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문헌 고찰을 위한 근거이론방법의 활용: 디지털 환경에서의 그림자 노동 개념 도출
박상철,이웅규,Park, Sangcheol,Lee, Woong-Kyu 한국지식경영학회 2019 지식경영연구 Vol.20 No.2
The objective of this paper is to present how to use Grounded Theory Methodology for conducting a literature review that produces new insights and conceptualizations. In this paper, we have employed Wolfswinkel et al.(2013)'s method, which is called by Grounded Theory Literature-Review Method, for a rigorous literature review. We have utilized this method to capture the concept and insights of individuals' shadow wok in digital environments. By analyzing the relevant literature based on Wolfswinkel et al.'s guide, we have extracted 73 codes in the coding steps and finally showed 12 categories by incorporating similar concepts from those codes. Based on the categories, we end this paper by developing the academic definitions of shadow work in digital environments.
YOLO 알고리즘 기반 국토위성영상의 선박 모니터링 가능성 평가 연구: 부산 신항과 캘리포니아 오클랜드항을 대상으로
박상철,박영빈,장소영,김태호,Park, Sangchul,Park, Yeongbin,Jang, Soyeong,Kim, Tae-Ho 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6
Maritime transport accounts for 99.7% of the exports and imports of the Republic of Korea; therefore, developing a vessel monitoring system for efficient operation is of significant interest. Several studies have focused on tracking and monitoring vessel movements based on automatic identification system (AIS) data; however, ships without AIS have limited monitoring and tracking ability. High-resolution optical satellite images can provide the missing layer of information in AIS-based monitoring systems because they can identify non-AIS vessels and small ships over a wide range. Therefore, it is necessary to investigate vessel monitoring and small vessel classification systems using high-resolution optical satellite images. This study examined the possibility of developing ship monitoring systems using Compact Advanced Satellite 500-1 (CAS500-1) satellite images by first training a deep learning model using satellite image data and then performing detection in other images. To determine the effectiveness of the proposed method, the learning data was acquired from ships in the Yellow Sea and its major ports, and the detection model was established using the You Only Look Once (YOLO) algorithm. The ship detection performance was evaluated for a domestic and an international port. The results obtained using the detection model in ships in the anchorage and berth areas were compared with the ship classification information obtained using AIS, and an accuracy of 85.5% and 70% was achieved using domestic and international classification models, respectively. The results indicate that high-resolution satellite images can be used in mooring ships for vessel monitoring. The developed approach can potentially be used in vessel tracking and monitoring systems at major ports around the world if the accuracy of the detection model is improved through continuous learning data construction.
Noise Removal for Level Set based Flower Segmentation
박상철,오강한,나인섭,김수형,양형정,이귀상,Park, Sang Cheol,Oh, Kang Han,Na, In Seop,Kim, Soo Hyung,Yang, Hyung Jeong,Lee, Guee Sang THE KOREAN INSTITUTE OF SMART MEDIA 2012 스마트미디어저널 Vol.1 No.2
본 연구에서는 노이즈를 제거하고 자연 영상에서 자동으로 꽃을 분할하는 후처리방법을 제시한다. 레벨 셋 알고리즘을 이용한 자연영상 꽃 분할에서는 레벨 셋이 에지 정보에만 의존하기 때문에 기대하지 않았던 분리된 노이즈들이 발생한다. 실험 결과는 제안 방법이 꽃 영역과 배경 영역의 많은 노이즈를 성공적으로 제거하였음을 보여준다. In this paper, post-processing step is presented to remove noises and develop a fully automated scheme to segment flowers in natural scene images. The scheme to segment flowers using a level set algorithm in the natural scene images produced unexpected and isolated noises because the level set relies only on the color and edge information. The experimental results shows that the proposed method successfully removes noises in the foreground and background.