• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.293-297
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• 2007

This thesis suggests a new algorithm to detects multiple moving objects using a CMODE(Correct Multiple Object DEtection) method in the color images acquired in real-time and to track the interested pedestrian using motion and hue information. The multiple objects are detected, and then shaking trees or moving cars are removed using structural characteristics and shape information of the man , the interested pedestrian can be detected, The first similarity judgment for tracking an interested pedestrian is to use the distance between the previous interested pedestrian's centroid and the present pedestrian's centroid. For the area where the first similarity is detected, three feature points are calculated using k-mean algorithm, and the second similarity is judged and tracked using the average hue value for the $3{\times}3$ area of each feature point. The zooming of camera is adjusted to track an interested pedestrian at a long distance easily and the FOV(Field of View) of camera is adjusted in case the pedestrian is not situated in the fixed range of the screen. As a experiment results, comparing the suggested CMODE method with the labeling method, an average approach rate is one fourth of labeling method, and an average detecting time is faster three times than labeling method. Even in a complex background, such as the areas where trees are shaking or cars are moving, or the area of shadows, interested pedestrian detection is showed a high detection rate of average 96.5%. The tracking of an interested pedestrian is showed high tracking rate of average 95% using the information of situation and hue, and interested pedestrian can be tracked successively through a camera FOV and zooming adjustment.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.277-280
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• 2001

The preprocessing is very important course in speech signal processing. It influence the compression-rate in speech coding and the recognition-rate in speech recognition etc. In this paper, we propose that minimizing window-influence method with pitch period and start points. The proposed method is available for voiced detection and word labeling.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.243-246
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• 2007

This paper aims to propose an intonation labeling method using Momel and to present results of analyzing a speech corpus consisting of 80 passages pronounced by 4 speakers (2 male and 2 female) using the proposed method. The results show that Momel works well enough to derive meaningful pitch targets, which could be labeled with H and L tones. On the other hand, the results of the analysis of Korean speech corpus correspond to earlier work.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.9
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• pp.1448-1455
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• 1990

In this paper, a range image segmentation method is proposed. This method consists of an initial segmentation stage by discontinuous edge detection and surface type labeling based on the sign of the principal curvatures. Initially type labeled image is oversegmented, this image is merged via stepwise optimal region merging stage based on polynomial function approxiamtion. The successful segmentation results are presented for two synthetic range images with noise and a real-world ERIM range image.

• Proceedings of the PSK Conference
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• 2003.04a
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• pp.291.1-291.1
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• 2003

The purpose of this study is to provide KFDA's guidance for premarket notification submission and labeling for prescription use drugs of abuse in vitro diagnostic devices. To evaluate in vitro diagnostic devices the following performance characteristics should be described in detail within the submission: analytical sensitivity or minimum detection limit, cutoff concentration, specificity and cross reactivity, interference, precision, method comparison and stability. (omitted)

• KIPS Transactions on Software and Data Engineering
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• v.6 no.9
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• pp.445-456
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• 2017

In this paper, we propose a method to detect spam tweets containing unhealthy information by using an n-gram dictionary under limited labeling. Spam tweets that contain unhealthy information have a tendency to use similar words and sentences. Based on this characteristic, we show that spam tweets can be effectively detected by applying a Naive Bayesian classifier using n-gram dictionaries which are constructed from spam tweets and normal tweets. On the other hand, constructing an initial training set requires very high cost because a large amount of data flows in real time in a twitter. Therefore, there is a need for a spam detection method that can be applied in an environment where the initial training set is very small or non exist. To solve the problem, we propose a method to generate pseudo-labels by utilizing twitter's retweet function and use them for the configuration of the initial training set and the n-gram dictionary update. The results from various experiments using 1.3 million korean tweets collected from December 1, 2016 to December 7, 2016 prove that the proposed method has superior performance than the compared spam detection methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.4
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• pp.359-365
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• 2011

This study aims at filtering ALS points into ground and non-ground effectively through labeling and window based algorithm by utilizing 2D adjacency based on scan line. Firstly, points adjacency is constructed through minimal search based on scan line. Connected component labeling algorithm is applied to classify raw ALS points into ground and non-ground by utilizing the adjacency structure. Then, some small objects are removed by morphology filtering, and isolated ground points are restored by IDW estimation. The experimental results shows that the method provides good filtering performance( about 97% accuracy) for diverse sites, and the overall processing takes less time than converting raw data into TIN or raster grid.

