• Journal of KIISE:Software and Applications
• /
• v.35 no.10
• /
• pp.616-626
• /
• 2008

The face shape extracted by the depth values has different appearance as the most important facial feature information and the face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by combining the multiple frequency domains for each depth image and depth fusion using fuzzy integral. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. It is used as the reference point to normalize for orientated facial pose and extract multiple areas by the depth threshold values. In the second step, we adopt as features for the authentication problem the wavelet coefficient extracted from some wavelet subband to use feature information. The third step of approach concerns the application of eigenface and Linear Discriminant Analysis (LDA) method to reduce the dimension and classify. In the last step, the aggregation of the individual classifiers using the fuzzy integral is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) show the highest recognition rate among the regions, and the depth fusion method achieves 98.6% recognition rate, incase of fuzzy integral.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.45 no.6
• /
• pp.139-146
• /
• 2008

Masked fake face detection using ordinary visible images is a formidable task when the mask is accurately made with special makeup. Considering recent advances in special makeup technology, a reliable solution to detect masked fake faces is essential to the development of a complete face recognition system. This research proposes a method for masked fake face detection that exploits reflectance disparity due to object material and its surface color. First, we have shown that measuring of albedo can be simplified to radiance measurement when a practical face recognition system is deployed under the user-cooperative environment. This enables us to obtain albedo just by grey values in the image captured. Second, we have found that 850nm infrared light is effective to discriminate between facial skin and mask material using reflectance disparity. On the other hand, 650nm visible light is known to be suitable for distinguishing different facial skin colors between ethnic groups. We use a 2D vector consisting of radiance measurements under 850nm and 659nm illumination as a feature vector. Facial skin and mask material show linearly separable distributions in the feature space. By employing FIB, we have achieved 97.8% accuracy in fake face detection. Our method is applicable to faces of different skin colors, and can be easily implemented into commercial face recognition systems.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.1-32
• /
• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• The Journal of the Petrological Society of Korea
• /
• v.17 no.1
• /
• pp.16-35
• /
• 2008

We study metamorphism of metasedimetary rocks and origin and evolution of leucogranite form Samcheok area, northeastern Yeongnam massif, South Korea. Metamorphic rocks in this area are composed of metasedimentary migmatite, biotite granitic gneiss and leucogranite. Metasedimentary rocks, which refer to major element feature of siliclastic sediment, are divided into two metamorphic zones based on mineral assemblages, garnet and sillimanite zones. According to petrogenetic grid of mineral assemblages, metamorhpic P-T conditions are $740{\sim}800^{\circ}C$ at $4.8{\sim}5.8\;kbar$ in the garnet zone and $640-760^{\circ}C$ at 2.5-4.5kbar in sillimanite zone. The leucogranite (Imwon leucogranite) is peraluminous granite which has high alumina index (A/CNK=1.31-1.93) and positive discriminant factor value (DF > 0). Thus, leucogranite is S-type granite generated from metasedimentary rocks. Major and trace element diagram ($R_1-R_2$ diagram and Rb vs. Y+Nb etc.) show collisional environment such as syn-collisional or volcanic arc granite. Because Rb/sr ratio (1.8-22.9) of leucogranites is higher than Sr/Ba ratio (0.21-0.79), leucogranite would be derived from muscovite dehydrate melting in metasedimentary rocks. Leucogranites have lower concentration of LREE and Eu and similar that of HREE relative to metasedimentary rocks. To examine difference of REEs between leucogranites and metasedimentary rocks, we perform modeling using volume percentage of a leucogranite and a metasedimenatry rock from study area and REE data of minerals from rhyolite (Nash and Crecraft, 1985) and melanosome of migmatite (Bea et al., 1994). Resultants of modeling indicate that LREE and HREE are controlled by monazites and garnet, respectively, although zircon is estimated HREE dominant in some leucogranite without garnet. Because there are many inclusions of accessary phases such as monazite and zircon in biotites from metasedimentary rocks. leucogranitic magma was mainly derived from muscovite-breakdown in metasedimenary rocks. Leucogranites can be subdivided into two types in compliance with Eu anomaly of chondrite nomalized REE pattern; the one of negative Eu anomaly is type I and the other is type II. Leucogranites have lower Eu concetnrations than that of metasedimenary rocks and similar that of both type. REE modeling suggest that this difference of Eu value is due to that of components of feldspars in both leucogranite and metasedimentary rock. The tendency of major ($K_2O$ and $Na_2O$) and face elements (Eu, Rb, Sr and Ba) of leucogranites also indicate that source magma of these two types was developed by anatexis experienced strong fractionation of alkali-feldspar. Conclusionally, leucogranites in this area are products of melts which was generated by muscovite-breakdown of metasedimenary rock in environment of continetal collision during high temperature/pressure metamorphism and then was fractionated and crystallized after extraction from source rock.

