2023

Jump to: 2022, 2021, 2020

25 pages, 6804 KiB

Open AccessArticle

Determination of Crop Soil Quality for Stevia rebaudiana Bertoni Morita II Using a Fuzzy Logic Model and a Wireless Sensor Network

by Angel-Primitivo Vejar-Cortés, Noel García-Díaz, Leonel Soriano-Equigua, Ana-Claudia Ruiz-Tadeo and José-Luis Álvarez-Flores

Appl. Sci. 2023, 13(17), 9507; https://doi.org/10.3390/app13179507 - 22 Aug 2023

Viewed by 1199

Abstract

Stevia rebaudiana Bertoni Morita II, a perennial plant native to Paraguay and Brazil, is also widely cultivated in the state of Colima, Mexico, for its use as a sweetener in food and beverages. The optimization of soil parameters is crucial for maximizing biomass [...] Read more.

Stevia rebaudiana Bertoni Morita II, a perennial plant native to Paraguay and Brazil, is also widely cultivated in the state of Colima, Mexico, for its use as a sweetener in food and beverages. The optimization of soil parameters is crucial for maximizing biomass production and stevioside levels in stevia crops. This research presents the development and implementation of a monitoring system to track essential soil parameters, including pH, temperature, humidity, electrical conductivity, nitrogen, phosphorus, and potassium. The system employs a wireless sensor network to collect quasi-real-time data, which are transmitted and stored in a web-based platform. A Mamdani-type fuzzy logic model is utilized to process the collected data and provide farmers an integrated assessment of soil quality. By comparing the quality data output of the fuzzy logic model with a linear regression model, the system demonstrated acceptable performance, with a determination coefficient of 0.532 for random data and 0.906 for gathered measurements. The system enables farmers to gain insights into the soil quality of their stevia crops and empowers them to take preventive and corrective actions to improve the soil quality specifically for stevia crops. Full article

► Show Figures

Figure 1

2022

Jump to: 2023, 2021, 2020

20 pages, 1174 KiB

Open AccessReview

A Review on Applications of Fuzzy Logic Control for Refrigeration Systems

by Juan Manuel Belman-Flores, David Alejandro Rodríguez-Valderrama, Sergio Ledesma, Juan José García-Pabón, Donato Hernández and Diana Marcela Pardo-Cely

Appl. Sci. 2022, 12(3), 1302; https://doi.org/10.3390/app12031302 - 26 Jan 2022

Cited by 26 | Viewed by 6060

Abstract

The use of fuzzy logic controllers in refrigeration and air conditioning systems, RACs, has as main objective to maintain certain thermal and comfort conditions. In this sense, fuzzy controllers have proven to be a viable option for use in RACs due to their [...] Read more.

The use of fuzzy logic controllers in refrigeration and air conditioning systems, RACs, has as main objective to maintain certain thermal and comfort conditions. In this sense, fuzzy controllers have proven to be a viable option for use in RACs due to their ease of implementation and their ability to integrate with other control systems and control improvements, as well as their ability to achieve potential energy savings. In this document, we present a review of the application of fuzzy controls in RACs based on vapor compression technology. Application information is discussed for each type of controller, according to its application in chillers, air conditioning systems, refrigerators, and heat pumps. In addition, this review provides detailed information on controller design, focusing on the potential to achieve energy savings; this design discusses input and output variables, number and type of membership functions, and inference rules. The future perspectives on the use of fuzzy control systems applied to RACs are shown as well. In other words, the information in this document is intended to serve as a guide for the creation of controller designs to be applied to RACs. Full article

► Show Figures

Figure 1

2021

Jump to: 2023, 2022, 2020

18 pages, 2067 KiB

Open AccessArticle

Fuzzy Logic in Selection of Maritime Search and Rescue Units

by Marzena Malyszko

Appl. Sci. 2022, 12(1), 21; https://doi.org/10.3390/app12010021 - 21 Dec 2021

Cited by 5 | Viewed by 2143

Abstract

The article discusses methods of ships assessment when determining their suitability for search and rescue action (SAR) at sea. Selection of the most preferable ships is one of the action planning elements. Due to various construction and equipment the civilian ships can only [...] Read more.

The article discusses methods of ships assessment when determining their suitability for search and rescue action (SAR) at sea. Selection of the most preferable ships is one of the action planning elements. Due to various construction and equipment the civilian ships can only perform rescue task to a certain degree. According to the Multi-Criteria Decision Analysis (MCDA), many parameters and data have to be compared in order to create a ranking of vessels ordered according to the coordinator’s preferences. When data are missing, incomplete or uncertain, a similar effect can be obtained using fuzzy logic. The author discussed the nature of the criteria, evaluation methods and presented a simulation of a ship study using fuzzy logic. The author developed fuzzy rules and presented the principle of operation of the controller. The article deals with the main principles of a decision support system (DSS) for the selection of ships in SAR operations. Full article

► Show Figures

Figure 1

15 pages, 1042 KiB

Open AccessArticle

The Evaluation of Software Security through Quantum Computing Techniques: A Durability Perspective

by Hashem Alyami, Mohd Nadeem, Abdullah Alharbi, Wael Alosaimi, Md Tarique Jamal Ansari, Dhirendra Pandey, Rajeev Kumar and Raees Ahmad Khan

Appl. Sci. 2021, 11(24), 11784; https://doi.org/10.3390/app112411784 - 11 Dec 2021

Cited by 23 | Viewed by 2518

Abstract

The primary goal of this research study, in the field of information technology (IT), is to improve the security and durability of software. A quantum computing-based security algorithm springs quite a lot of symmetrical approaches and procedures to ensure optimum software retreat. The [...] Read more.

