• Journal of the Korea Society of Computer and Information
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• v.19 no.4
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• pp.55-62
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• 2014

This paper proposes a fault detection method utilizing converted images of acoustic emission signals and texture analysis for identifying bearing's multi-faults which frequently occur in an induction motor. The proposed method analyzes three texture features from the converted images of multi-faults: multi-faults image's entropy, homogeneity, and energy. These extracted features are then used as inputs of a fuzzy-ARTMAP to identify each multi-fault including outer-inner, inner-roller, and outer-roller. The experimental results using ten times trials indicate that the proposed method achieves 100% accuracy in the fault classification.

• Geomechanics and Engineering
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• v.23 no.5
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• pp.441-454
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• 2020

The tunnel collapse, large deformation of surrounding rock, water and mud inrush are the major geological disasters in soft rock tunnel construction. Among them, tunnel collapse has the most serious impact on tunnel construction. Current research backed theories have certain limitations in identifying the collapse risk of soft rock tunnels. Examining the Zhengwan high-speed railway tunnel, eight soft rock tunnel collapse influencing factors were selected, and the combination of indicator weights based on the analytic hierarchy process and entropy weighting methods was obtained. The results show that the groundwater condition and the integrity of the rock mass are the main influencing factors leading to a soft rock tunnel collapse. A comprehensive fuzzy evaluation model for the collapse risk of soft rock tunnels is being proposed, and the real-time collapse risk assessment of the Zhengwan tunnel is being carried out. The results obtained via the fuzzy evaluation model agree well with the actual situation. A tunnel section evaluated to have an extremely high collapse risk and experienced a local collapse during excavation, verifying the feasibility of the collapse risk evaluation model. The collapse risk evaluation model proposed in this paper has been demonstrated to be a promising and innovative method for the evaluation of the collapse risk of soft rock tunnels, leading to safer construction.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.155-160
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• 2014

There are many image processing based algorithms and applications that fail when illumination change occurs. Therefore, the illumination change has to be detected then the illumination change occurred images need to be enhanced in order to keep the appropriate algorithm processing in a reality. In this paper, a new method for detecting illumination changes efficiently in a real time by using local region information and fuzzy logic is introduced. The effective way for detecting illumination changes in lighting area and the edge of the area was selected to analyze the mean and variance of the histogram of each area and to reflect the changing trends on previous frame's mean and variance for each area of the histogram. The ways are used as an input. The changes of mean and variance make different patterns w hen illumination change occurs. Fuzzy rules were defined based on the patterns of the input for detecting illumination changes. Proposed method was tested with different dataset through the evaluation metrics; in particular, the specificity, recall and precision showed high rates. An automatic parameter selection method was proposed for contrast limited adaptive histogram equalization method by using entropy of image through adaptive neural fuzzy inference system. The results showed that the contrast of images could be enhanced. The proposed algorithm is robust to detect global illumination change, and it is also computationally efficient in real applications.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.293-298
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• 2016

Document summarization is an important task in various areas where the goal is to select a few the most descriptive sentences from a given document as a succinct summary. Even without training data of human labeled summaries, there has been several interesting existing work in the literature that yields reasonable performance. In this paper, within the same unsupervised learning setup, we propose a more principled learning framework for the document summarization task. Specifically we formulate an optimization problem that expresses the requirements of both faithful preservation of the document contents and the summary length constraint. We circumvent the difficult integer programming originating from binary sentence selection via continuous relaxation and the low entropy penalization. We also suggest an efficient convex-concave optimization solver algorithm that guarantees to improve the original objective at every iteration. For several document datasets, we demonstrate that the proposed learning algorithm significantly outperforms the existing approaches.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.109-112
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• 2001

영상분할에 있어서 최적의 임계치를 구하는 것은 영상을 구성하고 있는 픽셀들을 의미있는 집단으로 나누는 거와 같으며 이를 위하여 퍼지화 정도를 측정하여 최소의 퍼지화 정도를 갖는 임계치를 최적의 임계치로 설정한다. 일반적으로 소속도는 하나의 픽셀과 그 픽셀이 속한 영역의 관계로 표현될 수 있는데 소속도 계산을 위한 엔트로피로 샤논(Shannon)함수를 사용한다[1]. Liang-Kai Huang에 의하여 제안된 알고리즘은 그 수렴속도 면에 있어서 많은 문제점을 갖고 있다[2]. 본 논문에서는 이런 수렴속도를 좀더 개선하기 위하여 SPOI(Simplified Fixed Point Iteration)를 제안하고 여러 가지 실험영상을 사용하여 졔안된 논문의 우수성을 보이고자 한다. 실험결과 적절한 임계치를 구하면서도 기존의 논문보다 속도면에서 상당히 우수한 특성을 보이고 있다.

