• Journal of Korean Society of Water and Wastewater
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• v.31 no.5
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• pp.409-414
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• 2017

In this study, we divided the process operation scenarios into three categories based on raw water temperature and turbidity. We will select and operate the process operation scenario according to the characteristics of the raw water. The number of algae in the DAF treated water has been analyzed to be less than 100 cells/mL. These results indicated that the DAF process is effective in removing the algae. In addition, the scenario of the integrated management decision algorithm of the DAF process was developed. DAF pilot plants ($500m^3/day$) process has shown a constantly sound performance for the treatment of raw water, yielding a significantly low level of turbidity (DAF treated water, 0.21~1.56 NTU).

• The KIPS Transactions:PartD
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• v.8D no.3
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• pp.203-210
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• 2001

These days data is being collected and accumulated in a wide variety of fields. Stored data itself is to be an information system which helps us to make decisions. An information system includes many kinds of necessary and unnecessary attribute. So many algorithms have been developed for finding useful patterns from the data and reasoning approximately new objects. We are interested in the simple and understandable rules that can represent useful patterns. In this paper we propose an algorithm which can reduce the information in the system to a minimum, based on a probabilistic rough set theory. The proposed algorithm uses a value that tolerates accuracy of classification. The tolerant value helps minimizing the necessary attribute which is needed to reason a new object by reducing conditional attributes. It has the advantage that it reduces the time of generalizing rules. We experiment a proposed algorithm with the IRIS data and Wisconsin Breast Cancer data. The experiment results show that this algorithm retrieves a small reduct, and minimizes the size of the rule under the tolerant classification rate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1C
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• pp.73-81
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• 2005

We propose an automatic modulation recognition scheme which extracts pre-defined key features from the received signal and then applies equal gain combining method to determine the used modulation. Moreover, we compare and analyze the performance of the proposed algorithm with that of decision-theoretic algorithm. Our scheme extracts five pre-defined key features from each data segment, a data unit for the key feature extraction, which are then averaged over all the segments to recognize the modulation according to the decision procedure. We check the performance of the proposed algorithm through computer simulations for analog modulations such as AM, FM, SSB and for digital modulations such as FSK2, FSK4, PSK2, and PSK4, by measuring recognition success rate varying SNR and data collection time. The result shows that the performance of the proposed scheme is comparable to that of the decision-theoretic algorithm with less complexity.

• Journal of the Korean Society of Hazard Mitigation
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• v.11 no.3
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• pp.49-56
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• 2011

This paper deals with a methodology for searching optimal route of hazard material (hazmat) vehicles. When we make a decision of hazmat optimal paths, there is a conflict between the public aspect which wants to minimize risk and the private aspect which has a goal of minimizing travel time. This paper presents Efficient Vector Labeling algorithm as a methodology for searching optimal path of hazmat transportation, which is intrinsically one of the multi-criteria decision making problems. The output of the presented algorithm is a set of Pareto optimal paths considering both risk and travel time at a time. Also, the proposed algorithm is able to identify non-dominated paths which are significantly different from each other in terms of links used. The proposed Efficient Vector Labeling algorithm are applied to test bed network and compared with the existing k-shortest path algorithm. Analysis of result shows that the proposed algorithm is more efficient and advantageous in searching reasonable alternative routes than the existing one.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.792-798
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• 2021

In this paper, an algorithm for determining the coagulant input rate in the drug-injection tank during the process of the water purification plant was derived through big data analysis and prediction based on artificial intelligence. In addition, analysis of big data technology and AI algorithm application methods and existing academic and technical data were reviewed to analyze and review application cases in similar fields. Through this, the goal was to develop an algorithm for determining the coagulant input rate and to present the optimal input rate through autonomous driving simulator and pilot operation of the coagulant input process. Through this study, the coagulant injection rate, which is an output variable, is determined based on various input variables, and it is developed to simulate the relationship pattern between the input variable and the output variable and apply the learned pattern to the decision-making pattern of water plant operating workers.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.443-453
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• 2000