• Journal of Korean Society of Transportation
• /
• v.33 no.3
• /
• pp.284-293
• /
• 2015

The objective of this paper is to determine the conditions of road surface by utilizing the images collected from closed-circuit television (CCTV) cameras installed on roadside. First, a technique was examined to detect wet surfaces at nighttime. From the literature reviews, it was revealed that image processing using polarization is one of the preferred options. However, it is hard to use the polarization characteristics of road surface images at nighttime because of irregular or no light situations. In this study, we proposes a new discriminant for detecting wet and dry road surfaces using CCTV image data at night. To detect the road surface conditions with night vision, we applied the wavelet packet transform for analyzing road surface textures. Additionally, to apply the luminance feature of night CCTV images, we set the intensity histogram based on HSI(Hue Saturation Intensity) color model. With a set of 200 images taken from the field, we constructed a detection criteria hyperplane with SVM (Support Vector Machine). We conducted field tests to verify the detection ability of the wet road surfaces and obtained reliable results. The outcome of this study is also expected to be used for monitoring road surfaces to improve safety.

• Journal of Intelligence and Information Systems
• /
• v.20 no.4
• /
• pp.43-58
• /
• 2014

As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.

• Journal of Korea Multimedia Society
• /
• v.11 no.11
• /
• pp.1501-1514
• /
• 2008

The face shape extracted by the depth values has different appearance as the most important facial information. The face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by multiple frequency domains for each depth image using the modified fuzzy c-mean algorithm. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. And the second step takes into consideration of the orientated frontal posture to normalize. Multiple contour line areas which have a different shape for each person are extracted by the depth threshold values from the reference point, nose tip. And then, the frequency component extracted from the wavelet subband can be adopted as feature information for the authentication problems. The third step of approach concerns the application of eigenface to reduce the dimension. And the linear discriminant analysis (LDA) method to improve the classification ability between the similar features is adapted. In the last step, the individual classifiers using the modified fuzzy c-mean method based on the K-NN to initialize the membership degree is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) showed the highest recognition rate among the extracted regions, and the proposed classification method achieved 98.3% recognition rate, incase of fuzzy cluster.

• Journal of Sasang Constitutional Medicine
• /
• v.8 no.1
• /
• pp.247-294
• /
• 1996

By means of the statistical data which has been collected with newly revised QSCC made use of the outpatient group examined at Kyung-Hee Medical Center and an open ordinary person group, the author proceeded statistical analysis for the validation study of the revised questionnaire itself. First, check the accurate discrimination rate by performing discriminant analysis on the statistical data of the patient group. And next, sought T-score by applying the norms gained in process of standadization of the open ordinary person group to the Sasang scale score of the outpatient group and investigated the distinctive feature between the subpopulations which was devided in the process of multivarite cluster analysis. The result was summarized as follows ; 1. The validity of the questionnaire was established through the fact that the accurate discrimination rate the ratio between predicted group and actual group was figured out 70.08%. 2. At the profile analysis the response to the relevant scale showed notable upward tendency in each constitutional group and therefore it seems to be pertinent in the field of constitutional discrimination. 3. In the observation of the power of expression through the profile analysis of each constitutional group the Soyang group demonstrated the most remarkable outcome, the Soeum group was the most inferior and the Taieum group revealed a sort of dual property. 4. What is called the group of seceder out of three subpopulation of each constitutional group distinguished definitely from the contrasted groups at the point of the distinctive profile feature and the content is like following description. (1) The seceder group of Soyang-in showed considerably passive disposition differently from general character of ordinary Soyang group and an appearance attracting the attention is that they demonstrated comparatively higher response at Soeum scale (2) The seceder group of Taieum-in gained low scores in general that informed the passive disposition of the group and the other way of the general property of Taieum group which showed accompanied ascension in Taiyang-Taieum scales they demonstrated sharply declined score at Taiyang scale (3) The seceder group of Soeum-in demonstrated distinctive property similar to the profile feature of Soyang group and it notifies that the passive property of Soeum group was diluted for the most part. According to the above result, the validity of newly revised questionnaire has been proven successfully and the property of seceder groups could be noticed to some degree through the profile analysis on the course of this study. The result of this study is expected to use as a research materials to produce next edition of the questionnaire and it is regarded that further inquisition about the difference between the seceder group and the contrasted group is required for the promotion of the questionnaire as it refered several times in the contents of the main discourse.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.18 no.6
• /
• pp.550-556
• /
• 1985

