• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.310-317
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• 2022

The mass attenuation coefficient is the primary physical parameter to model narrow beam gamma-ray attenuation. A new machine learning based approach is proposed to model gamma-ray shielding behavior of composites alternative to theoretical calculations. Two fuzzy logic algorithms and a neural network algorithm were trained and tested with different mixture ratios of vanadium slag/epoxy resin/antimony in the 0.05 MeV-2 MeV energy range. Two of the algorithms showed excellent agreement with testing data after optimizing adjustable parameters, with root mean squared error (RMSE) values down to 0.0001. Those results are remarkable because mass attenuation coefficients are often presented with four significant figures. Different training data sizes were tried to determine the least number of data points required to train sufficient models. Data set size more than 1000 is seen to be required to model in above 0.05 MeV energy. Below this energy, more data points with finer energy resolution might be required. Neuro-fuzzy models were three times faster to train than neural network models, while neural network models depicted low RMSE. Fuzzy logic algorithms are overlooked in complex function approximation, yet grid partitioned fuzzy algorithms showed excellent calculation efficiency and good convergence in predicting mass attenuation coefficient.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.306-314
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• 2000

In this paper we present a new approach to estimate link travel speed based on the hybrid neuro-fuzzy network. It combines the fuzzy ART algorithm for structure learning and the backpropagation algorithm for parameter adaptation. At first, the fuzzy ART algorithm partitions the input/output space using the training data set in order to construct initial neuro-fuzzy inference network. After the initial network topology is completed, a backpropagation learning scheme is applied to optimize parameters of fuzzy membership functions. An initial neuro-fuzzy network can be applicable to any other link where the probe car data are available. This can be realized by the network adaptation and add/modify module. In the network adaptation module, a CBR(Case-Based Reasoning) approach is used. Various experiments show that proposed methodology has better performance for estimating link travel speed comparing to the existing method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1203-1208
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• 2016

For young children who are not spontaneous or not accurate in verbal communication of their emotions and experiences, drawing is a good means of expressing their status in mind and thus drawing analysis with chromatics is a traditional tool for art therapy. Recently, children enjoy digital drawing via painting tools thus there is a growing needs to develop an automatic digital drawing analysis tool based on chromatics and art therapy theory. In this paper, we propose such an analyzing tool based on dominant color analysis. Technically, we use ART2 clustering and fuzzy logic to understand the fuzziness of subjects' status of mind expressed in their digital drawings. The frequency of color usage is fuzzified with respect to the membership functions. After applying fuzzy logic to this fuzzified central vector, we determine the dominant color and supporting colors from the digital drawings and children's status of mind is then analyzed according to the color-personality relationships based on Alschuler and Hattwick's historical researches.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.167-175
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• 2007

Fuzzy logic is used to represent qualitative knowledge and provides interpretability to a controlling system model in bioinformatics. This paper focuses on a bioinformatics data classification which is an important bioinformatics application. This paper reviews the two traditional controlling system models The sequence-based threshold controller have problems of optimal range decision for threshold readjustment and long processing time for optimal threshold induction. And the binary-based threshold controller does not guarantee for early system stability in the GPCR data classification for optimal threshold induction. To solve these problems, we proposes a fuzzy-based threshold controller for ART1 clustering in GPCR classification. We implement the proposed method and measure processing time by changing an induction recognition success rate and a classification threshold value. And, we compares the proposed method with the sequence-based threshold controller and the binary-based threshold controller The fuzzy-based threshold controller continuously readjusts threshold values with membership function of the previous recognition success rate. The fuzzy-based threshold controller keeps system stability and improves classification system efficiency in GPCR classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1428-1434
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• 2018

Canine cataract is developed with aging and can cause the blindness or surgical treatment if not treated timely. In this paper, we propose a method for extracting cataract suspicious areas automatically with FCM(Fuzzy C_Means) algorithm to overcome the weakness of previously attempted ART2 based method. The proposed method applies the fuzzy stretching technique and the Max-Min based average binarization technique to the dog eye images photographed by simple devices such as mobile phones. After applying the FCM algorithm in quantization, we apply the brightness average binarization method in the quantized region. The two binarization images - Max-Min basis and brightness average binarization - are ANDed, and small noises are removed to extract the final cataract suspicious areas. In the experiment with 45 dog eye images with canine cataract, the proposed method shows better performance in correct extraction rate than the ART2 based method.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.130-132
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• 2005

This paper presents two ART-based neural networks for the identification of gas mixtures subject to the drift. A fuzzy ARTMAP neural network is used for classifying $H_2S$, $NH_3$ and their mixture gases including a reference gas. The other fuzzy ART neural network is utilized to detect the drift of a tin oxide gas sensor by tracking a cluster center of the reference gas. After detecting the drift, the previous cluster center of each gas is updated as much as the drift of the reference gas. By the simulations, the proposed method is shown to compensate the drift on-line without making many categories of target gases compared with the previous studies.