The primary goal of this research study, in the field of information technology (IT), is to improve the security and durability of software. A quantum computing-based security algorithm springs quite a lot of symmetrical approaches and procedures to ensure optimum software retreat. The accurate assessment of software’s durability and security is a dynamic aspect in assessing, administrating, and controlling security for strengthening the features of security. This paper essentially emphasises the demarcation and depiction of quantum computing from a software security perspective. At present, different symmetrical-based cryptography approaches or algorithms are being used to protect different government and non-government sectors, such as banks, healthcare sectors, defense, transport, automobiles, navigators, weather forecasting, etc., to ensure software durability and security. However, many crypto schemes are likely to collapse when a large qubit-based quantum computer is developed. In such a scenario, it is necessary to pay attention to the security alternatives based on quantum computing. Presently, the different factors of software durability are usability, dependability, trustworthiness, and human trust. In this study, we have also classified the durability level in the second stage. The intention of the evaluation of the impact on security over quantum duration is to estimate and assess the security durability of software. In this research investigation, we have followed the symmetrical hybrid technique of fuzzy analytic hierarchy process (FAHP) and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS). The obtained results, and the method used in this estimation, would make a significant contribution to future research for organising software security and durability (SSD) in the presence of a quantum computer. Full article

► Show Figures

Figure 1

17 pages, 2055 KiB

Open AccessArticle

A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform

by Qiuying Chen and SangJoon Lee

Appl. Sci. 2021, 11(21), 9927; https://doi.org/10.3390/app11219927 - 24 Oct 2021

Cited by 5 | Viewed by 2820

Abstract

Health authorities have recommended the use of digital tools for home workouts to stay active and healthy during the COVID-19 pandemic. In this paper, a machine learning approach is proposed to assess the activity of users on a home workout platform. Keep is [...] Read more.

Health authorities have recommended the use of digital tools for home workouts to stay active and healthy during the COVID-19 pandemic. In this paper, a machine learning approach is proposed to assess the activity of users on a home workout platform. Keep is a home workout application dedicated to providing one-stop exercise solutions such as fitness teaching, cycling, running, yoga, and fitness diet guidance. We used a data crawler to collect the total training set data of 7734 Keep users and compared four supervised learning algorithms: support vector machine, k-nearest neighbor, random forest, and logistic regression. The receiver operating curve analysis indicated that the overall discrimination verification power of random forest was better than that of the other three models. The random forest model was used to classify 850 test samples, and a correct rate of 88% was obtained. This approach can predict the continuous usage of users after installing the home workout application. We considered 18 variables on Keep that were expected to affect the determination of continuous participation. Keep certification is the most important variable that affected the results of this study. Keep certification refers to someone who has verified their identity information and can, therefore, obtain the Keep certification logo. The results show that the platform still needs to be improved in terms of real identity privacy information and other aspects. Full article

► Show Figures

Figure 1

20 pages, 6553 KiB

Open AccessArticle

An Empirical Study of a Trustworthy Cloud Common Data Model Using Decentralized Identifiers

by Yunhee Kang, Jaehyuk Cho and Young B. Park

Appl. Sci. 2021, 11(19), 8984; https://doi.org/10.3390/app11198984 - 27 Sep 2021

Cited by 3 | Viewed by 2335

Abstract

The Conventional Cloud Common Data Model (CDM) uses a centralized method of user identification and credentials. This needs to be solved in a decentralized way because there are limitations in interoperability such as closed identity management and identity leakage. In this paper, we [...] Read more.

The Conventional Cloud Common Data Model (CDM) uses a centralized method of user identification and credentials. This needs to be solved in a decentralized way because there are limitations in interoperability such as closed identity management and identity leakage. In this paper, we propose a DID (Decentralized Identifier)-based cloud CDM that allows researchers to securely store medical research information by authenticating their identity and to access the CDM reliably. The proposed service model is used to provide the credential of the researcher in the process of creating and accessing CDM data in the designed secure cloud. This model is designed on a DID-based user-centric identification system to support the research of enrolled researchers in a cloud CDM environment involving multiple hospitals and laboratories. The prototype of the designed model is an extension of the encrypted CDM delivery method using DID and provides an identification system by limiting the use cases of CDM data by researchers registered in cloud CDM. Prototypes built for agent-based proof of concept (PoC) are leveraged to enhance security for researcher use of ophthalmic CDM data. For this, the CDM ID schema and ID definition are described by issuing IDs of CDM providers and CDM agents, limiting the IDs of researchers who are CDM users. The proposed method is to provide a framework for integrated and efficient data access control policy management. It provides strong security and ensures both the integrity and availability of CDM data. Full article

► Show Figures

Figure 1

20 pages, 3620 KiB

Open AccessFeature PaperArticle

Deep Reinforcement Learning-Based Network Routing Technology for Data Recovery in Exa-Scale Cloud Distributed Clustering Systems

by Dong-Jin Shin and Jeong-Joon Kim

Appl. Sci. 2021, 11(18), 8727; https://doi.org/10.3390/app11188727 - 18 Sep 2021

Cited by 4 | Viewed by 2774

Abstract

Research has been conducted to efficiently transfer blocks and reduce network costs when decoding and recovering data from an erasure coding-based distributed file system. Technologies using software-defined network (SDN) controllers can collect and more efficiently manage network data. However, the bandwidth depends dynamically [...] Read more.

