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An Efficient Evolutionary Algorithm for High-Speed Train Rescheduling Under a Partial Station Blockage

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SSRN

SSRN 2023年

作者： Wang, Rongsheng Zhang, Qi Dai, Xuewu Yuan, Zhiming Zhang, Tao Ding, Shuxin Jin, Yaochu China Academy of Railway Sciences Corporation Limited Beijing100081 China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China Train Operation Control Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Beijing100081 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Faculty of Technology Bielefeld University BielefeldD-33615 Germany Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. An HSTR model that is subject to train operation constraints is established to minimize the total train delay. The PS-SEGA framework consists of the following main steps. Firstly, a permutation-based encoding method is developed to define an efficient search space based on a train departure sequence. A decoding method is employed to output the rescheduled timetable and objective value based on the departure sequence. Moreover, a hybrid initialization method involving an efficient heuristic strategy (EHS) is put forward to accelerate the convergence speed of Evolutionary algorithms (EAs). Using problem-specific knowledge, EHS generates an efficient and feasible solution for the initial population. Finally, a restart strategy is presented to maintain genetic diversity. Compared with other advanced EAs and their improved variants also using the PS-SEGA framework, experimental results demonstrate the effectiveness of the proposed PS-SEGA for addressing HSTR scenarios under the partial station blockage. As for the scenarios that CPLEX can not obtain optimal solutions within 10 minutes, PS-SEGA can all provide quasi-optimal solutions in real-time. Furthermore, compared with the other two strategies (i.e., First-Scheduled-First-Served and EHS), PS-SEGA can find the best departure sequence that can output the timetable with a quasi-optimal solution. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

Energy-Constrained Computation Offloading in Space-Air-Ground Integrated Networks using Distributionally Robust Optimization

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arXiv 2022年

作者： Chen, Yali Ai, Bo Niu, Yong Zhang, Hongliang Han, Zhu The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

With the rapid development of connecting massive devices to the Internet, especially for remote areas without cellular network infrastructures, space-air-ground integrated networks (SAGINs) emerge and offload computation-intensive tasks. In this paper, we consider a SAGIN, where multiple low-earthorbit (LEO) satellites providing connections to the cloud server, an unmanned aerial vehicle (UAV), and nearby base stations (BSs) providing edge computing services are included. The UAV flies along a fixed trajectory to collect tasks generated by Internet of Things (IoT) devices, and forwards these tasks to a BS or the cloud server for further processing. To facilitate efficient processing, the UAV needs to decide where to offload as well as the proportion of offloaded tasks. However, in practice, due to the variability of environment and actual demand, the amount of arrival tasks is uncertain. If the deterministic optimization is utilized to develop offloading strategy, unnecessary system overhead or higher task drop rate may occur, which severely damages the system robustness. To address this issue, we characterize the uncertainty with a data-driven approach, and formulate a distributionally robust optimization problem to minimize the expected energy-constrained system latency under the worst-case probability distribution. Furthermore, the distributionally robust latency optimization algorithm is proposed to reach the suboptimal solution. Finally, we perform simulations on the realworld data set, and compare with other benchmark schemes to verify the efficiency and robustness of our proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Probability distributions

Nano Biochar Triggers High Adsorption of Oxytetracycline in Aqueous Environment: Effects of Solution Conditions and Particle Aggregation

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SSRN

SSRN 2022年

作者： Jia, Wenqiang Pan, Xiangrui Song, Jiaying Wang, Jian Sun, Weikai Xin, Yanjun Yan, Qinghua Zhou, Chengzhi Zheng, Hao Liu, Guocheng Qingdao Engineering Research Center for Rural Environment College of Resource and Environment Qingdao Agricultural University Qingdao266109 China State Key Laboratory of Urban Water Resource and Environment School of Environment Harbin Institute of Technology Harbin150090 China Institute of Coastal Environmental Pollution Control College of Environmental Science and Engineering Key Laboratory of Marine Environment and Ecology Ministry of Education Frontiers Science Center for Deep Ocean Multispheres and Earth System Ocean University of China Qingdao266100 China Sanya Oceanographic Institution Ocean University of China Sanya572000 China

