• Journal of Forest and Environmental Science
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• v.32 no.3
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• pp.280-290
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• 2016

Stem diameter variability is an essential inventory result that provides useful information in forest management decisions. Little has been done to explore the modelling potentials of standard deviation (SDD) and coefficient of variation (CVD) of diameter at breast height (dbh). This study, therefore, was aimed at developing and testing models for predicting SDD and CVD in stands of Pinus caribaea Morelet (pine) in south west Nigeria. Sixty temporary sample plots of size $20m{\times}20m$, ranging between 15 and 37 years were sampled, covering the entire range of pine in south west Nigeria. The dbh (cm), total and merchantable heights (m), number of stems and age of trees were measured within each plot. Basal area ($m^2$), site index (m), relative spacing and percentile positions of dbh at $24^{th}$, $63^{rd}$, $76^{th}$ and $93^{rd}$ (i.e. $P_{24}$, $P_{63}$, $P_{76}$ and $P_{93}$) were computed from measured variables for each plot. Linear mixed model (LMM) was used to test the effects of locations (fixed) and plots (random). Six candidate models (3 for SDD and 3 for CVD), using three categories of explanatory variables (i.e. (i) only stand size measures, (ii) distribution measures, and (iii) combination of i and ii). The best model was chosen based on smaller relative standard error (RSE), prediction residual sum of squares (PRESS), corrected Akaike Information Criterion ($AIC_c$) and larger coefficient of determination ($R^2$). The results of the LMM indicated that location and plot effects were not significant. The CVD and SDD models having only measures of percentiles (i.e. $P_{24}$ and $P_{93}$) as predictors produced better predictions than others. However, CVD model produced the overall best predictions, because of the lower RSE and stability in measuring variability across different stand developments. The results demonstrate the potentials of CVD in modelling stem diameter variability in relationship with percentiles variables.

• Steel and Composite Structures
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• v.51 no.4
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• pp.351-361
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• 2024

Accurate measurement of K_IC values for gas pipeline steels is important for assessing pipe safety using failure assessment diagrams. As direct measurement of K_IC was impossible for the API X70 pipeline steel, multi-specimen fracture tests were conducted to measure J_IC using three-point bend geometry. The J values were calculated from load-displacement (F-δ) plots, and the associated crack extensions were measured from the fracture surface of test specimens. Valid data points were found for the constructed J-Δa plot resulting in J_IC=356kN/m. More data points were added analytically to the J-Δa plot to increase the number of data points without performing additional experiments for different J-Δa zones where test data was unavailable. Consequently, displacement (δ) and crack-growth (Δa) from multi-specimen tests (with small displacements) were used simultaneously, resulting in the variation of Δa-δ (crack growth law) and δ-Δa obtained for this steel. For new Δa values, corresponding δ values were first calculated from δ-Δa. Then, corresponding J values for the obtained δ values were calculated from the area under the F-δ record of a full-fractured specimen (with large displacement). Given Δa and J values for new data points, the developed J-Δa plot with extra data points yielded a satisfactory estimation of J_IC=345kN/m with only a -3.1% error. This is promising and showed that the developed technique could ease the estimation of J_IC significantly and reduce the time and cost of expensive extra fracture toughness tests.

• Journal of Environmental Impact Assessment
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• v.29 no.4
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• pp.272-285
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• 2020

We investigated statistical evaluation parameters for 3D meteorological and air quality models and selected several quantitative indicator references, and summarized the reference values of the statistical parameters for domestic air quality modeling researcher. The finally selected 9 statistical parameters are MB (Mean Bias), ME (Mean Error), MNB (Mean Normalized Bias Error), MNE (Mean Absolute Gross Error), RMSE (Root Mean Square Error), IOA (Index of Agreement), R (Correlation Coefficient), FE (Fractional Error), FB (Fractional Bias), and the associated reference values are summarized. The results showed that MB and ME have been widely used in evaluating the meteorological model output, and NMB and NME are most frequently used for air quality model results. In addition, discussed are the presentation diagrams such as Soccer Plot, Taylor diagram, and Q-Q (Quantile-Quantile) diagram. The current results from our study is expected to be effectively used as the statistical evaluation parameters suitable for situation in Korea considering various characteristics such as including the mountainous surface areas.

• Journal of dental hygiene science
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• v.16 no.4
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• pp.257-262
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• 2016

The purpose of this study was to assess the correlation between integrated mineral loss (volume % mineral${\times}{\mu}m$, ${\Delta}Z_{TMR}$) determined using transverse microradiography (TMR) and integrated reflectivity ($dB{\times}{\mu}m$, ${\Delta}R_{OCT}$) determined using optical coherence tomography (OCT) for detecting early dental caries with lesion depth more than $200{\mu}m$. Sixty tooth specimens were made from sound bovine teeth. They were immersed in a demineralized solution for 20, 30, and 40 days. The ${\Delta}R_{OCT}$ was obtained from the cross-sectional OCT image. The ${\Delta}Z_{TMR}$ was obtained from the TMR image. The correlation between ${\Delta}R_{OCT}$ and ${\Delta}Z_{TMR}$ was examined using Pearson correlation. The Bland-Altman plot was constructed using the ${\Delta}R_{OCT}$ and ${\Delta}Z_{TMR}$ values. A significant correlation between ${\Delta}R_{OCT}$ and ${\Delta}Z_{TMR}$ was confirmed (r=0.491, p=0.003). Moreover, most of the difference between ${\Delta}R_{OCT}$ and ${\Delta}Z_{TMR}$ was included in the error section of the Bland-Altman plot. Therefore, OCT could be used as a substitute for TMR when analyzing mineral loss in early dental caries.

