• Journal of Broadcast Engineering
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• v.26 no.3
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• pp.296-307
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• 2021

A digital hologram (DH) is an ultra-high value-added video content that includes 3D information in 2D data. Therefore, its intellectual property rights must be protected for its distribution. For this, this paper proposes a watermarking method of DH using a deep neural network. This method is a watermark (WM) invisibility, attack robustness, and blind watermarking method that does not use host information in WM extraction. The proposed network consists of four sub-networks: pre-processing for each of the host and WM, WM embedding watermark, and WM extracting watermark. This network expand the WM data to the host instead of shrinking host data to WM and concatenate it to the host to insert the WM by considering the characteristics of a DH having a strong high frequency component. In addition, in the training of this network, the difference in performance according to the data distribution property of DH is identified, and a method of selecting a training data set with the best performance in all types of DH is presented. The proposed method is tested for various types and strengths of attacks to show its performance. It also shows that this method has high practicality as it operates independently of the resolution of the host DH and WM data.

• Journal of Dental Rehabilitation and Applied Science
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• v.38 no.3
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• pp.138-149
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• 2022

The clinical applicability of ceramics can be increased by analyzing the causes of fractures after fracture testing of dental ceramics. Fractography to analyze the cause of fracture of dental ceramics is being widely applied with the development of imaging technologies such as scanning electron microscopy. Setting the experimental conditions is important for accurate interpretation. The fractured specimens should be stored and cleaned to avoid contamination, and metal pretreatment is required for better observation. Depending on the type of fracture, there are dimple rupture, cleavage, and decohesive rupture mainly observed in metals, and fatigue fractures and conchoidal fractures observed in ceramics. In order to reproduce fatigue fracture in the laboratory, which is the main cause of fracture of ceramics, a dynamic loading for observing slow crack growth is essential, and the load conditions and number of loads must be appropriately set. A typical characteristic of a fracture surface of ceramic is a hackle, and the causes of fracture vary depending on the shape of hackle. Fractography is a useful method for in-depth understanding of fractures of dental ceramics, so it is necessary to follow the exact experimental procedure and interpret the results with caution.

• Journal of IKEEE
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• v.27 no.3
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• pp.340-344
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• 2023

In this paper, we propose a study on the development of deep learning structure to secure visibility of outdoor LED display board according to weather change. The proposed technique secures the visibility of the outdoor LED display board by automatically adjusting the LED luminance according to the weather change using deep learning using an imaging device. In order to automatically adjust the LED luminance according to weather changes, a deep learning model that can classify the weather is created by learning it using a convolutional network after first going through a preprocessing process for the flattened background part image data. The applied deep learning network reduces the difference between the input value and the output value using the Residual learning function, inducing learning while taking the characteristics of the initial input value. Next, by using a controller that recognizes the weather and adjusts the luminance of the outdoor LED display board according to the weather change, the luminance is changed so that the luminance increases when the surrounding environment becomes bright, so that it can be seen clearly. In addition, when the surrounding environment becomes dark, the visibility is reduced due to scattering of light, so the brightness of the electronic display board is lowered so that it can be seen clearly. By applying the method proposed in this paper, the result of the certified measurement test of the luminance measurement according to the weather change of the LED sign board confirmed that the visibility of the outdoor LED sign board was secured according to the weather change.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1177-1185
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• 2022

The water and sewage system is an infrastructure that provides safe and clean water to people. In particular, since the water and sewage pipelines are buried underground, it is very difficult to detect system defects. For this reason, the diagnosis of pipelines is limited to post-defect detection, such as system diagnosis based on the images taken after taking pictures and videos with cameras and drones inside the pipelines. Therefore, real-time detection technology of pipelines is required. Recently, pipeline diagnosis technology using advanced equipment and artificial intelligence techniques is being developed, but AI-based defect detection technology requires a variety of learning data because the types and numbers of defect data affect the detection performance. Therefore, in this study, various defect scenarios are implemented using 3D printing model to improve the detection performance when detecting defects in pipelines. Afterwards, the collected images are performed to pre-processing such as classification according to the degree of risk and labeling of objects, and real-time defect detection is performed. The proposed technique can provide real-time feedback in the pipeline defect detection process, and it would be minimizing the possibility of missing diagnoses and improve the existing water and sewerage pipe diagnosis processing capability.

