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Journal of Intelligence and Information Systems,
Vol. 4, No. 1, June 1998
A Genetic Algorithm Approach for Process Plan Selection on the CAPP
Chiung Moon, Hyungsoo Kim, and Sangjoon Lee
Vol. 4, No. 1, Page: 1 ~ 10
Keywords : CAPP, Process Plan Selection, 0-1 interger Programming
Abstract
Process planning is a very complex task and requires the dynamic informatioon of shop foor and market situations. Process plan selection is one of the main problems in the process planning. In this paper, we propose a new process plan selection model considering operation flexibility for the computer aided process planing. The model is formulated as a 0-1 integer programming considering realistic shop factors such as production volume, machining time, machine capacity, transportation time and capacity of tractors such as production volume, machining time, machine capacity, transportation time capacity of transfer device. The objective of the model is to minimize the sum of the processing and transportation time for all parts. A genetic algorithm a, pp.oach is developed to solve the model. The efficiency of the proposed a, pp.oach is verified with numerical examples.
An Intelligent User Interface using Rule-based Method
Young Soo Yang, Min Suk Lee, Jai Hie Kim, Moon Ik Chang, and Choong Shik Park
Vol. 4, No. 1, Page: 11 ~ 24
Keywords : intelligent user interface, rule-based system, object-oriented method
Abstract
A Hybrid Approach to Multiple Neural Networks and Genetic Programming : A Perspective of Engineering Design Application
Kyung Ho Lee, and Yun Seog Yeun
Vol. 4, No. 1, Page: 25 ~ 40
Keywords : Fedeted Agents, Neural Network, Genetic Programming
Abstract
A Comparative Analysis for the knowledge of Data Mining Techniques with Experties
Gwang-Yong Gim, Gwang-Ki Son, and On-Sun Hong
Vol. 4, No. 1, Page: 41 ~ 58
Keywords : AHP, Data Mining, ID3
Abstract
A Study on Building Methodology of Virtual Organization
Jung Youn Kim, and Kyoung Hoon Yang
Vol. 4, No. 1, Page: 59 ~ 77
Abstract
Typhoon Track Prediction using Neural Networks
Seongjin Park, and Sungzoon Cho
Vol. 4, No. 1, Page: 79 ~ 87
Keywords : Typhoon Tracking, Neural Networks, Time Series
Abstract
Expert System for Supporting the Design of Storage Tanks
Huang Duk Jo, and Geun Sik Jo
Vol. 4, No. 1, Page: 89 ~ 102
Abstract
Development of Diagnosis System Based on Alarm Processing
Hak-Yeong Chung, and Hyun-Shin Park
Vol. 4, No. 1, Page: 103 ~ 114
Abstract
A Hybrid Malfunction Diagnostic System using Rules and Cases
Jae Sik Lee, and Young Kil Kim
Vol. 4, No. 1, Page: 115 ~ 131
Keywords : Hybrid Systems, Case-based System, Rule-based Systems
Abstract
Customer service process is one of the most important processes in today's competitive business environment. Among the various activities of customer service process, equipment malfunction diagnosis activity should be performed fast and accurately. When a customer calls the service center and reports the observed symptoms, he/she describes them in layman's terms. Therefore, the customer-reported symptoms have not been considered helpful information for service representatives. However, in order to perform diagnosis activity fast and accurately, we need to make use of the customer-reported symptoms actively. In this research, we developed three systems called R-EMD (Rule-based Equipment Malfunction Diagnostic system), C-EMD (Case-based Equipment Malfunction Diagnostic system) and R&C-EMD (Rule & Case-based Equipment Malfunction Diagnostic system), each of which diagnoses equipment malfunctions using the customer-reported symptoms. R&C-EMD is a hybrid system that utilizes both rule-based and case-based technologies. The diagnosis rules used in R&C-EMD and R-EMD were not acquired from service manuals or interviews with service representatives. Rater, we extracted them directly from the past diagnosis cases based on symptoms' frequencies. By this way, we were able to overcome the knowledge acquisition bottleneck. Using the real 100 malfunction diagnosis cases, we evaluated the performances of R&C-EMC, R-EMD and C-EMD in terms of speed and accuracy. In diagnosis time, R&C-EMD took longer than R-EMD and shorter than C-EMD. However, R&C-EMC was the best in accuracy.
Relationships Between the Characteristics of the Business Data Set and Forecasting Accuracy of Prediction models
Won ha Lee, and Jong Uk Choi
Vol. 4, No. 1, Page: 133 ~ 147
Abstract
Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.
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