• The Plant Pathology Journal
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• v.36 no.5
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• pp.406-417
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• 2020

Early warning services for crop diseases are valuable when they provide timely forecasts that farmers can utilize to inform their disease management decisions. In South Korea, collaborative disease controls that utilize unmanned aerial vehicles are commonly performed for most rice paddies. However, such controls could benefit from seasonal disease early warnings with a lead time of a few months. As a first step to establish a seasonal disease early warning service using seasonal climate forecasts, we developed the EPIRICE Daily Risk Model for rice blast by extracting and modifying the core infection algorithms of the EPIRICE model. The daily risk scores generated by the EPIRICE Daily Risk Model were successfully converted into a realistic and measurable disease value through statistical analyses with 13 rice blast incidence datasets, and subsequently validated using the data from another rice blast experiment conducted in Icheon, South Korea, from 1974 to 2000. The sensitivity of the model to air temperature, relative humidity, and precipitation input variables was examined, and the relative humidity resulted in the most sensitive response from the model. Overall, our results indicate that the EPIRICE Daily Risk Model can be used to produce potential disease risk predictions for the seasonal disease early warning service.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.25 no.3
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• pp.89-100
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• 2017

In order for the international aviation community to efficiently and safely manage the gradual increase of air passenger demand, direction suggestions of airport traffic prediction based on future airport capacity requirements, airport design and infrastructure establishment is utilized by airport traffic data that is m comparable internationally. It is a global trend to pursue more efficient airport operating system structure to accept air passenger demand through more realistic comparable data in order to escape from the structure of reckless airport establishment and infrastructure composition based on passenger demand predictions referring to simple statistical data that has existed in the past. This study aimed to seek effective operational measures for the New Jeju airport scheduled to be opened in 2025 by time-series analysis. This study also analysed airport operation strategies, air traffic distribution strategies, cargo volume increase rates and its effectiveness of airports adopting the multi-airport system that have similar operational practices and geographical conditions. This study sought the most appropriate multi airport system application measures for New Jeju airport to promote efficiency and international competitiveness.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.361-366
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• 2013

Prediction of process parameters is very important in parameter design. If predictions are fairly accurate, the quality improvement process will be useful to save time and reduce cost. The concept of dual response approach based on response surface methodology has widely been investigated. Dual response approach may take advantages of optimization modeling for finding optimum setting of input factor by separately modeling mean and variance responses. This study proposes an alternative dual response approach based on machine learning techniques instead of statistical analysis tools. A hybrid neural network-genetic algorithm has been proposed for the purpose of parameter design. A neural network is first constructed to model the relationship between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Using empirical process data, process parameters can be predicted without performing real experimentations. A genetic algorithm is then applied to find the optimum settings of input factors, where the neural network is used to evaluate the mean and variance response. A drug formulation example from pharmaceutical industry has been studied to demonstrate the procedures and applicability of the proposed approach.

• Advances in environmental research
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• v.4 no.2
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• pp.83-104
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• 2015

Air Quality Index (AQI) is a pointer to broadcast short term air quality. This paper presents one day ahead AQI forecasting on seasonal basis for three major cities in Maharashtra State, India by using Artificial Neural Networks (ANN) and Genetic Programming (GP). The meteorological observations & previous AQI from 2005-2008 are used to predict next day's AQI. It was observed that GP captures the phenomenon better than ANN and could also follow the peak values better than ANN. The overall performance of GP seems better as compared to ANN. Stochastic nature of the input parameters and the possibility of auto-correlation might have introduced time lag and subsequent errors in predictions. Spectral Analysis (SA) was used for characterization of the error introduced. Correlational dependency (serial dependency) was calculated for all 24 models prepared on seasonal basis. Particular lags (k) in all the models were removed by differencing the series, that is converting each i'th element of the series into its difference from the (i-k)"th element. New time series is generated for all seasonal models in synchronization with the original time line & evaluated using ANN and GP. The statistical analysis and comparison of GP and ANN models has been done. We have proposed a promising approach of use of GP coupled with SA for real time prediction of seasonal multicity AQI.

• Smart Structures and Systems
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• v.4 no.5
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• pp.629-640
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• 2008

A new algorithm is proposed to determine optimal accelerometer locations (OAL) when a structure is identified by frequency domain system identification (SI) method. As a result, a guideline is presented for selecting OAL which can reflect modal response of a structure properly. The guideline is to provide a minimum number of necessary accelerometers with the variation in the number of measurable target modes. To determine OAL for SI applications effectively, the modal sensitivity effective independence distribution vector (MS-EIDV) is developed with the likelihood function of measurements. By maximizing the likelihood of the occurrence of the measurements relative to the predictions, Fisher Information Matrix (FIM) is derived as a function of mode shape sensitivity. This paper also proposes a statistical approach in determining the structural parameters with a presumed parameter error which reflects the epistemic paradox between the determination of OAL and the application of a SI scheme. Numerical simulations have been carried out to examine the proposed OAL algorithm. A two-span multi-girder bridge and a two-span truss bridge were used for the simulation studies. To overcome a rank deficiency frequently occurred in inverting a FIM, the singular value decomposition scheme has been applied.

