• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.1
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• pp.32-40
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• 2018

In this research, a method of deriving statistical weight prediction formula which is used during the conceptual design phase was studied and it was programmed using Microsoft Excel and verified by applying to jet transport aircraft. The database was built while referencing the variables of conventional wing weight estimation formulas and it was used for modeling the jet transport wing weight regression equation. The model was evaluated using the K-fold cross validation method to solve the overfitting problem of the model.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.1
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• pp.39-58
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• 2024

Tunnel Boring Machines (TBM) use multiple disc cutters to excavate tunnels through rock. These cutters wear out due to continuous contact and friction with the rock, leading to decreased cutting efficiency and reduced excavation performance. The rock's abrasivity significantly affects cutter wear, with highly abrasive rocks causing more wear and reducing the cutter's lifespan. The Cerchar Abrasivity Index (CAI) is a key indicator for assessing rock abrasivity, essential for predicting disc cutter life and performance. This study aims to develop a new method for effectively estimating CAI using rock strength, petrological characteristics, linear regression, and machine learning. A database including CAI, uniaxial compressive strength, Brazilian tensile strength, and equivalent quartz content was created, with additional derived variables. Variables for multiple linear regression were selected considering statistical significance and multicollinearity, while machine learning model inputs were chosen based on variable importance. Among the machine learning prediction models, the Gradient Boosting model showed the highest predictive performance. Finally, the predictive performance of the multiple linear regression analysis and the Gradient Boosting model derived in this study were compared with the CAI prediction models of previous studies to validate the results of this research.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.4 no.2
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• pp.1-11
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• 1984

This aims of this study is to analyse the correlationship between traffic accident s and traffic characteristic variables in at-grade intersections of urban area, to build up an accident forecasting model and to propose an evaluation method of hazardous at-grade intersections. The accident forecasting model is formulated by the use of residual indexes that is selected by principal component analysis and its statistical significance is tested by step-wise regression analysis. Effective countermeasures for safety can be established on the basis of identifying high accident intersections, because the validity of this model was examined and found to coincide with real world situations.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.103-110
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• 2005

Software Development is converting from structural to object oriented method. The later software development prefers the iterative process applications, not aterfall process and based on use case model, the requirements are expressed and based on this, analysis, design and coding are accomplished. Therefore, size of the software to be developed is estimated basing on use case and it is only possible to maintain the project success by estimating development effort, cost and development period. Even though development effort estimation models related current use case point. there is no appropriate development effort estimating. This paper shows, as a result of applying the development effort estimating model about UCP to the growth curve, a superior performance improvement to current statistical models. Therefore, estimation of development effort by applying this model, project development maintenance can be appropriately carried out.

• The Journal of Engineering Geology
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• v.19 no.2
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• pp.177-189
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• 2009

The shear strength has been managed as an important factor in soil mechanics. The shear strength estimation model was developed to evaluate the shear strength using only a few soil properties by the linear regression analysis model which is one of the statistical methods. The shear strength is divided into two part; one is the internal friction angle (${\phi}$) and the other is the cohesion (c). Therefore, some valid soil factors among the results of soil tests are selected through the correlation analysis using SPSS and then the model are formulated by the linear regression analysis based on the relationship between factors. Also, the developed model is compared with the result of direct shear test to prove the rationality of model. As the results of analysis about relationship between soil properties and shear strength, the internal friction angle is highly influenced by the void ratio and the dry unit weight and the cohesion is mainly influenced by the void ratio, the dry unit weight and the plastic index. Meanwhile, the shear strength estimated by the developed model is similar with that of the direct shear test. Therefore, the developed model may be used to estimate the shear strength of soils in the same condition of study area.

• The Journal of Bigdata
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• v.1 no.1
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• pp.9-17
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• 2016

This study identifies major features in water supply and introduces important factors in water services based on the information from data mining analysis of water quantity and water pressure measured from sensors. It also suggests more accurate methods using multiple regression analysis and neural network in predicting short term prediction of water demand in water service. A small block of a county is selected for the data collection and tests. There isa water demand on business such as public offices and hospitalstoo in this area. Real stream data from sensors in this area is collected. Among 2,728 data sets collected, 2,632 sets are used for modelling and 96 sets are used for testing. The shows that neural network is better than multiple regression analysis in their prediction performance.

• The Journal of Engineering Geology
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• v.14 no.4 s.41
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• pp.443-450
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• 2004

This study developed a prediction model of debris flow to predict a landslide probability on natural terrain composed of the Tertiary sedimentary and volcanic rocks using a logistic regression analysis. The landslides data were collected around Pohang, Gyeongbuk province where more than 100 landslides were occurred in 1998. Considered with basic characteristics of the logistic regression analysis, field survey and laboratory soil tests were performed for both slided points and not-slided points. The final iufluential factors on landslides were selected as six factors by the logistic regression analysis. The six factors are composed of two topographic factors and four geologic factors. The developed landslide prediction model has more than $90\%$ of prediction accuracy. Therefore, it is possible to make probabilistic and quantitative prediction of landslide occurrence using the developed model in this study area as well as the previously developed model for metamorphic and granitic rocks.

• The Journal of Engineering Geology
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• v.21 no.4
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• pp.361-367
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• 2011

The uniaxial compressive strength (UCS) is an important factor in the design and construction of surface and underground structures. However, the method employed to measure UCS is time consuming and expensive to apply in the field. Therefore, we developed a model to estimate UCS based on a few properties using linear regression analysis, which is a statistical method. To develop the model, valid factors from the test results were selected from a correlation analysis using a statistical program, and the model was formulated by linear regression based on the relationships among factors. UCS estimates derived from the model were compared with the results of UCS tests, to assess the reliability of the model. The relationship between rock properties and UCS indicates that the factors with the greatest influence on UCS are point load strength and shape facto r. The UCS values obtained using the model are in good agreement with the results of the UCS test. Therefore, the developed model may be used to estimate the UCS of rocks in regions with similar conditions to those of the present study area.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.61-69
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• 2023

Recently, as the fishery resources are depleted, expectations for productivity improvement by 'rearing fishery' in land farms are greatly rising. In the case of land farms, unlike ocean environments, it is easy to control and manage environmental and breeding factors, and has the advantage of being able to adjust production according to the production plan. On the other hand, unlike in the natural environment, there is a disadvantage in that operation costs may significantly increase due to the artificial management for fish growth. Therefore, profit maximization can be pursued by efficiently operating the farm in accordance with the planned target shipment. In order to operate such an efficient farm and nurture fish, an accurate growth prediction model according to the target fish species is absolutely required. Most of the growth prediction models are mainly numerical results based on statistical analysis using farm data. In this paper, we present a growth prediction model from a stochastic point of view to overcome the difficulties in securing data and the difficulty in providing quantitative expected values for inaccuracies that existing growth prediction models from a statistical point of view may have. For a stochastic approach, modeling is performed by introducing a Gaussian process regression method based on water temperature, which is the most important factor in positive growth. From the corresponding results, it is expected that it will be able to provide reference values for more efficient farm operation by simultaneously providing the average value of the predicted growth value at a specific point in time and the confidence interval for that value.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2007.12a
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• pp.259-260
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• 2007

The forecast of a container traffic has been very important for port plan and development Generally, statistic methods, such as regression analysis, ARIMA, have been much used for traffic forecasting. Recent research activities in forecasting with artificial neural networks(ANNs) suggest tint ANNs am be a promising alternative to the traditional linear methods. In this paper, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. The results with port traffic data indicate tint effectiveness can differ according to the ch1racteristics of ports.