• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.187-194
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• 2008

Because the types and severities of most engine faults are various and complex, it is not easy that the conventional model based fault detection approach like the GPA(Gas Path Analysis) method can monitor all engine fault conditions. Therefore this study proposed newly a diagnostic algorithm for isolating and diagnosing effectively the faulted components of the smart UAV propulsion system, which has been developed by KARI(Korea Aerospace Research Institute), using the fuzzy logic and the neural network algorithms. A precise performance model should be needed to perform the model-based diagnostics. The based engine performance model was developed using SIMULINK. For the work and mass flow matching between components of the steady-state simulation, the state-flow library was applied. The proposed steady-state performance model can simulate off-design point performance at various flight conditions and part loads, and in order to evaluate the steady-state performance model their simulation results were compared with manufacturer's performance deck data. According to comparison results, it was confirm that the steady-state model well agreed with the deck data within 3% in all flight envelop. The diagnosis procedure of the proposed diagnostic system has the following steps. Firstly after obtaining database of fault patterns through performance simulation, then secondly the diagnostic system was trained by the FFBP networks. Thirdly after analyzing the trend of the measuring parameters due to fault patterns, then fourthly faulted components were isolated using the fuzzy logic. Finally magnitudes of the detected faults were obtained by the trained neural networks. Because the detected faults have almost same as degradation values of the implanted fault pattern, it was confirmed that the proposed diagnostic system can detect well the engine faults.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.145-148
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• 2007

생명과학분야에서 마이크로어레이 기술은 세포에서의 RNA 발현 프로파일을 관찰할 수 있도록 함으로써 생명현상의 규명 및 약물개발 둥에서 분자수준의 생명현상에 대한 관찰과 분석이 가능 해지고 있다. 마이크로어레이 데이터분석에서는 특정한 처리나 과정에서 현저한 특성을 보이는 유전자를 식별하기 위한 분석뿐만 아니라 유전자 전체인 게놈수준에서의 분석도 이루어진다. 최근 유전자의 발현이 다양한 조절, 신호전달 및 대사경로에 의해서 영향을 받고 있다는 관점에서 게놈수준의 분석에 관심이 증가하고 있다. 약물반응 실험에서는 약물에 대한 게놈수준의 발현 프로파일을 관찰하는 것도 많은 정보를 제공할 수 있다. 약물실험에서는 대조군과 실험군들간에 관심 있는 상대적인 발현특성을 갖는 유전자군을 전체적으로 추출하는 것이 필요한 경우가 있다. 예를 들면 정상군은 두개의 실험군에 대해서 중간청도의 발현정도를 갖는 유전자군을 식별하는 분석을 하는 경우, 생물학적인 데이터의 특성상 절대값을 비교하는 방법으로는 유용한 유전자들을 효과적으로 식별해 낼 수 없다. 이 논문에서는 정상군과 실험군들의 발현정도값의 경향을 판단하기 위해서 각 유전자에 대해서 집단별 대표값을 선정하여 퍼지집합으로 집단의 값의 범위를 결정하고, 이를 이용하여 특정 패턴을 갖는 유전자들을 식별해내는 방법을 제안하고, 실제 데이터를 통해서 실험한 결과를 보인다.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.233-238
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• 1993

In this paper, two diagnosis algorithms using the simple fuzzy, cognitive map (FCM) that is an useful qualitative model are proposed. The first basic algorithm is considered as a simple transition of Shiozaki's signed directed graph approach to FCM framework. And the second one is an extended version of the basic algorithm. In the extension, three important concepts, modified temporal associative memory (TAM) recall, temporal pattern matching algorithm and hierarchical decomposition are adopted. As the resultant diagnosis scheme takes short computation time, it can be used for on-line fault diagnosis of large scale and complex processes that conventional diagnosis methods cannot be applied. The diagnosis system can be trained by the basic algorithm and generates FCM model for every experienced process fault. In on-line application, the self-generated fault model FCM generates predicted pattern sequences, which are compared with observed pattern sequences to declare the origin of fault. In practical case, observed pattern sequences depend on transport time. So if predicted pattern sequences are different from observed ones, the time weighted FCM with transport delay can be used to generate predicted ones. The fault diagnosis procedure can be completed during the actual propagation since pattern sequences of tvo different faults do not coincide in general.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1287-1292
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• 2005

This paper presents an effective pattern classification model by designing an artificial neural network based pattern classifiers for face recognition. First, a RGB image inputted from a frame grabber is converted into a HSV image which is similar to the human beings' vision system. Then, the coarse facial region is extracted using the hue(H) and saturation(S) components except intensity(V) component which is sensitive to the environmental illumination. Next, the fine facial region extraction process is performed by matching with the edge and gray based templates. To make a light-invariant and qualified facial image, histogram equalization and intensity compensation processing using illumination plane are performed. The finally extracted and enhanced facial images are used for training the pattern classification models. The proposed H-ART2 model which has the hierarchical ART2 layers and F-LVQ model which is optimized by fuzzy membership make it possible to classify facial patterns by optimizing relations of clusters and searching clustered reference patterns effectively. Experimental results show that the proposed face recognition system is as good as the SVM model which is famous for face recognition field in recognition rate and even better in classification speed. Moreover high recognition rate could be acquired by combining the proposed neural classification models.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.137-140
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• 2003

