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Synthesis of electric vehicle charging data: A real-world data-driven approach

Communications in Transportation Research 2024年第1期4卷 163-177页

作者： Zhi Li Zilin Bian Zhibin Chen Kaan Ozbay Minghui Zhong Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning NYU ShanghaiShanghai200126China Shanghai Key Laboratory of Urban Design and Urban Science NYU ShanghaiShanghai200126China Department of Civil and Urban Engineering New York UniversityNew York11201USA C2SMARTER Center(A Tier 1 USDOT UTC) Department of Civil and Urban EngineeringNew York UniversityNew York11201USA Shanghai Electric Vehicle Public Data Collecting Monitoringand Research CenterShanghai201805China

Nowadays,electric vehicles(EVs)are increasingly equipped with advanced onboard devices capable of collecting and recording real-time charging *** analysis of such data from a large-scale EV fleet plays a crucial role in supporting decision-making processes,particularly in the deployment of charging infrastructure and the formulation of EV-focused ***,the challenges of collecting these data are significant,primarily due to privacy concerns and the high costs associated with data *** response,this study introduces an innovative methodology for generating large-scale and diverse EV charging data,mirroring real-world patterns for cost-efficient and privacy-compliant ***,this approach combines Gibbs sampling and conditional density networks and was trained and validated using a real-world dataset consisting of approximately 1.65 million charging events from 3,777 battery EVs(BEVs)in Shanghai over a *** illustrate that the proposed model can effectively capture the underlying distribution of the original charging data,enabling the generation of synthetic samples that closely resemble real-world charging *** approach is readily employed for data imputation and augmentation,and it can also help simulate future charging distributions by conditional generation based on anticipated development premises.

关键词： Electric vehicles Charging data data augmentation data generation Gibbs sampling Conditional density network

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DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge Computing Networks

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 2202-2216页

作者： Lin, Bing Chen, Qiaoxin Chen, Xing Jia, Wen-Kang Lu, Yu Xiong, Neal N. Fujian Normal University College of Physics and Energy Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China Peking University School of Computer Science 100871 China Xiamen University Department of Informatics and Communication Engineering Xiamen361005 China Fuzhou University College of Computer and Data Science Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China Fujian Normal University College of Photonic and Electronic Engineering Fuzhou350117 China Fujian Normal University Concord University College Fuzhou350117 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States

Vehicular Edge Computing (VEC) is a feasible solution for autonomous driving as it can offload latency-sensitive and computation-intensive tasks from vehicle terminals to roadside units (RSUs) for real-time processing. Due to the high computational resource the workflow applications (i.e., autonomous driving) required, the small vehicle-to-RSUs communication range, and the scarce available resources for each vehicle, it might be difficult to accomplish complex workflow applications through the single-hop offloading paradigm in the Internet of Vehicles (IoV). In this paper, we propose a reinforcement learning (RL) based multi-hop computation offloading scheme for workflow applications to reduce their execution latency in the VEC networks, which considers the data dependency in workflow applications as well as the trust relationship and communication interference among RSUs. Firstly, a preprocessing method is employed to merge the cut-edges in a workflow to reduce the offloading scale of tasks and compress the encoding dimension of offloading solutions. Then, a Deep Q-network algorithm using Phase-optimal State update (DQPS) is proposed to update the offloading policy distribution, in which the deviation of the phase-optimal state from the current state is estimated as the reward of RL to promote the algorithm's convergence to adapt the dynamic VEC networks. Simulation results show that DQPS has the best performance compared to other benchmark schemes. Moreover, the latency of the identical applications can be reduced by 2.5%-25.3% through our offloading scheme with a multi-hop model compared to that with the single-hop paradigm in IoV. © 1975-2011 IEEE.

关键词： Computation offloading

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Inhibition of ALDH2 by disulfiram leads to synthetic lethality via ROS strikes twice in ARID1A-deficient cholangiocarcinoma

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Genes & Diseases 2023年第1期10卷 69-71页

作者： Tingming Liang Lin Jia Rui Duan Lulu Shen Dekang Ren Yujie Ren Yuyang Dou Li Guo Jiangsu Key Laboratory for Molecular and Medical Biotechnology School of Life ScienceNanjing Normal UniversityNanjingJiangsu210023China Department of Bioinformatics Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu ProvinceSchool of Geographic and Biologic InformationNanjing University of Posts and TelecommunicationsNanjingJiangsu 210023China

Synthetic lethality describes an interaction whereby the co-occurrence of two mutations leads to cell death but one mutation alone does not,which can be exploited for cancer therapeutics.1 Due to lacking effective nonsurgical treatment and early clinical diagnosis markers,patients have high mortality and low overall survival rates in cholangiocarcinoma(CCA).

