• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.47-57
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• 2010

The object detection is essential for identifying objects, location information, and user context-aware in the image. In this paper, we propose a robust object detection system. The System linearly transforms learning data obtained from the background images to Principal components. It organizes the Eigen-background with the selected Principal components which are able to discriminate between foreground and background. The Fuzzy-C-means (FCM) carries out clustering for images with inputs from the Eigen-background information and classifies them into objects and backgrounds. It used various patterns of backgrounds as learning data in order to implement a system applicable even to the changing environments, Our system was able to effectively detect partial movements of a human body, as well as to discriminate between objects and backgrounds removing noises and shadows without anyone frame image for fixed background.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.735-740
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• 2012

In this paper, we introduce design methodologies of polynomial radial basis function neural network classifier with the aid of Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA). By minimizing the information loss of given data, Feature data is obtained through preprocessing of PCA and LDA and then this data is used as input data of RBFNNs. The hidden layer of RBFNNs is built up by Fuzzy C-Mean(FCM) clustering algorithm instead of receptive fields and linear polynomial function is used as connection weights between hidden and output layer. In order to design optimized classifier, the structural and parametric values such as the number of eigenvectors of PCA and LDA, and fuzzification coefficient of FCM algorithm are optimized by Artificial Bee Colony(ABC) optimization algorithm. The proposed classifier is applied to some machine learning datasets and its result is compared with some other classifiers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5568-5587
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• 2018

This research uses artificial intelligence methods for computer network intrusion detection system modeling. Primary classification is done using self-organized maps (SOM) in two levels, while the secondary classification of ambiguous data is done using Sugeno type Fuzzy Inference System (FIS). FIS is created by using Adaptive Neuro-Fuzzy Inference System (ANFIS). The main challenge for this system was to successfully detect attacks that are either unknown or that are represented by very small percentage of samples in training dataset. Improved algorithm for SOMs in second layer and for the FIS creation is developed for this purpose. Number of clusters in the second SOM layer is optimized by using our improved algorithm to minimize amount of ambiguous data forwarded to FIS. FIS is created using ANFIS that was built on ambiguous training dataset clustered by another SOM (which size is determined dynamically). Proposed hybrid model is created and tested using NSL KDD dataset. For our research, NSL KDD is especially interesting in terms of class distribution (overlapping). Objectives of this research were: to successfully detect intrusions represented in data with small percentage of the total traffic during early detection stages, to successfully deal with overlapping data (separate ambiguous data), to maximize detection rate (DR) and minimize false alarm rate (FAR). Proposed hybrid model with test data achieved acceptable DR value 0.8883 and FAR value 0.2415. The objectives were successfully achieved as it is presented (compared with the similar researches on NSL KDD dataset). Proposed model can be used not only in further research related to this domain, but also in other research areas.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.151-155
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• 2019

Recently heart attack is 80% of the sudden death of elderly. The causes of a heart attack are complex and sudden, and it is difficult to predict the onset even if prevention or medical examination is performed. Therefore, early diagnosis and proper treatment are the most important. In this paper, we show the accuracy of normal and abnormal classification with neural network using weighted fuzzy function for accurate and rapid diagnosis of myocardial infarction. The data used in the experiment was data from the UCI Machine Learning Repository, which consists of 14 features and 303 sample data. The algorithm for feature selection uses the average of weight method. Two features were selected and removed. Heart attack was classified into normal and abnormal(1-normal, 2-abnormal) using the average of weight method. The test result for the diagnosis of heart attack using a weighted fuzzy neural network showed 87.66% accuracy.

• Journal of Navigation and Port Research
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• v.47 no.2
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• pp.75-84
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• 2023

Ships need to ensure safety during their navigation, which makes route determination highly important. It must be accompanied by a route following controller that can accurately follow the route. This study proposes a method for automatically generating the ship route based on deep reinforcement learning algorithm and following it using a route following controller. To generate a ship route, under keel clearance was applied to secure the ship's safety and navigation chart information was used to apply ship navigation related regulations. For the experiment, a target ship with a draft of 8.23 m was designated. The target route in this study was to depart from Busan port and arrive at the pilot boarding place of the Ulsan port. As a route following controller, a velocity type fuzzy P ID controller that could compensate for the limitation of a linear controller was applied. As a result of using the deep Q network, a route with a total distance of 62.22 km and 81 waypoints was generated. To simplify the route, the Douglas-Peucker algorithm was introduced to reduce the total distance to 55.67 m and the number of way points to 3. After that, an experiment was conducted to follow the path generated by the target ship. Experiment results revealed that the velocity type fuzzy P ID controller had less overshoot and fast settling time. In addition, it had the advantage of reducing the energy loss of the ship because the change in rudder angle was smooth. This study can be used as a basic study of route automatic generation. It suggests a method of combining ship route generation with the route following control.

