• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.5
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• pp.17-24
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• 2010

Recently the study of identifying bio-markers for disease diagnosis and prognosis has been actively performed. In particular, lots of attentions have been paid to the finding of pathway gene-sets differentially expressed in disease patients rather than the finding of individual gene markers. In this paper we propose a novel method to identify disease-related pathway gene-sets based on time-series microarray data. For this purpose, we firstly compute individual gene scores by the using maSigPro (microarray Significant Profiles) and then arrange all the genes in the decreasing order of the corresponding gene scores. The rank of each gene in the entire list is used to evaluate the statistical significance of candidate gene-sets with Wilcoxson rank sum test. For the generation of candidate gene-sets, MSigDB (Molecular Signatures Database) pathway information has been employed. The experiment was conducted with prostate cancer time-series microarray data and the results showed the usefulness of the proposed method by correctly identifying 6 out of 7 biological pathways already known as being actually related to prostate cancer.

• Journal of Global Scholars of Marketing Science
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• v.14
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• pp.125-144
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• 2004

The purpose of this study is to classify customer bye-mailing responsiveness on time-series analysis and RFM module and testify the effectiveness of grouping by ROI analysis. RFM (Recency, Frequency, Monetary Value) analysis are used for customer classification that is fundamental process of e-CRM application. ROI analysis were consisted of open, click-through, duration time, conversion rate, personalization and e-mail loyalty index. Major findings are as follows; Customer segmentation were loyal customer, odds customer, dormant customer, secession customer and observation customer by Activity email module. And Loyal, dormant and secession customer are segregated by RFM module. Loyal customer group have higher point of all ROI index than other groups. These results indicated that customer responsiveness of e-mailing and RFM analysis were appropriate methods to grouping the customer. Mid-small Internet Biz adapted marketing strategy by optimization of consumer information.

• 한국디지털정책학회:학술대회논문집
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• 2005.11a
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• pp.45-60
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• 2005

The purpose of this study is to classify customer by e-mailing responsiveness on time-series analysis and testify the effectiveness of grouping by ROI analysis. Response recency, response frequency and Activity(RFA) of e-mailing systems were adapted for Customer segmentations. ROI analysis were consisted of open, click-through, duration time, personalization, conversion rate and email loyalty index of email systems. Major findings are as follows: RFA analysis is used for customer segmentations that is fundamental process of e-CRM applications. Customer segmentations were loyal customer, odds customer, dormant customer, secession customer and observation customer by RFA grouping. Loyal customer group has high point in all ROI index compared to other groups. These results indicated that customer responsiveness of e-mailing systems were appropriate methods to grouping the customer with demographic variables. Therefore, effective e-mailing marketing strategy of e-Biz have suitable active DB and Behavior targeting is best approach to enforcing the target e-mailing marketing.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.581-584
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• 2018

최근 다양한 분야에서 대량의 데이터를 수집하여 처리하고 분석하는 빅데이터 기술이 활용되고 있다. 빅데이터 분석을 위해서는 데이터 시각화 기술이 필수적이다. 본 논문에서는 REST API를 사용하여 시계열 데이터베이스에 데이터를 질의하고, 응답받은 시계열 데이터를 다양한 형태의 차트로 시각화하는 마이크로서비스(Graphgen)를 설계하고 구현한다. 이 서비스는 데이터의 변동에 따라 실시간으로 시각화 객체를 갱신하며, 대용량 데이터 처리의 성능저하를 최소화하도록 개발된다. Graphgen은 InfluxDB와 OpenTSDB 시계열 데이터베이스와 Bokeh 시각화 라이브러리를 지원하며, 추후 서비스 확장이 용이하도록 개발된다. 또한 부하 분산과 통합 배포 관리를 위하여 컨테이너를 기반으로 개발된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.60-62
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• 2018

본 논문에서는 리눅스에서 제공하는 성능 분석 도구들을 활용하여 사용자가 원하는 모니터링 매트릭을 동적으로 등록하고 모니터링 할 수 있는 확장 가능한 하둡 응용 모니터링 서비스의 시계열 데이터 관리 방안을 다룬다. 본 논문에서는 이를 위해서 시계열 데이터를 위한 관계형 데이터베이스인 TimeScaleDB를 사용하였으며 동적으로 변경가능한 모니터링 메트릭 데이터가 하이퍼테이블의 관리를 통해서 구조화된 밀집 데이터 형태로 효율적으로 관리될 수 있음을 제시하였다.

