• MALSORI
• /
• no.67
• /
• pp.79-102
• /
• 2008

In this paper, we investigate the relation between Bayes factor and likelihood ratio test (LRT) approaches and apply the neighborhood information of Bayes factor to building an alternate hypothesis model of the LRT system. To consider the neighborhood approaches, we contemplate a distance measure between models and algorithms to be applied. We also evaluate several methods to improve performance of utterance verification using neighborhood information. Among these methods, the system which adopts anti-models built by collecting mixtures of neighborhood models obtains maximum error rate reduction of 17% compared to the baseline, linear and weighted combination of neighborhood models.

• Korean Journal of Child Studies
• /
• v.28 no.1
• /
• pp.55-69
• /
• 2007

This research examined age differences in the way 3-, 4-, 5-year-old children solve scientific problems involving magnetic and nonmagnetic objects. Their scientific process skills and scientific concepts were examined in 1) hypothesis setting, 2) hypothesis verification and 3) hypothesis application. Data was analyzed by one-way and two-way ANOVA and Scheffe. Children's scientific process skill presented differences by age in each phase of problem solving. That is, the scientific concept level demonstrated by 4-year-olds was higher than that of the 3-year-olds. That of the 5-year-olds was higher than the 4-year-olds. In addition, in all age groups, the children showed a higher level of understanding about magnetic and non-magnetic objects in the hypothesis application phase than in the hypothesis setting phase.

• Journal of the Korea Fashion and Costume Design Association
• /
• v.22 no.3
• /
• pp.89-98
• /
• 2020

The survey targeted 270 men and women in their from their 20s-50s who live in Seoul and Gyeonggi-do. The purpose of this study was to investigate the effect of the vocation of beauty worker on job satisfaction and self-development. The questionnaire was administered from January 5th to March 10th, 2020, and 243 responses were used for statistical analysis. 27 unanswered or unfaithful questionnaires were disregarded. Using SPSS WIN 21.0 was applied for statistical processing. First, the data was analyzed using descriptive statistical analysis, factor analysis, and reliability verification, For validity verification, one-way variance analysis, correlation analysis, and simple regression analysis were used. The results of this study are as follows. First, upon examining the hypothesis that vocation has a positive (+) effect on job satisfaction, a significant positive (+) influence was shown, and it was found that it was determined vocation is an important factor in job satisfaction for beauty industry professionals. Second, upon examining the hypothesis that vocation has a positive (+) effect on self- development, a significant positive (+) correlation was found between the self-development of beauty industry professionals and job satisfaction. Third, upon examining the hypothesis that occupational vocation has a positive (+) effect on self- development, a significant positive (+) correlation was shown between self-development and job satisfaction of beauty industry professionals.

• Nuclear Engineering and Technology
• /
• v.53 no.5
• /
• pp.1664-1675
• /
• 2021

In order to verify the statistical performance of the nonparametric tests used in the MARSSIM approach, all plausible contamination distribution types that can be encountered in a survey area should be investigated. As the first of such investigations, this study aims to perform the verification for normal distribution of the contamination in a survey area by simulating the collection of random samples from it through the Monte Carlo simulation. The results of the simulations conducted for a total of 81 simulation cases showed that Sign test and WRS test both exhibited an excellent statistical performance: 100% for the former and 98.8% for the latter. Therefore, in final status surveys of the MARSSIM approach, a high statistical performance can be expected in applying the nonparametric hypothesis tests to survey areas whose net contamination can be assumed to be normally distributed.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.8
• /
• pp.799-806
• /
• 2011

This paper proposes an algorithm to detect the stop line and crosswalk on the road surface using edge information and blob coloring. The detection has been considered as an important area of autonomous vehicle technologies. The proposed algorithm is composed of three phases: 1) hypothesis generation of stop lines, 2) hypothesis generation of crosswalks, and 3) hypothesis verification of stop lines. The last two phases are not performed if the first phase does not provide a hypothesis of a stop line. The last one is carried out by the combination of both hypotheses of stop lines and crosswalks, and determines the stop lines among stop line hypotheses. The proposed algorithm is proven to be effective through experiments with various images captured on the roads.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.34C no.10
• /
• pp.76-88
• /
• 1997

Based on the principles described in Part I, we have implemented a working prototype vision system using a feature structure called an LSG (local surface group) for generating object hypotheses. In order to verify an object hypothesis, we estimate the view of the hypothesized model object and render the model object for the computed view. The object hypothesis is then verified by finding additional features in the scene that match those present in the rendered image. Experimental results on synthetic and real range images show the effectiveness of the indexing scheme.

