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A facile data-driven battery capacity estimation framework for on-road plug-in hybrid electric vehicle

Progress in Natural science:Materials International 2025年第1期35卷 166-176页

作者： Jianlu Li Yanming Chen Tongxing Lei Jianguo Liu Guizheng Liu Zhaoyang Deng Xuebiao Wu Zhiyu Ding Yinghe Zhang Junwei Wu Yanan Chen Guangzhou Automobile Group Co. Ltd. School of Materials Science and Engineering Harbin Institute of Technology School of Materials and Environmental Engineering Shenzhen Polytechnic University School of Science Shenzhen Key Laboratory of Flexible Printed Electronics Technology Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application Harbin Institute of Technology School of Materials Science and Engineering Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education Tianjin Key Laboratory of Composite and Functional Materials Tianjin University

A data-driven framework with strong generalization capabilities is proposed to effectively extract features and easily access battery capacity. This framework can make highly accurate predictions for the battery capacities of plug-in electric vehicles. The feature extraction process is entirely based on statistics, which are always available and can be generalized to various types of battery data. An improved ampere-hour integral method can easily access battery capacity with just short-charging segments lasting 500 s. Several machine-learning models are trained to verify the framework's effectiveness, with the best model achieving a test error of 0.84 % based on leave-one-out validation. SHAP values are used to provide a reasonable interpretation of the relationships between the constructed features and model outputs. The proposed framework offers advantages such as reduced computational resources, wide generalization, and high prediction accuracy, showing great potential for battery management.

关键词： Battery capacity estimation Data-driven framework Lithium-ion battery Machine learning Plug-in hybrid electric vehicle

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TextFormer: A Query-based End-to-end Text Spotter with Mixed Supervision

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Machine Intelligence Research 2024年第4期21卷 704-717页

作者： Yukun Zhai Xiaoqiang Zhang Xiameng Qin Sanyuan Zhao Xingping Dong Jianbing Shen School of Computer Science Beijing Institute of TechnologyBeijing 100081China Department of Computer Vision Technology Baidu Inc.Beijing 100193China

End-to-end text spotting is a vital computer vision task that aims to integrate scene text detection and recognition into a unified *** methods heavily rely on region-of-interest(Rol)operations to extract local features and complex post-processing steps to produce final *** address these limitations,we propose TextFormer,a query-based end-to-end text spotter with a transformer ***,using query embedding per text instance,TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multitask *** allows for mutual training and optimization of classification,segmentation and recognition branches,resulting in deeper feature sharing without sacrificing flexibility or ***,we design an adaptive global aggregation(AGG)module to transfer global features into sequential features for reading arbitrarilyshaped texts,which overcomes the suboptimization problem of Rol ***,potential corpus information is utilized from weak annotations to full labels through mixed supervision,further improving text detection and end-to-end text spotting *** experiments on various bilingual(i.e.,English and Chinese)benchmarks demonstrate the superiority of our *** on the TDA-ReCTS dataset,TextFormer surpasses the state-of-the-art method in terms of 1-NED by 13.2%.

关键词： End-to-end text spotting arbitrarily-shaped texts transformer mixed supervision multitask modeling.

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Perception of premium services based on the evolution of hyper-service network in mobile edge computing

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Journal of Cloud Computing 2025年第1期14卷 1-17页

作者： Zhang, Xiaojun Liu, Zhizhong Chu, Dianhui Zhao, Meiyue School of Computer and control Engineering Yantai University Yantai264005 China School of Computer and control Engineering Harbin Institute of Technology Weihai264209 China

Recently, massive services from multiple domains are available on the network with the rapid development of Mobile Edge Computing (MEC) technology. These various services construct a service network through services’ collaboration, which can provide users with rich resources at anytime and anywhere. However, in MEC, due to the proliferation of services, as well as the dynamics of Quality of Service (QoS), it becomes more and more difficult for users to discover the premium services effectively, which seriously impacts users’ satisfaction and the utilization of resources. Therefore, how to proactively perceive the emergence of premium services in the service networks has become an challenge. To tackle this issue, we propose a method for perception of the premium services based on the evolution of hyper-service network (named as PPSHSN). First, based on the hypergraph theory, we construct a hyper-service network with the optimal connection method. Then, we develop the evolution mechanism of the hyper-service network and optimize the hyper-service network with the hyperdegree premium value evolution algorithm, thus to conduct the evolution of the service networks. Next, we devise the evaluation indexes based on the topological characteristics of hypergraphs, which are updated according to the evolution of the hyper-service network. Finally, we complete perception of premium services based on the evolution results of the hyper-service network. The effectiveness and feasibility of our proposed method are verified through extensive simulation experiments. © The Author(s) 2025.

