• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.652-655
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• 2007

Due to stagnation of domestic market, increasing number of domestic construction companies started to make inroads into foreign market recently. Yet compared to domestic market, there are much more risks in the foreign market which companies may confront. So deliberate and rational decision making skills are required. Accordingly, there has been many researches which analyzed the risk of individual markets and also studies covering decision support models. In this study, we suggest a model concerning financial issues when branching out into a new market, specially in the construction companies' point of view. For this we used a real options game which shows real competition status of a new market and deduced a feature of that market, Upon these results, we also suggest a model which helps firms to decide whether investing in the expansion is smart action or not. The model developed in this study is made in specific circumstances of limited conditions. The future study makes more realistic models considering subjects like disproportion in information and generalization of competing companies.

• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.563-575
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• 2009

This research has found logical data directly influencing forming consideration set and attitude and attitude strength under the choosing situation based on memory-base proposed by Priester et. al (2004). We've examined the possibility of model extension through physical salient strength according to the location of product display as an external stimulate factor and attitude and attitude strength, consideration set and role on variable choice. Especially, this research practically proposed the method measuring directly the attitude on behavior instead of seeing the intension of behavior or behavior by measuring the behavior itself based on existing experiment methods and applied logistics regression analysis. In conclusion, this research confirmed the possibility of generalization of this model by verifying appropriateness through logical background and actual analysis based on stimulus-base proposed model characters as an integrated model relation between attitude in stimulus-based relation and decision-making.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.3
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• pp.417-428
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• 2020

With the rapid development of information and communication technology, mobile devices have become an essential tool in our lives. Mobile devices are used as important evidence in criminal proof, as they accumulate data simultaneously with PIM functions while working with users most of the time. The mobile forensics is a procedure for obtaining digital evidence from mobile devices and should be collected and analyzed in accordance with due process, just like other evidence, and the integrity of the evidence is essential because it has aspects that are easy to manipulate and delete. Also, the adoption of evidence relies on the judges' liberalism, which necessitates the presentation of generalized procedures. In this paper, a mobile forensics model is presented to ensure integrity through the generalization of procedures. It is expected that the proposed mobile forensics model will contribute to the formation of judges by ensuring the reliability and authenticity of evidence.

• Wind and Structures
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• v.11 no.1
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• pp.35-50
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• 2008

Wind and temperature have been shown to be the critical sources causing changes in the modal properties of large-scale bridges. While the individual effects of wind and temperature on modal variability have been widely studied, the investigation about the effects of multiple environmental factors on structural modal properties was scarcely reported. This paper addresses the modeling of the simultaneous effects of wind and temperature on the modal frequencies of an instrumented cable-stayed bridge. Making use of the long-term monitoring data from anemometers, temperature sensors and accelerometers, a neural network model is formulated to correlate the modal frequency of each vibration mode with wind speed and temperature simultaneously. Research efforts have been made on enhancing the prediction capability of the neural network model through optimal selection of the number of hidden nodes and an analysis of relative strength of effect (RSE) for input reconstruction. The generalization performance of the formulated model is verified with a set of new testing data that have not been used in formulating the model. It is shown that using the significant components of wind speeds and temperatures rather than the whole measurement components as input to neural network can enhance the prediction capability. For the fundamental mode of the bridge investigated, wind and temperature together apply an overall negative action on the modal frequency, and the change in wind condition contributes less to the modal variability than the change in temperature.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.123-131
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• 2020

The pattern query in graph database has advantages of easy query expression and high query processing performance compared to relational database SQL. However, unlike the relational database, the graph database may not utilize the advantages of pattern query depending on modeling because the methodology for building the logical data model is not defined. In this study, in the is-a node modeling method that appears during the graph modeling process, we experiment that there is a difference in performance between graph pattern query when designing with a generalization model and designing with a specialization model. As a result of the experiment, it was shown that better performance can be obtained when the is-a node is designed as a specialization model. In addition, when writing a pattern query, we show that if a variable is bound to a node or edge, performance may be better than that of the variable of not bounded. The experimental results can be presented as an is-a node modeling method for pattern query and a graph query writing method in the graph database.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.1-10
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• 2021

Log data accumulated in the Learning Management System (LMS) provide high-quality information for the learning process of students. Until now, various studies have been conducted to predict students' academic achievement using LMS log data. However, previous studies were based on relatively small sample sizes of students and courses, limiting the possibility of generalization. This study developed and validated a deep neural network model for the early prediction of academic achievement of college students using massive LMS log data. To this end, we used 78,466,385 cases of LMS log data and 165,846 cases of grade data. The proposed model predicted the excellent-grade students with a high level of accuracy from the beginning of the semester. Meanwhile, the prediction accuracy for the moderate and underachieving groups was relatively low, but the accuracy improved as the time points of the prediction were delayed. This study is meaningful in that we developed an early prediction model based on a deep neural network with sufficient accuracy for practical utilization by only using LMS log data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.87-92
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• 2021

