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Method for In-Operando Contamination of Lithium Ion Batteries for Prediction of Impurity-Induced Non-Obvious Cell Damage

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BATTERIES-BASEL 2022年第4期8卷

作者： Hoeschele, Patrick Heindl, Simon Franz Schneider, Bernd Sinz, Wolfgang Ellersdorfer, Christian Graz Univ Technol Vehicle Safety Inst Inffeldgasse 23-1 A-8010 Graz Austria

The safety of lithium-ion batteries within electrified vehicles plays an important role. Hazards can arise from contaminated batteries resulting from non-obvious damages or insufficient production processes. A systematic examination requires experimental methods to provoke a defined contamination. Two prerequisites were required: First, the extent and type of contamination should be determinable to exclude randomness. Second, specimens should work properly before the contamination, enabling realistic behavior. In this study, two experimental methods were developed to allow for the first time a controlled and reproducible application of water or oxygen into 11 single-layer full cells (Li4Ti5O12/LiCoO2) used as specimens during electrical cycling. Electrochemical impedance spectroscopy was used to continuously monitor the specimens and to fit the parameters of an equivalent circuit model (ECM). For the first time, these parameters were used to calibrate a machine-learning algorithm which was able to predict the contamination state. A decision tree was calibrated with the ECM parameters of eight specimens (training data) and was validated by predicting the contamination state of the three remaining specimens (test data). The prediction quality proved the usability of classification algorithms to monitor for contaminations or non-obvious battery damage after manufacturing and during use. It can be an integral part of battery management systems that increases vehicle safety.

关键词： lithium-ion battery safety in operando contamination electrochemical impedance spectroscopy equivalent circuit model machine learning classification algorithm decision tree

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Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective 29

Where to Handle an Exception? Recommending Exception Handlin...

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29th IEEE/ACM International Conference on Program Comprehension (ICPC) / 18th IEEE/ACM International Conference on Mining Software Repositories (MSR)

作者： Jia, Xiangyang Chen, Songqiang Zhou, Xingqi Li, Xintong Yu, Run Chen, Xu Xuan, Jifeng Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China NYU Tandon Sch Engn New York NY 10003 USA

ISBN: (纸本)9781665414036

Exception handling is an effective mechanism to guarantee software reliability in modern programming languages. An exception interrupts the program execution and propagates backwards along the call chain until the exception is caught by an exception handler. In software development practices, developers may be confused in determining where to place the exception handler in the call chain. The reason is that exception handling requires a developer to take a comprehensive consideration from a global perspective of the software project. In this paper, we propose an automatic approach EHAdvisor, which recommends exception handling locations from the global perspective of the project. EHAdvisor first trains a binary classification model based on four types of features, including architectural features, project features, functional features, and exception features. Then, for a new code snippet with exceptions, EHAdvisor predicts the exception catching probability for each method in the call chain based on the classification model and recommends Top-K exception handling locations based on the probability ranking. We conducted experiments on a dataset from 29 high-quality open source projects. Experimental results show that EHAdvisor achieves an average Top-1 recommendation success rate of 70.83% for across-project location recommendation and an average Top-1 accuracy of 86.21% for intra-project recommendation. Experiments on the importance scores show that global features, such as project features and architectural features, are evidently important to the recommendation of exception handling locations.

关键词： exception handling location recommendation machine learning global features classification algorithm

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Intelligent recommendation system based on decision mode of archive translation tasks

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2022年 16卷 1048047页

作者： Chen, Lilan Chen, Yongsheng Guangdong Pharmaceut Univ Sch Foreign Languages Guangzhou Peoples R China Sun Yat Sen Univ Sch Informat Management Guangzhou Peoples R China

How to recruit, test, and train the intelligent recommendation system users, and how to assign the archive translation tasks to all intelligent recommendation system users according to the intelligent matching principles are still a problem that needs to be solved. With the help of proper names and terms in China's Imperial Maritime Customs archives, this manuscript aims to solve the problem. When the corresponding translation, domain or attributes of a proper name or term is known, it will be easier for some archive translation tasks to be completed, and the adaptive archive intelligent recommendation system will also improve the efficiency of intelligent recommendation quality of archive translation tasks. These related domains or attributes are different labels of these archives. To put it simply, multi-label classification means that the same instance can have multiple labels or be labelled into multiple categories, which is called multi-label classification. With the multi-label classification, archives can be classified into different categories, such as the trade archives, preventive archives, personnel archives, etc. The system users are divided into different professional domains by some tests, for instance, system users who are good at economic knowledge and users who have higher language skills. With these labels, the intelligent recommendation system can make the intelligent match between the archives and system users, so as to improve the efficiency and quality of intelligent archive translation tasks. In this manuscript, through multi-label classification, the intelligent recommendation system can realize the intelligent allocation of archive translation tasks to the system users. The intelligent allocation is realized through the construction of intelligent control model, and verifies that the intelligent recommendation system can improve the performance of task allocation over time without the participation of task issuers.

