• 로봇학회논문지
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• 제15권3호
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• pp.233-239
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• 2020

The aims of this paper is to develop a modular agricultural robot and its autonomous driving algorithm that can be used in field farming. Actually, it is difficult to develop a controller for autonomous agricultural robot that transforming their dynamic characteristics by installation of machine modules. So we develop for the model based control algorithm of rotary machine connected to agricultural robot. Autonomous control algorithm of agricultural robot consists of the path control, velocity control, orientation control. To verify the developed algorithm, we used to analytical techniques that have the advantage of reducing development time and risks. The model is formulated based on the multibody dynamics methods for high accuracy. Their model parameters get from the design parameter and real constructed data. Then we developed the co-simulation that is combined between the multibody dynamics model and control model using the ADAMS and Matlab simulink programs. Using the developed model, we carried out various dynamics simulation in the several rotation speed of blades.

• Journal of Advanced Marine Engineering and Technology
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• 제33권5호
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• pp.729-737
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• 2009

This paper introduces the machine tools feed system, which can be optimized the control's performance through simulation and the adjustment of the mechanical components. One method simulates the frequency response of the speed-loop with the design value using the MATLAB application, so that all of the interpolation axis can be equal to the response bandwidth, resulting in a high accuracy rate. The other method sees the mechanical component being adjusted by analyzing the results of various experiments. Lastly, this client's program is able to change the parameters that are related to the FFD, as well as the parameters in the friction compensation of the OPEN-CNC.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권2호
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• pp.143-148
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• 2012

This paper proposes a new method to improve performance of AdaBoost by using a distance weight function to increase the accuracy of its machine learning processes. The proposed distance weight algorithm improves classification in areas where the original binary classifier is weak. This paper derives the new algorithm's optimal solution, and it demonstrates how classifier accuracy can be improved using the proposed Distance Sensitive AdaBoost in a simulation experiment of pedestrian detection.

• 한국정밀공학회지
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• 제30권10호
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• pp.1087-1093
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• 2013

In this study, a measurement method of double ball-bar is proposed to measure the geometric errors of an ultra-precision roll mold machine tool. A volumetric error model of the machine tool is established to investigate the effects of the geometric errors to a radius error and a cylindricity of the roll mold. A measurement path is suggested for the geometric errors, and a ball-bar equation is derived to represent the relation between the geometric errors and a measured data of the double ball-bar. Set-up errors, which are inevitable at the double ball-bar installation, also are analyzed and are removed mathematically for the measurement accuracy. In addition, standard uncertainty of the measured geometric errors is analyzed to determine the experimental condition. Finally, the proposed method is tested and verified through simulation.

• Nuclear Engineering and Technology
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• 제55권5호
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• pp.1708-1717
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• 2023

The grade analysis of lead-zinc ore is the basis for the optimal development and utilization of deposits. In this study, a method combining Prompt Gamma Neutron Activation Analysis (PGNAA) technology and machine learning is proposed for lead-zinc mine borehole logging, which can identify lead-zinc ores of different grades and gangue in the formation, providing real-time grade information qualitatively and semi-quantitatively. Firstly, Monte Carlo simulation is used to obtain a gamma-ray spectrum data set for training and testing machine learning classification algorithms. These spectra are broadened, normalized and separated into inelastic scattering and capture spectra, and then used to fit different classifier models. When the comprehensive grade boundary of high- and low-grade ores is set to 5%, the evaluation metrics calculated by the 5-fold cross-validation show that the SVM (Support Vector Machine), KNN (K-Nearest Neighbor), GNB (Gaussian Naive Bayes) and RF (Random Forest) models can effectively distinguish lead-zinc ore from gangue. At the same time, the GNB model has achieved the optimal accuracy of 91.45% when identifying high- and low-grade ores, and the F₁ score for both types of ores is greater than 0.9.

