• Journal of The Korean Association For Science Education
• /
• v.32 no.2
• /
• pp.293-305
• /
• 2012

The purpose of this study was to analyze the secondary school students' reasoning types in regards to measurement and to get implications for science education. The subjects were 197 middle school students and 200 high school students. The PMQ1 written instrument was used to explore students' ideas. Students' ideas about measurement were classified in two types of point and set reasoning. The reasoning types distribution were analyzed by grade and measurement step such as data collection, data processing, and data comparison. Reasoning types distribution by measurement step indicated that set reasoning type showed high figures in data processing, but point reasoning type appeared in data collection, and data comparison. Set reasoning type increased significantly by grade in data comparison. The majority of students recognized that the true value of the measurand can not be determined.

• The Journal of Engineering Geology
• /
• v.11 no.2
• /
• pp.153-161
• /
• 2001

The quantitative evaluation of the stereo graphic projection, the limit equilibrium analysis, the finite difference analysis, the distinct element methocI is a analytical evaluation using many variables. Through the reliability analysis by the point estimation technique, uncertainty of other variables that have an effect on the stability of the rock slo~ was considered. The organized evaluation method of the approximate reasoning concept and using a fuzzy language was developed to confer and analysis the failure factors in planning and constructing the rock slope. Considering the result of the an- alysis, it was demonstrated that stability of entire sections can be evaluated through reliability analysis of point estimation technique. The results of stability evaluation by Fuzzy Approximate Reasoning is generally identical with the results of other existirw; analyses. As mentioned above, general and organized evaluation of special qualities of rock slope is possible using RMR Classification, Stereo Graphic Projection, Limit Equilibriwn Analysis, Finite Difference Analysis, Distinct Element Method, Point Estimation Technique, and Fuzzy Approximate Reasoning.

• Journal of Educational Research in Mathematics
• /
• v.16 no.2
• /
• pp.95-113
• /
• 2006

The study tries to differentiate the levels of mathematical reasoning from inductive reasoning to formal reasoning for teaching gradually. Because the formal point of view without the relation to objects has limitations in the creation of a new knowledge, our mathematics education needs consider the such characteristics. We propose an intuitive level of proof related in concrete operations and perceptual experiences as an intermediating step between inductive and formal reasoning. The key activity of the intuitive level is having insight on the generality of reasoning. The details of the process should pursuit the direction for going away from objects and near to formal reasoning. We need teach the mathematical reasoning gradually according to the appropriate level of reasoning more differentiated.

• Journal of Intelligence and Information Systems
• /
• v.6 no.1
• /
• pp.97-112
• /
• 2000

The basic idea of case-based reasoning is to solve a new problem using the previous problem-solving experiences. In this research we develop a case-based reasoning system for equipment malfunction diagnosis. We first divide the case base into clusters using the case-based clustering technique. Then we develop an appropriate case-based diagnostic system for each cluster. In other words for individual cluster a different case-based diagnostic system which uses different weights for attributes is developed. As a result multiple case-based reasoning system are operating to solve a diagnostic problem. In comparison to the performance of the single case-based reasoning system our system reduces the computation time by 50% and increases the accuracy by 5% point.

• The Journal of Korean Academic Society of Nursing Education
• /
• v.25 no.3
• /
• pp.378-387
• /
• 2019

Purpose: This article is a comprehensive review for concept clarification of critical thinking, clinical reasoning, and clinical judgment, which still lack a consensus and are of mixed use. Methods: Norris's method of concept clarification was used to review concepts that have no clear definition or conceptualization yet. Results: This review summarized literature from various disciplines, classified each concept based on similarities and differences, and provided hypothetic conceptual schema. Conclusion: Clinical reasoning and clinical judgment are clinical situation specific concepts, while critical thinking is a concept applied in general situations. Critical thinking is a broader concept and serves as a foundation for clinical reasoning and clinical judgment. Clinical reasoning precedes clinical judgment. Clinical judgement implies the end point or conclusion of clinical reasoning. Each of critical thinking, clinical reasoning, and clinical judgment is a cognitive and affective process not a psychomotor process. The concept of clinical competency involves action taken after the cognitive processes of clinical reasoning and clinical judgment.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.449-454
• /
• 1994

