scholarly journals (AUTOMATIC IDENTIFICATION SYSTEM (AIS) SATELIT DATA CORRECTION USING INTERPOLATION AND EXTRAPOLATION METHODE, (Case Study : LAPAN-A2 and LAPAN-A3 Satellite))

2019 ◽  
Vol 16 (2) ◽  
pp. 159
Author(s):  
Abdul Karim
Keyword(s):  
Big Data ◽  
Technology Use ◽  
Multiple Access ◽  
Automatic Identification ◽  
Identification System ◽  
Extrapolation Methods ◽  
Automatic Identification System ◽  
Valid Data ◽  
Time Division

Nasional Institute Aerounautics and Space (LAPAN) has two satellites (LAPAN-A2 and LAPAN-A3) that are carry Automatic Identification System (AIS) sensors. It can be use for monitoring Indonesian maritime. The altitude of the satellite about 642 Km and 500 km so it has a wide area covered and receive big data. The problem is the AIS technology use the Time Division Multiple Access (TDMA) system that has limitations in handling big data so that some data received can be damaged due to collision. Therefore, in this research has been done the analysis and correction data using interpolation and extrapolation methods. The results  is improvements of valid data about 22,6 % for LAPAN-A2 satellite and 20,8 % for LAPAN-A3 satellite.

Automatic identification system data-driven model for analysis of ship domain near bridge-waters

2021 ◽  
pp. 1-22
Author(s):  
Lei Jinyu ◽  
Liu Lei ◽  
Chu Xiumin ◽  
He Wei ◽  
Liu Xinglong ◽  
...  
Keyword(s):  
Least Squares Method ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Automatic Identification System ◽  
Domain Models ◽  
System Data ◽  
Quantification Model ◽  
Highly Correlated

Abstract The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.

Ship Operational Efficiency from AIS Data Using Big Data Technology

2022 ◽  
Vol 10 (1) ◽  
pp. 89-97
Author(s):  
Suraj Ingle
Keyword(s):  
Energy Efficiency ◽  
Big Data ◽  
Operational Efficiency ◽  
Fuel Oil ◽  
Automatic Identification ◽  
Identification System ◽  
Oil Consumption ◽  
Automatic Identification System ◽  
Public Data ◽  
Ship Operational Efficiency

Abstract: The Energy Efficiency Design Index (EEDI) is a necessary benchmark for all new ships to prevent pollution from ships. MARPOL has also applied the Ship Energy Efficiency Management Plan (SEEMP) to all existing ships. The Energy Efficiency Operational Indicator (EEOI) provided by SEEMP is used to measure a ship's operational efficiency. The shipowner or operator can make strategic plans, such as routing, hull cleaning, decommissioning, new construction, and so on, by monitoring the EEOI. Fuel Oil Consumption is the most important factor in calculating EEOI (FOC). It is possible to measure it when a ship is in operation. This means that the EEOI of a ship can only be calculated by the shipowner or operator. Other stakeholders, such as the shipbuilding firm and Class, or those who do not have the measured FOC, can assess how efficiently their ships are working relative to other ships if the EEOI can be determined without the real FOC. We present a method to estimate the EEOI without requiring the actual FOC in this paper. The EEOI is calculated using data from the Automatic Identification System (AIS), ship static data, and publicly available environmental data. Big data technologies, notably Hadoop and Spark, are used because the public data is huge. We test the suggested method with real data, and the results show that it can predict EEOI from public data without having to use actual FOC Keywords: Ship operational efficiency, Energy Efficiency Operational Indicator (EEOI), Fuel Oil Consumption (FOC), Automatic Identification System (AIS), Big data

scholarly journals Methodology of Acquiring Valid Data by Combining Oil Tankers’ Noon Report and Automatic Identification System Satellite Data

2019 ◽  
Vol 31 (3) ◽  
pp. 299-309 ◽  
Author(s):  
Ali Akbar Safaei ◽  
Hassan Ghassemi ◽  
Mahmoud Ghiasi
Keyword(s):  
Air Pollution ◽  
Fuel Consumption ◽  
Root Mean Square ◽  
Satellite Data ◽  
Expected Value ◽  
Automatic Identification ◽  
Identification System ◽  
Mean Square ◽  
Automatic Identification System ◽  
Valid Data

