• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.19 no.2
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• pp.197-215
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• 2015

Modeling water flow in variably saturated, porous media is important in many branches of science and engineering. Highly nonlinear relationships between water content and hydraulic conductivity and soil-water pressure result in very steep wetting fronts causing numerical problems. These include poor efficiency when modeling water infiltration into very dry porous media, and numerical oscillation near a steep wetting front. A one-dimensional finite element formulation is developed for the numerical simulation of variably saturated flow systems. First order backward Euler implicit and second order Crank-Nicolson time discretization schemes are adopted as a solution strategy in this formulation based on Picard and Newton iterative techniques. Five examples are used to investigate the numerical performance of two approaches and the different factors are highlighted that can affect their convergence and efficiency. The first test case deals with sharp moisture front that infiltrates into the soil column. It shows the capability of providing a mass-conservative behavior. Saturated conditions are not developed in the second test case. Involving of dry initial condition and steep wetting front are the main numerical complexity of the third test example. Fourth test case is a rapid infiltration of water from the surface, followed by a period of redistribution of the water due to the dynamic boundary condition. The last one-dimensional test case involves flow into a layered soil with variable initial conditions. The numerical results indicate that the Crank-Nicolson scheme is inefficient compared to fully implicit backward Euler scheme for the layered soil problem but offers same accuracy for the other homogeneous soil cases.

• Archives of design research
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• v.20 no.1 s.69
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• pp.21-32
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• 2007

To quantitatively measure or evaluate aesthetic factors is not easy in comparison with physical, functional, behavioral or economic factors. Yet aesthetic factors essentially play an important role in design modeling process. Despite its importance, research on aesthetic assessment or the interaction of aesthetic influential elements is insufficient. Therefore, this study is intended to find the relationships between visual preference and aesthetic variables of perceptual-cognitive dimension and affective dimension in commercial space design. According to the result of this substantiation research, aesthetic variables that give a positive effect on the preference of commercial space design are unity, order, and clarity in perceptual-cognitive dimension and 'pleasant', 'relaxing' in affective dimension. On the other side, aesthetic variables that give a negative effect on the preference are contrast, complexity, and ambiguity that is a contrary concept of clarity in perceptual-cognitive dimension and 'exciting', 'arousing' in affective dimension.

• Proceedings of the KIEE Conference
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• 2002.11d
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• pp.197-199
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• 2002

The invention of VHDL(Very High Speed Integrated Circuit Hardware Description Language), Technical language of Hardware, is a kind of turning point in digital circuit designing, which is being more and more complicated and integrated. Because of its excellency in expression ability of hardware, VHDL is not only used in designing Hardware but also in simulation for verification, and in exchange and conservation, composition of the data of designs, and in many other ways. Especially, It is very important that VHDL is a Technical language of Hardware standardized by IEEE, intenational body with an authority. The biggest problem in modern circuit designing can be pointed out in two way. One is a problem how to process the rapidly being complicated circuit complexity. The other is minimizing the period of designing and manufacturing to survive in a cutthroat competition. To promote the use of VHDL, more than a simple use of simulation by VHDL, it is requested to use VHDL in composing logical circuit with chip manufacturing. And, by developing the quality of designing technique, it can contribute for development in domestic industry related to ASIC designing. In this paper in designing SMPS(Switching mode power supply), programming PWM by VHDL, it can print static voltage by the variable load, connect computer to chip with byteblaster, and download in Max(EPM7064SLCS4 - 5)chip of ALTER. To achieve this, it is supposed to use VHDL in modeling, simulating, compositing logic and product of the FPGA chip. Despite its limit in size and operating speed caused by the specific property of FPGA chip, it can be said that this method should be introduced more aggressively because of its prompt realization after designing.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.41-46
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• 2010

In this paper, we propose a detection method of reduced computational complexity aimed at separating the moving objects from the background in a generic video sequence. In generally, indoor environments, it is difficult to accurately detect the object because environmental factors, such as lighting changes, shadows, reflections on the floor. First, the background image to detect an object is created. If an object exists in video, on a previously created background images for similarity comparison between the current input image and to detect objects through several operations to generate a mixture image. Mixed-use video and video inputs to detect objects. To complement the objects detected through the labeling process to remove noise components and then apply the technique of morphology complements the object area. Environment variable such as, lighting changes and shadows, to the strength of the object is detected. In this paper, we proposed that environmental factors, such as lighting changes, shadows, reflections on the floor, including the system uses mixture images. Therefore, the existing system more effectively than the object region is detected.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.645-652
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• 2011

The present study investigated the tracking system of a reflector to trace the movement of sun. The system was designed to minimize the error between the vertical vector of reflector and the position of sun. The proposed system was able to collect the sun lights at a point as a useful source of light energy and transmit the collected light to a remote area through optical fibers. Also the study successfully solved the controller design problem due to the complexity of modeling of the sun tracking system using a fuzzy logic controller which mimics human reasoning.

• Journal of Distribution Science
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• v.17 no.1
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• pp.105-115
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• 2019

Purpose - Previous research has shown that a very high level and a very low level of job scope can both be more stressful than intermediate levels of job scope. This study investigates the potential positive and negative effects of the number of accounts handled by sales personnel. The primary objective of this paper is to examine how the number of accounts salespeople handle affects their stress and performance. Research design, data, and methodology - This research conducted the data collection using a survey of salespeople in the pharmaceutical industry. I sent the survey to 420 salespeople, and received 318 usable responses. To assess measurement reliability and validity, I ran an exploratory and confirmatory factor analysis. I also employed structural equation modeling (SEM) to test all hypothesized effects in AMOS and also measured the interaction variable using Ping's (1996) approach. Results - These results show that there are linear and non-linear effects of the number of accounts handled by the salesperson on both role ambiguity and role conflict. First, the number of accounts handled by a salesperson is positively related to role ambiguity and role conflict. Second, the effect of the number of accounts handled on role ambiguity and role conflict decreases as the number of accounts handled by the salesperson increases. Third, as accounts increase from a low level, role stress increases; when the number of accounts reaches an optimal level, role stress decreases; and when the number of accounts increases to a high level, it can be detrimental to the salesperson's role stress. Fourth, while product complexity is positively related to role ambiguity, brand strength is negatively related to both role ambiguity and role conflict. Fifth, the greater the brand strength, the weaker the relationship will be between the number of accounts handled and salesperson role ambiguity. Finally, role ambiguity is positively related to salesperson performance. Conclusion - Too much and too little accounts increase the role ambiguity and role conflict of salespersons. Managers should identify the complex effect of the number of accounts handled by salespeople. Also, when products are complex, managers should provide training to eliminate any complex processes and complex information. These results suggest ways to decrease salespersons' role stress by ensuring an optimal level of the number of accounts and brand strength.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.