• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2006년도 춘계학술대회 논문집
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• pp.15-16
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• 2006

In-process surface roughness prediction is studied in this research. To implement in-process prediction, spindle displacement is introduced. Machined surface's roughness is assumed to be expressed in terms of spindle displacement. In-process measurement of spindle displacement is conducted using CCDS (cylindrical capacitive displacement sensor). Two prediction models are developed. One is simple linear model between measured surface roughness and values by spindle displacement. The other is multiple regression model including machining parameters like spindle speed, fee rate and radial depth of cut. Relation between machined surface roughness and roughness by spindle displacement are verified.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2001년도 추계학술대회논문집 II
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• pp.1384-1387
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• 2001

Presented is a method to predict strain responses from displacement measurements on a mechanical structure. The method consists of forming a transformation matrix, which is calculated from displacement and strain modal matrices. The modal matrices can be obtained by either finite element analysis or modal testing. One disadvantage of the method is that it requires displacements on all measuring points be measured simultaneously. The strain prediction method is applied to a simple simulated system.

• Geomechanics and Engineering
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• 제13권5호
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• pp.845-860
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• 2017

It is very important to study displacement prediction in geotechnical engineering. Nowadays, the grey system method, time series analysis method and artificial neural network method are three main methods. Based on the brief introduction, the three methods are analyzed comprehensively. Their merits and demerits, applied ranges are revealed. To solve the shortcomings of the artificial neural network method, a new prediction method based on new evolutionary neural network is proposed. Finally, through two real engineering applications, the analysis of three main methods and the new evolutionary neural network method all have been verified. The results show that, the grey system method is a kind of exponential approximation to displacement sequence, and time series analysis is linear autoregression approximation, while artificial neural network is nonlinear autoregression approximation. Thus, the grey system method can suitably analyze the sequence, which has the exponential law, the time series method can suitably analyze the random sequence and the neural network method almostly can be applied in any sequences. Moreover, the prediction results of new evolutionary neural network method is the best, and its approximation sequence and the generalization prediction sequence are all coincided with the real displacement sequence well. Thus, the new evolutionary neural network method is an acceptable method to predict the measurement displacements of geotechnical engineering.

• 한국지반환경공학회 논문집
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• 제20권6호
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• pp.15-22
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• 2019

본 연구에서는 현장조사, 실내시험 및 문헌자료를 기초로 지진 시 산사태 발생 위험지 예측 모델인 Newmark displacement model을 이용하여 위험지를 예측하였다. Newmark displacement model은 주로 지진의 정보와 해당 지역의 사면의 정보를 통해 산정되며, 사면의 안전율은 산지 토사재해 예측 프로그램인 LSMAP의 결과를 활용하였다. 연구대상 지역으로 과거 산사태가 발생한 부산의 백양산 일대를 선정하였다. 산사태 발생 해석 결과 Newmark displacement model을 활용한 지진 시 산사태 위험지 예측이 지진 계수가 미적용된 LSMAP의 산사태 위험지 예측보다 약 1.15배 넓은 지역을 위험지역으로 예측하는 것으로 나타났다.

• 한국정보통신학회논문지
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• 제18권4호
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• pp.920-926
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• 2014

본 연구에서는 지하굴착공사에서 굴착단계별 변위형상도를 미리 예측하는 기법에 관하여 역해석 알고리즘을 이용하여 현장시공사례에 적용하고 그 타당성을 검증하고자 한다. 이를 위해 IT 분야의 정보 처리 지식이 필요하고 전공융합의 필요성이 대두되어 건설 분야에 알고리즘의 적용을 시도하고자 한다. 본 연구의 기법에서 예측변위형상도는 굴착시공 시 연속벽체에 작용하는 토압과 연관이 있으며 이는 앵커 제거 후 시공하는 연속벽체의 안정성에 영향을 미치기 때문에 굴착단계별 변위형상도를 미리 예측하는 것은 아주 중요한 문제이다. 본 연구에서는 이를 위해 3곳의 지하연속벽을 사용한 현장에 반복적 역해석 알고리즘 기법을 세 단계로 적용하여 최종굴착 시 계측변위와 비교하였다. 그 결과 계측치와의 변위형상도가 잘 일치하고 있어 본 연구의 해석기법에 대한 타당성이 있음을 알 수 있었다.

