• Korean Journal of Applied Biomechanics
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• v.32 no.1
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• pp.1-8
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• 2022

Objective: This study proposes a methodology to analyze important variables that have a significant impact on the putting direction prediction using a machine learning-based putting direction prediction model trained with IMU sensor data. Method: Putting data were collected using an IMU sensor measuring 12 variables from 6 adult males in their 20s at K University who had no golf experience. The data was preprocessed so that it could be applied to machine learning, and a model was built using five machine learning algorithms. Finally, by comparing the performance of the built models, the model with the highest performance was selected as the proposed model, and then 12 variables of the IMU sensor were applied one by one to analyze important variables affecting the learning performance. Results: As a result of comparing the performance of five machine learning algorithms (K-NN, Naive Bayes, Decision Tree, Random Forest, and Light GBM), the prediction accuracy of the Light GBM-based prediction model was higher than that of other algorithms. Using the Light GBM algorithm, which had excellent performance, an experiment was performed to rank the importance of variables that affect the direction prediction of the model. Conclusion: Among the five machine learning algorithms, the algorithm that best predicts the putting direction was the Light GBM algorithm. When the model predicted the putting direction, the variable that had the greatest influence was the left-right inclination (Roll).

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.123-126
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• 2000

In this paper, the portable game machine called W"alking Beat" is designed and implemented not only to propose the new possibilities for the peripheral equipment market of portable acoustic machine but also to overcome the limitation of the acoustic simulation game machine such as the existing Beat Mania. The old game machine can be used only in a particular place, where it is installed. However, in order to get over the constraint on this space problem "Walking Beat Game Machine" is designed to facilitate the portability. In addition, the frequency analysis method using FFT algorithm is employed by regarding the music data for the portable digital acoustic machine as source so that the limitation that the existing game machine depends heavily on the previously registered game contents can be overcome. By making it possible to play games for all the music and putting an emphasis on multimedia trend only to listen to the music, it is speculated that it can contribute to the development of the new culture in the near future.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.10a
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• pp.213-218
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• 1997

Thermal deformation of machine origin of machine tools due to internal and external heat sources has been the most important problem to fabricate products with higher accuracy and performance. In order to solve this problem, GMDH models were constructed to estimate thermal deformation of machine origin for a vertical machining ceneter through measurement of temperature data of specific points on the machine tool. These models are nonlinear equations with high-order polynomials and implemented in a multilayered perceptron type network structure. Input variables and orders are automatically selected by correlation and optimization procedure. Sensors with small influence are deleted automatically in this algorithm. It was shown that the points of temperature measurement can be reduced without sacrificing the estimation accuracy of $\pm$5${\mu}{\textrm}{m}$. From the experimental result, it was confirmed that GMDH methodology was superior to least square models to estimate the thermal behavior of machine tools.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.367-372
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• 2003

This paper shows about the machine simulation embodiment when it happened NC equipment and between workpiece and interference collision at 5 axises processing of aircraft parts. And this research has been chosen because of the highest equipment interference occurrence rate at aircraft parts processing of 5 axises horizontal machine. It can verify simulation and machining process through correlation about their dynamic relations. interference, collision as embodied virtual manufacturing system of machining tool, workpiece, and holder etc. that is necessary element in shape of machine tool and function and processing in imagination ball. Also. it verified about interference and collision between NC equipment parts and workpiece, for applied machine simulation to NC Data of actuality aircraft parts of BULKHEAD and FRAME.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.420-426
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• 2022

Developing a machine learning model and putting it into production goes through a number of steps. Automated Machine Learning(AutoML) appeared to increase productivity and efficiency by automating inefficient tasks that occur while repeating this process whenever machine learning is applied. The high degree of automation of AutoML models allows non-experts to use machine learning models and techniques without the need to become machine learning experts. Automating the process of applying machine learning end-to-end with AutoML models has the added benefit of creating simpler solutions, generating these solutions faster, and often generating models that outperform hand-designed models. In this paper, the AutoML data is collected and AutoML's Color Petri net model is created and analyzed based on it.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.5
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• pp.1-6
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• 2011