关键词： Hypermedia systems

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Realizing the potential of Internet of Things (IoT) in Industrial applications

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Discover Internet of Things 2025年第1期5卷 1-16页

作者： Rathi, Bharat Thapaswi, S. Kambhampati, Meghana Jain, Vineet Akshay, P. Pandey, Trilok Nath Pradhan, Sunil Kumar School of Electronics Engineering Vellore Institute of Technology Chennai600127 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

The Internet of Things (IoT), which enables seamless connectivity and effective data exchange between physical items and digital systems, has completely changed the way we interact with our surroundings. This study evaluates IoT applications in healthcare, transportation, agriculture, and smart cities, highlighting advancements, opportunities, and challenges. Using a systematic literature review and case study analysis, it identifies essential IoT components, including sensors, communication protocols, data analytics, and security methods. Key findings demonstrate IoT's potential to enhance healthcare through real-time monitoring and cost reduction, optimize transportation systems for resource efficiency, improve agricultural productivity via precision farming, and enable sustainable urban development in smart cities. However, challenges like data privacy, interoperability, and scalability remain critical barriers to widespread adoption. This study underscores the need for interdisciplinary collaboration among researchers, policymakers, and industry stakeholders to address these challenges and unlock IoT's full potential. It provides actionable insights and future directions for realizing a connected, efficient, and sustainable IoT-driven future. © The Author(s) 2025.

关键词： Precision agriculture

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Analytical Formulation and Numerical Investigation of Electromechanical Dynamics in Circular Biconvex Diaphragm Capacitive Pressure Sensor

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IEEE Sensors Journal 2025年第11期25卷 18867-18875页

作者： Rukshana Bi, Gajula Motwani, Mayank Jindal, Sumit Kumar Vellore Institute of Technology School of Electronics Engineering Vellore632014 India Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India

Capacitive pressure sensors (CPSs) have attracted considerable interest due to their high sensitivity, low energy consumption, and potential for miniaturization, making them suitable for applications in automotive systems, consumer electronics, and industrial automation. The conventional designs of CPSs often face challenges such as limited sensitivity, mechanical instability, and difficulties in achieving increased sensitivity. This study presents a biconvex diaphragm using silicon, featuring a gradual thickness variation, with a maximum thickness at the center. This article offers a mathematical analysis of the sensor, featuring step-by-step derivations for parameters such as the maximum thickness of the diaphragm, working range, capacitance, and capacitive sensitivity. Finally, curves of capacitance and capacitive sensitivity with pressure are obtained in MATLAB, and the results are compared with those of prior works. The proposed sensor shows a more than threefold increase in capacitance and a nearly double improvement in capacitive sensitivity compared to the highest previously reported double touch mode capacitive pressure sensor (DTMCPS). The deflection of the diaphragm is obtained using COMSOL. © 2001-2012 IEEE.

关键词： MATLAB

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Surface flashover in 50 years:*** modification,structureoptimisation,and characteristics enhancement

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High Voltage 2025年第2期10卷 243-278页

