• Journal of Korean Society for Geospatial Information Science
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• v.17 no.3
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• pp.23-31
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• 2009

The border of the shorelines in a nation is an important factor in determining the border of a national territory, but Korea's shorelines are rapidly changing due to the recent rise in sea level from global warming and growth-centered economic policy over the decades of years. This research was done centering on the areas having well-preserved shorelines as they naturally are and other areas having damaged shorelines in their vicinities due to artificial structures at the two beaches located at the neighboring areas and having mutually homogeneous ocean conditions with each other. First, this research derived the shorelines using the aerial photographies taken from 1947 until 2007 and revised the tidal levels sounding data obtained from a hydrographical survey automation system consisting of Echosounder[Echotrac 3100] and Differential Global Positioning System[Beacon]by using topographical data and ships on land obtained by applying post-processing Kinematic GPS measuring method. In addition, this research evaluated the changes and dimensional variations for the last 60 years by dividing these determined shorelines into 5 sections. As a result, the Haewundae Beach showed a total of 29% decrease rate in dimension as of the year 2007 in comparison with the year 1947 due to a rapid dimensional decline centering on its west areas, while the dimension of the Gwanganri Beach showed an increase in its dimension amounting to a total of 69% due to the decrease in flow velocity by artificial structures built on both ends of the beach-forming accumulation; thus, it was found that there existed a big difference in deposition & accumulation tendency depending on neighboring environment in spite of the homogeneous ocean conditions.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.7
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• pp.884-891
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• 2016

A secondary injection system in a diesel engine has benefits: it can be controlled independently without interrupting engine control, it can be adapted to various layouts for exhaust systems, and it pose no reductant dilution problems compared to post injection systems in the combustion chamber or other supplemental reductant injections. In a secondary injection system, the efficiency of the catalyst depends on the method of reducing the supply. The reductant needs to be maintained and optimized with constant pressure, the positions and angles of injector is a very important factor. The concentration and amount of reductant can be changed by adjusting secondary injection conditions. However, secondary injection is highly dependent upon the type of injector, injection pressure, atomization, spray technology, etc. Therefore, it is necessary to establish injection conditions the spray characteristics must be well-understood, such as spray penetration, sauter mean diameter, spray angle, injection quantity, etc. Uniform distribution of the reductant corresponding to the maximum NOx reduction in the DeNOx catalyst system must also assured. With this goal in mind, the spray characteristics and impingement plate types of a secondary injector were analyzed using visualization and digital image processing techniques.

• Journal of Sasang Constitutional Medicine
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• v.22 no.1
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• pp.41-48
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• 2010

1. Objective There has been a few trials to diagnose Sasang Constitution by using EEG, but has not been studied intensively. For the purpose of practical diagnosis, the characteristics of EEG for each constitution should be studied first. Recently it has been shown that Sasang Constitution might be related to harm avoidance and novelty seeking in temperament and character profiles. Based on this finding, we propose a visual stimulation method to evoke a EEG response which may discriminate difference between constitutional groups. Through the experiment with this method, we tried to reveal the characteristics of EEG of each constitutional groups by the method of event-related potentials. 2. Methods: We used facial visual stimulation to verify the characteristics of EEG for each constitutional groups. To reveal characteristic in sensitivity and latency of response, we added several levels of noise to facial images. 6 male subjects(2 Taeeumin, 2 Soyangin, 2 Soeumin) participated in this study. All subjects are healthy 20's. To remove artifacts and slow modulation, we removed EOG contaminated data and renormalization is applied. To extract stimulation related components, normalized event-related potential method was used. 3. Results: From Oz channels, it is verified that facial image processing components are extracted. For lower level noise, components related to the visual stimulation were clearly shown in Oz, Pz, and Cz channels. Pz and Cz channels show differences among 3 constitutional groups in maximum around 200 msec. Especially moderate level of noise looks appropriate for diagnosis. 4. Conclusion: We verified that the visual stimulation with facial emotion might be a good candidate to evoke the differences between constitutional groups in EEG response. The differences shown in the experiment may imply that the process of emotion has distinct tendencies in latencies and sensitivity for each consitutional group. And this distinction might be related to the temperament profile of consitutional groups.

• Journal of the Korean Society for Railway
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• v.20 no.2
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• pp.165-172
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• 2017

High frequency squeal noise can be generated when a railroad vehicle runs a sharp curved section; this noise causes environmental complaints and excessive wear on the wheel and the railroad track. In this paper, curved squeal noise experiments on a commercial railway were carried out to investigate this phenomenon. The relationship of the squeal noise pressure level, the frequency characteristics, the railway running speed, and the modal behavior of the wheel were investigated. At the same time, the lateral motion of the wheel relative to the rail was captured using a video camera; wheel movement was calculated when the noise was generated. queal noise occurred at the highest level at the inner front wheel; this phenomena is considered to be related to the lateral vibration response characteristics of the wheel. It can be seen that the magnitude of this squeal noise is not directly related to the increase in vehicle speed.

• Geophysics and Geophysical Exploration
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• v.12 no.1
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• pp.77-84
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• 2009

Ground penetrating radar (GPR) is an effective tool for detecting shallow subsurface targets. In many GPR applications, these targets are veiled by the strong waves reflected from the ground surface, so that we need to apply a signal processing technique to separate the target signal from such strong signals. A pulse-compression technique is used in this research to compress the signal width so that it can be separated out from the strong contaminated clutter signals. This work introduces a filter algorithm to carry out pulse compression for GPR data, using a Wiener filtering technique. The filter is applied to synthetic and field GPR data acquired over a buried pipe. The discrimination method uses both the reflected signal from the target and the strong ground surface reflection as a reference signal for pulse compression. For a pulse-compression filter, reference signal selection is an important issue, because as the signal width is compressed the noise level will blow up, especially if the signal-to-noise ratio of the reference signal is low. Analysis of the results obtained from simulated and field GPR data indicates a significant improvement in the GPR image, good discrimination between the target reflection and the ground surface reflection, and better performance with reliable separation between them. However, at the same time the noise level slightly increases in field data, due to the wide bandwidth of the reference signal, which includes the higher-frequency components of noise. Using the ground-surface reflection as a reference signal we found that the pulse width could be compressed and the subsurface target reflection could be enhanced.

