• Journal of the Korea Safety Management & Science
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• v.8 no.2
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• pp.51-59
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• 2006

So far, there is no technique of quantitative evaluation on danger related to industrial accidents. Therefore, as an endeavor for obtaining technique of quantitative evaluation, this study presents feature analysis of industrial accidents in manufacturing field using QUEST algorithm. In order to analyze feature of industrial accidents, a retrospective analysis was performed in 10,536 subjects (10,313 injured people, 223 deaths). The sample for this work chosen from data related to manufacturing businesses during three years $(2002\sim2004)$ in Korea. The analysis results were very informative since those enable us to know the most important variables such as occurrence type, company size, and occurrence time which can affect injured people. Also, it is found that classification using QUEST algorithm which was performed in this study is very reliable.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.195-205
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• 2005

In decision tree analysis, C4.5 and CART algorithm have some problems of computational complexity and bias on variable selection. But QUEST algorithm solves these problems by dividing the step of variable selection and split point selection. When input variables are continuous, QUEST algorithm uses ANOVA F-test under the assumption of normality and homogeneity of variances. In this paper, we investigate the influence of violation of normality assumption and effect of the transformation of variables in the QUEST algorithm. In the simulation study, we obtained the empirical powers of variable selection and the empirical bias of variable selection after transformation of variables having various type of underlying distributions.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1881-1886
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• 2005

This paper proposes a novel approach of a precision attitude determination algorithm using UVF (Unit Vector Filter) measurements. The proposed method is superior to the conventional QUEST measurements based approaches because the estimation performance can be greatly enhanced by selecting brighter stars having better noise characteristics. The performance comparison with QUEST measurements is made to verify the usefulness of the proposed algorithm.

• Journal of Korea Game Society
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• v.9 no.4
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• pp.57-66
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• 2009

A quest system is one of the important parts in the MMORPG (Massive Multiplayer Online Role Playing Game) contents. Because of its complexity in combining various content components, quest reward design belongs to a complicated work in estimating quest reward levels correctly in the initial development stage. In this paper, we suggest a new quest reward design technique by considering optimal quest play paths. We model a quest reward problem as the TSP (Traveling Salesman Problem) and solve that by adopting genetic algorithms. With our system, game designers easily estimate the optimal quest play path and it can be useful in reducing the trial-errors in the initial quest design process.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.347-357
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• 2004

It is very important to select a split variable in constructing the classification tree. The efficiency of a classification tree algorithm can be evaluated by the variable selection bias and the variable selection power. The C4.5 has largely biased variable selection due to the influence of many distinct values in variable selection and the QUEST has low variable selection power when a continuous predictor variable doesn't deviate from normal distribution. In this thesis, we propose the SRT algorithm which overcomes the drawback of the C4.5 and the QUEST. Simulations were performed to compare the SRT with the C4.5 and the QUEST. As a result, the SRT is characterized with low biased variable selection and robust variable selection power.

• International Journal of Safety
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• v.5 no.1
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• pp.37-41
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• 2006

The major objective of the statistical analysis about industrial accidents is to determine the safety factors so that it is possible to prevent or decrease the number of future accidents by educating those who work in a given industrial field in safety management. So far, however, there exists no quantitative method for evaluating danger related to industrial accidents. Therefore, as a method for developing quantitative evaluation technique, this study presents feature analysis of industrial accidents in manufacturing field using QUEST algorithm. In order to analyze features of industrial accidents, a retrospective analysis was performed on 10,536 subjects (10,313 injured people, 223 deaths). The sample for this work was chosen from data related to manufacturing businesses during a three-year period ($2002{\sim}2004$) in Korea. This study used AnswerTree of SPSS and the analysis results enabled us to determine the most important variables that can affect injured people such as the occurrence type, the company size, and the time of occurrence. Also, it was found that the classification system adopted in the present study using QUEST algorithm is quite reliable.

• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.41-50
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• 2005

The various methods have been studied to develop discriminant model for pregnancy induced hypertension(PIH) as high risk pregnant. In this study, we adapt the approximate entropy which is the non-linear chaotic measuring method. Then, we develop a system to discriminant PIH pregnant using QUEST with S-PLUS.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.185-196
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• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

• Journal of the Korea Society for Simulation
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• v.22 no.4
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• pp.1-9
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• 2013

ADD (Agency for Defense Development) has developed the naval warfare simulation environment (QUEST), this paper describes the model library of naval weapon systems for the application of underwater warfare simulation included in the QUEST. Models are basically developed in order to measure the effectiveness and tactical development of underwater engagement between ships and weapons. Analyzing the mission space of underwater engagement and the functionality of the legacy models, we define standards of the model structure and developed the model components. Each components are the well-defined environment, system, subsystem, algorithm models, and the interfaces are defined between them. Users can construct a model in an efficient way to various warfare scenarios using the re-usable model components and co-work with the common model library.

• 한국데이터정보과학회:학술대회논문집
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• 2001.10a
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• pp.76-78
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• 2001

Variable selection in oriental medical research is considered. Decision tree analysis algorithms such as CHAID, CART, C4.5 and QUEST have been successfully applied to a medical research. Paralysis disease is a highly dangerous and murderous disease which accompanied with a great deal of severe physical handicap. In this paper, we explore the use of CHAID algorithm for selecting variables for evidence-diagnosis of paralysis, disease. Empirical results comparing our proposed method to the method using Wilks $\lambda$ given.