• Korean Journal of Applied Biomechanics
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• v.15 no.1
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• pp.207-219
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• 2005

The purpose of this study was to determine the effect of the foot rotation on the lower limb muscles. Fourteen subjects performed step-up/step-down at a cadence of 80 beats/min, exercises with the foot neutral, $35^{\circ}$ internally rotated, and $35^{\circ}$ externally rotated, respectively. For each variable, a one-way analysis of variance (ANOVA) was used to determine whether there were significant differences between genders and among the eight types of jump. When a significant difference was found in jump type, post hoc analyses were performed using the Tukey procedure. A confidence level of p < .05 was used to determine statistical significance. The results showed that significant changes in averaged IEMG values occurred with the internal rotation of the foot in the lateral gastrocnemius during the knee extension, and in the semitendinosus during the knee flexion. During the knee extension, however, the internal rotation of the foot produced a significantly lower Averaged IEMG values than the neutral foot position in the medial gastrocnemius. The results also found that the peak IEMG activity of the rectus femoris during the knee extension for the external rotation of the foot was Significantly higher than the corresponding values in the neutral position of the foot, while the intenal rotaion of the foot exhibited a significant difference with the neutral position of the foot in the semitendinosus during the knee flexion. In general, the foot rotation position did not influence the average IEMG and Peak IEMG values of most muscles. The practice of adopting foot rotation to selectively strengthen individual muscles of the lower limb was not supported by this study. The external rotation of the foot produced high muscle activities in the quadriceps during the knee extension. For the knee extension, therefore, maintaining a laterally rotated position should be need for stable and comfortable position.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.3
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• pp.361-367
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• 2010

The barrel cam, which is a type of cylindrical cam, has been widely used as a part of index drive units for automatic manufacturing machines. The axis of rotation of the barrel cam is orthogonal to the axis of rotation of the follower. The index drive rotates or dwells depending on the cam profile, while the cam rotates with a constant velocity. Continuous sliding contact between the barrel cam and the follower surfaces causes wearing of the adhesive between them. This study shows that the contact force between two sliding bodies is responsible for the wear of the barrel cam in the paper-cup-forming machine. This contact force is calculated by using the multibody dynamics model of the paper-cup-forming machine. The analytical result is validated by comparing it to the actual wear spots on the real product.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.6
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• pp.309-317
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• 2022

In this paper, we propose a machine learning-based copy-move forgery detection network with dual branches. Because the rotation or scaling operation is frequently involved in copy-move forger, the conventional convolutional neural network is not effectively applied in detecting copy-move tampering. Therefore, we divide the input into rotation-invariant and scaling-invariant features based on the wavelet coefficients. Each of the features is input to different branches having the same structure, and is fused in the combination module. Each branch comprises feature extraction, correlation, and mask decoder modules. In the proposed network, VGG16 is used for the feature extraction module. To check similarity of features generated by the feature extraction module, the conventional correlation module used. Finally, the mask decoder model is applied to develop a pixel-level localization map. We perform experiments on test dataset and compare the proposed method with state-of-the-art tampering localization methods. The results demonstrate that the proposed scheme outperforms the existing approaches.

• Structural Engineering and Mechanics
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• v.87 no.1
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• pp.19-28
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• 2023

This paper describes the experimental investigation of steel pallet rack beam-to-column connec-tions. Total behavior of moment-rotation (M-φ) curve and the effect of particular characteristics on the behavior of connection were studied and the associated load strain relationship and corre-sponding failure modes are presented. In this respect, an estimation of SPRBCCs moment and rotation are highly recommended in early stages of design and construction. In this study, a new approach based on Support Vector Machines (SVMs) coupled with discrete wavelet transform (DWT) is designed and adapted to estimate SPRBCCs moment and rotation according to four input parameters (column thickness, depth of connector and load, beam depth,). Results of SVM-WAVELET model was compared with genetic programming (GP) and artificial neural networks (ANNs) models. Following the results, SVM-WAVELET algorithm is helpful in order to enhance the accuracy compared to GP and ANN. It was conclusively observed that application of SVM-WAVELET is especially promising as an alternative approach to estimate the SPRBCCs moment and rotation.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.280-288
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• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.793-794
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• 2012

• Journal of the Korean Society for Precision Engineering
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• v.28 no.3
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• pp.265-274
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• 2011

This paper presents current state of the prediction simulator of structural characteristics of machinery equipment accuracy. Developed accuracy prediction simulator proceeds and estimates the structural analysis between the designer and simulator through the internet for convenience of designer. 3D CAD model which is input to the accuracy prediction simulator would simplified by the process of removing the small hole, fillet and chamfer. And the structural surface joints would be presented as the spring elements and damping elements for the structural analysis. The structural analysis of machinery equipment joints, containing rotary motion unit, linear motion unit, mounting device and bolted joint, are presented using Finite Element Method and their experiment. Finally, a general method is presented to tune the static stiffness at a rotation joint considering the whole machinery equipment system by interactive use of Finite Element Method and static load experiment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.10-14
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• 2005

This report deals with a feeding system of the Coaxal grinding machine, processing optical ferrule. This report also examines the applicability of using the feeding system for the Coaxial grinding machine, by mean of conducting performance estimation. The results are as follow; Repeatability of regulating wheel is $17{\mu}m$, R/W rotation accuracy is between $30\;\~\;40{\mu}m$. This means 'Rotation accuracy' is lower than the concentricity level. Backlash generation level at the feeding system of the grinding wheel is under $1{\mu}m$, thereby positioning accuracy is controlled within $2{\mu}m$ In terms of repeatability, you can find occasional error at the returning process from the starting point. This error is resulted from the measurement tolerance of the starting point sensor. We will get the repeatability level under control by $1{\mu}m$, through improving the soft-ware used and up-grading the sensor at the starting point.

• Bulletin of the Korean Space Science Society
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• 2003.10a
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• pp.26-26
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• 2003

During fabrication process for the large space optical surfaces, the traditional bound abrasive grinding with bronze bond cupped diamond wheel tools leaves the machine marks and the subsurface damage to be removed by subsequent loose abrasive lapping. We explored a new grinding technique for efficient quantitative control of precision CNC grinding for space optics materials such as Zerodur. The facility used is a NANOFORM-600 diamond turning machine with a custom grinding module and a range of resin bond diamond tools. The machining parameters such as grit number, tool rotation speed, work-piece rotation speed, depth of cut and feed rate were altered while grinding the work-piece surfaces of 20-100 mm in diameter. The input grinding variables and the resulting surface quality data were used to build grinding prediction models using empirical and multi-variable regression analysis methods. The effectiveness of the grinding prediction model was then examined by running a series of precision CNC grinding operation with a set of controlled input variables and predicted output surface quality indicators. The experiment details, the results and implications are presented.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.5
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• pp.132-139
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• 2008

To meet demand of big capacity and high speed rotation for washing machine, more stress from bending and twisting are complexly loaded onto the shaft supporting the horizontal drum, causing problems in fracture strength and fatigue life. Also, Vibration occurs due to the frequency of the rotating parts. But, shaft has various design factors such as diameter and distance between bearings according to configuration of shaft, the optimal values can't be easily determined. Using a design of experiment (DOE) based on the FEM (Finite Element Method), which has several advantages such as less computing, high accuracy performance and usefulness, this study was performed investigating the interaction effect between the various design factor as well as the main effect of the each design factor under bending, twist and vibration and proposed optimum design using center composition method among response surface derived from regression equation of simulation-based DOE.