• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.345-345
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• 2020

우리나라는 물산업 강국 도약을 위한 글로벌 경쟁력을 갖추기 위해 2015년 세계물포럼 이후 복합 수재해 대응과 능동적 하천관리 분야가 중요시되고 있다. 기존의 하천관리는 수위관측소 및 하도계획을 중심으로 이루어져왔으나 최근에는 항공측량, 드론 등 원격관리시스템에 기반을 둔 하천 통로에 대한 입체적인 3차원 관리로의 인식 전환이 고려되는 추세이다. 대부분 수위 및 하도를 중심으로 한 조사방식은 계측장비, 인력, 시간 등 많은 소요비용이 필요하게 되어 급변하는 하천 공간 조사 기술로 제한적 요소가 많아 현재의 하천조사 시스템으로는 하천관리에 필요한 데이터를 환경변화에 맞추어 신속하고 정확하게 취득하기가 어려운 실정으로 하천관리 고도화를 위해 필요한 자료를 적기에 신속하고 효율적으로 구축할 수 있는 기술 개발 필요한 실정이다. 사회 환경의 발달로 급변하는 환경에 맞는 적정한 하천관리를 위해서는 유역 및 하천에 대한 수문정보 생산 및 하상변동조사가 필요하고 첨단기술(ICT)을 활용하여 조사의 효율성, 안전성 및 정확성을 높일 필요성이 있다. 한편, 하천유역조사는 하천유역에 대한 다양한 정보 수집을 목표로 하나 그간 유역조사에 대한 체계적인 연구가 이루어지지 않아 수요자의 요구 자료 제공이 미흡한 실정이다. 최근 수심, 하상, 하천재료 등 하천법에 규정된 측량은 현장 직접계측을 통해 실시되고 있으며 측량의 공간적·시간적 범위의 변동으로 기존 방식의 비용이 급증하게 되어 신기술을 통한 효율화가 필요한 실정이다. 현재 하천조사를 위해 위성 및 유인항공기 등 다양한 방법이 활용되고 있으나 고빙용을 수반하고 위성영상은 공간해상도가 낮아 폭이 좁은 하천에 적용하는데 한계가 있다. 또한, 폭이 좁고 길게 형성된 하천의 특성상 항공측량 보다 드론이 효율적으로 드론에 레이저 광선으로 지형을 측량하는 장비를 탑재해 3차원 지형을 측량하는 방법이 유리하다. 따라서 본 연구에서는 동진강 상류 하천을 대상으로 드론을 활용할 경우 투입인력 및 소요시간 절감, 장비 및 인력 진입 불가지역에 대한 정보획득, 높은 공간해상도, 항공측량 대비 경제성이 높은 것으로 판단되어 드론을 활용하여 하천지형 모니터링을 실시하여 유역관리 및 수질모델링에 필요한 지도 생성 및 유역의 공간 정보를 획득하여 향후 유역관리 모형의 기초자료로 활용하고자 하였다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.363-373
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• 2020

During last decades numerous studies generating orthoimage have been carried out. Traditional methods require exterior orientation parameters of aerial images and precise 3D object modeling data and DTM (Digital Terrain Model) to detect and recover occlusion areas. Furthermore, it is challenging task to automate the complicated process. In this paper, we proposed a new concept of true orthoimage generation using DL (Deep Learning). DL is rapidly used in wide range of fields. In particular, GAN (Generative Adversarial Network) is one of the DL models for various tasks in imaging processing and computer vision. The generator tries to produce results similar to the real images, while discriminator judges fake and real images until the results are satisfied. Such mutually adversarial mechanism improves quality of the results. Experiments were performed using GAN-based Pix2Pix model by utilizing IR (Infrared) orthoimages, intensity from LiDAR data provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) through the ISPRS (International Society for Photogrammetry and Remote Sensing). Two approaches were implemented: (1) One-step training with intensity data and high resolution orthoimages, (2) Recursive training with intensity data and color-coded low resolution intensity images for progressive enhancement of the results. Two methods provided similar quality based on FID (Fréchet Inception Distance) measures. However, if quality of the input data is close to the target image, better results could be obtained by increasing epoch. This paper is an early experimental study for feasibility of DL-based true orthoimage generation and further improvement would be necessary.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.4
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• pp.19-32
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• 2021

