utility and strategies, and these choices subsequently alter the network structure, thereby influencing the prevalence of the disease. In addition, the incorporation of additional government incentives prompts individuals to opt for advanced preventive strategies, effectively containing disease transmission within the population. The construction of this model not only showcases the co-evolutionary process between network structure and disease transmission but also validates its efficacy through theoretical analysis and applications in real-world networks, offering a novel perspective towards the epidemic control.

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Photocatalytic Nitrogen Reduction for Ammonia Synthesis Accelerated by Overcoming Photo-Dember Effect

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Angewandte Chemie 2025年第26期137卷

作者： Peishen Li Yixuan Gao Alistair G. L. Borthwick Panpan Li Huixuan Zhang Fangyuan Chen Long Chen Fan Li Wen Liu College of Environmental Sciences and Engineering The Key Laboratory of Water and Sediment Sciences (Ministry of Education) Peking University Beijing 100871 P.R. China Beijing National Laboratory for Molecular Sciences Peking University Beijing 100871 P.R. China Institute of Infrastructure and Environment School of Engineering The University of Edinburgh King's Buildings Edinburgh EH9 3JL UK School of Engineering Computing and Mathematics University of Plymouth Drake Circus Plymouth PL4 8AA UK College of Materials Science and Engineering Sichuan University Chengdu 610065 P.R. China Beijing Key Lab for Source Control Technology of Water Pollution College of Environmental Science and Engineering Beijing Forestry University Beijing 100083 P.R. China

During photocatalytic nitrogen fixation for ammonia synthesis, the photo-Dember effect causes direct transmission of photogenerated electrons from the illuminated surface to the bottom of photocatalyst, thus significantly reducing the number of charge carriers migrating on the surface and nitrogen fixation efficiency. Herein, a bismuth oxychloride material with largely exposed (101) crystal plane and rich oxygen vacancies (BOC (101) -OVs) is synthesized, exhibiting a high NH 3 yield of 591.94 µmol g −1 h −1 ) after photocatalytic N 2 reduction under simulated solar light irradiation. The designed (101)/(001) interface in BOC (101) -OVs generates a self-built electric field ( E self ) on the material surface due to different atomic arrangements. Therefore, the newly developed material achieved >95% of photogenerated electrons changing the transfer path, i.e., from bulk phase transfer to surface lateral transfer path, thus escaping confinement by the photo-Dember effect. Meanwhile, after OVs construction, each adsorbed N 2 molecule simultaneously bonds with three Bi atoms of material through N 2p–Bi 6p bonding, accelerating the filling of high-energy electrons into the π* orbital of N 2 , leading to a new nitrogen reduction path with combined alternating hydrogenation and terminal hydrogenation. This study greatly advances the beneficial effect of charge carrier migration through overcoming the photo-Dember effect for ammonia synthesis.

关键词： Bismuth oxychloride Nitrogen fixation Oxygen Vacancy Photocatalysis Photo-Dember effect

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Investigation of the hyperchaos and control in the fractional order financial system with profit margin

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Partial Differential Equations in Applied Mathematics 2024年 9卷

作者： Johansyah, Muhamad Deni Sambas, Aceng Qureshi, Sania Zheng, Song Abed-Elhameed, Tarek M. Vaidyanathan, Sundarapandian Sulaiman, Ibrahim Mohammed Department of Mathematics Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu 22200 Malaysia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tamansari Gobras Tasikmalaya 46196 Indonesia Department of Computer Science and Mathematics Lebanese American University Beirut 13-5053 Lebanon Department of Mathematics Near East University Mersin 99138 Turkey Department of Basic Sciences and Related Studies Mehran University of Engineering and Technology Jamshoro 72060 Pakistan School of Data Science Zhejiang University of Finance and Economics Hangzhou 310018 China Institute of Mathematics and Interdisciplinary Sciences Zhejiang University of Finance and Economics 310018 China Department of Mathematics Faculty of Science Assiut University Assiut 71516 Egypt Centre for Control Systems Vel Tech University Avadi Tamil Nadu Chennai 600062 India Institute of Strategic Industrial Decision Modelling School of Quantitative Sciences Universiti Utara Malaysia Sintok Bukit Kayu Hitam Kedah 06010 Malaysia Faculty of Education and Arts Sohar University Sohar 311 Oman

