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Safety risk assessment for automotive battery pack based on deviation and outlier analysis of voltage inconsistency

JOURNAL OF CLEANER PRODUCTION 2024年 466卷

作者： Huang, Peifeng Liu, Shoutong Ren, Yinghui He, Yanyun Chao, Peipei Feng, Renlang Liu, Chuan Li, Zhen Bai, Zhonghao Hunan Univ State Key Lab Adv Design & Mfg Technol Vehicle Changsha Peoples R China China Automot Engn Res Inst Co Ltd Chongqing Peoples R China

Safety risk assessment is essential for evaluating the health status and averting sudden battery failures in electric vehicles. This study introduces a novel safety risk assessment approach for battery systems, addressing both cell and pack levels with three key indexes. The core of the assessment lies in representing the relative deviation of cell voltages through scatter diagrams across various stages of service life. Specifically, the study quantifies voltage deviations and deviation angles within different state of charge intervals to gauge safety risks at the cell level. Leveraging clustering algorithms aids in identifying outlier values. Furthermore, the dispersion of scatter points is utilized to assess safety risks at the pack level. Validation of this proposed model is conducted through cycling tests on battery modules with a deformed cell, demonstrating its efficacy in capturing inconsistent features of mechanical deformation abuse across the three indexes, thereby triggering alarms during operation. Moreover, the method is applied to assess the safety risks of five hazardous and accident-prone vehicles, enabling comprehensive evaluation of potential faulty cells and safety risks at the pack level. This proposed approach offers a fresh perspective on the comprehensive safety risk assessment of battery systems.

关键词： Voltage inconsistency Battery pack Safety risk assessment clustering algorithm

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A Novel Four-Step algorithm for Detecting a Single Circle in Complex Images

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SENSORS 2023年第22期23卷 9030-9030页

作者： Cao, Jianan Gao, Yue Wang, Chuanyang Soochow Univ Sch Mech & Elect Engn Suzhou 215137 Peoples R China

Single-circle detection is vital in industrial automation, intelligent navigation, and structural health monitoring. In these fields, the circle is usually present in images with complex textures, multiple contours, and mass noise. However, commonly used circle-detection methods, including random sample consensus, random Hough transform, and the least squares method, lead to low detection accuracy, low efficiency, and poor stability in circle detection. To improve the accuracy, efficiency, and stability of circle detection, this paper proposes a single-circle detection algorithm by combining Canny edge detection, a clustering algorithm, and the improved least squares method. To verify the superiority of the algorithm, the performance of the algorithm is compared using the self-captured image samples and the GH dataset. The proposed algorithm detects the circle with an average error of two pixels and has a higher detection accuracy, efficiency, and stability than random sample consensus and random Hough transform.

关键词： single-circle detection Canny edge detection clustering algorithm improved least squares method

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Detection and classification of football players with automatic generation of models

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OPTICAL ENGINEERING 2010年第1期49卷 017005-017005页

作者： Gomez, Jorge R. Herrero Jaraba, Elias Angel Montanes, Miguel Martinez Contreras, Francisco Orrite Urunuela, Carlos Univ Zaragoza Aragon Inst Engn Res E-50008 Zaragoza Spain

We focus on the automatic detection and classification of players in a football match. Our approach is not based on any a priori knowledge of the outfits, but on the assumption that the two main uniforms detected correspond to the two football teams. The algorithm is designed to be able to operate in real time, once it has been trained, and is able to detect partially occluded players and update the color of the kits to cope with some gradual illumination changes through time. Our method, evaluated from real sequences, gave better detection and classification results than those obtained by a system using a manual selection of samples to compute a Gaussian mixture model. (C) 2010 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3294885]

关键词： automatic segmentation SOM clustering algorithm

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Applying a thermodynamic-based method to reveal the failure laws of CFRP stiffened plates