장차전의 양상은 고도의 과학전, 타격수단의 다양화 등에 따라 고속이체기동전이 t행될 것이며, 이러한 전쟁양상은 정확하고, 보다 빠른 첩보와 정보의 수집 및 분석을 통하여 아군의 신속한 의사결정 및 대응을 요구한다. 이를 위하여 첩보와 정보 수집 및 분석을 자동화하기 위한 전장정보분석 자동화에 관한 연구가 국방과학연구소 주관으로 실시되고 있다. 따라서 이와 연계된 의사결정 자동화에 관한 연구가 필요하게 되었다. 본 연구는 이러한 요구에 부응할 수 있는 전장정보를 활용한 의사결정의 중요한 한 분야인 화력분배를 자동화하기 위한 전문가시스템의 지식베이스모듈에 대한 분석 및 설계에 관한 연구이다.기존에는 화력분야에 대한 아방책 선정까지를 자동화하는 전문가시스템 개발에 대한 연구가 수행되었으나, 본 연구에서는 자동화의 효율성을 높이기 위해서 아방책 선정에서 나아가 아군 화력자산의 파괴율을 고려하고, 지휘관의 의도에 부합하는 아군 화력자산을 배분하는 전문가시스템 개발에 관한 연구를 수행하였다. 본 연구에서는 화력분배 자동화를 위하여 화력분배와 관련된 현행 업무 관련 지식을 획득 및 분석하고 이를 바탕으로 화력자산 분배를 위한 규칙도출 시 개념설계, 상세설계, 알고리즘제시, 규칙추출예시를 하였으며 본 연구결과의 기대효과는 본문을 참고 바란다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.357-359
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• 2010

The Viterbi Decoding is one of the most researched areas of the convolutional decoding methods. In this paper, we use various parameters for the substantial Viterbi decoding and discuss some viterbi decoding methods. And, the viterbi algorithms of the methods, we discuss 'Hard Decision' and 'Soft Decision'. So, we compare differences of two methods about decoding methods, performance. Because of having various parameters and decision methods, we discuss the values of various parameter and decision methods in the Gaussian channel about the viterbi decoding methods.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11a
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• pp.65-68
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• 2003

컴퓨팅 환경이 유리쿼터스 환경으로 변해가면서 다양한 컨텐츠와 다양한 디바이스들이 등장하게 되었고, 디지털 컨텐츠를 보호하기 위해 DRM(Digital Rights Management) 기술이 적용된 서비스가 제공되고 있다. 그러나 현재 DRM 기술의 디지털 컨텐츠 보안 정책은 일정한 키 길이에 동일한 암호 알고리즘을 사용함으로써 비효율적이고, 사용자의 다양한 요구를 만족시키지 못하고 있다. 본 논문에서는 디지털 컨텐츠에 보안 정책을 효율적으로 적용하고 디바이스의 성능과 디지털 컨텐츠의 가치에 따라 의사 결정 방법인 MAUT(Multi-Attribute Utility Theory) 알고리즘을 이용하여 최적의 보안 등급을 동적으로 결정하는 컨텐츠 암호화 모듈을 설계하고 구현한다.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.3
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• pp.212-216
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• 2011

Fault detection technique for photovoltaic power systems is significant to dramatically reduce economic damage in industrial fields. This paper presents a novel fault detection approach using Fourier neural networks and stochastic decision making strategy for photovoltaic systems. We achieve neural modeling to represent its nonlinear dynamic behaviors through a gradient descent based learning algorithm. Next, a general likelihood ratio test (GLRT) is derived for constructing a decision malling mechanism in stochastic fault detection. A testbed of photovoltaic power systems is established to conduct real-time experiments in which the DC power line communication (DPLC) technique is employed to transfer data sets measured from the photovoltaic panels to PC systems. We demonstrate our proposed fault detection methodology is reliable and practicable over this real-time experiment.

• Journal of the Society of Disaster Information
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• v.16 no.3
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• pp.586-593
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• 2020

Purpose: This study aims to develop an algorithm for predicting sediment discharge by debris flow, and develop GIS-based decision support system for optimal arrangement of check dam. Method: The average stream width and flow length were used to predict the cumulative sediment discharge by debris flow. At this time, the amount of slope failure on source area and average flow length were utilized as input factors. Result: The predicted sediment discharge calculated through the algorithm was 1.1 times different on average compared to the actual sediment discharge by debris flow. In addition, the program is an objective indicator that selects the location and size of the check dam, and it can help practitioners make rational decisions. Conclusion: The soil erosion control works are being implemented every year. Therefore, it is expected that the GIS-based decision support system for location and size of the check dam will contribute to the prevention of sediment-related disasters.