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A comparative study of series hybrid approaches to model and predict the vehicle operating states

COMPUTERS & INDUSTRIAL ENGINEERING 2021年 162卷 107770-107770页

作者： Alizadeh, Morteza Ma, Junfeng Mississippi State Univ Dept Ind & Syst Engn 260 McCain Hall Mississippi State MS 39762 USA

With the growing complexity of modern vehicle system, the capability of modeling the behavior of different subsystems and predicting their forthcoming patterns become vital. It helps to extend vehicle's life cycle and control their maintenance costs. Leveraging statistical and deep learning techniques, the massive maintenance data can be used to model the behaviors of different subsystems of vehicle, predict the future trend, and consequently assist in making appropriate maintenance decisions. In this study, Auto-Regressive Integrated Moving Average (ARIMA), Multilayer Perceptrons Neural Network (MLPNN), and Wavelet Neural Network (WNN) were used to develop several series hybrid models (i.e. ARIMA-MLPNN, ARIMA-WNN, MLPNN-ARIMA, and WNN-ARIMA) to model and predict the operational behaviors of vehicle's subsystems. Moreover, a threshold-based anomaly detection method was developed for early detection of abnormalities. A real case study including three months records of 97 subsystems of an operating vehicle was used to validate the efficiency of these hybrid models. Results showed that the WNN-ARIMA model obtained the most accurate results compared with other hybrid models. A threshold-based anomaly detection approach was developed based on the residual errors of the WNN-ARIMA model. This approach precisely captures the abnormal states of various subsystems of the vehicle which can help to make more accurate decisions regarding the maintenance of the vehicle.

关键词： Vehicle Operating Subsystems behavior modeling and prediction Auto-Regressive Integrated Moving Average Wavelet Neural Network Multilayer Perceptrons Neural Network

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Predicting User Participation in Social Networking Sites

Predicting User Participation in Social Networking Sites

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11th IEEE International Conference on Intelligence and Security Informatics (IEEE ISI)

作者： Kong, Qingchao Mao, Wenji Zeng, Daniel Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781467362139;9781467362146

Social networking sites provide a convenient way for users to participate in discussion groups and communicate with others. While users situate in and enjoy such a social environment, it is important for various security related applications to understand, model and analyze participating users' behavior. In this paper, we make an attempt to model and predict user participation behavior in discussion groups of social networking sites. Our work employs a feature-based approach, which considers four types of features: thread features, content similarity, user behavior and social features. We conduct an empirical study on a popular social networking site in China, ***. The experimental results show the effectiveness of our approach.

关键词： user participation behavior modeling and prediction social networking sites

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An empirical study of cost-sensitive learning in cultural modeling

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INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT 2013年第3期11卷 437-455页

作者： Su, Peng Mao, Wenji Zeng, Daniel Shandong Jianzhu Univ Sch Management Engn Tsinan 250101 Shandong Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Arizona Dept Management Informat Syst Tucson AZ 85721 USA

Cultural modeling aims at developing behavioral models of groups and analyzing the impact of culture factors on group behavior using computational methods. Machine learning methods and in particular classification, play a central role in such applications. In modeling cultural data, it is expected that standard classifiers yield good performance under the assumption that different classification errors have uniform costs. However, this assumption is often violated in practice. Therefore, the performance of standard classifiers is severely hindered. To handle this problem, this paper empirically studies cost-sensitive learning in cultural modeling. We consider cost factor when building the classifiers, with the aim of minimizing total misclassification costs. We conduct experiments to investigate four typical cost-sensitive learning methods, combine them with six standard classifiers and evaluate their performance under various conditions. Our empirical study verifies the effectiveness of cost-sensitive learning in cultural modeling. Based on the experimental results, we gain a thorough insight into the problem of non-uniform misclassification costs, as well as the selection of cost-sensitive methods, base classifiers and method-classifier pairs for this domain. Furthermore, we propose an improved algorithm which outperforms the best method-classifier pair using the benchmark cultural datasets.

关键词： Cultural modeling Cost-sensitive learning Class imbalance problem Misclassification cost behavior modeling and prediction

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Predicting User Participation in Social Networking Sites

Predicting User Participation in Social Networking Sites

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IEEE International Conference on Intelligence and Security Informatics

作者： Qingchao Kong Wenji Mao Daniel Zeng State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467362146

关键词： User participation behavior modeling and prediction Social networking sites

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