• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.5
• /
• pp.473-478
• /
• 2013

In this paper, we develop the Heavy Rain Advisory Decision Model based on intelligent neuro-fuzzy algorithm RBFNNs by using KLAPS(Korea Local Analysis and Prediction System) Reanalysis data. the prediction ability of existing heavy rainfall forecasting systems is usually affected by the processing techniques of meteorological data. In this study, we introduce the heavy rain forecast method using the pre-processing techniques of meteorological data are in order to improve these drawbacks of conventional system. The pre-processing techniques of meteorological data are designed by using point conversion, cumulative precipitation generation, time series data processing and heavy rain warning extraction methods based on KLAPS data. Finally, the proposed system forecasts cumulative rainfall for six hours after future t(t=1,2,3) hours and offers information to determine heavy rain advisory. The essential parameters of the proposed model such as polynomial order, the number of rules, and fuzzification coefficient are optimized by means of Differential Evolution.

• Journal of Biomedical Engineering Research
• /
• v.19 no.5
• /
• pp.477-486
• /
• 1998

In this paper, we implemented the urine analysis system capable of measuring a qualitative and semi-quantitative and assay using strip. The analysis algorithm of urine analysis was adopted a fuzzy logic-based classifiers that was robust to external error factors such as temperature and electric power noises. The spectroscopic properties of 9 pads In a strip were studied to developing the urine analysis system was designed for robustnesss and stability. The urine analysis system was consisted of hardware and software. The hardware of the urine analysis system was based on one-chip microprocessor, and Its peripherals which composed of optic modulo, tray control, preamplifier, communication with PC, thermal printer and operating status indicator. The software of the urine analysis system was composed of system program and classification program. The system program did duty fort system control, data acquisition and data analysis. The classification program was composed of fuzzy inference engine and membership function generator. The membership function generator made triangular membership functions by statical method for quality control. Resulted data was transferred through serial cable to PC. The transferred data was arranged and saved be data acquisition program coded by C+ + language. The precision of urine analysis system and the stability of fuzzy classifier were evaluated by testing the standard urine samples. Experimental results showed a good stability states and a exact classification.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1534-1538
• /
• 2005

In this paper, we propose a method to detect contours of a face, eyes, and a mouth of a person in the color image in order to make an avatar automatically. First, we use the HSI color model to exclude the effect of various light conditions, and find skin regions in the input image by using the skin color defined on HS-plane. And then, we use deformable templates and genetic algorithm (GA) to detect contours of a face, eyes, and a mouth. Deformable templates consist of B-spline curves and control point vectors. Those represent various shapes of a face, eyes and a mouth. GA is a very useful search algorithm based on the principals of natural selection and genetics. Second, the avatar is automatically created by using GA-detected contours and Fuzzy C-Means clustering (FCM). FCM is used to reduce the number of face colors. In result, we could create avatars which look like handmade caricatures representing user's identity. Our approach differs from those generated by existing methods.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.6
• /
• pp.70-77
• /
• 2008

This paper proposes a new text candidate region detection method that uses genetic algorithm based on information of the segmented regions. In image segmentation, a classification of the pixels at each color channel and a reclassification of the region-unit for reducing inhomogeneous clusters are performed. EWFCM(Entropy-based Weighted C-Means) algorithm to classify the pixels at each color channel is an improved FCM algorithm added with spatial information, and therefore it removes the meaningless regions like noise. A region-based reclassification based on a similarity between each segmented region of the most inhomogeneous cluster and the other clusters reduces the inhomogeneous clusters more efficiently than pixel- and cluster-based reclassifications. And detecting text candidate regions is performed by genetic algorithm based on energy and variance of the directional edge components, the number, and a size of the segmented regions. The region information-based detection method can singles out semantic text candidate regions more accurately than pixel-based detection method and the detection results will be more useful in recognizing the text regions hereafter. Experiments showed the results of the segmentation and the detection. And it confirmed that the proposed method was superior to the existing methods.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.6
• /
• pp.533-538
• /
• 2013

The damage caused by Recent frequently occurring locality torrential rains is increasing rapidly. In case of densely populated metropolitan area, casualties and property damage is a serious due to landslides and debris flows and floods. Therefore, the importance of predictions about the torrential is increasing. Precipitation characteristic of the bad weather in Korea is divided into typhoons and torrential rains. This seems to vary depending on the duration and area. Rainfall is difficult to predict because regional precipitation is large volatility and nonlinear. In this paper, Very short-term precipitation forecasting pattern model is implemented using KLAPS data used by Korea Meteorological Administration. we designed very short term precipitation forecasting pattern model using GA-based RBFNNs. the structural and parametric values such as the number of Inputs, polynomial type,number of fcm cluster, and fuzzification coefficient are optimized by GA optimization algorithm.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.1
• /
• pp.195-201
• /
• 2014