• 로봇학회논문지
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• 제15권2호
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• pp.169-176
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• 2020

This paper studied the collision detection of robot manipulators for safe collaboration in human-robot interaction. Based on sensor-based collision detection, external torque is detached from subtracting robot dynamics. To detect collision using joint torque sensor data, a comparative study was conducted using data-based machine learning algorithm. Data was collected from the actual 3 degree-of-freedom (DOF) robot manipulator, and the data was labeled by threshold and handwork. Using support vector machine (SVM), decision tree and k-nearest neighbors KNN method, we derive the optimal parameters of each algorithm and compare the collision classification performance. The simulation results are analyzed for each method, and we confirmed that by an optimal collision status detection model with high prediction accuracy.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 추계학술대회 논문집
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• pp.381-386
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• 2002

Recently, The machine tools have been needed for high speed and accuracy to increase productivity. The most important thing to get a more stabilized machine is to know the frequncy response which has an effect on manufacture a lot. This problem should be considered seriously by many researchers. There are many programs about FEM. but just using FEM program to get information of the object is not enough to put our confidence in the stability of the structure design. Therefore, the purpose of this research is to make a study for proving one of the ways to design to produce stabilized a machine more efficiently by comparing FRT with Simulation through FEM. At this two tests, we can learn about the frequency response area causing resonance and we can reconfirm the result to trust.

• 한국CDE학회논문집
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• 제19권4호
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• pp.340-346
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• 2014

There are many factors that contribute to hit probability of the gun shot of ground combat vehicles. Aiming accuracy is mainly affected by the dynamic state of the vehicle. The stabilization error of the turret under system vibration is one of the major factors that affect the aiming accuracy. The vibration of the vehicle is affected by both the state of the road and the speed of the vehicle. This paper analyzes the aiming accuracy of the gun equipped on the GCV when the vehicle drives on the different roads and at different speed. The vertical displacement and the pitch angle of the gun are calculated and the impact points of the target are calculated. Distribution of the impact points on the target is greatly influenced by the pitch rotation rather than vertical displacement. And this aiming errors result in the errors of point of impacts on the target after the bullet flies through the air under trajectory equations. The GCV is modeled using a half-car model with 6 D.O.F. and the specifications of the M2 machine gun are used in trajectory calculation simulation and the target is located in 1000 m away from the gun.

• 한국생산제조학회지
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• 제8권6호
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• pp.55-63
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• 1999

The acceleration of the performance of machine tools influences the development of the semi-conductor and optical technology as the development of NC and measurement technology. Because the measurement has been done to unload condition without considering of mechanical stiffness in the case of machining center as we measure the quasi-static error of machine tools on general study people who works on the spot has many problems on the data value. Also there are no satisfiable results until now in spite of many studys about this because the deflections of the table and the shaft supporting a workpiece influence, influence the accuracy of the table and shaft supporting a workpiece influence the accuracy of the workpiece. And there is doubt about the inspection method of measured error. In this paper Therefor we will help working more accurately on the spot by measuring analyzing displaying the defoec-tion of the table and support shaft when we load on the table and the support shaft of machining center using laser interfer-ometer. Also we try to settle new conception of the measurement method and more accurate grasp of the deflection tenden-cy by verifing the tendency of the error measured through the comparison of the simulated error measured through the comparison of the simulated error using ANSYS a common finite element analysis program which is able to measure heat deformation material deformation and error resulted from this study.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.119-127
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• 2022

Most of the construction works are conducted outdoors, so the construction workers are affected by weather conditions such as temperature, humidity, and wind velocity which can be evaluated the thermal comfort as environmental factors. In our previous researches, it was found that construction accidents are usually occurred in the discomfort ranges. The safety management, therefore, should be planned in consideration of the thermal comfort and measured by a specialized simulation tool. However, it is very complex, time-consuming, and difficult to model. To address this issue, this study is aimed to develop a framework of a prediction model for improving the prediction accuracy about outdoor thermal comfort considering environmental factors using machine learning algorithms with hyperparameter tuning. This study is done in four steps: i) Establishment of database, ii) Selection of variables to develop prediction model, iii) Development of prediction model; iv) Conducting of hyperparameter tuning. The tree type algorithm is used to develop the prediction model. The results of this study are as follows. First, considering three variables related to environmental factor, the prediction accuracy was 85.74%. Second, the prediction accuracy was 86.55% when considering four environmental factors. Third, after conducting hyperparameter tuning, the prediction accuracy was increased up to 87.28%. This study has several contributions. First, using this prediction model, the thermal comfort can be calculated easily and quickly. Second, using this prediction model, the safety management can be utilized to manage the construction accident considering weather conditions.