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Rolling stock shunt operation planning in urban rail transit depots with maintenance consideration

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Transportation Research Part B: Methodological 2025年 199卷

作者： Wang, Dian Yao, Ling D'Ariano, Andrea Zhan, Shuguang Wang, Lisha School of Automotive and Transportation Engineering Hefei University of Technology Anhui Hefei230009 China Department of Civil Computer Science and Aeronautical Technologies Engineering Roma Tre University Rome00146 Italy School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China School of Architecture and Urban Planning Chongqing University Chongqing400030 China

This study investigates the daily rolling stock shunt operation planning in a rail depot. Given the layout of a depot and the rolling stocks (that arrive at and depart from this depot within an operation day) with given maintenance schedule, the studied problem lies in determining: 1) the position where each rolling stock is parked, outside washed, and/or maintained, and 2) the conflict-free shunting plan of rolling stocks to move within a depot. We transform the track-circuits in the depot into different multi-layer directed graphs to illustrate the shunting processes of rolling stocks. By means of these graphs, we formulate the studied problem as a mixed integer linear programming model by considering a more general variant of four requirements that make sense in practice but are not or rarely considered in previous works and by presenting a flexible technique to model the track capacity, to reduce the additional shunting movements of rolling stocks. Besides, we design a two-stage decomposition manner to efficiently solve real-life problem instances, wherein the problem in each stage is addressed by a presented logic-based Benders decomposition algorithm enhanced by customized acceleration mechanisms. Finally, a set of realistic and real-life instances with different scales (derived from the largest depot of the Chongqing Rail Transit Line 3 in China) are investigated. Computational results demonstrate that our best algorithm solves a real-life instance to optimality in approximately 8 s that is considerably shorter than the time of rail staffs to solve this instance manually, thus our approach can provide strong automatical computer-aided decision supports. Our approach is also very efficient in optimizing another objective that is also widely used, and can provide management insights to rail staffs. © 2025 Elsevier Ltd

关键词： Urban transportation

A Metric for Evaluating Neural Input Representation in Supervised Learning Networks

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arXiv 2020年

作者： Naveros, Francisco Carrillo, Richard R. Ros, Eduardo Luque, Niceto R. University of Granada Granada Spain Aging in Vision and Action Institut de la Vision Inserm–UPMC–CNRS Paris France Department of Computer Architecture and Technology University of Granada Granada Spain

Supervised learning has long been attributed to several feed-forward neural circuits within the brain, with particular attention being paid to the cerebellar granular layer. The focus of this study is to evaluate the input activity representation of these feed-forward neural networks. The activity of cerebellar granule cells is conveyed by parallel fibers and translated into Purkinje cell activity, which constitutes the sole output of the cerebellar cortex. The learning process at this parallel-fiber-to-Purkinje-cell connection makes each Purkinje cell sensitive to a set of specific cerebellar states, which are roughly determined by the granule-cell activity during a certain time window. A Purkinje cell becomes sensitive to each neural input state and, consequently, the network operates as a function able to generate a desired output for each provided input by means of supervised learning. However, not all sets of Purkinje cell responses can be assigned to any set of input states due to the network’s own limitations (inherent to the network neurobiological substrate), that is, not all input-output mapping can be learned. A key limiting factor is the representation of the input states through granule-cell activity. The quality of this representation (e.g., in terms of heterogeneity) will determine the capacity of the network to learn a varied set of outputs. Assessing the quality of this representation is interesting when developing and studying models of these networks to identify those neuron or network characteristics that enhance this representation. In this study we present an algorithm for evaluating quantitatively the level of compatibility/interference amongst a set of given cerebellar states according to their representation (granule-cell activation patterns) without the need for actually conducting simulations and network training. The algorithm input consists of a real-number matrix that codifies the activity level of every considered granule-cell in each

关键词： Brain

ExploreBP: A Simulation Tool for Mobile Browser Energy Optimization 11th

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ExploreBP: A Simulation Tool for Mobile Browser Energy Optim...

