• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.207-210
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• 2001

This Paper presents a design of the fuzzy decision maker analyzable of output result of ECG signals. The fuzzy decision maker proposed are divided into two groups whose functions are different each other. The one rules when decision of heart rates, The other decision values for an interval of each points of waveform using of which static state values and abnormal values. We have chosen several variable used for composing condition and action part by knowledge of an Expert The result of outputs with fuzzy rules suggested was a proved of satisfied with by classify ECG arrythmia signals

• Journal of Mechanical Science and Technology
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• v.14 no.4
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• pp.380-392
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• 2000

A typical production-distribution system consist of three main echelons representing the retailer, distributors, and a factory each with an on-site warehouse. The system is sufficiently general and realistic to represent many industrial situations. However, decision functions and parameters have been selected to apply particularly to the production and distribution of consumer durables. The flows included in the model are materials, orders, and those information flows needed to support the material and order-rate decisions. In this work, a realistic production-distribution system has been used as a basic model, which consists of three sectors: retailer, distributor, and factory. That system is a nonlinear 25th-order continuous system interconnected between the echelons. Using a modern control algorithm, a typical multi-echelon production-distribution system using a dynamic controller is numerically simulated in the nominal plant and in the perturbed plant when the piecewise constant manufacturing decision is limited by a factory manufacturing upper-limit due to capital equipment, manpower, and factory lotsize.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.237-246
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• 2020

this paper, a data modeling method based on decision-making conditions is proposed for making combat and battlefield management systems to be intelligent, which are also a decision-making support system. A picture of a robot seeing and perceiving like humans and arriving a point it wanted can be understood and be felt in body. However, we can't find an example of implementing a decision-making which is the most important element in human cognitive action. Although the agent arrives at a designated office instead of human, it doesn't support a decision of whether raising the market price is appropriate or doing a counter-attack is smart. After we reviewed a current situation and problem in control & command of military, in order to collect a big data for making a machine staff's advice to be possible, we propose a data modeling prototype based on decision-making conditions as a method to change a current control & command system. In addition, a decision-making tree method is applied as an example of the decision making that the reformed control & command system equipped with the proposed data modeling will do. This paper can contribute in giving us an insight of how a future AI decision-making staff approaches to us.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4549-4567
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• 2016

To cope with the complex electromagnetic environment of wireless communication systems, anti-jamming decision methods are necessary to keep the reliability of communication. Basing on the rule-reduced genetic algorithm (RRGA), an anti-jamming decision method is proposed in this paper to adapt to the fast channel variations. Firstly, the reduced decision rules are obtained according to the rough set (RS) theory. Secondly, the randomly generated initial population of the genetic algorithm (GA) is screened and the individuals are preserved in accordance with the reduced decision rules. Finally, the initial population after screening is utilized in the genetic algorithm to optimize the communication parameters. In order to remove the dependency on the weights, this paper deploys an anti-jamming decision objective function, which aims at maximizing the normalized transmission rate under the constraints of minimizing the normalized transmitting power with the pre-defined bit error rate (BER). Simulations are carried out to verify the performance of both the traditional genetic algorithm and the adaptive genetic algorithm. Simulation results show that the convergence rates of the two algorithms increase significantly thanks to the initial population determined by the reduced-rules, without losing the accuracy of the decision-making. Meanwhile, the weight-independent objective function makes the algorithm more practical than the traditional methods.

• Journal of Korea Game Society
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• v.11 no.6
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• pp.61-70
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• 2011

In this paper, we propose a defense NPC control model in the soccer game by applying the Decision Tree learning algorithm. The proposed model extracts the direction patterns and the action patterns generated by many soccer game users, and applies these patterns to the Decision Tree learning algorithm. Then, the proposed model decides the direction and the action according to the learned Decision Tree. Experimental results show that the proposed model takes some time to learn the Decision Tree while the proposed model takes 0.001-0.003 milliseconds to decide the direction and the action based on the learned Decision Tree. Therefore, the proposed model can control NPC in the soccer game system in real time. Also, the proposed model achieves higher accuracy than a previous model (Letia98); because the proposed model can utilize current state information, its analyzed information, and previous state information.

• Hwankyungkyoyuk
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• v.20 no.1
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• pp.90-105
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• 2007

The purpose of this study is to find the influence of environmental issue analysis instruction on the environmental decision making ability for grade 5 elementary school students. The study was done through pre and post testing control group structure. The object of this study is grade 5 of I elementary school students which were divided into 35 student test group and 54 student control group. Through studying references, the selection standard of appropriate environment issue and the environmental issue analysis instructing objective. Conducted the environment issue instructing based on the selected environment issue and instructing objective. The classes were held in total of 6 sessions in the chapters related to class objective and class content within the curriculum. The pre and post testing was done using environment decision making ability test sheet which was reconstructed by myself and the results were analyzed by t-test. As a result of comparing pre and post testing the students in test group showed significant results in the processes of problem recognition, evaluation of alternatives, behave planing (p<.001). As a result of comparing the differences of environment decision making ability of pre and post test of test group and control group, it showed significant results in the process of evaluation of alternatives(p<.00l). The environment issue analysis class has positive influence on the environment decision making abilities of the students but since the outcome of environment decision making ability is lower, there is a need for long term environment education plan and further studies to find whether the environment issues within the textbook is appropriate in the elementary student level, useful school aspect and the influence of environment issue analysis class on the change of values for individuals.

