• Title, Summary, Keyword: 단순평활법

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Adaptive Noise Reduction Algorithm for Image Based on Block Approach (블럭 방법에 근거한 영상의 적응적 잡음제거 알고리즘)

  • Kim, Yeong-Hwa
    • Communications for Statistical Applications and Methods
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    • v.19 no.2
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    • pp.225-235
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    • 2012
  • Noise reduction is an important issue in the field of image processing because image noise worsens the quality of the input image. The basic difficulty is that the noise and the signal are not easy to distinguish. Simple moothing is one of the most basic and important procedures to remove the noise, however, it does not consider the level of noise. This method effectively reduces the noise but the feature area is simultaneously blurred. This paper considers the block approach to detect noise and image features of the input image so that noise reduction could be adaptively applied. Simulation results show that the proposed algorithm improves the overall quality of the image by removing the noise according to the noise level.

Real-time Confidence interval estimation for Improved accuracy of Ship-inside the anomaly detection (선박내부 이상감지의 정확도 향상을 위한 실시간 신뢰구간 추정)

  • Kim, Yeong-Ju;Heo, yu Kyung;Jeong, Majung-A
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.721-723
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    • 2014
  • 본 논문은 선박내부의 센서데이터 이상감지를 위해 실시간 신뢰구간을 설정하고 신뢰구간을 초과하거나 미만이 되면 경보를 통해 관리자에게 알려주는 모니터링을 위한 신뢰구간 추정이다. 여기서, 이상 감지 예측의 정확도 향상을 위해 단순지수평활법과 이동평균법의 평균제곱오차를 비교 평가 하였다. 실험결과, 이동평균법의 평균제곱오차가 단순지수평활법 보다 적게 나와 선박 내부 모니터링을 위한 신뢰구간은 이동평균법을 적용하였다.

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Time Series Model을 이용한 주요항만 해상교통량 예측

  • Yu, Sang-Rok;Jeong, Jung-Sik;Kim, Cheol-Seung;Jeong, Jae-Yong
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • pp.133-135
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    • 2013
  • 장래의 해상교통량에 대한 정확한 예측은 항로설계 및 해상교통의 안전성 평가 측면에서 중요한 요소이다. 본 연구는 신뢰성 있는 해상교통량을 추정하기 위해 시계열 모델의 지수평활법과 ARIMA 모형을 이용하여 모형의 식별 및 진단 방안을 제시하였다. 제시된 방법의 효과를 검증하기 위하여 주요항만인 부산항, 광양항, 인천항, 평택항의 해상교통량을 예측하였다. 그 결과로 부산항은 ARIMA 모형, 광양항은 Winters 승법 모형, 인천항은 단순계절 모형, 평택항은 ARIMA 모형이 더 적합한 모형으로 알 수 있었으며, 각 항만별 계절에 따라 월별 교통량의 차이를 보이는 것으로 분석되었다. 본 연구 결과는 향후 항로 및 항만설계 또는 해상교통 안전성 평가에 보다 신뢰성 있는 추정치를 제공할 수 있을 것으로 보인다.

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A Study on the Travel Speed Estimation Using Bus Information (버스정보기반 통행속도 추정에 관한 연구)

  • Bin, Mi-Young;Moon, Ju-Back;Lim, Seung-Kook
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.12 no.4
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    • pp.1-10
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    • 2013
  • This study was conducted to investigate that bus information was used as an information of travel speed. To determine the travel speed on the road, bus information and the information collected from the point detector and the interval detection installed were compared. If bus information has the function of traffic information detector, can provide the travel speed information to road users. To this end, the model of recognizing the traffic patterns is necessary. This study used simple moving-average method, simple exponential smoothing method, Double moving average method, Double exponential smoothing method, ARIMA(Autoregressive integrated moving average model) as the existing methods rather than new approach methods. This study suggested the possibility to replace bus information system into other information collection system.

Airline In-flight Meal Demand Forecasting with Neural Networks and Time Series Models (인공신경망을 이용한 항공기 기내식 수요예측의 예측력 개선 방안에 관한 연구)

