• Management & Information Systems Review
• /
• v.29 no.4
• /
• pp.23-44
• /
• 2010

This study examines the value-relevance of accruals and discretionary accruals. Also, by examining the effects of the corporate life-cycle on these relationship, this study is able to provide evidence of the value-relevance of accruals and discretionary accruals measures in the economic context of life-cycle theory. This study uses results based on life-cycle classification methods developed by Anthony and Ramesh(1992), adjust Jones model and Dechow Dechev(2002) model. We classify firms using individuals variables(sales growth, capital expenditure growth, employee growth) and then use a composite score obtained from all variables for classification. Our sample consists of 272 firms listed in the Korean Stock Exchange during 14 years(1996-2009). Our final sample for regression variables consists of 2,448 firm-year observations. This evidence implies that the value-relevance of accruals and discretionary accruals in the growth and mature stage can have positive impact on the price but in the decline the value-relevance of accruals and discretionary accruals can have negative impact on the price. The results mean that discretionary accruals communicate managements' private information in the growth stage, but. earnings management in the decline stage. The results of this study suggest that corporate life cycle stages influence the value-relevance of accruals and discretionary accruals measures.

• 한국IT서비스학회:학술대회논문집
• /
• 2003.11a
• /
• pp.616-623
• /
• 2003

Since the computing environment changes very rapidly, the estimation of software effort is very difficult because it is not easy to collect a sufficient number of relevant cases from the historical data. If we pinpoint the cases, the number of cases becomes too small. However if we adopt too many cases, the relevance declines. So in this paper we attempt to balance the number of cases and relevance. Since many researches on software effort estimation showed that the neural network models perform at least as well as the other approaches, so we selected the neural network model as the basic estimator. We propose a search method that finds the right level of relevant cases for the neural network model. For the selected case set, eliminating the qualitative input factors with the same values can reduce the scale of the neural network model. Since there exists a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case set. To find the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the Case-Set Selection Algorithm. This algorithm can be adopted in the case-adaptive software effort estimation systems.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.7
• /
• pp.93-100
• /
• 2016

This paper shows what kind of influence the learning motivation factors have on the effectiveness of Flipped Learning Model through the case of operating a JAVA programming subject. The Flipped Learning Approach consisting of Before Class, Before or At Start of Class, and In Class provides the students with learning motivation as well as satisfies Keller's ARCS(Attention, Relevance, Confidence, Satisfaction) to keep them studying steadily. This research conducts the operation of Flipped Learning and gets Exploratory Factor Analysis and Reliability Analysis from the result of the course experience questionnaire at the end of the class. Given this survey result, Flipped Learning approach improves the learners' satisfaction in class and the effectiveness in the fields of understanding learning context more than does the previous lecture-based learning approach by pacing learning procedure and conducting self-directed learning.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.276-276
• /
• 2018

In this paper the potential of the Principle Component Analysis(PCA) technique that can be used to detect leaks in water pipe network blocks was evaluated. For this purpose the PCA was conducted to evaluate the relevance of the calculated outliers of a PCA model utilizing the recorded pipe flows and the recorded pipe leak incidents of a case study water distribution system. The PCA technique was enhanced by applying the computational algorithms developed in this study. The algorithms were designed to extract a partial set of flow data from the original 24 hour flow data so that the variability of the flows in the determined partial data set are minimal. The relevance of the calculated outliers of a PCA model and the recorded pipe leak incidents was analyzed. The results showed that the effectiveness of detecting leaks may improve by applying the developed algorithm. However, the analysis suggested that further development on the algorithm is needed to enhance the applicability of the PCA in detecting leaks in real-world water pipe networks.

• Journal of Digital Convergence
• /
• v.17 no.1
• /
• pp.159-168
• /
• 2019

The purpose of this study is to propose a strategy for creating an advertisement for Corporate Social Responsibility(CSR) based on the strategy of selecting an advertisement model in order to efficiently execute the advertisement of CSR. Data were analyzed by regression analysis. The results of this study are as follows: First, it is found that the value-relevance factors of the model are important regardless of the theme of CSR. In the economic field, the reliability of the model is important. But, the attractiveness of the model was more important in the environmental field. Second, it is effective to select a model with high value-relevance when selecting CSR advertising model. But, in the case of an expert, it is effective to select not only value addition, but also attractive and reliable models. Third, when producing CSR ads using emotional messages, it is important to consider the value and reliability of the advertising model. But, it is important to select the model considering the importance of the value and attractiveness of the advertisement model when producing the CSR advertisement using the informational message. It was found that it is very important to select a model that has a high relevance to the image of the social responsibility subject and the advertisement model when producing the social responsibility advertisement.

