• Journal of Korean Society of Transportation
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• v.28 no.6
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• pp.147-158
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• 2010

Studies on the distribution of traffic demands have been proceeding by providing traffic information for reducing greenhouse gases and reinforcing the road's competitiveness in the transport section, however, since it is preferentially required the extensive studies on the driver's behavior changing routes and its influence factors, this study has been developed a discriminant model for changing routes considering driving conditions including traffic conditions of roads and driver's preferences for information media. It is divided into three groups depending on driving conditions in group classification with the CART analysis, which is statistically meaningful. And, elements of the driving conditions and the preferred media affecting the change of paths are classified into statistical meaningful groups through the CHAID analysis, and the major factors affecting the change of paths are examined. Finally, the extent that driving conditions and preferred media affect a route change is examined through a discriminant analysis, and it is developed a discriminant model equation to predict a route change. As a result of building the discriminant model equation, it is shown that driving conditions affect a route change much more, the entire discriminant hit ratio is derived as 64.2%, and this discriminant equation shows high discriminant ability more than a certain degree.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.6
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• pp.1001-1006
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• 2013

In this paper, Recently, we describe the distinction mechanism analysis and injurious distinction mechanism performance analysis in order to determine harmfulness of the injurious multimedia which is being rapidly spread in the smart phone and Intelligent Robots. Based on the injurious mechanism distinction technologies, We defined individual injurious characteristics elements of multimedia(images and videos). Also, We analyze harmfulness of the injurious multimedia content by the visual characteristics modeling.

• Journal of IKEEE
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• v.17 no.3
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• pp.248-253
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• 2013

Frequency detector is a circuit that converts the frequency to a digital representation and finds its application in various fields such as modulator and synchronization circuitry. In this paper, a couple of first-order and second-order frequency discriminator structures are modeled and analyzed with their quantization noise sources. Also a delta-sigma frequency detector architecture is proposed. Through theoretical analysis and derived equations, the output noise is obtained, which is validated by simulation. The proposed all-digital frequency discriminator may be applied in the feedback path of the all-digital phase-locked loop.

• Korean Journal of Food Science and Technology
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• v.49 no.5
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• pp.469-473
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• 2017

The objective of this study was to develop methods for the discrimination of new and stale rice by using an electronic eye. To develop the discriminant, 107 rice samples produced in the years 2015 and 2016 were investigated. After the rice was treated with guaiacol, oxydol, and p-phenylenediamine reagents, an electronic eye was applied to discriminate between newly harvested rice and rice stored for 1 year. Out of the 4,096 color codes of the electronic eye, 31 color codes were identified for the discrimination of newly harvested rice and rice stored for 1 year. The classification ratio of newly harvested rice and rice stored for 1 year was 100% and the discrimination accuracy for unknown samples was 100%. In a total of 150 mixtures of new rice and stale rice, the discrimination accuracy was between 16.7 and 95.6%, depending on the mixing ratio. This capability of the electronic eye will be useful as a tool for discriminating the production year of rice.

• Korean Journal of Food Science and Technology
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• v.30 no.3
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• pp.473-479
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• 1998

Identification of rice species was attempted by three side (top, side and front) images of brown rice. Nine parameters of each image were area, aspect ratio, maximum diameter, minimum diameter, perimeter, roundness and red (R), green (G) and blue (B) pixel values of an image. Forty rice samples consisted of 19 species used for the study and total 27 image characteristics for a kernel were measured. For calibration and confirmation, 105 and 20 brown rice kernels per each sample were used respectively. For best identification of rice species, 24 image characteristics were selected for discriminant analysis. Average percentages for correct identification of rice species were 84.75% and 84.93% for calibration and confirmation data set, respectively. The highest and lowest percentage for correct identification were 99.05% for Nongan and 50.63% for Hwaseung respectively in calibration data. The confirmation data showed that the correct identification of Nongan or Paalgong was 100%, while that of Hwaseung was 47.62%. The result of the study showed that three side (top, side and front) image of brown rice was not suitable for identification of rice species suggesting that additional techniques are required for better discrimination of rice species.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.517-525
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• 1999

본 논문에서는 정준판별분석에서 분석자의 사전지식을 고려한 조건부 계량화(partial quantification)를 제안하고 있다. 연관된 수식을 라그랑지 승수법으로 유도하였으며 SAS/IML을 이용한 프로그램과 예제를 간략히 설명하였다.

