Topical Collection "Intelligence Systems and Sensors II"

Editor

Prof. Dr. Youngchul Bae
E-Mail Website
Collection Editor
Department of Electrical and Semiconductor Engineering, Chonnam National University, Daehak-ro 50 Yeosu, Jeonnam 59626, Korea
Interests: intelligent system; fuzzy and neural network; laser range finder; laser interferometer; localization; IoT sensors; chaos synchronization and control; robot control
Special Issues and Collections in MDPI journals

Topical Collection Information

Dear Colleagues,

I would like to cordially invite you to contribute a paper to a Special Issue of the open access journal Applied Sciences, entitled “Intelligent Systems and Sensors”, which aims to present recent developments in fuzzy, neural networks and artificial intelligence in numerous fields of real-life importance, including robots, social systems, and industries, to name but a few.

This Special Issue will focus on fuzzy, neural networks; neuro-fuzzy systems; and intelligent systems and sensors based on artificial intelligence. I invite you to submit your research on these topics as either original research papers or articles.

Prof. Dr. Youngchul Bae
Collection 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 collection 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 2000 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
  • 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
  • Others

Published Papers (7 papers)

2021

Jump to: 2020

Article
Deep Learning for Laryngopharyngeal Reflux Diagnosis
Appl. Sci. 2021, 11(11), 4753; https://doi.org/10.3390/app11114753 - 21 May 2021
Viewed by 433
Abstract
(1) Background: Deep learning has become ubiquitous due to its impressive performance in various domains, such as varied as computer vision, natural language and speech processing, and game-playing. In this work, we investigated the performance of recent deep learning approaches on the laryngopharyngeal [...] Read more.
(1) Background: Deep learning has become ubiquitous due to its impressive performance in various domains, such as varied as computer vision, natural language and speech processing, and game-playing. In this work, we investigated the performance of recent deep learning approaches on the laryngopharyngeal reflux (LPR) diagnosis task. (2) Methods: Our dataset is composed of 114 subjects with 37 pH-positive cases and 77 control cases. In contrast to prior work based on either reflux finding score (RFS) or pH monitoring, we directly take laryngoscope images as inputs to neural networks, as laryngoscopy is the most common and simple diagnostic method. The diagnosis task is formulated as a binary classification problem. We first tested a powerful backbone network that incorporates residual modules, attention mechanism and data augmentation. Furthermore, recent methods in transfer learning and few-shot learning were investigated. (3) Results: On our dataset, the performance is the best test classification accuracy is 73.4%, while the best AUC value is 76.2%. (4) Conclusions: This study demonstrates that deep learning techniques can be applied to classify LPR images automatically. Although the number of pH-positive images used for training is limited, deep network can still be capable of learning discriminant features with the advantage of technique. Full article
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Article
A Low-Cost System for Measuring Wind Speed and Direction Using Thermopile Array and Artificial Neural Network
Appl. Sci. 2021, 11(9), 4024; https://doi.org/10.3390/app11094024 - 28 Apr 2021
Viewed by 377
Abstract
Recent developments in wind speed sensors have mainly focused on reducing the size and moving parts to increase reliability and stability. In this study, the development of a low-cost wind speed and direction measurement system is presented. A heat sink mounted on a [...] Read more.
Recent developments in wind speed sensors have mainly focused on reducing the size and moving parts to increase reliability and stability. In this study, the development of a low-cost wind speed and direction measurement system is presented. A heat sink mounted on a self-regulating heater is used as means to interact with the wind changes and a thermopile array mounted atop of the heat sink is used to collect temperature data. The temperature data collected from the thermopile array are used to estimate corresponding wind speed and direction data using an artificial neural network. The multilayer artificial neural network is trained using 96 h data and tested on 72 h data collected in an outdoor setting. The performance of the proposed model is compared with linear regression and support vector machine. The test results verify that the proposed system can estimate wind speed and direction measurements with a high accuracy at different sampling intervals, and the artificial neural network can provide significantly a higher coefficient of determination than two other methods. Full article
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Article
Estimating Recycling Return of Integrated Circuits Using Computer Vision on Printed Circuit Boards
Appl. Sci. 2021, 11(6), 2808; https://doi.org/10.3390/app11062808 - 22 Mar 2021
Cited by 2 | Viewed by 604
Abstract
The technological growth of the last decades has brought many improvements in daily life, but also concerns on how to deal with electronic waste. Electrical and electronic equipment waste is the fastest-growing rate in the industrialized world. One of the elements of electronic [...] Read more.
The technological growth of the last decades has brought many improvements in daily life, but also concerns on how to deal with electronic waste. Electrical and electronic equipment waste is the fastest-growing rate in the industrialized world. One of the elements of electronic equipment is the printed circuit board (PCB) and almost every electronic equipment has a PCB inside it. While waste PCB (WPCB) recycling may result in the recovery of potentially precious materials and the reuse of some components, it is a challenging task because its composition diversity requires a cautious pre-processing stage to achieve optimal recycling outcomes. Our research focused on proposing a method to evaluate the economic feasibility of recycling integrated circuits (ICs) from WPCB. The proposed method can help decide whether to dismantle a separate WPCB before the physical or mechanical recycling process and consists of estimating the IC area from a WPCB, calculating the IC’s weight using surface density, and estimating how much metal can be recovered by recycling those ICs. To estimate the IC area in a WPCB, we used a state-of-the-art object detection deep learning model (YOLO) and the PCB DSLR image dataset to detect the WPCB’s ICs. Regarding IC detection, the best result was obtained with the partitioned analysis of each image through a sliding window, thus creating new images of smaller dimensions, reaching 86.77% mAP. As a final result, we estimate that the Deep PCB Dataset has a total of 1079.18 g of ICs, from which it would be possible to recover at least 909.94 g of metals and silicon elements from all WPCBs’ ICs. Since there is a high variability in the compositions of WPCBs, it is possible to calculate the gross income for each WPCB and use it as a decision criterion for the type of pre-processing. Full article
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Article
Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks
Appl. Sci. 2021, 11(4), 1573; https://doi.org/10.3390/app11041573 - 09 Feb 2021
Viewed by 508
Abstract
Arabic and Hindi handwritten numeral detection and classification is one of the most popular fields in the automation research. It has many applications in different fields. Automatic detection and automatic classification of handwritten numerals have persistently received attention from researchers around the world [...] Read more.
Arabic and Hindi handwritten numeral detection and classification is one of the most popular fields in the automation research. It has many applications in different fields. Automatic detection and automatic classification of handwritten numerals have persistently received attention from researchers around the world due to the robotic revolution in the past decades. Therefore, many great efforts and contributions have been made to provide highly accurate detection and classification methodologies with high performance. In this paper, we propose a two-stage methodology for the detection and classification of Arabic and Hindi handwritten numerals. The classification was based on convolutional neural networks (CNNs). The first stage of the methodology is the detection of the input numeral to be either Arabic or Hindi. The second stage is to detect the input numeral according to the language it came from. The simulation results show very high performance; the recognition rate was close to 100%. Full article
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Article
Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture
Appl. Sci. 2021, 11(3), 916; https://doi.org/10.3390/app11030916 - 20 Jan 2021
Cited by 3 | Viewed by 335
Abstract
Current intelligent information systems require complex database approaches managing and monitoring data in a spatio-temporal manner. Many times, the core of the temporal system element is created on the relational platform. In this paper, a summary of the temporal architectures with regards to [...] Read more.
Current intelligent information systems require complex database approaches managing and monitoring data in a spatio-temporal manner. Many times, the core of the temporal system element is created on the relational platform. In this paper, a summary of the temporal architectures with regards to the granularity level is proposed. Object, attribute, and synchronization group perspectives are discussed. An extension of the group temporal architecture shifting the processing in the spatio-temporal level synchronization is proposed. A data reflection model is proposed to cover the transaction integrity with reflection to the data model evolving over time. It is supervised by our own Extended Temporal Log Ahead Rule, evaluating not only collisions themselves, but the data model is reflected, as well. The main emphasis is on the data retrieval process and indexing with regards to the non-reliable data. Undefined value categorization supervised by the NULL_representation data dictionary object and memory pointer layer is provided. Therefore, undefined (NULL) values can be part of the index structure. The definition and selection of the technology of the master index is proposed and discussed. It allows the index to be used as a way to identify blocks with relevant data, which is of practical importance in temporal systems where data fragmentation often occurs. The last part deals with the syntax of the Select statement extension covering temporal environment with regards on the conventional syntax reflection. Event_definition, spatial_positions, model_reflection, consistency_model, epsilon_definition, monitored_data_set, type_of_granularity, and NULL_category clauses are introduced. Impact on the performance of the data manipulation operations is evaluated in the performance section highlighting temporal architectures, Insert, Update and Select statements forming core performance characteristics. Full article
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2020

