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OSE: On-Site State of Health Estimation for Li-ion Battery using Real-Time Field data

IEEE Transactions on Transportation Electrification 2025年第3期11卷 7452-7462页

作者： Chen, Yanwen Zhao, Cong Yang, Shusen Zhao, Peng Ren, Xuebin Han, Qing Yang, Yuqian Wu, Shuaijun Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics Shaanxi Xi'an710049 China Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics The Ministry of Education Key Lab for Intelligent Networks and Network Security Shaanxi Xi'an710049 China

On-site lithium-ion battery state of health (SoH) estimation is of crucial importance for reliable operations of electric vehicles (EVs). Yet, due to the low-quality of unlabeled real-time field data, diverse operating environments of in-service EVs, and limited computational capability of onboard devices, existing techniques established on data from well-controlled experimental environments are not practical for real world EVs' SoH estimation. Accurate and rapid SoH estimation based on field data of in-service EVs still remains quite challenging. To tackle this challenge, we present an on-site SoH estimation (OSE) method using in-service EV field data through a new knowledge embedded deep transfer learning (DTL) model. Initially, an universal data preprocessing approach integrating mechanism knowledge is designed to process low-quality data under diverse operating environments. Then, we develop a domain adaptive hybrid deep neural network (DAHDNN) model suitable for unlabeled field data, which can be deployed via edge cloud collaborative framework to meet actual computational capability. We demonstrate the superiority of our method across four real datasets, where, OSE's estimation error is decreased by up to 78.5% compared with the state-of-the-art methods. The results indicate that the proposed method has good generalizability and reliability for SoH estimation on real-time field data. © 2015 IEEE.

关键词： State of charge

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Effi-CNN: real-time vision-based system for interpretation of sign language using CNN and transfer learning

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Multimedia Tools and Applications 2025年第6期84卷 3137-3159页

作者： Pranav Katarya, Rahul Big Data Analytics and Web Intelligence Laboratory Computer Science & Engineering Department Delhi Technological University New Delhi India

The deaf and mute population has difficulty conveying their thoughts and ideas to others. Sign language is their most expressive mode of communication, but the general public is callow of sign language;therefore, the mute and deaf have difficulty communicating with others. A system that can correctly translate sign language motions to speech and vice versa in real time is required to overcome this communication barrier. Effi-CNN, a vision-based Sign Language Recognition (SLR) system employing transfer learning with EfficientNetB2, is proposed in this paper. We have also developed a system covering sign gestures to text in real time. Our approach was evaluated on eight publicly available datasets, including the Massey University gesture dataset, the ArSL2018 dataset, the MNIST-ASL dataset, and others. When comparing our results to state-of-the-art algorithms, the experimental findings showed that our proposed work is more successful than existing models. Our proposed model delivered an accuracy of 99.90 on MNIST dataset. The results show that our Effi-CNN surpasses most currently existing solutions and can categorize many gestures with a low error rate. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Hao, Yijun Yang, Shusen Li, Fang Zhang, Yifan Zhao, Cong Ren, Xuebin Zhao, Peng Xu, Chenren Wang, Shibo Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Xi’an710049 China Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China Peking University School of Computer Science Beijing100871 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University Xi'an710049 China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports 3.12x more demand when compared to related existing approaches. © 1983-2012 IEEE.

关键词： Network topology

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KNAS-TP: Domain Knowledge-driven Automated Construction of Uplink and Downlink Throughput Predictor in 5G 16

KNAS-TP: Domain Knowledge-driven Automated Construction of U...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Yang, Yuqian Zhao, Cong Xi'an Jiaotong University National Engineering Laboratory for Big Data Analytics Xi'an China

ISBN: (纸本)9798331506322

The advent of 5G networks marks a revolutionary shift in wireless communications, offering unprecedented improvements in speed, latency, and connectivity. Accurate uplink and downlink throughput prediction is crucial for 5G network Operations and Maintenance (O&M) tasks, yet existing methods constructing intuitive predictors without structural optimization often fail to adapt to dynamic 5 G environments due to the lack of effective utilization of domain knowledge. To address this, we present KNAS-TP, the first domain knowledge-driven automated construction method for 5G throughput prediction based on Neural Architecture Search (NAS) and the Attention mechanism. KNAS-TP incorporates domain knowledge in NAS search space establishment and loss function definitions, which includes the match degree evaluation of the complexity of the searched neural network structure and corresponding knowledge. Additionally, KNAS-TP enhances model using an Attention mechanism to captures the uneven impacts of deep factors, thereby improving final model training and prediction accuracy. Extensive experimental results demonstrate that our model outperforms all comparative methods, achieving up to 8.5 × lower throughput prediction MSE. The integration of domain knowledge is essential for robust and accurate throughput predictions, validating the effectiveness of our methodology in complex 5G scenarios. © 2024 IEEE.

