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1. (WO2016175425) VESSEL TRAFFIC SERVICE EXPERT SYSTEM USING DEEP LEARNING ALGORITHM, AND CONTROL METHOD THEREOF
Latest bibliographic data on file with the International Bureau   

Pub. No.:    WO/2016/175425    International Application No.:    PCT/KR2015/014180
Publication Date: 03.11.2016 International Filing Date: 23.12.2015
IPC:
G08G 3/00 (2006.01), G08G 3/02 (2006.01)
Applicants: KOREA INSTITUTE OF OCEAN SCIENCE & TECHNOLOGY [KR/KR]; (Sa-dong)787 Haean-ro, Sangnok-gu Ansan-si Gyeonggi-do 426-744 (KR)
Inventors: OH, Jae Yong; (KR).
KIM, Hye Jin; (KR)
Agent: KIM, Jung Su; (KR)
Priority Data:
10-2015-0061044 30.04.2015 KR
Title (EN) VESSEL TRAFFIC SERVICE EXPERT SYSTEM USING DEEP LEARNING ALGORITHM, AND CONTROL METHOD THEREOF
(FR) SYSTÈME EXPERT DE SERVICES DE TRAFIC MARITIME UTILISANT UN ALGORITHME D'APPRENTISSAGE EN PROFONDEUR, ET PROCÉDÉ DE COMMANDE ASSOCIÉ
(KO) 딥러닝 알고리즘을 이용한 해상교통관제 전문가 시스템 및 그 제어 방법
Abstract: front page image
(EN)The present invention relates to a vessel traffic service expert system using a deep learning algorithm, comprising: a vessel traffic service (VTS) center for providing, in real time, vessel traffic information including vessel information on sailing vessels and harbor area information; a vessel traffic information database unit for storing, in real time, the vessel traffic information provided from the VTS center; a vessel traffic service learning unit for receiving the vessel traffic information in a set range from the vessel traffic information database unit and generating control standard information which determines a vessel state and an area state by using the deep learning algorithm; a vessel traffic service analysis unit for generating, in real time, vessel traffic service information including the vessel state information and the area state information by comparing and analyzing the vessel traffic information and the control standard information which are being stored in real time; and an vessel traffic service information display unit for displaying the generated control standard information by means of an electronic navigation chart, wherein the control standard information includes vessel state standard information and area state standard information.
(FR)L'invention concerne un système expert de services de trafic maritime utilisant un algorithme d'apprentissage en profondeur, ledit système comprenant : un centre de services de trafic maritime (STM) permettant de fournir, en temps réel, des informations sur le trafic maritime comprenant des informations sur les navires et des informations sur la zone portuaire ; une unité de base de données d'informations sur le trafic maritime permettant de stocker, en temps réel, les informations sur le trafic maritime fournies par le centre STM ; une unité d'apprentissage de services de trafic maritime permettant de recevoir les informations sur le trafic maritime dans une plage définie en provenance de l'unité de base de données d'informations sur le trafic maritime et de générer des informations standard de commande qui déterminent l'état d'un navire et l'état d'une zone au moyen de l'algorithme d'apprentissage en profondeur ; une unité d'analyse de services de trafic maritime permettant de générer, en temps réel, des informations sur les services de trafic maritime comprenant les informations sur l'état du navire et les informations sur l'état de la zone en comparant et en analysant les informations sur le trafic maritime et les informations standard de commande qui sont stockées en temps réel ; et une unité d'affichage d'informations sur les services de trafic maritime permettant d'afficher les informations standard de commande générées au moyen d'une carte de navigation électronique, les informations standard de commande comprenant des informations standard sur l'état des navires et des informations standard sur l'état de la zone.
(KO)본 발명은 딥러닝(Deep Learning) 알고리즘을 이용한 해상교통관제 전문가 시스템에 관한 것으로, 운항중인 선박의 선박정보 및 항만영역 정보를 포함하는 해상교통 정보를 실시간으로 제공하는 VTS(Vessel Traffic Service) 센터와, 상기 VTS 센터에서 제공하는 해상교통 정보를 실시간 저장하는 해상교통 정보 데이터베이스부와, 상기 해상교통 정보 데이터베이스부로부터 설정된 범위의 상기 해상교통 정보를 제공받고, 딥러닝(Deep Learning) 알고리즘을 사용하여 선박상태 및 영역상태를 판단하는 관제기준 정보를 생성하는 해상교통관제 학습부와, 실시간 저장되는 상기 해상교통 정보 및 상기 관제기준 정보를 비교분석하여 선박상태 정보 및 영역상태 정보를 포함하는 해상관제 정보를 실시간 생성하는 해상교통관제 분석부 및 생성된 상기 해상관제 정보를 전자해도를 통해 표시하는 해상교통관제 정보 표시부를 포함하고, 상기 관제기준 정보는 선박상태 기준정보 및 영역상태 기준정보를 포함하는 것을 특징으로 한다.
Designated States: AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JP, KE, KG, KN, KP, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW.
African Regional Intellectual Property Organization (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, UG, ZM, ZW)
Eurasian Patent Organization (AM, AZ, BY, KG, KZ, RU, TJ, TM)
European Patent Office (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR)
African Intellectual Property Organization (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG).
Publication Language: Korean (KO)
Filing Language: Korean (KO)