• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.6
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• pp.122-130
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• 2008

In many applications of wireless sensor networks, sensed data can be classified either normal or urgent data according to its time criticalness. Normal data such as periodic monitoring is loss and delay tolerant, but urgent data such as fire alarm is time critical and should be transferred to a sink with reliable. In this paper, by exploiting these data characteristics, we propose a novel energy-efficient data-aware routing protocol for wireless sensor networks, which provides a high reliability for urgent data and energy efficiency for normal data. In the proposed scheme, in order to enhance network survivability and reliability for urgent data, each sensor node forwards only urgent data when its residual battery level is below than a threshold. Also, the proposed scheme uses different data delivery mechanisms depending on the data type. The normal data is delivered to the sink using a single-path-based data forwarding mechanism to improve the energy-efficiency. Meanwhile, the urgent data is transmitted to the sink using a directional flooding mechanism to guarantee high reliability. Simulation results demonstrate that the proposed scheme could significantly improve the network lifetime, along with high reliability for urgent data delivery.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1095-1106
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• 2014

Symbolic data analysis extends the data mining and exploratory data analysis to the knowledge mining, we can suggest the SDA tree model on clinical and genomic data with new knowledge mining SDA approach. Using SDA application for huge genomic SNP data, we can get the correlation the availability of understanding of hidden structure of HCC data could be proved. We can confirm validity of application of SDA to the tree structured progression model and to quantify the clinical lab data and SNP data for early diagnosis of HCC. Our proposed model constructs the representative model for HCC survival time and causal association with their SNP gene data. To fit the simple and easy interpretation tree structured survival model which could reduced from huge clinical and genomic data under the new statistical theory of knowledge mining with SDA.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.331-340
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• 2023

Predicting the survival time of a person in water (PIW) in maritime search and rescue (SAR) operations is an important concern. Although there have been many studies on survival models in marine-developed countries, it is difficult to apply them to Koreans in Korea's oceans because they were developed using marine distress data from the United Kingdom, United States, and Canada. Data on the survival time of a P IW were collected through interviews and surveys with a special rescue team from the Korea Coast Guard, SAR cases, press releases, and Korea Meteorological Administration data to address these issues. The maximum survival time (Korean) equation was developed by performing a regression analysis of this data, and the applicability to actual marine distress was reviewed and compared to the overseas survival model. By comprehensively using the maximum survival time (Korean), domestic SAR cases, and overseas survival models, guidelines for survival time and intensive and recommended search time were suggested. The study findings can contribute to decision-making, such as the input for search and rescue units. The findings can also help to determine the end of or reductions in SAR operations and explain policy decisions to the public and families of a PIW.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.153-155
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• 2019

국방무기체계 분야 중 단일 센터에 대규모 분산시스템을 구성하는 경우 신속한 데이터 처리를 위해 통신 미들웨어로 사용되는 DDS의 패킷을 튜닝하여 사용하고 있다. 하지만 향후 국방무기체계는 생존성 보장을 위해 분산시스템의 장소를 주/예비 센터로 이원화하면서 센터간에도 신속한 데이터 동기화 및 비상시 OO초 내 센터 임무전환까지 함께 요구하고 있다. 따라서 단일 센터에서 적용한 DDS 패킷 전송 방식을 WAN 환경에 적용 시 데이터 송수신간 패킷의 순서가 바뀌는 현상이 발생하여 데이터 공유가 제한될 수 있다. 본 연구에서는 이와같은 제한사항을 극복하기 위해 DDS를 적용한 LAN 구간의 기존 성능을 유지하면서 WAN 구간 데이터의 신뢰성 보장을 위한 TCP/IP 기반의 WAN Sync 설계를 제시하였다.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.109-119
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• 2003

In this paper we propose a local smoothing of the Nelson type estimator for the survival function based on an approximation by the Weibull distribution function. It appears that Mean Square Error and Bias of the smoothed estimator of the Nelson type survival function estimator is significantly smaller then that of the smoothed estimator of the Kaplan-Meier survival function estimator.

• The Journal of the Korea Contents Association
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• v.10 no.3
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• pp.53-63
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• 2010

Recently, various monitoring systems are implemented actively by using wireless sensor networks(WSN). When implementing WSN-based monitoring system, there are three important issues to consider. At First, we need to consider a sensor node failure detection method to support the ongoing monitoring. Secondly, because sensor nodes use limited battery power, we need an efficient data filtering method to reduce energy consumption. At Last, a reducing processing overhead method is necessary. The existing Kalman filtering scheme has good performance on data filtering, but it causes too much processing overhead to estimate sensed data. To solve these problems, we, in this paper, propose a new data filtering scheme based on data statical analysis. First, the proposed scheme periodically aggregates node survival massages to support a node failure detection. Secondly, to reduce energy consumption, it sends the sample data with a node survival massage and do data filtering based on those messages. Finally, it analyzes the sample data to estimate filtering range in a server. As a result, each sensor node can use only simple compare operation for filtering data. In addition, we show from our performance analysis that the proposed scheme outperforms the Kalman filtering scheme in terms of the number of sending messages.

