• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.465-472
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• 2015

We propose a novel plane detection in disparity space and evaluate its performance. Our method simplifies and makes scenes in disparity space easily dealt with by approximating various surfaces as planes. Moreover, the approximated planes can be represented in the same size as in the real world, and can be employed for obstacle detection and camera pose estimation. Using a stereo matching technique, our method first creates a disparity image which consists of binocular disparity values at xy-coordinates in the image. Slants of disparity values are estimated by exploiting a line simplification algorithm which allows our method to reflect global changes against x or y axis. According to pairs of x and y slants, we label the disparity image. 4-connected disparities with the same label are grouped, on which least squared model estimates plane parameters. N plane models with the largest group of disparity values which satisfy their plane parameters are chosen. We quantitatively and qualitatively evaluate our plane detection. The result shows 97.9%와 86.6% of quality in our experiment respectively on cones and cylinders. Proposed method excellently extracts planes from Middlebury and KITTI dataset which are typically used for evaluation of stereo matching algorithms.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.283-291
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• 2019

The aim of investigated the role of FABP5 in the hepatic lipogenesis and lipid metabolisms. Mice were overexpressed and silenced liver FABP5 using virus particles. Mice were fed a Western-type diet or regular chow for 1week and then sacrificed mouse after 24hr fasted. Liver homogenates were used for protein analysis by Western blot and mRNA levels by RT-PCR. Hepatic and serum lipids were analysed by thin-layer chromatography. Mice fed a Western-type or high saturated fat diet revealed large increases in FABP5 expression. However, FABP5 mRNA levels were drastically reduced under fasted. Hepatic TG was significantly increased FABP5-OEAV mice, but a significantly decreased hepatic free cholesterol under fed. The discovered a substantial decrease in hepatic TG mass with FABP5 silencing. In these data, presented evidence for an important role of FABP5 in hepatic lipogenesis and hepatic TG storage. FABP5 may also be a potential target in the treatment of NAFLD, metabolic syndrome, and obesity. Furthermore, studies to which transcription factors are involved in FABP5 expression and regulation.

• Journal of IKEEE
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• v.25 no.2
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• pp.273-279
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• 2021

In this paper, we propose an algorithm that shortens the learning time by performing individual learning using partitioning the deep learning structure. The proposed algorithm consists of four processes: network classification origin setting process, feature vector extraction process, feature noise removal process, and class classification process. First, in the process of setting the network classification starting point, the division starting point of the network structure for effective feature vector extraction is set. Second, in the feature vector extraction process, feature vectors are extracted without additional learning using the weights previously learned. Third, in the feature noise removal process, the extracted feature vector is received and the output value of each class is learned to remove noise from the data. Fourth, in the class classification process, the noise-removed feature vector is input to the multi-layer perceptron structure, and the result is output and learned. To evaluate the performance of the proposed algorithm, we experimented with the Extended Yale B face database. As a result of the experiment, in the case of the time required for one-time learning, the proposed algorithm reduced 40.7% based on the existing algorithm. In addition, the number of learning up to the target recognition rate was shortened compared with the existing algorithm. Through the experimental results, it was confirmed that the one-time learning time and the total learning time were reduced and improved over the existing algorithm.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Asia Marketing Journal
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• v.13 no.3
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• pp.55-77
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• 2011

