• Journal of Korean Society for Quality Management
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• v.21 no.2
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• pp.131-139
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• 1993

The newly proposed model of the future reliability results in earlier fault-fixes having a greater effect than the fault which make the greatest contribution to the overall failure rate tend to show themselves earlier, and so are fixed earlier. The suggested model allows a variety of reliability measures to be calculated. Predictions of total execution time(debugging time) is to achieve a target reliability. This model could also apply to computer-hardware reliability growth resulting from the elimination of design error and fault.

• Journal of Advanced Marine Engineering and Technology
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• v.41 no.1
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• pp.56-61
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• 2017

This paper presents a data prediction method and its application to haptic-based teleoperation systems. In general, time delays inevitably occur during data transmission in a network environment, which degrades the overall performance of haptic-based teleoperation systems. To address this situation, this paper proposes an autoregressive moving average (ARMA) model-based data prediction algorithm for estimating model parameters and predicting future data recursively in real time. The proposed method was applied to haptic data captured every 5 ms while bilateral haptic interaction was carried out by two users with an object in a virtual space. The results showed that the prediction performance of the proposed method had an error of less than 1 ms when predicting position-level data 100 ms ahead.

• Convergence Security Journal
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• v.14 no.4
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• pp.19-26
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• 2014

Mobile malicious code is typically spread by the worm, and although modeling techniques to analyze the dispersion characteristics of the worms have been proposed, only macroscopic analysis was possible while there are limitations in predicting on certain viruses and malicious code. In this paper, prediction methods have been proposed which was based on Markov chain and is able to predict the occurrence of future malicious code by utilizing the past malicious code data. The average value of the malicious code to be applied to the prediction model of Markov chain model was applied by classifying into three categories of the total average, the last year average, and the recent average (6 months), and it was verified that malicious code prediction possibility could be increased by comparing the predicted values obtained through applying, and applying the recent average (6 months).

• International Journal of Aeronautical and Space Sciences
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• v.12 no.4
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• pp.305-317
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• 2011

This paper presents an overview on flutter boundary prediction in tests which is principally based on a system stability measure, named Jury's stability criterion, defined in the discrete-time domain, accompanied with the use of autoregressive moving-average (AR-MA) representation of a sampled sequence of wing responses excited by continuous air turbulences. Stability parameters applicable to two-, three- and multi-mode systems, that is, the flutter margin for discrete-time systems derived from Jury's criterion are also described. Actual applications of these measures to flutter tests performed in subsonic, transonic and supersonic wind tunnels, not only stationary flutter tests but also a nonstationary one in which the dynamic pressure increased in a fixed rate, are presented. An extension of the concept of nonstationary process approach to an analysis of flutter prediction of a morphing wing for which the instability takes place during the process of structural morphing will also be mentioned. Another extension of analytical approach to a multi-mode aeroelastic system is presented, too. Comparisons between the prediction based on the digital techniques mentioned above and the traditional damping method are given. A future possible application of the system stability approach to flight test will be finally discussed.

• Journal of Korean Society of Forest Science
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• v.95 no.6
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• pp.778-783
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• 2006

Price prediction is essential to decisions of investment and shipment in oak mushroom cultivation. But predicting the prices of oak mushroom is very difficult because there are so many uncertain factors affecting the demand and the supply in the market. The Box-Jenkins methodology is one of strong tools in price prediction especially for the short-term using historical observations of time series. In this paper, the Box-Jenkins methodology is applied to find a model to forecast future oak mushroom prices. And out-of-sample test was conducted to check out the prediction accuracy. The result shows the high accuracy except for market disturbance period affected by unexpected weather change and reveals the usefulness of the model.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.131-140
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• 2021

It is becoming more important to utilize reclaimed lands in South Korea, due to the increasing competition for its usage among different sectors. However, the high groundwater level and poor permeability are exposing them to deterioration by salinization. Salinization is difficult to predict because the pattern changes according to various characteristics of soil and groundwater. In this study, the capillary rising time was studied by the water content profile in the soil. The prediction equation of soil salinity was developed based on simulation result of the CHEMFLO model. to enable prediction considering various soil water content and groundwater level. The two terms constituting the equation showed the coefficients of determination of 0.9816 and 0.9824, respectively. Using the prediction equation of the study, the surface salinity can be easily predicted from the initial surface salinity and the salinity of the groundwater. In the future, more precise predictions will be possible with the results of studies on the hydraulic characteristics of various reclaimed soils, changes in water content profile by seasonal and climate events.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.101-112
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• 2018

