• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.599-607
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• 2021

Street-view images, which are omnidirectional scenes centered on a specific location on the road, can provide various obstacle information for the pedestrians. Pedestrian network data for the navigation services should reflect the up-to-date obstacle information to ensure the mobility of pedestrians, including people with disabilities. In this study, the object detection model was trained for the bollard as a major obstacle in Seoul using street-view images and a deep learning algorithm. Also, a process for updating information about the presence and number of bollards as obstacle properties for the crosswalk node through spatial matching between the detected bollards and the pedestrian nodes was proposed. The missing crosswalk information can also be updated concurrently by the proposed process. The proposed approach is appropriate for crowdsourcing data as the model trained using the street-view images can be applied to photos taken with a smartphone while walking. Through additional training with various obstacles captured in the street-view images, it is expected to enable efficient information update about obstacles on the road.

• Journal of Korean Neurosurgical Society
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• v.66 no.4
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• pp.382-392
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• 2023

Objective : The use of indocyanine green videoangiography (ICG-VA) to assess blood flow in the brain during cerebrovascular surgery has been increasing. Clinical studies on ICG-VA have predominantly focused on qualitative analysis. However, quantitative analysis numerical modelling for time profiling enables a more accurate evaluation of blood flow kinetics. In this study, we established a multiple exponential modified Gaussian (multi-EMG) model for quantitative ICG-VA to understand accurately the status of cerebral hemodynamics. Methods : We obtained clinical data of cerebral blood flow acquired the quantitative analysis ICG-VA during cerebrovascular surgery. Varied asymmetric peak functions were compared to find the most matching function form with clinical data by using a nonlinear regression algorithm. To verify the result of the nonlinear regression, the mode function was applied to various types of data. Results : The proposed multi-EMG model is well fitted to the clinical data. Because the primary parameters-growth and decay rates, and peak center and heights-of the model are characteristics of model function, they provide accurate reference values for assessing cerebral hemodynamics in various conditions. In addition, the primary parameters can be estimated on the curves with partially missed data. The accuracy of the model estimation was verified by a repeated curve fitting method using manipulation of missing data. Conclusion : The multi-EMG model can possibly serve as a universal model for cerebral hemodynamics in a comparison with other asymmetric peak functions. According to the results, the model can be helpful for clinical research assessment of cerebrovascular hemodynamics in a clinical setting.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.72-76
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• 2021

판례는 일반인 또는 법률 전문가가 사건에 참조하기 위해 가장 먼저 참고할 수 있는 재판의 선례이다. 하지만 이러한 판례의 유용성에도 불구하고 현 대법원 판례 검색 시스템은 판례 검색에 용이하지 않다. 왜냐하면 법률 전문 지식이 없는 일반인은 검색 의도에 부합하는 검색 결과를 정확히 도출하는 데 어려움이 있으며, 법률 전문가는 검색에 많은 시간과 비용이 들게 되기 때문이다. 이미 해외에서는 유사 케이스 매칭 데이터셋을 구축하여 일반인과 전문가로 하여금 유사 판례 검색을 용이하게 할 뿐만 아니라 여러 자연어 처리 태스크에도 활용하고 있다. 하지만 국내에는 법률 AI와 관련하여 오직 법률과 관련한 세부 태스크 수행에 초점을 맞춘 연구가 많으며, 리소스로서의 유사 케이스 매칭 데이터셋은 구축되어 있지 않다. 이에 본 논문에서는 리소스로서의 판례 데이터셋을 위해 딥러닝 알고리즘 중 문서의 의미를 반영할 수 있는 Doc2Vec 임베딩 모델과 SBERT 임베딩 모델을 적용하여 판례 문서 간 유사도를 측정·비교하였다. 그 결과 SBERT 모델을 통해 도출된 유사 판례가 문서 간 내용적 유사성이 높게 나타났으며, 이를 통해 SBERT 모델을 이용하여 유사 판례 매칭 기초 데이터셋을 구축하였다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.835-843
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• 2023

Given the structure of XML data, path and tree pattern matching algorithms play an important role in XML query processing. To facilitate decisions or relationships between nodes, nodes in an XML tree are typically labeled in a way that can quickly establish an ancestor-descendant on relationship between two nodes. However, these techniques have the disadvantage of re-labeling existing nodes or recalculating certain values if insertion occurs due to sequential updates. Therefore, in current labeling techniques, the cost of updating labels is very high. In this paper, we propose a new labeling technique called Fast XML encoding, which supports the update of order-sensitive XML documents without re-labeling or recalculation. It also controls the length of the label by reusing deleted labels at the same location in the XML tree. The proposed reuse algorithm can reduce the length of the label when all deleted labels are inserted in the same location. The proposed technique in the experimental results can efficiently handle order-sensitive queries and updates.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.313-322
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• 2020

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1307-1312
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• 2023

In this paper, we propose a hybrid camera system that combines cameras with different focal lengths and LiDAR (Light Detection and Ranging) sensors to address the core components of autonomous driving perception technology, which include object recognition and distance measurement. We extract objects within the scene and generate precise location and distance information for these objects using the proposed hybrid camera system. Initially, we employ the YOLO7 algorithm, widely utilized in the field of autonomous driving due to its advantages of fast computation, high accuracy, and real-time processing, for object recognition within the scene. Subsequently, we use multi-focal cameras to create depth maps to generate object positions and distance information. To enhance distance accuracy, we integrate the 3D distance information obtained from LiDAR sensors with the generated depth maps. In this paper, we introduce not only an autonomous vehicle platform capable of more accurately perceiving its surroundings during operation based on the proposed hybrid camera system, but also provide precise 3D spatial location and distance information. We anticipate that this will improve the safety and efficiency of autonomous vehicles.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.2
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• pp.175-181
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• 2024

