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A Battery Swapping Service Management System Reliability-Based Design Concerning Station Information Delay

IEEE Transactions on Consumer Electronics 2025年第1期71卷 1063-1078页

作者： Li, Xinyu Cao, Yue Hu, Ziyi Zhang, Xu Liu, Zhi Lei, Xinhai Wuhan University School of Cyber Science and Engineering Wuhan430073 China Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan430250 China School of Computing Sciences Norwich United Kingdom University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan Wuhan Public Resources Trading Center Wuhan China

Since the long duration associated with traditional plug-in charging modes inevitably causes range anxiety to electric vehicle (EV) drivers, the battery swapping technology has emerged as a promising alternative. It thus becomes imperative to develop a reliable battery swapping service system, efficiently managing the large-scale energy replenishing demands from EVs. Firstly, this paper considers drivers' trip destination to minimize total trip duration, and the expense paid for battery swapping service to save users' service cost. By optimizing the requirement of battery swapping service during peak-price period, the expenditure for energy supplement can be reduced. Secondly, concerning in practice that the transmission delay is a crucial factor impacting the overall network and system reliability, this paper further investigates the impact of station information delay between the global controller and battery swapping stations (BSSs). By characterizing the delay into two types, namely, low and large delays, the scenario based on a known delay type are investigated. Thirdly, delay cases with multiple BSSs are also taken into account and a mixed decision-making method is developed. Finally, simulation results indicate the desirable performance of proposed scheme in satisfying user demand and alleviating impact of station information delay. © 1975-2011 IEEE.

关键词： Decision making

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Mitochondrial Network Segmentation from Fluorescence Microscopy Images using VGG-UNet 10

Mitochondrial Network Segmentation from Fluorescence Microsc...

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10th International Conference on Biosignals, Images and Instrumentation, ICBSII 2024

作者： Mohan, Ramya Prabha, S. Rajinikanth, V. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering TN Chennai602105 India Simats Center for Research and Innovation Saveetha School of Engineering Department of Cse TN Chennai602105 India

ISBN: (纸本)9798350350951

Automatic medical image examination plays a vital part in disease screening and decision making processes. This research aims to create deep-learning (DL) assisted Mitochondrial Network (MN) segmentation from the chosen Fluorescence Microscopy Images (FMI) with better accuracy. Several parts of this arrangement encompasses the following;(i) Image collection and resizing, (ii) extraction of the MN section using a chosen scheme, (iii) comparing the segmented section against the ground-truth (GT) and computing the performance metrics, and (iv) performance evaluation and validation of the proposed technique. In this work, a comparative analysis is presented using the UNet and its variants. The experimental findings affirm that the VGG-UNet-supported scheme outperforms alternatives, yielding an overall segmentation accuracy of >97%. This underscores the efficacy of the proposed methodology in advancing the automated examination of MN for the chosen FMI database. © 2024 IEEE.

关键词： Fluorescence microscopy

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Grape Cold Hardiness Prediction via Multi-Task Learning 37

Grape Cold Hardiness Prediction via Multi-Task Learning

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Saxena, Aseem Pesantez-Cabrera, Paola Ballapragada, Rohan Lam, Kin-Ho Keller, Markus Fern, Alan School of Electrical Engineering and Computer Science Oregon State University United States School of Electrical Engineering and Computer Science Washington State University United States Irrigated Agriculture Research and Extension Center Washington State University United States

ISBN: (纸本)9781577358800

Cold temperatures during fall and spring have the potential to cause frost damage to grapevines and other fruit plants, which can significantly decrease harvest yields. To help prevent these losses, farmers deploy expensive frost mitigation measures such as sprinklers, heaters, and wind machines when they judge that damage may occur. This judgment, however, is challenging because the cold hardiness of plants changes throughout the dormancy period and it is difficult to directly measure. This has led scientists to develop cold hardiness prediction models that can be tuned to different grape cultivars based on laborious field measurement data. In this paper, we study whether deep-learning models can improve cold hardiness prediction for grapes based on data that has been collected over a 30-year time period. A key challenge is that the amount of data per cultivar is highly variable, with some cultivars having only a small amount. For this purpose, we investigate the use of multi-task learning to leverage data across cultivars in order to improve prediction performance for individual cultivars. We evaluate a number of multi-task learning approaches and show that the highest performing approach is able to significantly improve over learning for single cultivars and outperforms the current state-of-the-art scientific model for most cultivars. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Forecasting

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Deep Broad Learning for Emotion Classification in Textual Conversations

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Tsinghua science and Technology 2024年第2期29卷 481-491页