Physical disintegration of macro biochars in natural environments produces nano biochars, thus affecting environmental processes (e.g., adsorption, migration) of antibiotic contaminants. However, understandings about the relationships between adsorption behaviors of antibiotics and unique physicochemical colloidal properties of nano biochar are relatively limited. In this study, a nano biochar (NSD700) was prepared from sawdust-derived biochar (SD700) by a combination method of ball milling, sonication, and centrifugation to contrastively investigate their adsorption for oxytetracycline (OTC). NSD700 had similar chemical structure properties of carbon fraction with SD700, whereas NSD700 possessed greater surface area and better porosity. The maximal sorption capacity of OTC on NSD700 was 30.7 mg/g, 1.95 times greater than that on SD700 (10.4 mg/g), primarily benefiting from more exposed surfaces and pores. OTC adsorption on NSD700 was inhibited under acidic and alkaline condition, and the inhibiting effect was more significant in comparison with that on SD700. OTC sorption onto NSD700 gradually decreased with increasing concentration of Na+, but which was not observed onto SD700. Aggregation kinetic of NSD700 varying with Na+ concentration demonstrated that the inhibiting effect might be affiliated with the destabilization and aggregation of NSD700, which could cause the shield of adsorption sites. This study provided insights for antibiotic adsorption characteristics on nano biochar and will help understanding the process and fate of antibiotics in the biochar-remediated environment. © 2022, The Authors. All rights reserved.

关键词： Porosity

Resource allocation for millimeter-wave train-ground communications in high-speed railway scenarios

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arXiv 2021年

作者： Zhang, Xiangfei Niu, Yong Mao, Shiwen Cai, Yunlong He, Ruisi Ai, Bo Zhong, Zhangdui Liu, Yiru State Key Laboratory of Rail Traffic Control and Safety Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China National Mobile Communications Research Laboratory Southeast University Nanjing211189 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of ISEE Zhejiang University China State Key Laboratory of Rail Traffic Control and Safety Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

With the development of wireless communication, higher requirements arise for train-ground wireless communications in high-speed railway (HSR) scenarios. The millimeter-wave (mm-wave) frequency band with rich spectrum resources can provide users in HSR scenarios with high performance broadband multimedia services, while the full-duplex (FD) technology has become mature. In this paper, we study train-ground communication system performance in HSR scenarios with mobile relays (MRs) mounted on rooftop of train and operating in the FD mode. We formulate a nonlinear programming problem to maximize network capacity by allocation of spectrum resources. Then, we develop a sequential quadratic programming (SQP) algorithm based on the Lagrange function to solve the bandwidth allocation optimization problem for track-side base station (BS) and MRs in this mm-wave train-ground communication system. Extensive simulation results demonstrate that the proposed SQP algorithm can effectively achieve high network capacity for train-ground communication in HSR scenarios while being robust to the residual self-interference (SI). Copyright © 2021, The Authors. All rights reserved.

关键词： Resource allocation

Effects and mechanisms of the main longitudinal beam shape on vortex-induced vibrations of Π-shaped composite girders

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Structures 2025年 78卷

作者： Kaiwen Li Yifei Sun Qingkuan Liu Yangxue Wang Qingcai Wang Binxuan Wang School of Civil Engineering Shijiazhuang Tiedao University Shijiazhuang 050043 China State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University Shijiazhuang 050043 China Key Laboratory of Roads and Railway Engineering Safety Control Ministry of Education Shijiazhuang 050043 China Innovation Center for Wind Engineering and Wind Energy Technology of Hebei Province Shijiazhuang 050043 China Research Center for Wind Engineering and Engineering Vibration Guangzhou University Guangzhou 510006 China

