Special Issue "Soft Computing in Applied Sciences and Industrial Applications"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (15 January 2021).

Special Issue Editors

Prof. Dr. Wei-Chang Yeh
E-Mail Website
Guest Editor
Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan
Interests: network reliability; SSO and soft computing; data mining; algorithm
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yonghui Lee
E-Mail Website
Guest Editor
School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW 2006, Australia
Interests: millimeter-wave wireless communications; machine-to-machine communications; cooperative communications; coding techniques; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chun-Cheng Lin
E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University (NYCU), Hsinchu, Taiwan
Interests: metaheuristic algorithms; machine learning; Internet of Things; wireless networks; information visualization; computational management science
Special Issues, Collections and Topics in MDPI journals
Dr. Omprakash Kaiwartya
E-Mail Website
Guest Editor
School of Science and Technology, Nottingham Trent University, Clifton Campus, Nottingham NG11 8NS, UK
Interests: internet of vehicles; electric vehicles; IoT use case of sensor networks
Special Issues, Collections and Topics in MDPI journals
Prof. Winncy Du
E-Mail Website
Guest Editor
Mechanical Engineering Department, San Jose State University, San Jose, CA, USA
Interests: sensors; robotics; mechatronics; automation; control

Special Issue Information

Dear Colleagues,

Soft computing, as a critical part of the domain of artificial intelligence, has increasingly become an important modern computational intelligence in artificial intelligence and applied science. The essential concept of soft computing is that the emergent collective intelligence of groups of simple agents possesses a powerful global search capability, and has been shown to be able to find the optimal solution within a rational time by numerous fields of studies using the soft computing concerned algorithms such as genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC) algorithms, and simplified swarm optimization (SSO). Therefore, soft computing has increasingly attracted numerous scholars, researchers, and scientists to engage in the research field related to soft computing, and in the application of resembling and simulating natural phenomena in soft computing used to solve larger problems in science and technology. Despite a significant amount of research on soft computing, many open issues and intriguing challenges remain in the field. This Special Collection will provide an excellent opportunity to present the latest scientific results and methods on the collaboration of Soft Computing in Applied Sciences and Industrial Applications, to discuss and exchange the latest advances in soft computing, and to explore the future directions in soft computing.

This Special Collection aims to provide an international open-access forum for the development, research, demonstration, and analysis of innovative knowledge and information related to all topics in soft computing. We welcome high-quality theoretical, conceptual, and empirical original research from all over the world.

Potential topics for submissions include, but are not limited to, the following:

  • Soft computing
  • Soft computing in applied sciences
  • Soft computing in industrial applications
  • Soft computing in reliability analytics
  • Soft computing in decision analysis
  • Soft computing for solving intelligent systems
  • Soft computing applied in safety, security, and risk management
  • Innovative research in soft computing
  • Soft computing in intelligent transportation systems
  • Soft computing in cloud-based manufacturing
  • Soft computing in sustainable manufacturing
  • Others

Prof. Wei-Chang Yeh
Prof. Dr. Yonghui Lee
Prof. Chun-Cheng Lin
Dr. Omprakash Kaiwartya
Prof. Winncy Du
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 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 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

  • soft computing
  • applied sciences
  • industrial applications

Published Papers (7 papers)

