• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 춘계학술대회 논문집
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• pp.3-8
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• 2004

When most manufacturing company produce grinding product operators decide grinding condition by experience and subjective judgment. The study on grinding manufacture have been developed to get the grinding condition with the same result when non-experienced or experienced worker work. The objective of this study is to develope the grinding condition and predict the result of grinding by neural network. Several discussions were made in following areas as; getting MRR with image processing, the architecture optimization of neural network with experiment design, analysis of the input neurons using sensitivity approach. The results showed that the developed approach was the best method in predicting grinding condition with respect to surface finish quality.

• 한국경영정보학회:학술대회논문집
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• 한국경영정보학회 2008년도 춘계학술대회
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• pp.52-63
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• 2008

Virtuality is a product of the information age, and as it plays a larger role in the activities of individuals, groups and organizations, the issue of how human behavior varies between virtual and physical space has become one of the most important questions facing the management environment of today. The purpose of this article is to examine how virtuality shapes individuals' social relationships within and outside their work groups. We developed a conceptual framework that explores the links between virtuality and social network based on computer-mediated communication theory and social network theory. Using data from 172 individuals of 42 project teams in 5 global business consulting firms, we tested cross-level hypotheses. The results of hierarchical linear modeling (HLM) indicate that virtuality significantly influences individual's internal tie strength as well as external bridging ties. The results also show the effects of virtual process via CMC vary along with the virtual context.

• 한국공작기계학회논문집
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• 제16권3호
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• pp.64-74
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• 2007

The continuous chip in turning operation deteriorates precision of workpiece and causes a hazardous condition to operator. Thus the chip form control becomes a very important task for reliable machining process. So, grooved chip breaker is widely used to obtain reliable discontinuous chip. However, developing new cutting insert having chip breaker takes long time and needs lots of research expense due to a couple of processes such as forming, sintering, grinding and coating of product and many different evaluation tests. In this paper, performance of commercial chip breaker is evaluated with neural network which is learned with a back propagation algorithm. For the evaluation, several important elements(depth of cut, land, breadth, radius) which directly influence the chip formation were chosen among commercial chip breakers and were used as input values of neural network. With the results of these input values, the performance evaluation method was developed and applied that method to the commercial tools.

• 전기학회논문지P
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• 제52권1호
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• pp.35-41
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• 2003

In this paper, a machine vision system for recognizing and classifying the patterns and marks engraved by die molding or laser marking on the glass panels of computer monitors is suggested and evaluated experimentally. The vision system is equipped with a neural network and an NGC pattern classifier including searching process based on normalized grayscale correlation and adaptive binarization. This system is found to be applicable even to the cases in which the segmentation of the pattern area from the background using ordinary blob coloring technique is quite difficult. The inspection process is accomplished by the use of the NGC hypothesis and ANN verification. The proposed pattern recognition system is composed of three parts: NGC matching process and the preprocessing unit for acquiring the best quality of binary image data, a neural network-based recognition algorithm, and the learning algorithm for the neural network. Another contribution of this paper is the method of generating the training patterns from only a few typical product samples in place of real images of all types of good products.

• 한국농공학회논문집
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• 제52권5호
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• pp.109-118
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• 2010

The objectives of this study are to analyze and compare importance degrees of rural amenity resources for rural development project using AHP and ANP (Analytic Network Process) which can be applied a complex decision making problem. For this study, I chose the 5 rural development project types and the 10 rural amenity resources as major criteria and formed the ANP network from relations with criteria. The importance degree matrix were derived by the results of AHP and several ANP analysis. As the results of this study, the importance degrees of 10 rural amenity resources are determined and the indigenous product resource is identified as the most important resource in general rural development project.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 하계종합학술대회논문집
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• pp.293-296
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• 1998

In this paper, we propose a matrix hypercube graph as a new topology for parallel computer and analyze its characteristics of the network parameters, such as degree, routing and diameter. N-dimensional matrix hypercube graph MH(2,n) contains 22n vertices and has relatively lower degree and smaller diameter than well-known hypercube graph. The matrix hypercube graph MH(2,n) and the hypercube graph Q2n have the same number of vertices. In terms of the network cost, defined as the product of the degree and diameter, the former has n2 while the latter has 4n2. In other words, it means that matrix hypercube graph MH(2,n) is better than hypercube graph Q2n with respect to the network cost.

• 한국지능시스템학회논문지
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• 제14권2호
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• pp.216-221
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• 2004

This paper describes an optimal synthesis method of binary neural network for pattern recognition. Our objective is to minimize the number of connections and the number of neurons in hidden layer by using a Newly Expanded and Truncated Learning Algorithm (NETLA) for the multilayered neural networks. The synthesis method in NETLA uses the Expanded Sum of Product (ESP) of the boolean expressions and is based on the multilayer perceptron. It has an ability to optimize a given binary neural network in the binary space without any iterative learning as the conventional Error Back Propagation (EBP) algorithm. Furthermore, NETLA can reduce the number of the required neurons in hidden layer and the number of connections. Therefore, this learning algorithm can speed up training for the pattern recognition problems. The superiority of NETLA to other learning algorithms is demonstrated by an practical application to the approximation problem of a circular region.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 1999년도 춘계학술대회논문집
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• pp.84-87
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• 1999

This paper presents a preform design method that combines the analytic method and inference of known knowledge with neural network. The analytic method is a finite element method that is used to simulate backward extrusion with pre-defined process parameters. The multi-layer network and back-propagation algorithm are utilized to learn the training examples from the simulation results. The design procedures are utilized to learn the training examples from the simulation results. The design procedures are two methods the first the neural network infer the deformed shape from the pre-defined processes parameters. The other the network infer the processes parameters from deformed shape. Especially the latest method is very useful to design the preform From the desired feature it is possible to determine the processes parameters such as friction stroke and tooling geometry. The proposed method is useful for shop floor to decide the processes parameters and preform shapes for producing sound product.

• 한국경영과학회지
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• 제31권1호
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• pp.1-13
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• 2006

In this paper we consider a design problem of a cyclic queueing network with two stages, each with a local buffer of limited capacity. Based on the theory of reversibility and product-form solution, we derive the throughput function of the network as a key performance measure to maximize. Two cases are considered. In case each stage consists of a single server, an optimal allocation policy of a given buffer capacity and work load between stages as well as the optimal number of customers is identified by exploiting the properties of the throughput function. In case each stage consists of multiple servers, the optimal policy developed for the single server case doesn't hold any more and an algorithm is developed to allocate with a small number of computations a given number of servers, buffer capacity as well as total work load and the total number of customers. The differences of the optimal policies between two cases and the implications of the results are also discussed. The results can be applied to support the design of certain manufacturing and computer/communication systems.

• 한국항해학회지
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• 제22권2호
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• pp.47-52
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• 1998

Recently , there have been considerable researches about the fusion of fuzzy logic and neural networks. The propose of thise researches is to combine the advantages of both. After the function of approximation using GMDP (Generalized Multi-Denderite Product)neural network for defuzzification operation of fuzzy controller, a new fuzzy-neural network is proposed. Fuzzy membership function of the proposed fuzzy-neural network can be adjusted by learning in order to be adaptive to the variations of a parameter or the external environment. To show the applicability of the proposed fuzzy-nerual network, the proposed model is applied to a speed control o fDC sevo motor. By the hardware implementation, we obtained the desriable results.