As a typical blunt body cross-section, Π-shaped composite girders are notably susceptible to vortex-induced vibrations (VIVs). Therefore, it is crucial to investigate their VIV characteristics and develop corresponding aerodynamic countermeasures. In the present study, synchronous pressure and vibration measurements, combined with computational fluid dynamics (CFD) simulations, were conducted for various Π-shaped composite girder models with different main longitudinal beam shapes. The effects of the main longitudinal beam height ( h ), bottom plate width ( b ), and bottom plate inclination angle ( β ) on the vertical VIV were systematically investigated. The underlying mechanisms of VIV for these parameters were discussed by analyzing the time-frequency characteristics of aerodynamic forces, the relationship between local lift forces and vortex-excited forces (VEFs), and flow field characteristics. The results indicate that reducing h , increasing b , or setting β to 20° can suppress vertical VIV, but the suppressive effect varies and is related to the wind attack angle. From the perspective of the flow field, the formation and movement of the large separation vortices at the leading edge of the girder are responsible for the observed vertical VIVs. The aforementioned parameter adjustments that exhibit an excellent VIV suppression effect can reduce the size and suppress the development of the leading-edge vortices, thereby improving the stability of the flow field. From the perspective of aerodynamic force characteristics, these adjustments can reduce the RMS value of the surface pressure coefficient. They can also shift the dominant frequency of the pressure spectrum away from the vibration frequency, weaken the dominant role of the vibration frequency in the overall lift, and greatly reduce the contribution of local lift to the overall vortex-excited forces (VEFs). As a result, VIV is suppressed.

关键词：

Blockchain for Energy Market: A Comprehensive Survey

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arXiv 2024年

作者： Jiang, Tianqi Luo, Haoxiang Yang, Kun Sun, Gang Yu, Hongfang Huang, Qi Vasilakos, Athanasios V. Glasgow College University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory of Optical Fiber Sensing and Communications Ministry of Education University of Electronic Science and Technology of China Chengdu611731 China Guangyuan Power Supply Company State Grid Sichuan Electric Power Company Guangyuan628033 China Peng Cheng Laboratory Shenzhen518066 China Sichuan Provincial Key Laboratory of Power System Wide-Area Measurement and Control University of Electronic Science and Technology of China Chengdu611731 China School of Information Engineering Southwest University of Science and Technology Mianyang621010 China Center for AI Research University of Agder Grimstad4879 Norway

The energy market encompasses the behavior of energy supply and trading within a platform system. By utilizing centralized or distributed trading, energy can be effectively managed and distributed across different regions, thereby achieving market equilibrium and satisfying both producers and consumers. Additionally, the energy market can address future production control and environmental concerns such as energy shortages and environmental deterioration. However, recent years have presented unprecedented challenges and difficulties for the development of the energy market. These challenges include regional energy imbalances, volatile energy pricing, high computing costs, and issues related to transaction information disclosure. These factors have hindered the smooth operation of the energy market. researchers widely acknowledge that the security features of blockchain technology can enhance the efficiency of energy transactions and establish the fundamental stability and robustness of the energy market. This type of blockchain-enabled energy market is commonly referred to as an energy blockchain. Currently, there is a burgeoning amount of research in this field, encompassing algorithm design, framework construction, and practical application. It is crucial to organize and compare these research efforts to facilitate the further advancement of energy blockchain. This survey aims to comprehensively review the fundamental characteristics of blockchain and energy markets, highlighting the significant advantages of combining the two. Moreover, based on existing research outcomes, we will categorize and compare the current energy market research supported by blockchain in terms of algorithm design, market framework construction, and the policies and practical applications adopted by different countries. Finally, we will address current issues and propose potential future directions for improvement, to provide guidance for the practical implementation of blockchain in the

关键词： Deterioration

Deep Learning Method for Path Loss Prediction in Mobile Communication systems

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Deep Learning Method for Path Loss Prediction in Mobile Comm...

