• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.225-226
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• 2006

A service level agreement (SLA) is a formal contract between a service provider and a subscriber that contains detailed technical specifications called service level specifications (SLSs). In this paper, we propose SLA data analysis system which provides an effective decision-making information to network service provider. The SLA data analysis system executes trend analysis and statistics analysis using SLA violation and refund information. The analysis results are presented in many ways such as tables, graphs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7A
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• pp.512-521
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• 2012

In this paper, we propose feasible combining rules for a decoding scheme of product codes to apply finite soft decision. Since the decoding scheme of product codes are based on complex tanh calculation with infinite soft decision, it requires high decoding complexity and is hard to practically implement. Thus, simple methods to construct look-up tables for finite soft decision are derived by analyzing the operations of the scheme. Moreover, we focus on using convolutional codes, which is popular for easy application of finite soft decision, as the horizontal codes of product codes so that the proposed decoding scheme can be properly implemented. Numerical results show that the performance of the product codes with convolutional codes using 4-bit soft decision approaches to that of same codes using infinite soft decision.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.05
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• pp.13-16
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• 1990

This paper describes a software algorithm for automatic diagnosis of arrhythmia using the criteria of Minnesota code manual. This algorithm represents more accurate and more objective information to medical doctor by standardizing the criteria of diagnosis of arrhythmia. Because this algorithm doesn't need complicated mathematic processing, it carries out the real-time automatic diagnosis that is very important in clinic. The Decision-Table technology suggests the proper results for the given conditions. So it expresses the complicated medical problems simply and clearly, those are not solved by the mathematical methods. The Decision-Tables have very simple structure and so it is very easy to correct or expand the system by adding or correcting some rules.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.331-337
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• 1997

In this paper we will discuss a type of inductive learning called learning from examples, whose task is to induce general descriptions of concepts from specific instances of these concepts. In many real life situations however new instances can be added to the set of instances. It is first proposed within the framework of rough set theory, for such cases, an algorithm to find minimal set of rules for decision tables without recalculation for overall set of instances. The method of learning presented here is based on a rough set concept proposed by Pawlak[2]. It is shown an algorithm to fund minimal set of rules using reduct change theorems giving criteria for minimum recalculation and an illustrative example.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.814-820
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• 2003

This paper presents a new DFD methodology to estimate the standard disassembly time and evaluate the disassemblablity of a product. The disassembly time is estimated with some decision and influencing factors of disassembly using motion analysis. The disassemblablity is evaluated using weights of the decision and influencing factors and disassembly difficulty scores. and the tables that include the time and stores of disassembly are suggested Finally, a new DFD support system is implemented to help designers to analyze the Product during the design process in concurrent engineering.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.400-405
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• 2013

The purpose of this study is to develop a composite platform for knowledge extractions, visualizations, and inference. Generally, the big data sets were frequently used in the healthcare and medical area. To help the knowledge managers/users working in the field, this study is focused on knowledge management (KM) based on Data Mining (DM), Knowledge Distribution Map (KDM), Decision Tree (DT), RDBMS, and SQL-inference. The proposed mechanism is composed of five key processes. Firstly, in Knowledge Parsing, it extracts logical rules from a big data set by using DM technology. Then it transforms the rules into RDB tables. Secondly, through Knowledge Maintenance, it refines and manages the knowledge to be ready for the computing of knowledge distributions. Thirdly, in Knowledge Distribution process, we can see the knowledge distributions by using the DT mechanism.Fourthly, in Knowledge Hierarchy, the platform shows the hierarchy of the knowledge. Finally, in Inference, it deduce the conclusions by using the given facts and data.This approach presents the advantages of diversity in knowledge representations and inference to improve the quality of computer-based medical diagnosis.

• Journal of Biomedical Engineering Research
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• v.11 no.1
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• pp.171-178
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• 1990

This paper describes a software algorithm for automatic diagnosis of arrhythmia using the criteria of Minnesota code manual. This algorithm provides more accurate and more objective information to medical doctor by standardizing the criteria of diagnosis of arrhythmia. Because this algorithm doesn't need complicated mathematic processing, it carries out the real-time automatic diagnosis that is very important in clinic. The Decision-Table technology suggests the proper results for the given conditions. So it can express clearly the complicated medical problems those are not solved by the mathematical methods. The Decision-Tables have very simple structure. Therefore, it is very easy to correct or expand the system by adding or correcting some rules.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.6
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• pp.151-159
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• 2003

This paper presents a new DFA methodology to estimate the standard assembly time and evaluate the assemblablity of a product. The assembly time is estimated with some decision and influencing factors of assembly using motion analysis. The assemblability is evaluated using weights of the decision and influencing factors and assembly difficulty scores, and the tables that include the time and score of assembly are suggested. Finally, a new DFA system supporting the DFA is implemented to help designers to analyze the product during the design process in concurrent engineering.

• IE interfaces
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• v.6 no.2
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• pp.37-51
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• 1993

This paper studies the economic replacement method for production facilities which requires huge investment but are necessary for higher productivity and competability of products. That is, the general models for the decision of economic life of properties which minimizes the total costs throughout the usage life(Life cycle costs) are generated. Main factors which make influences for the decision of econmic life can be divided by three. These are the change of salvage value, repair and maintenance costs, and availability of production facilities with the passage of usage time. In this paper, the real world data for these three factors are collected and analyzed for the extraction of representative standard forms with the passage of time. And general models for economic replacement methods and optimal usage terms are presented through tables with the combination of the standard forms of these three main factors.

• Journal of Communications and Networks
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• v.17 no.3
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• pp.306-320
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• 2015

Due to an explosive growth in services running in different datacenters, there is need for service selection and routing to deliver user requests to the best service instance. In current solutions, it is generally the client that must first select a datacenter to forward the request to before an internal load-balancer of the selected datacenter can select the optimal instance. An optimal selection requires knowledge of both network and server characteristics, making clients less suitable to make this decision. Information-Centric Networking (ICN) research solved a similar selection problem for static data retrieval by integrating content delivery as a native network feature. We address the selection problem for services by extending the ICN-principles for services. In this paper we present Queue and Latency, a network-driven service selection algorithm which maps user demand to service instances, taking into account both network and server metrics. To reduce the size of service router forwarding tables, we present a statistical method to approximate an optimal load distribution with minimized router state required. Simulation results show that our statistical routing approach approximates the average system response time of source-based routing with minimized state in forwarding tables.