• The Journal of the Korean life insurance medical association
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• v.22
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• pp.55-98
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• 2003

본 연구는 질병별사망율연구(疾病別死亡率硏究)의 이론적 기초를 제시하고 일본 선진보험사의 질병별사망율연구(疾病別死亡率硏究)의 경험을 고찰하고 국내 보험사들의 질병사망율연관데이터의 현황분석을 통해 향후 질병별사망율연구(疾病別死亡率硏究)의 결과를 얻기 위해 현재 보험사들이 전사적으로 준비하여야 할 것에 대해 제언을 하고자 한다. 사망률연구(mortality study)란 인구통계학적 개념을 기본으로 하여 역학적 연구방법의 하나인 코호트방법과 생존분석방법을 결합하여 인구집단(또는 피보험자 집단)을 대상으로 대량의 자료를 장기적으로 관찰하여 그 사망의 빈도와 분포를 기술하고 사망연관지수들을 알아내어 생명보험사업에 있어서 위험선택기술을 향상시키는 것이다. 초과사망을 및 사망비 산출의 실제를 생명표 방법론과 급성심근 경색증 환자의 생존 분석을 통해 알아본다. 생명표 방법론을 이용한 생존 분석방법이란 의학저널에서 발표된 논문을 사망률표로 변경하기 위한 필수적인 단계에 대한 것이다 관찰된 생존 곡선을 생명표 작성법의 한 방법인 비교 경험 사망률로 바꾸는데 초점을 두었다. 일본생명(日本生命)의 경우, 일본 협영생명(協塋生命)의 경우, 일본사망율조사(MA)위원회 생명보험사망을 연구고서등을 통해 질병별사망율연구(疾病別死亡率硏究)를 살펴 보았다. 일본은 질병별사망율(疾病別死亡率)을 구하기 위해서 1950년대 이후부터 체계적으로 자료를 모으고 축적, 분석하여 지속성을 유지하였다. 또한 일본MA위원회의 경우처럼 보험의학의사, 계리, 통계, 전산부서로 구성된 전담위원회의 통일된 협조가 질병별사망율연구(疾病別死亡率硏究)를 가능하게 하였다. 그리고 의학적인 관점에서 볼 때 일본보험의학계는 일본만의 독특한 질병분류로 분석하여 온 것이 특이하다. 질병별사망율연구(疾病別死亡率硏究)에 대해서는 모두가 필요성을 인정하면서도 구체적인 대비책은 없는 것이 우리나라 보험업계의 실정이다. 이러한 현실의 직접적인 이유는 질병별사망율연구(疾病別死亡率硏究)라는 것이 그 특성상 중장기적인 계획이며 많은 전문인력의 통합되고 집중된 노력이 요구되기 때문이다. 우리도 "생명보험사사망율연구위원회(Life Insurance Mortality Committee" (가칭)를 설치하고 장기적인 계획안을 먼저 만드는 것이 선행되어야 할 것이다. 지금부터 질병별사망율(疾病別死亡率) 데이터를 축적하고 매 5년 또는 매 10년마다 데이터를 분석한다면 질병별사망율(疾病別死亡率)에 대해 고유의 기술을 습득하는 것이 그리 먼 미래의 일만은 아닐 것이다.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.889-898
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• 2005

In this paper we propose a new estimator of relative odds ratio in the two-sample case of proportional odds model under censorship. Also, we show that the estimator consistent and asymptotically normal by using martingale-representation. The efficiency of the proposed is assessed through a simulation study.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.327-334
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• 2005

In this paper we propose a new estimator of relative odds ratio in the two-sample case of proportional odds model. Also, we show that the estimator is consistent and asymptotically normal. The efficiency of the proposed is assessed through a simulation study.

• Smart Media Journal
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• v.12 no.8
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• pp.18-25
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• 2023

The COVID-19 outbreak in 2020 caused continuous damage worldwode, especially the smart tourism industry was hit directly by the blockade of sky roads and restriction of going out. At a time when overseas travel and domestic travel have decreased significantly, the number of tourist hotels that are colsed and closed due to the continued deficit is increasing. Therefore, in this study, licensing data from the Ministry of Public Administraion and Security were collected and visualized to understand the operation status of the tourism and lodging industry. The machine learning classification algorithm was applied to implement the business status prediction model of the tourist hotel, the performance of the prediction model was optimized using the ensemble algorithm, and the performance of the model was evaluated through 5-Fold cross-validation. It was predicted that the survival rate of tourist hotels would decrease somewhat, but the actual survival rate was analyzed to be no different from before COVID-19. Through the prediction of the business status of the hotel industry in this paper, it can be used as a basis for grasping the operability and development trends of the entire tourism and lodging industry.