• Nuclear Engineering and Technology
• /
• v.25 no.3
• /
• pp.371-380
• /
• 1993

A model free controller utilizing a set of linguistic fuzzy logic of the human operator's experience is developed to control the steam generator water level in a pressurized water reactor. Only 9 rules for control action are generated from the inputs of water level error and mass flow error implicitly representing the time variation of the collapsed water level. The bell type membership functions of the premise side and the result side are tuned by the sensitivity study. This compact fuzzy logic controller shows a robust control during transient and no offset error and oscillation during steady state operation. For a multi-ramp power increase from start-up to full power, the proposed controller shows good performance for the entire range.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.11a
• /
• pp.377-380
• /
• 2012

본 연구에서는 학습데이터의 빈도요소를 반영하도록 수정된 구조의 FMM 신경망을 소개하고, 이로부터 패턴 분류를 위한 지식 표현을 생성하는 방법론을 제안한다. 하이퍼박스 멤버쉽함수는 5종류의 퍼지 분할을 기반으로 설정한 구간에 대하여 소속정도를 반영하여 결정하며, 각 차원별로 특징범위의 폭과 빈도 요소로부터 가중치 값이 학습된다. 본 연구에서는 제안된 이론을 수화인식 문제를 대상으로 고찰하였다. 인식 시스템의 구성은 특징추출을 위하여 3차원으로 확장된 구조의 CNN 모델을 사용하였으며, 수화패턴 데이터의 표현은 모션 히스토리 볼륨(Motion History Volume) 구조를 기반으로 하였다. 6종류의 수화패턴 동영상으로부터 27개 특징요소를 추출하고 이를 사용한 FMM 신경망의 학습과정과 지식의 추출 과정을 실험으로 보이고 그 유용성을 고찰한다.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.37 no.3
• /
• pp.1-10
• /
• 2009

Wooded areas that are a part of school campuses are one type of urban forest. Most schools located in an urban environment make an excellent setting for a forest in terms of location and area. These kinds of wooded spaces also make the city greener and healthier. As a place where students spend a great deal of time, schools can also be a venue for environmental education. The creation of wooded areas in schools currently has focused on the end result only; by ignoring student needs and participation, these areas have not had a significant influence on student environmental education. Previous studies based on questionnaire survey are significant in that they have quantified subjective qualitative data via Likert Scale. There has been, however, a problem in quantifying the more ambiguous subjective data. Therefore, this paper has attempted to investigate those factors that have an influence on student satisfaction with the wooded areas of their school campus using Fuzzy Theory with elementary school students in Gyeongsangbuk-do. A change was observed in terms of the ranking of arithmetic mean values of 'school peculiarity' and 'emotion evolution' and center of gravity, which has adopted Fuzzy Theory, proving that Fuzzy Theory could rationally objectify qualitative data such as human thoughts. In terms of the influential factors on the satisfaction with school forests(regression coefficient), 'school uniqueness(0.159)' was the highest, followed by 'many trees(0.142),' 'importance of nature(0.136)' and 'emotion evolution(0.130).' This paper may therefore be useful as basic data for objective questionnaire surveys and the development of school forests.

• Journal of Internet Computing and Services
• /
• v.17 no.6
• /
• pp.17-23
• /
• 2016

In the future network such as Internet of Things (IoT), the number of computing devices are expected to grow exponentially, and each of the things communicates with the others and acquires information by itself. Due to the growing interest in IoT applications, the broadcasting in Opportunistic ad-hoc networks such as Machine-to-Machine (M2M) is very important transmission strategy which allows fast data dissemination. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose a fuzzy logic based probabilistic multi-hop broadcast (FPMCAST) algorithm which statistically disseminates data accordingly to the remaining energy rate, the replication density rate of sending node, and the distance rate between sending and receiving nodes. In proposed FPMCAST, the inference engine is based the fuzzy rule base which is consists of 27 if-then rules. It maps input and output parameters to membership functions of input and output. The output of fuzzy system defines the fuzzy sets for rebroadcasting probability, and defuzzification is used to extract a numeric result from the fuzzy set. Here Center of Gravity (COG) method is used to defuzzify the fuzzy set. Then, the performance of FPMCAST is evaluated through a simulation study. From the simulation, we demonstrate that the proposed FPMCAST algorithm significantly outperforms flooding and gossiping algorithms. Specially, the FPMCAST algorithm has longer network lifetime because the residual energy of each node consumes evenly.

