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Reliability of electric vehicle charging infrastructure:A cross-lingual deep learning approach

Communications in Transportation Research 2023年第1期3卷 81-91页

作者： Yifan Liu Azell Francis Catharina Hollauer M.Cade Lawson Omar Shaikh Ashley Cotsman Khushi Bhardwaj Aline Banboukian Mimi Li Anne Web Omar Isaac Asensio School of Public Policy Georgia Institute of TechnologyAtlanta30332USA Sam Nunn School of International Affairs Georgia Institute of TechnologyAtlanta30332USA School of Civil&Environmental Engineering Georgia Institute of TechnologyAtlanta30332USA H.Milton Stewart School of Industrial and Systems Engineering Georgia Institute of TechnologyAtlanta30332USA School of Computer Science Georgia Institute of TechnologyAtlanta30332USA School of Computer Science Stanford UniversityPalo Alto94305USA School of Economics Georgia Institute of TechnologyAtlanta30332USA Institute for Data Engineering&Science(IDEaS) Georgia Institute of TechnologyAtlanta30332USA

Vehicle electrification has emerged as a global strategy to address climate change and emissions externalities from the transportation *** of charging infrastructure is needed to accelerate technology adoption;however,managers and policymakers have had limited evidence on the use of public charging stations due to poor data sharing and decentralized ownership across *** this article,we use machine learning based classifiers to reveal insights about consumer charging behavior in 72 detected languages including *** investigate 10 years of consumer reviews in East and Southeast Asia from 2011 to 2021 to enable infrastructure evaluation at a larger geographic scale than previously *** find evidence that charging stations at government locations result in higher failure rates with consumers compared to charging stations at private points of *** evidence contrasts with predictions in the *** European markets,where the performance is closer to *** also find that networked stations with communication protocols provide a relatively higher quality of charging services,which favors policy support for connectivity,particularly for underserved or remote areas.

关键词： Electric vehicles Consumer behavior Charging infrastructure Public policy Machine learning Natural language processing Transformer algorithms

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Target Threat Assessment Method based on RCS Feature Classifier and TOPSIS 15

Target Threat Assessment Method based on RCS Feature Classif...

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15th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2023

作者： Shan, Yujie Zhang, Jinhuan Dong, Jian Long, Jun Central South University School of Computer Science and Engineering Changsha China Central South University Big Data Institute Changsha China

ISBN: (纸本)9798350338874

Target threat assessment involves many uncertainties and is a tactical decision assessment problem with incomplete and uncertain information. In traditional target threat assessment, only the influence of the target's state attributes on its threat level is considered, while the target's type is ignored. To address this issue, radar target recognition is incorporated into the threat assessment process, and a method based on radar target recognition is proposed to improve the accuracy of threat assessment by incorporating Radar Cross Section (RCS) feature classifier into a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Using simulations, the practicability and viability of the above method are examined. © 2023 IEEE.

关键词： Radar target recognition

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From Time Series to Multi-modality: Classifying Multivariate Time Series via Both 1D and 2D Representations 9th

From Time Series to Multi-modality: Classifying Multivariat...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Yang, Chao Wang, Xianzhi Yao, Lina Long, Guodong Xu, Guandong School of Computer Science University of Technology Sydney Ultimo Australia School of Computer Science and Engineering UNSW Kensington Australia Australian AI Institute University of Technology Sydney Ultimo Australia Data Science Institute University of Technology Sydney Ultimo Australia CSIRO Data61 Canberra Australia

ISBN: (纸本)9783031466601

Multivariate time series classification is crucial for various applications such as activity recognition, disease diagnosis, and brain-computer interfaces. Deep learning methods have recently achieved promising performance thanks to their powerful representation learning capacity. However, existing deep learning-based classifiers rely solely on temporal information while disregarding clues from the frequency perspective. In this regard, we propose a novel method for classifying multivariate time series leveraging both temporal and frequency information. We first apply Short-Time Fourier Transform (STFT) to transform time series into spectrograms, which contain a 2D representation of frequency components and their temporal positions. In particular, for each variable, we generate spectrograms with varying frequencies and temporal resolutions under different window sizes. The transformation essentially adds a new modality to 1D time series and converts the multivariate time series classification into a multi-modality data classification task, making it possible to bring powerful backbones from computer vision fields to solve the time series classification problem. We then construct a dual-stream network based on the ResNet architecture that takes in both 1D and 2D representations for accurate multivariate time series classification. Our extensive experiments on 30 public datasets show our method outperforms multiple competitive state-of-the-art baselines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Time series

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Natural Language Query and Control Interface for database Using Afghan Language 16

Natural Language Query and Control Interface for Database Us...

