• Bulletin of the Korean Mathematical Society
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• v.44 no.3
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• pp.523-536
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• 2007

Sharp error estimates in approximating the Stieltjes integral with bounded integrands and bounded integrators respectively, are given. Applications for three point quadrature rules of n-time differentiable functions are also provided.

• Journal of Korean Elementary Science Education
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• v.30 no.2
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• pp.162-177
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• 2011

The purpose of this qualitative case study was to investigate the 'structure' of an elementary school teacher's practical knowledge concerning science experiment lessons. A female elementary teacher in the early career years participated in the study, and video recordings of her science experiment lessons as well as audio-taped interviews with her were analyzed by means of Elbaz's framework. The teacher expressed six images of science experiment lessons: 'Science is difficult', 'Experiments are dangerous', 'Experiments are accurate', 'A science experiment takes a long time', 'Science experiments are interesting', and 'Children are little scientists.' These images were supported by several principles and rules, most of which were clearly described. Among the images, principles, and rules, there were complex relationships with some working in synergy and some conflicting. In case of the image 'Children are little scientists', its subordinate principles and rules were not fully realized in the classroom. Implications for science teaching reform and science education research were discussed.

• Journal of Mechanical Science and Technology
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• v.14 no.6
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• pp.655-665
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• 2000

We propose an efficient algorithm for the purpose of satisfying a wide range of design constraints represented with equality and inequality equations as well as production rules. The algorithm employs simulated-annealing and a production rule inference engine and works on design constraints represented with networks. The algorithm fulfills equality constraints through constraint satisfaction processes like variable elimination while taking into account inequality constraints and inferring production rules. It can also reduce the load of the optimization procedure if necessary. We demonstrate the implementation of the algorithm with the result on machine tool design.

• Korea Journal of Hospital Management
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• v.16 no.4
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• pp.161-182
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• 2011

In today's competitive dental markets, it is of paramount importance to improve service quality and at the same time to use scarce resource efficiently. In this study, we present appointment policies and scheduling rules for private dental clinics to reduce the waiting time of patients and to increase the revenue by utilizing resource more effectively. This study validates the proposed appointment policies and scheduling rules based on simulation models. We show that the bottleneck-based appointment policy is the most effective among appointment policies, followed by the multiple-block appointment one. The shortest processing time among scheduling rules contributes most to the performance of the appointment system.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.1
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• pp.58-65
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• 2001

This paper proposes a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. And, it is guaranteed that he selected solution become the global optimal solution by optimizing the Akaikes information criterion expressing the quality of the inference rules. The trajectory tracking simulation and experiment of the polishing robot show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding mode controller provides reliable tracking performance during the polishing process.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.176-181
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• 2007

This paper presents evolvable neural networks based on a developmental model for navigation control of autonomous mobile robots in dynamic operating environments. Bio-inspired mechanisms have been applied to autonomous design of artificial neural networks for solving practical problems. The proposed neural network architecture is grown from an initial developmental model by a set of production rules of the L-system that are represented by the DNA coding. The L-system is based on parallel rewriting mechanism motivated by the growth models of plants. DNA coding gives an effective method of expressing general production rules. Experiments show that the evolvable neural network designed by the production rules of the L-system develops into a controller for mobile robot navigation to avoid collisions with the obstacles.

• The Journal of Information Systems
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• v.12 no.2
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• pp.1-20
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• 2003

This paper deals with the application of the genetic algorithm to the technical trading rule of the stock market. MACD(Moving Average Convergence & Divergence) and the Stochastic techniques are widely used technical trading rules in the financial markets. But, it is necessary to determine the parameters of these trading rules in order to use the trading rules. We use the genetic algorithm to obtain the appropriate values of the parameters. We use the daily KOSPI data of eight years during January 1995 and October 2002 as the experimental data. We divide the total experimental period into learning period and testing period. The genetic algorithm determines the values of parameters for the trading rules during the teaming period and we test the performance of the algorithm during the testing period with the determined parameters. Also, we compare the return of the genetic algorithm with the returns of buy-hold strategy and risk-free asset. From the experiment, we can see that the genetic algorithm outperforms the other strategies. Thus, we can conclude that genetic algorithm can be used successfully to the technical trading rule.

• Fashion & Textile Research Journal
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• v.10 no.6
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• pp.873-881
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• 2008

The purpose of this study is to examine the effects of sales person's perception of ethical management on job performance in department stores. Sales person's perception of ethical management consists of such things as fairness, looking for short-term profits and observing the rules. Job performance consists of such things as sales person's organizational commitment, Sales person's service delivery level, rational operations, and participational attitude. For these purposes, the author developed several hypotheses. The data was collected from 435 sales person's in department stores. The results of this study are as follows: First, fairness, looking for short-term profits, and observing the rules had a significantly positive effect on sales person's organizational commitment. Second, fairness and observing the rules had significantly positive effect on sales person's service delivery level. Third, fairness had a significantly positive effect on rational operation. Fifth, looking for short-term profits and observing the rules had significantly positive effect on participational attitude. At the end of this paper, limitations, further research directions, and implications are suggested.

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.513-522
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• 2001

Active database execute an action of active rule defined in advance which is triggered automatically, whenever an event with the matching event specifications occurs, its condition is evaluated. Because these rules may in turn trigger other rules, the set of rules may be triggered each other indefinitely, Therefore, we propose a termination analysis method to guarantee termination. This proposed method considers composite event as well as rule execution time. Above all, the method not only uses deactivation graph combined to trigger graph for exact analysis, but also improves the complexity of analysis. Also the proposed method enhances accuracy of analysis result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.240-259
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• 2020

Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.