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

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.46
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• pp.181-196
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• 1998

The aim of this research is the development of a CAPP system which can use the old experience of process planning to generate a process plan for a new part and learn from its own experience using the concept of stratified case-based reasoning(CBR). A process plan is determined through the hierarchical process planning procedure that is based on the hierarchical feature structure of a part. Each part and case have their own multiple abstractions that are determined by the feature structure of the part. Retrieving the case in stratified case-based process planning is accomplished by retrieving the abstraction that is most similar to the input part abstraction in each abstraction level of the case-base. A new process plan is made by the adaptation that translates the old case's process plan into the process plan of a new part. Operations, machines and tools, setups and operation sequence in each setup are determined in the adaptation of abstraction using some algorithms and the reasoning based on knowledge-base. By saving a new part and its process plan as a case, the system can use this new case in the future to generate a process plan of a similar part. That is, the system can learn its own experience of process planning. A new case is stored by adding the new abstractions that are required to save as the new abstraction to the existing abstractions in the case-base.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.121-129
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• 2006

Rule-based Expert system using Fuzzy technique inferences various rules by user's input condition and the most proper control signal. This system can lose objectivity by input condition which depends on user's decision. This paper can solve those problems by adding case-based system's technique. The traffic signal control expert system is proposed to store the cases based on the statistics, days, seasons and various circumstances and use them.

• Journal of Ship and Ocean Technology
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• v.5 no.3
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• pp.27-35
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• 2001

In this research, hybrid method with case-based reasoning and rule-based reasoning is applied. Using case-based reasoning, design experts'experience and know-how are effectively represented in order to obtain a proper configuration of midship section in the initial ship design stage. Since there is not sufficient domain knowledge available to us, traditional case-adaptation algorithms cannot be applied to our problem, i.e., creating the configuration of midship section. Thus, new case-adaptation algorithms not requiring any domain knowledge are developed antral applied to our problem. Using the knowledge representation of DnV rules, rule-based reasoning can perform deductive inference in order to obtain the scantling of midship section efficiently. The results from the case-based reasoning and the rule-based reasoning are examined by comparing the results with various conventional methods. And the reasonability of our results is verified by comparing the results wish actual values from parent ship.

• Asia pacific journal of information systems
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• v.25 no.4
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• pp.626-641
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• 2015

Conventional case-based reasoning (CBR) does not perform efficiently for high-volume datasets because of case retrieval time. To overcome this problem, previous research suggested clustering a case base into several small groups and retrieving neighbors within a corresponding group to a target case. However, this approach generally produces less accurate predictive performance than the conventional CBR. This paper proposes a new case-based reasoning method called the clustering-merging CBR (CM-CBR). The CM-CBR method dynamically indexes a search pool to retrieve neighbors considering the distance between a target case and the centroid of a corresponding cluster. This method is applied to three real-life medical datasets. Results show that the proposed CM-CBR method produces similar or better predictive performance than the conventional CBR and clustering-CBR methods in numerous cases with significantly less computational cost.

• The Journal of Society for e-Business Studies
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• v.9 no.3
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• pp.129-144
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• 2004

CASE tools supporting component based development should include functions for modeling, project management, and supporting other phases activities of the development process. Functional requirements of CASE tools supporting component based development are proposed in this paper. Required functions are identified by analyzing the existing CBD CASE tool, and are categorized into modeling, project management, and supporting other phases activities.

• The Transactions of the Korea Information Processing Society
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• v.2 no.5
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• pp.721-732
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• 1995

The IPSE(Integrated Project Support Environment) tool can be seen as a result of an attempt to synthesize the key aspects of language-centered, specific methodology-based and toolkit oriented environments, which are current CASE tools into an organic whole. The IPSE approach based on a process model is regarded as an effective way to implement integrated CASE. The PM-CASE(Process Model based CASE) tool is currently a prototype which draw diagrams describing processes by using a new modeling technique. Attributes related with a task of withen the process model should be defined an saved the database. These attributed are used to retrieve the information of products, and to call the tool related which the task. In this paper, TSEE(Process centered Software Engineering Environment) tools are compared and analyzed. By describing the basic concept, architecture and design of PM-CASE tool, a method of building an process model-based CASE tool is proposed be support an effect software development and management.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.237-240
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• 2005

This paper describer a design expert system which determines the design values of shadow mask using Case-Based Reasoning. In Case-Based Reasoning, it is important to both retrieve similar cases and adapt the cases to meet the design specifications exactly. Especially, the difficulty in automating the adaptation process will prevent the designers from using the design expert systems efficiently and easily. This paper explains knowledge-based design support systems for shadow mask through neural network-based case adaptation. Specifically, we developed 1) representing design knowledge and 2) adaptive case-based reasoning method using RBFN (Radial Basis Function Network).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.932-951
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• 2021

The effectiveness of testing in Model-based Testing (MBT) for Software Product Line (SPL) can be achieved by considering fault detection in test case. The lack of fault consideration caused test case in test suite to be listed randomly. Test Case Prioritization (TCP) is one of regression techniques that is adaptively capable to detect faults as early as possible by reordering test cases based on fault detection rate. However, there is a lack of studies that measured faults in MBT for SPL. This paper proposes a Test Case Prioritization (TCP) approach based on dissimilarity and string based distance called Last Minimal for Local Maximal Distance (LM-LMD) with Dice-Jaro-Winkler Dissimilarity. LM-LMD with Dice-Jaro-Winkler Dissimilarity adopts Local Maximum Distance as the prioritization algorithm and Dice-Jaro-Winkler similarity measure to evaluate distance among test cases. This work is based on the test case generated from statechart in Software Product Line (SPL) domain context. Our results are promising as LM-LMD with Dice-Jaro-Winkler Dissimilarity outperformed the original Local Maximum Distance, Global Maximum Distance and Enhanced All-yes Configuration algorithm in terms of Average Fault Detection Rate (APFD) and average prioritization time.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.883-889
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• 2009

The importance of cost estimate in early stage such has been increasing due to market change and severe competition in construction industry. Because the adjustable budget is only 20% after design stage, most of the crucial decisions to influence cost is made in the early stage. However, in the early stage, the project scope is not defined completely so that estimator has inaccurate information to make critical decision. Therefore, this research suggests the cost estimate method using case-based reasoning. Case-based reasoning is appropriate for the early cost estimating, as it has the strength of rapidity and convenience in cost estimation. This research analyzes 84 actual data of public apartment on the scale of 11~15 stories. In order to extract the most similar case, at the first step this research identifies influence factors and calculates attribute similarity. In case-based reasoning, the most challenging task is determining attribute weight. At the third step, this research calculates case similarity which is aggregated attribute similarity multipled by attribute weight. Finally, extracts the most similar case which has the highest score of case similarity.