This study investigate the interaction effects of bundle price discount framing and message framing on consumer's attitude of bundle component. Although each effect of bundle price discount framing and message framing has been explored individually, few attempts have been made to invest them jointly. This study tests the interaction effects of bundle price discount framing and message framing on consumer's evaluation of bundle component. Moreover, this research focuses on consumer's evaluation of individual bundle component while the existing research on bundling primarily focused on consumer's evaluation of the bundle. Prior research suggests that consumers are sensitive to the framing of prices and discounts in the presentation of the bundle offer. For example, there is considerable evidence that partitioning or consolidating the prices of a bundle can influence the attractiveness of the bundle offer. Similarly, there is evidence that an equivalent price reduction to the overall bundle, one of the individual products in the bundle, or distributed among the individual products in the bundle can alter the perceived attractiveness of the offer (e.g. Chakravarti, Krish, Paul, and Srivastava 2002; Hamilton and Srivastava 2008; Janiszewski and Cunha 2004; Johnson, Herrmann and Bauer 1999; ; Morwitz, Greenleaf, and Johnson 1998; Yadav 1994; 1995). In line with these earlier research, this research suggests that the bundle type can influence the consumer's evaluation of bundle component. There are two types of bundle - mixed-leader bundle and mixed-joint bundle. In mixed-leader bundling, the price of one of the two products is discounted when the other product is purchased at the regular price. In mixed-joint bundling, a single price is set when the two product are purchased jointly. This study supposes that the teeth whitening product is the leader product in a mixed-leader bundle. So bundle price discount framing is manipulated such as "Buy the teeth whitening product (regular price \80,000) and get 50% discount on the functional toothpaste(regular price \40,000), special set price \100,000" or "Buy the functional toothpaste and the teeth whitening product as a set and get discount for the set, special set price \60,000". Message framing is manipulated through the product claims described in an advertising bill. The positive framing presents that "Over 95% of users achieved the expected 2-3 shades of improvement in two weeks" where as the negative framing presents "less than 5% of users did not achieve the expected 2-3 shades of improvement in two weeks". This study uses hypothetical brand name of the teeth whitening product and the functional toothpaste This study is based on a 2x2 factorial design with bundle discount framing (mixed-leader bundle vs. mixed-joint bundle) and massage framing (positive vs. negative). The dependant variables are consumer's perceived quality and attitude of the teeth whitening product The data reveals that two dependant variables are correlated, so the data is analyzed with two-way MANOVA. This research explores the significant interaction effect of bundle discount framing and message framing on consumer's perceived quality and attitude of the teeth whitening product. When the message framing is positive, consumer's perceived quality and attitude of the teeth whitening product is higher in mixed-leader bundle than mixed-joint bundle condition. However, when the message framing is negative, consumer's evaluation is higher in mixed-joint bundle than mixed-leader bundle. The author explains this result by stating that consumers are less likely to use heuristics such as price-quality association and value discounting hypothesis(Raghubir 2004) in the negative message framing condition. Additionally, consumer's perceived risk of the teeth whitening product in the negative message framing condition can be more reduced by the bundle partner(e.g. the toothpaste) in mixed-joint bundle than mixed-leader bundle. Based on the results, marketing managers are advised to use different bundle type based on message framing of their product.

• The Korean Journal of Ecology
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• v.25 no.5
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• pp.341-348
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• 2002

Correlations among the island area, distance to mainland, latitude, longitude, human impacts, diversity and composition of vascular plants were investigated by analyzing data on 261 islands(10.3% of total number of islands in Korea) selected from the annual reports for 'the natural evironment survey of the uninhabited islands in Korea' published by 'Ministry of Environment' during three years from 1999. The area of surveyed 261 islands ranged 1,100 to 961,000㎡(average of 75,000㎡), and the distance to mainland ranged 0.15 to 51.5km (average of 14.9km). Total number of plant species recorded in those islands was 1,109 species throughout 30 families, and mean mumber of plant species of each island was 98.7 species. Native species were 1,003 species (90.4%), and exotic species were 106 species(9.6%). The families with the largest number of species was the Compositae with 114 species, and followed in the order of Gramineae(90), Leguminosae(54), and Rosaceae(53). The result of multi-dimensional scaling analysis based on the plant species composition showed that 261 islands were distinctly divided into two groups, western sea group(131 islands) and southern sea group(130 islands). The islands of western sea group(average area of 93,000㎡) had greatly larger area than them of southern sea group(average area of 57,000㎡), but the average number of species (average species of 192) per island were less than in southern sea group (average species of 233). And, the partitioning into two groups was responsible for the species restricted to southern than to western sea group. Therefore, this results suggest that the distribution pattern and the composition of plant species could be also affected by the latitude of the island. When the species-area model was applied to total island and plant species, these results indicate that the island area was the most significant predictor of plant species diversity, and the distance to mainland and the human impacts were also shown to be significant predictors of plant species richness. But when applied to both groups of islands by the stepwise selection method, the result showed that islands of southern sea group were greatly affected by the factors such as human impacts, distance to mainland and longitude than western sea group. For the purpose of conservation of natural ecosystem on the uninhabited islands in Korea, we will also examine how the human impacts and the invasion of exotic plant species will disturb the native species diversity.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.1
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• pp.16-37
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• 2018