A lot of manpower and budgets are being used to prevent fires, and only a small portion of the data generated during this process is used for disaster prevention activities. This study develops a prediction model of fire occurrence probability based on data mining in order to more actively use these data for disaster prevention activities. For this purpose, variables for predicting fire occurrence probability of various buildings were selected and data of construction administrative system, national fire information system, and Korea Fire Insurance Association were collected and integrated data set was constructed. After appropriate data cleansing and preprocessing, various data mining methodologies such as artificial neural network, decision trees, SVM, and Naive Bayesian were used to develop a prediction model of the fire occurrence probability of buildings. The most accurate model among the derived models is Linear SVM model which shows 68.42% as experimental data and 63.54% as verification data and it is the best model to predict fire occurrence probability of buildings. As this study develops the prediction model which uses only the set values of the specific ranges, future studies may explore more opportunites to use various setting values not shown in this study.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.230-240
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• 2020

Recently, the social debate about the fourth industrial revolution has been actively developed, and it is predicted that the 4th Industrial Revolution will have a great influence on our society, cities, residential and industrial spaces. Especially, it is anticipated that the technological development of the 4th Industrial Revolution will cause a wide range of changes in residential style and culture. Therefore, it is necessary to grasp the direction of future change in advance and proactively respond to future tasks and strategies need. The purpose of this study is to predict the direction and characteristics of the mid - to long - term changes in future housing that will be brought about by the 4th Industrial Revolution and to define future social, spatial and technological impacts and issues and to find policy measures for them. STEEP (V) as a methodology for forecasting future has been used. It is a process of deriving technical and social issues by using Big Data. It collects various keywords and draws out key issues and summarizes social change patterns related to each core issue. The proposed strategy for future housing prediction and countermeasures can be used as a basic data for future directions of housing policy and suggests a process for deriving reasonable and reasonable results from multiple data sets rather than accurate prediction.

• Journal of Technology Innovation
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• v.12 no.1
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• pp.189-218
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• 2004

In this paper, we estimate consumers' preferences for the key attributes of the future mobile telecommunication terminal using conjoint analysis. For statistical model, we estimate the mixed legit model which can reflect the heterogeneity of consumers' preferences, using the Bayesian approach with Gibbs sampling. The results show that there are large variations in consumers' preferences for the attributes of the future mobile telecommunication terminal, which justifies our using of mixed logit model. Also, the results show that most consumers prefer the medium size display and keyboard as input equipment. Additionally, far from general prediction, the results show that many consumers are indifferent to whether the future mobile telecommunication terminal is able to provide high quality internet service or not, and to operate many application programs and programs originally designed for PC. From those results, we can obtain some important implications for the R＆D strategies. Additionally, the results on the heterogeneity of consumers' preferences reveal that it is possible that the complete device convergence may not happen.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.10
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• pp.5891-5893
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• 2013

Incidence/mortality of liver cancer follow logistic curves because there is a limit reflecting the prevalence of hepatitis virus carriers in the cohort. The author fitted logistic curves to incidence/mortality data covering the nine five-year cohorts born in 1911-1955 of both sexes. Goodness-of-fit of logistic curves was sufficiently precise to be used for future predictions. Younger cohorts born in 1936 or later were predicted to show constant decline in incidence/mortality in the future. The male cohort born in 1931-35 showed an elevated incidence/mortality of liver cancer early in their lives supporting the previous claim that this particular cohort had suffered massive HCV infection due to nation-wide drug abuse in the 1950s. Declining case-fatality observed in younger cohorts suggested improved treatment of liver cancer. This study demonstrated that incidence/mortality of liver cancer follow logistic curves and fitted logistic formulae can be used for future prediction. Given the predicted decline of incidence/mortality in younger cohorts, liver cancer is likely to be lost to history in the not-so-distant future.