• 대한전기학회논문지
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• 제45권1호
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• pp.139-145
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• 1996

The input estimation method estimates maneuvering input acceleration in order to track a maneuvering target. In this paper, the optimal input estimator is derived by choosing the MAP hypothesis among maneuvering input transition hypotheses under the assumption that a maneuvering input acceleration is a semi-Markov process. The optimal input estimation method cannot be realized because the optimal filter should consider every maneuver onset time hypothesis from filter starting time to current time which increase rapidly. Hence the suboptimal filter using a sliding window is proposed. Since the proposed method can consider all hypotheses of input transitions inside the window, it is general enough to include Bogler's input estimation method. Simulation results show, however, that we can obtain a good performance even when the filter considering just one input transition in the window is used. (author). 9 refs., 3 figs., 1 tab.

• 한국영어학회지:영어학
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• 제1권4호
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• pp.587-608
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• 2001

When teaching English in elementary schools was introduced in Korea in 1997, the theoretical basis was the critical period hypothesis (CPH). The object of this study was to test whether the Korean situation satisfies the conditions for the CPH such as the amount of English input and needs. As a test for this, English input and needs were compared in Korea, the U.S.A. and Singapore. The items for English input were on a continuum of primary to secondary sources and the items for English needs were on a continuum of immediate to future needs. The 0-5 scale was used. The result showed that the total means of English input were 4.87, 4.62, and 1.05 for children in the U.S.A., Singapore and Korea respectively. The total means of English needs were 4.32, 3.81, and 1.52 for children in the U.S.A., Singapore and Korea respectively. These figures show that Korean children's levels of both input and needs were from “almost none” to “little,” while those of children in the U.S.A. and Singapore were from “much” to “very much.” This shows that teaching English in Korea presently is far from meeting the conditions that are expected by the CPH. As an alternative to explain what happens cognitively to Korean children, this paper suggests the automatization and proceduralization processes.

• 대한가정학회지
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• 제33권6호
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• pp.269-280
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• 1995

This study focused on examining the effects of Objective, Subjective variables on the household economic well-being. Data were collected from 254 financial managers in Seoul. City. Results show that the Causal model supported hypothesis. Almost of the hypothesis were supported and Perceived adequacy of resources variable was mediated between Input variables and Satisfaction with financial situation variable. The present study implicated that this model apply to family resource management research.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2003년도 하계학술대회 논문집
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• pp.46-49
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• 2003

The use of confidence measures for word/utterance verification has become art essential component of any speech input application. Confidence measures have applications to a number of problems such as rejection of incorrect hypotheses, speaker adaptation, or adaptive modification of the hypothesis score during search in continuous speech recognition. In this paper, we present a new utterance verification method using vowel string. Using subword HMMs of VCCV unit, we create anti-models which include vowel string in hypothesis words. The experiment results show that the utterance verification rate of the proposed method is about 79.5%.

• 한국게임학회 논문지
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• 제10권1호
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• pp.35-45
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• 2010

이 연구는 게임 안의 상황을 통제하는 조작 방법 중 실생활에서의 행위 경험과 유사한 조작도구를 사용하는 것이 몰입을 더욱 증가시킬 것이라는 가설을 세우고, 범용 조작도구와 전용 조작도구의 비교 실험을 통해 결과를 밝히고자 하였다. 이에 따라 게임 조작행위와 몰입간의 관계를 파악하여 연구의 과정을 도출하고 가설을 증명하였다. 연구결과 대부분의 플레이어들은 점수 결과에 상관없이 전용 조작도구를 사용하였을 때, 게임에 빠져들어 있었음을 느꼈고 만족도가 높았으며, 전용 조작도구의 사용이 몰입의 증가에 전체적으로 긍정적인 영향을 미쳤다. 대부분의 플레이어들이 도전, 주의집중, 즐거움, 현전 측면에서 전용조작도구가 훨씬 우세하다는 응답 결과를 나타냈고, 조작도구의 숙련과 통제는 두 조작도구가 비슷한 수준에서 선호되고 있는데, 전용 조작도구에서 실험 초반 호기심과 도전감을 강하게 나타냈다. 게임의 조작행위를 통해 몰입을 증가시키기 위해서는 현전감이 높고, 익숙해지기 쉬운 조작법과 정확한 조작이 가능한 조작 도구의 활용이 필요하며 이는 게임의 재미와 몰입을 더욱 증가시킨다는 결론에 이른다.