作者： Sancheng Peng Rong Zeng Hongzhan Liu Lihong Cao Guojun Wang Jianguo Xie Center for Linguistics and Applied Linguistics and Laboratory of Language Engineering and ComputingGuangdong University of Foreign StudiesGuangzhou 510006China Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices South China Normal UniversityGuangzhou 510006China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou 510006China Modern Education Technology Center Guangdong University of Foreign StudiesGuangzhou 510006China

Emotion classification in textual conversations focuses on classifying the emotion of each utterance from textual *** is becoming one of the most important tasks for natural language processing in recent ***,it is a challenging task for machines to conduct emotion classification in textual conversations because emotions rely heavily on textual *** address the challenge,we propose a method to classify emotion in textual conversations,by integrating the advantages of deep learning and broad learning,namely *** aims to provide a more effective solution to capture local contextual information(i.e.,utterance-level)in an utterance,as well as global contextual information(i.e.,speaker-level)in a conversation,based on Convolutional Neural Network(CNN),Bidirectional Long Short-Term Memory(Bi-LSTM),and broad *** experiments have been conducted on three public textual conversation datasets,which show that the context in both utterance-level and speaker-level is consistently beneficial to the performance of emotion *** addition,the results show that our proposed method outperforms the baseline methods on most of the testing datasets in weighted-average F1.

关键词： emotion classification textual conversation Convolutional Neural Network(CNN) Bidirectional Long Short-Term Memory(Bi-LSTM) broad learning

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Schema-Driven Information Extraction from Heterogeneous Tables

Schema-Driven Information Extraction from Heterogeneous Tabl...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Bai, Fan Kang, Junmo Stanovsky, Gabriel Freitag, Dayne Dredze, Mark Ritter, Alan College of Computing Georgia Institute of Technology United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel Artificial Intelligence Center SRI International Department of Computer Science Johns Hopkins University United States

ISBN: (纸本)9798891761681

In this paper, we explore the question of whether large language models can support cost-efficient information extraction from *** introduce schema-driven information extraction, a new task that transforms tabular data into structured records following a human-authored *** assess various LLM's capabilities on this task, we present a benchmark comprised of tables from four diverse domains: machine learning papers, chemistry literature, material science journals, and *** use this collection of annotated tables to evaluate the ability of open-source and API-based language models to extract information from tables covering diverse domains and data *** experiments demonstrate that surprisingly competitive performance can be achieved without requiring task-specific pipelines or labels, achieving F1 scores ranging from 74.2 to 96.1, while maintaining cost ***, through detailed ablation studies and analyses, we investigate the factors contributing to model success and validate the practicality of distilling compact models to reduce API reliance. © 2024 Association for Computational Linguistics.

关键词： Data mining

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Design and mechanism insight on SiC quantum dots sensitized inverse opal TiO_(2) with superior photocatalytic activities under sunlight

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Frontiers of Chemical science and engineering 2023年第12期17卷 1913-1924页

作者： Yingchao He Qiong Sun Likun Sun Zhixing Gan Liyan Yu Lifeng Dong College of Materials Science and Engineering Qingdao University of Science and TechnologyQingdao 266042China Center for Future Optoelectronic Functional Materials School of Computer and Electronic InformationNanjing Normal UniversityNanjing 210023China Department of Physics Hamline UniversitySt.PaulMN 55104USA

The combination of SiC quantum dots sensitized inverse opal TiO_(2) photocatalyst is designed in this work and then applied in wastewater purification under simulated *** various spectroscopic techniques,it is found that electrons transfer directionally from SiC quantum dots to inverse opal TiO_(2),and the energy difference between their conduction/valence bands can reduce the recombination rate of photogenerated carriers and provide a pathway with low interfacial resistance for charge transfer inside the *** a result,a typical type-II mechanism is proved to dominate the photoinduced charge transfer ***,the composite achieves excellent photocatalytic performances(the highest apparent kinetic constant of 0.037 min^(-1)),which is 6.2 times(0.006 min^(-1))and 2.1 times(0.018 min^(-1))of the bare inverse opal TiO_(2) and commercial P25 ***,the stability and non-toxicity of SiC quantum dots sensitized inverse opal TiO_(2) composite enables it with great potential in practical photocatalytic applications.

关键词： inverse opal TiO_(2) silicon carbide quantum dots quantum dot sensitized photocatalyst type-II charge transfer route

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Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning

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computers, Materials & Continua 2025年第1期82卷 1129-1150页

作者： Shijie Tang Yong Ding Huiyong Wang School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin541004China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic TechnologyGuilin541004China Institute of Cyberspace Technology HKCT Institute for Higher EducationHong Kong999077China School of Mathematics&Computing Science Guilin University of Electronic TechnologyGuilin541004China

As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are *** key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time *** address this issue,we propose an anomaly detection method based on distributed deep *** method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete *** use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation *** method can not only detect abnormal attacks but also locate the sensors that cause *** conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public *** experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.