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16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022

作者： Karimi, Sadullah Rasel, Annajiat Alim Abdullah, Matin Saad Brac University School of Data and Sciences Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (数字)9781665498104

ISBN: (纸本)9781665498104

The Afghan language, or Persian language, is one of the most widely used languages, with up to 110 million speakers worldwide. It is used in countries like Afghanistan, Azerbaijan, Iran, Iraq, Russia, Tajikistan, Turkmenistan, Uzbekistan, etc. The Persian language has insufficient resources and needs higher quality lexicon translation. The proposed Afghan Language Query and Control Interface (ALQCI) is based on NLIDB Approach. The Afghan language is used in this system to work with user-chosen data from a database. Retrieving information from the database requires knowledge of SQL Query Language or a well-designed interface. It is convenient for domain-expert users to retrieve data from the database. However, it may be challenging for non-expert users to access the database through SQL Query in the absence of a flexible user interface. This system helps overcome the challenge for those who speak Persian worldwide. The system may supplement or substitute less friendly user interfaces (UI). The system generates Structured Query Language (SQL) when a user inputs a spoken natural language query in the Persian language. The system works by allowing a user to enter a query in Persian, which the system then translates into a SQL query using the word mapping technique. The SQL is generated through semantic analysis. Afterwards, the generated SQL is used to extract knowledge using the Afghan language from a database. © 2022 IEEE.

关键词： Semantics

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Massively parallel algorithms for fully dynamic all-pairs shortest paths

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Frontiers of computer science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

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Joint Embedding of Local Structures and Evolutionary Patterns for Temporal Link Prediction 9th

Joint Embedding of Local Structures and Evolutionary Patte...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Chen, Tingxuan Long, Jun Yang, Liu Li, Guohui Luo, Shuai Xiao, Meihong School of Computer Science and Engineering Central South University Changsha China Big Data Institute Central South University Changsha China

ISBN: (纸本)9783031466632

Link prediction tackles the prediction of missing facts in an incomplete knowledge graph (KG) and has been widely explored in reasoning and information retrieval. The vast majority of existing methods perform link prediction on static KGs, with the assumption that the relational facts are generally correct. However, some facts may not be universally valid, as they tend to evolve. Despite the prevalence of temporal knowledge graphs (TKGs) with evolving facts, the studies on such data for temporal link prediction are still far from resolved. In this paper, we propose SiepNet, a novel graph neural network for temporal link prediction, driven by local Structural Information and Evolutionary Patterns. Specifically, SiepNet captures the local structural information based on a relation-aware GNN architecture, and incorporates temporal attention to model long- and short-range historical dependencies hidden in TKGs. Moreover, SiepNet integrates local structures and evolutionary patterns to enhance the semantic representation of evolving facts in TKGs. The extensive experiments on five real-world TKG datasets demonstrate the effectiveness of our approach SiepNet in temporal link prediction, compared with the state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge graph

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QUALYST: data Quality Assessment System for Thailand Open Government data 20

QUALYST: Data Quality Assessment System for Thailand Open Go...

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20th International Joint Conference on computer science and Software engineering, JCSSE 2023

作者： Samakit, Tanapat Anutariya, Chutiporn Buranarach, Marut School of Engineering and Technology Asian Institute of Technology Computer Science & Information Management Ict Department Thailand National Electronics and Computer Technology Center Data Science and Analytics Research Group Thailand

ISBN: (纸本)9798350300505

Open Government data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency, improve decision-making, enhance accountability, create economic opportunities, and encourage civic engagement. The OGD can help citizens understand the government and its legitimacy and transparency. Thus, when the government shares its data with people, it helps to create trust by being transparent, accountable, and promoting innovative solutions that benefit everyone. However, each published dataset has no indication of its quality assessment at all;thus, making it difficult for citizens to assess the reliability of the data from the OGD. Therefore, a data quality assessment for OGD should be developed. This will help create effective datasets which in turn help users understand the data. This study proposes QUALYST, a system that assesses Thailand's OGD dataset and validates it for analytic and visualization purposes. The study focuses on designing the data storage and implementing the assessment system. Furthermore, the proposed data quality dimensions, the developed data pipeline, and the assessment process are elaborated. Finally, the prototype system is demonstrated using Thailand's OGD datasets with examples in a visualized format. © 2023 IEEE.