In this study, difference in chemical composition of $PM_{2.5}$ observed between the year 2013 and 2015 at six air quality intensive monitoring stations (Bangryenogdo (BR), Seoul (SL), Daejeon (DJ), Gwangju (GJ), Ulsan (US), and Jeju (JJ)) was investigated and the possible factors causing their difference were also discussed. $PM_{2.5}$, organic and elemental carbon (OC and EC), and water-soluble ionic species concentrations were observed on a hourly basis in the six stations. The difference in chemical composition by regions was examined based on emissions of gaseous criteria pollutants (CO, $SO_2$, and $NO_2$), meteorological parameters (wind speed, temperature, and relative humidity), and origins and transport pathways of air masses. For the years 2013 and 2014, annual average $PM_{2.5}$ was in the order of SL ($${\sim_=}DJ$$)>GJ>BR>US>JJ, but the highest concentration in 2015 was found at DJ, following by GJ ($${\sim_=}SJ$$)>BR>US>JJ. Similar patterns were found in $SO{_4}^{2-}$, $NO_3{^-}$, and $NH_4{^+}$. Lower $PM_{2.5}$ at SL than at DJ and GJ was resulted from low concentrations of secondary ionic species. Annual average concentrations of OC and EC by regions had no big difference among the years, but their patterns were distinct from the $PM_{2.5}$, $SO{_4}^{2-}$, $NO_3{^-}$, and $NH_4{^+}$ concentrations by regions. 4-day air mass backward trajectory calculations indicated that in the event of daily average $PM_{2.5}$ exceeding the monthly average values, >70% of the air masses reaching the all stations were coming from northeastern Chinese polluted regions, indicating the long-range transportation (LTP) was an important contributor to $PM_{2.5}$ and its chemical composition at the stations. Lower concentrations of secondary ionic species and $PM_{2.5}$ at SL in 2015 than those at DJ and GJ sites were due to the decrease in impact by LTP from polluted Chinese regions, rather than the difference in local emissions of criteria gas pollutants ($SO_2$, $NO_2$, and $NH_3$) among the SL, DJ, and GJ sites. The difference in annual average $SO{_4}^{2-}$ by regions was resulted from combination of the difference in local $SO_2$ emissions and chemical conversion of $SO_2$ to $SO{_4}^{2-}$, and LTP from China. However, the $SO{_4}^{2-}$ at the sites were more influenced by LTP than the formation by chemical transformation of locally emitted $SO_2$. The $NO_3{^-}$ increase was closely associated with the increase in local emissions of nitrogen oxides at four urban sites except for the BR and JJ, as well as the LTP with a small contribution. Among the meterological parameters (wind speed, temperature, and relative humidity), the ambient temperature was most important factor to control the variation of $PM_{2.5}$ and its major chemical components concentrations. In other words, as the average temperature increases, the $PM_{2.5}$, OC, EC, and $NO_3{^-}$ concentrations showed a decreasing tendency, especially with a prominent feature in $NO_3{^-}$. Results from a case study that examined the $PM_{2.5}$ and its major chemical data observed between February 19 and March 2, 2014 at the all stations suggest that ambient $SO{_4}^{2-}$ and $NO_3{^-}$ concentrations are not necessarily proportional to the concentrations of their precursor emissions because the rates at which they form and their gas/particle partitioning may be controlled by factors (e.g., long range transportation) other than the concentration of the precursor gases.