关键词： Anomaly detection CPS deep learning MLP(multi-layer perceptron)

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Synthesizing CTA Image Data for Type-B Aortic Dissection using Stable Diffusion Models 46

Synthesizing CTA Image Data for Type-B Aortic Dissection usi...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Abaid, Ayman Ali Farooq, Muhammad Hynes, Niamh Corcoran, Peter Ullah, Ihsan University Hospital Galway University of Galway Newcastle Road Ireland University of Galway School of Computer Science Ireland University of Galway School of Engineering College of Science and Engineering Ireland University of Galway Insight Sfi Research Center for Data Analytics Ireland

ISBN: (纸本)9798350371499

Stable Diffusion (SD) has gained a lot of attention in recent years in the field of Generative AI thus helping in synthesizing medical imaging data with distinct features. The aim is to contribute to the ongoing effort focused on overcoming the limitations of data scarcity and improving the capabilities of ML algorithms for cardiovascular image processing. Therefore, in this study, the possibility of generating synthetic cardiac CTA images was explored by fine-tuning stable diffusion models based on user defined text prompts, using only limited number of CTA images as input. A comprehensive evaluation of the synthetic data was conducted by incorporating both quantitative analysis and qualitative assessment, where a clinician assessed the quality of the generated data. It has been shown that Cardiac CTA images can be successfully generated using using Text to Image (T2I) stable diffusion model. The results demonstrate that the optimized T2I CTA diffusion model successfully render images with features that are typically unique to acute type B aortic dissection (TBAD) medical conditions. © 2024 IEEE.

关键词： computerized tomography

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An Attention Mechanism Based AVOD Network for 3D Vehicle Detection

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-13页

作者： Yuan, Di Zhang, Haiping Shu, Xiu Liu, Qiao Chang, Xiaojun He, Zhenyu Shi, Guangming Guangzhou Institute of Technology Xidian University Xian China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China National Center for Applied Mathematics Chongqing Normal University Chongqing China Faculty of Engineering and Information Technology University of Technology Sydney Broadway New South Wales Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

With the continuous advancement of autonomous driving technology, 3D vehicle detection has become of widespread interest. The traditional aggregate view object detection (AVOD) framework has achieved some good results in 3D vehicle detection tasks. However, the complexity of the 3D vehicle detection scenario makes the current detection methods still not meet the actual requirements. To enhance the detection accuracy of 3D vehicle targets, we propose to equip an attention mechanism to improve the representation capability of feature maps, thereby further increasing the precision of 3D vehicle detection. Specifically, we have added the channel attention ECANet, spatial attention SANet, and mixed attention ECANet+SANet respectively into the image-based feature pyramid network of the AVOD detection framework, which can enhance the feature maps representation and improve the detection accuracy observably. The improved AVOD network is verified using the KITTI dataset. By showing the detection results of these attention mechanisms, it is found that the feature pyramid networks (FPN) module in the AVOD network based on Image has the best performance when integrating a mixed attention mechanism. In comparison to the original AVOD network, the detection results on the average precision index of the proposed method have improved by 2.29%, 2.81%, and 1.32% in the three indexes of simple, medium, and difficult, respectively. Extensive experiments have confirmed the practicality and efficacy of the AVOD network to equip the attention mechanisms for 3D vehicle detection. IEEE

关键词： Semantics

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AcLGB: A Lightweight DDoS Attack Detection Method 7th

AcLGB: A Lightweight DDoS Attack Detection Method

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7th International Conference on Information science, Communication and Computing, ISCC 2023

作者： Zeng, Fantao Cheng, Jieren Cao, Zhuyun Yang, Yue Sheng, Victor S. School of Computer Science and Technology Hainan University Haikou570228 China Hainan Blockchain Technology Engineering Research Center Haikou570228 China Department of Computer Science Texas Tech University TX79409 United States

ISBN: (纸本)9789819971602

With the development of Internet technology, distributed denial of service(DDoS) attack has always been a hot and difficult point in network *** network infrastructure and information security is also becoming more and more ***, cyber security is an arms race, as attacks develop and network traffic surges, intelligent solutions face the challenge of detecting sensitive changes in traffic *** this paper, we propose a lightweight Adaptive Clustering-based LightGBM(AcLGB) detection *** is a new DDoS traffic classification method and an effective lightweight detection *** introduce a new clustering technique to learn the clustering centers that can be used to extend the characteristics of a given *** solves the challenge of difficult detection when traffic characteristics change *** model separates the samples of different categories in the best way, and outperforms the current detection method with 99.98% detection accuracy. In the CIC-DDoS2019 data set, the detection time of 802s is better than other detection methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Denial-of-service attack

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