• The Korean Journal of Applied Statistics
• /
• v.30 no.5
• /
• pp.633-645
• /
• 2017

This study proposes a statistical procedure for analyzing container ship operation data that can help determine fuel consumption patterns. We first investigate the features that affect fuel consumption and develop the prediction model to find current fuel consumption. The ship data can be divided into two-type data. One set of operation data includes sea route, voyage information, longitudinal water speed, longitudinal ground speed, and wind, the other includes machinery data such as engine power, rpm, fuel consumption, temperature, and pressure. In this study, we separate the effects of external force on ships according to Beaufort Scale and apply a partial least squares regression to develop a prediction model.

• Bulletin of the Society of Naval Architects of Korea
• /
• v.21 no.4
• /
• pp.40-48
• /
• 1984

The computer program DASH(Direct Analysis of Ship's Hull), based on the direct calculating procedure as proposed at the 4th ISSC(1970), was developed. The DASH program is designed by the following calculation procedure: 1) Derivation of the design wave loads through the ship motion analysis based on the strip theory. 2) Stress analysis of the hull girder based on the 7-degree of the freedom beam theory including the warping torsion effect. 3) Long-term prediction of the stresses based on the statistical approach using sea-spectrums and ocean wave data in the ship's route. An example calculation was performed for the purpose of a demonstration of the present approach on the 16,200 DWT Oil Tanker. The results are discussed and compared with the conventional method.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.45 no.6
• /
• pp.183-193
• /
• 2003

Most construction works on the soft ground adopt instrumentation to manage settlement and stability of the embankment. The rapid progress of the information technologies and the digital data acquisition on the soft ground instrumentation has led to the fast-growing amount of data. Although valuable information about the behaviour of the soft ground may be hiding behind the data, most of the data are used restrictedly only for the management of settlement and stability. One of the critical issues on soft ground instrumentation is the long-term settlement prediction. Some observational settlement analysis methods are used for this purpose. But the reliability of the analysis results is remained in vague. The knowledge could be discovered from a large volume of experiences on the observational settlement analysis. In this article, we present a database to store settlement records and data mining procedure. A large volume of knowledge about observational settlement prediction were collected from the database by applying the filtering algorithm and knowledge discovery algorithm. Statistical analysis revealed that the reliability of observational settlement analysis depends on stay duration and estimated degree of consolidation.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 1999.08a
• /
• pp.438-446
• /
• 1999

Crop minimization of the top and bottom ends of hot rolled plate, in a plate, in a plate mill, has been investigated. The existing model to determine the edging pattern at the finishing rolling pass was not reasonable to get high width accuracy and rolling yields. New models including width prediction have been formulated by using neural network model of back propagation learning algorithm and statistical analysis based on the actual production rolling data to give the optimal pattern for minimizing trimming loss. Using these models, at a given rolling condition of broadside pass and finishing pass and the permissible condition of width variation, it was possible to minimize crip at the top and bottom ends according to optimum procedure in plate mill. An application to improve the plan view pattern reduced width variation by 23% and crop length by 30% on average with an effective fishtail crop shape.

• Proceedings of the Korean Reliability Society Conference
• /
• 2006.05a
• /
• pp.158-164
• /
• 2006

The creep behavior of newly developed composite geogrids which consists of PET yarns sheathed in PP were evaluated using SIM. For the SIM procedure, three test parameters, the applied loads, temperature steps and number of ribs were investigated, The study confirmed that temperature steps of 10 and 14$^{\circ}C$ up to 80$^{\circ}C$ are applicable for composite geogrids due to the different transition temperatures between two materials. At applied loads of 40 and 50%, only primary creep state was measured, while secondary creep state appeared at the applied loads of 60%, The lifetimes of composite geogrids were estimated at each of loading level using statistical reliability analysis technique. The results show that the lifetimes longer than 100 years can be predicted within 16 hours. Therefore, SIM is very effective and economical accelerated creep test methods, especially for lifetime prediction. This gives guidelines for users to select the appropriate factor of safety against creep considering the field condition within shorter test times.

• Communications for Statistical Applications and Methods
• /
• v.17 no.5
• /
• pp.697-712
• /
• 2010

This paper is concerned with the statistical analysis and development of stochastic models for the demand for life insurance policy loans. For these, firstly the characteristics of the regression trend, periodicity and dependence of the monthly demand for life insurance policy loans are investigated by a statistical analysis of the monthly demand data for the years 1999 through 2008. Secondly, the causal relationships between the demand for life insurance policy loans and the economic variables including unemployment rate and inflation rate for the period are investigated. The results show that inflation rate is main factor influencing policy loan demands. The overall evidence, however, failed to establish unidirectional causality relationships between the demand series and the other variables under study. Finally, based on these, univariate time series model and transfer function model where the demand series is related to one input series are derived, respectively, for the prediction of the demand for life insurance policy loans. A statistical procedure for using the model to predict the demand for life insurance policy loans is also proposed.

• Journal of Korean Society for Atmospheric Environment
• /
• v.7 no.2
• /
• pp.127-136
• /
• 1991

This paper is concerned with the development and application of a stochastic model for daily sulphur dioxide $(SO_2)$ concentrations in urban area (Seoul). For this, the characteristics of the regression trend, periodicity and dependence of the daily $SO_2$ concentration are investigated by a statistisical analysis of the daily average $SO_2$ values measured in Seoul area during 1989 $\sim$ 1990. Based on these, nonlinear regression time series model for the prediction of daily $SO_2$ concentrations is derived. A statistical procedure for using the model to predict the concentration level is also proposed.

• Journal of Intelligence and Information Systems
• /
• v.5 no.1
• /
• pp.95-101
• /
• 1999

In this paper, a comparison between different methods to combine predictions from neural networks will be given. These methods are bagging, bumping, and balancing. Those are based on the analysis of the ensemble generalization error into an ambiguity term and a term incorporating generalization performances of individual networks. Neural Networks and AI machine learning models are prone to overfitting. A strategy to prevent a neural network from overfitting, is to stop training in early stage of the learning process. The complete data set is spilt up into a training set and a validation set. Training is stopped when the error on the validation set starts increasing. The stability of the networks is highly dependent on the division in training and validation set, and also on the random initial weights and the chosen minimization procedure. This causes early stopped networks to be rather unstable: a small change in the data or different initial conditions can produce large changes in the prediction. Therefore, it is advisable to apply the same procedure several times starting from different initial weights. This technique is often referred to as training ensembles of neural networks. In this paper, we presented a comparison of three statistical methods to prevent overfitting of neural network.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.10a
• /
• pp.365-373
• /
• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Proceedings of the Korea Concrete Institute Conference
• /
• 1998.04a
• /
• pp.347-352
• /
• 1998

The purpose of this paper is to present and establish a procedure to predict the prestress forces during the service life of the structure. The statistical approach of this procedure is using the in-situ measurement data of the post-tensioning system to develop a nonlinear regression analysis. The method of least squares is used to fit a certain function a set of data. Use of a nonlinear model is achieved by its logarithmic transformation and sunsequent use of linear-regression theory. The regression analysis result can be used to check the prestress force during the service life so that the remaining prestress force is equal to or exceeds the design requirement. Results from the measurement data of PSC box girder bridge structure were used to demonstrate the procedures.