• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.1
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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.

• Korean Journal of English Language and Linguistics
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• v.1 no.4
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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.

• Journal of the Korean Home Economics Association
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• v.33 no.6
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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.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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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%.

• Journal of Korea Game Society
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• v.10 no.1
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• pp.35-45
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• 2010

This study sets a hypothesis on that the use of input control devices, which are similar to what we experience in real life, to control activities in games increases players' immersion rate, and compares general input control devices with dedicated input control devices in order to show appropriate results. Accordingly the process of the study is derived and the hypothesis is substantiated by understanding the relationship between game controlling activities and immersion rate. Overall satisfaction survey result on the use of dedicated devices shows that most players responded that they felt immersed enough in games when used dedicated devices and were highly satisfied. The use of the dedicated devices had positive impact on the increase of immersion rate in general. In order to increase immersion rate with controlling activities in games, the use of input control devices that are easy to handle and enable precise control is required, which shows that it will bring more fun and more increased immersion rate.

• Journal of Korea Multimedia Society
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• v.10 no.12
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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.

• Phonetics and Speech Sciences
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• v.7 no.4
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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).

• Journal of Digital Contents Society
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• v.6 no.4
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• pp.235-242
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• 2005

This Study examines influencing factors such as input, process, and output variables on1 student's satisfaction in cyber-education. That is to study on the effectiveness of input, process, and output variables for cyber-education and how does student's interaction moderate influencing factors and student satisfaction. The study was carried out through literature and empirical study. Questionnaire was used to varify the hypothesis based on which the input-process-output with system models were established. The result of hypothesis verification in this study is as follows : First, learning hour and grade showed a positive influence on the students' satisfaction in learning factors. Second reliant of professor, recognized teaming participate, and contents showed a positive influence on the students' satisfaction in system factors. Third, an interesting findings emerged throughout the analysis, showed that process variables were rather meaning factor than input variables.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.1-8
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• 2023

In this paper, we propose a training method of a recommendation learning model that analyzes the battlefield situation and recommends a suitable hypothesis for the current situation. The proposed learning model uses the preference determined by comparing the two hypotheses as a label data to learn which hypothesis best analyzes the current battlefield situation. Our model is based on Siamese neural network architecture which uses the same weights on two different input vectors. The model takes two hypotheses as an input, and learns the priority between two hypotheses while sharing the same weights in the twin network. In addition, a score is given to each hypothesis through the proposed post-processing ranking algorithm, and hypotheses with a high score can be recommended to the commander in charge.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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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.