• Analytical Science and Technology
• /
• v.37 no.1
• /
• pp.12-24
• /
• 2024

In this study, laser-induced breakdown spectroscopy (LIBS) was used to estimate the degree of rice polishing. As-threshed rice seeds were dehusked and polished for different times, and the resulting grains were analyzed using LIBS. Various atomic, ionic, and molecular emissions were identified in the LIBS spectra. Their correlation with the amount of polished-off matter was investigated. Na I and Rb I emission line intensities showed linear sensitivity in the widest range of polished-off-matter amount. Thus, univariate models based on those lines were developed to predict the weight percent of polished-off matter and showed 3-5 % accuracy performances. Partial least squares-regression (PLS-R) was also applied to develop a multivariate model using Si I, Mg I, Ca I, Na I, K I, and Rb I emission lines. It outperformed the univariate models in prediction accuracy (2 %). Our results suggest that LIBS can be a reliable tool for authenticating the degree of rice polishing, which is closed related to nutrition, shelf life, appearance, and commercial value of rice products.

• Journal of the Ergonomics Society of Korea
• /
• v.22 no.4
• /
• pp.59-78
• /
• 2003

Most regression models of anthropometric variables use stature and/or weight as regressors; however, these 'flat' regression models result in large errors for anthropometric variables having low correlations with the regressors. To develop more accurate regression models for anthropometric variables, this study proposed a method to estimate anthropometric variables in a hierarchical manner based on the relationships among the variables and a process to develop and improve corresponding regression models. By applying the proposed approach, a hierarchical estimation structure was constructed for 59 anthropometric variables selected for the occupant package design of a passenger car and corresponding regression models were developed with the 1988 US Army anthropometric survey data. The hierarchical regression models were compared with the corresponding flat regression models in terms of accuracy. As results, the standard errors of the hierarchical regression models decreased by 28% (4.3mm) on average compared with those of the flat models.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.12 no.5
• /
• pp.163-169
• /
• 2013

The objective of this study is to replace labor intensive forging processes by an automated system. For achieving it, an exclusive mechanism that consists of a positioner, an arm, and a hanger is configured to handle hot forging objects. Also, a structural analysis is applied to the horizontal motion unit, which is the most highly loaded, in order to verify its validity. In addition, its possibility is also verified through identifying the performance of the proposed system before applying it to sites. As a result, the major characteristic items, such as positioning accuracy, material diameter, object traveling weight, product failure rate, and forging process rate, in this system are perfectly verified for applying it to manufacturing sites.

• Computers and Concrete
• /
• v.11 no.6
• /
• pp.603-620
• /
• 2013

This paper improves genetic programming (GP) and weight genetic programming (WGP) and proposes soft-computing polynomials (SCP) for accurate prediction and visible polynomials. The proposed genetic programming system (GPS) comprises GP, WGP and SCP. To represent confined compressive strength and strain of circular concrete columns in meaningful representations, this paper conducts sensitivity analysis and applies pruning techniques. Analytical results demonstrate that all proposed models perform well in achieving good accuracy and visible formulas; notably, SCP can model problems in polynomial forms. Finally, concrete compressive strength and lateral steel ratio are identified as important to both confined compressive strength and strain of circular concrete columns. By using the suggested formulas, calculations are more accurate than those of analytical models. Moreover, a formula is applied for confined compressive strength based on current data and achieves accuracy comparable to that of neural networks.

• Journal of Information Processing Systems
• /
• v.15 no.1
• /
• pp.116-126
• /
• 2019

In this paper, an improved one-against-one support vector machine algorithm is used to classify multiple power quality disturbances. To solve the problem of parameter selection, an improved particle swarm optimization algorithm is proposed to optimize the parameters of the support vector machine. By proposing a new inertia weight expression, the particle swarm optimization algorithm can effectively conduct a global search at the outset and effectively search locally later in a study, which improves the overall classification accuracy. The experimental results show that the improved particle swarm optimization method is more accurate than a grid search algorithm optimization and other improved particle swarm optimizations with regard to its classification of multiple power quality disturbances. Furthermore, the number of support vectors is reduced.

• Structural Monitoring and Maintenance
• /
• v.10 no.2
• /
• pp.175-190
• /
• 2023

This article aims to quantitatively predict the snowmelt in extreme cold regions, considering a combination of grayscale and neural models. The traditional non-equidistant GM(1,1) prediction model is optimized by adjusting the time-distance weight matrix, optimizing the background value of the differential equation and optimizing the initial value of the model, and using the BP neural network for the first. The adjusted ice forecast model has an accuracy of 0.984 and posterior variance and the average forecast error value is 1.46%. Compared with the GM(1,1) and BP network models, the accuracy of the prediction results has been significantly improved, and the quantitative prediction of the ice sheet is more accurate. The monitoring and maintenance of the structure by quantitative prediction model by gray models was clearly demonstrated in the model.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.34 no.7
• /
• pp.71-78
• /
• 2006

Sun sensors are frequently implemented by satellites for attitude sensing, due to its simple manufacturability and light weight. A modeling of sun sensors has an important effect on the accuracy of satellite attitude determination. This paper addresses a new modeling of a 2-axis miniature fine sun sensor with improved accuracy. Unlike other previous algebraic modeling methods, the newly suggested physical modeling method takes into account the shadowing effect of the slit thickness. It was shown that a newly proposed sun sensor modeling provides a substantial accuracy improvement of 29% compared to the generic algebraic modeling. The proposed sensor modeling was validated using 2-axis fine sun sensors with FOV(Field of View) of ${\pm}60^{\circ}$ mounted on HAUSAT-2 small satellite, currently under development by SSRL(Space System Research Lab.) at Hankuk Aviation University, Korea.

• Structural Engineering and Mechanics
• /
• v.38 no.5
• /
• pp.651-674
• /
• 2011

This paper presents a novel method for predicting the failure probability of structural or mechanical systems subjected to random loads and material properties involving multiple design points. The method involves Multicut High Dimensional Model Representation (Multicut-HDMR) technique in conjunction with moving least squares to approximate the original implicit limit state/performance function with an explicit function. Depending on the order chosen sometimes truncated Cut-HDMR expansion is unable to approximate the original implicit limit state/performance function when multiple design points exist on the limit state/performance function or when the problem domain is large. Multicut-HDMR addresses this problem by using multiple reference points to improve accuracy of the approximate limit state/performance function. Numerical examples show the accuracy and efficiency of the proposed approach in estimating the failure probability.

• Journal of Advanced Marine Engineering and Technology
• /
• v.38 no.4
• /
• pp.444-450
• /
• 2014

Most researches on word sense disambiguation have used static sized context regardless of sentence patterns. This paper proposes to use dynamic sized context considering sentence patterns and distance between words for word sense disambiguation. We evaluated our system 12 words in 32,735sentences with Sejong POS and sense tagged corpus, and dynamic sized context showed 92.2% average accuracy for predicates, which is better than accuracy of static sized context.

• IE interfaces
• /
• v.11 no.3
• /
• pp.15-22
• /
• 1998

A new improved algorithm in edge location to subpixel accuracy using decent-based weight to spatial information is proposed in this paper and applied to automated visual inspection(AVI) system. An application of the new edge operator as an edge detector is also provided and compared with Tabatabai and Lyvers edge detectors. The existing algorithms located edger to subpixel accuracy using least-square or moment-based methods. The algorithms also use only spatial information or grey-level values to locate edges. However, the proposed algorithm consider the weighted sum of grey-levels values of each edge pattern. The results show that the proposed algorithm is relatively less biased and has smaller standard deviation than the edge operations developed by Tabatabai and Lyvers in the presence of noise.