In this study, face recognition system was designed based on polynomial Radial Basis Function Neural Networks(pRBFNNs) pattern classifier using 2-directional 2-dimensional principal component analysis algorithm. Existing one dimensional PCA leads to the reduction of dimension of image expressed by the multiplication of rows and columns. However $(2D)^2PCA$(2-Directional 2-Dimensional Principal Components Analysis) is conducted to reduce dimension to each row and column of image. and then the proposed intelligent pattern classifier evaluates performance using reduced images. The proposed pRBFNNs consist of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with the aid of fuzzy c-means clustering. In the conclusion part of rules. the connection weight of RBFNNs is represented as the linear type of polynomial. The essential design parameters (including the number of inputs and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. Using Yale and AT&T dataset widely used in face recognition, the recognition rate is obtained and evaluated. Additionally IC&CI Lab dataset is experimented with for performance evaluation.

• Journal of Sensor Science and Technology
• /
• v.22 no.2
• /
• pp.162-173
• /
• 2013

The purpose of this paper is to classify VOC gases by emulating the characteristics found in biological olfaction. For this purpose, we propose new signal processing method based a polymeric chemical sensor array consisting of 4096 sensors which is created by NEUROCHEM project. To remove unstable sensors generated in the manufacturing process of very large scaled chemical sensor array, we used discrete wavelet transformation and cosine similarity. And, to remove the supernumerary redundancy, we proposed the method of selecting candidates of representative sensor representing sensors with similar features by Fuzzy c-means algorithm. In addition, we proposed an improved algorithm for selecting representative sensors among candidates of representative sensors to better enhance classification ability. However, Classification for very large scaled sensor array has a great deal of time in process of learning because many sensors are used for learning though a redundancy is removed. Throughout experimental trials for classification, we confirmed the proposed method have an outstanding classification ability, at transient state as well as steady state.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.62 no.5
• /
• pp.696-704
• /
• 2013

In this paper, we introduce the LED emotional lighting system realized with the aid of both intelligent algorithm and RGB LED combined with White LED. Generally, the illumination is known as a design factor to form the living place that affects human's emotion and action in the light- space as well as the purpose to light up the specific space. The LED emotional lighting system that can express emotional atmosphere as well as control the quantity of light is designed by using both RGB LED to form the emotional mood and W LED to get sufficient amount of light. RBFNNs is used as the intelligent algorithm and the network model designed with the aid of LED control parameters (viz. color coordinates (x and y) related to color temperature, and lux as inputs, RGBW current as output) plays an important role to build up the LED emotional lighting system for obtaining appropriate color space. Unlike conventional RBFNNs, Fuzzy C-Means(FCM) clustering method is used to obtain the fitness values of the receptive function, and the connection weights of the consequence part of networks are expressed by polynomial functions. Also, the parameters of RBFNN model are optimized by using PSO(Particle Swarm Optimization). The proposed LED emotional lighting can save the energy by using the LED light source and improve the ability to work as well as to learn by making an adequate mood under diverse surrounding conditions.

• Journal of Korea Multimedia Society
• /
• v.12 no.1
• /
• pp.107-121
• /
• 2009

First reason for generation of standby power is because starting voltage must pass through from the source of electricity to IC. The second reason is due to current when IC is in operation. Purpose of this abstract is on structures of simple modules that automatically switch on or off through analysis of state on standby power and analysis of cutoff point patterns as well as application of algorithms. To achieve this, this paper is based on analysis of electric signals and modeling. Also, on/off cutoff criteria has been established for reduction of standby power. To find on/off cutoff point, that is executed algorithm of similar group and leading pattern group generation in the standby power state. Therefore, the algorithm was defined as an important parameter of the subtraction value of calculated between $1^{st}$ SCS, $2^{nd}$ SCS, and the median value of sampling coefficient per second from a wall outlet.

• Journal of the Korean Society for Precision Engineering
• /
• v.23 no.1 s.178
• /
• pp.64-71
• /
• 2006

Ultraprecision machining and MEMS technology have been taken more and more important position in machining of microparts. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro parts to micro products. Also, the method of micro-grooving using micro endmill is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This investigation deals with state monitoring using acoustic emission(AE) signal in the micro-grooving. Characteristic evaluation of AE raw signal, AE hit and frequency analysis for condition monitoring is presented. Also, the feature extraction of AE signal directly related to machining process is executed. Then, the distinctive micro endmill state according to the each tool condition is classified by the fuzzy C-means algorithm.