关键词： diagnosis mortality alone

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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Cooperative differential games guidance laws for multiple attackers against an active defense target

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Chinese Journal of Aeronautics 2022年第5期35卷 374-389页

作者： Fei LIU Xiwang DONG Qingdong LI Zhang REN School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang UniversityBeijing 100083China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100083China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of ChinaChongqing UniversityChongqing 400044China

This paper is concerned with a scenario of multiple attackers trying to intercept a target with active *** types of agents are considered in the guidance:The multiple attackers,the target and the defender,where the attackers aim to pursuit the target from different directions and evade from the defender *** guidance engagement is formulated in the framework of a zero-sum two-person differential game between the two opposing teams,such that the measurements on the maneuver of the target or estimations on the defending strategy of the defender can be *** of the attackers resides in two aspects:redundant interception under the threat of the defender and the relative intercept geometry with the *** miss distances,the relative intercept angle errors and the costs of the agents are combined into a single performance index of the *** formulation enables a unitary approach to the design of guidance laws for the *** minimize the control efforts and miss distances for the attackers,an optimization method is proposed to find the best anticipated miss distances to the defender under the constraint that the defender is endowed with a capture *** simulations with two cases are conducted to illustrate the effectiveness of the proposed cooperative guidance law.

关键词： Active defense Cooperative guidance Differential games Minimum effort optimization Relative intercept angle

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An Optimization Method of Energy Demand Forecasting Method Based on Neural Network 5

An Optimization Method of Energy Demand Forecasting Method B...

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5th International Conference on Smart Power and Internet Energy Systems, SPIES 2023

作者： Zhao, Yufan Song, Ruizhuo Li, Chengfeng School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education Beijing100083 China Clean Energy Branch of Cnooc Energy Technology & Services Limited Tianjin Tianjin300452 China

ISBN: (纸本)9798350371741

User-side energy demand forecasting is one of the essential functions of smart grids in the future. By forecasting electricity consumption from consumers, energy companies can make appropriate adjustments to the amount of electricity generated, reducing the burden on the grid and reducing energy waste. In this paper, an optimization method for energy prediction method based on neural network is proposed. Firstly, a dual-CNN structure is used to extract the state of the electrical equipment at the user and the power required for the state and predict it. Then, the neural network-based genetic algorithm was used to adjust the weight of the neural network. Both methods are more mature methods, and the combination of the two methods may be able to build on the original methods and achieve accurate prediction of energy demand. © 2023 IEEE.

关键词： Genetic algorithms

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Integrated Energy Management Platform for Monitoring, Operation and Maintenance 16th

Integrated Energy Management Platform for Monitoring, Operat...

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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Zhang, Tao Wang, Hailun Ye, Jun Yang, Xu Zhang, Yixin Li, Dongze Shi, Zinan School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education Beijing100083 China Xi’an Jiaotong University Xi’an710000 China Beijing SyiTsing Energy Technology Co. LTD. Beijing100083 China

ISBN: (纸本)9789819617760

A seamless and comprehensive system for the clean utilization of energy remains elusive, with energy dispatch failing to harmonize the overall picture and the integration of clean energy sources. The judicious employment of holistic smart energy holds immense importance for societal advancement. To foster a more rational and efficient approach to the development and administration of smart energy, and to ensure the optimal utilization of new energy resources, the intelligent integrated energy management platform's development and design methodologies are being optimized in tandem with the Internet of Things technology. This paper outlines the construction of an integrated energy intelligent management system and platform, leveraging the ‘cloud management edge side’ approach. It bridges the gap between the various tiers of the integrated energy service system, achieving a comprehensive integration of energy flow, information flow, and value flow within the system. This platform stands poised to support comprehensive energy operators, data, and network services across the region, spurring regional economic growth, and offering a diverse array of financial products and services, grounded in high-quality user resources and industrial structure service capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Self-supervised fast reconstruction method for diffusion MRI based on multi-directional assistance 17

