• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.1-9
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• 2024

In the development and mass production of polymers, there are many uncontrollable variables. Even small changes in chemical composition, structure, and processing conditions can lead to large variations in properties. Therefore, Traditional linear modeling techniques that assume a general environment often produce significant errors when applied to field data. In this study, we propose a new modeling method (GPR-SEM) that combines Structural Equation Modeling (SEM) and Gaussian Process Regression (GPR) to study the Friction-Coefficient and Flexural-Strength properties of Polyacetal resin, an engineering plastic, in order to meet the recent trend of using plastics in industrial drive components. And we also consider the possibility of using it for materials modeling with nonlinearity.

• The Journal of the Acoustical Society of Korea
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• v.14 no.5
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• pp.44-50
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• 1995

This paper describes a large-vocabulary continuous speech recognition system using continuous hidden Markov models for the Korean language. To improve the performance of the system, we study on the selection of speech modeling units, inter-word modeling, search algorithm, and grammars. We used triphones as basic speech modeling units, generalized triphones and function word-dependent phones are used to improve the trainability of speech units and to reduce errors in function words. Silence between words is optionally inserted by using a silence model and a null transition. Word pair grammar and bigram model based oil word classes are used. Also we implement a search algorithm to find N-best candidate sentences. A postprocessor reorders the N-best sentences using word triple grammar, selects the most likely sentence as the final recognition result, and finally corrects trivial errors related with postpositions. In recognition tests using a 3,000-word continuous speech database, the system attained $93.1\%$ word recognition accuracy and $73.8\%$ sentence recognition accuracy using word triple grammar in postprocessing.

• Journal of Positioning, Navigation, and Timing
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• v.4 no.1
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• pp.33-41
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• 2015

Compact Network Real-Time Kinematic (RTK) is a method that combines compact RTK and network RTK, and it can effectively reduce the time and spatial de-correlation errors. A network RTK user receives multiple correction information generated from reference stations that constitute a network, calculates correction information that is appropriate for one's own position through a proper combination method, and uses the information for the estimation of the position. This combination method is classified depending on the method for modeling the GPS error elements included in correction information, and the user position accuracy is affected by the accuracy of this modeling. Among the GPS error elements included in correction information, tropospheric delay is generally eliminated using a tropospheric model, and a combination method is then applied. In the case of a tropospheric model, the estimation accuracy varies depending on the meteorological condition, and thus eliminating the tropospheric delay of correction information using a tropospheric model is limited to a certain extent. In this study, correction information modeling accuracy performances were compared focusing on the Low-Order Surface Model (LSM), which models the GPS error elements included in correction information using a low-order surface, and a modified LSM method that considers tropospheric delay characteristics depending on altitude. Both of the two methods model GPS error elements in relation to altitude, but the second method reflects the characteristics of actual tropospheric delay depending on altitude. In this study, the final residual errors of user measurements were compared and analyzed using the correction information generated by the various methods mentioned above. For the performance comparison and analysis, various GPS actual measurement data were collected. The results indicated that the modified LSM method that considers actual tropospheric characteristics showed improved performance in terms of user measurement residual error and position domain residual error.

• Women's Health Nursing
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• v.29 no.1
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• pp.20-31
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• 2023

Purpose: As more newborns have received expanded newborn screening (NBS) for metabolic disorders, the overall number of false-positive results has increased. The purpose of this study was to explore the psychological impacts experienced by mothers related to the NBS process. Methods: An online parenting community in Korea was selected, and questions regarding NBS were collected using web crawling for the period from October 2018 to August 2021. In total, 634 posts were analyzed. The collected unstructured text data were preprocessed, and keyword analysis, topic modeling, and visualization were performed. Results: Of 1,057 words extracted from posts, the top keyword based on 'term frequency-inverse document frequency' values was "hypothyroidism," followed by "discharge," "close examination," "thyroid-stimulating hormone levels," and "jaundice." The top keyword based on the simple frequency of appearance was "XXX hospital," followed by "close examination," "discharge," "breastfeeding," "hypothyroidism," and "professor." As a result of LDA topic modeling, posts related to inborn errors of metabolism (IEMs) were classified into four main themes: "confirmatory tests of IEMs," "mother and newborn with thyroid function problems," "retests of IEMs," and "feeding related to IEMs." Mothers experienced substantial frustration, stress, and anxiety when they received positive NBS results. Conclusion: The online parenting community played an important role in acquiring and sharing information, as well as psychological support related to NBS in newborn mothers. Nurses can use this study's findings to develop timely and evidence-based information for parents whose children receive positive NBS results to reduce the negative psychological impact.