• Journal of Internet Computing and Services
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• v.25 no.5
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• pp.131-140
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• 2024

Aids to navigation, such as buoys used in maritime environments, play a crucial role in providing accurate information to navigating vessels, enabling them to precisely determine their position and maintain safe routes by marking surrounding hazardous areas. However, collisions between ships and these aids result in substantial costs for buoy damage and repair. While high-end equipment is currently used to prevent such accidents, its widespread adoption is hindered by cost concerns. This paper presents research on a maritime object detection algorithm utilizing embedded systems to address this issue. Previous studies employed the Hough transform for horizon detection, but its high computational demands posed challenges for real-time processing. To overcome this limitation, our approach first performs image segmentation, followed by an optimized Otsu algorithm for horizon detection. Subsequently, we establish a Region of Interest (ROI) based on the detected horizon, focusing on areas with a high risk of ship collision. Within this ROI, particularly below the horizon line, maritime objects are detected. A Convolutional Neural Network (CNN) model is then applied to determine whether the detected objects are ships. Objects classified as ships within the ROI are considered potential collision risks.

• The Korean Journal of Applied Statistics
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• v.37 no.6
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• pp.703-719
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• 2024

In this paper, we propose a procedure for identifying sports game highlights using online live comments. Highlights in sports games can be defined as important events that capture the viewers' attention, often involving scoring. Internet portals and many other platforms may edit game videos and provide highlight scenes. Since reviewing each game and identifying highlights manually can be labor-intensive and time-consuming, statistical learning procedures are beneficial for the automatic identification of highlights. In this paper, we extract words from preprocessed live comments, and divide videos into one-minute intervals to create a document-term matrix summarizing the frequency of word occurrences at each timestamp. Next, employing the log-odds ratios as weights, the weighted sum of the term frequencies at each timestamp is calculated to generate an event score. Then, the nonparametric kernel regression model is applied to event scores to estimate the underlying trend of event scores. The residual for each timestamp is obtained as the difference between underlying trend and the actual event score. Finally, we predict events as those instances whose residuals are widely deviated from the estimated trend levels. Applying this approach to real soccer games, we could predict actual highlights more accurately compared to simply using comment frequencies.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.2179-2183
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• 2008

지금까지 분포형 모형 개발에 대한 많은 노력이 있음에도 불구하고 여러 제약사항들에 의해 잠재력을 보여주는 정도로 활용되어 왔으나, 최근 급속도로 발전하는 컴퓨터의 계산능력, DEM 등 디지털정보의 구축이 진행되어 오고 있고, GIS 및 인공위성 영상기법의 발달로 공간적인 비균질성을 고려하여 유출과정에서 운동역학적인 이론을 기반으로 물의 흐름을 수리학적으로 추적해 나가는 물리적기반의 분포형 유출모형의 활용도가 높아지고 있다. 본 모형개발에 있어 이론적 배경이 된 모형은 1998년부터 일본 교토대학 방재연구소 코지리 연구실에서 개발 중인 Hydro-BEAM으로 유역 물순환의 건전성을 평가하기 위하여 장기간의 유역 내 유량, 수질을 시계열 및 공간적으로 파악하여 유역의 영향평가를 위해 개발된 물리적 기반의 격자구조를 가진 분포형 장기유출 모형이다. 유출량 계산은 유역내 수평 유출량산정 모듈로서 평면 분포형의 격자형을, 연직 분포형으로는 $A{\sim}B$층의 수평유출량은 하천으로 유입하고, C층은 하천유량에 영향을 미치지 않는 지하수층으로 가정하는 다층모형을 이용해서 A층, 지표 및 하도흐름은 운동파 법(kinematic wave)으로, $B{\sim}C$층의 유출량은 선형저류법으로 계산하는 모형이다. 본 연구에서는 격자흐름방향을 4방향에서 8방향으로 개선하였고, 모형의 각종 수문매개변수들을 GIS와 연계하여 직접 입력할 수 있도록 하였으며, 물리적기반의 침투과정을 모의할 수 있도록 Green & Ampt모듈을 추가하고, 향후 레이더 강우 및 수치예보강우의 홍수유출예측을 염두에 두고 격자강우량을 활용할 수 있도록 하는 등 홍수유출해석을 위한 분포형 강우-유출모형으로 개선 하였고, 이를 남강댐유역에 적용해 봄으로써 모형의 적용성을 검토해 보고자 하였다. 홍수기동안의 지표흐름과 지표하 흐름의 시간적 변화와 공간적 분포를 모의할 수 있었으며, 전처리과정으로서 ArcGIS 혹은 ArcView등의 GIS 프로그램을 이용하여 모형에 필요한 ASCII형태의 입력 매개 변수 자료들을 가공하였다. 또한 후처리과정으로서 모형의 수행결과인 유역내의 유출량 분포 등을 GIS상에서 나타낼 수 있도록 ASCII형태로 출력하도록 구성하였다. 남강댐유역을 대상으로 유역을 500m의 정방형 격자로 분할하고 수계망을 통하여 유역 출구까지 운동파이론에 의해 추적 계산하였으며, 수문곡선 비교결과 재현성 높은 결과를 보여주었다. 모형의 정확성 및 실용성에 대한 보다 정확한 평가를 위해서는 향후 다양한 강우 사상 혹은 다양한 크기의 유역에 대한 유출량의 재현성 및 매개변수 등에 검증이 이루어져야 할 것이다.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.111-124
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• 2010

