• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.96.2-96
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• 2002

In this paper, a new method for the mixed image separation is presented using the independent component analysis, the innovation process, and the expectation-maximization. In general, the independent component analysis (ICA) is one of the widely used statistical signal processing scheme that represents the information from observations as a set of random variables in the form of linear combinations of another statistically independent component variables. In various useful applications, ICA provides a more meaningful representation of the data than the principal component analysis through the transformation of the data to be quasi-orthogonal to each other, which can be utilized in linear p...

• Journal of Distribution Science
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• v.18 no.7
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• pp.73-81
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• 2020

Purpose: This research examines the factors that influence organic food purchasing decisions of kindergartens in Ho Chi Minh City, Vietnam. Research Design, Data, and Methodology: A mixed-method research was utilized in this study. It included a focus group of 10 participants and a survey of 304 respondents, (quantitative research) who are employed in the selected kindergartens, using both online and paper surveys based on nonprobability and convenient sampling. The SPSS and SmartPLS 3 software were used to analyze data. Results: a) Eight factors affect the purchase decision of kindergartens; b) Environment Attention, Normative Beliefs, Trust belief on brand, Cost of meal set, and Reference group positively affect Intention behavior; c) Feeling safe positively affect Perceived Quality Product. Perceived quality of product and Intention behavior positively affect organic food Purchase Decision of kindergartens. Conclusion: Eight factors affect organic food purchasing decisions of kindergartens in Ho Chi Minh City. This study offers recommendation and solutions for a stable output of organic products in Vietnam, and ways to popularize them within the community.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.355-361
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• 2010

This paper addresses a mathematical approach to decision making in a real-world material distribution situation. The problem is characterized by a low-volume and highly-varied mix of products, therefore there is a lot of material movement between the facilities. This study focuses especially on the transportation scheduler with a tool that can be used to quantitatively analyze the volume of material moved, the type of truck to be used, production schedules, and due dates. In this research, we have developed a mixed integer programming problem using the minimum cost, multiperiod, multi-commodity network flow approach that minimizes the overall material movement costs. The results suggest that the optimization approach provides a set of feasible solution routes with the objective of reducing the overall fleet cost.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.29 no.2
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• pp.94-108
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• 1993

In order to investigate the relation between the environmental properties and catch fluctuation of set net fishing ground located in the coastal waters of Yeosu, oceanographic observation and catches on the grounds were carried out from Jan. to Dec. in 1990 and 1992. The results obtained are summarized as follows; 1) Because of the surveyed area is a costal shallow water, the fishing ground was influenced largely by atmospheric phenomena such as air temperature. precipitation. etc. and so showed large variations in temperature and salinity yearly. The inner water flowed out mainly between Yeosu ad Namhe-do, and then through Kumo-do between Dolsan-do and Kumo-do. On the other hand, off shore water was supplied into the fishing ground from the vicinity of Sori-do and Yokchi-do. thus the fishing ground was occupied usually by various sources of water. 2) The water mass in the fishing ground were divided into the inner water(29.0～30.6$\textperthousand$) and the mixed water(31,7～32.2$\textperthousand$) and off shore water(32.3～32.8$\textperthousand$) accourding to the distribution of salinity from T-S diagram plotted all salinity data observed in 1990 and 1992. In summer the inner and mixing water which was formed by river flowed southerly and spread south-easterly in the vicinity of Kumo-do. The off shore water which supplied from the vicinity of Sori-do and Yokchi-do and inner water formed the thermal front and halo front in summer. 3) The fishes caught by the set net were arranged in the order of catch amounts as follows: Spanish mackerel>Horse mackerel >Hair tail>Common mackerel> Sardine> Anchovy. The Catches of anchovy and sardine were high in April to May and those of hair tail and horse mackerel in July to September, but spanish mackerel were caught during the whole period of fishing. When inner water and mixing water appeared respectively and inner water and mixing water speared together in the set net fishing ground, the set net showed a high catch.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.1
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• pp.21-31
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• 2002

This study applied each Neural Networks theory and Fuzzy Set theory to improve accuracy in remotely sensed images. Remotely sensed data have been used to map land cover. The accuracy is dependent on a range of factors related to the data set and methods used. Thus, the accuracy of maps derived from conventional supervised image classification techniques is a function of factors related to the training, allocation, and testing stages of the classification. Conventional image classification techniques assume that all the pixels within the image are pure. That is, that they represent an area of homogeneous cover of a single land-cover class. But, this assumption is often untenable with pixels of mixed land-cover composition abundant in an image. Mixed pixels are a major problem in land-cover mapping applications. For each pixel, the strengths of class membership derived in the classification may be related to its land-cover composition. Fuzzy classification techniques are the concept of a pixel having a degree of membership to all classes is fundamental to fuzzy-sets-based techniques. A major problem with the fuzzy-sets and probabilistic methods is that they are slow and computational demanding. For analyzing large data sets and rapid processing, alterative techniques are required. One particularly attractive approach is the use of artificial neural networks. These are non-parametric techniques which have been shown to generally be capable of classifying data as or more accurately than conventional classifiers. An artificial neural networks, once trained, may classify data extremely rapidly as the classification process may be reduced to the solution of a large number of extremely simple calculations which may be performed in parallel.

