Zhang et al., 2021 - Google Patents
A big data analytics method for the evaluation of ship-ship collision risk reflecting hydrometeorological conditionsZhang et al., 2021
View HTML- Document ID
- 16475531385299892925
- Author
- Zhang M
- Montewka J
- Manderbacka T
- Kujala P
- Hirdaris S
- Publication year
- Publication venue
- Reliability Engineering & System Safety
External Links
Snippet
This paper presents a big data analytics method for the evaluation of ship-ship collision risk in real operational conditions. The approach makes use of big data from Automatic Identification System (AIS) and nowcast data corresponding to time-dependent traffic …
- 238000011156 evaluation 0 title abstract description 13
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/66—Radar-tracking systems; Analogous systems where the wavelength or the kind of wave is irrelevant
- G01S13/72—Radar-tracking systems; Analogous systems where the wavelength or the kind of wave is irrelevant for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
- G01S13/723—Radar-tracking systems; Analogous systems where the wavelength or the kind of wave is irrelevant for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
- G01S13/726—Multiple target tracking
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhang et al. | A big data analytics method for the evaluation of ship-ship collision risk reflecting hydrometeorological conditions | |
Rong et al. | Spatial correlation analysis of near ship collision hotspots with local maritime traffic characteristics | |
Rong et al. | Data mining approach to shipping route characterization and anomaly detection based on AIS data | |
Zhang et al. | A machine learning method for the evaluation of ship grounding risk in real operational conditions | |
Rong et al. | Maritime traffic probabilistic prediction based on ship motion pattern extraction | |
Rong et al. | Ship collision avoidance behaviour recognition and analysis based on AIS data | |
Liu et al. | A quantitative method for the analysis of ship collision risk using AIS data | |
Fang et al. | Automatic identification system-based approach for assessing the near-miss collision risk dynamics of ships in ports | |
Zhang et al. | A systematic approach for collision risk analysis based on AIS data | |
Goerlandt et al. | An analysis of wintertime navigational accidents in the Northern Baltic Sea | |
Xin et al. | A simulation model for ship navigation in the “Xiazhimen” waterway based on statistical analysis of AIS data | |
Rawson et al. | A machine learning approach for monitoring ship safety in extreme weather events | |
Banda et al. | A risk analysis of winter navigation in Finnish sea areas | |
Hörteborn et al. | A revisit of the definition of the ship domain based on AIS analysis | |
Lee et al. | Maritime traffic route detection framework based on statistical density analysis from AIS data using a clustering algorithm | |
Tang et al. | Detection of abnormal vessel behaviour based on probabilistic directed graph model | |
Zhang et al. | Big data–based estimation for ship safety distance distribution in port waters | |
Kandel et al. | A data-driven risk assessment of Arctic maritime incidents: Using machine learning to predict incident types and identify risk factors | |
Liu et al. | Modelling dynamic maritime traffic complexity with radial distribution functions | |
Naus | Drafting route plan templates for ships on the basis of AIS historical data | |
Liu et al. | A data mining method for automatic identification and analysis of icebreaker assistance operation in ice-covered waters | |
Silveira et al. | Assessment of ship collision estimation methods using AIS data | |
Rawson et al. | Developing contextually aware ship domains using machine learning | |
Zhang et al. | A data-driven approach for ship-bridge collision candidate detection in bridge waterway | |
Rong et al. | A framework for ship abnormal behaviour detection and classification using AIS data |