In electrical power substations, bulky iron-core potential transformers (PTs) are installed in a tank of gas-insulated switchgear (GIS) to measure system voltages. This paper proposed a low-power voltage transformer (LPVT) that can replace the conventional iron-core PTs in response to the demand for the digitalization of substations. The prototype LPVT consists of a capacitive voltage divider (CVD) which is embedded in a spacer and an impedance matching circuit using passive components. The CVD was fabricated with a flexible PCB to acquire enough insulation performance and withstand vibration and shock during operation. The performance of the LPVT was evaluated at 80%, 100%, and 120% of the rated voltage (38.1 kV) according to IEC 61869-11. An accuracy correction algorithm based on LabVIEW was applied to correct the voltage ratio and phase error. The corrected voltage ratio and phase error were +0.134% and +0.079 min., respectively, which satisfies the accuracy CL 0.2. In addition, the voltage ratio of LPVT was analyzed in ranges of -40~+40℃, and a temperature correction coefficient was applied to maintain the accuracy CL 0.2. By applying the LPVT proposed in this paper to the same rating GIS, it can be reduced the length per GIS bay by 11%, and the amount of SF6 by 5~7%.

• Radiation Oncology Journal
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• v.24 no.4
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• pp.300-308
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• 2006

$\underline{Purpose}$: To develop a wireless CCTV system in semi-beam's eye view (BEV) to monitor daily patient setup in radiation therapy. $\underline{Materials\;and\;Methods}$: In order to get patient images in semi-BEV, CCTV cameras are installed in a custom-made acrylic applicator below the treatment head of a linear accelerator. The images from the cameras are transmitted via radio frequency signal (${\sim}2.4\;GHz$ and 10 mW RF output). An expected problem with this system is radio frequency interference, which is solved utilizing RF shielding with Cu foils and median filtering software. The images are analyzed by our custom-made software. In the software, three anatomical landmarks in the patient surface are indicated by a user, then automatically the 3 dimensional structures are obtained and registered by utilizing a localization procedure consisting mainly of stereo matching algorithm and Gauss-Newton optimization. This algorithm is applied to phantom images to investigate the setup accuracy. Respiratory gating system is also researched with real-time image processing. A line-laser marker projected on a patient's surface is extracted by binary image processing and the breath pattern is calculated and displayed in real-time. $\underline{Results}$: More than 80% of the camera noises from the linear accelerator are eliminated by wrapping the camera with copper foils. The accuracy of the localization procedure is found to be on the order of $1.5{\pm}0.7\;mm$ with a point phantom and sub-millimeters and degrees with a custom-made head/neck phantom. With line-laser marker, real-time respiratory monitoring is possible in the delay time of ${\sim}0.17\;sec$. $\underline{Conclusion}$: The wireless CCTV camera system is the novel tool which can monitor daily patient setups. The feasibility of respiratory gating system with the wireless CCTV is hopeful.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.307-314
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• 2008

This paper presents a new robust watermarking method for curves that uses informed-detection. To embed watermarks, the presented algorithm parameterizes a curve using the B-spline model and acquires the control points of the B-spline model. For these control points, 2D mesh are created by applying Delaunay triangulation and then the mesh spectral analysis is performed to calculate the mesh spectral coefficients where watermark messages are embedded in a spread spectrum way. The watermarked coefficients are inversely transformed to the coordinates of the control points and the watermarked curve is reconstructed by calculating B-spline model with the control points. To detect the embedded watermark, we apply curve matching algorithm using inflection points of curve. After curve registration, we calculate the difference between the original and watermarked mesh spectral coefficients with the same process for embedding. By calculating correlation coefficients between the detected and candidate watermark, we decide which watermark was embedded. The experimental results prove the proposed scheme is more robust than previous watermarking schemes against print-scan process as well as geometrical distortions.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.48-60
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• 2011

Nowadays, the 3D community is actively researching on 3D imaging and free-viewpoint video (FVV). The free-viewpoint rendering in multi-view video, virtually move through the scenes in order to create different viewpoints, has become a popular topic in 3D research that can lead to various applications. However, there are restrictions of cost-effectiveness and occupying large bandwidth in video transmission. An alternative to solve this problem is to generate virtual views using a single texture image and a corresponding depth image. A critical issue on generating virtual views is that the regions occluded by the foreground (FG) objects in the original views may become visible in the synthesized views. Filling this disocclusions (holes) in a visually plausible manner determines the quality of synthesis results. In this paper, a new approach for handling disocclusions using depth based inpainting algorithm in synthesized views is presented. Patch based non-parametric texture synthesis which shows excellent performance has two critical elements: determining where to fill first and determining what patch to be copied. In this work, a noise-robust filling priority using the structure tensor of Hessian matrix is proposed. Moreover, a patch matching algorithm excluding foreground region using depth map and considering epipolar line is proposed. Superiority of the proposed method over the existing methods is proved by comparing the experimental results.