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Investigation into Impedance Measurements for Rapid Capacity Estimation of Lithium-ion Batteries in Electric Vehicles

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Journal of Dynamics, Monitoring and Diagnostics 2024年第1期3卷 21-31页

作者： Xiaoyu Zhao Zuolu Wang Eric Li Haiyan Miao Centre for Efficiency and Performance Engineering University of HuddersfieldHuddersfieldHD13DHUK School of Computing Engineering&Digital TechnologiesTeesside UniversityMiddlesbroughTS13BXUK

With the dramatic increase in electric vehicles(EVs)globally,the demand for lithium-ion batteries has grown dramatically,resulting in many batteries being retired in the *** a rapid and robust capacity estimation method is a challenging work to recognize the battery aging level on service and provide regroup strategy of the retied batteries in secondary *** are still limitations on the current rapid battery capacity estimation methods,such as direct current internal resistance(DCIR)and electrochemical impedance spectroscopy(EIS),in terms of efficiency and *** address the challenges,this paper proposes an improved version of DCIR,named pulse impedance technique(PIT),for rapid battery capacity estimation with more ***,PIT is carried out based on the transient current excitation and dynamic voltage measurement using the high sampling frequency,in which the coherence analysis is used to guide the selection of a reliable frequency *** battery impedance can be extracted in a wide range of frequency bands compared to the traditional DCIR method,which obtains more information on the battery capacity ***,various statistical variables are used to extract aging features,and Pearson correlation analysis is applied to determine the highly correlated *** a linear regression model is developed to map the relationship between extracted features and battery *** validate the performance of the proposed method,the experimental system is designed to conduct comparative studies between PIT and EIS based on the two 18650 batteries connected in *** results reveal that the proposed PIT can provide comparative indicators to EIS,which contributes higher estimation accuracy of the proposed PIT method than EIS technology with lower time and cost.

关键词： electric vehicles electrochemical impedance spectroscopy lithium-ion battery pulse impedance technique rapid capacity estimation

A predictive model for ethylene-mediated auxin and cytokinin patterning in the Arabidopsis root

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Plant Communications 2024年第7期5卷 120-137页

作者： Simon Moore George Jervis Jennifer F.Topping Chunli Chen Junli Liu Keith Lindsey Department of Biosciences Durham UniversitySouth RoadDurham DH13LEUK Hubei Hongshan Laboratory College of Life Science and TechnologyHuazhong Agricultural UniversityWuhan 430070China National Key Laboratory for Germplasm Innovation and Utilization for Fruit and Vegetable Horticultural Crops Huazhong Agricultural UniversityWuhanHubei 430070China

The interaction between auxin and cytokinin is important in many aspects of plant *** measurements of both auxin and cytokinin concentration and reporter gene expression clearly show the coexistence of auxin and cytokinin concentration patterning in Arabidopsis root ***,in the context of crosstalk among auxin,cytokinin,and ethylene,little is known about how auxin and cytokinin concentration patterns simultaneously emerge and how they regulate each other in the Arabidopsis *** work utilizes a wide range of experimental observations to propose a mechanism for simultaneous patterning of auxin and cytokinin *** addition to revealing the regulatory relationships between auxin and cytokinin,this mechanism shows that ethylene signaling is an important factor in achieving simultaneous auxin and cytokinin patterning,while also predicting other experimental *** the mechanism with a realistic in silico root model reproduces experimental observations of both auxin and cytokinin *** made by the mechanism can be compared with a variety of experimental observations,including those obtained by our group and other independent experiments reported by other *** of these predictions include patterning of auxin biosynthesis rate,changes in PIN1 and PIN2 patterns in pin3,4,7 mutants,changes in cytokinin patterning in the pls mutant,PLS patterning,and various trends in different *** research reveals a plausible mechanism for simultaneous patterning of auxin and cytokinin concentrations in Arabidopsis root development and suggests a key role for ethylene pattern integration.

