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Applied Sciences
Special Issues
Innovation and Application of Intelligent System
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Innovation and Application of Intelligent System

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (8 April 2020) | Viewed by 10461

Special Issue Editors

Prof. Dr. Teen­-Hang Meen

E-Mail Website
Guest Editor
Department of Electronic Engineering National Formosa University, Yunlin 632, Taiwan
Interests: IOT devices; photovoltaic devices; STEM education
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wenbing Zhao

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH 44115, USA
Interests: fault-tolerant computing; computer and network security; peer-to-peer and grid computing; performance evaluation of distributed systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Cheng-Fu Yang

E-Mail Website
Guest Editor
Department of Aeronautical Engineering, Chaoyang University of Technology, Taichung 413, Taiwan
Interests: electronic ceramics; high-frequency communication materials; applied science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mechanical engineering and design innovations are both an academic and practical engineering field that involves systematic technological materialization through scientific principles and engineering designs. Technological innovation through mechanical engineering includes IT-based intelligent mechanical systems, mechanics and design innovations, and applied materials on nanosciences and nanotechnology. These new technologies which implant intelligence to machine systems are an interdisciplinary area combining conventional mechanical technology and new information technology.

The main goal of this Special Issue on “Innovation and Application of Intelligent Systems” is to discover new scientific knowledge relevant to IT-based intelligent mechanical systems, mechanics and design innovations, and applied materials on nanosciences and nanotechnology. We invite investigators to contribute their original research articles to this Special Issue. Potential topics include but are not limited to:

  • Intelligent mechanical manufacturing systems;
  • Mathematical problems on mechanical system design;
  • Smart electromechanical system analysis and design;
  • Applied materials on nanosciences and nanotechnology;
  • Computer-aided methods for mechanical design procedure and manufacture;
  • Computer and human–machine interaction;
  • Internet technology on mechanical system innovation;
  • Machine diagnostics and reliability;
  • Human–machine interaction/virtual reality and entertainment.
Prof. Dr. Teen­-Hang Meen
Prof. Dr. Wenbing Zhao
Prof. Dr. Cheng-Fu Yang
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

  • Smart electromechanical system analysis and design
  • Intelligent mechanical systems
  • Applied materials on nanosciences and nanotechnology

Published Papers (2 papers)

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14 pages, 4145 KiB  
Article
Multi-Objective Function for SWIPT System by SADDE
by Wei Chien, Chien-Ching Chiu, Yu-Ting Cheng, Wei-Lin Fang and Eng Hock Lim
Appl. Sci. 2020, 10(9), 3124; https://0-doi-org.brum.beds.ac.uk/10.3390/app10093124 - 30 Apr 2020
Cited by 4 | Viewed by 1725
Abstract
Simultaneous wireless information and power transfer (SWIPT) optimization with multiple objective function optimization is presented in the millimeter band in this paper. Three different objective functions that are used for harvest power (HP), capacity, and bit error rate (BER) were studied. There are [...] Read more.
Simultaneous wireless information and power transfer (SWIPT) optimization with multiple objective function optimization is presented in the millimeter band in this paper. Three different objective functions that are used for harvest power (HP), capacity, and bit error rate (BER) were studied. There are three different nodes in real environment for wireless power transfer (WPT) and SWIPT. The channel estimation calculated by shooting and bouncing ray/image techniques includes multi-path, fading effect, and path-loss in the real environment. We applied beamforming techniques at the transmitter to focus the transmitter energy in order to reduce the multi-path effect and adjust the length of the feed line on each array element in order to find the extremum of the objective functions by the self-adaptive dynamic differential evolution (SADDE) method. Numerical results showed that SWIPT node cannot achieve good performance by single objective function, but wireless power transfer (WPT) can. Nevertheless, both WPT and SWIPT nodes can meet the criteria by the multiple objective function. The harvesting power ratio as well as the BER and capacity can be improved by the multiple objective function to an acceptable level by only reducing a little harvesting energy compared to the best harvesting energy for the single objective function. Finally, the multiple optimization function cannot merely provide good information quality for SWIPT node but achieve good total harvesting power for WPT and SWIPT node as well. Full article
(This article belongs to the Special Issue Innovation and Application of Intelligent System)
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15 pages, 2601 KiB  
Article
Optimized YOLOv3 Algorithm and Its Application in Traffic Flow Detections
by Yi-Qi Huang, Jia-Chun Zheng, Shi-Dan Sun, Cheng-Fu Yang and Jing Liu
Appl. Sci. 2020, 10(9), 3079; https://0-doi-org.brum.beds.ac.uk/10.3390/app10093079 - 28 Apr 2020
Cited by 69 | Viewed by 8019
Abstract
In the intelligent traffic system, real-time and accurate detections of vehicles in images and video data are very important and challenging work. Especially in situations with complex scenes, different models, and high density, it is difficult to accurately locate and classify these vehicles [...] Read more.
In the intelligent traffic system, real-time and accurate detections of vehicles in images and video data are very important and challenging work. Especially in situations with complex scenes, different models, and high density, it is difficult to accurately locate and classify these vehicles during traffic flows. Therefore, we propose a single-stage deep neural network YOLOv3-DL, which is based on the Tensorflow framework to improve this problem. The network structure is optimized by introducing the idea of spatial pyramid pooling, then the loss function is redefined, and a weight regularization method is introduced, for that, the real-time detections and statistics of traffic flows can be implemented effectively. The optimization algorithm we use is the DL-CAR data set for end-to-end network training and experiments with data sets under different scenarios and weathers. The analyses of experimental data show that the optimized algorithm can improve the vehicles’ detection accuracy on the test set by 3.86%. Experiments on test sets in different environments have improved the detection accuracy rate by 4.53%, indicating that the algorithm has high robustness. At the same time, the detection accuracy and speed of the investigated algorithm are higher than other algorithms, indicating that the algorithm has higher detection performance. Full article
(This article belongs to the Special Issue Innovation and Application of Intelligent System)
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