• Korean Journal of Agricultural Science
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• v.42 no.4
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• pp.397-406
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• 2015

Quantitative traits are mostly controlled by a large number of genes. Some of these genes tend to have a large effect on quantitative traits in cattle and are known as major genes primarily located at quantitative trait loci (QTL). The genetic merit of animals can be estimated by genomic selection, which uses genome-wide SNP panels and statistical methods that capture the effects of large numbers of SNPs simultaneously. In practice, the accuracy of genomic predictions will depend on the size and structure of reference and training population, the effective population size, the density of marker and the genetic architecture of the traits such as number of loci affecting the traits and distribution of their effects. In this review, we focus on the structure of Hanwoo reference and training population in terms of accuracy of genomic prediction and we then discuss of genetic architecture of intramuscular fat(IMF) and marbling score(MS) to estimate genomic breeding value in real small size of reference population.

• Journal of Environmental Science International
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• v.20 no.12
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• pp.1541-1551
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• 2011

This study applied the Bayesian method for the quantification of the parameter uncertainty of spatial linear mixed model in the estimation of the spatial distribution of probability rainfall. In the application of Bayesian method, the prior sensitivity analysis was implemented by using the priors normally selected in the existing studies which applied the Bayesian method for the puppose of assessing the influence which the selection of the priors of model parameters had on posteriors. As a result, the posteriors of parameters were differently estimated which priors were selected, and then in the case of the prior combination, F-S-E, the sizes of uncertainty intervals were minimum and the modes, means and medians of the posteriors were similar to the estimates using the existing classical methods. From the comparitive analysis between Bayesian and plug-in spatial predictions, we could find that the uncertainty of plug-in prediction could be slightly underestimated than that of Bayesian prediction.

• Wind and Structures
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• v.12 no.6
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• pp.553-574
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• 2009

This paper presents statistical analysis results of wind speed and atmospheric turbulence data measured from more than 30 anemometers installed at 15 different height levels on 325 m high Beijing Meteorological Tower and is primarily intended to provide useful information on boundary layer wind characteristics for wind-resistant design of tall buildings and high-rise structures. Profiles of mean wind speed are presented based on the field measurements and are compared with empirical models' predictions. Relevant parameters of atmospheric boundary layer at urban terrain are determined from the measured wind speed profiles. Furthermore, wind velocity data in longitudinal, lateral and vertical directions, which were recorded from an ultrasonic anemometer during windstorms, are analyzed and discussed. Atmospheric turbulence information such as turbulence intensity, gust factor, turbulence integral length scale and power spectral densities of the three-dimensional fluctuating wind velocity are presented and used to evaluate the adequacy of existing theoretical and empirical models. The objective of this study is to investigate the profiles of mean wind speed and atmospheric turbulence characteristics over a typical urban area.

• Journal of Korean Tunnelling and Underground Space Association
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• v.13 no.6
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• pp.531-555
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• 2011

This study aims to derive the statistical models for the estimation of the required specifications of a rock TBM as well as for its cutterhead design suitable for a given rock mass condition. From a series of multi-variate regression analysis of 871 TBM driving data and 51 linear rock cutting test results, the optimum models were newly proposed to consider a variety of rock properties and mechanical cutting conditions. When the derived models were applied to two domestic shield tunnels, their predictions of cutter penetration depth, cutter acting forces and cutter spacing were very close to real TBM driving data, showing their high applicability.

• Transactions of Materials Processing
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• v.25 no.2
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• pp.102-108
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• 2016

The aim of the current study was to develop an advanced prediction model for the slab width spread during hot rough rolling. Rough rolling consists of both vertical rolling using a set of profiled edger rolls and horizontal rolling using a set of plain work rolls. FE-simulations were performed to investigate the influences of process variables such as initial slab width, initial thickness, sizing draft, edger roll draft and work roll draft on the final slab width variation. From a statistical analysis of the simulation results, an advanced model, which can predict the slab width spread during the edger rolling and horizontal rolling, was developed. The experimental hot rolling trials showed that the newly developed model provided fairly accurate predictions on the slab width spread during hot rough rolling process using a profiled edger rolls.