This research study proposes a novel hyperchaotic finance system with profit margin and then utilizes the Adomian Decomposition Method (ADM) to tackle the solution of the associated Caputo-derivative based fractional-order hyperchaotic finance system with profit margin. Numerical simulations and analyses, such as the evaluation of Lyapunov exponents (LE), the creation of bifurcation diagrams (BD), the complexity analysis (CA), and the 0-1 test, are used to get a full picture of the system. The results of our study demonstrate the occurrence of periodic, chaotic, and hyperchaotic dynamics in the system. Furthermore, the overall criteria for the existence and the uniqueness of the exact solutions for Caputo fractional-order models are presented. In addition, we present a control methodology for the fractional-order hyperchaotic financial system utilizing the Laplace transform and linear feedback control. Significantly, our research showcases a noteworthy correlation between the analytical results and numerical simulations, emphasizing the soundness and effectiveness of our methodology. © 2024 The Author(s)

关键词： Chaotic system Financial system Fractional order Hyperchaos

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Stable recovery of entangled weights: Towards robust identification of deep neural networks from minimal samples

arXiv

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arXiv 2021年

作者： Fiedler, Christian Fornasier, Massimo Klock, Timo Rauchensteiner, Michael Intelligent Control Systems Group Max Planck Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart70569 Germany Institute for Data Science in Mechanical Engineering RWTH-Aachen University Dennewartstraße 27 Aachen50868 Germany Department of Mathematics Bolzmannstraße 3 85748 Garching Germany Department of Numerical Analysis and Scientific Computing Simula Research Laboratory Oslo Norway Department of Mathematics University of San Diego California San Diego United States

In this paper we approach the problem of unique and stable identifiability of generic deep artificial neural networks with pyramidal shape and smooth activation functions from a finite number of input-output samples. More specifically we introduce the so-called entangled weights, which compose weights of successive layers intertwined with suitable diagonal and invertible matrices depending on the activation functions and their shifts. We prove that entangled weights are completely and stably approximated by an efficient and robust algorithm as soon as O(D2 × m) nonadaptive input-output samples of the network are collected, where D is the input dimension and m is the number of neurons of the network. Moreover, we empirically observe that the approach applies to networks with up to O(D × mL) neurons, where mL is the number of output neurons at layer L. Provided knowledge of layer assignments of entangled weights and of remaining scaling and shift parameters, which may be further heuristically obtained by least squares, the entangled weights identify the network completely and uniquely. To highlight the relevance of the theoretical result of stable recovery of entangled weights, we present numerical experiments, which demonstrate that multilayered networks with generic weights can be robustly identified and therefore uniformly approximated by the presented algorithmic pipeline. In contrast backpropagation cannot generalize stably very well in this setting, being always limited by relatively large uniform error. In terms of practical impact, our study shows that we can relate input-output information uniquely and stably to network parameters, providing a form of explainability. Moreover, our method paves the way for compression of overparametrized networks and for the training of minimal complexity *** Codes 65D15, 68T07, 90C26 Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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An Overlapping Community Detection Based Multi-Objective Evolutionary Algorithm for Diversified Social Influence Maximization

An Overlapping Community Detection Based Multi-Objective Evo...

引用

Congress on Evolutionary Computation

作者： Lei Zhang Fengiiao Sun Fan Cheng Haiping Ma Xiaoyan Sun Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Institutes of Physical Science and Information Technology Anhui University Hefei China School of Information and Control Engineering China University of Mining and Technology Xuzhou China

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

Influence maximization refers to selecting a group of nodes from a social network, which obtains the largest influence spread under a cascade model. However, most of the existing works only focused on the influence and ignored the diversity of influenced crowd. Thus, scholars have raised the issue of diversified social influence maximization recently, using the category information of nodes to design diversity indicator and introducing a trade-off parameter to balance the two objectives influence and diversity as one single objective for optimization. In fact, the category information of nodes in the network is usually difficult to be collected, thus the definition of diversity based on nodes' categories is not very general and accurate. In addition, it is very difficult to set the trade-off parameter, especially when there is no prior knowledge in real applications. To this end, we employ overlapping community structure information to design the diversity of nodes without any node's additional (e.g. category) information. Due to the two objectives of influence and diversity may be conflicting, a multi-objective evolutionary algorithm named MOEA-DIM is proposed to optimize the two objectives simultaneously, which does not need to set the tradeoff parameter between the two objectives. In MOEA-DIM, a network reduction strategy based on overlapping community structure is suggested to greatly reduce the search space. In addition, a population initialization strategy based on random walk is designed to accelerate the convergence of the algorithm. Experiments on six real-world datasets show that the proposed algorithm MOEA-DIM has promising performance in terms of both effectiveness and efficiency.