• Journal of the East Asian Society of Dietary Life
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• v.25 no.6
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• pp.942-951
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• 2015

This study compared nutrition knowledge, dietary behaviors, and checking behaviors of food and nutrition labels between Korean and Chinese university students to obtain useful data for development of an education program for healthy dietary life. The data were collected by a self-administered questionnaire in Korea and China. Frequencies, t tests, ${\chi}^2$ tests, Cronbach's ${\alpha}$, and Pearson's correlation coefficient analysis were conducted by SPSS Win. V.21.0. The levels of nutrition knowledge and dietary behaviors were not high. Korean students showed higher percentage of correct answers about nutrition knowledge and levels of dietary behaviors than Chinese students. The means of degree of checking contents of food labels were 3.46 points for Korean students and 3.11 for Chinese students. Both groups of students showed the highest degree of checking milk and dairy products. The degree of understanding nutritive component labeling of Chinese students was higher than that of Korean students. Both groups of students showed higher than normal levels of confidence about nutritive component labeling and necessity of education on food and nutrition labels. The most preferred method of education on food and nutrition labels was broadcast media for both groups of students. In addition, there were significant correlations among nutrition knowledge, dietary behaviors, checking degree of food labels, checking degree of nutritive component labeling, and experience of nutrition education.

• Journal of the Korean Home Economics Association
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• v.38 no.12
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• pp.29-45
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• 2000

The purposes of this study were to determine what factors influence risk perceptions of females and males for milk produced using food-related biotechnology, to test whether risk perceptions or other factors influence self-protection actions and to estimate milk demand response in light of self-protection actions and other economic and demographic factors. The expected utility model was applied to explain the way consumers would take self-protection actions regarding risk perceptions and to drive milk demand. Telephone interviews were conducted and the data were collected from households(females=1,029, males=437) nationwide in the U.S. And the data were analyzed by Heckman two-step method using the software package LIMDEP. Risk perceptions were found to be influenced not by demographic factors but by outrage factors as well as attitudinal factors in both females and males, although some factors were different. In addition, risk perceptions and labeling availability were found to significantly influence self-protection actions in both groups. Furthermore, as an important concern in this study, self-protection action was found to significantly influence milk demand in only male group, implying a consistent behavior of males. Also milk price and household size were found to significantly influence milk demand in both groups. In fact, the results did demonstrate that labeling availability significantly influenced self-protection actions. That is, in markets where labeled laternatives were present, concerned consumers were more likely to self protect by substituting to these products. A policy implication of this result is that labeling food products produced using biotechnology enhances consumer choice. Hence, consumer could express a more accurate demand response and reduce the perceived food safety risk. Furthermore, education for females might be necessary to have a consistent behavior because self-protection action did not significantly influence female's milk demand, though they have greater risk perceptions than males have.

• Journal of KIISE
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• v.43 no.7
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• pp.773-780
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• 2016

Semantic information and features are very important for Semantic Role Labeling(SRL) though many SRL systems based on machine learning mainly adopt lexical and syntactic features. Previous SRL research based on semantic information is very few because using semantic information is very restricted. We proposed the SRL system which adopts semantic information, such as named entity, word sense disambiguation, filtering adjunct role based on sense, synonym cluster, frame extension based on synonym dictionary and joint rule of syntactic-semantic information, and modified verb-specific numbered roles, etc. According to our experimentations, the proposed present method outperforms those of lexical-syntactic based research works by about 3.77 (Korean Propbank) to 8.05 (Exobrain Corpus) F1-scores.