• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.456-456
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• 2023

본 연구에서는 위성영상 상황판연계·표출시스템 적용을 위한 SNS 주민참여 콘텐츠를 개발하고, 재난대응 표준위기관리절차 기반의 주민참여정보 활용방안을 제시하고자 하였다. 재난상황관리에 있어 SNS의 활용을 통한 주민참여 확대적용 방안을 모색하고자 재난 대비·대응 관련 공공 및 정부부처에서 활용 중인 SNS 채널을 조사하였으며, 상황관리에 SNS를 적용한 선행사례를 분석하였다. 이를 기반으로 SNS를 적용한 예방안전 및 피해복원 확대 적용 방안을 제시하였으며, 신속한 위기대응을 위한 보조적인 의사결정 지원도구로서의 콘텐츠를 제안하였다. 먼저, 예방안전 및 피해복원 확대 적용을 위해 SNS에 주민들이 작성한 게시글을 웹 크롤링과 데이터 마이닝을 통해 분석하여 재난 상황인지와 상황판단 및 피해범위 추정에 활용하는 방안을 제시하였고, 이를 상황판연계·표출시스템에서 표출하기 위한 예시화면을 설계하였다. 또한, 연구 1차년에 수행했었던 위성영상을 활용한 재난상황대응 표준위기관리절차 중 위성영상정보에 주민참여정보를 연계·중첩하여 재난의 전조감지 단계부터 확산양상 및 피해범위를 확인하고, 재난기록을 분석하여 추후 발생된 재난에 선제적으로 대비할 수 있는 방안을 제시하였다. 그러나, 주민참여 기반 SNS 콘텐츠 적용을 위한 우선 해결사항으로는 재난상황판단 시, 정보의 정확성과 신뢰성 측면에서 의사결정을 위한 보조도구로서 활용을 할 것인지에 대한 중앙재난안전상황실과의 충분한 협의가 필요하며, 상황실에서 해당 콘텐츠를 활용하게 될 경우, SNS 정보의 행정망 방화벽 허용가능여부에 대한 추가 분석설계가 필요한 상황이다. 이를 위해 금년 연구수행에서 상황실 실무자 수요조사를 통해 SNS 정보 활용에 대한 반영여부를 결정할 예정이다.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.725-735
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• 2022

Currently, disasters occurring in Korea are characterized by unpredictability and complexity. Due to these features, property damage and human casualties are increasing. Since the initial response process of these disasters is directly related to the scale and the spread of damage, optimal decision-making is essential, and information of the site must be obtained through timely applicable sensors. However, it is difficult to make appropriate decisions because indiscriminate information is collected rather than necessary information in the currently operated Disaster and Safety Situation Office. In order to improve the current situation, this study proposed a framework that quickly collects various disaster image information, extracts information required to support decision-making, and utilizes it. To this end, a web-based display system and a smartphone application were proposed. Data were collected close to real time, and various analysis results were shared. Moreover, the capability of supporting decision-making was reviewed based on images of actual disaster sites acquired through CCTV, smartphones, and UAVs. In addition to the reviewed capability, it is expected that effective disaster management can be contributed if institutional mitigation of the acquisition and sharing of disaster-related data can be achieved together.

• Journal of Industrial Convergence
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• v.20 no.8
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• pp.77-84
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• 2022

This study aims to present a method for implementing a decision support system that can be used for selecting emerging technologies by applying a machine learning-based automatic classification technique. To conduct the research, the architecture of the entire system was built and detailed research steps were conducted. First, emerging technology candidate items were selected and trend data was automatically generated using a big data system. After defining the conceptual model and pattern classification structure of technological development, an efficient machine learning method was presented through an automatic classification experiment. Finally, the analysis results of the system were interpreted and methods for utilization were derived. In a DTW-kNN-based classification experiment that combines the Dynamic Time Warping(DTW) method and the k-Nearest Neighbors(kNN) classification model proposed in this study, the identification performance was up to 87.7%, and particularly in the 'eventual' section where the trend highly fluctuates, the maximum performance difference was 39.4% points compared to the Euclidean Distance(ED) algorithm. In addition, through the analysis results presented by the system, it was confirmed that this decision support system can be effectively utilized in the process of automatically classifying and filtering by type with a large amount of trend data.

• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.563-575
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• 2009

This research has found logical data directly influencing forming consideration set and attitude and attitude strength under the choosing situation based on memory-base proposed by Priester et. al (2004). We've examined the possibility of model extension through physical salient strength according to the location of product display as an external stimulate factor and attitude and attitude strength, consideration set and role on variable choice. Especially, this research practically proposed the method measuring directly the attitude on behavior instead of seeing the intension of behavior or behavior by measuring the behavior itself based on existing experiment methods and applied logistics regression analysis. In conclusion, this research confirmed the possibility of generalization of this model by verifying appropriateness through logical background and actual analysis based on stimulus-base proposed model characters as an integrated model relation between attitude in stimulus-based relation and decision-making.

