• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.05a
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• pp.186-189
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• 2005

본 논문에서는 LVQ를 퍼지화한 새로운 퍼지 학습 법칙들을 제안하였다. 퍼지 LVQ 학습법칙 1은 기존의 학습률 대신에 퍼지 학습률을 사용하였는데 이는 조건 확률의 퍼지화에 기반을 두고 있다. 퍼지 LVQ 학습법칙 2는 클래스들 사이에 존재하는 입력벡터가 결정 경계선에 대한 정보를 더 가지고 있는 것을 반영한 것이다. 이 새로운 퍼지 학습 법칙들을 improved IAFC(Integrted Adaptive Fuzzy Clustering)신경회로망에 적용하였다. improved IAFC신경회로망은 ART-1 (Adaptive Resonance Theory)신경회로망과 Kohonen의 Self-Organizing Feature Map의 장점을 취합한 퍼지 신경회로망이다. 제안한 supervised IAFC 신경회로망 1과 supervised IAFC neural 신경회로망 2의 성능을 오류 역전파 신경회로망의 성능과 비교하기 위하여 iris 데이터를 사용하였는데 Supervised IAFC neural network 2가 오류 역전파 신경회로망보다 성능이 우수함을 보여주었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.128-133
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• 1998

In this paper, we present a parallel framework for tracking moving objects. Parallel framework consists largely of two parts:Search Space Reduction(SSR) and Tracking(TR). SSR is further composed of fuzzy clustering and prediction based on Kalman filter. TR is done by boundarymatching using the Hausdorff distance based on distance transform.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.81-83
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• 2021

4차 산업 혁명과 함께 인공지능이 발전 하고 있다. 그 CNN 등 과 같은 이미지 관련 신경망들이 발전되어 가스 탐지와 같은 여러 분야에서 사용되고 있다. 하지만 가스 탐지는 Box 형태의 탐지가 일반적이고 Segmentation에 관한 연구는 있지만 연기와 같이 경계선이 불분명한 개체에 대해서는 연구가 미비하다. 본 논문에서는 Segmentation에 강력한 성능을 보이는 U-net을 활용하여 Box 형태가 아닌 Segmentation을 진행하여 픽셀단위로 연기를 탐지하고자 한다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.6
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• pp.15-24
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• 2011

As increase awareness of intelligent vehicle systems, many kinds of lane detection algorithm have been proposed. General boundary extraction method can bring good result in detection of lane on the road. But a shadow on the road, or other boundaries, such as horizontal lines can be detected. The method using morphological operations was used to extract information about Lane. By applying HSV color model for color information of lane, the candidate of the lane can be extracted. In this paper, the lane detection region was set by Hough transformation using the candidate of the lane. By extracting lane markings on the lane detection region, lane detection method can bring good result.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1051-1060
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• 2018

This study aims to propose instituting the serious games of language-based cognitive enhancement for the BIF children. The program consists of 4 cognitive areas (perception, attention, working memory, knowledge inference) in 4 language dimensions (phoneme, word, sentence, discourse). 16 games of 4 areas/2 dimensions with 3 difficulty levels were implemented in a mobile station and pilot-tested to children including BIFs. The results from the pilot tests supported for the validity and effectiveness of our games: Children's game performance correlated with their IQ scores (overall and sub-areas) revealing significant differences between the groups. The stroop scores in pre-and-post training hinted the increase of children's cognitive control.

• The Magazine of the IEIE
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• v.22 no.4
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• pp.51-63
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• 1995

멀티미디어 사용자 인터페이스는 사용자의 동작을 통한 입력, 사용자에 대한 시스템의 응답, 사용자와 시스템 사이의 대화를 관리하고 운영하는 환경이다. 최근에 신호처리 기술, 인식 기술, 소프트웨어 기술, 인공지능 기술을 기반으로한 MMUI에 관한 연구활동이 많은 관심을 끌고 있다. 사용자가 시스템에 정보를 보다 자연스럽고 다양하게 제공하고 시스템으로부터 필요한 정보를 다양한 형태로 효율적으로 제공하기 위한 MMUI를 설계하기 위하여 분야의 경계선을 넘어 상호 동조하는 연구팀을 구성하여 활발히 진행되고 있다. MMUI개발자는 비디오나 오디오 입출력을 고려해야 하므로 이러한 매체들을 통합하는 문제와 시간에 따라 변하는 시스템을 위한 모델링을 하는 문제들을 대면하게 된다. MMUI개발자들이 대면하는 대표적인 문제는 매체 병합, 모델과 메타포(metaphor) 사용, 네비게이션(navigation),입출력 다루기, 동기화, 새로운 매체 개발이다. 그러므로 본 고에서는 멀티미디어 제품 및 서비스를 위한 MMUI기술과 그 연구방향을 살펴본다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.291-294
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• 2007

비디오 시스템으로 영상 획득하는 과정에서 다양한 조명상태는 얼굴인식에 있어서 심각한 영향을 준다. 본 논문에서는 웨이블렛 변환을 이용하여 영상을 나눈 후 각각의 영역에 얼굴영상을 인식에 알맞은 조명상태로 변환한다. 변환과정은 얼굴을 히스토그램 지정연산으로 명암에 대해 개선과 경계부분을 강화하여 얼굴인식을 위한 영상을 만들어 인식률을 높였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.391-396
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• 2003

This paper proposes a new approach that converts continuous-valued attributes to categorical-valued ones considering the distribution of target attributes(classes). In this approach, It can be possible to get optimal interval boundaries by considering the distribution of data itself without any requirements of parameters. For each attributes, the distribution of target attributes is projected to one-dimensional space. And this space is clustered according to the criteria like as the density value of each target attributes and the amount of overlapped areas among each density values of target attributes. Clusters which are made in this ways are based on the probabilities that can predict a target attribute of instances. Therefore it has an interval boundaries that minimize a loss of information of original data. An improved performance of proposed discretization method can be validated using C4.5 algorithm and UCI Machine Learning Data Repository data sets.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.95-103
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• 1998

This paper deals with a problem occuring in recognition of tactile images due to the effects of imposed force at a me urement moment. Tactile image of a contact surface, used for recognition of the surface type, varies depending on the forces imposed so that a false recognition may result in. This paper fuzzifies two parameters of the contour of a tactile image with the membership function formed by considering the imposed force. Two fuzzifed paramenters are fused by the average Minkowski's dist; lnce. The proposed algorithm was implemented on the multisensor system cnmposed of an optical tact le sensor and a 6 axes forceltorque sensor. By the experiments, the proposed algorithm has shown average recognition ratio greater than 869% over all imposed force ranges and object models which is about 14% enhancement comparing to the case where only the contour information is used. The pro- ~oseda lgorithm can be used for end-effectors manipulating a deformable or fragile objects or for recognition of 3D objects by implementing on multi-fingered robot hand.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.179-190
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• 2022

Data imbalance refers to a phenomenon in which the number of data in one category is too large or too small compared to another category. Due to this, it has been raised as a major factor that deteriorates performance in machine learning that utilizes classification algorithms. In order to solve the data imbalance problem, various ovrsampling methods for amplifying prime number distribution data have been proposed. Among them, SMOTE is the most representative method. In order to maximize the amplification effect of minority distribution data, various methods have emerged that remove noise included in data (SMOTE-IPF) or enhance only border lines (Borderline SMOTE). This paper proposes a method to ultimately improve classification performance by improving the processing method for anomaly data in the traditional SMOTE method that amplifies minority classification data. The proposed method consistently presented relatively high classification performance compared to the existing methods through experiments.