关键词： intelligent recommender system multi-label classification decision model classification algorithm adaptive

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Shallow Versus Deep Neural Networks in Gear Fault Diagnosis

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2020年第3期35卷 1338-1347页

作者： Cirrincione, Giansalvo Kumar, Rahul Ranjeev Mohammadi, Ali Kia, Shahin Hedayati Barbiero, Pietro Ferretti, Jacopo Univ Picardie Jules Verne Lab LTI F-80025 Amiens France Univ South Pacific Sch Engn & Phys Suva 9072 Fiji Univ Padua Dept Ind Engn I-35122 Padua Italy Univ Picardie Jules Verne Lab MIS F-80025 Amiens France Politecn Torino Dept Math Sci I-10129 Turin Italy Politecn Torino DET Dept Elect & Telecommun I-10129 Turin Italy Univ Torino Dipartimento Sci Chirurg I-10124 Turin Italy

Accurate gear defect detection in induction machine-based systems is a fundamental issue in several industrial applications. At this aim, shallow neural networks, i.e., architectures with only one hidden layer, have been used after a feature extraction step from vibration, torque, acoustic pressure and electrical signals. Their additional complexity is justified by their ability in extracting its own features and in the very high-test classification rates. These signals are here analyzed, both geometrically and topologically, in order to estimate the class manifolds and their reciprocal positioning. At this aim, the different states of the gears are studied by using linear (Pareto charts, biplots, principal angles) and nonlinear (curvilinear component analysis) techniques, while the class clusters are visualized by using the parallel coordinates. It is deduced that the class manifolds are compact and well separated. This result justifies the use of a shallow neural network, instead of a deep one, as already remarked in the literature, but with no theoretical justification. The experimental section confirms this assertion, and also compares the shallow neural network results with the other machine learning techniques used in the literature.

关键词： Gears Feature extraction Vibrations Neural networks Principal component analysis Electronic mail Fault detection classification algorithm fault detection fault diagnosis gears induction motors multilayer perceptron neural networks principal component analysis vibrations

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A Computer Aided Approach for River Styles-Inspired Characterization of Large Basins: The Magdalena River (Colombia)

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WATER 2020年第4期12卷

作者： Nardini, Andrea Yepez, Santiago Zuniga, Lislie Gualtieri, Carlo Dolores Bejarano, Maria Fdn CREACUA Riohacha 440001 La Guajira Colombia Univ Politecn Madrid UPM Escuela Montes PhD Program Adv Forestry Engn Madrid 28040 Spain Univ Concepcion UdeC Fac Ciencias Forestales Dept Manejo Bosques & Medio Ambiente Concepcion 407374 Chile Univ Concepcion UdeC Lab Geotecnol & Modelamiento Recursos Nat LGM Concepcion 407374 Chile Univ Napoli Federico II Civil Architectural & Environm Engn Dept DICEA I-80125 Naples Italy Univ Politecn Madrid UPM Escuela Montes Madrid 28040 Spain

This paper addresses the geomorphic characterization and classification of large rivers in a framework of scarce information. This is inspired by the River Styles Framework with some modifications that make the process more straightforward and accessible to practitioners and more applicable to large basins, while reducing the subjective, expert-based inputs, as the process is now more systematic. To this aim, it utilizes innovative criteria and some computer-aided procedures and tools based on GIS, Excel and Python. This approach sheds light on the character and the behavior of rivers, which is key to informing planning, management and restoration. The application to the Magdalena River (Colombia) illustrates the characterization and classification process and the type of results, which ultimately highlight the great geomorphic diversity of that river. The process is applicable to many other rivers worldwide.

关键词： river styles Magdalena River geomorphic response river behavior classification algorithm

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Comparison of machine learning methods for the construction of a standalone gait diagnosis device

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IET SIGNAL PROCESSING 2020年第4期14卷 243-250页

作者： Han, Yi Chiew Wong, Kiing Ing Murray, Iain Curtin Univ Malaysia Dept Elect & Comp Engn CDT250 Miri Malaysia Curtin Univ Australia Sch Engn Comp & Math Sci Perth WA Australia

