Special Issue "Intelligence Systems and Sensors"

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

Deadline for manuscript submissions: 30 April 2020.

Special Issue Editor

Guest Editor
Prof. Youngchul Bae E-Mail
Department of Electrical and Semiconductor Engineering, Chonnam National University, Daehak-ro 50 Yeosu, Jeonnam 59626, Korea
Phone: 82-10-8996-6839
Interests: Chaotic dynamics in power system, chaotic control

Special Issue Information

Dear Colleagues,

I would like to cordially invite you to contribute a paper to Special Issue of the open access journal Applied Sciences, entitled “Intelligent systems and sensors”, which aims to present recent developments of fuzzy, Neural network and artificial Intelligence in the fields of real life including robot, social system, industries  and so on.

This Special Issue contains fuzzy, neural networks, neuro-fuzzy systems, intelligent systems and sensors, based on artificial intelligent. I invite you to submit your research on these topics, in the form of original research papers and articles.

Prof. Dr. Youngchul Bae
Guest Editor

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 papers will be 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 1500 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

  • Artificial Intelligence
  • Complex Systems
  • Computational Intelligence 
  • Evolutionary Computing 
  • Fault Detection and Diagnosis 
  • Fuzzy Control 
  • Fuzzy Sets and Logic 
  • Fuzzy Systems 
  • Granular Computing 
  • Intelligent Communications 
  • Intelligent Electronics 
  • Intelligent Electrical Systems 
  • Information Fusion 
  • Intelligent Control 
  • Intelligence sensors 
  • Smart sensors 
  • Imaging sensors
  • Sensor application 
  • Others
  • Intelligent Manufacturing Systems
  • Intelligent Medical Systems
  • Intelligent Systems
  • Intelligent Transportation Systems
  • Machine Learning 
  • Mathematical Models 
  • Neural Networks
  • Neuro-Fuzzy Systems 
  • Robotics 
  • Social Systems
  • Web Intelligence and Interaction

Published Papers (5 papers)