• Geophysics and Geophysical Exploration
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• v.12 no.4
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• pp.347-354
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• 2009

Physical properties of rocks are strongly dependant on details of pore micro-structures, which can be used for quantifying relations between physical properties of rocks through pore-scale simulation techniques. Recently, high-resolution scan techniques, such as X-ray microtomography and high performance computers make it possible to calculate permeability from pore micro-structures of rocks. We try to extend this simulation methodology to velocity and electrical conductivity. However, the smoothing effect during tomographic inversion creates artifacts in pore micro-structures and causes inaccurate property estimation. To mitigate this artifact, we tried to use sharpening filter and neural network classification techniques. Both methods gave noticeable improvement in pore structure imaging and accurate estimation of permeability and electrical conductivity, which implies that our method effectively removes the smoothing effect in pore structures. However, the calculated velocities showed only incremental improvement. By comparison between thin section images and tomogram, we found that our resolution is not high enough, and it is mainly responsible for the inaccuracy in velocity despite the successful removal of the smoothing effect. In conclusion, our methods can be very useful for pore-scale modeling, since it can create accurate pore structure without the smoothing effect. For accurate velocity estimation, the resolution of pore structure should be at least three times higher than that for permeability simulation.

• Geophysics and Geophysical Exploration
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• v.17 no.4
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• pp.231-241
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• 2014

Recently, single channel seismic survey for engineering purpose have been used widely taking advantage of simple processing. However it is very difficult to obtain high fidelity subsurface image by single channel seismic due to insufficient fold coverage. Recently, seismic waveform inversion in multi channel seismic survey is utilized for accurate subsurface imaging even in complex terrains. In this paper, we propose the seismic waveform inversion algorithm for velocity model building using a single channel seismic data. We utilize the Gauss-Newton method and assume that subsurface model is 1-Dimensional. Seismic source estimation technique is used and offset effect is also corrected by removing delay time by offset. Proposed algorithm is verified by applying modified Marmousi2 model, and applied to field data set obtained in port of Busan.

• Geophysics and Geophysical Exploration
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• v.23 no.2
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• pp.97-114
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• 2020

Recently, many studies have been actively conducted on the application of machine learning in all branches of science and engineering. Studies applying machine learning are also rapidly increasing in all sectors of seismic exploration, including interpretation, processing, and acquisition. Among them, fault detection is a critical technology in seismic interpretation and also the most suitable area for applying machine learning. In this study, we introduced various machine learning techniques, described techniques suitable for fault detection, and discussed the reasons for their suitability. We collected papers published in renowned international journals and abstracts presented at international conferences, summarized the current status of the research by year and field, and intensively analyzed studies on fault detection using machine learning. Based on the type of input data and machine learning model, fault detection techniques were divided into seismic attribute-, image-, and raw data-based technologies; their pros and cons were also discussed.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.3
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• pp.261-270
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• 2017

Bad sitting postures are known to cause for a variety of diseases or physical deformation. However, it is not easy to fit right sitting posture for long periods of time. Therefore, methods of distinguishing and inducing good sitting posture have been constantly proposed. Proposed methods were image processing, using pressure sensor attached to the chair, and using the IMU (Internal Measurement Unit). The method of using IMU has advantages of simple hardware configuration and free of various constraints in measurement. In this paper, we researched on distinguishing sitting postures with a small amount of data using just one IMU. Feature extraction method was used to find data which contribution is the least for classification. Machine learning algorithms were used to find the best position to classify and we found best machine learning algorithm. Used feature extraction method was PCA(Principal Component Analysis). Used Machine learning models were five : SVM(Support Vector Machine), KNN(K Nearest Neighbor), K-means (K-means Algorithm) GMM (Gaussian Mixture Model), and HMM (Hidden Marcov Model). As a result of research, back neck is suitable position for classification because classification rate of it was highest in every model. It was confirmed that Yaw data which is one of the IMU data has the smallest contribution to classification rate using PCA and there was no changes in classification rate after removal it. SVM, KNN are suitable for classification because their classification rate are higher than the others.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.2 s.308
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• pp.21-32
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• 2006

The license plate recognition (LPR) unit consists of the following core components: plate region segmentation, individual character extraction, and character recognition. Out of the above three components, accuracy in the performance of plate region segmentation determines the overall recognition rate of the LPR unit. This paper proposes an algorithm for segmenting the license plate region on the front or rear of a vehicle in a fast and accurate manner. In the case of the proposed algorithm images are captured on the spot where unmanned monitoring of illegal parking and stowage is performed with a variety of roadway environments taken into account. As a means of enhancing the segmentation performance of the on-the-spot-captured images of license plate regions, the proposed algorithm uses a mathematical model for license plate colors to convert color images into digital data. In addition, this algorithm uses Gaussian smoothing and double threshold to eliminate image noises, one-pass boundary tracing to do region labeling, and MBR to determine license plate region candidates and extract individual characters from the determined license plate region candidates, thereby segmenting the license plate region on the front or rear of a vehicle through a verification process. This study contributed to addressing the inability of conventional techniques to segment the license plate region on the front or rear of a vehicle where the frame of the license plate is damaged, through processing images in a real-time manner, thereby allowing for the practical application of the proposed algorithm.