Research has been conducted to efficiently transfer blocks and reduce network costs when decoding and recovering data from an erasure coding-based distributed file system. Technologies using software-defined network (SDN) controllers can collect and more efficiently manage network data. However, the bandwidth depends dynamically on the number of data transmitted on the network, and the data transfer time is inefficient owing to the longer latency of existing routing paths when nodes and switches fail. We propose deep Q-network erasure coding (DQN-EC) to solve routing problems by converging erasure coding with DQN to learn dynamically changing network elements. Using the SDN controller, DQN-EC collects the status, number, and block size of nodes possessing stored blocks during erasure coding. The fat-tree network topology used for experimental evaluation collects elements of typical network packets, the bandwidth of the nodes and switches, and other information. The data collected undergo deep reinforcement learning to avoid node and switch failures and provide optimized routing paths by selecting switches that efficiently conduct block transfers. DQN-EC achieves a 2.5-times-faster block transmission time and 0.4-times-higher network throughput than open shortest path first (OSPF) routing algorithms. The bottleneck bandwidth and transmission link cost can be reduced, improving the recovery time approximately twofold. Full article

► Show Figures

Figure 1

18 pages, 6711 KiB

Open AccessArticle

A Study on Building a “Real-Time Vehicle Accident and Road Obstacle Notification Model” Using AI CCTV

by Chaeyoung Lee, Hyomin Kim, Sejong Oh and Illchul Doo

Appl. Sci. 2021, 11(17), 8210; https://doi.org/10.3390/app11178210 - 03 Sep 2021

Cited by 4 | Viewed by 4238

Abstract

This research produced a model that detects abnormal phenomena on the road, based on deep learning, and proposes a service that can prevent accidents because of other cars and traffic congestion. After extracting accident images based on traffic accident video data by using [...] Read more.

This research produced a model that detects abnormal phenomena on the road, based on deep learning, and proposes a service that can prevent accidents because of other cars and traffic congestion. After extracting accident images based on traffic accident video data by using FFmpeg for model production, car collision types are classified, and only the head-on collision types are processed by using the deep learning object-detection algorithm YOLO (You Only Look Once). Using the car accident detection model that we built and the provided road obstacle-detection model, we programmed, for when the model detects abnormalities on the road, warning notification and photos that captures the accidents or obstacles, which are then transferred to the application. The proposed service was verified through application notification simulations and virtual experiments using CCTVs in Daegu, Busan, and Gwangju. By providing services, the goal is to improve traffic safety and achieve the development of a self-driving vehicle sector. As a future research direction, it is suggested that an efficient CCTV control system be introduced for the transportation environment. Full article

► Show Figures

Figure 1

16 pages, 3253 KiB

Open AccessArticle

Ambient Sound Recognition of Daily Events by Means of Convolutional Neural Networks and Fuzzy Temporal Restrictions

by Aurora Polo-Rodriguez, Jose Manuel Vilchez Chiachio, Cristiano Paggetti and Javier Medina-Quero

Appl. Sci. 2021, 11(15), 6978; https://doi.org/10.3390/app11156978 - 29 Jul 2021

Cited by 6 | Viewed by 2088

Abstract

The use of multimodal sensors to describe activities of daily living in a noninvasive way is a promising research field in continuous development. In this work, we propose the use of ambient audio sensors to recognise events which are generated from the activities [...] Read more.

The use of multimodal sensors to describe activities of daily living in a noninvasive way is a promising research field in continuous development. In this work, we propose the use of ambient audio sensors to recognise events which are generated from the activities of daily living carried out by the inhabitants of a home. An edge–fog computing approach is proposed to integrate the recognition of audio events with smart boards where the data are collected. To this end, we compiled a balanced dataset which was collected and labelled in controlled conditions. A spectral representation of sounds was computed using convolutional network inputs to recognise ambient sounds with encouraging results. Next, fuzzy processing of audio event streams was included in the IoT boards by means of temporal restrictions defined by protoforms to filter the raw audio event recognition, which are key in removing false positives in real-time event recognition. Full article

► Show Figures

Figure 1

20 pages, 4493 KiB

Open AccessArticle

Development of a Fuzzy Logic Controller for Small-Scale Solar Organic Rankine Cycle Cogeneration Plants

by Luca Cioccolanti, Simone De Grandis, Roberto Tascioni, Matteo Pirro and Alessandro Freddi

Appl. Sci. 2021, 11(12), 5491; https://doi.org/10.3390/app11125491 - 13 Jun 2021

Cited by 4 | Viewed by 1912

Abstract

Solar energy is widely recognized as one of the most attractive renewable energy sources to support the transition toward a decarbonized society. Use of low- and medium-temperature concentrated solar technologies makes decentralized power production of combined heating and power (CHP) an alternative to [...] Read more.