In this research, the authors investigate the feasibility of selecting three-dimensional thigh and shank angles as the features of machine learning methods. Four common machine learning techniques, i.e. random forest, k-nearest neighbour, support vector machine and perceptron, were compared in terms of accuracy and memory usage so that a real-time standalone gait diagnosis device can be constructed using low-end inertial measurement units (IMUs). With proper re-sampling and normalisation, they discovered that the support vector machine and perceptron resulted in the top two highest accuracies (96-99%) among the four machine learning methods. The memory requirement of the perceptron is the lowest among the machine learning methods. Therefore, perceptron was selected as the classification algorithm for the standalone gait diagnosis device. The trained perceptron was transferred to the thigh and shank's IMUs to process the data locally in real-time. The constructed standalone gait diagnosis device lit up green or red light emitting diodes when normal or abnormal gaits were detected, respectively. This standalone device was further tested in real-life and achieved a mean classification accuracy of 96.50%.

关键词： pattern classification perceptrons learning (artificial intelligence) support vector machines patient diagnosis gait analysis medical diagnostic computing biomedical equipment standalone device machine learning support vector machine perceptron real-time standalone gait diagnosis device low-end inertial measurement units classification algorithm three-dimensional thigh angles three-dimensional shank angles

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Improving feature selection performance for classification of gene expression data using Harris Hawks optimizer with variable neighborhood learning

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BRIEFINGS IN BIOINFORMATICS 2021年第5期22卷 bbab097页

作者： Qu, Chiwen Zhang, Lupeng Li, Jinlong Deng, Fang Tang, Yifan Zeng, Xiaomin Peng, Xiaoning Hunan Normal Univ Sch Med Dept Pathol & Pathophysiol Changsha 410081 Peoples R China Hunan Normal Univ Coll Math & Stat Changsha Peoples R China Baise Univ Sch Informat Engn Baise Peoples R China Jishou Univ Sch Med Dept Pathol & Pathophysiol Jishou Peoples R China Cent South Univ Dept Epidemiol & Hlth Stat Xiangya Publ Hlth Sch Changsha Peoples R China

Gene expression profiling has played a significant role in the identification and classification of tumor molecules. In gene expression data, only a few feature genes are closely related to tumors. It is a challenging task to select highly discriminative feature genes, and existing methods fail to deal with this problem efficiently. This article proposes a novel metaheuristic approach for gene feature extraction, called variable neighborhood learning Harris Hawks optimizer (VNLHHO). First, the F-score is used for a primary selection of the genes in gene expression data to narrow down the selection range of the feature genes. Subsequently, a variable neighborhood learning strategy is constructed to balance the global exploration and local exploitation of the Harris Hawks optimization. Finally, mutation operations are employed to increase the diversity of the population, so as to prevent the algorithm from falling into a local optimum. In addition, a novel activation function is used to convert the continuous solution of the VNLHHO into binary values, and a naive Bayesian classifier is utilized as a fitness function to select feature genes that can help classify biological tissues of binary and multi-class cancers. An experiment is conducted on gene expression profile data of eight types of tumors. The results show that the classification accuracy of the VNLHHO is greater than 96.128% for tumors in the colon, nervous system and lungs and 100% for the rest. We compare seven other algorithms and demonstrate the superiority of the VNLHHO in terms of the classification accuracy, fitness value and AUC value in feature selection for gene expression data.

关键词： gene feature selection variable neighborhood learning Harris Hawks algorithm classification algorithm

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Mangrove monitoring revealed by MDPrePost-Net using archived Landsat imageries

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REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT 2023年 32卷

作者： Dimyati, Muhammad Umarhadi, Deha Agus Jamaluddin, Ilham Awanda, Disyacitta Widyatmanti, Wirastuti Univ Indonesia Fac Math & Nat Sci Dept Geog Depok 16424 Indonesia Univ Gadjah Mada Fac Forestry Dept Forest Management Yogyakarta 55281 Indonesia Natl Cent Univ Dept Comp Sci & Informat Engn 300 Jhongda Rd Jhongli 32001 Taoyuan Taiwan Univ Gadjah Mada Fac Geog Dept Geog Informat Sci Bulaksumur 55281 Yogyakarta Indonesia