关键词： Arabidopsis root auxin patterning cytokinin patterning ethylene signaling in silico digital root spatiotemporal modeling

A Survey of Crime Scene Investigation Image Retrieval Using Deep Learning

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Journal of Beijing Institute of Technology 2024年第4期33卷 271-286页

作者： Ying Liu Aodong Zhou Jize Xue Zhijie Xu Xi’an University of Posts and Telecommunications(XUPT) Xi’an 710121China University of Huddersfield Huddersfield HD13DHUK

Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal ***,with the rapid development of deep learning,data-driven paradigm has become the mainstreammethod of CSI image feature extraction and representation,and in this process,datasets provideeffective support for CSI retrieval ***,there is a lack of systematic research onCSI image retrieval methods and ***,we present an overview of the existing worksabout one-class and multi-class CSI image retrieval based on deep *** to theresearch,based on their technical functionalities and implementation methods,CSI image retrievalis roughly classified into five categories:feature representation,metric learning,generative adversar-ial networks,autoencoder networks and attention ***,We analyzed the remain-ing challenges and discussed future work directions in this field.

关键词： crime scene investigation(CSI)image image retrieval deep learning

黄土高原彬长矿区土壤侵蚀特征及其与环境变化的关系

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农业工程学报 2024年第5期40卷 158-167页

作者：李朋飞黄霖霖臧宇哲胡晋飞张晓晨白晓姚顽强西安科技大学测绘科学与技术学院西安710054 Department of Geography Durham UniversityDurham DH13 LEUK

黄土高原矿区煤炭产量占全国70%以上,大规模开采活动导致区域内生态环境退化与土壤侵蚀加剧。然而,当前鲜有矿区土壤侵蚀时空特征及其与环境变化关系的定量研究,限制了煤矿区土地复垦与生态修复进展。为了探究矿区土壤侵蚀特征及其与环... 详细信息

黄土高原矿区煤炭产量占全国70%以上,大规模开采活动导致区域内生态环境退化与土壤侵蚀加剧。然而,当前鲜有矿区土壤侵蚀时空特征及其与环境变化关系的定量研究,限制了煤矿区土地复垦与生态修复进展。为了探究矿区土壤侵蚀特征及其与环境变化的关系,为黄土高原煤矿区生态环境治理提供理论指导,该研究以彬长矿区为研究区,利用修订的通用土壤流失方程(revised universal soil loss equation,RUSLE)分别模拟了矿区全区2003—2019年及沉陷区2014—2019年土壤侵蚀速率,探究了矿区全区及沉陷区土壤侵蚀特征,定量研究了降雨、地形和植被对土壤侵蚀的影响。结果表明:1)2003—2019年间,彬长矿区土壤侵蚀速率整体呈显著上升趋势(P<0.05),年份间差异较大,2003年土壤侵蚀最为严重,侵蚀速率为85.56 t/(hm~2·a),2009年侵蚀程度最低,侵蚀速率为15.54 t/(hm^(2)·a)。微度、轻度侵蚀主要分布于城区、黄土塬和河流阶地等平坦地区,其空间分布在不同年份间基本保持一致,其余侵蚀类型主要分布于煤矿开采区、沟壑区及河道周边,且侵蚀强度空间异质性较大。2)2014—2019年间矿区全区及沉陷区土壤侵蚀速率表现出明显的季节周期性变化。春秋季节土壤侵蚀强度多表现为微度、轻度侵蚀,冬季侵蚀速率接近于0,夏季土壤侵蚀速率为一年中最高,是矿区水土流失治理的重点时段。3)彬长矿区土壤侵蚀速率与降雨量呈极显著正相关(P<0.01),沉陷区土壤侵蚀速率与降雨量、沉陷量及植被覆盖度均呈极显著正相关(P<0.01),降雨量是土壤侵蚀的主要影响因子,且开采沉陷加剧了土壤侵蚀,沉陷区的沉陷量不同,土壤侵蚀程度也不同。该研究方法探明了矿区的土壤侵蚀变化规律及环境变化对土壤侵蚀的影响,可为黄土高原煤矿区土壤侵蚀防治和生态环境恢复提供决策支持和科学依据。

