• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.367-372
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• 1998

In this paper, the mathematical model of a cut with an inverted flexible beam and a concentrated tip mass was derived. The characteristic equation for calculating the natural frequencies of the cart-beam-mass system was obtained and the motion of the system was analyzed through unconstrained modal analysis. A good positioning response of the cart without excessive vibrational motion of the tip mass could be obtained through numerical simulation using PID controller with the feedback of both the position of the cart and the deflection of the beam.

• Speech Sciences
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• v.14 no.2
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• pp.91-103
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• 2007

The purpose of this paper is to develop a model enabling to predict the prosody of Korean text-to-speech synthesis using the CART and SKES algorithms. CART prefers a prediction variable in many instances. Therefore, a partition method by F-Test was applied to CART which had reduced the number of instances by grouping phonemes. Furthermore, the quality of the text-to-speech synthesis was evaluated after applying the SKES algorithm to the same data size. For the evaluation, MOS tests were performed on 30 men and women in their twenties. Results showed that the synthesized speech was improved in a more clear and natural manner by applying the SKES algorithm.

• Journal of the Ergonomics Society of Korea
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• v.1 no.2
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• pp.3-9
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• 1982

This study demonstrates that certain personal and task factors can be modelled to predict slip potential as well as back loadings durings dynamic pushing and pulling tasks. Such tasks are com- mon to many manual material handling jobs in industry and the results of this work will hopefully be of use in improved job design. The objective of this research is to formulate and validate a dynamic biomechanical model of pushing and pulling a cart. For pushing and pulling tasks, the model can : (1) estimate foot forces for given hand forces, and (2) estimate tors muscle and vertabral column loadings. In order to formulate and validate the model, experiments involving pushing and pulling of a cart were performed. These experiments produced data of the following type : (1) dynamic forces on the feet, (2) hand forces required to move the cart, (3) body motions as functions of various cart motion and (4) back muscle actions. The model was validated using three different methods; precision was tested using correlation between predicted and measured results, accuracy using standard error between of predicted and measured results, and intuitive comparison of predicted results using sensitivity analyses.

• Proceedings of the Korean Nuclear Society Conference
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• 2003.05a
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• pp.54.1-54.1
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• 2003

• Proceedings of the Korean Nuclear Society Conference
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• 2002.10a
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• pp.52-52
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• 2002

• Proceedings of the Korean Nuclear Society Conference
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• 2002.10a
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• pp.53-53
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• 2002

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.343-354
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• 2005

The goal of this paper is to compare classification performances and to find a better classifier based on the characteristics of data. The compared methods are CART with two ensemble algorithms, bagging or boosting and SVM. In the empirical study of twenty-eight data sets, we found that SVM has smaller error rate than the other methods in most of data sets. When comparing bagging, boosting and SVM based on the characteristics of data, SVM algorithm is suitable to the data with small numbers of observation and no missing values. On the other hand, boosting algorithm is suitable to the data with number of observation and bagging algorithm is suitable to the data with missing values.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.149-161
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• 2006

The most important component in decision tree algorithm is the rule for split variable selection. Many earlier algorithms such as CART and C4.5 use greedy search algorithm for variable selection. Recently, many methods were developed to cope with the weakness of greedy search algorithm. Most algorithms have different selection criteria depending on the type of variables: continuous or nominal. However, ordinal type variables are usually treated as continuous ones. This approach did not cause any trouble for the methods using greedy search algorithm. However, it may cause problems for the newer algorithms because they use statistical methods valid for continuous or nominal types only. In this paper, we propose a ordinal variable selection method that uses Cramer-von Mises testing procedure. We performed comparisons among CART, C4.5, QUEST, CRUISE, and the new method. It was shown that the new method has a good variable selection power for ordinal type variables.

• Safety and Health at Work
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• v.11 no.3
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• pp.307-313
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• 2020

Background: Many tasks in industrial and health care setting are involved with pushing and pulling tasks up or down on a ramp. An efficient method of moving cart which reduces the risk of low back pain should be concerned. This study aimed to investigate the effects of handling types (HTs) and slope on lumbar spinal load during moving a cart on a ramp. We conducted a 2 × 2 × 4 factorial design with three main factors: 2 HTs, 2 handling directions of moving a cart and 4 degrees of ramp slope. Methods: Thirty healthy male workers performed 14 tasks consist of moving a cart up and down on the ramp of 0°, 10°, 15°, and 20° degrees with pushing and pulling methods. Joint angles from a 3D motion capture system combined with subject height, body weight, and hand forces were used to calculate the spinal load by the 3DSSPP program. Results: Our results showed significant effect of HT, handling directions and slope on compression and shear force of the lumbar spine (p < 0.001). When the ramp gradient increased, the L4/5 compression forces increased in both pushing and pulling (p < 0.001) Shear forces increased in pulling and decreased in pushing in all tasks. At high slopes, pulling generated more compression and shear forces than that of pushing (p < 0.01). Conclusion: Using the appropriate technique of moving a cart on the ramp can reduce the risk of high spinal load, and the pushing is therefore recommended for moving a cart up/down on ramp gradients.

• Journal of Korea Water Resources Association
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• v.43 no.7
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• pp.597-608
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• 2010

In this study, a method for soil moisture estimation was proposed to obtain the nationwide soil moisture distribution map using on-site soil moisture observations, rainfall, surface temperature, NDVI, land cover, effective soil depth, and CART (Classification And Regression Tree) algorithm. The method was applied to the Yong-dam dam basin since the soil moisture data (4 sites) of the basin were reliable. Soil moisture observations of 3 sites (Bu-gui, San-jeon, Cheon-cheon2) were used for training the algorithm and 1 site (Gye-buk2) was used for the algorithm validation. The correlation coefficient between the observed and estimated data of soil moisture in the validation sites is about 0.737. Results show that even though there are limitations of the lack of reliable soil moisture observation for various land use, soil type, and topographic conditions, the soil moisture estimation method using ancillary data and CART algorithm can be a reasonable approach since the algorithm provided a fairly good estimation of soil moisture distribution for the study area.