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Statistical Learning: Technologies and Industrial Applications
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Statistical Learning: Technologies and Industrial Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 10202

Special Issue Editors

Dr. Xun Shen

E-Mail Website
Guest Editor
Faculty of Engineering, Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Fuchu, Japan
Interests: automotive control; intelligent driving system; chance constrained optimization; computation; statistics
Special Issues, Collections and Topics in MDPI journals
Dr. Shuai Li

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN, USA
Interests: health-promoting intelligent building and civil infrastructure systems; cyber-eco additive manufacturing technologies; autonomous vehicles and robotics; in silico modeling and simulation
Dr. Tinghui Ouyang

E-Mail Website
Guest Editor
Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2 Chome-3-26 Aomi, Koto City, Tokyo 135-0064, Japan
Interests: machine learning; data mining; anomaly detection; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Alan Wai Hou Lio

E-Mail Website
Guest Editor
Department of Wind Energy, Technical University of Denmark, Frederiksborgvej 399, 114, S18, 4000 Roskilde, Denmark
Interests: control and state estimation of dynamical systems; model predictive control; kalman filtering

Special Issue Information

Dear Colleagues,

Statistical learning is an important topic that has been well-studied in diverse research areas and application domains. It generally involves using big data or sparse data to model systems' behavior, design the optimal data-driven control policy or decision-making policy. Statistical learning from big data or sparse data can be very helpful to investigate the industrial systems' characteristics and guarantee industrial systems' stability, security, and economy. As the development of intelligent industries and sensor systems grows, large amounts of data become easily available, and challenges have arisen in industrial systems' statistical learning, especially how to accomplish statistical learning for big data and sparse data more efficiently. Three typical cases are the study within energy-related systems, traffic systems involved with autonomous driving vehicles, IoT-based civil infrastructure systems.

These systems can involve various data formats, more complex data structures, and very sparse data making statistical learning a challenge. Currently, under the development of deep learning, big data analytics, data recovery, residual analysis, and other technologies, many promising results have been achieved in statistical learning for the above industrial applications. However, many challenging problems remain unsolved due to the uncertain and complex nature of energy, traffic systems, and IoT-based civil infrastructure systems. Therefore, new techniques and advanced engineering applications on statistical learning in energy systems, traffic systems and IoT-based civil infrastructure systems still appeal to a wide range of scholars and industries. This Special Issue is aimed at providing selected contributions on advances in the theoretical findings, technologies, and industrial applications of statistical learning.

Potential topics includes, but are not limited to:
・Theory development on statistical learning
-Sparse data analytics
-Machine learning
  -Deep learning
  -Graph theory
  -Data-driven control
・Statistical learning and data driven control in energy-related industrial systems
・Statistical learning in traffic systems
・Statistical learning in IoT-based civil infrastructure systems

Dr. Xun Shen
Dr. Shuai Li
Dr. Tinghui Ouyang
Dr. Alan Wai Hou Lio
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • statistical learning
  • sparse data analytics
  • data-driven control policy

Published Papers (5 papers)

