Special Issue "Smart Processing for Systems under Uncertainty or Perturbation"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 9 October 2020.

Special Issue Editors

Prof. Dr. Sanghyuk Lee
Website
Guest Editor
Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, 215123 China
Interests: control theory; data analysis; fuzzy set theory; robust controller design; energy optimization
Prof. Dr. Mihail Popescu
Website
Guest Editor
University of Missouri, Columbia, MO 65211, USA
Interests: computational intelligence; fuzzy logic; image processing; sensor informatics; eldercare technology
Dr. Eneko Osaba
Website1 Website2
Guest Editor
Tecnalia Research & Innovation, 48160 Derio, Spain
Interests: bioinspired optimization; combinatorial optimization; artificial intelligence; metaheuristics; swarm intelligence
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of artificial intelligence and learning algorithms, data processing and decision procedures have become possible for complex systems, large scale systems, and high-dimensional systems. Such achievements are supported by the development of high specification hardware. However, we are faced with output reliability issues due to variations in external inputs such as disturbances or uncertainty, because computation and processing relies on straight through processing. In this regard, we are focusing on research into robust performance with respect to external inputs from smart devices such as smart sensors or smart actuators. Hence, we are emphasizing aspects that give rise to effective and efficient solutions involving control algorithms for the implementation of smart devices. Generally, finding an optimal or suboptimal solution for a complex process with internal or external variation is challenging. As feasible methodologies, analytical approaches or metaheuristic algorithms are considered, and these include bio-inspired algorithms, artificial intelligence, and machine learning.

In this regard, we hope this Special Issue provides the opportunity to share our research ideas related to data networking and process optimization for smart systems. In this open forum, we are more than happy to receive your submissions and share research ideas relating to algorithms, optimization, and hardware implementations for complex system reliability.

Potential topics include, but are not limited to:

  • Smart device applications in process control to improve performance;
  • Smart device development and application to large-scale systems under disturbance;
  • Intelligent algorithm design and application to industrial processes;
    • Applications of deep learning to large-scale systems for efficiency;
  • Optimization algorithm development to large-scale system with uncertainty;
    • Energy internet with efficient network communication;
    • Data communication efficiency in large-scale systems;
    • Nature-inspired algorithm application to smart grids;
  • Other related fields.

Prof. Dr. Sanghyuk Lee
Prof. Dr. Mihail Popescu
Dr. Eneko Osaba
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. Electronics is an international peer-reviewed open access monthly 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

  • large-scale system
  • smart device
  • optimization
  • efficiency processing
  • smart grid
  • process control
  • disturbance and uncertainty
  • nature-inspired computation

Published Papers (6 papers)