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年

作者： Wang, Tianlong Gao, Weicheng Zou, Xionghui Shen, Zijie Huang, Haojie Zhang, Xuekun Liu, Bai Harbin Inst Technol Dept Astronaut Sci & Mech Harbin 150001 Peoples R China Harbin Inst Technol Sch Civil Engn Harbin Peoples R China Harbin Inst Technol Sch Civil Engn Key Lab Struct Dynam Behav & Control Minist Educ Harbin Peoples R China COMAC Shanghai Aircraft Mfg Co Ltd Composites Ctr Shanghai Peoples R China Harbin Inst Technol Sch Elect Engn & Automat Harbin Peoples R China

The high specific strength and stiffness of carbon fiber-reinforced polymer (CFRP) stiffened plates make them popular in aerospace and other fields. Nevertheless, CFRP stiffened plates have unique characteristics and complicated and diversified failure patterns, so it is impossible to determine its failure law with a uniform failure criterion accurately. Its load-carrying capacity has always been calculated on the conservative side of empirical estimation. This work uses the stressing state theory to investigate the stressing state evolution of composite stiffened plates under axial loading. It conducts three CFRP stiffened plates of the same configuration from a thermodynamic perspective. By equating stiffened plates to thermodynamic systems, the measured strain data are modeled as state variables, and renormalization and clustering algorithms are applied to determine the phase transition loads of the structure, while the characteristic points before and after normalization are verified for their reasonableness and stability. Accordingly, this work reveals the failure laws of CFRP stiffened plates by applying a thermodynamic-based method. It characterizes the structural failure features by defining catastrophe points throughout the failure process, which provides a new reference for the failure analysis and strength design of CFRP stiffened plates. [GRAPHICS]

关键词： CFRP stiffened plates thermodynamic-based method network-free renormalization clustering algorithm phase transition loads

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Complex Drivers of Riparian Soil Oxygen Variability Revealed Using Self-Organizing Maps

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WATER RESOURCES RESEARCH 2023年第6期59卷 e2022WR034022-e2022WR034022页

作者： Lancellotti, Brittany V. Underwood, Kristen L. Perdrial, Julia N. Schroth, Andrew W. Roy, Eric D. Adair, Carol E. Univ Vermont Rubenstein Sch Environm & Nat Resources Burlington VT 05405 USA Univ Virgin Isl Virgin Isl EPSCoR Kingshill VI USA Univ Vermont Civil & Environm Engn Burlington VT USA Univ Vermont Dept Geol Burlington VT USA

Oxygen (O-2) regulates soil reduction-oxidation processes and therefore modulates biogeochemical cycles. The difficulties associated with accurately characterizing soil O-2 variability have prompted the use of soil moisture as a proxy for O-2, as O-2 diffusion into soil water is much slower than in soil air. The use of soil moisture alone as a proxy measurement for O-2 could result in inaccurate O-2 estimations. For example, O-2 may remain high during cool months when soil respiration rates are low. We analyzed high-frequency sensor data (e.g., soil moisture, CO2, gas-phase soil pore O-2) with a machine learning technique, the Self-Organizing Map, to pinpoint suites of soil conditions associated with contrasting O-2 regimes. At two riparian sites in northern Vermont, we found that O-2 levels varied seasonally, and with soil moisture. For example, 47% of low O-2 levels were associated with wet and cool soil conditions, whereas 32% were associated with dry and warm conditions. Contrastingly, the majority (62%) of high O-2 conditions occurred under dry and warm conditions. High soil moisture levels did not always lead to low O-2, as 38% of high O-2 values occurred under wet and cool conditions. Our results highlight challenges with predicting soil O-2 solely based on water content, as variable combinations of soil and hydrologic conditions can complicate the relationship between water content and O-2. This indicates that process-based ecosystem and denitrification models that rely solely on soil moisture to estimate O-2 may need to incorporate other site and climate-specific drivers to accurately predict soil O-2.