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Research

Article
Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning
Appl. Sci. 2021, 11(4), 1965; https://doi.org/10.3390/app11041965 - 23 Feb 2021
Cited by 4 | Viewed by 836
Abstract
Despite the promising results obtained by deep learning methods in the field of medical image segmentation, lack of sufficient data always hinders performance to a certain degree. In this work, we explore the feasibility of applying deep learning methods on a pilot dataset. [...] Read more.
Despite the promising results obtained by deep learning methods in the field of medical image segmentation, lack of sufficient data always hinders performance to a certain degree. In this work, we explore the feasibility of applying deep learning methods on a pilot dataset. We present a simple and practical approach to perform segmentation in a 2D, slice-by-slice manner, based on region of interest (ROI) localization, applying an optimized training regime to improve segmentation performance from regions of interest. We start from two popular segmentation networks, the preferred model for medical segmentation, U-Net, and a general-purpose model, DeepLabV3+. Furthermore, we show that ensembling of these two fundamentally different architectures brings constant benefits by testing our approach on two different datasets, the publicly available ACDC challenge, and the imATFIB dataset from our in-house conducted clinical study. Results on the imATFIB dataset show that the proposed approach performs well with the provided training volumes, achieving an average Dice Similarity Coefficient of the whole heart of 89.89% on the validation set. Moreover, our algorithm achieved a mean Dice value of 91.87% on the ACDC validation, being comparable to the second best-performing approach on the challenge. Our approach provides an opportunity to serve as a building block of a computer-aided diagnostic system in a clinical setting. Full article
(This article belongs to the Special Issue Soft Computing in Applied Sciences and Industrial Applications)
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Article
Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks
Appl. Sci. 2020, 10(23), 8644; https://doi.org/10.3390/app10238644 - 03 Dec 2020
Cited by 2 | Viewed by 706
Abstract
With the emergence of problems on environmental pollutions, lithium batteries have attracted considerable attention as an efficient and nature-friendly alternative energy storage device owing to their advantages, such as high power density, low self-discharge rate, and long life cycle. They are widely used [...] Read more.
With the emergence of problems on environmental pollutions, lithium batteries have attracted considerable attention as an efficient and nature-friendly alternative energy storage device owing to their advantages, such as high power density, low self-discharge rate, and long life cycle. They are widely used in numerous applications, from everyday items, such as smartphones, wireless vacuum cleaners, and wireless power tools, to transportation means, such as electric vehicles and bicycles. In this paper, the state of charge (SOC) of each cell of the lithium battery pack was estimated in real time using two types of neural networks: Multi-layer Neural Network (MNN) and Long Short-Term Memory (LSTM). To determine the difference in the SOC estimation performance under various conditions, the input values were compared using 2, 6, and 8 input values, and the difference according to the use of temperature variable data was compared, and finally, the MNN and LSTM. The differences were compared. Real-time SOC was estimated using the method with the lowest error rate. Full article
(This article belongs to the Special Issue Soft Computing in Applied Sciences and Industrial Applications)
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Article
Using Simplified Swarm Optimization on Multiloop Fuzzy PID Controller Tuning Design for Flow and Temperature Control System
Appl. Sci. 2020, 10(23), 8472; https://doi.org/10.3390/app10238472 - 27 Nov 2020
Viewed by 723
Abstract
This study proposes the flow and temperature controllers of a cockpit environment control system (ECS) by implementing an optimal simplified swarm optimization (SSO) fuzzy proportional-integral-derivative (PID) control. The ECS model is considered as a multiple-input multiple-output (MIMO) and second-order dynamic system, which is [...] Read more.
This study proposes the flow and temperature controllers of a cockpit environment control system (ECS) by implementing an optimal simplified swarm optimization (SSO) fuzzy proportional-integral-derivative (PID) control. The ECS model is considered as a multiple-input multiple-output (MIMO) and second-order dynamic system, which is interactive. In this work, we use five methods to design and compare the PID controllers in MATLAB and Simulink, including Ziegler–Nicolas PID tuning, particle swarm optimization (PSO) PID, SSO PID, and the combination of the fuzzy theory with PSO PID and SSO PID, respectively. The main contribution of this study is the pioneering implementation of SSO in a fuzzy PI/PID controller. Moreover, by adding the original gain parameters Kp, Ki, and Kd in the PID controller with delta values, which are calculated by fuzzy logic designer, we can tune the parameters of PID controllers in real time. This makes our control system more accurate, adaptive, and robust. Full article
(This article belongs to the Special Issue Soft Computing in Applied Sciences and Industrial Applications)
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Article
Predicting and Modeling Wildfire Propagation Areas with BAT and Maximum-State PageRank
Appl. Sci. 2020, 10(23), 8349; https://doi.org/10.3390/app10238349 - 24 Nov 2020
Cited by 6 | Viewed by 519
Abstract
The nature and characteristics of free-burning wildland fires have significant economic, safety, and environmental impacts. Additionally, the increase in global warming has led to an increase in the number and severity of wildfires. Hence, there is an increasing need for accurately calculating the [...] Read more.
The nature and characteristics of free-burning wildland fires have significant economic, safety, and environmental impacts. Additionally, the increase in global warming has led to an increase in the number and severity of wildfires. Hence, there is an increasing need for accurately calculating the probability of wildfire propagation in certain areas. In this study, we firstly demonstrate that the landscapes of wildfire propagation can be represented as a scale-free network, where the wildfire is modeled as the scale-free network whose degree follows the power law. By establishing the state-related concepts and modifying the Binary-Addition-Tree (BAT) together with the PageRank, we propose a new methodology to serve as a reliable tool in predicting the probability of wildfire propagation in certain areas. Furthermore, we demonstrate that the proposed maximum-state PageRank used in the methodology can be implemented separately as a fast, simple, and effective tool in identifying the areas that require immediate protection. The proposed methodology and maximum-state PageRank are validated in the example generated from the Barabási-Albert model in the study. Full article
