• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2007.11a
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• pp.977-982
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• 2007

There have been many efforts in automatic object recognition using computing technologies. Especially in the development of automated construction equipment, automatic object recognition is very important issue for the proper equipment maneuvering. 3D laser scanning, which uses (time-of-flight) method to construct the 3-dimensional information, is applied to the civil earth work environment for its high accuracy, quick data collection, and object recognition capability that will be developed by the authors in the future. The 3D earth model is also used as a fundamental information for intelligent earth work task planning. This paper presents the analysis of the 3D laser scanner market and selection of the most optimum 3D scanner for the intelligent earth work planning. As well as the hardware configuration for the automated 3D earth modeling is developed but also the software structure and detailed user interface are designed in this research. In addition, it is presented in this paper that the accuracy comparison test between TotalStation(R) which is a traditional survey tool and ScanStation(R). The accuracy test is done by relative distance measurement using known targets.

• Knowledge Management Research
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• v.23 no.2
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• pp.37-60
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• 2022

The growth of virtual reality games and the popularization of blockchain technology are bringing significant changes to the formation of the metaverse industry ecosystem. Especially, after Meta acquired Oculus, a VR device and application company, the growth of VR-based metaverse services is accelerating. In this study, the concept that supports leisure activities in the metaverse environment is explored realting to game-like features in VR apps, which differentiates traditional mobile apps based on a smart phone device. Using exploratory text mining methods and network analysis approches, 241 apps registed in the Oculus Quest 2 App Store were analyzed. Analysis results from a quasi-network show that a leisure concept is closely related to various genre features including a game and tourism. Additionally, the anlaysis results of G & F model indicate that the leisure concept is distictive in the view of gateway brokerage role. Those results were also confirmed in LDA topic modeling analysis.

• Food Science and Preservation
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• v.15 no.6
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• pp.810-815
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• 2008

Improving quality is a very important component of maintaining competitiveness of agricultural products. However, evaluation of 'high quality' indicates it is a very abstract concept and independent of some quality attributes, leading to differences in the perception of quality. Thus, there is a pressing need to objectively define 'high quality' and to develop basic technologies for its measurement, for application in the production, storage and distribution of competitive agricultural products. To objectively quantify apple quality, a survey was conducted on consumer preferences and awareness of quality attributes including color, taste, flavor and shape. The survey questionnaire targeted male and female adults (463 persons) ranging in age from 20 to 59 years. The questionnaire was based on purchases made at a wholesale market (50.1%) or a traditional market (18.8%). The majority of purchases were as small packets (62.0%) or as individual pieces (20.5%). Apples of moderate size (fist size, 60.5%) were preferred over small (4.3%) or large (32.6%) apples. The questionnaire provided consumer data on external quality attributes including color, shape and variety. Taste attributes were evaluated in relation to the balance between sour and sweet taste, and flavors peculiar to apples.

• The Research Journal of the Costume Culture
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• v.9 no.3
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• pp.511-522
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• 2001

Various art trends of the 20th century that contributed to the creation and development of abstract art had showed the transition from the convention of mere representation of the object to the formative sensitivity emphasizing self-expression. Noticing that such trends had influenced the fashion industry to move toward a free and individualized style, this study attempts to express the formative way from the existing art to wear, especially, based on early 20th century paintings, 5 casual wears were made which applied korean materials and silhouettes that are functional, sample and show traditional korea beauty. The purpose of this study is to search for a solution to expand the world market by producing dresses utilizing our own tradition that can be distinguished in the global market and that derive inspiration from the formative of the sensitivity of the paintings during the first half of the 20th century. It also aims to let national economy as a high-added industry. The result of this study are as follows: First, the expression method and element of various styles of art such as Fauvism, Expressionism and Cubism, during the period of transition to abstract art, clearly presented the direction toward the artistic liberation and made possible a new formative artistic expression of dress in the early years of the 20th century. Their ideas inspired the dress designers of the time with a reformative and creative sense of fashion and have greatly contributed to the development of a new era of uniqueness and individuality. Second, the color and the simplicity of form of the early 20th century paintings are suitable fro utilizing a motive of functional dresses and express unique and concise modern beauty. Third, it was confirmed that utilizing our tradition in contemporary dress can be a significant method of creation in which the uniqueness and creativity of Korean dress can be expressed, distinguishing it on the global scene, as well as inspire the originality and pride of our culture. Fourth, a possibility has been discovered. It is the functionality and uniqueness of aesthetic expression technique of the contemporary arts that can contribute to the fashion of tomorrow, by searching a modern fashion which was affected by the past and also by taking a look at the trend of modern fashion as the same field as casula wear.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.9
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• pp.307-314
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• 2015