• 한국멀티미디어학회논문지
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• 제10권12호
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• pp.1559-1565
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• 2007

The many target-detection methods that use forward-looking infrared (FUR) images can deal with large targets measuring $70{\times}40$ pixels, utilizing their shape features. However, detection small targets is difficult because they are more obscure and there are many target-like objects. Therefore, few studies have examined how to detect small targets consisting of fewer than $30{\times}10$ pixels. This paper presents a small target detection method using clutter rejection with stochastic hypothesis testing for FLIR imagery. The proposed algorithm consists of two stages; detection and clutter rejection. In the detection stage, the mean of the input FLIR image is first removed and then the image is segmented using Otsu's method. A closing operation is also applied during the detection stage in order to merge any single targets detected separately. Then, the residual of the clutters is eliminated using statistical hypothesis testing based on the t-test. Several FLIR images are used to prove the performance of the proposed algorithm. The experimental results show that the proposed algorithm accurately detects small targets (Jess than $30{\times}10$ pixels) with a low false alarm rate compared to the center-surround difference method using the receiver operating characteristics (ROC) curve.

• 말소리와 음성과학
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• 제7권4호
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• pp.3-8
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• 2015

In this paper, we investigate an input dimension reduction method using continuous word vector in deep neural network language model. In the proposed method, continuous word vectors were generated by using Google's Word2Vec from a large training corpus to satisfy distributional hypothesis. 1-of-${\left|V\right|}$ coding discrete word vectors were replaced with their corresponding continuous word vectors. In our implementation, the input dimension was successfully reduced from 20,000 to 600 when a tri-gram language model is used with a vocabulary of 20,000 words. The total amount of time in training was reduced from 30 days to 14 days for Wall Street Journal training corpus (corpus length: 37M words).

• 디지털콘텐츠학회 논문지
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• 제6권4호
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• pp.235-242
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• 2005

본 연구는 대학에서 사이버교육에 영향을 미치는 요인을 분석하고자 실시되었다. 먼저 사이버교육의 개념과 그 특성을 고찰하고 사이버교육의 평가준거들에 대한 선행연구들을 분석하여 본 연구에 적합한 교육효과 관련 변인들을 선정하였다. 연구 모형은 체제론에 입각한 투입-과정-산출 모형을 적용하여 투입변인은 교수, 학생, 학습 환경이고, 과정변인은 교육내용, 상호작용, 강의참여이며, 산출변인은 교육효과성과 교육만족도로 하였다. 분석은 투입-과정-산출과정에서 관련 변인들이 어떤 관계가 있으며, 실제 사이버교육에서 어느 정도 영향을 미치는지를 분석하였다. 이를 분석하기 위해서 사이버강의에 참여하고 있는 190명의 학생들을 대상으로 실증적인 연구를 하였다. 주요 연구결과에 따른 결론을 보면 다음과 같다. 첫째 학생들이 사이버교육에 참여한 시간이 많거나 성적이 높을수록 사이버교육 효과에도 긍정적으로 인식한다. 둘째, 사이버교육이 잘 이루어지기 위해서는 처음에는 교수의 역할이 중요하다. 셋째, 사이버교육의 효과를 높이기 위해서는 사이버교육의 교육내용과 학생들의 강의참여가 중요하다.

• 한국컴퓨터정보학회논문지
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• 제28권1호
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• pp.1-8
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• 2023

점점 더 복잡해지고 다양해지는 무기체계와 급격하게 변화하는 전장정보에 따라서, 인공지능을 사용한 전장 상황 분석 연구의 필요성이 대두되고 있다. 본 논문에서는 전장 상황을 분석하여 현재 상황에 적합한 가설을 추천해주는 분석결과 추천 학습모델의 학습 및 설계 방안을 제안한다. 학습 모델은 두 가설을 비교하여 결정되는 선호 여부를 레이블 데이터로 활용하여, 어떠한 가설이 현재 전장상황을 잘 분석하고 있는지 학습한다. 또한 후처리 랭킹 알고리즘을 통하여 각각의 가설에 대한 종합점수를 부여하고, 점수가 높은 상위 가설들을 지휘관에게 추천할 수 있음을 확인한다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.547-550
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• 2009

The object recognition mechanism of human being is not well understood yet. On research of animal experiment using an ape, however, neurons that respond to simple shape (e.g. circle, triangle, square and so on) were found. And Hypothesis has been set up as human being may recognize object as combination of such simple shapes. That mechanism is called Figure Alphabet Hypothesis, and those simple shapes are called Figure Alphabet. As one way to research object recognition algorithm, we focused attention to this Figure Alphabet Hypothesis. Getting idea from it, we proposed the feature extraction algorithm for object recognition. In this paper, we described recognition of binarized images of multifont alphabet characters by the recognition model which combined three-layered neural network in the feature extraction algorithm. First of all, we calculated the difference between the learning image data set and the template by the feature extraction algorithm. The computed finite difference is a feature quantity of the feature extraction algorithm. We had it input the feature quantity to the neural network model and learn by backpropagation (BP method). We had the recognition model recognize the unknown image data set and found the correct answer rate. To estimate the performance of the contriving recognition model, we had the unknown image data set recognized by a conventional neural network. As a result, the contriving recognition model showed a higher correct answer rate than a conventional neural network model. Therefore the validity of the contriving recognition model could be proved. We'll plan the research a recognition of natural image by the contriving recognition model in the future.