11th EAI International Conference on Simulation Tools and Techniques, SIMUTools 2019

作者： Zhang, Jin Wei, Xin Liu, Zhen Liu, Fangxin Li, Tao Lu, Tingjuan Gong, Xiaoli Nankai University Tianjin China IT Department Chinese PLA 117 Hospital Hangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030322151

The browser is one of the most commonly used applications. Users tend to pursue a good user experience and care more about the performance of the browser, while ignoring the power consumption of the browser. This paper proposes a method to reduce the energy consumption of web browsing. In order to better quantify the user experience, this paper uses the first screen load time as the evaluation metric of user experience. First, according to the relationship between the network speed and the first screen load time, find the most suitable primary frequency at a specific network speed, and define the point as the balance point. When the primary frequency is greater than the primary frequency corresponding to the balance point, the first screen load time will almost never change. The balance points of different web pages are also different. Then adjust the CPU frequency according to the balance point of the webpage and the network speed, which can reduce the browser energy consumption and reduce the impact on the user experience. At the same time, this paper proposes a simulation tool ExploreBP, which is used to simulate the working state of the network speed and different web pages to find the optimal energy consumption configuration. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2019.

关键词： Frequency modulation

On the comparison of classic and deep keypoint detector and descriptor methods 11

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On the comparison of classic and deep keypoint detector and ...

11th International Symposium on Image and Signal Processing and Analysis, ISPA 2019

作者： Bojanic, David Bartol, Kristijan Pribanic, Tomislav Petkovic, Tomislav Donoso, Yago Diez Mas, Joaquim Salvi Faculty of Electrical Engineering and Computing University of Zagreb Zagreb EU Croatia Faculty of Science Yamagata University Yamagata Japan University of Girona Department of Computer Architecture and Technology Girona EU Spain

ISBN: (纸本)9781728131405

The purpose of this study is to give a performance comparison between several classic hand-crafted and deep key-point detector and descriptor methods. In particular, we consider the following classical algorithms: SIFT, SURF, ORB, FAST, BRISK, MSER, HARRIS, KAZE, AKAZE, AGAST, GFTT, FREAK, BRIEF and RootSIFT, where a subset of all combinations is paired into detector-descriptor pipelines. Additionally, we analyze the performance of two recent and perspective deep detector-descriptor models, LF-Net and SuperPoint. Our benchmark relies on the HPSequences dataset that provides real and diverse images under various geometric and illumination changes. We analyze the performance on three evaluation tasks: keypoint verification, image matching and keypoint retrieval. The results show that certain classic and deep approaches are still comparable, with some classic detector-descriptor combinations overperforming pretrained deep models. In terms of the execution times of tested implementations, SuperPoint model is the fastest, followed by ORB. © 2019 IEEE.

关键词： Deep learning

A Neuronal Network Model of the Primate Visual System: Color Mechanisms in the Retina, LGN and V1

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A Neuronal Network Model of the Primate Visual System: Color...

作者： Martínez-Cañada, Pablo Morillas, Christian Pelayo, Francisco Department of Computer Architecture and Technology University of Granada Granada Spain Centro de Investigacion en Tecnologias de la Informacion y de Las Comunicaciones University of Granada Granada Spain

Color plays a key role in human vision but the neural machinery that underlies the transformation from stimulus to perception is not well understood. Here, we implemented a two-dimensional network model of the first stages in the primate parvocellular pathway (retina, lateral geniculate nucleus and layer 4Cβ in V1) consisting of conductance-based point neurons. Model parameters were tuned based on physiological and anatomical data from the primate foveal and parafoveal vision, the most relevant visual field areas for color vision. We exhaustively benchmarked the model against well-established chromatic and achromatic visual stimuli, showing spatial and temporal responses of the model to disk- and ring-shaped light flashes, spatially uniform squares and sine-wave gratings of varying spatial frequency. The spatiotemporal patterns of parvocellular cells and cortical cells are consistent with their classification into chromatically single-opponent and double-opponent groups, and nonopponent cells selective for luminance stimuli. The model was implemented in the widely used neural simulation tool NEST and released as open source software. The aim of our modeling is to provide a biologically realistic framework within which a broad range of neuronal interactions can be examined at several different levels, with a focus on understanding how color information is processed. © 2019 World Scientific Publishing Company.

关键词： Color

NeuroPod: a real-time neuromorphic spiking CPG applied to robotics

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arXiv 2019年

作者： Gutierrez-Galan, Daniel Dominguez-Morales, Juan P. Perez-Peña, Fernando Linares-Barranco, Alejandro Robotics and Computer Technology Lab Universidad de Sevilla Spain Department of Computer Architecture and Technology Universidad de Cádiz Spain

