• Asian Pacific Journal of Cancer Prevention
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• 제16권14호
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• pp.5829-5834
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• 2015

Objective: To describe and analyze the epidemiological characteristics of lung cancer mortality in China from 1991 to 2013, forecast the future five-year trend and provide scientific evidence for prevention and management of lung cancer. Materials and Methods: Mortality data for lung cancer in China from 1991 to 2013 were used to describe epidemiological characteristics. Trend surface analysis was applied to analyze the geographical distribution of lung cancer. Four models, curve estimation, time series modeling, gray modeling (GM) and joinpoint regression, were performed to forecast the trend for the future. Results: Since 1991 the mortality rate of lung cancer increased yearly. The rate for males was higher than that for females and rates in urban areas were higher than in rural areas. In addition, our results showed that the trend will continue to increase in the ensuing 5 years. The mortality rate increased from age 45-50 and peaked in the group of 85 years old. Geographical analysis indicated that people living in northeast China provinces and the coastal provinces in eastern China had a higher mortality rate for lung cancer than those living in the centre or western Chinese provinces. Conclusions: The standardized mortality rate of lung cancer has constantly increased from 1991 to 2013, and been predicted to continue in the ensuing 5 years. Further efforts should be concentrated on education of the general public to increase prevention and early detection. Much better prevention and management is needed in high mortality areas (northeastern and eastern parts of China) and high risk populations (45-50-year-olds).

• 전기전자학회논문지
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• 제21권1호
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• pp.13-23
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• 2017

공항의 안정적인 운영을 위하여 승객의 단기예측은 매우 중요하다. 본 논문에서는 인천공항의 출입국자 예측을 위하여 출입국자의 대부분을 차지하는 한국인과 중국인의 출국자의 예측 모델링을 수행하였다. 예측 모델링 정확도 향상을 위해 네이버와 바이두 검색 트렌드 데이터를 활용하였다. 출국자 수들과 관련 검색 트렌드 데이터 간 Granger Causality 테스트를 수행하여 상관관계가 있음을 확인하였다. "출국자 수" 단독으로 예측하는 것보다 "출국자 수"와 "검색어 트렌드" 자료를 합하여 예측하는 것이 정확도가 향상됨을 알 수 있었다. 이는 검색이 어떤 일을 수행하기 전에 하는 행위이기 때문이고, 검색 트렌드 데이터 내에 태생적으로 예측 기재가 존재함을 본 연구를 통하여 확인할 수 있었다.

• 한국컴퓨터정보학회논문지
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• 제20권1호
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• pp.93-101
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• 2015

현대 사회는 다양한 범죄가 발생하고 있다. 범죄를 예방하기 위해서는 범죄를 예측 하는 것이 필요하고, 범죄 예측에 관한 다양한 연구가 진행 중에 있다. 범죄 관련 데이터는 검찰청에서 1년에 한번 통계처리를 하여 발표하고 있다. 그러나 통계처리 된 자료는 현재 시점을 기준으로 약 2년 전의 자료로 현재 발생하는 범죄에 대한 데이터로 적합하지 않다. 본 논문은 범죄를 예측하는 데이터로 네이버 트랜드를 적용했다. 네이버 트랜드의 웹 검색 트래픽을 이용하면, 현재 발생하는 범죄에 대한 관심도 데이터를 얻을 수 있다. 네이버 웹 검색 트래픽 데이터를 이용하여 범죄를 예측할 수 있는 모델링을 구성하였고, 예측 이론으로 마코프 체인을 적용하였다. 다양한 범죄 중 살인, 방화, 강간을 대상으로 예측 모델링에 적용하였고, 결과 값을 분석하였다. 그 결과 실제 발생한 범죄 발생 빈도수를 기준으로 20%이내의 유사한 결과를 얻었다. 향후에는 계절의 특성을 고려한 범죄 예측 모델링에 대한 연구를 진행할 예정이다

• 전기학회논문지
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• 제62권1호
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• pp.140-142
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• 2013

Cognitive radio (CR) has been recently proposed to dynamically access unused-spectrum. Since the spectrum availability for opportunistic access is determined by spectrum sensing, sensing control is identified as one of the most crucial issues of cognitive radio networks. Out-of-band sensing to find an available channels to sense. Sensing is also required in case of spectrum hand-off. Sensing process needs to be done very fast in order to enhance the quality of service (QoS) of the CR nodes, and transmission not to be cut for longer time. During the sensing, the PU(primary user) detection probability condition should be satisfied. We adopt a channel prediction method to find target channels. Proposed prediction method combines chaotic global method and chaotic local method for channel idle probability prediction. Global method focus on channel history information length and order number of prediction model. Local method focus on local prediction trend. Through making simulation, Proposed method can find an available channel with very high probability, total sensing time is minimized, detection probability of PU's are satisfied.