In order to evaluate the marine mollusc muscle as foodstuff not only from the biochemical aspect but also from the view point of food science, we have analyzed the protein and amino acid compositions of the echiurid (Urechis unicinctus) and sea hare (Aplysia kurodai) muscle. The protein quality of the muscles was also investigated using in vitro methods based on in vitro digestibility, predicted digestibility, computed PER (C-PER) and discriminant computed PER(DC-PER). The remarkable feature of the protein compositions of the both muscles was that water soluble protein occupied a large amount of the muscle protein with fairly lower contents of the salt soluble protein. From the analysis of SDS-PAG electrophoresis, the sarcoplasmic proteins in the echiurid and the sea hare muscles were composed of 15 and 10 subunits, respectively. The free amino acid compositions of the total amino acids in the echiurid and sea hare muscle were characterized with $75\%$ of glycine and alanine, and with $78\%$ of taurine, respectively. The amino acid anaylsis of both muscle proteins showed that the echiurid muscle was rich in glycine, aspartic acid, glutamic acid, arginine and lysine, but was poor in cysteine, while the sea hare muscle was rich in glycine, glutamic acid, aspartic acid and arginine, but was negligible in cysteine and tryptophan. In the total amino acid profiles of the freeze dried muscles in echiurid and sea hare, there was not found a significant difference compared to the amino acid compositions of the muscle proteins. Predicting the protein quality of the echiurid and sea hare muscles using the in vitro method, it was apparently low compared to the muscle protein of fishes.

• Journal of Global Scholars of Marketing Science
• /
• v.18 no.1
• /
• pp.59-80
• /
• 2008

Recently, e-sports are growing with potentiality as a new industry with conspicuous profit model. But studies that dealing with e-sports are not enough. Hence, proposes of this paper are both to establish basic model that is for the design of e-sport marketing strategy and to contribute toward future studies which are related to e-sports. Recently, the researches to explain sports-sponsorship through the identification theory have been discovered. Many researches say that somewhat proper identification is a requirement for most sponsors to improve the their images which is essential to sponsorship activity. Consequently, the research for sponsorship associated with identification in the e-sports, not in the physical sports is the core sector of this study. We extracted the variables from online's major characteristics and existing sport sponsorship researches. First, because e-sports mean the tournaments or leagues in the use of online game, the main event of the game is likely to call it online game. Online media's attributes are distinguished from those of offline. Especially, interactivity, anonymity, and expandibility as a e-sport game attributes are able to be mentioned. So, these inherent online attributes are examined on the relationship with flow. Second, in physical sports games, Fisher(1998) revealed that team similarity and team attractivity were positively related to team identification. Wann(1996) said that the result of former game influenced the evaluation of the next game, then in turn has an effect on the identification of team supporters. Considering these results in the e-sports side, e-sports gamer' attractivity, similarity, and match result seem to be important precedent variables of the identification with a gamer. So, these e-sport gamer attributes are examined on the relationship with both flow and identification with a gamer. Csikszentmihalyi(1988) defined the term flow as feeling status for him to be making current positive experience optimally. Hoffman and Novak(1996) also said that if a user experienced the flow he would visit a website without any reward. Therefore flow might be positively associated with user's identification with a gamer. And, Swanson(2003) disclosed that team identification influenced the positive results of sponsorship, which included attitude toward sponsors, sponsor patronage, and satisfaction with sponsors. That is, identification with a gamer expect to be connected with corporation identification significantly. According to the above, we can design the following research model. All variables used in this study(interactivity, anonymity, expandibility, attractivity, similarity, match result, flow, identification with a gamer, and identification with a sponsor) definitely were defined operationally underlying precedent researches. Sample collection was carried out to the person who has an experience to have enjoyed e-sports during June 2006. Much portion of samples is men because much more men than women enjoy e-sports in general. Two-step approach was used to test the hypotheses. First, confirmatory factor analysis was committed to guarantee the validity and reliability of variables. The results showed that all variables had not only intensive and discriminant validity, but also reliability. Then, research model was examined with fully structural equation using LISREL 8.3 version. The fitness of the suggested model mostly was at the acceptable level. Shortly speaking about the results, first of all, in e-sports game attributes, only interactivity which is called a basic feature in online situation affected flow positively. Secondly, in e-sports gamer's attributes, similarity with a gamer and match result influenced flow positively, but there was no significant effect in the relationship between the attractivity of a gamer and flow. And as expected, similarity had an effect on identification with a gamer significantly. But unexpectedly attractivity and match result did not influence identification with a gamer significantly. Just the same as the fact verified in the many precedent researches, flow greatly influenced identification with a gamer, and identification with a gamer continually had an influence on the identification with a sponsor significantly. There are some implications in these results. If the sponsor of e-sports supports the pro-game player who absolutely should have the superior ability to others and is similar to the user enjoying e-sports, many amateur gamers will feel much of the flow and identification with a pro-gamer, and then after all, feel the identification with a sponsor. Such identification with a sponsor leads people enjoying e-sports to have purchasing intention for products produced by the sponsor and to make a positive word-of-mouth for those products or the sponsor. For the future studies, we recommend a few ideas. Based on the results of this study, it is necessary to find new variables relating to the e-sports, which is not mentioned in this study. For this work to be possible, qualitative research seems to be needed to consider the inherent e-sport attributes. Finally, to generalize the results related to e-sports, a wide range of generations not a specific generation should be researched.