Acting as a global vital entity in the coastal ecosystem, mangroves are currently facing major threats to destruction due to anthropogenic activities. Restoration and rehabilitation measures of mangroves are being carried out, including in Indonesia. To support conservation actions in many islands in Indonesia, spatio-temporal information on mangroves is required. Remote sensing with advanced techniques such as machine learning and deep learning has proven the capability to spatially observe mangroves. This study aims to monitor mangrove change in three study areas, i.e., South Sumatra, North Kalimantan, and Southeast Sulawesi, using a fully convolutional network (FCN)-based MDPrePost-Net. This method was developed originally to assess the man-grove degradation due to a major event (i.e., Hurricane irma 2017 in southwest Florida), whereas this study adopts it for an extended observation period (1989-2022 for South Sumatra, 1991-2021 for North Kalimantan, and 1990-2021 for Southeast Sulawesi) using medium-resolution Landsat imageries. We observed that the mangroves remain stable within the national parks designated by the government. Outside the national parks, mangrove conversion massively occurred even though the areas are assigned as protection forests. The classification results showed satisfactory accuracy values of more than 84%. The maps produced have an advantage in the spatial change analysis compared to the global datasets such as Global Mangrove Watch version 3.0. Our method has a limitation when cloudless images are not available. The integration with Synthetic Aperture Radar (SAR) images and a rigorous cloud removal method for the optical images may improve the results of mangrove monitoring.

关键词： Deep learning Fully convolutional network Remote sensing classification algorithm Wetland

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Assessment of machine learning classifiers in mapping the cocoa-forest mosaic landscape of Ghana

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SCIENTIFIC AFRICAN 2023年 20卷

作者： Ashiagbor, George Asare-Ansah, Akua Oparebea Amoah, Emmanuel Boakye Asante, Winston Adams Mensah, Yaw Asare Kwame Nkrumah Univ Sci & Technol KNUST Fac Renewable Nat Resources FRNR Kumasi Ghana Kwame Nkrumah Univ Sci & Technol KNUST Dept Geomat Engn Kumasi Ghana

The absence of clear-cut directives on the optimal classifier for land-use land-cover (LULC) classification of Ghana's cocoa landscape presents a practice gap. This has resulted in monitoring challenges since it is difficult to effectively compare land cover maps because they differ in the classifiers. In this paper, we explored the performance of four commonly used machine learning classifiers in the cocoa landscape to accurately determine the option that best segregates the different vegetation classes. Specifically, the accuracy with which k-Nearest Neighbors (kNN), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF) classifiers mapped the cocoa landscape in Juaboso, Ghana, was compared. A pre-processed Sentinel-2 image, 352 training and 151 validation points collected through field Global Positioning System survey were used. The image was classified using the kNN, ANN, SVM and RF classifiers. The accuracies of the LULC maps were assessed using overall accuracy (OA), Cohens' kappa (k). Also, practitioners with practical knowledge of the land cover classes and their distribution in the landscape subjected the map to visual inspection. The OA and k values indicated RF (OA = 84.77%, k = 0.801), kNN (OA = 84.11%, k = 0.796), ANN (OA = 76.13%, k = 0.7), and SVM (OA = 81.45%, k = 0.762) all performed well in classifying the landscape with a satisfactory agreement. Additionally, there are no clear-cut classifier that experts in remote sensing should apply while mapping Ghana's cocoa environment. At any point, using the classifier that most accurately represents the landscape is crucial and should be prioritized. Therefore, guidance on the choice of classification algorithms by researchers and practitioners for mapping the cocoa landscape of Ghana must not be limited to the overall accuracies and kappa only. Instead, operationalising a mapping and validation framework that incorporates experts' review will yield a LULC map that better

关键词： Remote sensing classification algorithm High forest zone of Ghana Cocoa deforestation Expert validation

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algorithm using supervised subspace learning and non-local representation for pose variation recognition

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IET COMPUTER VISION 2020年第7期14卷 528-537页

作者： Liao, Mengmeng Wang, Changzhi Gu, Xiaodong Fudan Univ Dept Elect Engn Shanghai 200433 Peoples R China

Pose variation has been one of the challenges of face recognition. To solve this challenge, the authors propose a classification algorithm using supervised subspace learning and non-local representation (SSLNR). In SSLNR, they first propose a supervised subspace learning algorithm (SSLA). SSLA includes three different terms. The first term is the difference term, which can reduce the intra-class differences. The second term is the block-diagonal regularisation term, which promotes the samples to be represented by intra-class samples. The last one is the noise robust term. Then, the original samples are mapped to the learned subspace by using SSLA. Thus, the intra-class differences of the samples mapped to the learned subspace are reduced. Finally, those mapped samples are classified by proposed non-local constraint-based extended sparse representation classifier. SSLNR is extensively evaluated using four databases, namely Georgia Tech, Label faces in the wild, FEI and CVL. Experimental results show that SSLNR achieves better performance than some state-of-the-art algorithms, such as DARG and RRNN.

关键词： sample mapping supervised subspace learning algorithm learned subspace pose variation recognition block-diagonal regularisation term image classification learning (artificial intelligence) feature extraction nonlocal constraint-based extended sparse representation classifier intra-class differences image representation classification algorithm nonlocal representation intra-class samples pose estimation SSLA SSLNR face recognition noise robust term

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