Jump to: 2021

Article
Application of the Fuzzy System for an Emotional Pattern Generator
Appl. Sci. 2020, 10(19), 6930; https://doi.org/10.3390/app10196930 - 03 Oct 2020
Viewed by 774
Abstract
Nowadays, the end-user’s emotional engagement on commercial products has become more and more highlighted. Product developers’ and designers’ jobs are beyond the aesthetic face to new requirements that improve the user experience. One of the most important user demands is customization. Based on [...] Read more.
Nowadays, the end-user’s emotional engagement on commercial products has become more and more highlighted. Product developers’ and designers’ jobs are beyond the aesthetic face to new requirements that improve the user experience. One of the most important user demands is customization. Based on the new manufacturing technologies, there are some new opportunities to customize mass production output. Many automotive companies offer a wide variety of car components produced by request on the customers. The customization establishes the emotional engagement, but specific psychological background required to achieve it. This article deals with the scientific background of users’ emotions and geometric patterns (as an important feature of product customization). The related research covers the algorithmic generation of these patterns and soft computing control on this procedure. The method was developed based on fuzzy logic, which integrates the psychological aspects in the mathematical method. Full article
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Article
Application of Machine Learning in the Control of Metal Melting Production Process
Appl. Sci. 2020, 10(17), 6048; https://doi.org/10.3390/app10176048 - 01 Sep 2020
Cited by 1 | Viewed by 830
Abstract
This paper presents the application of machine learning in the control of the metal melting process. Metal melting is a dynamic production process characterized by nonlinear relations between process parameters. In this particular case, the subject of research is the production of white [...] Read more.
This paper presents the application of machine learning in the control of the metal melting process. Metal melting is a dynamic production process characterized by nonlinear relations between process parameters. In this particular case, the subject of research is the production of white cast iron. Two supervised machine learning algorithms have been applied: the neural network and the support vector regression. The goal of their application is the prediction of the amount of alloying additives in order to obtain the desired chemical composition of white cast iron. The neural network model provided better results than the support vector regression model in the training and testing phases, which qualifies it to be used in the control of the white cast iron production. Full article
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