This paper describes new PID control methods based on the fuzzy logic. PID gains are retuned after evaluating control performances of transient responses in terms of performance features. The retuning procedure is based on fuzzy rules and reasoning accumulated from the knowledge of experts on PID gain scheduling. For the case that the retuned PID gains result in worse CLDR (characteristics of load disturbance rejection) than the initial gains, an on-line tuning scheme of the set-point weighting parameter is, proposed. This is based on the fact that the set-point weighting method efficiently reduce either overshoot or undershoot without any degradation of CLDR. The set-point weighting parameter is adjusted at each sampling instant by the fuzzy rules and reasoning. As a result, better control performances were achived in comparison with die controllers tuned by the Z-N (Ziegler-Nichols) parameter tuning formula or by the fixed set-point weighting parameter.

• Proceedings of the KIEE Conference
• /
• 1989.07a
• /
• pp.241-245
• /
• 1989

A knowledge based expert system is a computer program that emulates the reasoning process of a human expert in a specific problem domain. This paper presents an expert system to diagnose the various faults in power system. The developed expert system is represented considering two points; the possibility of solution and the fast processing speed. As uncertainties exist in the facts and rules which comprise the knowledge base of the expert system, Certainty Factor, which is based on the confirmation theory is used for the inexact reasoning. Also, as the diagnosis problem requires the inductive reasoning process in nature, the solution is imperfect and not unique in general. So the expert system is designed to generate all the possible hypothesis in order of the possibility and also it can explain the propagation procedure of the faults for each solution using the built in backtracking mechanism. In realization of the expert system, the processing speed is greatly dependent upon the problem representation, reasoning scheme and search strategy. So, in this paper the fault diagnosis problem itself is analysed from the view point of Artificial Intelligence and as a result, the expert system has the following basic features. 1) The certainty factor is adopted in the inference engine for inexact reasoning. 2) Problem apace is represented using the problem reduction technique. 3) Bidirectional reasoning scheme is used. 4) Best first search strategy is adopted for rapid processing. The expert system was developed us ing PROLOG language.

• 한국데이터정보과학회:학술대회논문집
• /
• 2003.10a
• /
• pp.57-72
• /
• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

• Journal of the Korean Data and Information Science Society
• /
• v.14 no.3
• /
• pp.641-651
• /
• 2003

This study suggests the hybrid models for interest rate forecasting using structural changes (or change points). The basic concept of this proposed model is to obtain significant intervals caused by change points, to identify them as the change-point groups, and to reflect them in interest rate forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in the U. S. Treasury bill rate dataset. The second phase is to forecast the change-point groups with data mining classifiers. The final phase is to forecast interest rates with backpropagation neural networks (BPN). Based on this structure, we propose three hybrid models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported model, (2) case-based reasoning (CBR)-supported model, and (3) BPN-supported model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the prediction ability of hybrid models to reflect the structural change.

• The Journal of Information Systems
• /
• v.15 no.2
• /
• pp.95-123
• /
• 2006

It is needed to evaluate information systems actively which has already developed to improve future performance of the organization and foster the activation of information system. The introduction or development of information system also can bring about a organizational success. To measure exactly the organizational performance of information systems, it is needed to develop a new valuation model for a specific information system from a objective pint of view, as well as to equip a standard methodology using BSC measurement. The information system valuation from a objective point of view is of importance as the basic information for the decision to obtain information system. This paper takes aim at investigating a new information system valuation model and developing a information system valuation system using case-based reasoning for predicting currency value of information system in each organization. A new information system valuation system is developed as a web-enabling base. Using this, users are able to estimate the value of specific information system on a real time efficiently.