关键词： 5G mobile communication systems

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Forecasting of stock price index using support vector regression with multivariate empirical mode decomposition

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Journal of Systems and Information Technology 2022年第2期24卷 75-95页

作者： Huang, Yanmei Deng, Changrui Zhang, Xiaoyuan Bao, Yukun Center for Big Data Analytics Jiangxi University of Engineering Xinyu China

Purpose: Despite the widespread use of univariate empirical mode decomposition (EMD) in financial market forecasting, the application of multivariate empirical mode decomposition (MEMD) has not been fully investigated. The purpose of this study is to forecast the stock price index more accurately, relying on the capability of MEMD in modeling the dependency between relevant variables. Design/methodology/approach: Quantitative and comprehensive assessments were carried out to compare the performance of some selected models. data for the assessments were collected from three major stock exchanges, namely, the standard and poor 500 index from the USA, the Hang Seng index from Hong Kong and the Shanghai Stock Exchange composite index from China. MEMD-based support vector regression (SVR) was used as the modeling framework, where MEMD was first introduced to simultaneously decompose the relevant covariates, including the opening price, the highest price, the lowest price, the closing price and the trading volume of a stock price index. Then, SVR was used to set up forecasting models for each component decomposed and another SVR model was used to generate the final forecast based on the forecasts of each component. This paper named this the MEMD-SVR-SVR model. Findings: The results show that the MEMD-based modeling framework outperforms other selected competing models. As per the models using MEMD, the MEMD-SVR-SVR model excels in terms of prediction accuracy across the various data sets. Originality/value: This research extends the literature of EMD-based univariate models by considering the scenario of multiple variables for improving forecasting accuracy and simplifying computability, which contributes to the analytics pool for the financial analysis community. © 2020, Emerald Publishing Limited.

关键词： Empirical mode decomposition

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U-Park: A User-Centric Smart Parking Recommendation System for Electric Shared Micromobility Services

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5179-5193页

作者： Yan, Sen OConnor, Noel E. Liu, Mingming Dublin City University SFI Insight Centre for Data Analytics School of Electronic Engineering DublinD09 V209 Ireland

Electric shared micromobility services (ESMSs) has become a vital element within the mobility as a service framework, contributing to sustainable transportation systems. However, existing ESMS face notable design challenges such as shortcomings in integration, transparency, and user-centered approaches, resulting in increased operational costs and decreased service quality. A key operational issue for ESMS revolves around parking, particularly ensuring the availability of parking spaces as users approach their destinations. For instance, a recent study illustrated that nearly 13% of shared e-bike users in Dublin, Ireland, encounter difficulties parking their e-bikes due to inadequate planning and guidance. In response, we introduce U-Park, a user-centric smart parking recommendation system designed for ESMS, providing tailored recommendations to users by analyzing their historical mobility data, trip trajectory, and parking space availability. We present the system architecture, implement it, and evaluate its performance using real-world data from an Irish-based shared e-bike provider, MOBY Bikes. Our results illustrate U-Park's ability to predict a user's destination within a shared e-bike system, achieving an approximate accuracy rate of over 97.60%, all without requiring direct user input. Experiments have proven that this predictive capability empowers U-Park to suggest the optimal parking station to users based on the availability of predicted parking spaces, improving the probability of obtaining a parking spot by 24.91% on average and 29.66% on maximum when parking availability is *** Statement—ESMSs, such as shared e-bikes, play a key role in sustainable urban mobility. However, ESMS often face parking challenges, affecting service quality and operating costs.U-Park, our user-centric smart parking recommendation system,solves this problem by accurately predicting the user’s destination and suggesting the best parking space. With over 97.60% predic

关键词： Recommender systems

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ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing

ProbSAINT: Probabilistic Tabular Regression for Used Car Pri...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Madhusudhanan, Kiran Behrens, Gunnar Stubbemann, Maximilian Schmidt-Thieme, Lars University Of Hildesheim Information Systems and Machine Learning Lab Vwfs Data Analytics Research Center Germany Volkswagen Financial Services Ag Data Analytics & Ai Engineering United States

ISBN: (纸本)9798350362480

Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics. With the recent surge in online marketplaces and increased demand for used cars, accurate pricing would benefit both buyers and sellers by ensuring fair transactions. However, the transition towards automated pricing algorithms using machine learning necessitates the comprehension of model uncertainties, specifically the ability to flag predictions that the model is unsure about. Although recent literature proposes the use of boosting algorithms or nearest neighbor-based approaches for swift and precise price predictions, encapsulating model uncertainties with such algorithms presents a complex challenge. We introduce ProbSAINT, a model that offers a principled approach for uncertainty quantification of its price predictions, along with accurate point predictions that are comparable to state-of-the-art boosting techniques. Furthermore, acknowledging that the business prefers pricing used cars based on the number of days the vehicle was listed for sale, we show how ProbSAINT can be used as a dynamic forecasting model for predicting price probabilities for different expected offer durations. Our experiments further indicate that ProbSAINT is especially accurate in instances where it is highly certain. This proves the applicability of its probabilistic predictions in real-world scenarios where trustworthiness is crucial. © 2024 IEEE.

