• Journal of Korea Technology Innovation Society
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• v.10 no.3
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• pp.383-405
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• 2007

In this paper we estimate knowledge production function for 15 South Korean industry sectors using panel data. To accommodate the influence of inter-sectoral interactions on the creation of knowledge, we estimate parameters for related knowledge production functions using the Dynamic Seemingly Unrelated Regression(DSUR) model proposed by Mark et al. (2005). We find the elasticity of knowledge production with respect to the size of research staff to be 0.25 and that with respect to the existing stock of knowledge to be 0.35. The fact that the elasticity of new knowledge creation with regard to the existing knowledge stock is below 1 in South Korea corroborates the view that the rate of long-term growth of her economy is chiefly determined by the elasticity related to production functions of goods and services and the rate of population growth, and that her government policy, to ensure a continued growth for the Korean economy, must shift the focus of R&D policies from the current direct intervention-centered model to one consisting of indirect measures, namely supporting knowledge management and diffusion and the creation of a knowledge sharing system. In terms of R&D policy implications it could be consider that the national knowledge production system should strengthen the cumulative process of knowledge accumulation and population for research and development. Our country R&D policy, also, need to adopt a global approach to increase knowledge stock at the highest levels of a country.

• Journal of Korea Technology Innovation Society
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• v.13 no.2
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• pp.231-251
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• 2010

The numbers of SCI paper or patent in science and technology are expected to be related with the number of researcher and knowledge stock (R&D stock, paper stock, patent stock). The results of the regression model showed that severe multicollinearity existed and errors were made in the estimation and testing of regression coefficients. To solve the problem of multicollinearity and estimate the effect of the independent variable properly, principal component regression model were applied for three cases with S&T knowledge production. The estimated principal component regression function was transformed into original independent variables to interpret properly its effect. The analysis indicated that the principal component regression model was useful to estimate the effect of the highly correlate production factors and showed that the number of researcher, R&D stock, paper or patent stock had all positive effect on the production of paper or patent.

• Korean Journal of Computational Design and Engineering
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• v.11 no.2
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• pp.77-87
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• 2006

It is necessary to change the existing design process to cope with a short life-cycle product and various customer's demands. Also a frequent design change may delay the whole design process and it will increase the unit cost of the production. New alternatives or techniques have emerged to solve the existing design problems, such as a knowledge based engineering, an intelligent CAD, a function based design, and so on. In this paper, we propose a hybrid design system with a knowledge based design methodology and a function based design technique. The knowledge based design is good at a frequent design change and the function based design is effective to extract a core design behavior. In an early design process, the system utilizes a core design behavior through the function based design process. On the other hand, the system manages complicated design issues with the knowledge based design technique in the detailed design process. We conclude that the hybrid design system can bring fair effects on implementing an efficient design environment in aspect of time and expense.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1989.04a
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• pp.71-76
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• 1989

Structural design, generally engineering design, is a complex process combinding design knowledges and analysis techniques. While analysis techniques can be automated in an algorithmic fashion, relatively little work has been done in the area of the design automation. An effect approach method for the automation of the engineering design may be a hybrid system, in which design knowledges, specification requirements and interpretations are represented using an expert system methodology and numerically intensive operations of the design process are implemented using an algorithmic language such as FORTRAN. The purpose of this paper is concentrated on the knowledge of K-CLIPS(KAIST-C Language Integrated Production System) used to design and implement this hybrid system. In K-CLIPS, some representation methods : frame, production rule, fact and user defined function are used to construct the knowledge base. The hierarchical knowledges could be expressed more naturally with a little number of rules than other plain production systems.