• Journal of the korean academy of Pediatric Dentistry
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• v.48 no.2
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• pp.221-228
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• 2021

The aim of this study was to evaluate the use of easily accessible machine learning application to identify mesiodens, and to compare the ability to identify mesiodens between trained model and human. A total of 1604 panoramic images (805 images with mesiodens, 799 images without mesiodens) of patients aged 5 - 7 years were used for this study. The model used for machine learning was Google's teachable machine. Data set 1 was used to train model and to verify the model. Data set 2 was used to compare the ability between the learning model and human group. As a result of data set 1, the average accuracy of the model was 0.82. After testing data set 2, the accuracy of the model was 0.78. From the resident group and the student group, the accuracy was 0.82, 0.69. This study developed a model for identifying mesiodens using panoramic radiographs of children in primary and early mixed dentition. The classification accuracy of the model was lower than that of the resident group. However, the classification accuracy (0.78) was higher than that of dental students (0.69), so it could be used to assist the diagnosis of mesiodens for non-expert students or general dentists.

• Journal of The Korean Society of Grassland and Forage Science
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• v.38 no.1
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• pp.53-60
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• 2018

Yield prediction model for mixed pasture was developed with a shortage that the relationship between dry matter yield (DMY) and days of summer depression (DSD) was not properly reflected in the model in the previous research. Therefore, this study was designed to eliminate the data of the regions with distinctly different climatic conditions and then investigate their relationships DMY and DSD using the data in each region separately of regions with distinct climatic characteristics and classify the data based on regions for further analysis based on the previous mixed pasture prediction model. The data set used in the research kept 582 data points from 11 regions and 41 mixed pasture types. The relationship between DMY and DSD in each region were analyzed through scatter plot, correlation analysis and multiple regression analysis in each region separately. In the statistical analysis, DMY was taken as the response variable and 5 climatic variables including DSD were taken as explanatory variables. The results of scatter plot showed that negative correlations between DMY and DSD were observed in 7 out of 9 regions. Therefore, it was confirmed that analyzing the relationship between DMY and DSD based on each region is necessary and 5 regions were selected (Hwaseong, Suwon, Daejeon, Siheung and Gwangju) since the data size in these regions is large enough to perform the further statistical analysis based on large sample approximation theory. Correlation analysis showed that negative correlations were found between DMY and DSD in 3 (Hwaseong, Suwon and Siheung) out of the 5 regions, meanwhile the negative relationship in Hwaseong was confirmed through multiple regression analysis. Therefore, it was concluded that the interpretability of the yield prediction model for mixed pasture could be improved based on constructing the models using the data from each region separately instead of using the pooled data from different regions.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.237-246
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• 2022

Among the technologies of the 4th industrial revolution, drones that have grown rapidly and are being used in various industries can be operated by the pilot directly or can be operated automatically through programming. In order to be controlled by a pilot or to operate automatically, it is essential to predict and analyze the optimal path for the drone to move without obstacles. In this paper, after securing and analyzing the pilot training dataset through the unmanned aerial vehicle piloting training platform designed through prior research, the profile of the dataset that should be preceded to search and derive the optimal route of the unmanned aerial vehicle was designed. The drone pilot training data includes the speed, movement distance, and angle of the drone, and the data set is visualized to unify the properties showing the same pattern into one and preprocess the properties showing the outliers. It is expected that the proposed big data-based profile can be used to predict and analyze the optimal movement path of an unmanned aerial vehicle.

• Phonetics and Speech Sciences
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• v.9 no.1
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• pp.19-26
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• 2017

The main objective of this study is to investigate the influence of utterance internal pause in English speaking evaluation. To avoid possible confusion with other errors caused by segmental and prosodic inaccuracy, stem utterances with two different length obtained from a native speaker were manipulated to make a set of stimuli tokens through insertion of pauses whose length and position vary. After a total of 90 participants classified into three proficiency groups rated the stimuli, the scored data set was statistically analyzed in terms of the mixed effects model. It was confirmed that predictors such as pause length, pause position and utterance length significantly influence raters' evaluation scores. Especially, a dominating effect was found in such a way that raters gradually deducted scores in accordance with the increase of pause duration. In another experiment, a tree-based statistical learning technique was utilized to check which of the significant predictors played a more influential role than others. The findings in this paper are expected to be practically informative for both the test takers who are preparing for an English speaking test and the raters who desire to develop more objective rubric of speaking evaluation.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2007.04a
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• pp.67-70
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• 2007

As construction industry is requiring competitive power and technique in national construction market with rapid fluctuation of construction environment and development, requirements upgrading performance in construction materials are increasing. But, national lightweight aggregate and lightweight concrete's inappropriateness when produced are also increasing. And there are not international standard of aggregates in using these construction materials because standards and characteristics of aggregate in each countries are different. Therefore, in this study, lightweight aggregate acquired due to wide range of use is tested and mixed for concrete to gain practicality and set the authorized manual in international. Also, basic data will be proposed to set a standard for concrete by analyzing lightweight aggregate characteristics. When lightweight aggregate absorptivity is high, concrete shows low strength and when it's density is low, concrete shows low weight of unit volume. Furthermore, compressive strength of lightweight aggregate is steep in first and longtime material age is tendency to cause low strength increasing rate.