• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.11-13
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• 2008

This study proposes the method to use existing digital maps as one of the technologies to exclude individual differences that occur in the process of manually determining GCP for the geometric correction of KOMPSAT images and applying it to the images and to automate the generation of ortho-images. It is known that, in case high-resolution satellite images are corrected geometrically by using RPC, first order polynomials are generally applied as the correction formula in order to obtain good results. In this study, we matched the corresponding objects between 1:25,000 digital map and a KOMPSAT image to obtain the coefficients of the zero order polynomial and showed the differences in the pixel locations obtained through the matching. We performed proximity corrections using the Boolean operation between the point data of the surface linear objects and the point data of the edge objects of the image. The surface linear objects are road, water, building from topographic map.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.6
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• pp.879-884
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• 2012

Photoacoustic Tomography (PAT) is a promising medical imaging modality by reason of its particularity. It combines optical imaging contrast of optical imaging with the spatial resolution of ultrasound imaging and can demonstrate change of biological feature in an image. For that reason, many studies are in progress to apply this technic for diagnosis. But, real-time PAT system is necessary to confirm a biological reaction induced by external stimulation immediately. Thus, we developed a real-time PAT system using linear array transducer and self-developed Data acquisition board (DAQ) resources, To evaluate the feasibility and performance of our proposed system, two type of phantom test were also performed. As a result of those experiments, the proposed system shows enough performance and confirm its usefulness.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.320-325
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• 1999

An absolute type shaft encoder which utilized moire fringe will be presented in this paper. Linear moire fringe is commonly used to measure the displacement of the linear motion. However, an absolute encoder which measure the rotation angle of a shaft is operated usually with a code disk which the gray code pattern is printed on. Such encoder has inherently resolution limit because of the patterning mechanism and sensing mechanism. In order to measure the position of fringes which move as the code disk rotates, neural network was developed in this paper. Formerly fringe position is usually measured by a sophisticated software, which needs a little long calculation time. However, using neural network method can eliminate such calculation time, even though it needs learning job. The proposed method is verified through several experiments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.1085-1087
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• 2002

Deformation of soft tissue is known inhomogeneous and non-linear in general. In this study, we propose a measurement methodology of local/global strain during soft tissue elongation precisely using laser extensometer which has high accuracy, resolution and is possible to measure global/local strain. The mechanical tensile test are performed on tibialis cranialis, flexor hallucis longus, extensor digitorum longus of swine hindlimb. In order to measure target displacement, reflective marker is attached to detect elongation on specimen using surgical adhesive. The result of this study is to show that laser extensometer is valid to measure longitudinal elongation which is inhomogeneous and non-linear fur soft tissue.

• Journal of Ocean Engineering and Technology
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• v.7 no.1
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• pp.99-106
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• 1993

The use of Hermite cubic element, as a possible finite element computation of transport equations containing shocks, has been invesigated. In the present paper the hermite cubic elements are applied to both linear and nonlinear scalar one and two dimensional equations. In the one dimensional problems, numerical results by the hermite cubic element show better than those by the linear element, and the steady state solution by the hermite cubic element yields result with good resolution. This fact proves the superiority of the hermite cubic element in space differencing. In two dimensional case, the results by the hermite cubic element shows a boundary instability, and the use of higher order time differencing method may be necessary for fixing the problem.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.54-59
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• 2005

Recently, the ultra-precision stage is widely used in the fields of the nano-technology, specially in AFMs(Atomic Force Microscope) and STMs(Scanning Tunneling Microscope). In this paper, the ultra-precision stage which consists of flexure hinges, piezoelectric actuator, and ultra-precision linear encoder, is designed and developed. The guide mechanism which consisted of flexure hinges is analyzed by Finite Element Method. And we derived the transfer function of the system in 1st order system from step responses according to the magnitude. We performed simulation for the model to tune the control gain and applied the gains to the developed system. Experimental results found that the stage can be controlled in 5 nm resolution by PID controller.

• International Journal of Contents
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• v.9 no.3
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• pp.1-9
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• 2013

In this paper, we propose a fast and accurate system for detecting pedestrians from a static image. Histogram of Oriented Gradients (HOG) is a well-known feature for pedestrian detection systems but extracting HOG is expensive due to its high dimensional vector. It will cause long processing time and large memory consumption in case of making a pedestrian detection system on high resolution image or video. In order to deal with this problem, we use Principal Components Analysis (PCA) technique to reduce the dimensionality of HOG. The output of PCA will be input for a linear SVM classifier for learning and testing. The experiment results showed that our proposed method reduces processing time but still maintains the similar detection rate. We got twenty five times faster than original HOG feature.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.157-157
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• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• ETRI Journal
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• v.44 no.4
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• pp.624-640
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• 2022

Taxi-demand forecasting and hotspot prediction can be critical in reducing response times and designing a cost effective online taxi-booking model. Taxi demand in a region can be predicted by considering the past demand accumulated in that region over a span of time. However, other covariates-like neighborhood influence, sociodemographic parameters, and point-of-interest data-may also influence the spatiotemporal variation of demand. To study the effects of these covariates, in this paper, we propose three models that consider different covariates in order to select a set of independent variables. These models predict taxi demand in spatial units for a given temporal resolution using linear and ensemble regression. We eventually combine the characteristics (covariates) of each of these models to propose a robust forecasting framework which we call the combined covariates model (CCM). Experimental results show that the CCM performs better than the other models proposed in this paper.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.2
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• pp.37-47
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• 1995

In recent years, it has been variously developed for testing the accuracy of circular motion of NC machine tools, for example Telescoping Ball Bar Method by Bryan, Circular test Method by Knapp and $r^{-{\theta} }$ Method by Tsutsumi etc., but these methods are all 2-dimentional measuring methods on plane. These simple methods of circular motion accuracy test of NC machine tools have been studied by many reserchers as above, but it is not yet settled in the code of measuring methods of motion errors of NC machine tools, because of errors of measuring units and sensors, and also especially the difficulties of centering of measuring units and the spindle of machining center. In this paper, in use of 2 rotary encoders and 1 magnetic type linear scale with resolution of 0.5 .mu. m, it has become possible for measuring of 3 dimentional space motion accuracy.