作者： Zhen Li Ji Liu Yoshimichi Ohki George Chen Shengtao Li Key Laboratory of Engineering Dielectrics and Its Application Ministry of EducationHarbin University of Science and TechnologyHarbinHeilongjiangChina State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong UniversityXi'anShaanxiChina Research Institute for Materials Science and Technology Waseda UniversityShinjukuJapan School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Surface flashover is a gas-solid interface insulation failure that significantly jeopardises the secure operation of advanced electronic,electrical,and spacecraft *** the widespread application of numerous material modification and structure optimisation technologies aimed at enhancing surface flashover performance,the influence mechanisms of the present technologies have yet to be systematically discussed and *** review aims to introduce various material modification technologies while demonstrating their influence mechanisms on flashover performances by establishing relationships among‘microscopic structure-mesoscopic charge transport-macroscopic insulation failure’.Moreover,it elucidates the effects of chemical structure on surface trap parameters and surface charge transport concerning flashover *** review categorises and presents structure optimisation technologies that govern electric field *** identified technologies highlight that achieving a uniform tangential electric field and reducing the normal electric field can effectively enhance flashover ***,this review proposes recommendations encompassing mathematical,chemical,evaluation,and manufacturing *** systematic summary of current technologies,their influence mechanisms,and associated advantages and disadvantages in improving surface insulation performance is anticipated to be a pivotal component in flashover and future dielectric theory.

关键词： material modification structure optimization surface flashover structure optimisation technologies chemical structure charge transport insulation failure

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Composite spectral spatial pixel CNN for land-use hyperspectral image classification with hybrid activation function

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Multimedia Tools and Applications 2025年第12期84卷 10527-10550页

作者： Banerjee, Anasua Swain, Satyajit Rout, Minakhi Bandyopadhyay, Mainak School of Computer Engineering Kalinga Institute of Industrial Technology-Deemed-to-be-University Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Silchar India

Deep learning methods have played a prominent role in the development of computer visualization in recent years. Hyperspectral imaging (HSI) is a popular analytical technique based on spectroscopy and visible imaging which examines the pattern of light in a target and recognizes objects based on varying spectral properties. However, in remote sensing, detecting surface material via HSI analysis is a critical and difficult task. The performance of spectral-spatial data exploitation is well established to outperform typical spectral pixel-wise techniques. Because of its great feature extraction ability, convolutional neural networks (CNN) have emerged as a potent deep learning approach. CNN translates the input features of an image into an equivalent CNN feature map, in addition to naturally combining spectral and spatial information. However, spectral-spatial properties when combined with pixel-wise extraction can learn more minute details of objects present on the earth’s terrain. In this paper, a noble Composite Spectral Spatial Pixel CNN model for the classification of hyperspectral data is presented which is an amalgamation of 3D-2D-1D CNN. While the 3D and 2D CNN exploit the spectral-spatial features effectively, 1D CNN works on pixel-wise feature extraction. Further, to optimize the classification performance of the proposed model, a new hybrid activation function Flatten-T Swish is also used which is the combination of ReLU and Swish function. The proposed model is compared with other state-of-the-art models based on three popular HSI datasets, and it is found that the proposed model performs better among others in terms of classification and computation time, giving 98.87% accuracy for Indian Pines, 99.92% accuracy for Pavia University, and 99.99% accuracy for Salinas Valley dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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Multilabel Black-Box Adversarial Attacks Only With Predicted Labels

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1284-1297页

作者： Kong, Linghao Luo, Wenjian Ye, Zipeng Zhou, Qi Jia, Yan Harbin Institute of Technology Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China

Multilabel adversarial examples have become a threat to deep neural network models (DNNs). Most of the current work on multilabel adversarial examples are focused on white-box environments. In this article, we focus on a black-box environment where the available information is extremely limited: a label-only black-box environment. Under the label-only black-box environment, the attacker can only obtain the predicted labels, and cannot obtain any other information such as the model's internal structure, parameters, the training dataset, and the output prediction confidence. We propose a label-only black-box attack framework, and through this framework to implement two black-box adversarial attacks: multi-label boundary-based attack (ML-BA) and multilabel label-only black-box attack (ML-LBA). The ML-BA is developed by transplanting the boundary-based attack in the multiclass domain to the multilabel domain, and the ML-LBA is based on differential evolution. Experimental results show that both the proposed algorithms can achieve the hiding single label attack in label-only black-box environments. Besides, ML-LBA requires fewer queries and its perturbations are significantly less. This demonstrates the effectiveness of the proposed label-only black-box attack framework and the advantageous of differential evolution in optimizing high-dimensional problems. © 2020 IEEE.