• Honam Mathematical Journal
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• v.44 no.3
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• pp.461-472
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• 2022

In optimization theory, hypo-convergence is considered as an effective tool by providing the convergence of supremum values under some conditions. This feature makes it different from other types of convergence. Therefore, we have defined the hypo-convergence of a sequence of fuzzy sets due to the increasing interest in fuzzy set theory in recent years. After giving a theoretical framework, we deal with the optimization process by using a sequential characterization of hypo-convergence of sequence of fuzzy sets. Since the maximization process in optimization theory is beyond the presence of hypo-convergence, we give some conditions to satisfy the convergence of supremum values. Furthermore, we show how sequence of fuzzy sets and fuzzy numbers differ in the convergence of the supremum values.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.01a
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• pp.227-230
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• 2018

본 논문에서는 ART2를 적용하여 X-Ray 영상에서 손목 골절을 추출하는 방법을 제안한다. 제안된 방법에서는 X-ray 영상에서 손목에서의 요골을 추출하기 위해서 요골 및 척골 부위를 ROI 영역으로 설정한다. 설정된 ROI 영역에서 명암 대비를 강조하기 위해 사다리꼴 형태의 Fuzzy Stretching 기법을 적용한다. 사다리꼴 형태의 Fuzzy Stretching 기법이 적용된 ROI 영역에 ART2 기법을 적용하여 요골 및 척골 영역에서 골절이 존재하지 않은 영역을 제거한다. 골절이 존재하지 않은 영역이 제거된 ROI 영역에 다시 ART2 기법을 적용하여 골절의 후보 영역을 추출한다. 추출된 후보 골절 영역을 라벨링한 후, 뼈의 가장자리에 존재하는 골절 후보 영역을 제거한다. 그리고 남아 있는 골절 후보 영역 중에서 가장 큰 두 개의 영역을 골절 부위 영역으로 판단하여 최종적으로 골절 부위 영역를 추출한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.12
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• pp.954-961
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• 2013

In this paper, we propose a new learning method using a variable learning to improve pattern recognition in the FCSR(Fast Commit Slow Recode) learning method of the Fuzzy ART. Traditional learning methods have used a fixed learning rate in updating weight vector(representative pattern). In the traditional method, the weight vector will be updated with a fixed learning rate regardless of the degree of similarity of the input pattern and the representative pattern in the category. In this case, the updated weight vector is greatly influenced from the input pattern where it is on the boundary of the category. Thus, in noisy environments, this method has a problem in increasing unnecessary categories and reducing pattern recognition capacity. In the proposed method, the lower similarity between the representative pattern and input pattern is, the lower input pattern contributes for updating weight vector. As a result, this results in suppressing the unnecessary category proliferation and improving pattern recognition capacity of the Fuzzy ART in noisy environments.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.07a
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• pp.271-273
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• 2013

본 논문에서는 비파괴 검사를 통하여 얻은 세라믹 영상에 퍼지 기법과 ART2 기법을 적용하여 결함을 검출하는 방법을 제안한다. 제안된 방법은 세라믹 소재로 얻어진 영상에서 결함의 구간을 설정하기 위해 퍼지 스트레칭 기법을 적용하여 명암도를 대비시킨다. 명암 대비가 강조된 영상에서 퍼지 이진화 기법을 적용한 후, 상/하 경계선에 가장 많이 분포된 곳을 Max, Min으로 설정하고, Max+20, Min-20을 결함 구간으로 설정한다. 설정한 결함 구간 내의 비파괴 세라믹 영상에서 ART2 알고리즘 기법을 적용하여 세라믹 영상의 결함을 검출한다. 본 논문에서 제안된 방법을 비파괴 세라믹 영상을 대상으로 실험한 결과, 제안된 방법이 기존의 세라믹 결함 검출 방법보다 비파괴 세라믹 영상에서 다양한 형태의 결함이 검출되는 것을 확인하였다.