• Geomechanics and Engineering
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• v.34 no.6
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• pp.697-726
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• 2023

Brittleness as an important property of rock plays a crucial role both in the failure process of intact rock and rock mass response to excavation in engineering geological and geotechnical projects. Generally, rock brittleness indices are calculated from the mechanical properties of rocks such as uniaxial compressive strength, tensile strength and modulus of elasticity. These properties are generally determined from complicated, expensive and time-consuming tests in laboratory. For this reason, in the present research, an attempt has been made to predict the rock brittleness indices from simple, inexpensive, and quick laboratory test results namely dry unit weight, porosity, slake-durability index, P-wave velocity, Schmidt rebound hardness, and point load strength index using multiple linear regression, exponential regression, support vector machine (SVM) with various kernels, generating fuzzy inference system, and regression tree ensemble (RTE) with boosting framework. So, this could be considered as an innovation for the present research. For this purpose, the number of 39 rock samples including five igneous, twenty-six sedimentary, and eight metamorphic were collected from different regions of Iran. Mineralogical, physical and mechanical properties as well as five well known rock brittleness indices (i.e., B₁, B₂, B₃, B₄, and B₅) were measured for the selected rock samples before application of the above-mentioned machine learning techniques. The performance of the developed models was evaluated based on several statistical metrics such as mean square error, relative absolute error, root relative absolute error, determination coefficients, variance account for, mean absolute percentage error and standard deviation of the error. The comparison of the obtained results revealed that among the studied methods, SVM is the most suitable one for predicting B₁, B₂ and B₅, while RTE predicts B₃ and B₄ better than other methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.1
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• pp.177-185
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• 2009

Cluster analysis or clustering is a kind of unsupervised learning method in which a set of data points is divided into a given number of homogeneous groups. Fuzzy clustering method, one of the most popular clustering method, allows a point to belong to all the clusters with different degrees, so produces more intuitive and natural clusters than hard clustering method does. Even more some of fuzzy clustering variants have noise-immunity. In this paper, we improved the Possibilistic Fuzzy C-Means (PFCM), which generates a membership matrix as well as a typicality matrix, using Gath-Geva (GG) method. The proposed method has a focus on the boundaries of clusters, which is different from most of the other methods having a focus on the centers of clusters. The generated membership values are suitable for the classification-type applications. As the typicality values generated from the algorithm have a similar distribution with the values of density function of Gaussian distribution, it is useful for Gaussian-type density estimation. Even more GG method can handle the clusters having different numbers of data points, which the other well-known method by Gustafson and Kessel can not. All of these points are obvious in the experimental results.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.970-976
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• 2007

In this study, Polynomial Network Pattern Classifier(PNC) based on Fuzzy Inference Mechanism is designed and its parameters such as learning rate, momentum coefficient and fuzzification coefficient are optimized by means of Particle Swarm Optimization. The proposed PNC employes a partition function created by Fuzzy C-means(FCM) clustering as an activation function in hidden layer and polynomials weights between hidden layer and output layer. Using polynomials weights can help to improve the characteristic of the linear classification of basic neural networks classifier. In the viewpoint of linguistic analysis, the proposed classifier is expressed as a collection of "If-then" fuzzy rules. Namely, architecture of networks is constructed by three functional modules that are condition part, conclusion part and inference part. The condition part relates to the partition function of input space using FCM clustering. In the conclusion part, a polynomial function caries out the presentation of a partitioned local space. Lastly, the output of networks is gotten by fuzzy inference in the inference part. The proposed PNC generates a nonlinear discernment function in the output space and has the better performance of pattern classification as a classifier, because of the characteristic of polynomial based fuzzy inference of PNC.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.9
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• pp.1905-1912
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• 2009

Clustering results based on the FCM algorithm sometimes produces undesirable clustering result through data distribution in the clustered space because data is classified by comparison with membership degree which is calculated by the Euclidean distance between input vectors and clusters. Symmetrical measurement of clusters and fuzzy theory are applied to the classification to tackle this problem. The enhanced FCM algorithm has a low impact with the variation of changing distance about each cluster, middle of cluster and cluster formation. Improved hybrid network of applying FCM algorithm is proposed to classify patterns effectively. The proposed enhanced FCM algorithm is applied to the learning structure between input and middle layers, and normalized delta learning rule is applied in learning stage between middle and output layers in the hybrid network. The proposed algorithms compared with FCM-based RBF network using Max_Min neural network, FMC-based RBF network and HCM-based RBF network to evaluate learning and recognition performances in the two-dimensional coordinated data.