• Journal of the Korea Computer Industry Society
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• v.3 no.8
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• pp.1039-1048
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• 2002

It is difficult and laborious to distinguish between the original and the plagiarism about the electrical documents or on-line received documents, specially student homeworks because in many case, the homeworks are written on the same subject. Existing methods are not appropriate to solve this problem, which find the most appropriate category using the expression frequency of index term in documents to be classified. In this paper, a new classification method was proposed to distinguish between the original and the plagiarism about documents which were written similarly which is based on the syntagma vector - except the similar syntagma tree structure and non-index term.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.205-215
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• 2017

The brain magnetic resonance images (MRI) is an important imaging biomarker in Alzheimer's disease (AD) as the cerebral atrophy has been shown to strongly associate with cognitive symptoms. The decrease of volume estimates in different structures of the medial temporal lobe related to memory correlates with the decline of cognitive functions in neurodegenerative diseases. During the past decades several methods have been developed for quantifying the disease related atrophy of hippocampus from MRI. Special effort has been dedicated to separate AD and mild cognitive impairment (MCI) related modifications from normal aging for the purpose of early detection and prediction. We trained a multi-class support vector machine (SVM) with probabilistic outputs on a sample (n = 58) of 20 normal controls (NC), 19 individuals with MCI, and 19 individuals with AD. The model was then applied to the cross-validation of same data set which no labels were known and the predictions. This study presents data on the association between MRI quantitative parameters of hippocampus and its quantitative structural changes examination use on the classification of the diseases.

• Journal of Power Electronics
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• v.9 no.2
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• pp.232-242
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• 2009

In this paper, a matrix converter with improved low frequency output performance is proposed by achieving a one-step commutation owing to a general commutation circuit applicable to n-phase to m-phase matrix converters. The commutation circuit consists of simple resister and capacitor components, leading to a very stable, reliable and robust operation. Also, it requires no extra sensing information to achieve commutation, allowing for a one-step commutation like a conventional dead time commutation. With the dead time commutation strategy applied, the distortion caused by commutation delay is analyzed and compensated, therefore leading to better output linear behavior. In this paper, detailed commutation procedures of the R-C commutation circuit are analyzed. A selection of specific semiconductor switches and commutation circuit components is also provided. Finally, the effectiveness of the proposed commutation method is verified through a two-phase to single-phase matrix converter and the feasibility of the compensation approach is shown by an open loop space vector modulated three-phase matrix converter with a passive load.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.515-520
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• 2006

계량기 숫자 인식은 일반적으로 사용되고 있는 아날로그 계량기에 카메라를 부착하여, 검침 시 숫자 계기판 영상을 전송받고, 그 영상으로부터 숫자를 추출 및 인식하는 기술이다. 계량기 숫자 인식에서는 카메라의 설치 상태 및 기타 환경적인 요인들로 인해 숫자 계기판 영상의 일관성 있는 취득이 어렵게 된다. 본 논문에서는 숫자 인식에 악영향을 미치는, 취득 영상의 상태 변화를 보정해주기 위해 잡영 제거 및 윤곽보존 숫자강화를 제안하였다. 잡영 제거를 위해 잡영을 분포 위치에 따라서 세 가지 타입으로 나누었으며, 각 타입별로 잡영 제거를 하였다. 윤곽보존 숫자강화 과정에서는 일반적인 이진화 기법이 가지는 테두리 정보손실을 최소화할 수 있도록, 숫자 테두리의 명도를 보존하면서 숫자 중심부분의 밝기를 강화시켰다. 전처리 전/후의 인식률 비교 실험을 위해 SVM(Support Vector Machines)을 사용하였으며, 학습 데이터 1,409장과 조명 상태를 달리하여 취득한 1,782의 테스트 데이터를 실험 데이터로 사용하였다. 실험 결과, 81.09%라는 성능 향상을 확인하였으며 이는 제안한 전처리 기법이 조명으로 인한 데이터의 상태 변화 문제를 해결해줌으로써 인식 성능 향상에 크게 기여한다는 것을 입증해준다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.163-172
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• 2016

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

• International Journal of Contents
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• v.15 no.4
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• pp.113-119
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• 2019

It has been reported that large amounts of information on agri-foods were delivered to consumers through television and social networks, and the information may influence consumers' behavior. The purpose of this paper was first to analyze relations of social network service and broadcasting program on paprika consumption in the aspect of amounts to purchase and identify potential factors that can promote paprika consumption; second, to develop prediction models of paprika consumption by using structured and unstructured big data. By using data 2010-2017, cross-correlation and time-series prediction algorithms (autoregressive exogenous model and vector error correction model), statistically significant correlations between paprika consumption and television programs/shows and blogs mentioning paprika and diet were identified with lagged times. When paprika and diet related data were added for prediction, these data improved the model predictability. This is the first report to predict paprika consumption by using structured and unstructured data.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10d
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• pp.561-565
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• 2006