• Science of Emotion and Sensibility
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• v.1 no.2
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• pp.93-103
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• 1998

효율적이고 체계적인 퍼지제어를 위해 조작자의 제어동작을 모델링하거나 공정을 모델링하는 기법이 필요하고, 또한 퍼지 추론시에 조건부의 기여도(contribution factor)의 결정과 동작부의 제어량의 결정이 추론의 결과에 중요하다. 본 논문에서는 추론시 조건부의 기여도와 동작부의 세어량이 퍼지 엔트로피의 개념하에서 수행되는 적응 퍼지 추론시스템을 제시한다. 제시된 시스템은 전방향 신경회로망의 토대위에서 구현되며 주건부의 기여도가 퍼지 엔트로피에 의하여 구해지고, 동작부의 제어량은 확장된 퍼지 엔트로피에 의하여 구해진다. 이를 위한 학습 알고리즘으로는 역전파 알고리즘을 이용하여 조건부의 파라미터의 동정을 하고 동작부 파라미터의 동정에는 국부해에 보다 강인한 유전자 알고리즘을 이용하다. 이러한 모델링 기법을 임펄스 잡음과 가우시안 잡음이 첨가된 영상에 적용하여 본 결과, 영상복원시에 발생되는 여러 가지의 경우에 대한 적응성이 보다 양호하게 유지되었고, 전체영상의 20%의 데이터만으로도 객관적 화질에 있어서 기존의 추론 방법에 비해 향상을 보였다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1999.11a
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• pp.388-395
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• 1999

본 논문의 목적은 퍼지 엔트로피를 이용하여 비선형신호를 예측하는 것이다. 이 방법은 분할된 여러 부 공간(subspace)에 대해 입력 데이터로부터 퍼지 엔트로피를 이용하여 각각의 규칙에 등급을 정하여 불필요한 제어규칙을 제거하여 바람직한 규칙베이스를 구성하도록 한 것이다. 적용되는 퍼지 신경망의 기본적인 구조는 퍼지 제어기의 규칙베이스와 추론의 과정을 신경회로망을 이용하여 구현하며 퍼지 제어규칙의 매개변수들은 역전파 알고리즘에 의해 적응되어진다. 또한 매개변수의 수를 줄이기 위하여 제어규칙의 결론부의 출력값은 신경망의 가중치로 구성하였다. 결국 퍼지 신경망의 복잡도를 줄일 수 있다. Mackey-Glass 시계열의 예측에 대한 컴퓨터 시뮬레이션을 통하여 본 논문에서 제안한 방법의 효율성을 입증하고, 제안된 방법을 EEG 생리신호 분석에 이용될 수 있다.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.1-13
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• 2002

In this paper, we propose an object tracking system which can be convinced of moving area shaped on objects through color sequential images, decided moving directions of foot messengers or vehicles of image sequences. In static camera, we suggests a new evaluating method extracting co-occurrence matrix with feature vectors of RGB after analyzing and blocking difference images, which is accessed to field of camera view for motion. They are energy, entropy, contrast, maximum probability, inverse difference moment, and correlation of RGB color vectors. we describe how to analyze and compute corresponding relations of objects between adjacent frames. In the clustering, we apply an algorithm of FCM(fuzzy c means) to analyze matching and clustering problems of adjacent frames of the featured vectors, energy and entropy, gotten from previous phase. In the matching phase, we also propose a method to know correspondence relation that can track motion each objects by clustering with similar area, compute object centers and cluster around them in case of same objects based on membership function of motion area of adjacent frames.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.20-27
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• 2006

Multi-level thresholding is a method that is widely used in image segmentation. However most of the existing methods are not suited to be directly used in applicable fields and moreover expanded until a step of image segmentation. This paper proposes region-based multi-level thresholding as an image segmentation method. At first we classify pixels of each color channel to two clusters by using EWFCM(Entropy-based Weighted Fuzzy C-Means) algorithm that is an improved FCM algorithm with spatial information between pixels. To obtain better segmentation results, a reduction of clusters is then performed by a region-based reclassification step based on a similarity between regions existing in a cluster and the other clusters. The clusters are created using the classification information of pixels according to color channel. We finally perform a region merging by Bayesian algorithm based on Kullback-Leibler distance between a region and the neighboring regions as a post-processing method as many regions still exist in image. Experiments show that region-based multi-level thresholding is superior to cluster-, pixel-based multi-level thresholding, and the existing mettled. And much better segmentation results are obtained by the post-processing method.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2773-2784
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• 2023

It is essential to evaluate the blasting-enhanced permeability (BEP) feasibility of a low-permeability sandstone-type uranium deposit. In this work, the mineral composition, reservoir physical properties and rock mechanical properties of samples from sandstone-type uranium deposits were first measured. Then, the reformability evaluation method was established by the analytic hierarchy process-entropy weight method (AHP-EWM) and the fuzzy mathematics method. Finally, evaluation results were verified by the split Hopkinson Pressure Bar (SHPB) experiment and permeability test. Results show that medium sandstone, argillaceous sandstone and siltstone exhibit excellent reformability, followed by coarse sandstone and fine sandstone, while the reformability of sandy mudstone is poor and is not able to accept BEP reservoir stimulation. The permeability improvement and the distribution of damage fractures before and after the SHPB experiment confirm the correctness of evaluation results. This research provides a reformability evaluation method for the BEP of the low-permeability sandstone-type uranium deposit, which contributes to the selection of the appropriate regional and stratigraphic horizon of the BEP and the enhanced ISL of the low-permeability sandstone-type uranium deposit.