• Journal of Korean Biological Nursing Science
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• v.25 no.2
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• pp.131-142
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• 2023

Purpose: The aim of this study was to assess lipid levels and to identify groups with poor lipid control group among patients with dyslipidemia. Methods: Data from 1,399 Korean patients with dyslipidemia older than 20 years were extracted from the Korea National Health and Nutrition Examination Survey. Complex sample analysis and decision-tree analysis were conducted with using SPSS for Windows version 27.0. Results: The mean levels of total cholesterol (TC), triglyceride (TG), low density lipoprotein-cholesterol (LDL-C), and high density lipoprotein cholesterol were 211.38±1.15 mg/dL, 306.61±1.15 mg/dL, 118.48±1.08 mg/dL, and 42.39±1.15 mg/dL, respectively. About 61% of participants showed abnormal lipid control. Poor glycemic control groups (TC ≥ 200 mg/dL or TG ≥ 150 mg/dL or LDL-C ≥ 130 mg/dL) were identified through seven different pathways via decision-tree analysis. Poor lipid control groups were categorized based on patients' characteristics such as gender, age, education, dyslipidemia medication adherence, perception of dyslipidemia, diagnosis of myocardial infarction or angina, diabetes mellitus, perceived health status, relative hand grip strength, hemoglobin A1c, aerobic exercise per week, and walking days per week. Dyslipidemia medication adherence was the most significant predictor of poor lipid control. Conclusion: The findings demonstrated characteristics that are predictive of poor lipid control and can be used to detect poor lipid control in patients with dyslipidemia.

• Journal of the Korea Safety Management & Science
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• v.15 no.2
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• pp.243-253
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• 2013

This research paper introduces the application and implementation of medical decision metrics that classifies medical decision-making into four different metrics using statistical diagnostic tools, such as confusion matrix, normal distribution, Bayesian prediction and Receiver Operating Curve(ROC). In this study, the metrics are developed based on cross-section study, cohort study and case-control study done by systematic literature review and reformulated the structure of type I error, type II error, confidence level and power of detection. The study proposed implementation strategies for 10 quality improvement activities via 14 medical decision metrics which consider specificity and sensitivity in terms of ${\alpha}$ and ${\beta}$. Examples of ROC implication are depicted in this paper with a useful guidelines to implement a continuous quality improvement, not only in a variable acceptance sampling in Quality Control(QC) but also in a supplier grading score chart in Supplier Chain Management(SCM) quality. This research paper is the first to apply and implement medical decision-making tools as quality improvement activities. These proposed models will help quality practitioners to enhance the process and product quality level.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.253-265
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• 2016

We propose an obstacle classification method using multi-decision factors and decision sections based on Single 2D LiDAR. The existing obstacle classification method based on single 2D LiDAR has two specific advantages: accuracy and decreased calculation time. However, it was difficult to classify obstacle type, and therefore accurate path planning was not possible. To overcome this problem, a method of classifying obstacle type based on width data was proposed. However, width data was not sufficient to enable accurate obstacle classification. The proposed algorithm of this paper involves the comparison between decision factor and decision section to classify obstacle type. Decision factor and decision section was determined using width, standard deviation of distance, average normalized intensity, and standard deviation of normalized intensity data. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 2D LiDAR-based method, thus demonstrating the possibility of obstacle type classification using single 2D LiDAR.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.27-30
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• 2018

We design an expert system for tomato smart farm using decision trees and construct a control system with decision structure similar to that of farmers by using the data generated by factors that vary depending on the surrounding environment of each house. At present, Smart farm's control system does not control itself like the way farmers have done so far. Therefore, the dependency of smart farm control system is still not high. Direct intervention by farmers is indispensable for environmental control based on surrounding environment such as sensor value in smart farm. Therefore, we aimed to design a controller that incorporates decision trees into the expert system to make a system similar to the decision making of farmers. Prior to controlling the equipment in the house, it automatically selects the most direct effect among the various environmental factors, and then builds an expert system for complex control by including criteria for decision making by farmers. This study focused on deriving results using data without using heavy tools. Data is coming out of many smart farms at present. We expect this to be a standard for a methodology that allows farmers to access quickly and easily and reduce direct intervention.