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Research

Open AccessArticle
Outdoor Particulate Matter Correlation Analysis and Prediction Based Deep Learning in the Korea
Electronics 2020, 9(7), 1146; https://doi.org/10.3390/electronics9071146 - 15 Jul 2020
Abstract
Particulate matter (PM) has become a problem worldwide, with many deleterious health effects such as worsened asthma, affected lungs, and various toxin-induced cancers. The International Agency for Research on Cancer (IARC) under the World Health Organization (WHO) has designated PM as a group [...] Read more.
Particulate matter (PM) has become a problem worldwide, with many deleterious health effects such as worsened asthma, affected lungs, and various toxin-induced cancers. The International Agency for Research on Cancer (IARC) under the World Health Organization (WHO) has designated PM as a group 1 carcinogen. Although Korea Environment Corporation forecasts the status of outdoor PM four times a day, whichever is higher among PM10 and PM2.5. Korea Environment Corporation forecasts for the stages of PM. It remains difficult to predict the value of PM when going out. We correlate air quality and solar terms, address format, and weather data, and PM in the Korea. We analyzed the correlation between address format, air quality data, and weather data, and PM. We evaluated performance according to the sequence length and batch size and found the best outcome with a sequence length of 7 days, and a batch size of 96. We performed PM prediction using the Long Short-Term Recurrent Unit (LSTM), the Convolutional Neural Network (CNN), and the Gated Recurrent Unit (GRU) models. The CNN model suffered the limitation of only predicting from the training data, not from the test data. The LSTM and GRU models generated similar prediction results. We confirmed that the LSTM model has higher accuracy than the other two models. Full article
(This article belongs to the Special Issue Smart Processing for Systems under Uncertainty or Perturbation)
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Open AccessArticle
EEG Self-Adjusting Data Analysis Based on Optimized Sampling for Robot Control
Electronics 2020, 9(6), 925; https://doi.org/10.3390/electronics9060925 - 02 Jun 2020
Abstract
Research on electroencephalography (EEG) signals and their data analysis have drawn much attention in recent years. Data mining techniques have been extensively applied as efficient solutions for non-invasive brain–computer interface (BCI) research. Previous research has indicated that human brains produce recognizable EEG signals [...] Read more.
Research on electroencephalography (EEG) signals and their data analysis have drawn much attention in recent years. Data mining techniques have been extensively applied as efficient solutions for non-invasive brain–computer interface (BCI) research. Previous research has indicated that human brains produce recognizable EEG signals associated with specific activities. This paper proposes an optimized data sampling model to identify the status of the human brain and further discover brain activity patterns. The sampling methods used in the proposed model include the segmented EEG graph using piecewise linear approximation (SEGPA) method, which incorporates optimized data sampling methods; and the EEG-based weighted network for EEG data analysis, which can be used for machinery control. The data sampling and segmentation techniques combine normal distribution approximation (NDA), Poisson distribution approximation (PDA), and related sampling methods. This research also proposes an efficient method for recognizing human thinking and brain signals with entropy-based frequent patterns (FPs). The obtained recognition system provides a foundation that could to be useful in machinery or robot control. The experimental results indicate that the NDA–PDA segments with less than 10% of the original data size can achieve 98% accuracy, as compared with original data sets. The FP method identifies more than 12 common patterns for EEG data analysis based on the optimized sampling methods. Full article
(This article belongs to the Special Issue Smart Processing for Systems under Uncertainty or Perturbation)
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Open AccessArticle
A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments
Electronics 2020, 9(4), 686; https://doi.org/10.3390/electronics9040686 - 23 Apr 2020
Abstract
As current data centers and servers are growing in size by orders of magnitude when needed, load balancing is a great concern in scalable computing systems, including mobile edge cloud computing environments. In mobile edge cloud computing systems, a mobile user can offload [...] Read more.
As current data centers and servers are growing in size by orders of magnitude when needed, load balancing is a great concern in scalable computing systems, including mobile edge cloud computing environments. In mobile edge cloud computing systems, a mobile user can offload its tasks to nearby edge servers to support real-time applications. However, when users are located in a hot spot, several edge servers can be overloaded due to suddenly offloaded tasks from mobile users. In this paper, we present a load balancing algorithm for mobile devices in edge cloud computing environments. The proposed load balancing technique features an efficient complexity by a graph coloring-based implementation based on a genetic algorithm. The aim of the proposed load balancing algorithm is to distribute offloaded tasks to nearby edge servers in an efficient way. Performance results show that the proposed load balancing algorithm outperforms previous techniques and increases the average CPU usage of virtual machines, which indicates a high utilization of edge servers. Full article
(This article belongs to the Special Issue Smart Processing for Systems under Uncertainty or Perturbation)
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Open AccessArticle
Development of Curriculum Design Support System Based on Word Embedding and Terminology Extraction