Fuel consumption of marine vessels plays an important role in both generating air pollution and ship operational expenses where the global environmental concerns toward air pollution and economics of shipping operation are being increased. In order to optimize ship fuel consumption, the fuel consumption prediction for her envisaged voyage is to be known. To predict fuel consumption of a ship, noon report (NR) data are available source to be analysed by different techniques. Because of the possible human error attributed to the method of NR data collection, it involves risk of possible inaccuracy. Therefore, in this study, to acquire pure valid data, the NR raw data of two very large crude carriers (VLCCs) composed with their respective Automatic Identification System (AIS) satellite data. Then, well-known models i.e. K-Mean, Self-Organizing Map (SOM), Outlier Score Base (OSB) and Histogram of Outlier Score Base (HSOB) methods are applied to the collected tankers NR during a year. The new enriched data derived are compared to the raw NR to distinguish the most fitted methodology of accruing pure valid data. Expected value and root mean square methods are applied to evaluate the accuracy of the methodologies. It is concluded that measured expected value and root mean square for HOSB are indicating high coherence with the harmony of the primary NR data.

scholarly journals Recreational vessels without Automatic Identification System (AIS) dominate anthropogenic noise contributions to a shallow water soundscape

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Line Hermannsen ◽  
Lonnie Mikkelsen ◽  
Jakob Tougaard ◽  
Kristian Beedholm ◽  
Mark Johnson ◽  
...  
Keyword(s):  
Noise Pollution ◽  
Marine Species ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
High Speeds ◽  
Recreational Vessels ◽  
Vessel Noise ◽  
Noise Models

Abstract Recreational boating is an increasing activity in coastal areas and its spatiotemporal overlap with key habitats of marine species pose a risk for negative noise impacts. Yet, recreational vessels are currently unaccounted for in vessel noise models using Automatic Identification System (AIS) data. Here we conduct a case study investigating noise contributions from vessels with and without AIS (non-AIS) in a shallow coastal area within the Inner Danish waters. By tracking vessels with theodolite and AIS, while recording ambient noise levels, we find that non-AIS vessels have a higher occurrence (83%) than AIS vessels, and that motorised recreational vessels can elevate third-octave band noise centred at 0.125, 2 and 16 kHz by 47–51 dB. Accordingly, these vessels dominated the soundscape in the study site due to their high numbers, high speeds and proximity to the coast. Furthermore, recreational vessels caused 49–85% of noise events potentially eliciting behavioural responses in harbour porpoises (AIS vessels caused 5–24%). We therefore conclude that AIS data would poorly predict vessel noise pollution and its impacts in this and other similar marine environments. We suggest to improve vessel noise models and impact assessments by requiring that faster and more powerful recreational vessels carry AIS-transmitters.

scholarly journals Generating a node in an AIS-based routing graph for improved Estimated Time of Arrival. (Big) Data challenge: using AIS for generating a routing graph

2019 ◽  
pp. 45-56
Author(s):  
Carsten Hilgenfeld ◽  
Nina Vojdani ◽  
Frank Heymann ◽  
Evamarie Wiessner ◽  
Bettina Kutschera ◽  
...  
Keyword(s):  
Big Data ◽  
International Exchange ◽  
Automatic Identification ◽  
Identification System ◽  
Time Of Arrival ◽  
Grid Node ◽  
Vessel Length ◽  
Automatic Identification System ◽  
Global Data

For the international exchange of goods, an exact estimated time of arrival (ETA), especially in case of delays, is of great importance. Using global data of the automatic identification system (AIS) a grid node is generated. The sum of such nodes and their connections form a routing graph. As an example, with one node of in total more than 100,000 nodes it is described how this point gets the maximum vessel length and draft assigned.

scholarly journals Collision Mitigation Algorithm for Space Base Ais System

2020 ◽  
Vol 9 (6) ◽  
pp. 870-877
Keyword(s):  
High Speed ◽  
Viterbi Algorithm ◽  
Automatic Identification ◽  
Identification System ◽  
Description Language ◽  
Automatic Identification System ◽  
Interference Cancelation ◽  
Collision Mitigation ◽  
Time Division ◽  
Algorithm Implementation

Automatic Identification System gain demand and liking to be installed in navigational frameworks for collision mitigation due to more extensive inclusion. The as of late created satellite Automatic identification system gives better exactness than the prior utilized Terrestrial Automatic identification system. Space based Automatic identification system uses GMSK to regulate the message. The regulated Automatic ID System message is then transmitted & gotten among boats & satellite Automatic ID System over self-organize time division multiple access medium. The regular single axis spacecraft Autometic ID System receiver failed to decode the message precisely due to message cover. Using GMSK demodulation, filter and concept of Viterbi algorithm will try to obtain original message from cover message. In this paper Interference cancelation algorithm implementation corrects the covered AIS message and decode the obtain data. The receiver, transmitter, & Interference cancelation are sketched in very high-speed description language (VHDL) Language.

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