Solar energy is widely recognized as one of the most attractive renewable energy sources to support the transition toward a decarbonized society. Use of low- and medium-temperature concentrated solar technologies makes decentralized power production of combined heating and power (CHP) an alternative to conventional energy conversion systems. However, because of the changes in solar radiation and the inertia of the different subsystems, the operation control of concentrated solar power (CSP) plants is fundamental to increasing their overall conversion efficiency and improving reliability. Therefore, in this study, the operation control of a micro-scale CHP plant consisting of a linear Fresnel reflector solar field, an organic Rankine cycle unit, and a phase change material thermal energy storage tank, as designed and built under the EU-funded Innova Microsolar project by a consortium of universities and companies, is investigated. In particular, a fuzzy logic control is developed in MATLAB/Simulink by the authors in order to (i) initially recognize the type of user according to the related energy consumption profile by means of a neural network and (ii) optimize the thermal-load-following approach by introducing a set of fuzzy rules to switch among the different operation modes. Annual simulations are performed by combining the plant with different thermal load profiles. In general, the analysis shows that that the proposed fuzzy logic control increases the contribution of the TES unit in supplying the ORC unit, while reducing the number of switches between the different OMs. Furthermore, when connected with a residential user load profile, the overall electrical and thermal energy production of the plant increases. Hence, the developed control logic proves to have good potential in increasing the energy efficiency of low- and medium-temperature concentrated solar ORC systems when integrated into the built environment. Full article

► Show Figures

Figure 1

15 pages, 1376 KiB

Open AccessArticle

A Modified Quad Q Network Algorithm for Predicting Resource Management

by Yeonggwang Kim, Jaehyung Park, Jinyoung Kim, Junchurl Yoon, Sangjoon Lee and Jinsul Kim

Appl. Sci. 2021, 11(11), 5154; https://doi.org/10.3390/app11115154 - 01 Jun 2021

Viewed by 2079

Abstract

As the resource management systems continues to grow, the resource distribution system is expected to expand steadily. The demand response system enables producers to reduce the consumption costs of an enterprise during fluctuating periods in order balance the supply grid and resell the [...] Read more.

As the resource management systems continues to grow, the resource distribution system is expected to expand steadily. The demand response system enables producers to reduce the consumption costs of an enterprise during fluctuating periods in order balance the supply grid and resell the remaining resources of the product to generate revenue. Q-learning, a reinforcement learning algorithm based on a resource distribution compensation mechanism, is used to make optimal decisions to schedule the operation of smart factory appliances. In this paper, we proposed an effective resource management system for enterprise demand response using a Quad Q Network algorithm. The proposed algorithm is based on a Deep Q Network algorithm that directly integrates supply-demand inputs into control logic and employs fuzzy inference as a reward mechanism. In addition to using uses the Compare Optimizer method to reduce the loss value of the proposed Q Network Algorithm, Quad Q Network also maintains a high accuracy with fewer epochs. The proposed algorithm was applied to market capitalization data obtained from Google and Apple. Also, we verified that the Compare Optimizer used in Quad Q Network derives the minimum loss value through the double operation of Double Q value. Full article

► Show Figures

Figure 1

20 pages, 14051 KiB

Open AccessArticle

Using Fuzzy Control for Feed Rate Scheduling of Computer Numerical Control Machine Tools

by Cheng-Jian Lin, Chun-Hui Lin and Shyh-Hau Wang

Appl. Sci. 2021, 11(10), 4701; https://doi.org/10.3390/app11104701 - 20 May 2021

Cited by 7 | Viewed by 2156

Abstract

In industrial processing, workpiece quality and processing time have recently become important issues. To improve the machining accuracy and reduce the cutting time, the cutting feed rate will have a significant impact. Therefore, how to plan a dynamic cutting feed rate is very [...] Read more.

In industrial processing, workpiece quality and processing time have recently become important issues. To improve the machining accuracy and reduce the cutting time, the cutting feed rate will have a significant impact. Therefore, how to plan a dynamic cutting feed rate is very important. In this study, a fuzzy control system for feed rate scheduling based on the curvature and curvature variation is proposed. The proposed system is implemented in actual cutting, and to verify the data an optical three-dimensional scanner is used to measure the cutting trajectory of the workpiece. Experimental results prove that the proposed fuzzy control system for dynamic cutting feed rate scheduling increases the cutting accuracy by 41.8% under the same cutting time; moreover, it decreases the cutting time by 50.8% under approximately the same cutting accuracy. Full article

► Show Figures

Figure 1

26 pages, 4825 KiB

Open AccessArticle

Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning

by Farzin Piltan and Jong-Myon Kim

Appl. Sci. 2021, 11(10), 4602; https://doi.org/10.3390/app11104602 - 18 May 2021

Cited by 37 | Viewed by 3355

Abstract

In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing [...] Read more.