캐리커처의 일반적인 의미는 어떤 사람이나 사물의 특징을 추출하여 익살스럽게 풍자한 그림이나 글이다. 다시 말해, 캐리커처는 사람의 얼굴에서 특징을 잡아 과장하거나 왜곡하여 그린 데생이라고 한다. 컴퓨터를 이용한 기존의 캐리커처 제작방법으로는, 입력 이미지 좌표의 통계적인 차이값을 이용하는 PICASSO System 방법[1], 제작자의 애매한 느낌을 퍼지 논리를 이용하여 표현하는 방법, 이미지를 와핑하는 방법, 여러 단계의 벡터 필드 변환을 이용하는 방법등이 연구되어 왔다. 본 논문에서는 실시간 또는 준비된 영상을 입력으로 받아 저장한 후, 네 단계의 과정으로 처리한 후 최종적으로 캐리커처된 이미지를 생성하게 된다. 각 단계별 처리 내용으로는 첫번째 단계에서는 영상에서 얼굴을 검출하고 두번째 단계에서는 특정 얼굴부위의 기하학적 정보를 좌표값으로 추출한다. 세번째 단계에서는 전 단계에서 얻은 좌표값으로 로컬 와핑 기법을 이용하여 영상을 변환한다. 네 번째 단계에서는 변형된 영상으로 퍼지 논리를 이용하여 보다 개선된 윤곽선 이미지로 변환하여 캐리커처 이미지를 얻는다. 본 논문에서는 영상 인식, 변환 및 윤곽선 검출 및 둥의 여러 가지 영상 처리 기법을 이용하여 기존의 캐리커처 제작 방식보다 간단하고, 복잡한 연산 과정이 없는 캐리커처 제작 시스템을 구현하였다.

• Journal of Biomedical Engineering Research
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• v.20 no.2
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• pp.149-154
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• 1999

In anatomical aspects, magnetic resonance image offers more accurate information than other medical images such as X ray, ultrasonic and CT images. This paper introduces a method that segments and detects lesion for 2 dimensional axial MR brain images automatically. Image segmentation process consists of 2 stages. First stage extracts cerebrum region using thresholding and morphology. In the second stage, white matter, gray matter and cerebrospinal fluid in the cerebrum are extracted using FCM, We could improve processing time as removing uninterested region. Finally symmetry measure and anatomical Knowledge are used to detect lesion.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.316-321
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• 2008

Advanced computer network technology enables computers to be connected in an open network environment. Despite the growing numbers of security threats to networks, most intrusion detection identifies security attacks mainly by detecting misuse using a set of rules based on past hacking patterns. This pattern matching has a high rate of false positives and can not detect new hacking patterns, which makes it vulnerable to previously unidentified attack patterns and variations in attack and increases false negatives. Intrusion detection and analysis technologies are thus required. This paper investigates the asymmetric costs of false errors to enhance the performances the detection systems. The proposed method utilizes the network model to consider the cost ratio of false errors. By comparing false positive errors with false negative errors, this scheme achieved better performance on the view point of both security and system performance objectives. The results of our empirical experiment show that the network model provides high accuracy in detection. In addition, the simulation results show that effectiveness of anomaly traffic detection is enhanced by considering the costs of false errors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.8B
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• pp.1490-1499
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• 1999

This paper proposes a new handwritten numeral recognition method using numeral shape grasps and FSOM (Fuzzy Self-Organizing Map). The proposed algorithm is based on the idea that numeral input data with similar shapes are classified into the same class. Shapes of numeral data are created using lines of external-contact and the class of numeral data is determined by template matching of the shapes. Each class of numeral data has FSOM and feature extraction method, respectively. In this paper, we divide the numeral database into the 16 classes. The divided FSOM model allows not only an independent learning phase of SOM but also step-by-step learning. Experiments using Concordia University handwritten numeral database proved that the proposed algorithm is effective to improve recognition accuracy.

• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1970-1979
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• 1999

This paper proposes an AVI system which recognizes the license plate and the driver's face simultaneously using passive AVI techniques. For this, firstly, the pro-processing algorithm independent of the environment is proposed and region extraction of the car number plate and the driver's face is described. Secondly, characters are separated and recognition parameters are extracted from target regions. Thirdly, template matching of car number plate is performed and the fuzzy relation matrix of driver face is made for the final recognition processes. The merits of the proposed system are following : Pre-processing is accomplished regardless of the environment. The application areas of conventional AVI system can be expanded in the content that the driver's face is also recognized in the proposed system compared with only the number plast is recognized in the existing systems.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.1-15
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• 2006

In Korea, a resident registration card has various personal information such as a present address, a resident registration number, a face picture and a fingerprint. A plastic-type resident card currently used is easy to forge or alter and tricks of forgery grow to be high-degree as time goes on. So, whether a resident card is forged or not is difficult to judge by only an examination with the naked eye. This paper proposed an automatic recognition method of a resident card which recognizes a resident registration number by using a refined ART2-based RBF network newly proposed and authenticates a face picture by a template image matching method. The proposed method, first, extracts areas including a resident registration number and the date of issue from a resident card image by applying Sobel masking, median filtering and horizontal smearing operations to the image in turn. To improve the extraction of individual codes from extracted areas, the original image is binarized by using a high-frequency passing filter and CDM masking is applied to the binaried image fur making image information of individual codes better. Lastly, individual codes, which are targets of recognition, are extracted by applying 4-directional contour tracking algorithm to extracted areas in the binarized image. And this paper proposed a refined ART2-based RBF network to recognize individual codes, which applies ART2 as the loaming structure of the middle layer and dynamicaly adjusts a teaming rate in the teaming of the middle and the output layers by using a fuzzy control method to improve the performance of teaming. Also, for the precise judgement of forgey of a resident card, the proposed method supports a face authentication by using a face template database and a template image matching method. For performance evaluation of the proposed method, this paper maked metamorphoses of an original image of resident card such as a forgey of face picture, an addition of noise, variations of contrast variations of intensity and image blurring, and applied these images with original images to experiments. The results of experiment showed that the proposed method is excellent in the recognition of individual codes and the face authentication fur the automatic recognition of a resident card.