• 대한산업공학회지
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• 제20권4호
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• pp.5-21
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• 1994

We have described the optimal process route selection model for the PCB(printed circuit board) auto-insertion line. This PCB assembly line is known as a FFL(flexible flow line) which produces a range of products keeping the flow shop properties. Under FFL environments, we have emphasized the balancing of work-loads in order to maximize total productivity of PCB auto-insertion line. So we have developed a heuristic algorithm based on a work-order selection rule and min-max concept for the job route selection model.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2006년도 춘계학술발표회 논문집
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• pp.211-216
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• 2006

The Constrained Linear Spectral Unmixing(CLSU) is investigated for sub-pixel image processing, Its result is the abundance map which mean fractions of endmember existing in a mixed pixel. Compared to the Linear Spectral Unmixing using least square method, CLSU uses the NNLS (Non-Negative Least Square) algorithm to guarantee that the estimated fractions are constrained. But, CLSU gets Into difficulty in image processing due to select endmember at a user's disposition. In this study, endmember selection optimization method using entropy in the error-image analysis is proposed. In experiments which is used hyperion image, it is shown that our method can select endmember number than CLSU based on unsupervised endemeber selection.

• Asian Pacific Journal of Cancer Prevention
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• 제15권23호
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• pp.10539-10542
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• 2015

A method to predict the risk of lung cancer is proposed, based on two feature selection algorithms: Fisher and ReliefF, and BP Neural Networks. An appropriate quantity of risk factors was chosen for lung cancer risk prediction. The process featured two steps, firstly choosing the risk factors by combining two feature selection algorithms, then providing the predictive value by neural network. Based on the method framework, an algorithm LCRP (lung cancer risk prediction) is presented, to reduce the amount of risk factors collected in practical applications. The proposed method is suitable for health monitoring and self-testing. Experiments showed it can actually provide satisfactory accuracy under low dimensions of risk factors.

• 한국경영과학회지
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• 제16권2호
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• pp.61-61
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• 1991

We propose a strategic R&D resource allocation and project selection model based on national R&D policy and objectives. First, contributions to R&D policy and objectives for each R&D area are evaluated by using analytical hierarchy process (AHP). Second, fuzzy Delphi are proposed to estimate R&D budget for each R&D area. Then, a project selection grid is also introduced to implement two-phased evaluation for R&D project selection. We also discuss how to improve the consistency in AHP and how to reduce the pairwise comparison in AHP. The proposed model enables the decision makers to allocate R&D budget, and to evaluate and select the R&D proposals based on both the contribution to national R&D policy and objectives, and the size of each R&D area concurrently

• 기술혁신연구
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• 제13권2호
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• pp.223-237
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• 2005

This paper explains that tacit knowledge is a critical component for the success of government-led R&D project selection, where rapid and accurate decision making need to be made under lack of information circumstances. It also explores ways to fully exploit the tacit knowledge of experts participating in the Korean government's R&D project selection process. Some of these include: (1) strategic attention from the top officials, (2) forming self-organizing teams, (3) establishing a horizontal and risk-taking culture, (4) encouraging a sense of responsibility in creating and sharing tacit knowledge, and (5) providing a seamless monitoring system and training.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권1호
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• pp.21-28
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• 2005

This paper proposes a new method for accelerating the search speed of genetic algorithms by taking derivative evaluation and conditional random selection into account in their evolution process. Derivative evaluation makes genetic algorithms focus on the individuals whose fitness is rapidly increased. This accelerates the search speed of genetic algorithms by enhancing exploitation like steepest descent methods but also increases the possibility of a premature convergence that means most individuals after a few generations approach to local optima. On the other hand, derivative evaluation under a premature convergence helps genetic algorithms escape the local optima by enhancing exploration. If GAs fall into a premature convergence, random selection is used in order to help escaping local optimum, but its effects are not large. We experimented our method with one combinatorial problem and five complex function optimization problems. Experimental results showed that our method was superior to the simple genetic algorithm especially when the search space is large.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.983-984
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• 2008

This paper proposes a variable break that can offset prediction error as well as a pre-selection methods, based on the variable break, for enhanced unit selection. In Japanese, a sentence consists of several APs (Accentual phrases) and MPs (Major phrases), and the breaks between these phrases must predicted to realize text-to-speech systems. An MP also consists of several APs and plays a decisive role in making synthetic speech natural and understandable because short pauses appear at its boundary. The variable break is defined as a break that is able to change easily from an AP to an MP boundary, or from an MP to an AP boundary. Using CART (Classification and Regression Trees), the variable break is modeled stochastically, and then we pre-select candidate units in the unit-selection process. As the experimental results show, it was possible to complement a break prediction error and improve the naturalness of synthetic speech.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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• pp.505-508
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• 2003

The current paper proposes a new vertex selection scheme for polygon-based contour ceding. To efficiently characterize the shape of an object, we incorporate the curvature information in addition to the conventional maximum distance criterion in vertex selection process. The proposed method consists of “two-step procedure.” At first, contour pixels of high curvature value are selected as key vertices based on the curvature scale space (CSS), thereby dividing an overall contour into several contour-segments. Each segment is considered as an open contour whose end points are two consecutive key vertices and is processed independently. In the second step, vertices for each contour segment are selected using progressive vertex selection (PVS) method in order to obtain minimum number of vertices under the given maximum distance criterion ( $D_{MAX}$). Experimental results are presented to compare the approximation performances of the proposed and conventional methods.s.

• 응용통계연구
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• 제23권2호
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• pp.357-366
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• 2010

Many tree algorithms have been developed for regression problems. Although they are regarded as good algorithms, most of them suffer from loss of prediction accuracy when there are many noise variables. To handle this problem, we propose the multi-step GUIDE, which is a regression tree algorithm with a variable selection process. The multi-step GUIDE performs better than some of the well-known algorithms such as Random Forest and MARS. The results based on simulation study shows that the multi-step GUIDE outperforms other algorithms in terms of variable selection and prediction accuracy. It generally selects the important variables correctly with relatively few noise variables and eventually gives good prediction accuracy.

• 경영과학
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• 제16권2호
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• pp.61-77
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• 1999

We propose a strategic R&D resource allocation and project selection model based on national R&D policy and objectives. First, contributions to R&D policy and objectives for each R&D area are evaluated by using analytical hierarchy process (AHP). Second, fuzzy Delphi are proposed to estimate R&D budget for each R&D area. Then, a project selection grid is also introduced to implement two-phased evaluation for R&D project selection. We also discuss how to improve the consistency in AHP and how to reduce the pairwise comparison in AHP. The proposed model enables the decision makers to allocate R&D budget, and to evaluate and select the R&D proposals based on both the contribution to national R&D policy and objectives, and the size of each R&D area concurrently.