Self-supervised fast reconstruction method for diffusion MRI...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Wu, Yuxin Zhang, Jian Wang, Lihui Cao, Ying Wu, Linhai Wu, Yunjia Ye, Chen Zhu, Yuemin Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 France

ISBN: (纸本)9798350387384

Diffusion MRI (dMRI) is widely used for the detection and diagnosis of various pathologies due to its non-invasive nature. However, its signal acquisition process is relatively slow and susceptible to motions. In order to accelerate reconstruction process, many deep learning-based methods have been developed in recent years to achieve promising results, while most of them are supervised based methods and require fully sampled data which are difficult to obtain in practical applications to train the network. Self-supervised based methods were proposed to deal with this problem to a certain extent, however, they are devoted only to conventional MRI data, ignoring the correlation between the directions of dMRI data. To address this issue, we propose a self-supervised fast reconstruction method for dMRI based on multi-directional assistance. The method better utilizes the information between dMRI data directions by Partition-Replace operation, cleverly constructing data pairs and designing effective self-supervised strategy for bi-directional prediction, which can overcome the problem of data incompleteness and guide the model to reconstruct unsampled signals in spite of the difficulty of acquiring fully sampled data. Experimental results show that our method achieves better reconstruction in the absence of fully sampled data, even with an 8-fold acceleration factor. © 2024 IEEE.

关键词： Self-supervised learning

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Structure-and Logic-Aware Heterogeneous Graph Learning for Recommendation 40

Structure-and Logic-Aware Heterogeneous Graph Learning for R...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Li, Anchen Yang, Bo Huo, Huan Hussain, Farookh Khadeer Xu, Guandong College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Aalto University Espoo Finland School of Computer Science University of Technology Sydney Sydney Australia Education University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798350317152

Recently, there has been a surge in recommendations based on heterogeneous information networks (HINs), attributed to their ability to integrate complex and rich semantics. Despite this advancement, most HIN-based recommenders overlook two critical aspects. First, they often fail to consider HIN's heterophily nature, hindering the capture of non-local structures in HINs. Second, most methods lack the capability for logical reasoning. In this paper, we propose a novel structure-and logic-aware heterogeneous graph learning framework for recommender systems (SLHRec). Our SLHRec contains a structure-aware module and a logic-aware module. The former uses network geometry to construct non-local neighborhoods for nodes in HINs, and then introduces a graph neural network to integrate constructed neighbors for modeling the heterophily of HINs. The logic-aware module uses the Markov logic network (MLN) to infuse logic rules into heterogeneous graph learning, thereby boosting logic reasoning in recommendations. Furthermore, we utilize contrastive learning to model cooperative signals between modules, enabling them to complement each other. In the prediction stage, both modules contribute to generating recommendations. Compared with several strong recommender baselines, our SLHRec achieves superior performance on four real-world datasets. © 2024 IEEE.

关键词： Semantics

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Analysis and modeling for illuminance and signal-to-noise of smart traffic information system

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Journal of engineering and Applied Science 2022年第1期69卷 1-17页

作者： Chen, Yaoting Chen, Huanting School of Business Minnan Normal University Zhangzhou363000 China Analysis and Application for Business Big Data of Fujian Provincial Key Laboratory Zhangzhou363000 China College of Physics and Information Engineering Minnan Normal University Zhangzhou363000 China

The traffic lights using by light emitting diode (LED) are able to indicate and provide the information about the ambient. The signal transimission performance of traffic system is related to a variety of factors such as the signal-to-noise ratio (SNR), illuminance. Several factors are influence on illumiance and SNR, such as eatsink temperature and driver parameters. It has not been any substantial investigate on the operated effects of traffic light with sinusoidal waveform driver on illuminance and SNR. With the use of heatsink temperature and driver parameters of the LED source with sinusoidal waveform driver, prediction modeling for the illuminance and SNR of the mixed white LED sources with heatsink temperature, frequency, amplitude voltage, bias voltage and amplification factors are proposed in this paper to enhance the illuminance and SNR model of LED source inside smart traffic information system. The proposed model should carry out a series of calibration step for illuminance, heatsink temperature and driver parameters, which are easy for traffic designer and source system designers to follow in the traffic information system. The proposed model has been measured for the mixed white LED sources, with reasonably good agreements between theoretical and practical results. © 2022, The Author(s).

关键词： Signal to noise ratio

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