Electronics 2020, 9(4), 608; https://doi.org/10.3390/electronics9040608 - 03 Apr 2020
Abstract
The principles of computer skills have been included in primary and secondary educated since the early 2000s, and the reform of curricula is related to the development of IT. Therefore, curricula should reflect the latest technological trends and needs of society. The development [...] Read more.
The principles of computer skills have been included in primary and secondary educated since the early 2000s, and the reform of curricula is related to the development of IT. Therefore, curricula should reflect the latest technological trends and needs of society. The development of a curriculum involves the subjective judgment of a few experts or professors to extract knowledge from several similar documents. More objective extraction needs to be based on standardized terminology, and professional terminology can help build content frames for organizing curricula. The purpose of this study is to develop a smart system for extracting terms from the body of computer science (CS) knowledge and organizing knowledge areas. The extracted terms are composed of semantically similar knowledge areas, using the word2vec model. We analyzed a higher-education CS standards document and compiled a dictionary of technical terms with a hierarchical clustering structure. Based on the developed terminology dictionary, a specialized system is proposed to enhance the efficiency and objectivity of terminology extraction. The analysis of high school education courses in India and Israel using the technical term extraction system found that (1) technical terms for Software Development Fundamentals were extracted at a high rate in entry-level courses, (2) in advanced courses, the ratio of technical terms in the areas of Architecture and Organization, Programming Languages, and Software Engineering areas was high, and (3) electives that deal with advanced content had a high percentage of technical terms related to information systems. Full article
(This article belongs to the Special Issue Smart Processing for Systems under Uncertainty or Perturbation)
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Open AccessArticle
Scalable Algorithms for Maximizing Spatiotemporal Range Sum and Range Sum Change in Spatiotemporal Datasets
Electronics 2020, 9(3), 514; https://doi.org/10.3390/electronics9030514 - 20 Mar 2020
Abstract
In this paper, we introduce the three-dimensional Maximum Range-Sum (3D MaxRS) problem and the Maximum Spatiotemporal Range-Sum Change (MaxStRSC) problem. The 3D MaxRS problem tries to find the 3D range where the sum of weights across all objects inside is maximized, and the [...] Read more.
In this paper, we introduce the three-dimensional Maximum Range-Sum (3D MaxRS) problem and the Maximum Spatiotemporal Range-Sum Change (MaxStRSC) problem. The 3D MaxRS problem tries to find the 3D range where the sum of weights across all objects inside is maximized, and the MaxStRSC problem tries to find the spatiotemporal range where the sum of weights across all objects inside is maximally increased. The goal of this paper is to provide efficient methods for data analysts to find interesting spatiotemporal regions in a large historical spatiotemporal dataset by addressing two problems. We provide a mathematical explanation for each problem and propose several algorithms for them. Existing methods tried to find the optimal region over two-dimensional datasets or to monitor a burst region over two-dimensional data streams. The majority of them cannot directly solve our problems. Although some existing methods can be used or modified to solve the 3D MaxRS problems, they have limited scalability. In addition, none of them can be used to solve the MaxStRS-RC problem (a type of MaxStRSC problem). Finally, we study the performance of the proposed algorithms experimentally. The experimental results show that the proposed algorithms are scalable and much more efficient than existing methods. Full article
(This article belongs to the Special Issue Smart Processing for Systems under Uncertainty or Perturbation)
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Open AccessArticle
Development of Fashion Product Retrieval and Recommendations Model Based on Deep Learning
Electronics 2020, 9(3), 508; https://doi.org/10.3390/electronics9030508 - 19 Mar 2020
Abstract
The digitization of the fashion industry diversified consumer segments, and consumers now have broader choices with shorter production cycles; digital technology in the fashion industry is attracting the attention of consumers. Therefore, a system that efficiently supports the searching and recommendation of a [...] Read more.
The digitization of the fashion industry diversified consumer segments, and consumers now have broader choices with shorter production cycles; digital technology in the fashion industry is attracting the attention of consumers. Therefore, a system that efficiently supports the searching and recommendation of a product is becoming increasingly important. However, the text-based search method has limitations because of the nature of the fashion industry, in which design is a very important factor. Therefore, we developed an intelligent fashion technique based on deep learning for efficient fashion product searches and recommendations consisting of a Sketch-Product fashion retrieval model and vector-based user preference fashion recommendation model. It was found that the “Precision at 5” of the image-based similar product retrieval model was 0.774 and that of the sketch-based similar product retrieval model was 0.445. The vector-based preference fashion recommendation model also showed positive performance. This system is expected to enhance consumers’ satisfaction by supporting users in more effectively searching for fashion products or by recommending fashion products before they begin a search. Full article
(This article belongs to the Special Issue Smart Processing for Systems under Uncertainty or Perturbation)
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