• Journal of Korea Technology Innovation Society
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• v.10 no.1
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• pp.47-72
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• 2007

Ideas do not become exhausted, and there are no diminishing returns in the creation of knowledge. Nonetheless, growth ultimately ceases in this simplest model of endogeneous innovation. The reasons are similar to those that are discussed in the context of the neoclassical model of capital accumulation. Even if the resource cost of creating new goods does not rise, the economic return to invention may decline as the number of available products increases. When the rate of return to R&D falls to the level of the discount rate, private agents cease to be willing to defer consumption in order to invest in product development. But, if we treat knowledge capital as a public capital considering of its non-appropriable benefits, economic growth can be sustained in the economy. Romer(1986) has pointed out that growth might be sustainable if the accumulation of knowledge is not subject to long-run diminishing returns. Actually Romer assumed diminishing returns in the production of private knowledge from available resources, but increasing returns in the production of output from labor and total (public and private) knowledge. His condition for the sustainability of long-run growth amounts to an assumption that the diminishing returns in the former activity do not outweigh the increasing returns in the latter. The Johansen(1988) cointegration test method is used for finding long-run equilibrium relationship between R&D input and the product innovation. Test results indicate the existence of cointegrating equation between each pair of regression variables including dependent variable in the knowledge production function. And, the signs of cointegrating vectors are well accord to the prediction of sustainable growth. In the empirical analysis, from all cases of the form for the knowledge production function, we could not reject the null hypothesis that R&D spillover effect is significant($H_{0}:\;{\gamma}=1$). In summary, we showed that considering goodness of fit of regression model, we can see that the empirical evidence is strongly in favor of the character of knowledge as the public knowledge capital. So, we can expect that by product innovation, economic growth can be sustained in the Korean economy.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.2
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• pp.40-45
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• 2005

This paper has presented an application of an optimum grinding conditions based on the fuzzy logic. Fuzzy logic can handle vague and uncertain knowledge, and presents a scheme for integrating data with various kinds of grinding data. Especially, this research is capable of determining the grinding conditions taking into account some fuzzy membership function represented for trapezoidal form such as hardness and surface roughness of workpiece, material tensile strength and elongation, and requirement of grinding method. Larsen's fuzzy production method utilizing the fuzzy production rule can be applied on the establishment of grinding conditions, and also the output value obtained by the center of gravity method can effectively utilize the optimum grinding conditions.

• Knowledge Management Research
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• v.21 no.1
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• pp.27-40
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• 2020

Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

• Korean Journal of Child Studies
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• v.18 no.2
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• pp.73-85
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• 1997

The purpose of the present study was to examine the role of semantic and syntactic knowledge in the first language acquisition of Korean classifiers. The elicited classifiers production test(EPT) was conducted to 105 children aged from 2 to 7. EPT consisted of 16 classifiers and two items for each classifier. 32 items were divided into 2 major semantic features: animacy and inanimacy. The semantic features of inanimacy were subcategorized into 3 features such as neutral, shape and function. The results revealed that; 1) children produced the correct structure of classification from the very early age with correct word order of the noun phrase showing early fundamental syntactic knowledge; 2) The earliest response pattern was to respond to all nouns in the same way using a neutral classifier showing no apparent semantic basis for their choice; 3) Children didn't show any preference for animate, shape, or function classifiers.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.665-679
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• 2017

When measuring productive efficiency, often it is necessary to have knowledge of the production frontier function that shows the maximum possible output of production units as a function of inputs. Canonical parametric forms of the frontier function were initially considered under the framework of stochastic frontier model; however, several additional nonparametric methods have been developed over the last decade. Efforts have been recently made to impose shape constraints such as monotonicity and concavity on the non-parametric estimation of the frontier function; however, most existing methods along that direction suffer from unnecessary non-smooth points of the frontier function. In this paper, we propose methods to estimate the smooth frontier function with monotonicity for stochastic frontier models and investigate the effect of imposing a monotonicity constraint into the estimation of the frontier function and the finite dimensional parameters of the model. Simulation studies suggest that imposing the constraint provide better performance to estimate the frontier function, especially when the sample size is small or moderate. However, no apparent gain was observed concerning the estimation of the parameters of the error distribution regardless of sample size.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.287-289
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• 2021

Smart farming education service based on network are important factors for farming sector. The lack of time and space has to lead to their limited appliance to farmers. Limited information support and low background knowledge in farming production is a lot of trial and error in farming production. Smart farming education as a service based on cloud provide the farming information that is the farming knowledge, farming skill, and farmer's experiences and knowhow, etc. And real-time information such as climate change, soil environment and market trends is very important. This paper proposes a framework for applying farming education service based on cloud. It consists of smart farming function and smart farming education function