To monitor the environment of land surface change is considered as an important research field since those parameters are related with land use, climate change, meteorological study, agriculture modulation, surface energy balance, and surface environment system. For the change detection, many different methods have been presented for distributing more detailed information with various tools from ground based measurement to satellite multi-spectral sensor. Recently, using high resolution satellite data is considered the most efficient way to monitor extensive land environmental system especially for higher spatial and temporal resolution. In this study, we use two different spatial resolution satellites; the one is SPOT/VEGETATION with 1 km spatial resolution to detect coarse resolution of the area change and determine objective threshold. The other is Landsat satellite having high resolution to figure out detailed land environmental change. According to their spatial resolution, they show different observation characteristics such as repeat cycle, and the global coverage. By correlating two kinds of satellites, we can detect land surface change from mid resolution to high resolution. The K-mean clustering algorithm is applied to detect changed area with two different temporal images. When using solar spectral band, there are complicate surface reflectance scattering characteristics which make surface change detection difficult. That effect would be leading serious problems when interpreting surface characteristics. For example, in spite of constant their own surface reflectance value, it could be changed according to solar, and sensor relative observation location. To reduce those affects, in this study, long-term Normalized Difference Vegetation Index (NDVI) with solar spectral channels performed for atmospheric and bi-directional correction from SPOT/VEGETATION data are utilized to offer objective threshold value for detecting land surface change, since that NDVI has less sensitivity for solar geometry than solar channel. The surface change detection based on long-term NDVI shows improved results than when only using Landsat.

• Progress in Medical Physics
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• v.15 no.4
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• pp.202-209
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• 2004

Flat panel based digital radiography (DR) systems have recently become useful and important in the field of diagnostic radiology. For DRs with amorphous silicon photosensors, CsI(TI) is normally used as the scintillator, which produces visible light corresponding to the absorbed radiation energy. The visible light photons are converted into electric signal in the amorphous silicon photodiodes which constitute a two dimensional array. In order to produce good quality images, detailed behaviors of DR detectors to radiation must be studied. The relationship between air exposure and the DR outputs has been investigated in many studies. But this relationship was investigated under the condition of the fixed tube voltage. In this study, we investigated the relationship between the DR outputs and X-ray in terms of the absorbed energy in the detector rather than the air exposure using SPEC-l8, an X-ray energy spectrum model. Measured exposure was compared with calculated exposure for obtaining the inherent filtration that is a important input variable of SPEC-l8. The absorbed energy in the detector was calculated using algorithm of calculating the absorbed energy in the material and pixel values of real images under various conditions was obtained. The characteristic curve was obtained using the relationship of two parameter and the results were verified using phantoms made of water and aluminum. The pixel values of the phantom image were estimated and compared with the characteristic curve under various conditions. It was found that the relationship between the DR outputs and the absorbed energy in the detector was almost linear. In a experiment using the phantoms, the estimated pixel values agreed with the characteristic curve, although the effect of scattered photons introduced some errors. However, effect of a scattered X-ray must be studied because it was not included in the calculation algorithm. The result of this study can provide useful information about a pre-processing of digital radiography.

• Geophysics and Geophysical Exploration
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• v.14 no.4
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• pp.289-297
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• 2011

Chirp sub-bottom profilers (SBP) data are comparatively higher-resolution data than other seismic data and it's raw signal can be used as a final section after conducting basic filtering. However, Chirp SBP signal has possibility to include various noise in high-frequency band and to provide the distorted image for the complex geological structure in time domain. This study aims at the goal to establish the workflow of Chirp SBP data processing for enhanced image and to analyze the proper parameters for the domestic continental shelf. After pre-processing, we include the dynamic S/N filtering to eliminate the high-frequency component noise, the dip scan stack to enhance the continuity of reflection events and finally the post-stack depth migration to correct the distorted structure on the time domain sections. We demonstrated our workflow on the data acquired by domestically widely used equipments and then we could obtain the improved seismic sections of depth domain. This workflow seems to provide the proper seismic section to interpretation when applied to data processing of Chirp SBP that are largely used for domestic acquisition.