• Journal of the Korean Mathematical Society
• /
• v.51 no.6
• /
• pp.1291-1304
• /
• 2014

In 2005 Kadiri proved that the Riemann zeta function ${\zeta}(s)$ does not vanish in the region $$Re(s){\geq}1-\frac{1}{R_0\;{\log}\;{\mid}Im(s){\mid}},\;{\mid}Im(s){\mid}{\geq}2$$ with $R_0=5.69693$. In this paper we will show that $R_0$ can be taken $R_0=5.68371$ using Kadiri's method together with Platt's numerical verification of Riemann Hypothesis.

• The Journal of the Acoustical Society of Korea
• /
• v.20 no.2
• /
• pp.27-34
• /
• 2001

Utterance verification is used in variable vocabulary word recognition to reject the word that does not belong to in-vocabulary word or does not belong to correctly recognized word. Utterance verification is an important technology to design a user-friendly speech recognition system. We propose a new utterance verification algorithm for no-training utterance verification system based on the minimum verification error. First, using PBW (Phonetically Balanced Words) DB (445 words), we create no-training anti-phoneme models which include many PLUs(Phoneme Like Units), so anti-phoneme models have the minimum verification error. Then, for OOV (Out-Of-Vocabulary) rejection, the phoneme-based confidence measure which uses the likelihood between phoneme model (null hypothesis) and anti-phoneme model (alternative hypothesis) is normalized by null hypothesis, so the phoneme-based confidence measure tends to be more robust to OOV rejection. And, the word-based confidence measure which uses the phoneme-based confidence measure has been shown to provide improved detection of near-misses in speech recognition as well as better discrimination between in-vocabularys and OOVs. Using our proposed anti-model and confidence measure, we achieve significant performance improvement; CA (Correctly Accept for In-Vocabulary) is about 89％, and CR (Correctly Reject for OOV) is about 90％, improving about 15-21％ in ERR (Error Reduction Rate).

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.37 no.8A
• /
• pp.639-647
• /
• 2012

Vision-based on-road vehicle detection is one of the key techniques in automotive driver assistance systems. However, due to the huge within-class variability in vehicle appearance and environmental changes, it remains a challenging task to develop an accurate and reliable detection system. In general, a vehicle detection system consists of two steps. The candidate locations of vehicles are found in the Hypothesis Generation (HG) step, and the detected locations in the HG step are verified in the Hypothesis Verification (HV) step. Since the final decision is made in the HV step, the HV step is crucial for accurate detection. In this paper, we propose using a reduced multivariate polynomial pattern classifier (RM) for the HV step. Our experimental results show that the RM classifier outperforms the well-known Support Vector Machine (SVM) classifier, particularly in terms of the fast decision speed, which is suitable for real-time implementation.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.12
• /
• pp.63-73
• /
• 2009

This paper presents a vehicle detection method that uses convolution matching method based on a simple color information. An input image is expressed as 8 oriented color expression(Red, Green, Blue, White, Black, Cyan, Yellow, Magenta) considering an orientation of a pixel color vector. It makes the image very reliable and strong against changes of illumination condition or environment. This paper divides the vehicle detection into a hypothesis generation step and a hypothesis verification step. In the hypothesis generation step, the vehicle candidate region is found by vertical edge and shadow. In the hypothesis verification step, the convolution matching and the complexity of image edge are used to detect real vehicles. It is proved that the proposed method has the fast and high detection rate on various experiments where the illumination source and environment are changed.