• Journal of the Korean Society for information Management
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• v.32 no.3
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• pp.377-396
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• 2015

This study proposed a key-frame automatic extraction method for video storyboard surrogates based on users' cognitive responses, EEG signals and discriminant analysis. Using twenty participants, we examined which ERP pattern is suitable for each step, assuming that there are five image recognition and process steps (stimuli attention, stimuli perception, memory retrieval, stimuli/memory comparison, relevance judgement). As a result, we found that each step has a suitable ERP pattern, such as N100, P200, N400, P3b, and P600. Moreover, we also found that the peak amplitude of left parietal lobe (P7) and the latency of FP2 are important variables in distinguishing among relevant, partial, and non-relevant frames. Using these variables, we conducted a discriminant analysis to classify between relevant and non-relevant frames.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.63 no.2
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• pp.98-105
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• 2018

The sale of brown rice batches composed of rice produced in different years is prohibited in Korea. Thus, new methods for the identification of the year of production are critical for maintaining the distribution of high quality brown rice. Here, we describe the exploitation of an enzyme that can be used to discriminate between freshly harvested and one-year-old brown rice. The degree of enzyme activity was visualized through freshness test with Guaiacol, Oxydol, and p-phenylenediamine reagents. With electronic eye equipment, we selected 29 color codes for identifying new brown rice and old brown rice. The discrimination power of selected color codes showed a minimum of 0.263 to a maximum of 0.922 and an average value of 0.62. The accuracy with which new brown rice and old brown rice could be identified was 100% in principal component analysis (PCA) and discriminant function analysis (DFA). The DFA analysis had greater discriminatory power than did the PCA analysis. A verification test using new brown rice, old brown rice, or a mixture of the two was then performed to validate our method. The accuracy of identification of new and old brown rice was 100% in both cases, whereas mixed brown rice samples were correctly classified at a rate of 96.9%. Additionally, in order to test whether the discriminant constructed in winter can be applied to samples collected in summer, new and old brown rice stored for 8 months were collected and tested. Both new and old brown rice collected in summer were classified as old brown rice and showed 50% identification accuracy. We were able to attribute these observations to changes in enzyme content over time, and therefore we conclude, it will be necessary to develop discriminants that are specific to distinct storage periods in the near future.

• Journal of Conservation Science
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• v.21
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• pp.89-100
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• 2007

This study analyze concentration of minor and trace elements on 47 white porcelains excavated from Dudong-ri, Baekryeon-ri, Sachon-ri kilns in Gyeonsangnam-do by NAA(neutron activation analysis) and try to classify the provenance and characteristics according to the analytical result. Each kilns are divided into the group by PCA(principal component analysis) and LDA(linear discrimination analysis) using 17 elements; Ba Ce, Co, Cr, Cs, Dy, Eu, Hf, La Lu, Rb, Sc, Sm, Ta, Th, V, Yb. The contribution elements are Dy, Sm, La, Ce, Lu, Sc. And soft and hard white porcelains are similar with the chemical composition of the use materials therefore the difference of the chemical composition not confirmed a cause. The analytical results of the fine(I) and poor(II) quality white porcelains presume the difference of the povenance of clay materials or the poduction process such as difference purify and additive materials.

• Journal of Gifted/Talented Education
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• v.15 no.1
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• pp.67-84
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• 2005

In this study we investigated the adequacy of tools for distinction gifted students through the comparison these mutual relation on the basis of data, like paper test, the depths interview score, and the rest data((TTCT: Torrance Tests of Creative Thinking, IQ test, FASP: Find A Shape Puzzle, V.T: Visualization Tests and Exp: experimental ability test), and analysis data of EEG test for examining the adequacy of tools for identification gifted students. So, we developed Brain Wave gifted Index(G-Index) for finding another distinction ability as using brain waves data. The standard of index development use gifted brain characteristic in closed-eyes rest state which is judged like that characteristic of distinction between gifted and normal students is the most clear and consistence. That is, the degree of unified pattern between each object and gifted PCA pattern was defined by Pearson method which added spatial mutual index to weight concept. This refer to mean number of spatial PCA pattern. Searching for the possibility of distinction gifted gave distinction effect in 76%. The result of regression analysis on the basis of mutual relation between the rest data is . The probability formula for distinct gifted group is as follow. $$P=\frac 1{1+e^{-[-0.018(TTCT)+0.057(IQ)+1.916(FASP)+0.682(V.T)+0.088(Exp.)+0.034(G-Index)-57.510]}}$$ The result of this calculation showed that probability for distinct in gifted group was very good(95.0%). On the basis of upper result, tools for identification gifted students should be estimated as using many-sided estimation data whatever possible. And following study about development, and operation of tools for distinction suitable to gifted student in science should be progressed.