• Atmosphere
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• v.29 no.5
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• pp.551-566
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• 2019

Quantitative understanding of a random error that is associated with Lagrangian particle dispersion modeling is a prerequisite for backward-in-time mode simulations. This study aims to quantify the random error of the WRF-FLEXPART model and suggest an optimum number of the Lagrangian particles for backward-in-time simulations over the Seoul metropolitan area. A series of backward-in-time simulations of the WRF-FLEXPART model has conducted at two receptor points by changing the number of Lagrangian particles and the relative error, as a quantitative indicator of random error, is analyzed to determine the optimum number of the release particles. The results show that in the Seoul metropolitan area a 1-day Lagrangian transport contributes 80~90% in residence time and ~100% in atmospheric enhancement of carbon monoxide. The relative errors in both the residence time and the atmospheric concentration enhancement are larger when the particles release in the daytime than in the nighttime, and in the inland area than in the coastal area. The sensitivity simulations reveal that the relative errors decrease with increasing the number of Lagrangian particles. The use of small number of Lagrangian particles caused significant random errors, which is attributed to the random number sampling process. For the particle number of 6000, the relative error in the atmospheric concentration enhancement is estimated as -6% ± 10% with reduction of computational time to 21% ± 7% on average. This study emphasizes the importance of quantitative analyses of the random errors in interpreting backward-in-time simulations of the WRF-FLEXPART model and in determining the number of Lagrangian particles as well.

• Journal of Positioning, Navigation, and Timing
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• v.2 no.2
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• pp.123-130
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• 2013

Autonomous agricultural machines that are operated in small-scale farmland frequently experience turning and changes in direction. Thus, unlike when they are operated in large-scale farmland, the steering control systems need to be controlled precisely so that travel errors can be minimized. This study aims to develop a control algorithm for improving the path tracking performance of a steering system by analyzing the effect of the setting of the waypoint, which serves as the reference point for steering when an autonomous agricultural machine moves along a path or a coordinate, on control errors. A simulation was performed by modeling a 26-hp tractor steering system and by applying the equations of motion of a tractor, with the use of a computer. Path tracking errors could be reduced using an algorithm which sets the waypoint for steering on a travel path depending on the radius of curvature of the path and which then controls the speed and steering angle of the vehicle, rather than by changing the steering speed or steering ratio which are dependent on mechanical performance.

• Journal of Korean Society for Quality Management
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• v.24 no.4
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• pp.190-206
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• 1996

In case measurements are made on units of production in time order, it is reasonable to expect that the measurement errors will sometimes be first order autocorrelated, and a technique to test such autocorrelation is required to give good control of the productive process. Tool-wear process provide an example for which regression model can sometimes be useful in modeling and controlling the process. For the control of such process, we present a simple method for testing first order autocorrelation in regression errors. The method is based on Bayesian test method via Bayes factor and derived by observing that in general, a Bayes factor can be written as the product of a quantity called the Savage-Dickey density ratio and a correction factor ; both terms are easily estimated from Gibbs sampling technique. Performance of the method is examined by means of Monte Carlo simulation. It is noted that the test not only achieves satisfactory power but eliminates the inconvenience occurred in using the well-known Durbin-Watson test.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.2 s.95
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• pp.201-210
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• 1999

One of the major limitations of productivity and quality in metal cutting is the machining accuracy of machine tools. The machining accuracy is affected by geometric and thermal errors of the machine tools. Therefore, a key requirement for improving te machining accuracy and product quality is to reduce the geometric and thermal errors of machine tools. This study models geometric error for error analysis and develops on-machine measurement system by which the volumetric erors are measured. The geometric error is modeled using form shaping function(FSF) which is defined as the mathematical relationship between form shaping motion of machine tool and machined surface. The constant terms included in the error model are found from the measurement results of on-machine measurement system. The developed on-machine measurement system consists of the spherical ball artifact (SBA), the touch probe unit with a star type stylus, the thermal data logger and the personal computer. Experiments, performed with the developed measurement system, show that the system provides a high measuring accuracy, with repeatability of ${\pm}2{\mu}m$ in X, Y and Z directions.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.1-10
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• 2001

In the reverse engineering, surfaces are modeled for new products by interpolating the digitized data points obtained by measuring the existing shapes. However, many measuring or deviation errors are happened during the measuring process. If these errors are ignored, designers could get undesirable results. Therefore, it is important to handle such errors and fairing procedure with the esthetics criteria is needed during surface modeling process. This paper presents algorithms for the fairing of B-spline surfaces. The algorithms are based on automatic repositioning of control points for B-spline surfaces. New positions of the control points are determined by solving a non-linear programming of which the objective functions are derived variously using derived surfaces and constraints are established by distance measures between the original and the modified control points. Changes in surface shapes are analyzed by illustrations of their shapes and continuous plotting of gaussian and mean curvatures.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.2
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• pp.168-176
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• 2015

Due to the reduction in device feature size, transient faults (soft errors) in logic circuits induced by radiations increase dramatically. Many researches have been done in modeling and analyzing the susceptibility of sequential circuit elements caused by soft errors. However, to the best knowledge of the authors, there is no work which has well considerated the feedback characteristics and the multiple clock cycles of sequential circuits. In this paper, we present a new method for evaluating the susceptibility of sequential circuit elements to soft errors. The proposed method uses four Error Propagation Probability Matrixs (EPPMs) to represent the error propagation probability of logic gates and flip-flops in current clock cycle. Based on the predefined matrix union operations, the susceptibility of circuit elements in multiple clock cycles can be evaluated. Experimental results on ISCAS'89 benchmark circuits show that our method is more accurate and efficient than previous methods.