(This article belongs to the Special Issue Soft Computing in Applied Sciences and Industrial Applications)
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Article
Advanced Prediction of Roadway Broken Rock Zone Based on a Novel Hybrid Soft Computing Model Using Gaussian Process and Particle Swarm Optimization
Appl. Sci. 2020, 10(17), 6031; https://doi.org/10.3390/app10176031 - 31 Aug 2020
Cited by 3 | Viewed by 592
Abstract
A simple and accurate evaluation method of broken rock zone thickness (BRZT), which is usually used to describe the broken rock zone (BRZ), is meaningful, due to its ability to provide a reference for the roadway stability evaluation and support design. [...] Read more.
A simple and accurate evaluation method of broken rock zone thickness (BRZT), which is usually used to describe the broken rock zone (BRZ), is meaningful, due to its ability to provide a reference for the roadway stability evaluation and support design. To create a relationship between various geological variables and the broken rock zone thickness (BRZT), the multiple linear regression (MLR), artificial neural network (ANN), Gaussian process (GP) and particle swarm optimization algorithm (PSO)-GP method were utilized, and the corresponding intelligence models were developed based on the database collected from various mines in China. Four variables including embedding depth (ED), drift span (DS), surrounding rock mass strength (RMS) and joint index (JI) were selected to train the intelligence model, while broken rock zone thickness (BRZT) is chosen as the output variable, and the k-fold cross-validation method was applied in the training process. After training, three validation metrics including variance account for (VAF), determination coefficient (R2) and root mean squared error (RMSE) were applied to describe the predictive performance of these developed models. After comparing performance based on a ranking method, the obtained results show that the PSO-GP model provides the best predictive performance in estimating broken rock zone thickness (BRZT). In addition, the sensitive effect of collected variables on broken rock zone thickness (BRZT) can be listed as JI, ED, DS and RMS, and JI was found to be the most sensitive factor. Full article
(This article belongs to the Special Issue Soft Computing in Applied Sciences and Industrial Applications)
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Article
An Effective Method for Minimizing Electric Generation Costs of Thermal Systems with Complex Constraints and Large Scale
Appl. Sci. 2020, 10(10), 3507; https://doi.org/10.3390/app10103507 - 19 May 2020
Cited by 2 | Viewed by 747
Abstract
In this paper, an improved antlion optimization algorithm (IALO) was proposed to search for promising solutions for optimal economic load dispatch (ELD) problems to minimize electrical generation fuel costs in power systems with thermal units and to ensure all constraints are within operating [...] Read more.
In this paper, an improved antlion optimization algorithm (IALO) was proposed to search for promising solutions for optimal economic load dispatch (ELD) problems to minimize electrical generation fuel costs in power systems with thermal units and to ensure all constraints are within operating ranges. IALO can be more effective than the original method, called the antlion optimization algorithm (ALO), because of the high performance of the applied modifications on the new solutions searching process. In order to evaluate the abilities of the IALO method, we completed many tests on thermal generating systems including 10, 15, 20, 30, 60, 80, and 90 units with different constraints and fuel-consuming characteristics. The results suggest that the offered method is superior to the ALO method with more stable search ability, faster convergence velocity, and shorter calculation times. Furthermore, the obtained results of the IALO method are much better than those of almost all the other methods used to solve problems for the same systems. As a result, IALO is suggested to be a highly effective method, and it can be applied to other problems in power systems instead of ALO, which has a lower performance. Full article
(This article belongs to the Special Issue Soft Computing in Applied Sciences and Industrial Applications)
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Article
Reliability Analysis of an Air Traffic Network: From Network Structure to Transport Function
Appl. Sci. 2020, 10(9), 3168; https://doi.org/10.3390/app10093168 - 01 May 2020
Cited by 2 | Viewed by 851
Abstract
To scientifically evaluate the reliability of air traffic networks, a definition of air traffic network reliability is proposed in this paper. Calculation models of the connectivity reliability, travel-time reliability, and capacity reliability of the air traffic network are constructed based on collected historical [...] Read more.
To scientifically evaluate the reliability of air traffic networks, a definition of air traffic network reliability is proposed in this paper. Calculation models of the connectivity reliability, travel-time reliability, and capacity reliability of the air traffic network are constructed based on collected historical data, considering the current status and the predicted future evolution trends. Considering the randomness and fuzziness of factors affecting reliability, a comprehensive evaluation model of air traffic networks based on the uncertainty transformation model is established. Finally, the reliability of the US air traffic network is analyzed based on data published by the Transportation Statistics Bureau of the US Department of Transportation. The results show that the connectivity reliability is 0.4073, the capacity reliability is 0.8300, the travel-time reliability is 0.9180, and the overall reliability evaluated is “relatively reliable”. This indicates that although the US structural reliability is relatively low, the US air traffic management is very efficient, and the overall reliability is strong. The reliability in nonpeak hours is much higher than that in peak hours. The method can identify air traffic network reliability efficiently. The main factors affecting reliability can be found in the calculation process, and are beneficial for air traffic planning and management. The empirical analysis also reflects that the evaluation model based on the uncertainty transformation model can transform the quantitative data of network structure and traffic function into the qualitative language of reliability. Full article
(This article belongs to the Special Issue Soft Computing in Applied Sciences and Industrial Applications)
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