• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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• pp.761-764
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• 2000

This paper presents a Fuzzy Neural Network (FNN) system to decide whether or not the right information of lanes can be extracted from gray-level images of road scene. The decision of noisy level of input images has been required because much noises usually deteriorates the performance of feature detection based on image processing and lead to erroneous results. As input parameters to FNN, eight noisiness indexes are constructed from a cumulative distribution function (CDF) and proved the indexes being classifiers of images as the good and the bad corrupted by sources of noise by correlation analysis between input images and the indexes. Considering real-time processing and discrimination efficiency, the proposed FNN is structured by eight input parameters, three fuzzy variables and single output. We conduct much experiments and show that our system has comparable performance in terms of false-positive rates.

• 전기전자학회논문지
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• 제4권2호
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• pp.173-180
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• 2000

본 논문에서는 퍼지이론을 적용하여 평형감과 생체신호의 관계를 자동해석하고 평가하였다. 온도자극법을 실시하여 현기증을 유발하였고, 생체신호의 정량화를 통하여 생체신호와 현기증과의 상관관계를 제시하였다. Table-Lookup 방법을 이용한 적응퍼지규칙 설계에 의하여 생체신호의 유기적인 관계를 객관적으로 분석하여 피험자의 평형감 정도를 판단하는데 있어서 좋은 결과를 나타내었다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2005년도 춘계학술발표대회
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• pp.427-430
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• 2005

사람의 홍채는 태어날 때 한번 정해지면 평생 변화하지 않는 특성을 가지고 있으며, 개개인별로 모양이 모두 다른 것으로 알려져 있다. 이에, 본 논문에서는 홍채영상 취득시 조명에 의한 동공의 크기 변화에 민감하지 않은 2차원의 홍채패턴을 취득하여, 2D Gabor Wavelet과 Fuzzy LDA를 이용하여 특징 벡터를 추출한다. 인식과정에서는 correlation 계수를 이용하여 서로 다른 홍채의 특징 값에 대해 유사도를 측정하고 유사도가 가장 큰 대상을 찾게 된다. 이때, 4개 방향의 Gabor Wavelet을 거쳐 얻어진 영상에 대해 최고의 값을 인식 대상자로 인정하므로 오 인식 될 확률을 최소화 할 수 있다. 제안한 알고리듬의 유용성을 확인하기 위해 대상자 50명에 대하여 각각 6회씩 촬영한 두 가지 데이터베이스(CASIA, CBNU)를 이용하였으며, 실험 결과 90% 이상의 높은 인식률을 얻었다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.158-164
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• 1999

This paper investigated and evaluated the NASA's Shuttle Imaging Radar-C (SIR-C) multiple frequency SAR data for differential backscattering effects of microwave from the surface geological materials overlying the skarn type mineralization. Although an integrated approach in mineral exploration is more cost effective and is well in use, there are still many technical and scientific issues to be further investigated and researched. In this study we have reprocessed several sets of previously surveyed exploration data and experimented with fuzzy logic digital fusion of the preprocessed data with respect to chosen exploration targets. Among the numerous fuzzy logic operators, which are currently available for a data driven integrated exploration strategy, we used varying combinations of fuzzy MIN, fuzzy MAX, and fuzzy SUM operators along with Gamma operator for fusion of exploration data, including the contact metamorphic zone information. The final exploration target tested was a skarn type W-Mo-F mineralization in the study area. The fuzzy logic derived mineral potential anomaly almost exactly matched the differential backscattering anomalies on the C-band and L-band SIR_C data when overlaid on each other. Although this high degree of correlation between these two data sets is remarkable, the differential backscattering anomaly over the skarn type W-Mo-F mineralization in the study area requires further investigation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권1호
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• pp.1-3
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• 2009

Recently, Carlsson, Full\acute{e}$r and Majlender [1] presented the concept of possibilitic correlation representing an average degree of interaction between marginal distribution of a joint possibility distribution as compared to their respective dispersions. They also formulated the weak and strong forms of the possibilistic Cauchy-Schwarz inequality. In this paper, we define a new probability measure. Then the weak and strong forms of the Cauchy-Schwarz inequality are immediate consequence of probabilistic Cauchy-Schwarz inequality with respect to the new probability measure.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1998년도 추계학술대회
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• pp.201-202
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• 1998

In this paper we suggested an automated method for detecting and counting rapid eye movement(REM) using EOG during sleep. This method is formulated by two step fuzzy logic. At first step, the velocity and the distance of single channel eye movement are used for the fuzzy input to get the possibility of being REM at each EOG. At second step, the two possibility values of both EOG from the first step and the correlation coefficient of both eye movements are used for the fuzzy logic input, and the output is the final possibility of being Rapid Eye Movement. We applied this algorithm to the normal and narcoleptic sleep data and compared the difference. We found the possibility that the count of REM can be a parameter that has significant physiological meanings.