关键词： Optimization Encoding

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A Pipelining Loop Optimization Method for Dataflow Architecture

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Journal of Computer Science & Technology 2018年第1期33卷 116-130页

作者： Xu Tan Xiao-Chun Ye Xiao-Wei Shen Yuan-Chao Xu Da Wang Lunkai Zhang Wen-Ming Li Dong-Rui Fan Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 1000J9 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125 China College of Information Engineering Capital Normal University Beijing 100048 China Department of Computer Science The University of Chicago Chicago IL 60637 U.S.A.

With the coming of exascale supercomputing era, power efficiency has become the most important obstacle to build an exascale system. Dataflow architecture has native advantage in achieving high power efficiency for scientific applications. However, the state-of-the-art dataflow architectures fail to exploit high parallelism for loop processing. To address this issue, we propose a pipelining loop optimization method （PLO）, which makes iterations in loops flow in the processing element （PE） array of dataflow accelerator. This method consists of two techniques, architecture-assisted hardware iteration and instruction-assisted software iteration. In hardware iteration execution model, an on-chip loop controller is designed to generate loop indexes, reducing the complexity of computing kernel and laying a good f（mndation for pipelining execution. In software iteration execution model, additional loop instructions are presented to solve the iteration dependency problem. Via these two techniques, the average number of instructions ready to execute per cycle is increased to keep floating-point unit busy. Simulation results show that our proposed method outperforms static and dynamic loop execution model in floating-point efficiency by 2.45x and 1.1x on average, respectively, while the hardware cost of these two techniques is acceptable.

关键词： dataflow model control-flow model loop optimization exascale computing scientific application

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Extracting Non-Gaussian Governing Laws from Data on Mean Exit Time

arXiv

引用

arXiv 2020年

作者： Zhang, Yanxia Duan, Jinqiao Jina, Yanfei Li, Yang Department of Mechanics Beijing Institute of Technology Beijing100081 China Department of Applied Mathematics College of Computing Illinois Institute of Technology ChicagoIL60616 United States State Key Laboratory of Mechanics and Control of Mechanical Structures College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Applied Mathematics College of Computing Illinois Institute of Technology ChicagoIL60616 United States

Motivated by the existing difficulties in establishing mathematical models and in observing the system state time series for some complex systems, especially for those driven by non-Gaussian Lévy motion, we devise a method for extracting non-Gaussian governing laws with observations only on mean exit time. It is feasible to observe mean exit time for certain complex systems. With the observations, a sparse regression technique in the least squares sense is utilized to obtain the approximated function expression of mean exit time. Then, we learn the generator and further identify the stochastic differential equations through solving an inverse problem for a nonlocal partial differential equation and minimizing an error objective function. Finally, we verify the efficacy of the proposed method by three examples with the aid of the simulated data from the original systems. Results show that the method can apply to not only the stochastic dynamical systems driven by Gaussian Brownian motion but also those driven by non-Gaussian Lévy motion, including those systems with complex rational drift. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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Human Motion Prediction Based on Visual Tracking 4

Human Motion Prediction Based on Visual Tracking

引用

4th International Conference on Robotics and Automation engineering, ICRAE 2019

作者： Zou, Juncheng Yin, Weihua Wang, Everett X. Wang, Jiancheng Lu, Yan-Feng School of Mechanical Engineering Beijing University of Science and Technology China Institute of Science and Technology Innovation Dongguan University of Technology China School of Information Engineering Guangdong University of Technology Guangzhou China Cloud Computing Center Chinese Academy of Sciences China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728147406

ISBN: (纸本)9781728147406

Following and moving according to human motion is an important task for mobile robots. To ensure more compliant motion planning and execution of mobile robots, they can not be controlled by real-time visual information. In the tracking process, robots need to recognize and track the target then, plan the motion and execute the motion. In practical applications, the environments are very complex, such as illumination, shadows and occlusion, which the traditional visual tracking algorithms often deviate or lose the target. Therefore, to achieve fast human motion tracking, it is necessary to predict human motions by video prediction. In this paper, we propose a human motion tracking algorithm of mobile robot based on video prediciton. (1) The multi-layer generation adversarial loop network, which trained by off-line video dataset and learn how to predict human motion. (2) We used the pre-trained model to predict the state of the tracking target in the video. (3) This video prediction model was integrated into human-to-robot tracking algorithms of mobile robot following human system. Experiments show that the proposed algorithm can track human motion at a certain speed and precision. © 2019 IEEE.

关键词： Mobile robots

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