It is well known that any maintenance works for aircrafts must be carried out strictly in accordance with the specified maintenance manuals, especially for military airplanes. According to our previous studies, the largest portion of the maintenance jobs for military aircrafts is found to be related to the assembly/disassembly of various parts, which requires precise understanding of the work procedures as well as correlation between interconnected parts let alone grasping of the exact shapes of parts involved. However, the conventional manuals for aircraft maintenance have failed to provide enough information required for the efficient maintenance except for simple texts and vague pictures, which are far from being sufficient sets of information. On the contrary, unlike incomplete conventional manuals with poor contents, 3D modeling SW could provide us with not only powerful visualization tool even to see through inside any assembly but also freedom to watch parts under test from any angle we want. In addition, the maintenance personnels could learn the precise maintenance procedures through repeatedly watching 3D animated version of the maintenance work as if they were on the field. In this study, we have suggested the efficient procedures to develop 3D manual for aircraft maintenance using 3D modeling SW, Solidworks and implemented a 3D maintenance manual for Integrated Drive Generator(IDG) in Boeing 747. Characteristics of the developed 3D manual has been analyzed in comparison with the conventional ones as well. It is shown that the suggested method could be easily applied to develop a 3D maintenance manual for commercial airplanes since the maintenance works involving assembly/disassembly of major parts are very similar regardless of aircraft types.

• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.244-249
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• 2017

Steganalysis is to detect information hidden by steganography inside general data such as images. There are stegoanalysis techniques that use machine learning (ML). Existing ML approaches to steganalysis are based on extracting features from stego images and modeling them. Recently deep learning-based methodologies have shown significant improvements in detection accuracy. However, all the existing methods, including deep learning-based ones, have a critical limitation in that they can only detect stego images that are created by a specific steganography method. In this paper, we propose a generalized steganalysis method that can model multiple types of stego images using deep learning. Through various experiments, we confirm the effectiveness of our approach and envision directions for future research. In particular, we show that our method can detect each type of steganography with the same level of accuracy as that of a steganalysis method dedicated to that type of steganography, thereby demonstrating the general applicability of our approach to multiple types of stego images.

• Journal of Satellite, Information and Communications
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• v.10 no.1
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• pp.56-61
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• 2015

In this paper, the 3D positioning of IoT sensors with the Unity engine of android platform based home-hub was proposde for IoT(Internet of Things) users. Especially, the monitoring of IoT sensor and battery status was designed with the clustering of IoT sensor's position. The 3D positioning of RSSI(received signal strength indicator) and angle for new IoT sensor according to clustering method was described with the cooperation of beacon and received arrival signal time. This unity engine based smart hub platform can monitor the working situation of IoT sensors, and apply 3D video with texture for the life-cycling of many IoT sensors simultaneously. rs was described with RSSI(received signal strength indicator) and received angle.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.1
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• pp.45-52
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• 2001

The proximal isovelocity surface area (PISA) method is an effective way of measuring the regurgitant blood flow rate in the mitral valve. This method defines the modelling required to describe the geometry of the isotach of the PISA. In the normal PISA flow model, the flow rate is calculated assuming that the surface of the isotach is either hemispherical or non-hemispherical numerically. However, this paper evaluated the estimate flow rate using a direct surface subdivision flow model based on the height field after isotach extraction using a region-based scheme. To validate the proposed method, the various PISA flow models were compared using pusatile color Doppler images with flow rates ranging from $30\;cm^3/sec\;to\;60\;cm^3/sec$ flow rate. Whereas the hemispherical flow model had a mean value of $29\;cm^3/sec$ and underestimated the measured flow rate by 35%, the proposed model and non-hemispherical model produced a c;ame mean value of $45\;cm^3/sec$, moreover, both flow models produced a similar pulsatile flow rate.

