• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.458-463
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• 1998

This paper presents an autopilot design method for STT missiles using the intelligent control technique and multiple controllers. The mixed $H_2/H_{\infty}$ control technique is applied for each controller design and the control gains are implemented by using the genetic searching algorithm. To facilitate automatic switching of multiple controllers under different operating conditions, an error based switching scheme is also combined with the multiple controllers at a higher level, which constitutes a hierarchical intelligent control system. It is shown via computer simulation that the proposed autopilot outperforms the conventional one.

• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.234-243
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• 2013

In order to point out the best utility of a product (or a service), marketers need to clearly understand and measure the preference of the consumers. Among numerous marketing analysis techniques, the conjoint analysis is one of the popular tools for market research. One of the issues with this tool is the lack of feedback for the respondents. This paper proposes personal stepwise conjoint analysis based on an interactive Web-questionnaire allowing respondents to receive a diagnosis of their evaluation and giving the possibility to reconsider their evaluation. To validate our proposal, experimentation with forty-two respondents is also demonstrated. Experimental results, potential modifications and improvements are detailed in this paper.

• Journal of the Korea Society of Computer and Information
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• v.22 no.10
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• pp.9-17
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• 2017

The student dropout prediction is an indispensable for many intelligent systems to measure the educational system and success rate of all university. Therefore, in this paper, we propose an intelligent dropout prediction system that minimizes the situation by adopting the proactive process through an effective model that predicts the students who are at risk of dropout. In this paper, the main data sets for students dropout predictions was used as questionnaires and university information. The questionnaire was constructed based on theoretical and empirical grounds about factor affecting student's performance and causes of dropout. University Information included student grade, interviews, attendance in university life. Through these data sets, the proposed dropout prediction model techniques was classified into the risk group and the normal group using statistical methods and Naive Bays algorithm. And the intelligence dropout prediction system was constructed by applying the proposed dropout prediction model. We expect the proposed study would be used effectively to reduce the students dropout in university.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.275-280
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• 2009

A Genetic Algorithm (GA) is a kind of search techniques used to find exact or approximate solutions to optimization and searching problems. This paper discusses the design of a genetic algorithm-based intelligent parking system. This is a search strategy based on the model of evolution to solve the problem of parking systems. A genetic algorithm for an optimal solution is used to find a series of optimal angles of the moving vehicle at a parking space autonomously. This algorithm makes the planning simpler and the movement more effective. At last we present some simulation results.

• Journal of the Semiconductor & Display Technology
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• v.23 no.2
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• pp.71-75
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• 2024

This research focuses on processing unstructured data efficiently, containing various formulas in document processing and management regarding the terms and rules of domestic insurance documents using text mining techniques. Through parsing and compilation technology, document context, content, constants, and variables are automatically separated, and errors are verified in order of the document and logic to improve document accuracy accordingly. Through document debugging technology, errors in the document are identified in real time. Furthermore, it is necessary to predict the changes that intelligent document processing will bring to document management work, in particular, the impact on documents and utilization tasks that are double managed due to various formulas and prepare necessary capabilities in the future.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.89-95
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• 2016

In a software testing domain, test case prioritization techniques improve the performance of regression testing, and arrange test cases in such a way that maximum available faults be detected in a shorter time. User-sessions and cookies are unique features of web applications that are useful in regression testing because they have precious information about the application state before and after making changes to software code. This approach is in fact a user-session based technique. The user session will collect from the database on the server side, and test cases are released by the small change configuration of a user session data. The main challenges are the effectiveness of Average Percentage Fault Detection rate (APFD) and time constraint in the existing techniques, so in this paper developed an intelligent framework which has three new techniques use to manage and put test cases in group by applying useful criteria for test case prioritization in web application regression testing. In dynamic weighting approach the hybrid criteria which set the initial weight to each criterion determines optimal weight of combination criteria by evolutionary algorithms. The weight of each criterion is based on the effectiveness of finding faults in the application. In this research the priority is given to test cases that are performed based on most common http requests in pages, the length of http request chains, and the dependency of http requests. To verify the new technique some fault has been seeded in subject application, then applying the prioritization criteria on test cases for comparing the effectiveness of APFD rate with existing techniques.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.595-602
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• 2009

Fuzzy rules, which represent the behavior of their system, are sensitive to fuzzy clustering techniques. If the classification abilities of such clustering techniques are improved, their systems can work for the purpose more accurately because the capabilities of the fuzzy rules and parameters are enhanced by the clustering techniques. Thus, this paper proposes a new hierarchically structured clustering algorithm that can enhance the classification abilities. The proposed clustering technique consists of two clusters based on correlationship and statistical characteristics between data, which can perform classification more accurately. In addition, this paper uses difference data sets to reflect the patterns and regularities of the original data clearly, and constructs multiple fuzzy systems to consider various characteristics of the differences suitably. To verify effectiveness of the proposed techniques, this paper applies the constructed fuzzy systems to the field of time series prediction, and performs prediction for nonlinear time series examples.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.110.5-110
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• 2002

$\textbullet$Introduction $\textbullet$Previous Works $\textbullet$IKF and IIMM Method $\textbullet$Simulation Results $\textbullet$Conclusions

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.68-77
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• 2017

In order to implement the smart home environment, we need an intelligence service platform that learns the user's life style and behavioral patterns, and recommends appropriate services to the user. The intelligence service platform should embed a couple of effective and efficient data mining algorithms for learning from the data that is gathered from the smart home environment. In this study, we evaluate the suitability of data mining algorithms for smart home intelligent service platforms. In order to do this, we first develop an intelligent service scenario for smart home environment, which is utilized to derive functional and technical requirements for data mining algorithms that is equipped in the smart home intelligent service platform. We then evaluate the suitability of several data mining algorithms by employing the analytic hierarchy process technique. Applying the analytical hierarchy process technique, we first score the importance of functional and technical requirements through a hierarchical structure of pairwise comparisons made by experts, and then assess the suitability of data mining algorithms for each functional and technical requirements. There are several studies for smart home service and platforms, but most of the study have focused on a certain smart home service or a certain service platform implementation. In this study, we focus on the general requirements and suitability of data mining algorithms themselves that are equipped in smart home intelligent service platform. As a result, we provide a general guideline to choose appropriate data mining techniques when building a smart home intelligent service platform.

• Journal of Internet of Things and Convergence
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• v.6 no.1
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• pp.39-43
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• 2020

The Internet of Things plays a role as an important element technology of the 4th Industrial Revolution. This study is currently developing intelligent cars with IT technology, and is at a time when the development of intelligent cars is active and network data communication is possible. However, security solutions are needed as security is still at a weak stage, which can be threatened by intrusions into the network from outside. In this paper, in order to improve security of intelligent cars without causing security problems, we will apply blockchain technology, propose biometric authentication techniques using users' biometric information, and continue to study them in the future.