http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
우리나라 해안 형태를 고려한 국가 해안 쓰레기 자료 시각화 기법 연구
한재림,김태훈,이철용,최현우 한국디지털콘텐츠학회 2023 한국디지털콘텐츠학회논문지 Vol.24 No.10
The Ministry of Oceans and Fisheries has been carrying out the Korea National Beach Litter Monitoring Program since 2008to establish policies for preventing and managing marine waste. Korea is surrounded by the sea on three sides and has a complexcoastline, making it difficult to intuitively understand survey data by expressing it on a map. In addition, big data accumulated over a long period of time, such as coastal garbage surveys, is difficult to convey information intuitively. Therefore, visualizationtechnology that helps to intuitively derive insights by easily summarizing these data is needed. We devised a circular bar charttechnique considering the coastal shape of Korea for visualization of coastal waste survey data. This is meaningful in thatinformation can be easily delivered by expressing the complex elements of coastal waste by region, year, and type clearly andeffectively.
선박패스(V-Pass) 자료를 활용한 어업활동 지도 제작 연구 - 남해동부해역을 중심으로 -
한재림 ( Jae-rim Han ),김태훈 ( Tae-hoon Kim ),최은영 ( Eun Yeong Choi ),최현우 ( Hyun-woo Choi ) 한국지리정보학회 2021 한국지리정보학회지 Vol.24 No.1
해양공간계획은 해양을 체계적이고 합리적으로 관리하기 위해 9가지 용도구역으로 지정한다. 그중 하나가 어업활동의 보호와 육성을 비롯한 수산물의 지속 가능한 생산을 위해 필요한 어업활동보호구역이다. 본 연구는 V-Pass 자료를 활용하여 어업활동 지도를 제작하고 어업활동 밀집 공간을 도출함으로써 어업활동보호구역 지정에 필요한 요소 중 하나인 어업활동 공간을 정량적으로 파악하고자 한다. 이를 위해 V-Pass 자료를 정적 정보와 동적 정보가 결합된 데이터셋 구축, 어선 속도 계산, 어업활동 지점 추출, 비어업활동 공간 내의 자료 제거와 같은 전처리를 수행하였다. 최종적으로 선별된 V-Pass 점 자료를 이용하여 커널밀도추정으로 어업활동 지도를 제작하고 어업활동이 밀집된 공간을 분석하였다. 또한 어선의 업종과 계절에 따라 어업활동의 공간분포는 차이가 있음을 확인하였다. 본 연구를 통해 수행한 대용량 V-Pass 자료의 전처리 기법과 어업활동의 공간밀도 분석 방법은 향후 어업활동에 대한 공간특성평가 연구에 기여할 것으로 기대된다. Marine spatial planning(MSP) designates the marine as nine kinds of use zones for the systematic and rational management of marine spaces. One of them is the fishery protection zone, which is necessary for the sustainable production of fishery products, including the protection and fosterage of fishing activities. This study intends to quantitatively identify the fishing activity space, one of the elements necessary for the designation of fisheries protection zones, by mapping of fishery activities using V-Pass data and deriving the fishery activity concentrated zone. To this end, pre-processing of V-Pass data was performed, such as constructing a dataset that combines static and dynamic information, calculating the speed of fishing vessels, extracting fishing activity points, and removing data in non-fishing activity zone. Finally, using the selected V-Pass point data, a fishery activity map was made by kernel density estimation, and the concentrated space of fishery activity was analyzed. In addition, it was confirmed that there is a difference in the spatial distribution of fishing activities according to the type of fishing vessel and the season. The pre-processing technique of large volume V-Pass data and the mapping method of fishing activities performed through this study are expected to contribute to the study of spatial characteristics evaluation of fishing activities in the future.
V-Pass 자료를 활용한 어업활동 패턴 분석 - 울릉도 해역 중심 -
한재림(Jae-Rim Han),김태훈(Taehoon Kim),김범규(Bum-Kyu Kim),최현우(Hyun-Woo Choi) 한국해양환경·에너지학회 2021 한국해양환경·에너지학회 학술대회논문집 Vol.2021 No.10
해양수산부에서는 바다를 보존하고 어민들의 삶을 보호하기 위해 해양공간관리계획을 통해 어업활동보호구역을 지정하고 있다. 어업활동보호구역은 연근해조업실적, 어선활동자료 등 어업활동을 입증할 객관적 데이터를 기준으로 지정되어야 한다. 본 연구에서는 2018년 대용량 V-Pass 자료를 이용해 울릉도 해역에서 활동하는 어선의 이동과 어업활동 특성을 분류하고, 공간밀도분석을 통해 어업활동의 시공간적 분포를 탐색하는 것을 목적으로 한다. 이를 위해 V-Pass 자료로부터 어선 속도 계산, 비 어업활동 공간 내 자료 제거와 같은 전처리를 수행하였다. V-Pass 자료를 이용한 어업활동 분석 결과는 어업활동 핫스팟을 발견할 수 있디. 하지만 어종과 생산량을 알아내기는 사실상 어려운 일이다. 따라서 어업생산통계자료와 V-Pass 어업활동 빈도분석 자료를 활용하여 월별 어업활동이 집중적으로 수행된 공간에서의 대상 어종의 생산량을 유추하는 실험도 함께 수행하였다. 향후 어업생산 통계자료와 V-Pass 자료를 이용한 어업활동공간 분석 방법론의 개발이 필요하다. The Ministry of Oceans and Fisheries designates a fishing activity protection zone through a Marine Spatial Planning to preserve the sea and protect the lives of fishermen. Fishing activity protection zones should be designated based on objective data to prove fishing activities, such as coastal marine fishing performance and fishing vessels activity data. The purpose of this study is to classify the movement and fishing characteristics of fishing vessels operating on Ulleung Island in 2018 using large volume of V-Pass data and to explore the spatio-temporal distribution of fishing activities through spatial density analysis. To this end, preprocessing such as calculating the speed of fishing vessels and removing data in the non-fishing activity space was performed from the V-Pass data. The results of fishing activity analysis using V-Pass data can detect fishing activity hot spots. However, it is difficult to find out the species and production of fish. Therefore, an experiment was also conducted to infer the production of target fish species in the space where monthly fishing activities were intensively performed using the statistical data on fishery production and the frequency analysis data of V-Pass fishing activities. In the future, it is necessary to develop a fishing activity space analysis methodology using statistical data on fishery production and V-Pass data.
Random Forest Classifier-based Ship Type Prediction with Limited Ship Information of AIS and V-Pass
전호군,한재림 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.4
Identifying ship types is an important process to prevent illegal activities on territorial waters and assess marine traffic of Vessel Traffic Services Officer (VTSO). However, the TerrestrialAutomatic Identification System (T-AIS) collected at the ground station has over 50% of vessels that do not contain the ship type information. Therefore, this study proposes a method of identifying ship types through the Random Forest Classifier (RFC) from dynamic and static data of AIS and V-Pass for one year and the Ulsan waters. With the hypothesis that six features, the speed, course, length, breadth, time, and location, enable to estimate of the ship type, four classification models were generated depending on length or breadth information since 81.9% of ships fully contain the two information. The accuracy were average 96.4% and 77.4% in the presence and absence of size information. The result shows that the proposed method is adaptable to identifying ship types.