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Industrial Applications of Data Intelligence
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Industrial Applications of Data Intelligence

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 5873

Special Issue Editors

Dr. Long Wang

E-Mail Website
Guest Editor
Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
Interests: machine learning; data mining; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Huang

E-Mail Website
Guest Editor
Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong, China
Interests: machine learning; computational intelligence; renewable energy systems; complex systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in sensor and data storage technologies have enabled the cumulation of a large amount of data from industrial systems. Both structural and non-structural data from industrial systems are collected, including time-series, text, image, and sound formats, among others. Since data can systematically describe the status of industrial systems, their utilization has seen growing interest from the industry and data intelligence methods are in high demand. Meanwhile, theoretical development in related disciplines, such as machine learning, computer vision, evolutionary computation, and signal processing, has provided effective ways of analyzing and utilizing the collected data. The recent success of applying data-driven methods in different domains, including intelligent manufacturing, the energy internet, and smart healthcare, has proved the potential of employing data intelligence algorithms for solving real problems in various industrial fields.

This Special Issue will present the latest work from researchers on the applications of data intelligence algorithms in industrial systems, especially considering data fusion approaches to integrating different data sources and fusing structural and non-structural information.

Dr. Long Wang
Dr. Chao Huang
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

  • machine learning
  • deep learning
  • moblie computing
  • industrial applications

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Published Papers (2 papers)

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Research

15 pages, 3710 KiB  
Article
A Soft Measurement Method for the Tail Diameter in the Growing Process of Czochralski Silicon Single Crystals
by Lei Jiang, Da Teng and Yue Zhao
Appl. Sci. 2024, 14(4), 1569; https://doi.org/10.3390/app14041569 - 16 Feb 2024
Cited by 1 | Viewed by 1629
Abstract
In the Czochralski silicon single crystal growth process, the tail diameter is a key parameter that cannot be directly measured. In this paper, we propose a real-time soft measurement method that combines a deep belief network (DBN) and a support vector regression (SVR) [...] Read more.
In the Czochralski silicon single crystal growth process, the tail diameter is a key parameter that cannot be directly measured. In this paper, we propose a real-time soft measurement method that combines a deep belief network (DBN) and a support vector regression (SVR) network based on system identification to accurately predict the crystal diameter. The main steps of the proposed method are as follows: First, we address the delay problem of the effects of the temperature and crystal pulling speed on the tail diameter growth by using a back propagation (BP) neural network based on the mean impact value (MIV) method to determine the optimal delay time. Second, we construct a prediction model of the tail diameter by using the DBN network with the temperature and crystal pulling speed as input variables in the crystal growth process. Third, we improve the DBN network by using the SVR network to enhance its linear regression capability. We also employ the ant colony optimization (ACO) algorithm to obtain the optimal parameters of the SVR network. Finally, we compare the performance of the DBN-ACO-SVR network based on system identification with the DBN and SVR networks, and the results show that our method can effectively deal with the delay problem and achieve the accurate prediction of the tail diameter in the Czochralski silicon single crystal growth process. Full article
(This article belongs to the Special Issue Industrial Applications of Data Intelligence)
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16 pages, 14938 KiB  
Article
Machine Learning-Based Garbage Detection and 3D Spatial Localization for Intelligent Robotic Grasp
by Zhenwei Lv, Tingyang Chen, Zhenhua Cai and Ziyang Chen
Appl. Sci. 2023, 13(18), 10018; https://doi.org/10.3390/app131810018 - 5 Sep 2023
Cited by 6 | Viewed by 3193
Abstract
Garbage detection and 3D spatial localization play a crucial role in industrial applications, particularly in the context of garbage trucks. However, existing approaches often suffer from limited precision and efficiency. To overcome these challenges, this paper presents an algorithmic architecture that leverages advanced [...] Read more.
Garbage detection and 3D spatial localization play a crucial role in industrial applications, particularly in the context of garbage trucks. However, existing approaches often suffer from limited precision and efficiency. To overcome these challenges, this paper presents an algorithmic architecture that leverages advanced techniques in computer vision and machine learning. The proposed approach integrates cutting-edge computer vision methodologies to improve the precision of waste classification and spatial localization. By utilizing RGB-D data captured by the RealSenseD415 camera, the algorithm incorporates state-of-the-art computer vision algorithms and machine learning models, including the Yolactedge model, for real-time instance segmentation of garbage objects based on RGB images. This enables the accurate prediction of garbage class and the generation of masks for each instance. Furthermore, the predicted masks are utilized to extract the point cloud corresponding to the garbage instances. The oriented bounding boxes of the segmented point cloud is calculated as the spatial location information of the garbage instances using the DBSCAN clustering algorithm to remove the interfering points. The findings indicate that the proposed approach can run at a maximum speed of 150 FPS. The usefulness of the proposed method in achieving accurate garbage recognition and spatial localization in a vision-driving robot grasp system has been tested experimentally on datasets that were custom-collected. The results demonstrate the algorithmic architecture’s ability to transform waste management procedures while also enabling intelligent garbage sorting and enabling robotic grasp applications. Full article
(This article belongs to the Special Issue Industrial Applications of Data Intelligence)
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