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Journal of Intelligence and Information Systems,
Vol. 3, No. 2, December 1997
A Study for searching optimized combination of Spent light water reactor fuel to reuse as heavy water reactor fuel by using evolutionary algorithm
Jongil Ahn, Kyung Sook Jung, and Tae Choong Chung
Vol. 3, No. 2, Page: 1 ~ 9
Abstract
These papers propose an evolutionary algorithm for re-using output of waste fuel of light water reactor system in nuclear power plants. Evolutionary algorithm is useful for optimization of the large space problem. The wastes contain several re-useable elements, and they should be carefully selected and blended to satisfy requirements as input material to the heavy water nuclear reactor system. This problem belongs to a NP-hard like the 0/1 Knapsack problem. Two evolutionary strategies are used as a, pp.oximation algorithms in the highly constrained combinatorial optimization problem. One is the traditional strategy, using random operator with evaluation function, and the other is heuristic based search that uses the vector operator reducing between goal and current status. We also show the method, which performs the feasible teat and solution evaluation by using the vectorized data in problem. Finally, We compare the simulation results of using random operator and vector operator for such combinatorial optimization problems.
A Hybrid Genetic Algorithm for Solving Nonlinear Optimization Problems
Young-Soo Yun, Chi-Ung Moon, and Sang-Yong Yi
Vol. 3, No. 2, Page: 11 ~ 22
Abstract
A Study on Qualitative Reasoning about Motion in Space
Hyun-Kyung Kim
Vol. 3, No. 2, Page: 23 ~ 32
Abstract
Case-Based Reasoning Framework for Data Model Reuse
Jae Sik Lee, and Jae Hong Han
Vol. 3, No. 2, Page: 33 ~ 55
Keywords : Data Model, Data Modeling, Software Reuse, Case-based Reasoning
Abstract
A data model is a diagram that describes the properties of different categories of data and the associations among them within a business or information system. In spite of its importance and usefulness, data modeling activity requires not only a lot of time and effort but also extensive experience and expertise. The data models for similar business areas are analogous to one another. Therefore, it is reasonable to reuse the already-developed data models if the target business area is similar to what we have already analyzed before. In this research, we develop a case-based reasoning system for data model reuse, which we shall call CB-DM Reuser (Case-Based Data Model Reuser). CB-DM Reuse consists of four subsystems : the graphic user interface to interact with end user, the data model management system to build new data model, the case base to store the past data models, and the knowledge base to store data modeling and data model reusing knowledge. We present the functionality of CB-DM Reuser and show how it works on real-life a, pp.ication.
Expert System for Predicting the Stock Market Timing Using Candlesticks Chart
Kang Hee Lee, In Sil Yang, and Geun Sik Jo
Vol. 3, No. 2, Page: 57 ~ 70
Abstract
Reasoning of Uncertain Constraints in a Generalized Job-shop Scheduling
Namkee Chung, Seungyoung Chung, and Minsu Suh
Vol. 3, No. 2, Page: 71 ~ 82
Keywords : Job-shop scheduling, CSP model, Dispatching
Abstract
A Meta-learning Approach that Learns the Bias of a Classifier
Yeung-Joon Kim, Chul-Eui Hong, and Yoon-Ho Kim
Vol. 3, No. 2, Page: 83 ~ 91
Keywords : genetic algorithms, inductive learning, Bayesian appoaches, meta-learning appoach
Abstract
DELVAUX is an inductive learning environment that learns Bayesian classification rules from a set o examples. In DELVAUX, a genetic a, pp.oach is employed to learn the best rule-set, in which a population consists of rule-sets and rule-sets generate offspring by exchanging some of their rules. We have explored a meta-learning a, pp.oach in the DELVAUX learning environment to improve the classification performance of the DELVAUX system. The meta-learning a, pp.oach learns the bias of a classifier so that it can evaluate the prediction made by the classifier for a given example and thereby improve the overall performance of a classifier system. The paper discusses the meta-learning a, pp.oach in details and presents some empirical results that show the improvement we can achieve with the meta-learning a, pp.oach.
A Knowledge-Based Mastitis Diagnostic System for Dairy Participants in USA
Tae Woon Kim, and Jae Deuk Lee
Vol. 3, No. 2, Page: 93 ~ 104
Keywords : Mastitis, Somatic Cell Count, Fuzzy set, Knowledge-based System
Abstract
The major economic health problem of dairy cattle is mastitis which can affect 10 to 50% of cow-quarters. This health problem is difficult for many dairy farmers and health advisors to understand, diagnose and control. Without special laboratory testing, most mastitis is overlooked. Estimates of annual mastitis cast per cow vary from $50 to $200. For the nearly 9 million cows in the United States, annual loss to the dairy industry amounts to over one billion. A knowledge-based decision aid has been developed to evaluate mastitis data retrieved electronically from two of nine U. S. regional dairy records processing centers. Heuristic rules to diagnose herd mastitis problems were collected and incorporated into the system from various domain experts. This system information. It allows users to select mastitis control schemes with various degrees of aggressiveness and teaches commonly accepted mastitis control practices.
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