• Journal of Astronomy and Space Sciences
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• 제29권4호
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• pp.389-395
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• 2012

The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system (ANFIS). An ANFIS produces a control signal for one of the three axes of a spacecraft's body frame, so in total three ANFISs are constructed for 3-axis attitude control. The fuzzy inference system of the ANFIS is initialized using a subtractive clustering method. The ANFIS is trained by a hybrid learning algorithm using the data obtained from attitude control simulations using state-dependent Riccati equation controller. The training data set for each axis is composed of state errors for 3 axes (roll, pitch, and yaw) and a control signal for one of the 3 axes. The stability region of the ANFIS controller is estimated numerically based on Lyapunov stability theory using a numerical method to calculate Jacobian matrix. To measure the performance of the ANFIS controller, root mean square error and correlation factor are used as performance indicators. The performance is tested on two ANFIS controllers trained in different conditions. The test results show that the performance indicators are proper in the sense that the ANFIS controller with the larger stability region provides better performance according to the performance indicators.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2818-2820
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• 2002

In this paper, we design neurofuzzy networks architecture by means of linear fuzzy inference. The proposed neurofuzzy networks are equivalent to linear fuzzy rules, and the structure of these networks is composed of two main substructures, namely premise part and consequence part. The premise part of neurofuzzy networks use fuzzy space partitioning in terms of all variables for considering correlation between input variables. The consequence part is networks constituted as first-order linear form. The consequence part of neurofuzzy networks in general structure(for instance ANFIS networks) consists of nodes with a function that is a linear combination of input variables. But that of the proposed neurofuzzy networks consists of not nodes but networks that are constructed by connection weight and itself correspond to a linear combination of input variables functionally. The connection weights in consequence part are learned by back-propagation algorithm. For the evaluation of proposed neurofuzzy networks. The experimental results include a well-known NASA dataset concerning software cost estimation.

• 대한인간공학회:학술대회논문집
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• 대한인간공학회 1996년도 춘계학술대회논문집
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• pp.113-127
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• 1996

In Human Reliability Analysis(HRA), the uncertainties involved in many factors that affect human reliability have to be represented as the quantitative forms. Conventional probability- based human reliability theory is used to evaluate the effect of those uncertainties but it is pointed out that the actual human reliability should be different from that of conventional one. Conventional HRA makes use of error rates, however, it is difficult to collect data enough to estimate these error rates, and the estimates of error rates are dependent only on engineering judgement. In this paper, the error possibility that is proposed by Onisawa is used to represent human reliability, and the error possibility is obtained by use of fuzzy reasoning that plays an important role to clarify the relation between human reliability and human error. Also, assuming these factors are connected to the top event through Fault Tree structure, the influence and correlation of these factors are measured by fuzzy operation. When a fuzzy operation is applied to Fault Tree Analysis, it is possible to simplify the operation applying the logic disjuction and logic conjuction to structure function, and the structure of human reliability can be represented as membership function of the top event. Also, on the basis of the the membership function, the characteristics of human reliability can be evaluated by use of the concept of pattern recognition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1224-1242
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• 2018

Aiming at motion analysis and compensation, it is essential to conduct motion detection with images. However, motion detection and tracking from low-altitude images obtained from an unmanned aerial system may pose many challenges due to degraded image quality caused by platform motion, image instability and illumination fluctuation. This research tackles these challenges by proposing a modified joint transform correlation algorithm which includes two preprocessing strategies. In spatial domain, a modified fuzzy edge detection method is proposed for preprocessing the input images. In frequency domain, to eliminate the disturbance of self-correlation items, the cross-correlation items are extracted from joint power spectrum output plane. The effectiveness and accuracy of the algorithm has been tested and evaluated by both simulation and real datasets in this research. The simulation experiments show that the proposed approach can derive satisfactory peaks of cross-correlation and achieve detection accuracy of displacement vectors with no more than 0.03pixel for image pairs with displacement smaller than 20pixels, when addition of image motion blurring in the range of 0~10pixel and 0.002variance of additive Gaussian noise. Moreover,this paper proposes quantitative analysis approach using tri-image pairs from real datasets and the experimental results show that detection accuracy can be achieved with sub-pixel level even if the sampling frequency can only attain 50 frames per second.