International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Zhuocheng Xu Huiyun Cao Yue Yin Xiaohan Zhang Lina Wu Danping He Yuanbo Wang School of Electronic and Information Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Rail Traffic Control and Safety Beijing China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing China Frontiers Science Center for Smart High-speed Railway System Beijing China CHN energy shuohuang railway development company Ltd Hebei China

ISBN: (纸本)9781665418133

The study of wireless channel propagation characteristics is important to channel availability analysis and wireless network optimization. The existing deterministic channel models are greatly restricted by the high complexity. This paper proposes a deep learning (DL) method for path loss (PL) prediction in mobile communication systems, which uses satellite images and neural network (NN) to achieve efficient and reliable prediction. A Deep Neural Network (DNN) framework is established based on a Convolutional Neural Network (CNN) and a Back Propagation Neural Network (BPNN), where CNN extracts environmental features of propagation paths from satellite images and BPNN predicts PL according to the distance features and environmental features. The results show that DNN proposed in this paper can reduce the time cost with satisfying accuracy compared to the traditional Ray-tracing method with the lack of a detailed model of the scene and material parameters. The DL method for making a quick prediction of the PL in this paper can be used to optimize the mobile communication systems.

关键词： Deep learning Satellites Wireless networks Ray tracing Predictive models Feature extraction Mobile communication

Deep neural network based interpolation of sparse samples for time-dense power load forecasting

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Deep neural network based interpolation of sparse samples fo...

International Conference on Computer engineering and Applications (ICCEA)

作者： Bo Li Ruifeng Zhao Jiangang Lu Kuo Xin Jinhua Huang Guanqiang Lin Jinrong Chen Xueyue Pang Shunxin Wu Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd. Guangzhou Guangdong China Electric Power Dispatching and Control Center of China Southern Power Grid Co. Ltd. Guangzhou Guangdong China Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangzhou Guangdong China Huizhou Power Supply Bureau of Guangdong Power Grid Co. Ltd. Huizhou Guangdong China Foshan Power Supply Bureau of Guangdong Power Grid Co. Ltd. Foshan Guangdong China China Energy Engineering Group Guangdong Electric Power Design Institute Co. Ltd. Guangzhou Guangdong China State Key Laboratory of Power Equipment & System Security and New Technology School of Electrical Engineering Chongqing University Chongqing China

Large amount of power load data with high-sampling rate plays a crucial role in the training of time-dense power load forecasting network. However, in many cases, there is a large amount of data with low-sampling rate, which can't directly train a time-dense forecasting network. Therefore, this paper proposes a deep learning based dataset interpolation method, which uses the available high-sampling rate power load datasets to train a dataset interpolation network, then interpolates the low--sampling rate datasets with trained network to obtain high-sampling rate datasets, and finally uses the interpolated datasets for time-dense forecasting network training. The results show that the data obtained based on the method proposed in this paper fits the ground-truth closer than the traditional interpolation, and the results of the network trained based on this dataset are more accurate in the subsequent forecasting tasks.

关键词：

Content Distribution based on Joint V2I and V2V Scheduling in mmWave Vehicular Networks

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arXiv 2022年

作者： Su, Lan Niu, Yong Han, Zhu Ai, Bo He, Ruisi Wang, Yibing Wang, Ning Su, Xiang The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China The Norwegian University of Science and Technology Norway and University of Oulu Finland

With the explosive growth of vehicle applications, vehicular networks based on millimeter wave (mmWave) bands have attracted interests from both academia and industry. mmWave communications are able to utilize the huge available bandwidth to provide multiple Gbps transmission rates among vehicles. In this paper, we address the content distribution scheduling problem in mmWave vehicular networks. It has been challenging for all vehicles in the same network to complete content downloading due to the limited communication resources of roadside units (RSUs) and the high mobility of vehicles. We propose a joint vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) scheduling scheme to minimize the total number of content distribution time slots from a global optimization perspective. In the V2I phase, the RSU serially transmits integrity content to vehicles, which are selected according to the vehicular network topology and transmission scheduling scheme. In the V2V phase, full-duplex communications and concurrent transmissions are exploited to achieve content sharing between vehicles and improve transmission efficiency. Performance evaluations demonstrate that our proposed scheme reduces the number of time slots and significantly improves system throughput when compared with other schemes, especially under large-size file transfers and a large number of vehicles. Copyright © 2022, The Authors. All rights reserved.

关键词： Millimeter waves