• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.121-129
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• 2017

Demand forecasting is one of the most critical tasks in defense logistics, because the failure of the task can bring about a huge waste of budget. Up to date, ROK-MND(Republic of Korea - Ministry of National Defense) has analyzed past component consumption data with time-series techniques to predict each component's demand. However, the accuracy of the prediction still needs to be improved. In our study, we attempted to find consumption pattern using data mining techniques. We gathered an 18,476 component consumption data first, and then derived diverse features to utilize them in identification of demanding patterns in the consumption data. The results show that our approach improves demand forecasting with higher accuracy.

• Journal of the Korea Safety Management & Science
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• v.7 no.4
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• pp.61-71
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• 2005

Many researches have been focused on the analysis of industry disasters in order to reduce them. As a similar endeavor, this paper provides a propensity analysis of injured people from various industries using classification and regression tree(CART), a data mining algorithm. The sample for this work was chosen from 25,157data related to various industries during one year ( $2003.2\sim2004.1$ ) at Kangwon-Do in Korea. For the purpose of this paper, eight independent variables (injured date, injured time, injured month, type of Injured person, continuous service period, sex, company size, age)are taken from injured person group. According to the analysis result, it is found that five out of the eight factors that are predicted as significant have salient effects. Factors of season, time/hour, day of the week, or month which disasters happened do not show any significant effect. This paper provides common features of injured people. The provided analysis result will be helpful as a starting point for root cause analysis and reduction of industry disasters and also for development of a guideline of safety management.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.296-300
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• 2012

최근 지구 온난화에 따른 이상 기후변화로 인해 게릴라성 집중호우와 같은 다양한 강우패턴이 발생되고 있다. 특히 집중호우의 빈도 및 규모가 커지고 있으며 피해 또한 증가하고 있다. 이에 대한 대안으로 하도의 정비, 댐 건설, 제방의 증고와 같은 구조적인 대책과 홍수예경보, 홍수보험, 통합홍수관리와 같은 비구조적인 대책에 대한 접근이 이루어지고 있다. 그러나 미래 기후변화에 대한 예측의 한계와 구조적 대책의 물리적 한계를 감안할 때 구조적 대책에 의한 방법만으로 변화하는 기후에 대응하여 홍수재해를 완벽하게 대처하기에는 부족한 것이 사실이다. 따라서 비구조적 대책에 의한 홍수피해저감이 절실히 필요하다. 따라서 본 연구에서는 국제수문개발계획 대표유역인 낙동강유역에 위치한 위천유역을 연구대상으로 선택하였고 이러한 중소규모의 유역에서 홍수예경보의 한계를 극복하고 신뢰성을 높이기 위하여 홍수유출시에 일어나는 유역내의 복잡한 물리적인 현상을 직접 고려하지 않고 입력자료와 출력자료의 관계로부터 학습과 추론을 통해 결론을 도출해내는 신경망, 퍼지, 유전자 알고리즘과 같은 Date Mining 기법을 사용하여 자동우량과 자동수위에 의한 하류 홍수예경보시스템을 구축하기 위해 수위를 예측하였다.

• Journal of the Korea Society of Computer and Information
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• v.17 no.2
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• pp.139-147
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• 2012

In this paper, we discuss the structure of biological knowledge discovery system based on text mining and automatic inference. Given a set of biology documents, the system produces a new hypothesis in an integrated manner. The text mining module of the system first extracts the 'event' information of predefined types from the documents. The inference module then produces a new hypothesis based on the extracted results. Such an integrated system can use information more up-to-date and diverse than other automatic knowledge discovery systems use. However, for the success of such an integrated system, the precision of the text mining module becomes crucial, as any hypothesis based on a single piece of false positive information would highly likely be erroneous. In this paper, we propose a probabilistic filtering method that filters out false positives from the extraction results. Our proposed method shows higher performance over an occurrence-based baseline method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.63-71
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• 2011

The current Web portal sites provide significant keywords with high popularity or importance; specifically, user-friendly services such as tag clouds and associated word search are provided. However, in general, since news articles are classified only with their date and categories, it is not easy for users to find other articles related to some articles while reading news articles classified with categories. And the conventional associated keyword service has not satisfied users sufficiently because it depends only upon user queries. This paper proposes a way of searching news articles by utilizing the keywords tightly associated with users' queries. Basically, the proposed method discovers a set of keyword association patterns by using the association rule mining technique that extracts association patterns for keywords by focusing upon sentences containing some keywords. The method enables users to navigate the space of associated keywords hidden in large news articles.