关键词： Transparency

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Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection 24

Dual-Modeling Decouple Distillation for Unsupervised Anomaly...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Xinyue Wang, Jianyuan Leng, Biao Zhang, Shuo School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer & Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400706868

Knowledge distillation based on student-teacher network is one of the mainstream solution paradigms for the challenging unsupervised Anomaly Detection task, utilizing the difference in representation capabilities of the teacher and student networks to implement anomaly localization. However, over-generalization of the student network to the teacher network may lead to negligible differences in representation capabilities of anomaly, thus affecting the detection effectiveness. Existing methods address the possible over-generalization by using differentiated students and teachers from the structural perspective or explicitly expanding distilled information from the content perspective, which inevitably results in an increased likelihood of underfitting of the student network and poor anomaly detection capabilities in anomaly center or edge. In this paper, we propose Dual-Modeling Decouple Distillation (DMDD) for the unsupervised Anomaly Detection. In DMDD, a Decouple Student-Teacher Network is proposed to decouple the initial student features into normality and abnormality features. We further introduce Dual-Modeling Distillation based on normal-anomalous image pairs, fitting normality features of anomalous image and the teacher features of the corresponding normal image, widening the distance between abnormality features and the teacher features in anomalous regions. Synthesizing these two distillation ideas, we achieve anomaly detection which focuses on both edge and center of anomaly. Finally, a Multi-perception Segmentation Network is proposed to achieve focused anomaly map fusion based on multiple attention. Experimental results on MVTec AD show that DMDD surpasses SOTA localization performance of previous knowledge distillation-based methods, reaching 98.85% on pixel-level AUC and 96.13% on PRO. © 2024 ACM.

关键词： Students

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Analysis and Classification of Network Security Attacks Using Domain Generation Algorithm

Analysis and Classification of Network Security Attacks Usin...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Gomathi, S. Samundeeswari, S. Ponmanirai, S. Nanthini, S. Vandayar Engineering College Department of Computer Science and Engineering Thanjavur India Saveetha School of Engineering Simats University Department of Computational Intelligence Thandalam India Saveetha School of Engineering Simats University Department of Department of Data Science Thandalam India

ISBN: (纸本)9798350313987

In the realm of cybersecurity, the evolution of network attacks necessitates innovative approaches for threat prediction and mitigation. Traditional methods often fall short in identifying novel attack patterns or adapting swiftly to emerging threats. This study explores the application of Domain Generative Algorithms (DGAs) in the context of network attack prediction. DGAs leverage advanced machine learning techniques to generate synthetic data samples representing various network attack scenarios. By training generative models on diverse and large-scale datasets comprising both historical attack data and benign network traffic, the algorithms learn intricate patterns intrinsic to different types of attacks. Through, systematic analysis of these synthetic attack instances, IDS and IPS algorithms can be fine-tuned to recognize subtle attack patterns that might elude conventional signature-based detection methods. This research underscores the pivotal role of DGAs in revolutionizing the landscape of network attack prediction. By harnessing the power of generative algorithms, organizations can proactively strengthen their cybersecurity posture, effectively mitigating the risks posed by an ever-evolving spectrum of network attacks. © 2023 IEEE.

关键词： data Traffic Denial of Services Domain Generation Algorithm Malware Network security Pattern Analysis

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A Novel Hybrid AI Framework for Real-Time Prediction and Risk Assessment of Vector-Borne Epidemics using Spatiotemporal data 4

A Novel Hybrid AI Framework for Real-Time Prediction and Ris...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Munirathnam, Rajesh Maindola, Meenakshi Bhargav, H.K. Patil, Prachi Ramesh Bhatt, Pooja Natrayan, L. TechnoGen Inc. Data & Analytics Division NJ United States Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Department of Computer Science and Engineering Tumakuru India Jagdambha College of Engineering and Technology Department of Electronics and Telecommunication Engineering Yavatmal India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Computer science and Engineering Vadodara India Saveetha School of Engineering Simats Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350352931

Vector-borne epidemics constitute major threats to global health that call for improved modeling systems for managing diseases. In order to incorporate such factors into an actual model, a traditional forecasting process may fail to grasp the nature of disease transmission adequately. We present a hybrid AI system based on LSTM and GCN for forest bug outbreak detection in real time. The framework also integrated a spatial-temporal analysis of climate variability, vector population fluctuations, and human mobility with climate variables across 15 regions over a five-year period (2019-2023). The hybrid framework also produced superior results, achieving a 94.2% prediction rate for health risk disease outbreaks, compared to a 17.3% improvement in traditional machine learning. When used to distinguish between high-risk and low-risk zones, the model demonstrated the following metrics: sensitivity of 92.8% and specificity of 95.1%. Real-time risk assessment demonstrated 89.6% effectiveness in indicating early warning and the best prediction lead time average of 21 days. This framework significantly enhances the prediction of vector-borne diseases by offering a dependable, real-time platform for disease surveillance and risk assessment. Its high accuracy and early warning capabilities make the system a useful tool in epidemiological and public health campaigns and control measures. © 2024 IEEE.

关键词： Prediction models

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