• The Mathematical Education
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• v.46 no.2 s.117
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• pp.193-205
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• 2007

In this paper, we focus on the product rule and sum rule which are considered as the most fundamental counting tools of Combinatorics. Despite of the importance of these rules in both educational and social aspects, they are taught superficially in class. We take the survey through both internet and questionaire to investigate how thoroughly students understand the rules. Then we discuss about the results of the survey and suggest effective teaching methods to improve students' understanding of these rules.

• Journal of Environmental Science International
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• v.14 no.6
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• pp.533-542
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• 2005

In this study, an optimization technique was developed from the application of Allocation Rule. Average Allocation coefficients of the Andong and Imha dam compare constant water supply condition with vary water supply condition that are above the contribute ratio $67\%\~50\%$ the Andong dam in Rule(A)-Rule(C). In the Refill Season, Andong dam water supply contribution is higher than Imha dam at the Control point water supply. In the Allocation analysis results, Rule(A) is calculated storage ratio because Andong dam contribute to Control point larger than Imha dam which Andong dam storage is larger than Imha dam storage. Rule(B) calculated sum of the storage and inflow ratio for Andong dam and Imha dam, as Andong dam contribution is higher than Imha dam. Rule(C) calculated that sum of storage, inflow and water supply is divided average storage ratio, as the best results of the Allocation coefficients and water supply capacity. The results of storage analysis is larger vary water supply condition than constant water supply condition and the results of water supply analysis is larger vary water supply condition than constant water supply condition. Water supply deficit is decrease $30\%$ for vary water supply condition.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.299-302
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• 1996

The purpose of this study is developing scheduling logic and program for machining scheduling of automobile part production line with unbalanced processing time. Three rules are developed to minimize slack and effect on other job. Fourth rule is suggested which considered weighted sum of three factors. schedule is generated totally 1, 000 times and then optimal weight parameter is selected. The program is developed to schedule situation to compare with the performance measure, total finish time of machine and total tardiness of part. As a result, the rule which considered weighted sum of three factors is effective for both measure.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.520-523
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• 1997

This paper presents FPGA implementation of fuzzy controller using Product-Sum inference method. Product-Sum inference method has much better performance than other inference methods. This fuzzy controller is composed of several digital modules, e.g. fuzzifier, rule base, adder, multiplier, select center and divider, and is operated by error and error variation. We synthesized the fuzzy controller and performed wave simulation using Xilinx VHDL tool(ViewLogic, ViewSim).

• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.227-230
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• 2003

We can not distinguish semantism of the feature of the current language “big”, “small”, “beautiful”. But we study artificial linguistic interface work and convert natural language to digital binary linguistic theory, we should define the basical conversion process. When we utilize the sum of product fuzzy theory and the visible numerical value, we can establish reasoning rule of input language. Fuzzy theory should be converted to general resulting rule.

• IE interfaces
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• v.10 no.2
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• pp.57-67
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• 1997

This paper is a report of a simulation study that investigates a dynamic approach to scheduling jobs in a conventional shop floor, especially in an assemoly-type welding process. We consider both JIT arrival and JIT delivery. Various dispatching rules are tested for the following performance measures; mean flow time, rate of tardy jobs, mean tardiness, sum of mean tardiness and mean earliness. The results indicate that SPT rule is the best for the mean flow time. MSLACK that we suggest in this paper, is the best for the mean tardiness and the sum of mean tardiness and mean earliness. However, it is not clear that which rule is the best for the rate of tardy jobs.

• Journal of the Korean Society of Safety
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• v.21 no.3 s.75
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• pp.16-21
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• 2006

This paper presents a knowledge-based electrical fire cause diagnosis system using the fuzzy reasoning. The cause diagnosis of electrical fires may be approached either by studying electric facilities or by investigating cause using precision instruments at the fire site. However, cause diagnosis methods for electrical fires haven't been systematized yet. The system focused on database(DB) construction and cause diagnosis can diagnose the causes of electrical fires easily and efficiently. The cause diagnosis system for the electrical fire was implemented with entity-relational DB systems using Access 2000, one of DB development tools. Visual Basic is used as a DB building tool. The inference to confirm fire causes is conducted on the knowledge-based by combined approach of a case-based and a rule-based reasoning. A case-based cause diagnosis is designed to match the newly occurred fire case with the past fire cases stored in a DB by a kind of pattern recognition. The rule-based cause diagnosis includes intelligent objects having fuzzy attributes and rules, and is used for handling knowledge about cause reasoning. A rule-based using a fuzzy reasoning has been adopted. To infer the results from fire signs, a fuzzy operation of Yager sum was adopted. The reasoning is conducted on the rule-based reasoning that a rule-based DB system built with many rules derived from the existing diagnosis methods and the expertise in fire investigation. The cause diagnosis system proposes the causes obtained from the diagnosis process and showed possibility of electrical fire causes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.318-322
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• 2016

A quad-copter or quad rotor system is an unmanned flying machine having four engines, which their thrust force is produced by four propellers. Its stable control is very important and has widely been studied. It is a typical example of a nonlinear system. So, it is difficult to get a desired control performance by conventional control algorithms. In this paper, we propose the design of a vectored sum defuzzification based fuzzy logic system for the hovering control of a quad-copter. We first summarize its dynamics and introduce a vectored sum defuzzification scheme. And then we design a vectored sum defuzzification based fuzzy logic system. for the hovering control of the quad-copter. Finally, in order to check the feasibility of the proposed system we present some simulation examples.

• Honam Mathematical Journal
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• v.25 no.1
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• pp.95-115
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• 2003

Consider the problem of estimating a $p{\times}1$ mean vector ${\theta}(p{\geq}4)$ under the quadratic loss, based on a sample $X_1,\;{\cdots}X_n$. We find an optimal decision rule within the class of Lindley type decision rules which shrink the usual one toward the mean of observations when the underlying distribution is that of a variance mixture of normals and when the norm $||{\theta}-{\bar{\theta}}1||$ is known, where ${\bar{\theta}}=(1/p)\sum_{i=1}^p{\theta}_i$ and 1 is the column vector of ones. When the norm is restricted to a known interval, typically no optimal Lindley type rule exists but we characterize a minimal complete class within the class of Lindley type decision rules. We also characterize the subclass of Lindley type decision rules that dominate the sample mean.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.211-218
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• 1994

In this paper we develope a hybrid learning rule to improve the robustness of multi-layer Perceptions. In most neural networks the activation of a neuron is deternined by a nonlinear transformation of the weighted sum of inputs to the neurons. Investigating the behaviour of activations of hidden layer neurons a new learning algorithm is developed for improved robustness for multi-layer Perceptrons. Unlike other methods which reduce the network complexity by putting restrictions on synaptic weights our method based on error-backpropagation increases the complexity of the underlying proplem by imposing it saturation requirement on hidden layer neurons. We also found that the additional gradient-descent term for the requirement corresponds to the Hebbian rule and our algorithm incorporates the Hebbian learning rule into the error back-propagation rule. Computer simulation demonstrates fast learning convergence as well as improved robustness for classification and hetero-association of patterns.