• Journal of Ocean Engineering and Technology
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• v.34 no.3
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• pp.167-179
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• 2020

Recently, the increasing importance of artificial intelligence (AI) technology has led to its increased use in various fields in the shipbuilding and marine industries. For example, typical scenarios for AI include production management, analyses of ships on a voyage, and motion prediction. Therefore, this study was conducted to predict a response amplitude operator (RAO) through AI technology. It used a neural network based on one of the types of AI methods. The data used in the neural network consisted of the properties of the vessel and RAO values, based on simulating the in-house code. The learning model consisted of an input layer, hidden layer, and output layer. The input layer comprised eight neurons, the hidden layer comprised the variables, and the output layer comprised 20 neurons. The RAO predicted with the neural network and an RAO created with the in-house code were compared. The accuracy was assessed and reviewed based on the root mean square error (RMSE), standard deviation (SD), random number change, correlation coefficient, and scatter plot. Finally, the optimal model was selected, and the conclusion was drawn. The ultimate goals of this study were to reduce the difficulty in the modeling work required to obtain the RAO, to reduce the difficulty in using commercial tools, and to enable an assessment of the stability of medium/small vessels in waves.

• Journal of Forest and Environmental Science
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• v.28 no.2
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• pp.63-67
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• 2012

Crown ratio is the ratio of live crown length to tree height. It is often used as an important predictor variable for tree growth equation. It indicates tree vigor and is a useful parameter in forest health assessment. The objective of the study was to develop crown ratio prediction models for Tectona grandis. Based on the data set from the temporary sample plots, several non linear equations including logistics, Chapman Richard and exponential functions were tested. These functions were evaluated in terms of coefficient of determination ($R^2$) and standard error of the estimate (SEE). The significance of the estimated parameters was also verified. Plot of residuals against estimated crown ratios were observed. Although the logistic model had the highest $R^2$ and the least SEE, Chapman-Richard and Exponential functions were observed to be more consistent in their predictive ability; and were therefore recommended for predicting crown ratio in the stand.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.172.6-172
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• 2001

In recent years, many attempts have been made to predict the behavior of bonds, currencies, stock, or other economic markets. Most previous experiments used multilayer perceptrons(MLP) for stock market forecasting, The Kospi 200 Index is modeled using different neural networks and fuzzy system predictions. In this paper, a multilayer perceptron architecture, a dynamic polynomial neural network(DPNN) and a fuzzy system are used to predict the Kospi 200 index. The results of prediction is compared with the root mean squared error(RMSE) and the scatter plot. The results show that the fuzzy system is performing slightly better than DPNN and MLP. We can develop the desired fuzzy system by learning methods ...

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1989.10a
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• pp.65-70
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• 1989

Until recently post-processing of finite element model has been Heavily relied on expensive graphic peripheral devices. For this reason many engineers and researchers can not afford to access to the graphics. with the aid of inexpensive personal computers very econmical post-processor graphics program called MICRO-POST has been developed in conjunction with low-cost printers and plotters. Model geometry or results of analysis for the unlimitted meshes either produced by mainframe or microcomputer can be easily and economi-call presented in a number of different graphic devices. The paper presents the procedure obtaining the device the independent graphics, and the structure and functions of the program. It also describes a new error-preventive dialogue type input technique to control the plot operation in an interactive manner. Through the post-processing examples for the general purpose finite element programs, it demonstrates the usefulness of the program.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.7-12
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• 2003

In recent years, many attempts have been made to predict the behavior of bonds, currencies, stock or other economic markets. Most previous experiments used the neural network models for the stock market forecasting. The KOSPI 200 (Korea Composite Stock Price Index 200) is modeled by using different neural networks and fuzzy logic. In this paper, the neural network, the dynamic polynomial neural network (DPNN) and the fuzzy logic employed for the prediction of the KOSPI 200. The prediction results are compared by the root mean squared error (RMSE) and scatter plot, respectively. The results show that the performance of the fuzzy system is little bit worse than that of the DPNN but better than that of the neural network. We can develop the desired fuzzy system by optimization methods.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.37-39
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• 2014

현대에 집필되는 소설이나 시나리오 등의 스토리는 점점 플롯이 복합해지고 캐릭터가 입체적으로 변해가고 있다. 여러 개의 이야기가 동시다발로 진행되고, 다양한 캐릭터들이 복잡한 관계로 얽히면서 갈등 구조를 형성하기 때문에 작가는 창작 과정에서 작품 속 캐릭터들에 대한 정보를 정확하게 인지하고 관리해야 하는 부담이 점점 커지고 있다. 창작 과정에서 생산되는 수많은 정보들에 대한 작가의 인지적 부담은 정보에 대한 작가의 잘못된 기억과 혼란을 초래할 수 있으며, 이는 결국 작품의 완전성과 무결성을 저해하는 요인이 된다. 따라서 본 논문에서는 작가가 작성한 원고를 분석해 캐릭터의 대화 이력을 추적하고, 캐릭터가 아는 정보와 모르는 정보가 무엇인지 추적하여 작품의 모순을 막고 작가의 창작 활동을 도와주는 작품 분석 시스템을 소개한다.