In this study, the application of an intelligent digital twin integrated with machine learning for bearing anomaly detection and crack size identification will be observed. The intelligent digital twin has two main sections: signal approximation and intelligent signal estimation. The mathematical vibration bearing signal approximation is integrated with machine learning-based signal approximation to approximate the bearing vibration signal in normal conditions. After that, the combination of the Kalman filter, high-order variable structure technique, and adaptive neural-fuzzy technique is integrated with the proposed signal approximation technique to design an intelligent digital twin. Next, the residual signals will be generated using the proposed intelligent digital twin and the original RAW signals. The machine learning approach will be integrated with the proposed intelligent digital twin for the classification of the bearing anomaly and crack sizes. The Case Western Reserve University bearing dataset is used to test the impact of the proposed scheme. Regarding the experimental results, the average accuracy for the bearing fault pattern recognition and crack size identification will be, respectively, 99.5% and 99.6%. Full article

► Show Figures

Figure 1

26 pages, 10167 KiB

Open AccessFeature PaperArticle

Building an Operational Solution Assistant System for Foreign SMEs in ROK

by Hong-Danh Thai and Jun-Ho Huh

Appl. Sci. 2021, 11(10), 4510; https://doi.org/10.3390/app11104510 - 15 May 2021

Cited by 2 | Viewed by 2547

Abstract

Foreign Direct Investment (FDI) is an important resource that helps accelerate the development of the country’s economy, add substantial funding to growth and facilitate technology transfer. Republic of Korea (ROK) is one of the world’s developed countries with dynamic economy, advanced science and [...] Read more.

Foreign Direct Investment (FDI) is an important resource that helps accelerate the development of the country’s economy, add substantial funding to growth and facilitate technology transfer. Republic of Korea (ROK) is one of the world’s developed countries with dynamic economy, advanced science and technology. In recent years, the Korean government has continuously formulated tax policies, policies to support the business economy and import policies to support foreign businesses in Korea. The Pangyo Valley Creative Economy Valley is being groomed as a global startup hub in Asia. Small and medium enterprises (SMEs) in foreign countries are increasingly interested and eager to seek investment opportunities in the Korean market. Nonetheless, for these companies, language barriers and cultural and institutional differences make it more difficult and time-consuming to learn about the Korean market (such as investment trends, laws, visa policies, taxes and business establishment issues in Korea, etc.). In this study, we explored the process of searching information and seeking investment opportunities and built a business consulting and support application in the first stages of starting a business in ROK to increase effectiveness and save time, which is also an innovative business practice in Use-case ROK. We designed our Virtual Assistant system that can crawl and analyze data on foreign investments in ROK from open data resource websites (data.co.kr) and used analytic and aggregation techniques to explore trends in investments of foreign enterprises. We also researched the process of searching information and seeking investment opportunities for SMEs when investing in ROK, government support policies, laws and taxes as well as a number of other related issues. We built datasets and used Natural Language Processing (NLP) together with Natural Language Understanding (NLU) algorithms to build chatbot applications. Friendly framework for new developers to add and build up the dataset of AI Assistant is built by providing input intent data function, input Entity data function, input utterance data function as well as training and test function. In addition, we built a web-app connected to the server to visualize all the results of research so that SMEs owners can easily use and look for information on investments. Based on the research results, we can make recommendations to SMEs in keeping with the changing investment trends in ROK. Full article

► Show Figures

Figure 1

14 pages, 5108 KiB

Open AccessArticle

Level-Based Learning Algorithm Based on the Difficulty Level of the Test Problem

by You-Sik Hong, Chang-Pyoung Han and Seong-Soo Cho

Appl. Sci. 2021, 11(10), 4380; https://doi.org/10.3390/app11104380 - 12 May 2021

Cited by 1 | Viewed by 1731

Abstract

These days, because of the coronavirus, all countries are introducing online university systems. Online universities have the advantage of allowing students to take classes anytime, anywhere, 24 h a day, but lectures are given in a non-face-to-face manner between instructors and students. Thus, [...] Read more.

These days, because of the coronavirus, all countries are introducing online university systems. Online universities have the advantage of allowing students to take classes anytime, anywhere, 24 h a day, but lectures are given in a non-face-to-face manner between instructors and students. Thus, while students are taking classes on a web-based basis, the problem arises that concentration on the lectures is significantly reduced. Therefore, in order to solve these problems, in this paper, we propose a level-wise learning algorithm based on the difficulty level of the test problem, and we present the simulation results. In order to improve this problem, in this paper, we propose an automatic music recommendation algorithm based on fuzzy reasoning that can improve the level of learning and lecture concentration, and we show our results on developing a web-based, smart e-learning software. As a result of computer simulation, it was proved that the learning test method, considering by level the difficulty of the test and the incorrect answer rate, was more effective than the existing test method, judged the student’s grades fairly, and improved the risk of unfairly failing the test by 30%. Full article

► Show Figures

Figure 1

15 pages, 3684 KiB

Open AccessArticle

A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering

by Sun-Young Ihm, Shin-Eun Lee, Young-Ho Park, Aziz Nasridinov, Miyeon Kim and So-Hyun Park

Appl. Sci. 2021, 11(8), 3719; https://doi.org/10.3390/app11083719 - 20 Apr 2021

Cited by 3 | Viewed by 2115

Abstract

Collaborative filtering (CF) is a recommendation technique that analyzes the behavior of various users and recommends the items preferred by users with similar preferences. However, CF methods suffer from poor recommendation accuracy when the user preference data used in the recommendation process is [...] Read more.