关键词： self-organizing map clustering algorithm riparian zone soil oxygen soil moisture high frequency data

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A Quantification Method for Supraharmonic Emissions Based on Outlier Detection algorithms

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ENERGIES 2021年第19期14卷 6404页

作者： Zhou, Hui Gui, Zesen Zhang, Jiang Zhou, Qun Liu, Xueshan Ma, Xiaoyang Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China

Based on outlier detection algorithms, a feasible quantification method for supraharmonic emission signals is presented. It is designed to tackle the requirements of high-resolution and low data volume simultaneously in the frequency domain. The proposed method was developed from the skewed distribution data model and the self-tuning parameters of density-based spatial clustering of applications with noise (DBSCAN) algorithm. Specifically, the data distribution of the supraharmonic band was analyzed first by the Jarque-Bera test. The threshold was determined based on the distribution model to filter out noise. Subsequently, the DBSCAN clustering algorithm parameters were adjusted automatically, according to the k-dist curve slope variation and the dichotomy parameter seeking algorithm, followed by the clustering. The supraharmonic emission points were analyzed as outliers. Finally, simulated and experimental data were applied to verify the effectiveness of the proposed method. On the basis of the detection results, a spectrum with the same resolution as the original spectrum was obtained. The amount of data declined by more than three orders of magnitude compared to the original spectrum. The presented method will benefit the analysis of quantification for the amplitude and frequency of supraharmonic emissions.

关键词： supraharmonic outlier detection data distribution DBSCAN clustering algorithm

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Evaluation Method of Water Injection Development Effects in Fractured Vuggy Carbonate Reservoirs: Case Study of FI7 Strike-Slip Fault Zone

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ENERGIES 2024年第22期17卷 5611页

作者： Geng, Jie Xiao, Long Yue, Ping Xiao, Yun Yang, Hongnan Qu, Simin Wang, Xia Southwest Petr Univ Natl Key Lab Oil & Gas Reservoir Geol & Exploitat Chengdu 610500 Peoples R China PetroChina Tarim Oilfield Co Korla 841000 Peoples R China PetroChina Southwest Oil & Gas Co Chengdu 610051 Peoples R China

As the main method of secondary development, water injection has been widely used in fractured vuggy carbonate reservoirs. By using an evaluation index system combined with an integrated approach to objectively evaluate and support targeted and operational adjustments of water injection development effects in fractured vuggy carbonate reservoirs, a comprehensive evaluation method is established in this study. The CRITIC method is used as the main approach, and the analytic hierarchy process, the entropy weight method, and the coefficient of variation method are used as sub-methods. Additionally, the clustering centres are divided using the clustering method to reduce the error caused by the irrational distribution of the relevant evaluation index data. The method is used to evaluate and analyse the effects of water injection development, using the FI7 fault zone of the Halahatang oilfield as an example. The application results show that the evaluation method is feasible and effective for cases with small data volume, fewer computational resources, and less time. This study has a certain reference significance for the evaluation of the effect of water injection development in similar fractured vuggy reservoirs.

关键词： fractured vuggy reservoirs water injection development recovery efficiency evaluation system CRITIC method clustering algorithm

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Research on optimization of setup time for product in the multi-product production system

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MICROPROCESSORS AND MICROSYSTEMS 2021年 80卷 103618-103618页

作者： Li, Xiaoyan Tongji Univ Coll Econ & Management Shanghai Peoples R China

Consumers' consumption habits are more and more personalized and diversified, which makes the multi-product production system has been applied extensively in the factory worldwide. This brings a difficult problem to a large number of manufacturing enterprises: how to optimize the setup time of the product to achieve the purpose of improving the time efficiency. Based on this problem, this paper proposes the TCP technology for the optimization of setup time, that is, using the Times Series model, the clustering algorithm, and the Parallel Job technology in the Single Minute Exchange of Die (SMED), to form an application framework focusing on optimizing the product setup time. The validity of the technology is verified by a case study. This paper enriches the research field of setup time optimization, production planning, and the application of the clustering algorithm in the multi-product production system. It provides a new way for manufacturing enterprises to pursue an excellent efficiency of product setup time.