• 정보처리학회 논문지
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• 제13권6호
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• pp.251-259
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• 2024

초미세먼지, 특히 지름이 2.5㎛ 이하인 PM2.5는 인체 건강과 경제에 큰 피해를 주는 오염물질이다. 본 연구는 대한민국 서울 지역을 중심으로, 2017년부터 2022년까지 자료를 수집하여 PM2.5 데이터 분석 및 데이터 경향성 변화 추이를 분석하고, PM2.5 중기 예측 모델을 개발하는 것을 목표로 한다. 수집, 생산된 대기질 및 기상 데이터, 재분석 데이터, 수치모델 예측 데이터를 바탕으로, 모델을 학습하고 이를 통합한 경향성 변화에도 대응할 수 있는 앙상블 기법을 제안한다. 본 연구에서 제안하는 앙상블 기법은 PM2.5 농도 예측 성능 면에서 기존 모델 대비 미래 D+3~D+6 예측일 F1 Score 기준 평균 2019년 약 42.16%, 2021년 약 58.92%, 2022년 약 34.79% 높은 성능을 보였다. 제안한 모델은 변화하는 환경 조건에도 성능을 유지함으로써 안정적인 예측을 가능하게 하며, 기존 딥러닝 기반 PM2.5 단기 예측보다 먼 예측을 수행하는 중기 예측 모델을 제시한다.

• 한국세라믹학회지
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• 제46권2호
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• pp.161-169
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• 2009

MLCC (Multi-layer Ceramic Capacitor) is the most important passive component in electronic devices such as HHP, PC and digital display. The development trend of MLCC is a miniaturization with increasing the capacitance. In this paper, a development history of the high capacitance MLCC in Korea was introduced, and the necessity of the finer $BaTiO_3$ was explained in the viewpoint of the issued electrical and dielectric properties of high capacitance MLCC. The bottleneck technologies to realize the high capacitance was shortly introduced, followed by the prediction of the development trend of MLCC in near future.

• 한국정보시스템학회지:정보시스템연구
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• 제29권1호
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• pp.289-301
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• 2020

Purpose This study analyzes whether Internet search of cryptocurrency has a causal relationship to return and volatility of cryptocurrency. Design/methodology/approach Google Trend was used as a measure of the level of Internet search, and the parametric tests of Granger causality in the 1st moment and the 2nd moment were adopted as the analysis method. We used Bitcoin's dollar-based price, which is the No. 1 market value among cryptocurrency. Findings The results showed that the Internet search measured by Google Trends has a causal relationship to cryptocurrency in both average and volatility, while there is a difference in causality and its degree according to the search area and category that Google Trend user should set. Because the Granger causality is based on the improvement of prediction, the analysis results of this study indicate that Internet search can be used as a leading indicator in predicting return and volatility of cryptocurrency.

• 농업과학연구
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• 제43권4호
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• pp.507-521
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• 2016

Landslides are one of the most common natural hazards causing significant damage and casualties every year. In Korea, the increasing trend in landslide occurrence in recent decades, caused by climate change, has set off an alarm for researchers to find more reliable methods for landslide prediction. Therefore, an accurate landslide-susceptibility assessment is fundamental for preventing landslides and minimizing damages. However, analyzing the stability of a natural slope is not an easy task because it depends on numerous factors such as those related to vegetation, soil properties, soil moisture distribution, the amount and duration of rainfall, earthquakes, etc. A variety of different methods and techniques for evaluating landslide susceptibility have been proposed, but up to now no specific method or technique has been accepted as the standard method because it is very difficult to assess different methods with entirely different intrinsic and extrinsic data. Landslide prediction methods can fall into three categories: empirical, statistical, and physical approaches. This paper reviews previous research and surveys three groups of landslide prediction methods.

• 품질경영학회지
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• 제42권2호
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• pp.221-234
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• 2014

Purpose: The Purpose of this study is to develop a model for predicting agent churn group in the cosmetics industry. We develope two models, pattern model and matrix model, which are compared regarding the prediction accuracy of churn agents. Finally, we try to conclude if there is statistically significant difference between two models by empirical study. Methods: We develop two models using the part of RFM(Recency, Frequency, Monetary) method which is one of customer segmentation method in traditional CRM study. In order to ensure which model can predict churn agents more precisely between two models, we used CRM data of cosmetics company A in China. Results: Pattern model and matrix model have been developed. we find out that there is statistically significant differences between two models regarding the prediction accuracy. Conclusion: Pattern model and matrix model predict churn agents. Although pattern model employed the trend of monetary mount for six months, matrix model that used the amount of sales per month and the duration of the employment is better than pattern model in prediction accuracy.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.150-150
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• 2022

This study investigates the roles of decomposition methods on high accuracy in daily rainfall prediction from light gradient boosting machine (LightGBM) algorithm. Here, empirical mode decomposition (EMD) and singular spectrum analysis (SSA) methods were considered to decompose and reconstruct input time series into trend terms, fluctuating terms, and noise components. The decomposed time series from EMD and SSA methods were used as input data for LightGBM algorithm in two hybrid models, including empirical mode-based light gradient boosting machine (EMDGBM) and singular spectrum analysis-based light gradient boosting machine (SSAGBM), respectively. A total of four parameters (i.e., temperature, humidity, wind speed, and rainfall) at a daily scale from 2003 to 2017 is used as input data for daily rainfall prediction. As results from statistical performance indicators, it indicates that the SSAGBM model shows a better performance than the EMDGBM model and the original LightGBM algorithm with no decomposition methods. It represents that the accuracy of LightGBM algorithm in rainfall prediction was improved with the SSA method when using multivariate dataset.