Machine tool errors have to be characterized and predicted to improve machine tool accuracy. Therefore, it is very important to assess errors in machine tools. Volumetric error analysis has been developed by many researchers. This paper presents a useful technique for analyzing the volumetric errors in machine tools using the ball bar. The volumetric error model is proposed in specific vertical machining center and the program is developed for generating NC code, acquiring the ball bar data, and analyzing the volumetric errors. The developed system assesses the volumetric errors such as positional, straightness, squareness, and back lash. Also this system analyzes the dynamic performance such as servo gain mismatch. The radial data acquired by ball bar on 3D space is used for analyzing these errors. It is convenient to test the volumetric errors on 3D space because all errors are calculated at once. The developed system has been tested using an actual vertical machining center.

• International Journal of CAD/CAM
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• v.12 no.1
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• pp.1-8
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• 2012

This study analyzes the characteristics of two different kinds of squat exercise through physical experiments and a computer simulation, i.e. one with a free weight and the other with a Smith machine are studied. This study also proposes a new design for the Smith machine, which has both the advantages of each type based on the results of the analysis. The muscle force and level of stimulation of the lower extremities during squatting were calculated by running an inverse dynamics analysis program on a musculoskeletal model together with the measured motion data. The calculated results were verified by comparing with the measured EMG data. The analysis showed that squatting using free weight is more effective than squatting using the Smith machine. Meanwhile, in order to design an improved Smith machine, which is the final goal of this study, the trajectory of the barbell of the subjects during free weight squatting was measured on the sagittal plane. The measurement showed that the average slope of the trajectory of the barbell is tilted backward by $10.7^{\circ}$. Based on this measurement, this study proposes a tilted design for an improved Smith machine.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3431-3437
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• 2021

In this study, one-dimensional gamma ray source positions are estimated using a plastic scintillating optical fiber, two photon counters and via data processing with a machine learning algorithm. A nonlinear regression algorithm is used to construct a machine learning model for the position estimation of radioactive sources. The position estimation results of radioactive sources using machine learning are compared with the theoretical position estimation results based on the same measured data. Various tests at the source positions are conducted to determine the improvement in the accuracy of source position estimation. In addition, an evaluation is performed to compare the change in accuracy when varying the number of training datasets. The proposed one-dimensional gamma ray source position estimation system with plastic scintillating fiber using machine learning algorithm can be used as radioactive leakage scanners at disposal sites.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.5
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• pp.297-304
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• 2020

In this study, the prediction method was reviewed to process a cylindrical plate forming using machine learning as a data-driven approach by roll bending equipment. The calculation of the forming variables was based on the analysis using the mechanical relationship between the material properties and the roll bending machine in the bending process. Then, by applying the finite element analysis method, the accuracy of the deformation prediction model was reviewed, and a large number data set was created to apply to machine learning using the finite element analysis model for deformation prediction. As a result of the application of the machine learning model, it was confirmed that the calculation is slightly higher than the linear regression method. Applicable results were confirmed through the machine learning method.

• Transactions of Materials Processing
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• v.32 no.2
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• pp.74-80
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• 2023

A deformation behavior of commercially pure titanium (CP-Ti) is highly dependent on material and processing parameters, such as deformation temperature, deformation direction, and strain rate. This study aims to predict the multivariable and nonlinear tensile behavior of CP-Ti using machine learning based on three algorithms: artificial neural network (ANN), light gradient boosting machine (LGBM), and long short-term memory (LSTM). The predictivity for tensile behaviors at the cryogenic temperature was lower than those in the room temperature due to the larger data scattering in the train dataset used in the machine learning. Although LGBM showed the lowest value of root mean squared error, it was not the best strategy owing to the overfitting and step-function morphology different from the actual data. LSTM performed the best as it effectively learned the continuous characteristics of a flow curve as well as it spent the reduced time for machine learning, even without sufficient database and hyperparameter tuning.