关键词： Multi-product production system Setup time clustering algorithm Time efficiency improvement

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CH-Bin: A convex hull based approach for binning metagenomic contigs

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2022年 100卷 107734页

作者： Chandrasiri, Sunera Perera, Thumula Dilhara, Anjala Perera, Indika Mallawaarachchi, Vijini Univ Moratuwa Dept Comp Sci & Engn Moratuwa 10400 Sri Lanka Australian Natl Univ Sch Comp Canberra ACT 2600 Australia Flinders Univ S Australia Flinders Accelerator Microbiome Explorat Bedford Pk SA 5042 Australia

Metagenomics has enabled culture-independent analysis of micro-organisms present in environmental samples. Metagenomics binning, which involves the grouping of contigs into bins that represent different taxonomic groups, is an important step of a typical metagenomic workflow followed after assembly. The majority of the metagenomic binning tools represent the composition and coverage information of contigs as feature vectors consisting of a large number of dimensions. However, these tools use traditional Euclidean distance or Manhattan distance metrics which become unreliable in the high dimensional space. We propose CH-Bin, a binning approach that leverages the benefits of using convex hull distance for binning contigs represented by high dimensional feature vectors. We demonstrate using experimental evidence on simulated and real datasets that the use of high dimensional feature vectors to represent contigs can preserve additional information, and result in improved binning results. We further demonstrate that the convex hull distance based binning approach can be effectively utilized in binning such high dimensional data. To the best of our knowledge, this is the first time that composition information from oligonucleotides of multiple sizes has been used in representing the composition information of contigs and a convex hull distance based binning algorithm has been used to bin metagenomic contigs. The source code of CH-Bin is available at https://***/kdsuneraavinash/CH-Bin.

关键词： Convex hull Convex hull distance Metagenomic binning Multiple k values High dimensional data clustering clustering algorithm

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Robust and interpretable mineral identification using laser-induced breakdown spectroscopy mapping

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SPECTROCHIMICA ACTA PART B-ATOMIC SPECTROSCOPY 2023年第1期206卷

作者： Capela, Diana Ferreira, Miguel F. S. Lima, Alexandre Dias, Filipa Guimara, Diana Jorge, Pedro A. S. Silva, Nuno A. INESC TEC Ctr Appl Photon Rua Campo Alegre 687 P-4169007 Porto Portugal Univ Porto Fac Ciencias Dept Fis & Astron Rua Campo Alegre 687 P-4169007 Porto Portugal Univ Porto Fac Ciencias Dept Geociencias Ambiente & Ordenamento Terr Rua Campo Alegre 687 P-4169007 Porto Portugal Pole Univ Porto ICT Inst Earth Sci P-4169007 Porto Portugal

Fast and precise identification of minerals in geological samples is of paramount importance for the study of rock constituents and for technological applications in the context of mining. However, analyzing samples based only on the extrinsic properties of the minerals such as color can often be insufficient, making additional analysis crucial to improve the accuracy of the methods. In this context, Laser-induced breakdown spectroscopy mapping is an interesting technique to perform the study of the distribution of the chemical elements in sample surfaces, thus allowing deeper insights to help the process of mineral identification. In this work, we present the development and deployment of a processing pipeline and algorithm to identify spatial regions of the same mineralogical composition through chemical information in a fast and automatic way. Furthermore, by providing the necessary labels to the results on a training sample, we can turn this unsupervised methodology into a classifier that can be used to generalize and classify minerals in similar but unseen samples. The results obtained show good accuracy in reproducing the expected mineral regions and extend the interpretability of previous unsupervised methods with a visualization tool for cluster assignment, thus paving for future applications in contexts requiring high-throughput mineral identification systems, such as mining.

关键词： LIBS Mineral identification Mapping clustering algorithm

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