关键词：土壤矿区侵蚀黄土高原沉陷环境因子 RUSLE模型

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基于高分辨率孪生网络的无人机目标跟踪算法

吉林大学学报（工学版） 2024年第5期54卷 1426-1434页

作者：王殿伟张池房杰许志杰西安邮电大学通信与信息工程学院西安710121 哈德斯菲尔德大学计算机与工程学院哈德斯菲尔德HD13DH

针对无人机(UAV)目标跟踪任务中目标尺寸小、尺度变化明显和视点改变频繁等问题,本文提出一种基于高分辨率孪生网络的无人机目标跟踪算法。首先,利用改进高分辨率网络作为特征提取主干网络,并且采用动态多模板策略挖掘视频的帧间信息;然... 详细信息

针对无人机(UAV)目标跟踪任务中目标尺寸小、尺度变化明显和视点改变频繁等问题,本文提出一种基于高分辨率孪生网络的无人机目标跟踪算法。首先,利用改进高分辨率网络作为特征提取主干网络,并且采用动态多模板策略挖掘视频的帧间信息;然后,构建多帧特征融合模块,得到利于目标定位的融合特征;最后,选取无锚框策略定位目标位置,得到精确的跟踪结果。实验结果表明:本文算法在DTB70数据集测试的成功率和准确率分别为66.0%和84.7%,在UAV123数据集测试的成功率和准确率分别为65.7%和84.3%,有效地提升了目标跟踪性能。

关键词：计算机视觉目标跟踪无人机高分辨率孪生网络多帧特征

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基于改进Double-Head RCNN的无人机航拍图像小目标检测算法

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北京航空航天大学学报 2024年第7期50卷 2141-2149页

作者：王殿伟胡里晨房杰许志杰西安邮电大学通信与信息工程学院西安710121 哈德斯菲尔德大学计算机与工程学院哈德斯菲尔德HD13DH

为解决无人机航拍图像中小目标特征信息少且容易被噪声干扰导致现有算法漏检率和误检率高的问题,提出一种改进Double-Head Region-卷积神经网络(RCNN)的无人机航拍图像小目标检测算法。在骨干网络ResNet-50上引入Transformer和可变形卷... 详细信息

为解决无人机航拍图像中小目标特征信息少且容易被噪声干扰导致现有算法漏检率和误检率高的问题,提出一种改进Double-Head Region-卷积神经网络(RCNN)的无人机航拍图像小目标检测算法。在骨干网络ResNet-50上引入Transformer和可变形卷积(DCN)模块,更有效提取小目标特征信息和语义信息;提出一种基于内容感知特征重组(CARAFE)的特征金字塔网络(FPN)结构模块,解决特征融合过程中小目标被背景噪声干扰而丢失特征信息的问题;在区域建议网络中针对小目标尺度分布特点重新设置Anchor生成尺度,进一步提升小目标检测性能。在VisDrone-DET2021数据集上的实验结果表明:所提算法能提取更具有表征能力的小目标特征信息和语义信息,对比Double-Head RCNN算法,所提算法的参数量增加了9.73×10^(6),FPS损失了0.6,但是AP、AP50和AP75分别提升了2.6%、6.2%和2.1%,APs提升了3.1%。

关键词：小目标检测无人机航拍图像 Double-Head RCNN Transformer 内容感知特征重组

Development of Long-Range,Low-Powered and Smart IoT Device for Detecting Illegal Logging in Forests

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Journal of Dynamics, Monitoring and Diagnostics 2024年第3期3卷 190-198页

作者： Samuel Ayankoso Zuolu Wang Dawei Shi Wenxian Yang Allan Vikiru Solomon Kamau Henry Muchiri Fengshou Gu Centre for Efficiency and Performance Engineering University of HuddersfieldHuddersfield HD13DHUK School of Computing and Engineering Sciences Strathmore UniversityMadarakaNairobi***Kenya

Forests promote the conservation of biodiversity and also play a crucial role in safeguarding theenvironment against erosion,landslides,and climate ***,illegal logging remains a significant threatworldwide,necessitating the development of automatic logging detection systems in *** paper proposesthe use of long-range,low-powered,and smart Internet of Things(IoT)nodes to enhance forest *** research framework involves developing IoT devices for forest sound classification andtransmitting each node’s status to a gateway at the forest base station,which further sends the obtained datathrough cellular connectivity to a cloud *** key issues addressed in this work include sensor and boardselection,Machine Learning(ML)model development for audio classification,TinyML implementation on amicrocontroller,choice of communication protocol,gateway selection,and power consumption *** the existing solutions,the developed node prototype uses an array of two microphone sensors forredundancy,and an ensemble network consisting of Long Short-Term Memory(LSTM)and ConvolutionalNeural Network(CNN)models for improved classification *** model outperforms LSTM and CNNmodels when used independently and also gave 88%accuracy after ***,this solutiondemonstrates cost efficiency and high potential for scalability.

