• 한국항만경제학회지
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• 제39권2호
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• pp.129-141
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• 2023

본 연구는 오차교정모형을 활용해 건화물선과 유조선 일간 해상운임의 동태적 특성과 예측 정확도를 분석한다. 공적분된 시계열 자료의 오차를 계산하기 위해 본 연구는 공통 확률적 추세 모형(Common Stochastic Trend Model, CSTM 모형)과 벡터오차교정모형(Vector Error Correction Model, VECM 모형)을 활용한다. 먼저, CSTM 모형의 오차를 사용한 오차교정모형이 VECM 모형의 경우보다 교정계수(adjustment speed coefficient)가 경제학적 이론에 더 부합하는 결과를 보인다. 나아가 조정결정계수(adjR²) 측면에서도 CSTM 모형의 경우가 VECM 모형에 비해 모형 적합도가 큰 것으로 나타난다. 둘째, 예측 정확도를 판단하는 지표인 평균 절대 오차와 평균 절대 척도 오차를 살펴보면, CSTM 모형의 오차를 이용한 모형이 VECM 모형의 오차를 이용한 모형보다 총 15가지 경우 중에 12가지 경우에서 예측 정확도가 높은 것을 확인할 수 있다. 미래 연구주제로서 1) 두 가지 오차를 모두 활용하는 분석 및 예측 과제, 2) 원자재 및 에너지 자원 시장의 데이터를 추가하는 과제, 3) 오차항의 부호에 따라 교정계수를 다르게 추정하는 과제 등을 제시한다.

• 한국정밀공학회지
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• 제30권5호
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• pp.507-512
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• 2013

This paper proposes a new model to compensate for errors of a five-axis machine tool. A matrix with error components, that is, an error matrix, is separated from the error synthesis model of a five-axis machine tool. Based on the kinematics and inversion of the error matrix which can be obtained not by using a numerical method, an error compensation model is established and used to calculate compensation values of joint variables. The proposed compensation model does not need numerical methods to find the compensation values from the error compensation model, which includes nonlinear equations. An experiment using a double ball-bar is implemented to verify the proposed model.

• Journal of Mechanical Science and Technology
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• 제15권11호
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• pp.1482-1489
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• 2001

Thermally-induced errors originating from machine tool errors have received significant attention recently because high speed and precise machining is now the principal trend in manufacturing proce sses using CNC machine tools. Since the thermal error model is generally a function of temperature, the thermal error compensation system contains temperature sensors with the same number of temperature variables. The minimization of the number of variables in the thermal error model can affect the economical efficiency and the possibility of unexpected sensor fault in a error compensation system. This paper presents a thermal error model with minimum number of variables using a fuzzy logic strategy. The proposed method using a fuzzy logic strategy does not require any information about the characteristics of the plant contrary to numerical analysis techniques, but the developed thermal error model guarantees good prediction performance. The proposed modeling method can also be applied to any type of CNC machine tool if a combination of the possible input variables is determined because the error model parameters are only calculated mathematically-based on the number of temperature variables.

• 한국정밀공학회지
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• 제16권3호통권96호
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• pp.215-221
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• 1999

The object of the thermal error compensation system in machine tools is improving the accuracy of a machine tool through real time error compensation. The accuracy of the machine tool totally depends on the accuracy of thermal error model. A thermal error model can be obtained by appropriate combination of temperature variables. The proposed method for optimal variable selection in the thermal error model is based on correlation grouping and successive regression analysis. Collinearity matter is improved with the correlation grouping and the judgment function which minimizes residual mean square is used. The linear model is more robust against measurement noises than an engineering judgement model that includes the higher order terms of variables. The proposed method is more effective for the applications in real time error compensation because of the reduction in computational time, sufficient model accuracy, and the robustness.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 춘계학술대회 논문집
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• pp.703-707
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• 2000

Machine tool errors have to be characterized and predicted to improve machine tool accuracy. A real-time error compensation system has been developed based on volumetric error synthesis model which is composed of machine tool errors. This paper deals with new algorithm about verification of machine tool errors. This new algorithm uses a simplified volumetric error synthesis model. This simplified model is constructed with only main components among the error components of the machines. This main error components are analyzed by three-dimensional helical ball bar test. By substituting result of helical ball bar test fer simplified model, we could find that obtained error components are closed to real error components.