무선 모바일 애드 혹 네트워크에서 에너지 효율을 위한 많은 라우팅 알고리즘이 연구되어 왔다. 이러한 제안된 다른 알고리즘들을 살펴보면, 에너지 효율 뿐 만 아니라 성능의 향상을 위한 노력도 기울여 왔다. 일반적으로 목적지 노드가 전송받은 RREQ의 메트릭을 통하여 에너지 효율적인 경로를 선택 하게 되는데, 이것은 결국 데이터 전송 지연이 발생하게 된다. 그리고 경로가 설정된 이후에 경로를 유지하는데 있어서, 노드의 이동이나 배터리의 소진으로 링크가 끊기게 되면 경로를 다시 찾는 과정에서 제어 패킷을 다시 플러딩 하기 때문에 여러 노드의 에너지 소비를 초래하게 된다. 본 논문에서는 이러한 문제들을 해결하기 위해 기존의 AODV(On-Demand Distance Vector Routing) 프로토콜을 기반으로 다음과 같은 방법을 제안한다. 먼저 RREQ를 받은 노드가 남은 배터리의 양을 고려하여 전송의 여부를 결정하는 방법을 제안하고, 경로를 유지하는데 있어서 전송의 세기를 통해 노드의 이동성을 인지하고 링크가 끊기기 전에 다른 노드로 경로를 대체함으로써 경로 재설정을 통한 에너지 소비를 줄이는 방안을 제안한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2005.11a
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• pp.181-184
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• 2005

다양한 처리능력을 가진 단말기들은 복잡한 네트워크 환경의 호환성을 제공하기 위해서, 전송 네트워크 채널이 허용하는 범위내로 부호화된 비디오의 비트율을 적응적으로 맞춰 주어야 한다. 트랜스코더는 특정 비트율로 부호화 되어 있는 비디오를 원하는 비트율로 다시 변환하기 위해서 복호화한 후 다시 부호화의 과정을 거쳐야 하기 때문에 이에 따른 계산량의 증가와 더불어 전송시간에 문제가 발생한다. 이를 해결하기 위한 한 가지 방법으로 제안된 것이 프레임 건너뜀 기법, 즉 시간적 해상도 변환 트랜스코딩이다. 비디오를 부호화하는 과정에서 계산량을 가장 많이 차지하는 움직임 추정과정의 계산량을 줄임으로써 트랜스코딩을 수행하는데 소모되는 시간과 노력을 크게 줄이고, 건너뛰지 않고 남아있는 프레임에 더 많은 비트를 할당하여 요구되는 화질을 유지할 수 있다. 본 논문에서 움직임 벡터의 방향성을 고려하여 제안한 기법인 E-FDVS (Efficient-Forward Dominant Vector Selection)는 움직임 벡터의 방향성을 고려하여 매크로블록과 움직임 벡터의 차이를 보상하여 움직임 벡터를 재추정한다. 실험에서는 MPEG-2 영상에 대해서 제안한 방법을 적용하여 기존의 방법들에 비해서 성능이 우수함을 보인다.

• Journal of the Korea Society of Computer and Information
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• v.5 no.2
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• pp.90-97
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• 2000

This paper describes an speaker-adaptive speech recognition system which make a reliable recognition of speech signal for new speakers. In the Proposed method, an speech spectrum of new speaker is adapted to the reference speech spectrum by using Parameters of a 1st linear transformation network at the front of phoneme classification neural network. And the recognition system is based on semicontinuous HMM(hidden markov model) which use the multilayer perceptron as a fuzzy vector quantizer. The experiments on the isolated word recognition are performed to show the recognition rate of the recognition system. In the case of speaker adaptation recognition, the recognition rate show significant improvement for the unadapted recognition system.

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.153-160
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• 2008

One of the most important features of the Mobile Manipulator is redundant freedom. Using it's redundant freedom, a Mobile Manipulator can move in various modes, and perform dexterous motions. In this paper, to improve robot job performance, two robots -mobile robot, task robot- are joined together to perform a job, we studied the optimal position and posture of a Mobile Manipulator to achieve a minimum of movement of each robot joint. Kinematics of mobile robot and task robot is solved. Using the mobility of a Mobile robot, the weight vector of robots is determined. Using the Gradient method, global motion trajectory is minimized, so the job which the Mobile Manipulator performs is optimized. The proposed algorithm is verified with PURL-II which is Mobile Manipulator combined Mobile robot and task robot, and the results are discussed.