• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.1
• /
• pp.101-108
• /
• 2010

In this paper, we proposed a new lazy classifier with fuzzy k-nearest neighbors approach and feature selection which is based on reconstruction error. Reconstruction error is the performance index for locally linear reconstruction. When a new query point is given, fuzzy k-nearest neighbors approach defines the local area where the local classifier is available and assigns the weighting values to the data patterns which are involved within the local area. After defining the local area and assigning the weighting value, the feature selection is carried out to reduce the dimension of the feature space. When some features are selected in terms of the reconstruction error, the local classifier which is a sort of polynomial is developed using weighted least square estimation. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods such as standard neural networks, support vector machine, linear discriminant analysis, and C4.5 trees.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.1
• /
• pp.153-158
• /
• 2010

This paper showed the possibilities of performance improvement of Brain-Computer Interface (BCI) decreasing classification error rates of EEG signals by applying Variance-Considered Machine (VCM) which proposed in our previous study. BCI means controlling system such as computer by brain signals. There are many factors which affect performances of BCI. In this paper, we used suggested algorithm as a classification algorithm, the most important factor of the system, and showed the increased correct rates. For the experiments, we used data which are measured during imaginary movements of left hand and foot. The results indicated that superiority of VCM by comparing error rates of the VCM and SVM. We had shown excellence of VCM with theoretical results and simulation results. In this study, superiority of VCM is demonstrated by error rates of real data.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.5
• /
• pp.406-411
• /
• 2013

Big data issue has been considered in diverse fields. Also, big data learning has been required in all areas such as engineering and social science. Statistics and machine learning algorithms are representative tools for big data learning. In this paper, we study learning tools for big data and propose an efficient methodology for big data learning via legacy data to practical application. We apply our big data learning to patent analysis, because patent is one of big data. Also, we use patent analysis result for technology forecasting. To illustrate how the proposed methodology could be applied in real domain, we will retrieve patents related to big data from patent databases in the world. Using searched patent data, we perform a case study by text mining preprocessing and multiple linear regression of statistics.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.5
• /
• pp.383-389
• /
• 2016

In this paper, we present the hull blasting machine with vision-based weld bead recognition device for cleaning shipment exterior wall. The purpose of this study is to introduce the mechanism design of the high efficiency hull blasting machine using the vision system to recognize the weld bead. Therefore, we have developed a robot mechanism and drive controller system of the hull blasting robot. And hull blasting characteristics such as the climbing mechanism, vision system, remote controller and CAN have been discussed and compared with the experimental data. The hull blasting robots are able to remove rust or paint at anchor, so the re-docking is unnecessary. Therefore, this can save time and cost of undergoing re-docking process and build more vessels instead. The robot uses sensors to navigate safely around the hull and has a filter system to collect the fouling removed. We have completed a pilot test of the robot and demonstrated the drive control and CAN communication performance.

• Journal of Advanced Navigation Technology
• /
• v.22 no.5
• /
• pp.472-477
• /
• 2018

In this paper, we develop interactive content services for preventing depression of users through an intelligent Internet of Things(IoT) mirror system. For interactive content services, an IoT mirror device measures attention and meditation data from an EEG headset device and also measures facial expression data such as "sad", "angery", "disgust", "neutral", " happy", and "surprise" classified by a multi-layer perceptron algorithm through an webcam. Then, it sends the measured data to an oneM2M-compliant IoT server. Based on the collected data in the IoT server, a machine learning model is built to classify three levels of depression (RED, YELLOW, and GREEN) given by a proposed merge labeling method. It was verified that the k-nearest neighbor (k-NN) model could achieve about 93% of accuracy by experimental results. In addition, according to the classified level, a social network service agent sent a corresponding alert message to the family, friends and social workers. Thus, we were able to provide an interactive content service between users and caregivers.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.4
• /
• pp.295-300
• /
• 2016

Internet of things which helps communication between human and thing and between things by connecting networks on them is developing. Develops of Internet of things, network technique, and smart machine result interest on home network system. In this paper, we suggested a system with the home network to the dormitory using pressure sensors, infrared sensor, ultrasonic sensor, switch, arduino, raspberrypi, android application. Smart dormitory system based on the internet of things provide information whether public things in dormitory like laundry machine, computer, treadmill is being used now, and package is arrived through android application.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.6
• /
• pp.768-773
• /
• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

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

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.3
• /
• pp.297-305
• /
• 2008

Recently, kernel methods have attracted great interests in the areas of pattern classification, function approximation, and anomaly detection. The role of the kernel is particularly important in the methods such as SVM(support vector machine) and KPCA(kernel principal component analysis), for it can generalize the conventional linear machines to be capable of efficiently handling nonlinearities. This paper considers the problem of combining radar and rain gauge observations utilizing the regression approach based on the kernel-based gaussian process and support vector learning. The data-assimilation results of the considered methods are reported for the radar and rain gauge observations collected over the region covering parts of Gangwon, Kyungbuk, and Chungbuk provinces of Korea, along with performance comparison.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.3
• /
• pp.326-331
• /
• 2010

This paper presents the design and implementation of a data acquisition system with a large number of multi-channels for manufacturing machine. The system having a throughput of 800-ch analog signals has been designed with Quartus II tool and Cyclone II FPGA. The proposed system is suitable for the large scale data handling in order to distinguish whether the operation is correct or not. The designed system is composed of a control unit, voltage divider and USB interface. To reduce the data throughput, we utilized an algorithm which can extract the same data from the achieved data. The test results of the system adapted to a manufacturing machine, show a relevant data acquisition operation of 800 channels in short time.