• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.495-504
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• 2013

Evolutionary algorithms have been applied to multi-objective optimization problems by approximation methods using computational intelligence. Those methods have been improved gradually in order to generate more exactly many approximate Pareto optimal solutions. The paper introduces a new method using support vector machine to find an approximate Pareto frontier in multi-objective optimization problems. Moreover, this paper applies an evolutionary algorithm to the proposed method in order to generate more exactly approximate Pareto frontiers. Then a decision making with two or three objective functions can be easily performed on the basis of visualized Pareto frontiers by the proposed method. Finally, a few examples will be demonstrated for the effectiveness of the proposed method.

• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.203-208
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• 2005

We propose an algorithmic approach for handling linguistic values defined in the same linguistic variable. Using the proposed approach, we can explicitly capture the differences of individuals' subjectivity with respect to linguistic values defined in the same linguistic variable. The proposed approach can be employed as a useful tool for discovering hidden relationship among linguistic values defined in the same linguistic variable. Consequently, it provides a basis for improving the precision of knowledge acquisition in the development of fuzzy systems including fuzzy expert systems, fuzzy decision tree, fuzzy cognitive map, ok. In this paper, we apply the proposed approach to a collective linguistic assessment among multiple experts.

• Journal of Korea Multimedia Society
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• v.16 no.3
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• pp.354-365
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• 2013

Node deployment is one of the important problems in achieving good quality of service in wireless sensor network. The purpose of this paper is to develop an interactive system that supports user's decision makings in designing sensor fields. The system provides grid-based initial deployment algorithm supporting three types of node deployment pattern, area-fill, path-cover, and barrier-cover deployment pattern. After initial deployment, an iterative refinement algorithm can be applied, which takes care of the irregularity of the deployment area and the heterogeneity of sensors. The proposed system helps users to effectively deploy nodes in the sensor field, analyse the detection performance of the deployment, and perform network simulations. The developed system can be utilized as a part of the development environment of the surveillance sensor network system.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.161-170
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• 2020

In this study, develop the seismic acceleration measurement data conversion and signal processing algorithms for improve the operational efficiency of the seismic acceleration measurement system installed for public facilities. Through the analysis of the seismic acceleration time history data, the evaluation methods and criteria and evaluating the safety of buildings were proposed. The system was applied to the test bed building to verify its operation and usability. It is expected to be used as a decision making support data and determining the direction and priority of disaster response in the event of an earthquake.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.295-306
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• 2009

Query execution time minimization is an important objective in distributed database design. While total time minimization is an objective for On Line Transaction Processing (OLTP), response time minimization is for Decision Support queries. We formulate the sub-query allocation problem using analytical models and solve with genetic algorithm (GA). We show that query execution plans with total time minimization objective are inefficient from response time perspective and vice versa. The procedure is tested with simulation experiments for queries of up to 20 joins. Comparison with exhaustive enumeration indicates that GA produced optimal solutions in all cases in much less time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.167-168
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• 2019

Lately, variety of algorithms using encryption technology has been adopted as methods of unlocking smartphone. It is advancing toward the direction to solve through human biometrics technology which has already succeeded in commercialization. These include finger print recognition, face recognition, and iris recognition. In this study, we selected biometrics recognition technology and pattern recognition and password input methods which are already commercialized as evaluation items. The evaluation items are five algorithms including finger print recognition, face recognition iris recognition, pattern recognition and password input method. Based on these algorithms, analytic hierarchy process is used to analyze the preference of smartphone users. Also, the theoretical implications are presented based on the analysis results.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.21-31
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• 2012

