• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.553-561
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• 2012

Various statistical models to Estimate GDP (measured as a nation's economic situation) have been developed. In this paper an autoregressive distributed lag model, factor model, and a Bayesian VAR model estimate quarterly GDP as a single model; the combined estimates were evaluated to compare a single model. Subsequently, we suggest that some combined models are better than a single model to estimate quarterly GDP.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.1
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• pp.19-32
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• 2002

This paper examines the effect of the producer-supplier quality contract parameters and the magnitude of the quality related variables on the quality of the final products. Our analysis focuses on the parties' equilibrium behavior In a quality game environment where the supplier should choose among the two production technologies, one requiring high cost but producing high quality Parts and the other requiring low cost but producing low quality parts and where the producer should decide whether to do the inspection of the parts. The game framework is employed to depict the potential conflicts existing between the Producer and the supplier because the Producer can not observe the supplier's choice and each party wants the other to bear the cost of producing high quality products. In our model, we specifically consider the competitive situation where the producer competes with a firm producing the same product. We employ the market share attraction model to Incorporate the competitive situation and completely characterize the equilibrium by using the Nash equilibrium concept for the game solution. Our results show that the equilibrium depends on the contract terms and the magnitude of the quality related variables. Compared to the non-competitive situation, the probability of producer's Inspection and the probability of supplier's choosing the high quality technology increases in a competitive situation. This is true even when the competitor's quality is lower than the producer's lowest. As a result, the quality of the final product increases In a competitive situation. And as the failure cost borne by the supplier increases, the probability of choosing the high quality technology Increases and the probability of inspection decreases. The net effect of this results in the decrease of the final product quality.

• Education of Primary School Mathematics
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• v.5 no.2
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• pp.71-94
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• 2001

The purposes of this study were to design small group collaborative learning models for developing the creativity and to analyze the effects on applying the models in mathematics teaching and loaming. The meaning of open education in mathematics learning, the relation of creativity and inquiry learning, the relation of small group collaborative learning and creativity, and the relation of assessment and creativity were reviewed. And to investigate the relation small group collaborative learning and creativity, we developed three types of small group collaborative learning model- inquiry model, situation model, tradition model, and then conducted in elementary school and middle school. As a conclusion, this study suggested; (1) Small group collaborative learning can be conducted when the teacher understands the small group collaborative learning practice in the mathematics classroom and have desirable belief about mathematics instruction. (2) Students' mathematical anxiety can be reduced and students' involvement in mathematics learning can be facilitated, when mathematical tasks are provided through inquiry model and situation model. (3) Students' mathematical creativity can be enhanced when the teacher make classroom culture that students' thinking is valued and teacher's authority is reduced. (4) To develop students' mathematical creativity, the interaction between students in small group should be encouraged, and assessment of creativity development should be conduced systematically and continuously.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.9
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• pp.45-52
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• 2021

Stock price fluctuations affect investor returns, particularly, in this pandemic situation that has triggered stock market shocks. As a result of this situation, investors prefer to move their money into a safer portfolio. Therefore, in this study, we approach an efficient portfolio model using smart beta and combining others to obtain a fast method to predict investment stock returns. Smart beta is a method to selects stocks that will enter a portfolio quickly and concisely by considering the level of return and risk that has been set according to the ability of investors. A smart beta portfolio is efficient because it tracks with an underlying index and is optimized using the same techniques that active portfolio managers utilize. Using the logistic regression method and the data of 100 low volatility stocks listed on the Indonesia stock exchange from 2009-2019, an efficient portfolio model was made. It can be concluded that an efficient portfolio is formed by a group of stocks that are aggressive and actively traded to produce optimal returns at a certain level of risk in the long-term period. And also, the portfolio selection model generated using the smart beta, beta, alpha, and stock variants is a simple and fast model in predicting the rate of return with an adjusted risk level so that investors can anticipate risks and minimize errors in stock selection.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.1
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• pp.53-59
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• 2000

Data from a village household dairy survey was compared with technical parameters of three model farms (0.2, 0.4 and 0.8 ha in extent) established by the Mid-country Livestock Development Centre (MLDC). In terms of land size, about 67% of the 250 dairy farmers interviewed corresponded with the MLDC models, but only 33% of the farmers were keeping dairy cattle under conditions comparable to the MLDC models (no regular off-farm income). In the 0.2 ha category, village farmers kept more cows, and in the other two categories the village farmers kept less cows than their MLDC model counterparts. In all three categories, the milk production per cow was higher in the model farms (1540 to 2137 vs. 1464 to 1508 litres/cow/year), and this could be attributed to higher feeding levels of concentrates in the model farms as compared to the village farmers (430 to 761 vs. 233 to 383 kg/cow/year). The amount of milk produced from fodder was higher in the village situation in comparison to the models. In the mid country, dairy production seems to depend on access to fodder resources rather than on the extent of land owned. Except in the 0.8 ha village category, the highest contribution to the total income was made by the dairy component (44 to 60%). With 0.8 ha village farmers, the income contribution from dairy and crops was similar (41%). Income from other livestock was important for the 0.2 ha MLDC model, but for all other categories their contribution to total income ranged from 0 to 10%. Access to fodder resources outside own-farm land is vital for economic dairy production. As such, an in-depth analysis of feed resources available and their accessibility needs to be further investigated.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.107-111
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• 2006