Bias, variance, error and learning are important factors for performance in modeling a big data based recommendation system. The recommendation model in this system must reduce complexity while maintaining the explanatory diagram. In addition, the sparsity of the dataset and the prediction of the system are more likely to be inversely proportional to each other. Therefore, a product recommendation model has been proposed through learning the similarity between products by using a factorization method of the sparsity of the dataset. In this paper, the generalization ability of the model is improved by applying the max-norm regularization as an optimization method for the loss function of this model. The solution is to apply a stochastic projection gradient descent method that projects a gradient. The sparser data became, it was confirmed that the propsed regularization method was relatively effective compared to the existing method through lots of experiment.

• Structural Engineering and Mechanics
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• v.91 no.5
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• pp.443-457
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• 2024

At present, the traditional concrete surface inspection methods based on artificial vision have the problems of high cost and insecurity, while the computer vision methods rely on artificial selection features in the case of sensitive environmental changes and difficult promotion. In order to solve these problems, this paper introduces deep learning technology in the field of computer vision to achieve automatic feature extraction of structural damage, with excellent detection speed and strong generalization ability. The main contents of this study are as follows: (1) A method based on DeepLabV3+ convolutional neural network model is proposed for surface detection of post-earthquake structural damage, including surface damage such as concrete cracks, spaling and exposed steel bars. The key semantic information is extracted by different backbone networks, and the data sets containing various surface damage are trained, tested and evaluated. The intersection ratios of 54.4%, 44.2%, and 89.9% in the test set demonstrate the network's capability to accurately identify different types of structural surface damages in pixel-level segmentation, highlighting its effectiveness in varied testing scenarios. (2) A semantic segmentation model based on DeepLabV3+ convolutional neural network is proposed for the detection and evaluation of post-earthquake structural components. Using a dataset that includes building structural components and their damage degrees for training, testing, and evaluation, semantic segmentation detection accuracies were recorded at 98.5% and 56.9%. To provide a comprehensive assessment that considers both false positives and false negatives, the Mean Intersection over Union (Mean IoU) was employed as the primary evaluation metric. This choice ensures that the network's performance in detecting and evaluating pixel-level damage in post-earthquake structural components is evaluated uniformly across all experiments. By incorporating deep learning technology, this study not only offers an innovative solution for accurately identifying post-earthquake damage in civil engineering structures but also contributes significantly to empirical research in automated detection and evaluation within the field of structural health monitoring.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.413-418
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• 2020

It is necessary to manage the prediction accuracy of the machine learning model to prevent the decrease in the performance of the grid network condition prediction model due to overfitting of the initial training data and to continuously utilize the prediction model in the field by maintaining the prediction accuracy. In this paper, we propose an automation technique for maintaining the performance of the model, which increases the accuracy and reliability of the prediction model by considering the characteristics of the power grid state data that constantly changes due to various factors, and enables quality maintenance at a level applicable to the field. The proposed technique modeled a series of tasks for maintaining the performance of the power grid condition prediction model through the application of the workflow management technology in the form of a workflow, and then automated it to make the work more efficient. In addition, the reliability of the performance result is secured by evaluating the performance of the prediction model taking into account both the degree of change in the statistical characteristics of the data and the level of generalization of the prediction, which has not been attempted in the existing technology. Through this, the accuracy of the prediction model is maintained at a certain level, and further new development of predictive models with excellent performance is possible. As a result, the proposed technique not only solves the problem of performance degradation of the predictive model, but also improves the field utilization of the condition prediction model in a complex power grid system.

• Journal of the Korean Chemical Society
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• v.58 no.5
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• pp.478-489
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• 2014

The purposes of this study were to develop teaching strategy enhancing the activity to explore variables and to examine the instructional influences on students' creative thinking skills and critical thinking skills. In this study, a model using listing-excluding-controlling variables (DPAS model) was designed and applied to the existing 'Teaching model for the enhancement of the creative problem solving skills'. And it was implemented to preservice science teachers for the one semester. Results indicated that the experimental group presented statistically meaningful improvement in creative thinking skills, especially in recognizing problems, making hypothesis, controlling of variables and interpreting & transforming of data (p<.05). In addition, the strategy contributed to improve critical thinking skills, especially in making hypothesis and making conclusion & generalization (p<.05).