关键词： Automotive industry

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Traffic Speed Forecasting in Congested Conditions: Evaluation and Improvements 8

Traffic Speed Forecasting in Congested Conditions: Evaluatio...

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8th International Conference on Intelligent Traffic and Transportation, ICITT 2024 - incorporating the 8th International Conference on Communication and Network Technology, ICCNT 2024

作者： Jagadeesh, George Rosario Thia, Kai Xin Artificial Intelligence - Data Analytics Strategic Technology Centre ST Engineering Ltd. Singapore

ISBN: (纸本)9781643685601

Traffic forecasts are especially valuable in congested conditions for travelers and traffic managers. While deep learning-based forecasting models perform impressively on data from mostly normal traffic conditions, their performance in congested conditions has not been adequately examined and improved upon. Towards addressing this gap, we first present and apply a method for determining the spatial and temporal extents of congestion episodes in a traffic speed dataset. We evaluate two deep learning models and a baseline model on data within the congestion extents and analyze the results. Subsequently, we propose and evaluate two simple modifications related to the input data features, which improve the prediction accuracy of a state-of-the-art model in congested conditions. Overall, this study indicates the need for more targeted research efforts towards developing robust prediction models that perform well in congested conditions. © 2024 The Authors.

关键词： Prediction models

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Pricing Used Vehicles at Volkswagen Financial Services AG

Pricing Used Vehicles at Volkswagen Financial Services AG

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Jawed, Shayan Stening, Jan Schmidt-Thieme, Lars University of Hildesheim Vwfs Data Analytics Research Center Hildesheim Germany Data Analytics & Ai Engineering Volkswagen Financial Services Ag Braunschweig Germany

ISBN: (纸本)9798350324457

In this work, we develop machine learning algorithms for pricing used vehicles for B2B sales at Volkswagen Financial Services. The B2B pricing dataset is structured as Tabular data, however unlike commonly available tabular datasets, it is also time-indexed. To exploit the temporal component and improve prediction accuracy, we design a Sequential network that processes sequences of vehicles. We transform the data from a Tabular representation to a sequential representation by appending previously sold vehicles and their prices as additional features for a target vehicle to be priced. The sequential network, PriceNet, embeds each of the multiple vehicles through a dedicated Embedding module, and uses a series of Convolutional layers that learn sequential features relating to price trends and seasonalities. We show that PriceNet can improve performance over the state-of the-art deep-learning based Tabular baselines, Tab-Transformer and ***, this paper also covers related aspects such as Chronological Validation strategies for hyperparameter tuning models for time-indexed data. Notably, we also observed that Gradient Boosted Decision Tree algorithms, outperformed all the models in prediction accuracy. For this class of models, we therefore also designed a new Quantile level tuning approach that tunes the quantile level based on Out-of-sample chronological validation data. By tuning the quantile level, we can probabilistically determine whether to overshoot or undershoot in the case of temporal covariate shifts for out-of-sample testing data observed after the validation split. © 2023 IEEE.

关键词： Convolutional Neural networks Gradient Boosted Decision Trees Hyperparameter Optimization Quantile Learning Tabular data

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A comprehensive study on fault tolerance in stream processing systems

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Frontiers of Computer Science 2022年第2期16卷 80-97页

作者： Xiaotong WANG Chunxi ZHANG Junhua FANG Rong ZHANG Weining QIAN Aoying ZHOU School of Data Science and Engineering East China Normal UniversityShanghai 200062China Advanced Data Analytics Laboratory of Soochow University Suzhou 215006China

Stream processing has emerged as a useful technology for applications which require continuous and low latency computation on infinite streaming *** stream processing systems(SPSs)usually require distributed deployment on clusters of servers in face of large-scale of data,it is especially common to meet with failures of processing nodes or communication networks,but should be handled seriously considering service quality.A failed system may produce wrong results or become unavailable,resulting in a decline in user experience or even significant financial ***,a large amount of fault tolerance approaches have been proposed for *** approaches often have their own priorities on specific performance concerns,e.g.,runtime overhead and recovery ***,there is a lack of a systematic overview and classification of the state-of-the-art fault tolerance approaches in SPSs,which will become an obstacle for the development of ***,we investigate the existing achievements and develop a taxonomy of the fault tolerance in ***,we propose an evaluation framework tailored for fault tolerance,demonstrate the experimental results on two representative open-sourced SPSs and exposit the possible disadvantages in current ***,we specify future research directions in this domain.

关键词： fault tolerance performance evaluation stream processing

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