According to IT(information technology) industry progress. our life is gradually convenient. The proliferation of environmental pollution and the threat of diseases proportional to the progress comes to be high gradually. We must prevent dangerous diseases which threatens the life of the human. Or we are bumped against irrevocable serious situation. In spite of the situation. managing one's own health against modern busy lifestyle is very difficult. Therefore, we need to manage our health situation by using sensors based on ubiquitous IT environment. In this paper. we propose a diagnostic model which is able to diagnose and prevent a cerebrovascular disease based on ubiquitous technology. Also. as a step of implementing the u-health care diagnosis system, the diagnosis model of cerebrovascular disease plays an important role to decide a clinic result. In the future, by using this model. we may improve our welfare and health.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.411-416
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• 2008

The development of games has brought about the birth of game characters that are visually very realistic. At present, one sees much enthusiasm for giving the characters emotions through such devices as avatars and emoticons. However, in a freely changing environment of games, the devices merely allow for the expression of the value derived from a first input rather than creating expressions of emotion that actively respond to their surroundings. As such, there are as of yet no displays of deep emotions among game characters. In light of this, the present article proposes the 'CROSS(Character Reaction on Specific Situation) Model AE Engine' for game characters in order to develop characters that will actively express action and emotion within the environment of the changing face of games. This is accomplished by classifying the emotional components applicable to game characters based on the OCC model, which is one of the most well known cognitive psychological models. Then, the situation of game playing analysis of the commercialized RPG game is systematized by ontology.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.1-8
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• 2023

In this paper, we propose a training method of a recommendation learning model that analyzes the battlefield situation and recommends a suitable hypothesis for the current situation. The proposed learning model uses the preference determined by comparing the two hypotheses as a label data to learn which hypothesis best analyzes the current battlefield situation. Our model is based on Siamese neural network architecture which uses the same weights on two different input vectors. The model takes two hypotheses as an input, and learns the priority between two hypotheses while sharing the same weights in the twin network. In addition, a score is given to each hypothesis through the proposed post-processing ranking algorithm, and hypotheses with a high score can be recommended to the commander in charge.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.101-125
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• 2022

Recently, with the development of computing technology and the improvement of the cloud environment, deep learning technology has developed, and attempts to apply deep learning to various fields are increasing. A typical example is anomaly detection, which is a technique for identifying values or patterns that deviate from normal data. Among the representative types of anomaly detection, it is very difficult to detect a contextual anomaly that requires understanding of the overall situation. In general, detection of anomalies in image data is performed using a pre-trained model trained on large data. However, since this pre-trained model was created by focusing on object classification of images, there is a limit to be applied to anomaly detection that needs to understand complex situations created by various objects. Therefore, in this study, we newly propose a two-step pre-trained model for detecting abnormal situation. Our methodology performs additional learning from image captioning to understand not only mere objects but also the complicated situation created by them. Specifically, the proposed methodology transfers knowledge of the pre-trained model that has learned object classification with ImageNet data to the image captioning model, and uses the caption that describes the situation represented by the image. Afterwards, the weight obtained by learning the situational characteristics through images and captions is extracted and fine-tuning is performed to generate an anomaly detection model. To evaluate the performance of the proposed methodology, an anomaly detection experiment was performed on 400 situational images and the experimental results showed that the proposed methodology was superior in terms of anomaly detection accuracy and F1-score compared to the existing traditional pre-trained model.

• Journal of The Korean Association For Science Education
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• v.19 no.4
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• pp.516-527
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• 1999

The purpose of this study was to find out the responses of students confronted with discrepant situation about inertia concept. We had developed two discrepant situation patterns according to conceptual change model(Kwon, 1989). And we examined the students' responses to the discrepant situations of the 3 inertia problems by interview method. In the result of this study. many students in discrepant situation 1 failed to explain the situation Some students gave up their preconception and showed new conception that was different from preconception. But most of their new conceptions were not scientific conception. In discrepant situation 2. the major characteristics of students responses were the assimilation strategies. Many students modified their preconception in some part but didn't change it. After discrepant situation. students' conceptions were changed more diverse conceptions than preconceptions.