• 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.

• Smart Media Journal
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• v.13 no.1
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• pp.9-17
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• 2024

This study aims to develop an integrated agricultural distribution network management system to improve the quality, profit, and decision-making efficiency of agricultural products. We adopt two key techniques: crop maturity detection based on the YOLOX target detection algorithm and market price prediction based on the Prophet model. By training the target detection model, it was possible to accurately identify crops of various maturity stages, thereby optimizing the shipment timing. At the same time, by collecting historical market price data and predicting prices using the Prophet model, we provided reliable price trend information to shipping decision makers. According to the results of the study, it was found that the performance of the model considering the holiday factor was significantly superior to that of the model that did not, proving that the effect of the holiday on the price was strong. The system provides strong tools and decision support to farmers and agricultural distribution managers, helping them make smart decisions during various seasons and holidays. In addition, it is possible to optimize the distribution network of agricultural products and improve the quality and profit of agricultural products.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.737-738
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• 2009

IT 환경이 급속도로 발전해 가고 있다. 기업 내에서 생성 및 보관되는 데이터의 양은 시간이 흐름에 따라 급격히 증가하고 있으며, 기업의 의사결정에 중요한 데이터의 가치는 변화를 거듭한다. 데이터의 증가는 곧 그것을 관리하는 비용의 증가를 불러오고, 방대한 데이터 속에서 진정한 정보의 가치를 창출하는 것이 어려운 상황에 직면해 있다. 이러한 기업의 비즈니스 프로세스에 맞춰 효과적인 ILM(Information Lifecycle Management)의 구현은 반드시 필요하다. 본 연구는 데이터의 가치를 보다 효과적이며 비용 효율적으로 사용할 수 있는 ILM의 구현을 위해 Database 기반의 Archiving System 구축 방법론을 제시하고 이를 바탕으로 실제 ILM이 구현된 기업의 구축 효과 및 성능 평가를 통해 그 타당성에 대하여 연구하는 것을 그 목적으로 한다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.2
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• pp.1-11
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• 2014

In this paper, travel speed patterns were deducted based on historical DSRC travel speed data using Decision Tree technique to improve availability of the massive amount of historical data. These patterns were designed to reflect spatio-temporal vicissitudes in reality by generating pattern units classified by months, time of day, and highway sections. The study area was from Seoul TG to Ansung IC sections on Gyung-bu highway where high peak time of day frequently occurs in South Korea. Decision Tree technique was applied to categorize travel speed according to day of week. As a result, five different pattern groups were generated: (Mon)(Tue Wed Thu)(Fri)(Sat)(Sun). Statistical verification was conducted to prove the validity of patterns on nine different highway sections, and the accuracy of fitting was found to be 93%. To reduce travel pattern errors against individual travel speed data, inclusion of four additional variables were also tested. Among those variables, 'traffic condition on previous month' variable improved the pattern grouping accuracy by reducing 50% of speed variance in the decision tree model developed.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.367-374
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• 1999

This paper suggests an integrated method based on concept ascension and attribute reduction for efficient induction of decision rules from a large database. We study an automatic scheme to generate concept trees by a clustering technique, a method for generalizing databases by the concept ascension technique, an optimal reduction method by means of attributes reduction using the sibmificance of attributes, and an efficient way of reduction of attribute values applying the discernible matrix and functions. The method can be used for the decision making tasks such as an investment planning or price evaluation, the construction of knowledge bases for diagnosis of defects or medical diagnosis, data analysis such as marketing or experimental data, information retrieval for high level inquiries, and so on.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.47-50
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• 2005

센서와 모바일 기술의 발달로 인해 다양한 센서에서 수집된 스트림 데이터를 처리하는 연구들이 많이 수행되고 있다. 다차원 속성의 스트림 데이터는 센서에서 주기적으로 수집되어 버퍼링 후 처리되기 때문에 기존의 투플 기반의 데이터 분류 기법에 적합하지 않다. 따라서 이 논문에서는 윈도우 기반의 스트림 데이터 분류를 위해 각 속성의 평균과 표준편차 값을 이용하여 투플 기반으로 변환하는 기법을 제안한다. 제안된 기법의 타당성은 투플 기반 데이터 분류 기법(의사결정트리, 단순 베이지안 분류기, 베이지안 신뢰 네트워크)에 의한 정확도 측정에 기반 한다. 로봇에서 수집된 센서 데이터를 이용한 실험 결과, 높은 정확도로 제안된 기법이 타당함을 증명하였으며 베이지안 신뢰 네트워크 기법이 다른 기법에 비해 우수함을 발견하였다.