• Journal of KIISE:Databases
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• v.37 no.3
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• pp.127-136
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• 2010

With rapid growth of information and computer communication technologies, the numbers of digital documents including multimedia data have been recently exploded. In particular, news video database and news video mining have became the subject of extensive research, to develop effective and efficient tools for manipulation and analysis of news videos, because of their information richness. However, many research focus on browsing, retrieval and summarization of news videos. Up to date, it is a relatively early state to discover and to analyse the plentiful latent semantic knowledge from news videos. In this paper, we propose the news video mining system based on multi-modal approach and text mining, which uses the visual-textual information of news video clips and their scripts. The proposed system systematically constructs a taxonomy of news video stories in automatic manner with hierarchical clustering algorithm which is one of text mining methods. Then, it multilaterally analyzes the topics of news video stories by means of time-cluster trend graph, weighted cluster growth index, and network analysis. To clarify the validity of our approach, we analyzed the news videos on "The Second Summit of South and North Korea in 2007".

• Spatial Information Research
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• v.19 no.1
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• pp.127-133
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• 2011

Recently, with the advances in sensor techniques and net work computing, Ubiquitous Sensor Network (USN) has been received a lot of attentions from various communities. The sensor nodes distributed in the sensor network tend to continuously generate a large amount of data, which is called stream data. Sensor stream data arrives in an online manner so that it is characterized as high-speed, real-time and unbounded and it requires fast data processing to get the up-to-date results. The data stream has many application domains such as traffic analysis, physical distribution, U-healthcare and so on. Therefore, there is an overwhelming need of a USN middleware for processing such online stream data to provide corresponding services to diverse applications. In this paper, we propose a novel USN middleware which can provide users both context-aware service and meaningful sequential patterns. Our proposed USN middleware is mainly focused on location based applications which use stream location data. We also show the implementation of our proposed USN middleware. By using the proposed USN middleware, we can save the developing cost of providing context aware services and stream sequential patterns mainly in location based applications.

• The Journal of Sustainable Design and Educational Environment Research
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• v.18 no.2
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• pp.1-11
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• 2019

The purpose of this study is to make objective decisions in the use of closed schools through the development of utilization indicators for the efficient use of closed schools, which is expected to increase continuously. The research phase was largely carried out by drawing preliminary indicators for use in closed schools, drawing final indicators using big data, and quantifying indicators, and finally objectifying them through quantification. The institution intends to apply and verify the facility based on future indicators. This study has implications for the application of big data analysis methods that have not been attempted in planning and research for the use of closed school facilities to date.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.10-16
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• 2023

The ever-increasing amount of data generated by various industries and systems has led to the development of data mining techniques as a means to extract valuable insights and knowledge from such data. The electrical energy industry is no exception, with the large amounts of data generated by SCADA systems. This study focuses on the analysis of historical data recorded in the SCADA database of the Libyan Electricity Company. The database, spanned from January 1st, 2013, to December 31st, 2022, contains records of daily date and hour, energy production, temperature, humidity, wind speed, and energy consumption levels. The data was pre-processed and analyzed using the WEKA tool and the Apriori algorithm, a supervised machine learning technique. The aim of the study was to extract association rules that would assist decision-makers in making informed decisions with greater efficiency and reduced costs. The results obtained from the study were evaluated in terms of accuracy and production time, and the conclusion of the study shows that the results are promising and encouraging for future use in the Libyan Electricity Company. The study highlights the importance of data mining and the benefits of utilizing machine learning technology in decision-making processes.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.748-750
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• 2023

Facial expression recognition technology's rapid growth and development have garnered significant attention in recent years. This technology holds immense potential for various applications, making it crucial to stay up-to-date with the latest trends and advancements. Simultaneously, it is essential to identify and address the challenges that impede the technology's progress. Motivated by these factors, this study aims to understand the latest trends, future directions, and challenges in facial expression recognition technology by utilizing text mining to analyze papers published between 2020 and 2023. Our research focuses on discerning which aspects of these papers provide valuable insights into the field's recent developments and issues. By doing so, we aim to present the information in an accessible and engaging manner for readers, enabling them to understand the current state and future potential of facial expression recognition technology. Ultimately, our study seeks to contribute to the ongoing dialogue and facilitate further advancements in this rapidly evolving field.