关键词： Generative adversarial networks

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Development of the Internet of smart orchard Things based on multi-sensors and LoRa technology

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Intelligent and Converged Networks 2023年第4期4卷 342-354页

作者： Pingchuan Zhang Xu Chen Shan Li Caihong Zhang Yanjun Hu School of Computer Science and Technology Henan Institute of Science and TechnologyXinxiang 453000China

With the rapid growth of science and technology,the Internet of Things(IoT)technology has matured and attracted the attention of many *** development of agricultural modernization leads to the gradual emergence of intelligent management gradually taking root in agricultural *** many technologies in the IoT technologies,low-power Wide Area Network(WAN)technology has the characteristics of reliable and stable transmission with long distance and low power *** is very useful for data transmission in special environments,especially for orchards in mountainous *** paper proposed a new agricultural Internet of Things in orchard management based on multi-sensors,such as DHT11 for temperature/humidity and GY-30 for illumination,the Long Range(LoRa)technology for transmitting the collected data or control command between the terminal and data cloud center,*** a low-power IoT sensor network in the orchard can remotely measure the parameters in the *** WAN is used to transmit data to the central *** order to reduce power consumption and cost,a single monitoring node selects two power supplies,a solar power supply and a power supply,and the power supply can be turned on remotely by users in special *** in different environments in the peach orchard show that the monitoring system has enough reliability and accuracy,and is suitable for environmental monitoring in orchards in remote areas or areas with complex terrain.

关键词： Internet of Things LoRa smart orchard sensor network multi-sensors

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ZIF-derived N-doped carbon nanorods supporting bimetallic CoFe single-atoms/nanoclusters as bifunctional oxygen electrocatalysts for stable Zn-air batteries

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Rare Metals 2024年第11期43卷 5769-5780页

作者： Hong-Shuang Fan Fei-Xiang Ma Zi-Hao Liu Wen-Hui Wang Zheng-Qi Liu Xiong-Yi Liang Yue Du Yang-Yang Li Liang Zhen Cheng-Yan Xu Sauvage Laboratory for Smart Materials School of Materials Science and EngineeringHarbin Institute of Technology(Shenzhen)Shenzhen 518055China School of Civil and Environmental Engineering Harbin Institute of Technology(Shenzhen)Shenzhen 518055China Department of Materials Science and Engineering City University of Hong KongHong Kong 999077China Peng Cheng Laboratory Shenzhen 518055China MOE Key Laboratory of Micro-Systems and Micro-Structures Manufacturing Harbin Institute of TechnologyHarbin 150080China

High-performance bifunctional oxygen electrocatalysts that simultaneously boost the sluggish oxygen reduction reaction(ORR)and oxygen evolution reaction(OER)need to be developed for advanced rechargeable Znair battery *** this work,a zeolitic imidazolate framework(ZIF)-phase conversion associated with a subsequent thermal fixing strategy was developed to fabricate bimetallic CoFe single atoms/clusters embedded in N-doped carbon(denoted as CoFe-N-C)nanorods,which can serve as efficient bifunctional ORR/OER *** observation and X-ray absorption spectroscopy analysis confirm the co-existence of highly active Co/Fe-N_(x) dual sites and CoFe alloy nanoclusters.X-ray photoelectron spectroscopy(XPS)results prove that implanting secondary Fe atoms into Co-N-C matrix nanorods can induce electronic redistribution of atomic Co/Fe active sites and generate synergistic effects,which would optimize the adsorption energy of the reaction intermediates and thus enhance the bifunctional ORR/OER *** bimetallic CoFe-N-C nanorods exhibit significantly enhanced bifunctional ORR/OER activity and stability than the monometallic Co/Fe-N-C nanorods in alkaline electrolytes in terms of a very positive half-wave potential of 0.90 V(*** hydrogen electrode(RHE))for ORR,and an overpotential of 440 mV to reach current density of 10 mA·cm^(-2)for OER,yielding a small overpotential gap of 0.77 ***,the rechargeable Zn-air batteries using bimetallic CoFe-N-C nanorods as air-cathode catalyst demonstrates peak power density of200.7 mW·cm^(-2)and robust cycling stability of up to200 h,corresponding to 1200 discharge-charge cycles.

关键词： Bimetallic electrocatalyst Single-atom catalysts Oxygen reduction reaction Oxygen evolution reaction Zn-air batteries

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