• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2098-2106
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• 2014

In the area of clustering, there are numerous approaches to construct clusters in the input space. For regression problem, when forming clusters being a part of the overall model, the relationships between the input space and the output space are essential and have to be taken into consideration. Conditional Fuzzy C-Means (c-FCM) clustering offers an opportunity to analyze the structure in the input space with the mechanism of supervision implied by the distribution of data present in the output space. However, like other clustering methods, c-FCM focuses on the distribution of the data. In this paper, we introduce a new method, which by making use of the ambiguity index focuses on the boundaries of the clusters whose determination is essential to the quality of the ensuing classification procedures. The introduced design is illustrated with the aid of numeric examples that provide a detailed insight into the performance of the fuzzy classifiers and quantify several essentials design aspects.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.724-730
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• 1993

Most of clustering methods usually employ the center of a cluster to assign the input data into a cluster. When the shape of a cluster could not be easily represented by the center of cluster, however, it is difficult to assign input data into a proper cluster using previous methods. In this paper, to overcome such a difficulty, a cluster is to be represented as a collection of several subclusters. And membership functions are used to represent how much input data belong to subclusters. Then the position of each subcluster is adoptively corrected by use of a competitive learning neural network. To show the validity of the proposed method, a numerical example is illustrated, where FMMC(Fuzzy Min-Max Clustering) algorithm is compared with the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.94-99
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• 1993

In this paper, the method for navigation and obstacle avoidance of an autonomous mobile robot is proposed. It is based on the fuzzy inference system which enables to deal with imprecise and uncertain information, and on the neural network which enables to learn input and output pattern data obtained from ultrasonic sensors. For autonomous navigation, the wall-following navigation utilizing input and output data by an expert's control action is constructed. An approach by the neural network is developed for the obstacle avoidance because of the redundant input data. For an autonomous navigation, the fuzzy control and the control of the neural network are integrated and its feasibility is demonstrated by means of experiment.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.786-789
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• 2002

A recurrent artificial neural network (ANN) model is developed to simulate hydrocarbon recovery process at petroleum-contaminated site. The groundwater extraction rate, vacuum pressure, and saturation hydraulic conductivity are selected as the input variables, while the cumulative hydrocarbon recovery volume is considered as the output variable. The experimental data fer establishing the ANN model are from implementation of a multiphase flow model for dual phase remediation process under different input variable conditions. The complex nonlinear and dynamic relationship between input and output data sets are then identified through the developed ANN model. Reasonable agreements between modeling results and experimental data are observed, which reveals high effectiveness and efficiency of the neural network approach in modeling complex hydrocarbon recovery behavior.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.759-760
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• 2006

In this paper, The author designed on high speed VLD(Variable Length Decoder) using shift buffer. Variable Length Decoder is received N bit data from input block and decode the input signal using Shifting Buffer, Length Decoder and Symbol Decoder blocks. The inner part of shifting buffer in proposed Variable Length Decoder is filled input data and then operating therefore, the proposed structure can improve the decoded speed. And in this paper we applying pipeline structure therefore data is decoded in every clock.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.2
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• pp.137-143
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• 1989

System modeling from input-output data is generally carried out in two steps. The first step is to determine the form of the model. In the second step, the parameters of the model in an appropriate form are estimated from input-output data. This paper presents a method, via single term Walsh functions, for simultaneous estimation of the order and the parameters of linear systems from input-output data. The estimation of the model order is based on minimizing an error function, which is defined by Desai and Fairman. Unknown system parameters are recursively estimated by the least square method.

• IE interfaces
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• v.19 no.4
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• pp.291-299
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• 2006

In order to enhance sustainable war fighting capabilities, it is important to maintain a good ammunition support system. In this paper, we evaluate the performance of ammunition companies using Imprecise Data Envelopment Analysis (IDEA)-BCC and IDEA-Additive model, which can deal with imprecise data in DEA. The input variables of IDEA models were selected by stepwise multiple regression analysis. With the regression model, we could choose the number of soldiers, officers, and ammunition warehouses as input variables that have significant effects on the output performance. Then, we applied the IDEA-BCC model with the concept of potential efficiency. The results of the model indicate that 8 out of 16 ammunition companies are efficient, 7 are inefficient, and 1 is potentially efficient. We could also identify the possible input excesses and output shortfalls to reach the efficient frontier using the IDEA-Additive model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.700-712
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• 2011

Due to recently advanced electrical devices, human can access computer network regardless of working location or time restriction. However, currently widely used mouse, joystick, and trackball input system are not easy to carry and they bound user hands exclusively within working space. Those make user inconvenient in Ubiquitous environments.. In this paper, we propose multiple dimension human motion detection system based on wireless sensor networks. It is a portable input device and provides easy installation process and unbinds user hands during input processing stages. Our implemented system is comprised of three components. One is input unit that senses user motions and transmits collected data to receiver. Second is receiver that conveys the received data to application, which runs on server computer. Third is application that performs command operations according to received data. Experiments shows that proposed system accurately detect the characteristics of user arm motions and fully support corresponding input requests.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.48-52
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• 2001

In this paper presents an accurate AFM used that is free from the Z-directional distortion of a servo actuator is described. Two mathematical correction methods by the in-situ self-calibrationare employed in this AFM. One is the method by the integration, and the other is the method by inverse function of the calibration curve. The in situ self-calibration method by the integration, the derivative of the calibration curve function of the PZT actuator is calculated from the profile measurement data sets which are obtained by repeating measurements after a small Z-directional shift. Input displacement at each sampling point is approximately estimated first by using a straight calibration line. The derivative is integrated with reference to the approximate input to obtain the approximate calibration curve. Then the approximation of the input value of each sampling point is improved using the obtained calibration curve. Next the integral of the derivative is improved using the newly estimated input values. As a result of repeating these improving process, the calibration curve converges to the correct one, and the distortion of the AFM image can be corrected. In the in situ self-calibration through evaluating the inverse function of the calibration curve, the profile measurement data sets were used during the data processing technique. Principles and experimental results of the two methods are presented.

• Journal of Korea Multimedia Society
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• v.11 no.2
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• pp.247-256
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• 2008

Recently, natural disasters like flooding damages have frequently occurred as to typhoons and local downpours affected by the climate changes. Many researches have actively been studied in analysing runoff models, the verification of their parameters, and the inflow on surfaces in order to lessen the damages. However, much time and effort needs in generating input files of the models in most current researches. Therefore, in this paper we develop a system for generating a simulation input data automatically. This system is connected to the EPA-SWMM based on the spatial data in the UIS systems and consists the simulation module for analysing urban flooding and the SWMM simulator module. Also, we construct a prototype using a range of regular inundation to generate a simulation input file. This system gives advantages showing inundation areas based on the map viewer as well as lessening errors of input data and simulation time.