Collaborative filtering (CF) is a recommendation technique that analyzes the behavior of various users and recommends the items preferred by users with similar preferences. However, CF methods suffer from poor recommendation accuracy when the user preference data used in the recommendation process is sparse. Data imputation can alleviate the data sparsity problem by substituting a virtual part of the missing user preferences. In this paper, we propose a k-recursive reliability-based imputation (k-RRI) that first selects data with high reliability and then recursively imputes data with additional selection while gradually lowering the reliability criterion. We also propose a new similarity measure that weights common interests and indifferences between users and items. The proposed method can overcome disregarding the importance of missing data and resolve the problem of poor data imputation of existing methods. The experimental results demonstrate that the proposed approach significantly improves recommendation accuracy compared to those resulting from the state-of-the-art methods while demanding less computational complexity. Full article

► Show Figures

Figure 1

23 pages, 9055 KiB

Open AccessArticle

Erasure-Coding-Based Storage and Recovery for Distributed Exascale Storage Systems

by Jeong-Joon Kim

Appl. Sci. 2021, 11(8), 3298; https://doi.org/10.3390/app11083298 - 07 Apr 2021

Cited by 4 | Viewed by 2370

Abstract

Various techniques have been used in distributed file systems for data availability and stability. Typically, a method for storing data in a replication technique-based distributed file system is used, but due to the problem of space efficiency, an erasure-coding (EC) technique has been [...] Read more.

Various techniques have been used in distributed file systems for data availability and stability. Typically, a method for storing data in a replication technique-based distributed file system is used, but due to the problem of space efficiency, an erasure-coding (EC) technique has been utilized more recently. The EC technique improves the space efficiency problem more than the replication technique does. However, the EC technique has various performance degradation factors, such as encoding and decoding and input and output (I/O) degradation. Thus, this study proposes a buffering and combining technique in which various I/O requests that occurred during encoding in an EC-based distributed file system are combined into one and processed. In addition, it proposes four recovery measures (disk input/output load distribution, random block layout, multi-thread-based parallel recovery, and matrix recycle technique) to distribute the disk input/output loads generated during decoding. Full article

► Show Figures

Figure 1

19 pages, 21225 KiB

Open AccessArticle

A New Switching Adaptive Fuzzy Controller with an Application to Vibration Control of a Vehicle Seat Suspension Subjected to Disturbances

by Do Xuan Phu, Van Mien and Seung-Bok Choi

Appl. Sci. 2021, 11(5), 2244; https://doi.org/10.3390/app11052244 - 03 Mar 2021

Cited by 9 | Viewed by 1982

Abstract

This paper proposes a new switching adaptive fuzzy controller and applies it to vibration control of a vehicle seat suspension equipped with a semi-active magnetorheological (MR) damper. The proposed control system consists of three functioned filters: (1) Filter 1: a model of interval [...] Read more.

This paper proposes a new switching adaptive fuzzy controller and applies it to vibration control of a vehicle seat suspension equipped with a semi-active magnetorheological (MR) damper. The proposed control system consists of three functioned filters: (1) Filter 1: a model of interval type 2 fuzzy to compensate disturbances; (2) Filter 2: a ‘switching term’ to evaluate the magnitude of disturbance; and (3) Filter 3: a group of adaptation laws to enhance the robustness of control input. These filters play a role of powerful shields to improve control performance and guarantee the stability of the applied system subjected to external disturbances. After embedding a PID (proportional-integral-derivative) model into Riccati-like equation, main control parameters are updated based on the adaptation laws. The proposed controller is then synthesized in two different cases: high disturbance and small disturbance. For the high disturbance, a special type of sliding surface function, which relates to an exponential function and its t-norm, is used to increase the energy of control system. For the small disturbance, the energy from the modified t-norm of the sliding surface is neglected to reduce the energy consumption with maintaining the desired performance. To demonstrate the effectiveness of the proposed controller, a vehicle seat suspension installed with controllable MR damper is adopted to reflect the robustness against external disturbances corresponding to road excitations. It is validated from computer simulation that the proposed controller can provide better vibration control performance than other existing robust controllers showing excellent control stability with well-reduced displacement and velocity at the position of the seat. Full article

► Show Figures

Figure 1

22 pages, 12738 KiB

Open AccessFeature PaperArticle

A Comparison of Fuzzy-Based Energy Management Systems Adjusted by Nature-Inspired Algorithms

by Diego Arcos-Aviles, Diego Pacheco, Daniela Pereira, Gabriel Garcia-Gutierrez, Enrique V. Carrera, Alexander Ibarra, Paúl Ayala, Wilmar Martínez and Francesc Guinjoan

Appl. Sci. 2021, 11(4), 1663; https://doi.org/10.3390/app11041663 - 12 Feb 2021

Cited by 11 | Viewed by 2090

Abstract

The growing energy demand around the world has increased the usage of renewable energy sources (RES) such as photovoltaic and wind energies. The combination of traditional power systems and RESs has generated diverse problems due especially to the stochastic nature of RESs. Microgrids [...] Read more.