• The Korean Journal of Pesticide Science
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• v.4 no.2
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• pp.11-17
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• 2000

Optimum parameters were investigated for the simultanious analysis of 24 pesticide residuces using gas-liquid chromatography with electron capture detection. Electronic pressure control(EPC) on column enhanced resolution of 24 analyzes. Using DB-17, SPB-608, and Ultra-2 capillary column without EPC incomplete separation was observed in some pairs of pesticides. When EPC function was adopted, no severe overlapping was observed on SPB-608 column in every pesticides except vinclozolin/acetochlor pair. Total running time was 45 min, much shorter than $69{\sim}81$ min when used without EPC. Limit of determination of each analyze ranged $0.1{\sim}12.9$ ng/mL.

• International Journal of Highway Engineering
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• v.13 no.3
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• pp.111-119
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• 2011

The noise prediction model, KRON-2006, in South Korea has been developed for obtaining the average noise level. The model is based on an outdoor sound propagation method based on ISO9613 and ASJ Model-1998 and supports the analysis of the linear noise source, such as highway, for obtaining Leq. Because of that, the model can't obtain Lmax, Lmin from the time series noise profile based on traffic at every moment. In order to address this problem, the real time noise prediction model based on traffic simulation using GIS model and algorithm is proposed. It can predict the vehicle point noise level based on vehicle type, speed generated from traffic simulation by using headway and obtain Lmax, Lmin as integrating the noise profile generated from it at every moment. An evalution of the noise prediciton model using field measurements finds good agreement between predicted and measured noise levels at 1m, 8m, 15m from curb of the near side lane.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.9
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• pp.2535-2544
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• 2009

The brain-wave research provides relatively various information for brain condition in safety. The counselor or measuree will must easily reduce user TCO about the series of process for the measurement, analysis, and management of brain-wave, and can access to the desired information in real time. While the traditional method for brain-wave process was processed manually by the judgment of a specialist. In this paper the developed system is smart brain-wave care system based on optimizing and combining the 3-Tier client/server by IT with brain-wave technology including BQT. This system was developed in the real-time service with a completely automated process by the conveniently web interface. Our system currently gave a service at the field, and the collected data on DB were provided to researchers for the use of clinical research.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.2
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• pp.95-109
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• 2023

In the 4th industrial revolution, changes in the technological paradigm have had a direct impact on the maintenance system of ships. The 2-stroke low speed engine system integrates with the core equipment required for propulsive power. The Condition Based Management (CBM) is defined as a technology that predictive maintenance methods in existing calender-based or running time based maintenance systems by monitoring the condition of machinery and diagnosis/prognosis failures. In this study, we have established a framework for CBM technology development on our own, and are engaged in engineering-based failure analysis, data development and management, data feature analysis and pre-processing, and verified the reliability of failure mode DB using LSTM algorithms. We developed various simulated failure mode scenarios for 2-stroke low speed engine and researched to produce data on onshore basis test_beds. The analysis and pre-processing of normal and abnormal status data acquired through failure mode simulation experiment used various Exploratory Data Analysis (EDA) techniques to feature extract not only data on the performance and efficiency of 2-stroke low speed engine but also key feature data using multivariate statistical analysis. In addition, by developing an LSTM classification algorithm, we tried to verify the reliability of various failure mode data with time-series characteristics.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.