Recently, market trends of auxiliary storage device HDD and SSD are interchangeable. In the future, the SSD is expected to be used more popular than HDD as an auxiliary storage device. The TRIM command technique has been proposed and used effectively due to the development of the SSD. The TRIM command techniques can be used to solve the problem of Freezing SSD that operating system cooperates with the SSD. The TRIM command techniques are performed in the idle time of the internal SSD that are actually deleted when a user deletes the data. However, in the point of view of computer forensics, the digital crime is increasing year by year due to lack of data recovery. Thus, this rate of arrest is insufficient. In this paper, I propose a solution that selectively manages data to delete based on advantage of the stability and the write speed of the TRIM command. Through experiments, It is verified by measuring the performance of the traditional method and selected method.

• The KIPS Transactions:PartD
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• v.17D no.4
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• pp.253-258
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• 2010

Traditional frequent pattern mining considers equal profit/weight value of every item. Weighted Frequent Pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery by considering different weights for different items. Existing algorithms in this area are based on fixed weight. But in our real world scenarios the price/weight/importance of a pattern may vary frequently due to some unavoidable situations. Tracking these dynamic changes is very necessary in different application area such as retail market basket data analysis and web click stream management. In this paper, we propose a novel concept of dynamic weight and an algorithm DWFPM (dynamic weighted frequent pattern mining). Our algorithm can handle the situation where price/weight of a pattern may vary dynamically. It scans the database exactly once and also eligible for real time data processing. To our knowledge, this is the first research work to mine weighted frequent patterns using dynamic weights. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using dynamic weights.

• Korean Journal of Food Science and Technology
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• v.33 no.4
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• pp.395-400
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• 2001

The objectives of this study are to establish efficient pretreatment concentration and rehydration process for the production of the high quality of freeze-dried Mook, a traditional gel food in Korea, as an instant food. Effect of immersion in sucrose solution as pretreatment before freeze-drying on the rehydration efficiency and quality characteristics was studied. The rehydration efficiency of non-treated Mook was the highest. The rehydration efficiency increased as the concentration of sucrose increased. The texture of rehydrated Mook treated in sucrose solution was decreased with increase in rehydration temperature. The Mook treated at 60% sucrose solution was somewhat similar to the market selling Mook in the quality and the treatment prevented color and texture degradation.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.711-716
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• 2006

Since the introduction of digital camera to the mass market, the number of digital photos owned by an individual is growing at an alarming rate. This phenomenon naturally leads to the issues of difficulties while searching and browsing in the personal digital photo archive. Traditional approach typically involves content-based image retrieval using computer vision algorithms. However, due to the performance limitations of these algorithms, at least on the casual digital photos taken by non-professional photographers, more recent approaches are centered on time-based clustering algorithms, analyzing the shot times of photos. These time-based clustering algorithms are based on the insight that when these photos are clustered according to the shot-time similarity, we have "event clusters" that will help the user browse through her photo archive. It is also reported that one of the remaining problems with the time-based approach is that people perceive events in different scales. In this paper, we present an adaptive time-based clustering algorithm that exploits the usage history of digital photos in order to infer the user's preference on the event granularity. Experiments show significant performance improvements in the clustering accuracy.