The growing energy demand around the world has increased the usage of renewable energy sources (RES) such as photovoltaic and wind energies. The combination of traditional power systems and RESs has generated diverse problems due especially to the stochastic nature of RESs. Microgrids (MG) arise to address these types of problems and to increase the penetration of RES to the utility network. A microgrid includes an energy management system (EMS) to operate its components and energy sources efficiently. The objectives pursued by the EMS are usually economically related to minimizing the operating costs of the MG or maximizing its income. However, due to new regulations of the network operators, a new objective related to the minimization of power peaks and fluctuations in the power profile exchanged with the utility network has taken great interest in recent years. In this regard, EMSs based on off-line trained fuzzy logic control (FLC) have been proposed as an alternative approach to those based on on-line optimization mixed-integer linear (or nonlinear) programming to reduce computational efforts. However, the procedure to adjust the FLC parameters has been barely addressed. This parameter adjustment is an optimization problem itself that can be formulated in terms of a cost/objective function and is susceptible to being solved by metaheuristic nature-inspired algorithms. In particular, this paper evaluates a methodology for adjusting the FLC parameters of the EMS of a residential microgrid that aims to minimize the power peaks and fluctuations on the power profile exchanged with the utility network through two nature-inspired algorithms, namely particle swarm optimization and differential evolution. The methodology is based on the definition of a cost function to be optimized. Numerical simulations on a specific microgrid example are presented to compare and evaluate the performances of these algorithms, also including a comparison with other ones addressed in previous works such as the Cuckoo search approach. These simulations are further used to extract useful conclusions for the FLC parameters adjustment for off-line-trained EMS based designs. Full article

► Show Figures

Figure 1

19 pages, 5793 KiB

Open AccessArticle

Chinese Character Image Completion Using a Generative Latent Variable Model

by In-su Jo, Dong-bin Choi and Young B. Park

Appl. Sci. 2021, 11(2), 624; https://doi.org/10.3390/app11020624 - 11 Jan 2021

Cited by 5 | Viewed by 2128

Abstract

Chinese characters in ancient books have many corrupted characters, and there are cases in which objects are mixed in the process of extracting the characters into images. To use this incomplete image as accurate data, we use image completion technology, which removes unnecessary [...] Read more.

Chinese characters in ancient books have many corrupted characters, and there are cases in which objects are mixed in the process of extracting the characters into images. To use this incomplete image as accurate data, we use image completion technology, which removes unnecessary objects and restores corrupted images. In this paper, we propose a variational autoencoder with classification (VAE-C) model. This model is characterized by using classification areas and a class activation map (CAM). Through the classification area, the data distribution is disentangled, and then the node to be adjusted is tracked using CAM. Through the latent variable, with which the determined node value is reduced, an image from which unnecessary objects have been removed is created. The VAE-C model can be utilized not only to eliminate unnecessary objects but also to restore corrupted images. By comparing the performance of removing unnecessary objects with mask regions with convolutional neural networks (Mask R-CNN), one of the prevalent object detection technologies, and also comparing the image restoration performance with the partial convolution model (PConv) and the gated convolution model (GConv), which are image inpainting technologies, our model is proven to perform excellently in terms of removing objects and restoring corrupted areas. Full article

► Show Figures

Figure 1

2020

Jump to: 2023, 2022, 2021

19 pages, 4194 KiB

Open AccessArticle

Dynamic Topology Model of Q-Learning LEACH Using Disposable Sensors in Autonomous Things Environment

by Jae Hyuk Cho and Hayoun Lee

Appl. Sci. 2020, 10(24), 9037; https://doi.org/10.3390/app10249037 - 17 Dec 2020

Cited by 13 | Viewed by 2379

Abstract

Low-Energy Adaptive Clustering Hierarchy (LEACH) is a typical routing protocol that effectively reduces transmission energy consumption by forming a hierarchical structure between nodes. LEACH on Wireless Sensor Network (WSN) has been widely studied in the recent decade as one key technique for the [...] Read more.

Low-Energy Adaptive Clustering Hierarchy (LEACH) is a typical routing protocol that effectively reduces transmission energy consumption by forming a hierarchical structure between nodes. LEACH on Wireless Sensor Network (WSN) has been widely studied in the recent decade as one key technique for the Internet of Things (IoT). The main aims of the autonomous things, and one of advanced of IoT, is that it creates a flexible environment that enables movement and communication between objects anytime, anywhere, by saving computing power and utilizing efficient wireless communication capability. However, the existing LEACH method is only based on the model with a static topology, but a case for a disposable sensor is included in an autonomous thing’s environment. With the increase of interest in disposable sensors which constantly change their locations during the operation, dynamic topology changes should be considered in LEACH. This study suggests the probing model for randomly moving nodes, implementing a change in the position of a node depending on the environment, such as strong winds. In addition, as a method to quickly adapt to the change in node location and construct a new topology, we propose Q-learning LEACH based on Q-table reinforcement learning and Fuzzy-LEACH based on Fuzzifier method. Then, we compared the results of the dynamic and static topology model with existing LEACH on the aspects of energy loss, number of alive nodes, and throughput. By comparison, all types of LEACH showed sensitivity results on the dynamic location of each node, while Q-LEACH shows best performance of all. Full article

► Show Figures

Figure 1

17 pages, 5443 KiB

Open AccessArticle

Detection of Smoking in Indoor Environment Using Machine Learning

by Jae Hyuk Cho

Appl. Sci. 2020, 10(24), 8912; https://doi.org/10.3390/app10248912 - 14 Dec 2020

Cited by 15 | Viewed by 3923

Abstract

Revealed by the effect of indoor pollutants on the human body, indoor air quality management is increasing. In particular, indoor smoking is one of the common sources of indoor air pollution, and its harmfulness has been well studied. Accordingly, the regulation of indoor [...] Read more.