• 한국소음진동공학회논문집
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• 제21권2호
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• pp.112-119
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• 2011

This paper is concerned with the measurement of static and dynamic displacement by image processing(IP) and study for prediction method of velocity and acceleration. To measure the displacement visually, the measurement system consists of a telephoto zoom camera, CCD(charge coupled device) image device and a computer. The specific target on the white board is used to calculate the displacement of the structure. The captured image is then converted into a pixel-based data and then analyzed numerically. The limitation of the system depends on the image capturing speed and the pixel-size of image. In this paper, we developed for the displacement measurement using the image processing method. The proposed method enables us to measure the vibration displacement, velocity and acceleration directly without any contact. The current resolution for the displacement measurement can be seen from the results.

• Structural Engineering and Mechanics
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• 제67권2호
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• pp.105-113
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• 2018

To improve the prediction accuracy of the strong-unloading rock slope performance and obtain the range of variation in the slope displacement, a new displacement time-series prediction model is proposed, called the fuzzy information granulation (FIG)-genetic algorithm (GA)-back propagation neural network (BPNN) model. Initially, a displacement time series is selected as the training samples of the prediction model on the basis of an analysis of the causes of the change in the slope behavior. Then, FIG is executed to partition the series and obtain the characteristic parameters of every partition. Furthermore, the later characteristic parameters are predicted by inputting the earlier characteristic parameters into the GA-BPNN model, where a GA is used to optimize the initial weights and thresholds of the BPNN; in the process, the numbers of input layer nodes, hidden layer nodes, and output layer nodes are determined by a trial method. Finally, the prediction model is evaluated by comparing the measured and predicted values. The model is applied to predict the displacement time series of a strong-unloading rock slope in a hydropower station. The engineering case shows that the FIG-GA-BPNN model can obtain more accurate predicted results and has high engineering application value.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2010년도 추계학술대회 논문집
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• pp.527-532
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• 2010

This paper is concerned with the measurement of static and dynamic displacement by image processing(IP) and study for prediction method of velocity and acceleration. To measure the displacement visually, the measurement system consists of a telephoto zoom camera, ccd image device and a computer. The specific target on the white board is used to calculate the displacement of the structure. The captured image is then converted into a pixel-based data and then analyzed numerically. The limitation of the system depends on the image capturing speed and the pixel-size of image. In this paper, we developed for the displacement measurement using the image processing method. The proposed method enables us to measure the vibration measurement, velocity and acceleration directly without any contact. The current resolution of the displacement measurement is limited to 1/100 millimeter scale.

• 대한기계학회논문집A
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• 제30권11호
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• pp.1455-1460
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• 2006

The relationship between fatigue crack growth behavior and cyclic crack tip opening displacement is studied. An elastic-plastic finite element analysis (FEA) is performed to examine the growth behavior of fatigue crack, where the contact elements are used in the mesh of the crack tip area. We investigate the relationship between the reversed plastic zone size and the changes of the cyclic crack tip opening displacement along the crack growth. We investigate the effect of the element size when predict fatigue crack opening behavior using the cyclic crack tip opening displacement obtained from FEA. The cyclic crack tip opening displacement is related to fatigue crack opening behavior.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 추계학술대회논문집
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• pp.844-848
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• 2005

A method to predict the strain responses from the measurements of displacement responses is considered. The method uses a transformation matrix which is composed of a displacement modal matrix and a strain modal matrix. The method can predict strains at points where displacements are not measured as well as at displacement measuring points. One of the drawbacks of the strain prediction method is that the displacement responses must be measured at many points on a structure simultaneously. This difficulty can be overcome by measuring the FRFs between displacements at a reference point and other point in sequence with a two channel measuring equipment This procedure is based on the assumption that the characteristics of excitation applied to the structure do not vary with time.