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Research

18 pages, 3099 KiB  
Article
Granular Description of Uncertain Data for Classification Rules in Three-Way Decision
by Xinhui Zhang and Tinghui Ouyang
Appl. Sci. 2022, 12(22), 11381; https://doi.org/10.3390/app122211381 - 9 Nov 2022
Cited by 1 | Viewed by 1026
Abstract
Considering that data quality and model confidence bring threats to the confidence of decision-making, a three-way decision with uncertain data description is more meaningful in system analyses. In this paper, an advanced method for forming classification rules in three-way decisions is proposed. This [...] Read more.
Considering that data quality and model confidence bring threats to the confidence of decision-making, a three-way decision with uncertain data description is more meaningful in system analyses. In this paper, an advanced method for forming classification rules in three-way decisions is proposed. This method firstly constructs information granules for describing uncertain data in decision-making; meanwhile, information entropy is introduced in Granular Computing (GrC) to realize a better uncertainty description. Then, based on the constructed uncertainty descriptors, fuzzy rules are formed aiming at the common decision-making processes, namely classification problems. Finally, experiments on both synthetic and publicly available data are implemented. Discussions on numerical results validate the feasibility of the proposed method for forming three-way classification rules. Moreover, classification rules with consideration of uncertain data are demonstrated to be better performed than traditional methods with an improvement of 1.35–4.26% in decision-making processes. Full article
(This article belongs to the Special Issue Statistical Learning: Technologies and Industrial Applications)
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21 pages, 3254 KiB  
Article
Extension of DBSCAN in Online Clustering: An Approach Based on Three-Layer Granular Models
by Xinhui Zhang, Xun Shen and Tinghui Ouyang
Appl. Sci. 2022, 12(19), 9402; https://doi.org/10.3390/app12199402 - 20 Sep 2022
Cited by 5 | Viewed by 2766
Abstract
In big data analysis, conventional clustering algorithms have limitations to deal with nonlinear spatial datasets, e.g., low accuracy and high computation cost. Aiming at these problems, this paper proposed a new DBSCAN extension algorithm for online clustering, which consists of three layers, considering [...] Read more.
In big data analysis, conventional clustering algorithms have limitations to deal with nonlinear spatial datasets, e.g., low accuracy and high computation cost. Aiming at these problems, this paper proposed a new DBSCAN extension algorithm for online clustering, which consists of three layers, considering DBSCAN, granular computing (GrC), and fuzzy rule-based modeling. Firstly, making use of DBSCAN algorithms’ advantages at extracting structural information, spatial data are clustered via DBSCAN into structural clusters, which are subsequently described by structural information granules (IG) via GrC. Secondly, based on the structural IGs, a series of granular models are constructed in the medium space, and utilized to form fuzzy rules to guide clustering on spatial data. Finally, with the help of structural IGs and granular rules, a rule-based modeling method is constructed in the output space for online clustering. Experiments on a synthetic toy dataset and a typical spatial dataset are implemented in this paper. Numerical results validate the feasibility to the proposed method in online spatial data clustering. Moreover, comparative studies with conventional methods and existing DBSCAN variants demonstrate the superiorities of the proposed method, as well as accuracy improvement and computation overhead reduction. Full article
(This article belongs to the Special Issue Statistical Learning: Technologies and Industrial Applications)
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17 pages, 9018 KiB  
Article
A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing Condition
by Aina Wang, Yingshun Li, Zhao Yao, Chongquan Zhong, Bin Xue and Zhannan Guo
Appl. Sci. 2022, 12(8), 3854; https://doi.org/10.3390/app12083854 - 11 Apr 2022
Cited by 4 | Viewed by 1722
Abstract
Rotating machinery is a key piece of equipment for tremendous engineering operations. Vibration analysis is a powerful tool for monitoring the condition of rotating machinery. Furthermore, vibration signals have the characteristics of time series. Hence, it is necessary to monitor the condition of [...] Read more.
Rotating machinery is a key piece of equipment for tremendous engineering operations. Vibration analysis is a powerful tool for monitoring the condition of rotating machinery. Furthermore, vibration signals have the characteristics of time series. Hence, it is necessary to monitor the condition of vibration signal series to avoid any catastrophic failure. To this end, this paper proposes an effective condition monitoring strategy under a hybrid method framework. First, we add variational mode decomposition (VMD) to preprocess the data points listed in a time order into a subseries, namely intrinsic mode functions (IMFs). Then the framework of the hybrid prediction model, namely the autoregressive moving average (ARMA)-artificial neural network (ANN), is adopted to forecast the IMF series. Next, we select the sensitive modes that contain the prime information of the original signal and that can imply the condition of the machinery. Subsequently, we apply the support vector machine (SVM) classification model to identify the multiple condition patterns based on the multi-domain features extracted from sensitive modes. Finally, the vibration signals from the Case Western Reserve University (CWRU) laboratory are utilized to verify the effectiveness of our proposed method. The comparison results demonstrate advantages in prediction and condition monitoring. Full article
(This article belongs to the Special Issue Statistical Learning: Technologies and Industrial Applications)
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17 pages, 750 KiB  
Article
Finite-Time Set Reachability of Probabilistic Boolean Multiplex Control Networks
by Yuxin Cui, Shu Li, Yunxiao Shan and Fengqiu Liu
Appl. Sci. 2022, 12(2), 883; https://doi.org/10.3390/app12020883 - 16 Jan 2022
Cited by 3 | Viewed by 1465
Abstract
This study focuses on the finite-time set reachability of probabilistic Boolean multiplex control networks (PBMCNs). Firstly, based on the state transfer graph (STG) reconstruction technique, the PBMCNs are extended to random logic dynamical systems. Then, a necessary and sufficient condition for the finite-time [...] Read more.
This study focuses on the finite-time set reachability of probabilistic Boolean multiplex control networks (PBMCNs). Firstly, based on the state transfer graph (STG) reconstruction technique, the PBMCNs are extended to random logic dynamical systems. Then, a necessary and sufficient condition for the finite-time set reachability of PBMCNs is obtained. Finally, the obtained results are effectively illustrated by an example. Full article
(This article belongs to the Special Issue Statistical Learning: Technologies and Industrial Applications)
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21 pages, 6524 KiB  
Article
Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation
by Fei-Xue Wang, Qian Peng, Xin-Liang Zang and Qi-Fan Xue
Appl. Sci. 2021, 11(24), 12137; https://doi.org/10.3390/app112412137 - 20 Dec 2021
Cited by 6 | Viewed by 2355
Abstract
Adaptive cruise control (ACC), as a driver assistant system for vehicles, not only relieves the burden of drivers, but also improves driving safety. This paper takes the intelligent pure electric city bus as the research platform, presenting a novel ACC control strategy that [...] Read more.
Adaptive cruise control (ACC), as a driver assistant system for vehicles, not only relieves the burden of drivers, but also improves driving safety. This paper takes the intelligent pure electric city bus as the research platform, presenting a novel ACC control strategy that could comprehensively address issues of tracking capability, driving safety, energy saving, and driving comfort during vehicle following. A hierarchical control architecture is utilized in this paper. The lower controller is based on the nonlinear vehicle dynamics model and adjusts vehicle acceleration with consideration to the changes of bus mass and road slope by extended Kalman filter (EKF). The upper controller adapts Model Predictive Control (MPC) theory to solve the multi-objective optimal problem in ACC process. Cost functions are developed to balance the tracking distance, driving safety, energy consumption, and driving comfort. The simulations and Hardware-in-the-Loop (HIL) test are implemented; results show that the proposed control strategy ensured the driving safety and tracking ability of the bus, and reduced the vehicle’s maximum impact to 5 m/s3 and the State of Charge (SoC) consumption by 10%. Vehicle comfort and energy economy are improved obviously. Full article
(This article belongs to the Special Issue Statistical Learning: Technologies and Industrial Applications)
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