Threat Evaluation(TE) which has air intelligence attained by identifying friend or foe evaluates the target's threat degree, so it provides information to Weapon Assignment(WA) step. Most of TE data are passed by sensor measured values, but existing techniques(fuzzy, bayesian network, and so on) have many weaknesses that erroneous linkages and missing data may fall into confusion in decision making. Therefore we need to efficient Threat Evaluation system that can refine various sensor data's linkages and calculate reliable threat values under unpredictable war situations. In this paper, we suggest new threat evaluation system based on information fusion JDL model, and it is principle that combine fuzzy which is favorable to refine ambiguous relationships with bayesian network useful to inference battled situation having insufficient evidence and to use learning algorithm. Finally, the system's performance by getting threat evaluation on an air defense scenario is presented.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.310-315
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• 2014

The domestic maritime environment shows higher frequency of maritime accidents amidst greater traffic volume arising from increasing international seaborne trade and maritime leisure activities. To reduce such maritime accidents, there exist various kinds of safety navigation devices in the ship bridge aimed to mitigate burdens of navigators and support their accurate decision making. Amongst is the AIS considered very important, which is an automatic tracking system to assist understanding of the circumstances in the vicinity by receiving information of other ships and also sending its own; where the information contains errors initially, however, such wrong information is periodically transmitted, accordingly giving rise to hindrance sometimes in decision making by shore operators or ship navigators at sea. This study is to propose the error data and field management algorithm using fuzzy theory toward improving reliability and accuracy in ship related information received from AIS.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.287-295
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• 2017

Since Google's AlphaGo defeated a world champion of Go players in 2016, there have been many interests in the deep learning. In the financial sector, a Robo-Advisor using deep learning gains a significant attention, which builds and manages portfolios of financial instruments for investors.In this paper, we have proposed the a deep learning algorithm geared toward identification and forecast of the KOSPI index direction,and we also have compared the accuracy of the prediction.In an application of forecasting the financial market index direction, we have shown that the Robo-Advisor using deep learning has a significant effect on finance industry. The Robo-Advisor collects a massive data such as earnings statements, news reports and regulatory filings, analyzes those and recommends investors how to view market trends and identify the best time to purchase financial assets. On the other hand, the Robo-Advisor allows businesses to learn more about their customers, develop better marketing strategies, increase sales and decrease costs.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.380-380
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• 2020

가뭄 피해 극복을 위한 인공 함양지 통합관리시스템의 일부로써 지표수-지하수 연계 특성 분석용 의사결정을 전달하는 인공지능 스마트 계측기의 필요성이 꾸준히 제기되어 왔으나 실용성과 효율성을 동시에 갖춘 계측기는 시장에 출시되지 않았다. 기존의 계측기는 단순 측정이 목적이었으며 분석을 위해서는 일정 기간 직접 계측하여 분석하거나, 계측데이터를 원격 망을 통하여 서버로 전송하고 관리자가 데이터를 해석하는 방식을 취하였다. 또한, 수질 계측과 수질의 미소 변동성을 동시에 계측하여 수질 변화상태를 판단 할 수 있는 수질 계측기는 상품화되지 않아 다목적 수질 분석에 한계점을 갖고 있다. 이러한 한계점이 기존의 지하수 수질 계측기로는 불가능한 수중 라돈을 채수 없이 계측 가능하도록 하고, 순간 수질 변화 및 수질 변화 요인분석이 가능한 계측을 위하여 라돈, 전도도, 수위, 수온 및 필름형 pH 센서를 개발하여 적용한 다항목 계측기로 통합하는 연구가 필요한 이유이다. 개발한 계측기는 빅데이터 기반의 지능형 수질 변동성 분석 알고리즘을 내장하고 수직 깊이 방향의 다중심도 계측이 가능하도록 핵심적인 통신 연결성을 확보하였고 다양한 수질에서 견딜 수 있으며 특히 인공함양에서 발생하는 철, 망간에 부식되지 않는 재질을 이용하여 설계한 '지표수-지하수 연계 특성 분석용 다심도 및 인공지능 스마트 계측장치'이다. 본 장치는 기존 지하수 수질 계측기에서는 불가능하였던 순간 수위변화 및 수위변화 요인분석이 가능한 계측을 위하여 초당 측정 샘플링 주파수(10Hz)를 높인 계측회로를 개발하여 적용하였다.