Revealed by the effect of indoor pollutants on the human body, indoor air quality management is increasing. In particular, indoor smoking is one of the common sources of indoor air pollution, and its harmfulness has been well studied. Accordingly, the regulation of indoor smoking is emerging all over the world. Technical approaches are also being carried out to regulate indoor smoking, but research is focused on detection hardware. This study includes analytical and machine learning approach of cigarette detection by detecting typical gases (total volatile organic compounds, CO₂ etc.) being collected from IoT sensors. In detail, data set for machine learning was built using IoT sensors, including training data set securely collected from the rotary smoking machine and test data set gained from actual indoor environment with spontaneous smokers. The prediction accuracy was evaluated with accuracy, precision, and recall. As a result, the non-linear support vector machine (SVM) model showed the best performance with 93% in accuracy and 88% in the F1 score. The supervised learning k-nearest neighbors (KNN) and multilayer perceptron (MLP) models also showed relatively fine results, but shows effectivity simplifying prediction with binary classification to improve accuracy and speed. Full article

► Show Figures

Figure 1

17 pages, 3204 KiB

Open AccessArticle

A Fuzzy Rule-Based GIS Framework to Partition an Urban System Based on Characteristics of Urban Greenery in Relation to the Urban Context

by Barbara Cardone and Ferdinando Di Martino

Appl. Sci. 2020, 10(24), 8781; https://doi.org/10.3390/app10248781 - 08 Dec 2020

Cited by 2 | Viewed by 1550

Abstract

We present a Geographical Information System (GIS)-based framework implementing a Mamdani fuzzy rule-based system to partition in an unsupervised mode an urban system in urban green areas. The proposed framework is characterized by high usability and flexibility. The study area is partitioned into [...] Read more.

We present a Geographical Information System (GIS)-based framework implementing a Mamdani fuzzy rule-based system to partition in an unsupervised mode an urban system in urban green areas. The proposed framework is characterized by high usability and flexibility. The study area is partitioned into homogeneous regions regarding the characteristics of public green areas and relations with the residents and buildings. The urban system is initially partitioned into microzones, given the smallest areas in which a census of the urban system is taken in terms of resident population, type and number of buildings and properties, and industrial and service activities. During a pre-processing phase, the values of specific indicators defined by a domain expert, which characterize the type of urban green area and the relationship with the residents and buildings, are calculated for each microzone. Subsequently, the fuzzy rule-based system component is executed to classify each microzone based on the fuzzy rule set constructed by the domain expert. Spatially adjoining microzones belonging to the same class are dissolved to form homogeneous areas called urban green contexts. The membership degrees of the microzones to the fuzzy set of their class are used to evaluate the reliability of the classification of the urban green context. We test our framework on the municipality of Pozzuoli, Italy, comparing the results with the ones obtained in a supervised manner by the expert appropriately partitioning and classifying the urban study area based on his knowledge of it. Full article

► Show Figures

Figure 1

13 pages, 7685 KiB

Open AccessArticle

Design and Implementation of a Big Data Evaluator Recommendation System Using Deep Learning Methodology

by Sukil Cha, Mun Y. Yi and Sekyoung Youm

Appl. Sci. 2020, 10(22), 8000; https://doi.org/10.3390/app10228000 - 11 Nov 2020

Cited by 2 | Viewed by 1696

Abstract

As the number of researchers in South Korea has grown, there is increasing dissatisfaction with the selection process for national research and development (R&D) projects among unsuccessful applicants. In this study, we designed a system that can recommend the best possible R&D evaluators [...] Read more.

As the number of researchers in South Korea has grown, there is increasing dissatisfaction with the selection process for national research and development (R&D) projects among unsuccessful applicants. In this study, we designed a system that can recommend the best possible R&D evaluators using big data that are collected from related systems, refined, and analyzed. Our big data recommendation system compares keywords extracted from applications and from the full-text of the achievements of the evaluator candidates. Weights for different keywords are scored using the term frequency–inverse document frequency algorithm. Comparing the keywords extracted from the achievement of the evaluator candidates’, a project comparison module searches, scores, and ranks these achievements similarly to the project applications. The similarity scoring module calculates the overall similarity scores for different candidates based on the project comparison module scores. To assess the performance of the evaluator candidate recommendation system, 61 applications in three Review Board (RB) research fields (system fusion, organic biochemistry, and Korean literature) were recommended as the evaluator candidates by the recommendation system in the same manner as the RB’s recommendation. Our tests reveal that the evaluator candidates recommended by the Korean Review Board and those recommended by our system for 61 applications in different areas, were the same. However, our system performed the recommendation in less time with no bias and fewer personnel. The system requiresrevisions to reflect qualitative indicators, such as journal reputation, before it can entirely replace the current evaluator recommendation process. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

The Development and Application of Fuzzy Logic

Share This Topical Collection

Editors

Topical Collection Information

Published Papers (24 papers)

2023

Jump to: 2022, 2021, 2020

2022

Jump to: 2023, 2021, 2020

2021

Jump to: 2023, 2022, 2020

2020

Jump to: 2023, 2022, 2021

Further Information

Guidelines

MDPI Initiatives

Follow MDPI