• The Journal of Bigdata
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• v.6 no.2
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• pp.71-84
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• 2021

Since small and medium-sized enterprises fell short of the securement of technological competitiveness in the field of big data and artificial intelligence (AI) field-core technologies of the Fourth Industrial Revolution, it is important to strengthen the competitiveness of the overall industry through technology commercialization. In this study, we aimed to propose a priority related to technology transfer and commercialization for practical use of public research results. We utilized public research performance information, improving missing values of 6T classification by deep learning model with an ensemble method. Then, we conducted topic modeling to derive the converging fields of big data and AI. We classified the technology fields into four different segments in the technology portfolio based on technology activity and technology efficiency, estimating the potential of technology commercialization for those fields. We proposed a priority of technology commercialization for 10 detailed technology fields that require long-term investment. Through systematic analysis, active utilization of technology, and efficient technology transfer and commercialization can be promoted.

• Journal of the Korean Society of Radiology
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• v.11 no.5
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• pp.371-377
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• 2017

General phantom for practical X-ray photography Practical phantom is an indispensable textbook for radiology, but it is difficult for existing commercially available phantom to be equipped with various kinds of phantom because it is an expensive import. Using 3D printing technology, I would like to make the general phantom for practical X-ray photography less expensive and easier. We would like to use a skeleton model that was produced based on CT image data using a 3D printer of FDM (Fused Deposition Modeling) method as a phantom for general X-ray imaging. 3D slicer 4.7.0 program is used to convert CT DICOM image data into STL file, convert it to G-code conversion process, output it to 3D printer, and create skeleton model. The phantom of the completed phantom was photographed by X - ray and CT, and compared with actual medical images and phantoms on the market, there was a detailed difference between actual medical images and bone density, but it could be utilized as a practical phantom. 3D phonemes that can be used for general X-ray practice can be manufactured at low cost by utilizing 3D printers which are low cost and distributed and free 3D slicer program for research. According to the future diversification and research of 3D printing technology, it will be possible to apply to various fields such as health education and medical service.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.487-501
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• 2020

As a viable option for retrieval of LST (Land Surface Temperature), this paper presents a DNN (Deep Neural Network) based approach using 148 Landsat 8 images for South Korea. Because the brightness temperature and emissivity for the band 10 (approx. 11-㎛ wavelength) of Landsat 8 are derived by combining physics-based equations and empirical coefficients, they include uncertainties according to regional conditions such as meteorology, climate, topography, and vegetation. To overcome this, we used several land surface variables such as NDVI (Normalized Difference Vegetation Index), land cover types, topographic factors (elevation, slope, aspect, and ruggedness) as well as the T₀ calculated from the brightness temperature and emissivity. We optimized four seasonal DNN models using the input variables and in-situ observations from ASOS (Automated Synoptic Observing System) to retrieve the LST, which is an advanced approach when compared with the existing method of the bias correction using a linear equation. The validation statistics from the 1,728 matchups during 2013-2019 showed a good performance of the CC=0.910~0.917 and RMSE=3.245~3.365℃, especially for spring and fall. Also, our DNN models produced a stable LST for all types of land cover. A future work using big data from Landsat 5/7/8 with additional land surface variables will be necessary for a more reliable retrieval of LST for high-resolution satellite images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.5
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• pp.379-387
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• 2006

The real world is spatially modelled conceptually either as discrete objects or earth surface. The generated data models are then usually represented as vector and raster respectively. Although there are limited cases where only one data model is sufficient to solve the spatial problem at hand, it is now generally accepted that GIS should be able to handle various types of data model. Recent advances in spatial technology introduced even more variety of heterogeneous data models and the need is ever growing to handle and manage efficiently these large variety of spatial data. The OGC (Open GIS Consortium), an international organization pursuing standardization in the geospatial industry. recently introduced the GMLJP2 (Geographic Mark-Up Language JP2) format which enables store and handle heterogeneous spatial data. The GMLJP2 format, which is based on the JP2 format which is an abbreviation for JPEG2000 wavelet image compression format, takes advantage of the versatility of the GML capabilities to add extra data on top of the compressed image. This study takes a close look into the GMLJP2 format to analyse and exploit its potential to handle and mange hetergeneous spatial data. Aerial image, digital map and LIDAR data were successfully transformed end archived into a single GMLJP2 file. A simple viewing program was made to view the heterogeneous spatial data from this single file.