• 한국정밀공학회지
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• 제19권4호
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• pp.124-131
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• 2002

In this paper, an extended volumetric error model considering backlash in a three-axis machine tool was proposed and utilized for calculating the volumetric error of the machine tool at any position in three-dimensional workspace. Backlashes are interrelated; i.e. the angular backlash affects the straightness errors which then affect talc calculated squareness errors. Therefore, a new concept was introduced to define the backlash of squareness errors to incorporate the backlash of squareness error into the volumetric error, and the characteristics of the backlash of squareness error were investigated. The effects of backlash errors were assessed, by experiments. for 21 geometric errors of a machine tool. The backlash error was shown to be one of the systematic errors of a machine tool. And a generalized volumetric error model formulator for three-axis machine tools was developed, which allowed us to formulate machine tool synthesis error models far all possible machine tool configurations only with machine tool topology information. Based on these volumetric error model and model formulator, a computer-aided volumetric error analysis system was developed for a three-axis machine tool in this paper. Then the volumetric error at an arbitrary position can be obtained, and displayed in a three-dimensional graphic form.

• 제어로봇시스템학회논문지
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• 제18권5호
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• pp.502-508
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• 2012

Heliostat, as a concentrator to reflect the incident solar energy to the receiver, is the most important system in the tower-type solar thermal power plant since it determines the efficiency and ultimately the overall performance of solar thermal power plant. Thus, a good sun tracking ability as well as a good optical property of it are required. Heliostat sun tracking system uses usually an open loop control system. Thus the sun tracking error caused by heliostat's geometrical error, optical error and computational error cannot be compensated. Recently use of sun tracking error model to compensate the sun tracking error has been proposed, where the error model is obtained from the measured ones. This work is a development of heliostat sun tracking error measurement and compensation method using BCS (Beam Characterization System). We first developed an image processing system to measure the sun tracking error optically. Then the measured error is modeled in linear polynomial form and neural network form trained by the extended Kalman filter respectively. Finally error models are used to compensate the sun tracking error. We also developed the necessary image processing algorithms so that the heliostat optical properties such as maximum heat flux intensity, heat flux distribution and total reflected heat energy could be analyzed. Experimentally obtained data shows that the heliostat sun tracking accuracy could be dramatically improved using either linear polynomial type error model or neural network type error model. Neural network type error model is somewhat better in improving the sun tracking performance. Nevertheless, since the difference between two error models in compensation of sun tracking error is small, a linear error model is preferred in actual implementation due to its simplicity.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 춘계학술대회 논문집
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• pp.632-635
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• 2002

In this paper, a general procedure to determine machine tool errors from the on-machine measurement (OMM) data is described. First, a parameterized error model of a machine tool is illustrated by approximating error components as linear function of axis positions, and a modified error model is proposed which includes backlash effects. To determine the unknown model coefficient vectors of the forward and backward error model, an artifact with 8 cutes is made and calibrated on CMM. Then, lower-left and upper-right cube corners are measured with a touch-trigger probe mounted on the machine tool spindle. Measured error data are used to determine the coefficient vectors. The positioning errors in the XY plane at the fixed z position are simulated for the forward and backward error model.

• 한국생산제조학회지
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• 제9권6호
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• pp.152-157
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• 2000

In the control of CNC machine tools, it is significant for precise machining to reduce the contour error. The object of servo-control is reduction of contour error and tracking error. In past studies, there were two approaches to control a servo-system. One was to eliminate axial tracking errors, and the other was to control contour errors. The Cross-coupled controller(CCC) was introduced fro ma veiwpoint of contour error model. Recently, for machining part with free form surfaces, we propose a new contour error model based on curve interpolator. It is presented here that performance of CCC using proposed model is enhanced. Therefore, we can make more precise parts with the curve interpolator and the new contour error model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.405-423
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• 2022

The distance vector-hop (DV-Hop) is one of the emblematic algorithms that use node connectivity for locating, which often accompanies by a large positioning error. To reduce positioning error, the bio-inspired algorithm and weight optimization model are introduced to address positioning. Most scholars argue that the weight value decreases as the hop counts increases. However, this point of view ignores the intrinsic relationship between the error and weight. To address this issue, this paper constructs the relationship model between error and hop counts based on actual communication characteristics of sensor nodes in wireless sensor network. Additionally, we prove that the error converges to 1/6CR when the hop count increase and tendency to infinity. Finally, this paper presents a modified error-oriented weight positioning model, and implements it with genetic algorithm. The experimental results demonstrate excellent robustness and error removal.