  • Lee, Young-Chan;Seo, Chang-Gab
    • The Journal of Information Systems
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    • v.10 no.2
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    • pp.151-164
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    • 2001
  • 현재의 항공사 기내식 수요예측 시스템으로는 항공기 운항의 지연이나 초과 주문으로 인한 손실 문제를 해결하기 힘든 것으로 알려져 있다. 이러한 문제를 해결하기 위해 본 연구에서는 항공기 기내식 시계열 자료만을 입력변수로 사용한 단순인공신경망모형(simple neural network model), 단순인공신경망모형에 전통적인 시계열 기법(본 연구에서는 지수 평활법)의 예측 결과를 입력변수로 추가한 혼합인공신경망모형(hybrid neural network model), 그리고 혼합인공신경 망 모형에 상관관계가 높은 다른 시계열 자료(본 논문에서는 유사 노선의 다른 항공기 기내식 시계열 자료)를 인공신경망의 입력변수로 추가시킨 하이퍼혼합인공신경망모형(hyper hybrid neural network model)을 새로운 항공기 기내식 수요예측 기법으로 제안하고, 이들 모형의 예측력을 비교 분석하였다. 분석 결과 하이퍼혼합인공신경망 모형의 예측력이 가장 우수한 것으로 나타나, 인공신경 망을 기반으로 한 수요예측에 있어 상관관계가 높은 다른 시계열 자료를 입력변수로 추가함으로써 인공신경망모형의 예측력을 개선시킬 수 있음을 알 수 있었다

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The Study for Software Future Forecasting Failure Time Using Time Series Analysis. (시계열 분석을 이용한 소프트웨어 미래 고장 시간 예측에 관한 연구)

  • Kim, Hee-Cheul;Shin, Hyun-Cheul
    • Convergence Security Journal
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    • v.11 no.3
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    • pp.19-24
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    • 2011
  • Software failure time presented in the literature exhibit either constant monotonic increasing or monotonic decreasing, For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offer information of outline content. In this paper, we discuss forecasting failure time case of failure time censoring. In this study, time series analys is used in the simple moving average and weighted moving averages, exponential smoothing method for predict the future failure times, Empirical analysis used interval failure time for the prediction of this model. Model selection using the mean square error was presented for effective comparison.

A Study on the prediction of Advertising Expenditure (계량적 통계분석을 통한 매체별 광고비 예측 연구)

  • Han, Sangpil;Yu, Seung Yeob
    • Journal of Digital Convergence
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    • v.12 no.9
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    • pp.111-121
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    • 2014
  • This study is designed to predict the total ad expenditure of Korea, and six media ad expenditures in 5 years based on the past 20 years ad expenditure date. We use annual data published by Cheil Worldwide advertising data analysis. Time series, SUR method, exponential smoothing method and regression analysis were used for exploring the data. The results showed that the total advertising expenditure in 2018 is predicted to 10,873 billion wons. On the basis of the findings, implications are discussed for academicians as well as practitioners.

The Study of Prediction Model of Gas Accidents Using Time Series Analysis (시계열 분석을 이용한 가스사고 발생 예측 연구)

  • Lee, Su-Kyung;Hur, Young-Taeg;Shin, Dong-Il;Song, Dong-Woo;Kim, Ki-Sung
    • Journal of the Korean Institute of Gas
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    • v.18 no.1
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    • pp.8-16
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    • 2014
  • In this study, the number of gas accidents prediction model was suggested by analyzing the gas accidents occurred in Korea. In order to predict the number of gas accidents, simple moving average method (3, 4, 5 period), weighted average method and exponential smoothing method were applied. Study results of the sum of mean-square error acquired by the models of moving average method for 4 periods and weighted moving average method showed the highest value of 44.4 and 43 respectively. By developing the number of gas accidents prediction model, it could be actively utilized for gas accident prevention activities.

A Case Study on Crime Prediction using Time Series Models (시계열 모형을 이용한 범죄예측 사례연구)

  • Joo, Il-Yeob
    • Korean Security Journal
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    • no.30
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    • pp.139-169
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    • 2012
  • The purpose of this study is to contribute to establishing the scientific policing policies through deriving the time series models that can forecast the occurrence of major crimes such as murder, robbery, burglary, rape, violence and identifying the occurrence of major crimes using the models. In order to achieve this purpose, there were performed the statistical methods such as Generation of Time Series Model(C) for identifying the forecasting models of time series, Generation of Time Series Model(C) and Sequential Chart of Time Series(N) for identifying the accuracy of the forecasting models of time series on the monthly incidence of major crimes from 2002 to 2010 using IBM PASW(SPSS) 19.0. The following is the result of the study. First, murder, robbery, rape, theft and violence crime's forecasting models of time series are Simple Season, Winters Multiplicative, ARIMA(0,1,1)(0,1,1), ARIMA(1,1,0 )(0,1,1) and Simple Season. Second, it is possible to forecast the short-term's occurrence of major crimes such as murder, robbery, burglary, rape, violence using the forecasting models of time series. Based on the result of this study, we have to suggest various forecasting models of time series continuously, and have to concern the long-term forecasting models of time series which is based on the quarterly, yearly incidence of major crimes.

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