关键词： illegal logging forest monitoring internet of things nodes TinyML sound classification

Online battery model parameters identification approach based on bias-compensated forgetting factor recursive least squares

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Green Energy and Intelligent Transportation 2024年第4期3卷 12-22页

作者： Dong Zhen Jiahao Liu Shuqin Ma Jingyu Zhu Jinzhen Kong Yizhao Gao Guojin Feng Fengshou Gu School of Mechanical Engineering Hebei University of TechnologyTianjin300401 China Advanced Equipment Research Institute Co. Ltd.of HEBUTTianjin300401 China College of Information Science and Engineering Hohai UniversityNanjing210098 China School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai200240 China School of Computing and Engineering University of HuddersfieldHuddersfield HD13DHUK

Accuracy of a lithium-ion battery model is pivotal in faithfully representing actual state of battery,thereby influencing safety of entire electric *** estimation of battery model parameters using key measured signals is ***,measured signals inevitably carry random noise due to complex real-world operating environments and sensor errors,potentially diminishing model estimation *** the challenge of accuracy reduction caused by noise,this paper introduces a Bias-Compensated Forgetting Factor Recursive Least Squares(BCFFRLS)***,a variational error model is crafted to estimate the average weighted variance of random ***,an augmentation matrix is devised to calculate the bias term using augmented and extended parameter vectors,compensating for bias in the parameter *** assess the proposed method's effectiveness in improving parameter identification accuracy,lithium-ion battery experiments were conducted in three test conditions—Urban Dynamometer Driving Schedule(UDDS),Dynamic Stress Test(DST),and Hybrid Pulse Power Characterization(HPPC).The proposed method,alongside two contrasting methods—the offline identification method and Forgetting Factor Recursive Least Squares(FFRLS)—was employed for battery model parameter *** analysis reveals substantial improvements,with the mean absolute error reduced by 25%,28%,and 15%,and the root mean square error reduced by 25.1%,42.7%,and 15.9%in UDDS,HPPC,and DST operating conditions,respectively,when compared to the FFRLS method.

关键词： Lithium-ion battery Battery model Recursive least squares Parameter identification

电商物流发展制约因素识别——基于ISM-MICMAC模型分析

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商业经济研究 2024年第4期 99-102页

作者：崔尧孙家豪西安财经大学陕西西安710100 英国杜伦大学英国杜伦DH13LE

电商物流发展在新零售背景下面临新旧多重障碍,亟待转型升级。本文基于德尔菲法梳理出电商物流发展制约因素,运用ISM模型将阻碍电商物流发展的因素划分为直接因素、间接因素、深层因素3个类别,最后运用MICMAC模型的驱动力-依赖度分析框... 详细信息

电商物流发展在新零售背景下面临新旧多重障碍,亟待转型升级。本文基于德尔菲法梳理出电商物流发展制约因素,运用ISM模型将阻碍电商物流发展的因素划分为直接因素、间接因素、深层因素3个类别,最后运用MICMAC模型的驱动力-依赖度分析框架将以上因素划分为Ⅰ联系、Ⅱ依赖、Ⅲ自治、Ⅳ独立4个集群。结果表明:电商物流阻碍因素共包含5个层次19个因子,满意度、政策制度和市场竞争是深层阻碍因素;间接因素包含因子数最多,且各因子存在同层级和跨层级多种影响路径;管理水平和服务平台建设属于Ⅲ自治集群,具有低驱动力和低依赖度特征,是电商物流平台较容易调控因素,在转型发展中应优先考虑。

关键词：新零售电商物流阻碍因素 ISM-MICMAC模型