Initially, robots were developed with the aim of making our life easier, carrying out repetitive or dangerous tasks for humans. Although they were able to perform these tasks, the latest generation of robots are being designed to take a step further, by performing more complex tasks that have been carried out by smart animals or humans up to date. To this end, inspiration needs to be taken from biological examples. For instance, insects are able to optimally solve complex environment navigation problems, and many researchers have started to mimic how these insects behave. Recent interest in neuromorphic engineering has motivated us to present a real-time, neuromorphic, spike-based Central Pattern Generator of application in neurorobotics, using an arthropod-like robot. A Spiking Neural Network was designed and implemented on SpiNNaker. The network models a complex, online-change capable Central Pattern Generator which generates three gaits for a hexapod robot locomotion. Reconfigurable hardware was used to manage both the motors of the robot and the real-time communication interface with the Spiking Neural Networks. Real-time measurements confirm the simulation results, and locomotion tests show that NeuroPod can perform the gaits without any balance loss or added delay. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural networks

Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications

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arXiv 2020年

作者： Yang, Helin Xiong, Zehui Zhao, Jun Niyato, Dusit Xiao, Liang Wu, Qingqing School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Information and Communication Engineering Key Laboratory of Digital Fujian on IoT Communication Architecture and Security Technology Xiamen University Xiamen361005 China Department of Electrical and Computer Engineering National University of Singapore Singapore119260 Singapore

In this paper, we study an intelligent reflecting surface (IRS)-aided wireless secure communication system for physical layer security, where an IRS is deployed to adjust its reflecting elements to secure the communication of multiple legitimate users in the presence of multiple eavesdroppers. Aiming to improve the system secrecy rate, a design problem for jointly optimizing the base station (BS)’s beamforming and the IRS’s reflecting beamforming is formulated considering different quality of service (QoS) requirements and time-varying channel conditions. As the system is highly dynamic and complex, and it is challenging to address the non-convex optimization problem, a novel deep reinforcement learning (DRL)-based secure beamforming approach is firstly proposed to achieve the optimal beamforming policy against eavesdroppers in dynamic environments. Furthermore, post-decision state (PDS) and prioritized experience replay (PER) schemes are utilized to enhance the learning efficiency and secrecy performance. Specifically, PDS is capable of tracing the environment dynamic characteristics and adjust the beamforming policy accordingly. Simulation results demonstrate that the proposed deep PDS-PER learning based secure beamforming approach can significantly improve the system secrecy rate and QoS satisfaction probability in IRS-aided secure communication systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Dynamic Resilient Network Games with Applications to Multi-Agent Consensus

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arXiv 2020年

作者： Nugraha, Yurid Cetinkaya, Ahmet Hayakawa, Tomohisa Ishii, Hideaki Zhu, Quanyan Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo101-8430 Japan Department of Computer Science Tokyo Insitute of Technology Yokohama226-8502 Japan Department of Electrical and Computer Engineering New York University BrooklynNY11201 United States

A cyber security problem in a networked system formulated as a resilient graph problem based on a game-theoretic approach is considered. The connectivity of the underlying graph of the network system is reduced by an attacker who removes some of the edges whereas the defender attempts to recover them. Both players are subject to energy constraints so that their actions are restricted and cannot be performed continuously. For this two-stage game, which is played repeatedly over time, we characterize the optimal strategies for the attacker and the defender in terms of edge connectivity and the number of connected components of the graph. The resilient graph game is then applied to a multi-agent consensus problem. We study how the attacks and the recovery on the edges affect the consensus process. Finally, we also provide numerical simulation to illustrate the results. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

An evolutionary deep learning method for short-term wind speed prediction: A case study of the lillgrund offshore wind farm

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arXiv 2020年

作者： Neshat, Mehdi Nezhad, Meysam Majidi Abbasnejad, Ehsan Tjernberg, Lina Bertling Garcia, Davide Astiaso Alexander, Bradley Wagner, Markus Optimization and Logistics Group School of Computer Science University of Adelaide Australia Sapienza University of Rome Italy Australian Institute for Machine Learning The University of Adelaide Australia School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Planning Design and Technology of Architecture Sapienza University of Rome Italy

Accurate short-term wind speed forecasting is essential for large-scale integration of wind power generation. However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task. This study uses a new hybrid evolutionary approach that uses a popular evolutionary search algorithm, CMA-ES, to tune the hyper-parameters of two long-term-short-term (LSTM) ANN models for wind prediction. The proposed hybrid approach is trained on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea. Two forecasting horizons including ten-minutes ahead (absolute short term) and one-hour ahead (short term) are considered in our experiments. Our experimental results indicate that the new approach is superior to five other applied machine learning models, i.e., polynomial neural network (PNN), feed-forward neural network (FNN), nonlinear autoregressive neural network (NAR) and adaptive neuro-fuzzy inference system (ANFIS), as measured by five performance criteria. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems