Int. J. Fuzzy Log. Intell. Syst.最新文献_第10页

Automatic Classification of Sleep Stage from an ECG Signal Using a Gated-Recurrent Unit 利用门控循环单元从心电信号中自动分类睡眠阶段

Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-09-30 DOI: 10.5391/IJFIS.2020.20.3.181

E. Urtnasan, Yeewoong Kim, Joung-Uk Park, Sooyong Lee, Kyoung-Joung Lee

{"title":"Automatic Classification of Sleep Stage from an ECG Signal Using a Gated-Recurrent Unit","authors":"E. Urtnasan, Yeewoong Kim, Joung-Uk Park, Sooyong Lee, Kyoung-Joung Lee","doi":"10.5391/IJFIS.2020.20.3.181","DOIUrl":"https://doi.org/10.5391/IJFIS.2020.20.3.181","url":null,"abstract":"A healthy sleep structure is clinically very important for overall health. The sleep structure can be represented by the percentage of different sleep stages during the total sleep time. In this study, we proposed a method for automatic classiﬁcation of sleep stages from an electrocardiogram (ECG) signal using a gated-recurrent unit (GRU). The proposed method performed multiclass classiﬁcation for three-class sleep stages such as awake, light, and deep sleep. A deep structured GRU was used in the proposed method, which is a common recurrent neural network. The proposed deep learning (SleepGRU) model consists of a 5-layer GRU and is optimized by batch-normalization, dropout, and Adam update rules. The ECG signal was recorded during nocturnal polysomnography from 112 subjects, and was normalized and segmented into units of 30-second duration. To train and evaluate the proposed method, the training set consisted of 80,316 segments from 89 subjects, and the test set used 20,079 segments from 23 subjects. We achieved good performances with an overall accuracy of 80.43% and F1-score of 80.07% for the test set. The proposed method can be an alternative and useful tool for sleep monitoring and sleep screening, which have previously been manually evaluated by a sleep technician or sleep expert.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122671882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Swarm Intelligence for Additive White Gaussian Noise Level Estimation 加性高斯白噪声水平估计的群体智能

Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-09-30 DOI: 10.5391/IJFIS.2020.20.3.169

Heri Prasetyo, U. Salamah

引用次数: 1

Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-09-30 DOI: 10.5391/IJFIS.2020.20.3.201

S. I. Baek, Yang-Eui Song, Yong Kyu Lee

{"title":"A Similarity-Based Software Recommendation Method Reflecting User Requirements","authors":"S. I. Baek, Yang-Eui Song, Yong Kyu Lee","doi":"10.5391/IJFIS.2020.20.3.201","DOIUrl":"https://doi.org/10.5391/IJFIS.2020.20.3.201","url":null,"abstract":"Existing software recommendation methods consider only the usage frequencies of software as far as we know. In this study, we propose a software recommendation method reflecting user requirements based on both the Boolean model and vector space model. A function matrix and function vector are made from the functional specification of each software type and stored in the database. First, it creates a requirement vector from a user’s functional requirements of the desired software. Second, it makes a list of software with the same functions wanted using the function matrix based on the Boolean model. Third, the cosine similarities are calculated between the requirement vector and function vectors of the software in the list based on the vector space model. Finally, a software recommendation list is generated in descending order of similarity. Based on the experiment results, appropriate software well suited for user requirements can be recommended. This is because we searched for software that satisfies each user’s requirements by using the cosine similarity function of information retrieval and recommended it according to the ranking. In the future, performance can be improved by utilizing statistical search techniques.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130325801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Numerical Solutions of Fuzzy Fractional Delay Differential Equations 模糊分数阶时滞微分方程的数值解

Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-09-30 DOI: 10.5391/IJFIS.2020.20.3.247

V. Padmapriya, M. Kaliyappan, A. Manivannan

引用次数: 3

Development of a Mecanum-Wheeled Mobile Robot for Dynamic- and Static-Obstacle Avoidance Based on Laser Range Sensor 基于激光距离传感器的机动轮式动、静态避障机器人研制

Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-09-30 DOI: 10.5391/IJFIS.2020.20.3.188

Musa Matli, A. Albayrak, R. Bayir

{"title":"Development of a Mecanum-Wheeled Mobile Robot for Dynamic- and Static-Obstacle Avoidance Based on Laser Range Sensor","authors":"Musa Matli, A. Albayrak, R. Bayir","doi":"10.5391/IJFIS.2020.20.3.188","DOIUrl":"https://doi.org/10.5391/IJFIS.2020.20.3.188","url":null,"abstract":"This study aims to present an idea about the practical consequences of using mobile robots with Mecanum wheels. For mobile robots, an approach is proposed to avoid obstacles without location and map information. This approach is presented using a series of developed solutions. This article shares the process on how a set of discussed conceptual methodologies can be applied as well as their practical results. This method is provided using fuzzy logic and gap tracking. LIDAR is used to recognize obstacles around the mobile robot. By using the LIDAR, the robot detects gaps around it and moves according to fuzzy logic. The fuzzy logic consists of three inputs, an output, and 45 rules. The ﬁrst of the membership functions represents the membership function that replaces the obstacle. The second membership function calculates the distance to the obstacle. The ﬁnal login membership function is used to determine the angle between the obstacle and robot view. The output membership function represents the membership function that moves the robot. The results are analyzed under three different scenarios with ﬁve different experiments for each scenario. The results show that the mobile robot can avoid obstacles without location and map information. We believe that the proposed method can be used in mobile robots such as guard and service robots.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122207517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Fully Automated Parallel-Processing R Package for High-Dimensional Multiple-Phenotype Analysis Considering Population Structure 考虑种群结构的高维多表型分析的全自动并行处理R包

Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-09-30 DOI: 10.5391/IJFIS.2020.20.3.219

Gi Ju Lee, Sung Min Park, Junghyun Jung, J. W. Joo

引用次数: 0

Report Timing Control Method for Monitoring WSN Applications 监控WSN应用的报告定时控制方法

Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-06-30 DOI: 10.5391/ijfis.2020.20.2.105

Jaesung Park, Changyong Yoon

引用次数: 0

CNN-Based Recognition Algorithm for Four Classes of Roads 基于cnn的四类道路识别算法

Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-06-30 DOI: 10.5391/ijfis.2020.20.2.114

Sung-Min Cho, Byung-Jae Choi

引用次数: 1

Collision Avoidance Algorithm of Mobile Robots at Grid Map Intersection Point 网格地图交点移动机器人避碰算法

Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-06-30 DOI: 10.5391/ijfis.2020.20.2.96

Young-In Choi, Jae-Hoon Cho, Yong-Tae Kim

引用次数: 2

Automatic Determination of the Number of Clusters for Semi-Supervised Relational Fuzzy Clustering 半监督关系模糊聚类中聚类数的自动确定

Int. J. Fuzzy Log. Intell. Syst. Pub Date : 2020-06-30 DOI: 10.5391/ijfis.2020.20.2.156

Norah Ibrahim Fantoukh, M. Ismail, Ouiem Bchir

{"title":"Automatic Determination of the Number of Clusters for Semi-Supervised Relational Fuzzy Clustering","authors":"Norah Ibrahim Fantoukh, M. Ismail, Ouiem Bchir","doi":"10.5391/ijfis.2020.20.2.156","DOIUrl":"https://doi.org/10.5391/ijfis.2020.20.2.156","url":null,"abstract":"Semi-supervised clustering relies on both labeled and unlabeled data to steer the clustering process towards optimal categorization and escape from local minima. In this paper, we propose a novel fuzzy relational semi-supervised clustering algorithm based on an adaptive local distance measure (SSRF-CA). The proposed clustering algorithm utilizes side-information and formulates it as a set of constraints to supervise the learning task. These constraints are expressed using reward and penalty terms, which are integrated into a novel objective function. In particular, we formulate the clustering task as an optimization problem through the minimization of the proposed objective function. Solving this optimization problem provides the optimal values of different objective function parameters and yields the proposed semi-supervised clustering algorithm. Along with its ability to perform data clustering and learn the underlying dissimilarity measure between the data instances, our algorithm determines the optimal number of clusters in an unsupervised manner. Moreover, the proposed SSRF-CA is designed to handle relational data. This makes it appropriate for applications where only pairwise similarity (or dissimilarity) information between data instances is available. In this paper, we proved the ability of the proposed algorithm to learn the appropriate local distance measures and the optimal number of clusters while partitioning the data using various synthetic and real-world benchmark datasets that contain varying numbers of clusters with diverse shapes. The experimental results revealed that the proposed SSRF-CA accomplished the best performance among other state-of-the-art algorithms and conﬁrmed the outperformance of our clustering approach.","PeriodicalId":354250,"journal":{"name":"Int. J. Fuzzy Log. Intell. Syst.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124156185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2