• Journal of Digital Contents Society
• /
• v.2 no.2
• /
• pp.103-115
• /
• 2001

This paper proposes the two algorithms which generate a minimal decision rule and approximate inference operation, adapted the rough set and the factor space theory in fuzzy knowledge base. The generation of the minimal decision rule is executed by the data classification technique and reduct applying the correlation analysis and the Bayesian theorem related attribute factors. To retrieve the specific object, this paper proposes the approximate inference method defining the membership function and the combination operation of t-norm in the minimal knowledge base composed of decision rule. We compare the suggested algorithms with the other retrieval theories such as possibility theory, factor space theory, Max-Min, Max-product and Max-average composition operations through the simulation generating the object numbers and the attribute values randomly as the memory size grows. With the result of the comparison, we prove that the suggested algorithm technique is faster than the previous ones to retrieve the object in access time.

• Science of Emotion and Sensibility
• /
• v.7 no.3
• /
• pp.15-25
• /
• 2004

The regression analysis and Adaptive -Network based Fuzzy-inference system (ANFIS) were applied to the explanation on human's visual texture of cotton fabrics with 7 mechanical properties. The ANFIS uses the structure with fuzzy membership function and neural network. The results obtained by the statistical analysis through the coefficient of correlation and regression analysis showed that subjective texture had a linear relationship with mechanical properties. But It had a relatively low coefficient of determination and was difficult that the statistical analysis explained other relationship with the exception of a lineality and interaction among mechanical properties. Comparing the statistical analysis, the ANFIS was an effective tool to explain human's non-linear perceptions and their interactions. But to apply ANFIS to human's perceptions more effectively, it is necessary to discriminate effective input variables through controlling the properties of samples.

• The Journal of the Korea Contents Association
• /
• v.11 no.6
• /
• pp.1-7
• /
• 2011

The methodology of KOSPI forecast has been considered as one of the most difficult problem to develop accurately since short-term KOSPI is correlated with various factors including politics and economics. In this paper, we presents a methodology for forecasting short-term trends of stock price for five days using the feature selection method based on a neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by removing the worst input features one by one. A technical indicator are selected for preprocessing KOSPI data in the first step. In the second step, thirty-nine numbers of input features are produced by wavelet transforms. Twelve numbers of input features are selected as the minimized numbers of input features from thirty-nine numbers of input features using the non-overlap area distribution measurement method. The proposed method shows that sensitivity, specificity, and accuracy rates are 72.79%, 74.76%, and 73.84%, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.7
• /
• pp.350-356
• /
• 2017

With increasing attention to real-time body detection, active research is being conducted on human body detection based on skin color. Despite this, most existing skin detection methods utilize static skin color models and have detection rates in images, in which colors are distorted. This study proposed a method of detecting the skin region using a fuzzy classification of the gradient map, saturation, and Cb and Cr in the YCbCr space. The proposed method, first, creates a gradient map, followed by a saturation map, CbCR map, fuzzy classification, and skin region binarization in that order. The focus of this method is to rigorously detect human skin regardless of the lighting, race, age, and individual differences, using features other than color. On the other hand,the borders between these features and non-skin regions are unclear. To solve this problem, the membership functions were defined by analyzing the relationship between the gradient, saturation, and color features and generate 108 fuzzy rules. The detection accuracy of the proposed method was 86.35%, which is 2~5% better than the conventional method.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.6 no.2
• /
• pp.231-236
• /
• 2011

The binarization algorithm frequently applies to one part of the preprocessing phase for a variety of image processing techniques such as image recognition and image analysis, etc. So it is important that binarization algorithm is determined by the selection of threshold value for binarization in image processing. The previous algorithms could get the proper threshold value in the case that shows all the difference of brightness between background and object, but if not, they could not get the proper threshold value. In this paper, we propose the efficient fuzzy binarization method which first, segments the brightness range of gray_scale images to 2 intervals to perform car license plate binarization and applies fuzzy member function to each intervals. The experiment for performance evaluation of the proposed binarization algorithm showed that the proposed algorithm generates the more effective threshold value than the previous algorithms in car license plate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2003.10a
• /
• pp.801-804
• /
• 2003

The autopilot system of vessel is proposed to take service safety sorority, to elevate service efficiency, to decrease labor and to improve working environment. Ultimate purpose of it is to minimize the number of crew by guaranteeing economical efficiency of shipping service. Recently, the research is being achieving to compensate various nonlinear parameters of vessel and apply it is course keeping control, track keeping control, roll-rudder stabilization, dynamic ship positioning and automatic mooring control etc. using optimizing control technique. Relation between rudder angle controlled by steering machine of vessel and ship-heading angle, and load condition of ship are nonlinear, which affect various parameters of shipping service. The speed and direction of waves, velocity and quantity of wind, which also cause the non-linearity of it. Therefore the autopilot system of ship requires the robust control algorithm can overcome various non-linearity. On this paper, we design the autopilot system of ship, which overcome nonlinear parameters and disturbance of it using Fuzzy Algorithm, evaluate the proposed algorithm and its excellence through simulation