• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.22-25
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• 2003

It is usually difficult for a single user to have all the essential knowledge on various Rapid Prototyping processes and techniques. It is therefore necessary to capture knowledge and experience of users of expert level into a decision-support system which provides quicker and more interactive way to select proper RP process and/or machine. rather than reading reports on benchmarking studies and comparing tables and graphs. In this paper two algorithms are presented, which may be used in such a decision-support system. together with its applications. The one is an extended PRES(Project Evaluation and Selection) algorithm which applies weighting factors of each attribute. The other is a LCE(Linear Confidence Equation) algorithm which is proposed to apply user's input requirements as well as weighting factors.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.11 s.176
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• pp.202-209
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• 2005

In this paper, benchmark tests of Rapid Prototyping(RP) are presented to evaluate characteristics of various RP Systems and Processes, and several decision-support systems are developed to select RP Machine/Process suitable to user's requirements. Results of the RP benchmark tests are applied to the recently developed RP machines for the purpose of analyzing attributes such as dimensional accuracy, surface roughness, build cost, build time, and etc. Decision-making support systems are also developed, which contain not only new LCE (Linear Confidence Equation) algorithm but also modified PRES and MDS algorithm. Those algorithms are proved to be effective in that reasonably acceptable results are obtained on several cases of different inputs.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.579-585
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• 2012

Many different semi-supervised learning algorithms have been proposed for use wit unlabeled data. However, most of them focus on classification problems. In this paper we propose a semi-supervised regression algorithm called the semi-supervised local constant estimator (SSLCE), based on the local constant estimator (LCE), and reveal the asymptotic properties of SSLCE. We also show that the SSLCE has a faster convergence rate than that of the LCE when a well chosen weighting factor is employed. Our experiment with synthetic data shows that the SSLCE can improve performance with unlabeled data, and we recommend its use with the proper size of unlabeled data.

• Management Science and Financial Engineering
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• v.10 no.2
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• pp.1-28
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• 2004

The integrated supply chain of business partners for e-Commerce in cyber space is defined as Logistics Chain if the cooperative activities are logistics-related. Logistics Chain could be managed effectively and efficiently by cooperative technologies of logistics chain execution. In this paper, we propose a routing and scheduling algorithm based on the Tabu search by adding geographical information into existing constraint for pick-up and delivery process to minimize service time and cost in logistics chain. And, we also consider an uncertainty processing for the tracing of moving object to control pick-up and delivery vehicles based on GPS/GIS/ITS. Uncertainty processing is required to minimize amount of telecommunication and database on vehicles tracing. Finally, we describe the Logistics Chain Execution (LCE) system to perform plan and control activities for postal logistics chain. To evaluate practical effects of the routing and scheduling system, we perform a pretest for the performance of the tabu search algorithm. And then we compare our result with the result of the pick-up and delivery routing plan generated manually by postmen.