• The Mathematical Education
• /
• v.55 no.2
• /
• pp.209-232
• /
• 2016

This study analyzed the process of steps in a program introducing Renzulli's enrichment triad model and various levels of posing open-ended problems of those who participated in the program for mathematics-gifted elementary students. As results, participants showed their abilities of problem posing related to real life in a program introducing Renzulli's enrichment triad model. From eighteen mathematical responses, gifted students were generally outstanding in terms of producing problems that demonstrated high quality completion, communication, and solvability. Amongst these responses from fifteen open-ended problems, all of which showed that the level of students' ability to devise questions was varied in terms of the problems' openness (varied possible outcomes), complexity, and relevance. Meanwhile, some of them didn't show their ability of composing problem with concepts, principle and rules in complex level. In addition, there are high or very high correlations among factors of mathematical problems themselves as well as open-ended problems themselves, and between mathematical problems and open-ended problems. In particular, factors of mathematical problems such as completion, communication, and solvability showed very high correlation with relevance of the problems' openness perspectives.

• Coupled systems mechanics
• /
• v.7 no.1
• /
• pp.95-109
• /
• 2018

The relevance of turbulent mixing in estuarine numerical models for stratified two-layer shallow water flows is analysed in this paper. A one-dimensional numerical model was developed for this purpose by extending an immiscible two-layer model with an additional source term, which accounts for turbulent mixing effects, namely the entrainment of fluid from the lower to the upper layer. The entrainment rate is quantified by an empirical equation as a function of the bulk Richardson number. A finite volume method based on an approximated Roe solver was used to solve the governing coupled system of partial differential equations. A comparison of numerical results with and without entrainment is presented to illustrate the influence of entrainment on both the salt-water intrusion length and lower layer dynamics. Furthermore, one example is given to demonstrate how entrainment terms may help to stabilize the numerical scheme and prevent a possible loss of hyperbolicity. Finally, the model with entrainment is validated by comparing the numerical results to field measurements.

• Structural Engineering and Mechanics
• /
• v.68 no.6
• /
• pp.691-700
• /
• 2018

Ground granulated blast furnace slag (GGBS) is a by product obtained from iron and steel industries, useful in the design and development of high quality cement paste/mortar and concrete. This paper investigates the applicability of relevance vector machine (RVM) based regression model to predict the compressive strength of various GGBS based concrete mixes. Compressive strength data for various GGBS based concrete mixes has been obtained by considering the effect of water binder ratio and steel fibres. RVM is a machine learning technique which employs Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM is an extension of support vector machine which couples probabilistic classification and regression. RVM is established based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 70% of the data has been used for development of RVM model and 30% of the data is used for validation. The predicted compressive strength for GGBS based concrete mixes is found to be in very good agreement with those of the corresponding experimental observations.

• International conference on construction engineering and project management
• /
• 2011.02a
• /
• pp.176-181
• /
• 2011

A role of cost modeler is that of facilitating design process by the systematic application of cost factors so as to maintain sensible and economic relationships between cost, quantity, utility and appearance. These relationships help to achieve the client's requirements within an agreed budget. The purpose of this study is to develop a parametric cost estimating model for the early design stage by using the multi-dimensional system of OLAP (On-line Analytical Processing) based on the case of quantity data related to architectural design features. The parametric cost estimating models have been adopted to support decision making in the early design stage. These models typically use a similar instance or a pattern of historical case. In order to effectively use this type of data model, it is required to set data classification and prediction methods. One of the methods is to find the similar class in line with attribute selection measure in the multi-dimensional data model. Therefore, this research is to analyze the relevance attribute influenced by architectural design features with the subject of case-based quantity data used for the parametric cost estimating model. The relevance attributes can be analyzed by Analytical Characterization. It helps determine what attributes to be included in the OLAP multi-dimension.

• Journal of KIISE:Software and Applications
• /
• v.32 no.1
• /
• pp.1-9
• /
• 2005

In this paper we propose a modified fuzzy min-max neural network model for pattern classification and discuss the usefulness of the model. We define a new hypercube membership function which has a weight factor to each of the feature within a hyperbox. The weight factor makes it possible to consider the degree of relevance of each feature to a class during the classification process. Based on the proposed model, a knowledge extraction method is presented. In this method, a list of relevant features for a given class is extracted from the trained network using the hyperbox membership functions and connection weights. Ft)r this purpose we define a Relevance Factor that represents a degree of relevance of a feature to the given class and a similarity measure between fuzzy membership functions of the hyperboxes. Experimental results for the proposed methods and discussions are presented for the evaluation of the effectiveness and feasibility of the proposed methods.