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Research

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Open AccessArticle
Around View Monitoring-Based Vacant Parking Space Detection and Analysis
Appl. Sci. 2019, 9(16), 3403; https://doi.org/10.3390/app9163403 - 19 Aug 2019
Abstract
Accelerated urbanization and the ensuing rapid increase in urban populations led to the need for a tremendous number of parking spaces. Automated parking systems coupled with new parking lot layouts can effectively address the need. However, most automated parking systems available on the [...] Read more.
Accelerated urbanization and the ensuing rapid increase in urban populations led to the need for a tremendous number of parking spaces. Automated parking systems coupled with new parking lot layouts can effectively address the need. However, most automated parking systems available on the market today use ultrasonic sensors to detect vacant parking spaces. One limitation of this method is that a reference vehicle must be parked in an adjacent space, and the accuracy of distance information is highly dependent on the positioning of the reference vehicle. To overcome this limitation, an around view monitoring-based method for detecting parking spaces and algorithms analyzing the vacancy of the space are proposed in this study. The framework of the algorithm comprises two main stages: parking space detection and space occupancy classification. In addition, a highly robust analysis method is proposed to classify parking space occupancy. Two angles of view were used to detect features, classified as road or obstacle features, within the parking space. Road features were used to provide information regarding the possible vacancy of a parking space, and obstacle features were used to provide information regarding the possible occupancy of a parking space. Finally, these two types of information were integrated to determine whether a specific parking space is occupied. The experimental settings in this study consisted of three common settings: an indoor parking lot, an outdoor parking lot, and roadside parking spaces. The final tests showed that the method’s detection rate was lower in indoor settings than outdoor settings because lighting problems are severer in indoor settings than outdoor settings in around view monitoring (AVM) systems. However, the method achieved favorable detection performance overall. Furthermore, we tested and compared performance based on road features, obstacle features, and a combination of both. The results showed that integrating both types of features produced the lowest rate of classification error. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Open AccessArticle
Sensorless Air Flow Control in an HVAC System through Deep Learning
Appl. Sci. 2019, 9(16), 3293; https://doi.org/10.3390/app9163293 - 11 Aug 2019
Abstract
Sensor-based intelligence is essential in future smart buildings, but the benefits of increasing the number of sensors come at a cost. First, purchasing the sensors themselves can incur non-negligible costs. Second, since the sensors need to be physically connected and integrated into the [...] Read more.
Sensor-based intelligence is essential in future smart buildings, but the benefits of increasing the number of sensors come at a cost. First, purchasing the sensors themselves can incur non-negligible costs. Second, since the sensors need to be physically connected and integrated into the heating, ventilation, and air conditioning (HVAC) system, the complexity and the operating cost of the system are increased. Third, sensors require maintenance at additional costs. Therefore, we need to pursue the appropriate technology (AT) in terms of the number of sensors used. In the ideal scenario, we can remove excessive sensors and yet achieve the intelligence that is required to operate the HVAC system. In this paper, we propose a method to replace the static pressure sensor that is essential for the operation of the HVAC system through the deep neural network (DNN). Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Open AccessArticle
TIR-MS: Thermal Infrared Mean-Shift for Robust Pedestrian Head Tracking in Dynamic Target and Background Variations
Appl. Sci. 2019, 9(15), 3015; https://doi.org/10.3390/app9153015 - 26 Jul 2019
Abstract
Thermal infrared (TIR) pedestrian tracking is one of the major issues in computer vision. Mean-shift is a powerful and versatile non-parametric iterative algorithm for finding local maxima in probability distributions. In existing infrared data, and mean-shift-based tracking is generally based on the brightness [...] Read more.
Thermal infrared (TIR) pedestrian tracking is one of the major issues in computer vision. Mean-shift is a powerful and versatile non-parametric iterative algorithm for finding local maxima in probability distributions. In existing infrared data, and mean-shift-based tracking is generally based on the brightness feature values. Unfortunately, the brightness is distorted by the target and background variations. This paper proposes a novel pedestrian tracking algorithm, thermal infrared mean-shift (TIR-MS), by introducing radiometric temperature data in mean-shift tracking. The thermal brightness image (eight-bits) was distorted by the automatic contrast enhancement of the scene such as hot objects in the background. On the other hand, the temperature data was unaffected directly by the background change, except for variations by the seasonal effect, which is more stable than the brightness. The experimental results showed that the TIR-MS outperformed the original mean-shift-based brightness when tracking a pedestrian head with successive background variations. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Open AccessArticle
Vessel Trajectory Prediction Model Based on AIS Sensor Data and Adaptive Chaos Differential Evolution Support Vector Regression (ACDE-SVR)
Appl. Sci. 2019, 9(15), 2983; https://doi.org/10.3390/app9152983 - 25 Jul 2019
Abstract
There are difficulties in obtaining accurate modeling of ship trajectories with traditional prediction methods. For example, neural networks are prone to falling into local optima and there are a small number of Automatic Identification System (AIS) information samples regarding target ships acquired in [...] Read more.
There are difficulties in obtaining accurate modeling of ship trajectories with traditional prediction methods. For example, neural networks are prone to falling into local optima and there are a small number of Automatic Identification System (AIS) information samples regarding target ships acquired in real time at sea. In order to improve the accuracy of ship trajectory predictions and solve these problems, a trajectory prediction model based on support vector regression (SVR) is proposed. Ship speed, course, time stamp, longitude and latitude from AIS data were selected as sample features and the wavelet threshold de-noising method was used to process the ship position data. The adaptive chaos differential evolution (ACDE) algorithm was used to optimize the internal model parameters to improve convergence speed and prediction accuracy. AIS sensor data corresponding to a certain section of the Tianjin Port ships were selected, on which SVR, Recurrent Neural Network (RNN) and Back Propagation (BP) neural network model trajectory prediction simulations were carried out. A comparison of the results shows that the trajectory prediction model based on ACDE-SVR has higher and more stable prediction accuracy, requires less time and is simple, feasible and efficient. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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Review

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Open AccessReview
Information Fusion for Multi-Source Material Data: Progress and Challenges
Appl. Sci. 2019, 9(17), 3473; https://doi.org/10.3390/app9173473 - 22 Aug 2019
Abstract
The development of material science in the manufacturing industry has resulted in a huge amount of material data, which are often from different sources and vary in data format and semantics. The integration and fusion of material data can offer a unified framework [...] Read more.
The development of material science in the manufacturing industry has resulted in a huge amount of material data, which are often from different sources and vary in data format and semantics. The integration and fusion of material data can offer a unified framework for material data representation, processing, storage and mining, which can further help to accomplish many tasks, including material data disambiguation, material feature extraction, material-manufacturing parameters setting, and material knowledge extraction. On the other side, the rapid advance of information technologies like artificial intelligence and big data, brings new opportunities for material data fusion. To the best of our knowledge, the community is currently lacking a comprehensive review of the state-of-the-art techniques on material data fusion. This review first analyzes the special properties of material data and discusses the motivations of multi-source material data fusion. Then, we particularly focus on the recent achievements of multi-source material data fusion. This review has a few unique features compared to previous studies. First, we present a systematic categorization and comparison framework for material data fusion according to the processing flow of material data. Second, we discuss the applications and impact of recent hot technologies in material data fusion, including artificial intelligence algorithms and big data technologies. Finally, we present some open problems and future research directions for multi-source material data fusion. Full article
(This article belongs to the Special Issue Intelligence Systems and Sensors)
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