scholarly journals Near-Optimal Weather Routing by Using Improved A* Algorithm

2020 ◽  
Vol 10 (17) ◽  
pp. 6010
Author(s):  
Yong Woo Shin ◽  
Misganaw Abebe ◽  
Yoojeong Noh ◽  
Sangbong Lee ◽  
Inwon Lee ◽  
...  
Keyword(s):  
Operating Conditions ◽  
Weather Data ◽  
Computational Time ◽  
Automatic Identification ◽  
Identification System ◽  
Time Of Arrival ◽  
Climate Data ◽  
A Algorithm ◽  
Grid Deformation ◽  
Ship Operation

With soaring oil prices worldwide, determining the most optimal routes for economical ship operation has become an important issue. Optimizing ship routes is economically important for ship operation, but it is also essential to meet the standards of environmental regulations recently imposed by the International Maritime Organization. For this purpose, various algorithms for determining ship routes have been developed to ensure the economical operation of ships via utilization of marine climate data and Automatic Identification System (AIS) data. However, such algorithms require a large amount of computational time and do not provide optimal routes because they do not consider practical operating conditions, such as weather and ocean conditions. In this study, an improved A* algorithm using AIS and weather data is proposed to overcome the limitation of the original A* algorithm, one of the most widely used path-finding algorithms. The improved A* algorithm uses an adaptive grid system that efficiently explores nodes according to map grid deformation by latitude. It finds economical routes by minimizing the estimated time of arrival generated by machine learning through 16-way node exploration. For verification of the proposed method, the original A* algorithm and improved A* algorithm were compared through a case study.

scholarly journals Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping

2020 ◽  
Vol 10 (7) ◽  
pp. 2325 ◽  
Author(s):  
Misganaw Abebe ◽  
Yongwoo Shin ◽  
Yoojeong Noh ◽  
Sangbong Lee ◽  
Inwon Lee
Keyword(s):  
Machine Learning ◽  
Operating Conditions ◽  
Weather Data ◽  
Automatic Identification ◽  
Identification System ◽  
Learning Approaches ◽  
Speed Prediction ◽  
Accuracy Result ◽  
One Year ◽  
Ship Speed

As oil prices continue to rise internationally, shipping costs are also increasing rapidly. In order to reduce fuel costs, an economical shipping route must be determined by accurately predicting the estimated arrival time of ships. A common method in the evaluation of ship speed involves computing the total resistance of a ship using theoretical analysis; however, using theoretical equations cannot be applied for most ships under various operating conditions. In this study, a machine learning approach was proposed to predict ship speed over the ground using the automatic identification system (AIS) and noon-report maritime weather data. To train and validate the developed model, the AIS and marine weather data of the seventy-six vessels for a period one year were used. The model accuracy result shows that the proposed data-driven model has a satisfactory capability to predict the ship speed based on the chosen features.

scholarly journals Positioning Error Analysis of Ranging-Mode Using AIS Signals in China

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Kai Zheng ◽  
Qing Hu ◽  
Jingbo Zhang
Keyword(s):  
Measurement Errors ◽  
Phase Locking ◽  
Automatic Identification ◽  
Identification System ◽  
Time Of Arrival ◽  
Positioning Error ◽  
North East ◽  
The North ◽  
Error Distributions ◽  
Shift Keying

In order to provide resilient position, navigation, and time (PNT) information forE-Navigation, the ranging-mode (R-Mode) positioning using automatic identification system (AIS) signals is encouraged. As the accuracy is the key for the positioning system, this paper investigates the position error of the R-Mode positioning based on AIS shore-based station in China. The measurement errors of Gaussian filtered minimum shift keying (GMSK) demodulation based on carrier phase locking loop are investigated in theory. The dilution of precision (DOP) for time of arrival (TOA) and time difference of arrival (TDOA) used in R-Mode positioning of AIS is discussed in two measurement mechanisms. The positioning error distributions in the North, East, and South Sea regions of China based on the existing AIS shore-based stations are evaluated. The positioning accuracy is at the meter level in the most traffic dense areas to meet the requirements for vessel navigation.

scholarly journals Design and Development of an AIoT Architecture for Introducing a Vessel ETA Cognitive Service in a Legacy Port Management Solution

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8133
Author(s):  
Clara I. Valero ◽  
Enrique Ivancos Pla ◽  
Rafael Vaño ◽  
Eduardo Garro ◽  
Fernando Boronat ◽  
...  
Keyword(s):  
Traffic Management ◽  
Automatic Identification ◽  
Identification System ◽  
Time Of Arrival ◽  
Legacy System ◽  
Cognitive Component ◽  
Cognitive Services ◽  
Oceanographic Data ◽  
Reducing Costs ◽  
Easy Integration

Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT principles is presented. The architecture incorporates a new cognitive component that enables the incorporation of intelligent services to the FIWARE framework, allowing to modernize IoT infrastructures with Artificial Intelligence (AI) technologies. This allows to extend the effective life of the legacy system, using existing assets and reducing costs. Using the architecture, a cognitive service capable of predicting with high accuracy the vessel port arrival is developed and integrated in a legacy sea traffic management solution. The cognitive service uses automatic identification system (AIS) and maritime oceanographic data to predict time of arrival of ships. The validation has been carried out using the port of Valencia. The results indicate that the incorporation of AI into the legacy system allows to predict the arrival time with higher accuracy, thus improving the efficiency of port operations. Moreover, the architecture is generic, allowing an easy integration of the cognitive services in other domains.

scholarly journals Usage of Real Time Machine Vision in Rolling Mill

2021 ◽  
Vol 13 (7) ◽  
pp. 3851
Author(s):  
Jiří David ◽  
Pavel Švec ◽  
Vít Pasker ◽  
Romana Garzinová
Keyword(s):  
Rolling Mill ◽  
Optical Character Recognition ◽  
Rolling Process ◽  
Operating Conditions ◽  
Inspection System ◽  
Automatic Identification ◽  
Identification System ◽  
Successful Implementation ◽  
Technical Equipment ◽  
Automatic Inspection

This article deals with the issue of computer vision on a rolling mill. The main goal of this article is to describe the designed and implemented algorithm for the automatic identification of the character string of billets on the rolling mill. The algorithm allows the conversion of image information from the front of the billet, which enters the rolling process, into a string of characters, which is further used to control the technological process. The purpose of this identification is to prevent the input pieces from being confused because different parameters of the rolling process are set for different pieces. In solving this task, it was necessary to design the optimal technical equipment for image capture, choose the appropriate lighting, search for text and recognize individual symbols, and insert them into the control system. The research methodology is based on the empirical-quantitative principle, the basis of which is the analysis of experimentally obtained data (photographs of billet faces) in real operating conditions leading to their interpretation (transformation into the shape of a digital chain). The first part of the article briefly describes the billet identification system from the point of view of technology and hardware resources. The next parts are devoted to the main parts of the algorithm of automatic identification—optical recognition of strings and recognition of individual characters of the chain using artificial intelligence. The method of optical character recognition using artificial neural networks is the basic algorithm of the system of automatic identification of billets and eliminates ambiguities during their further processing. Successful implementation of the automatic inspection system will increase the share of operation automation and lead to ensuring automatic inspection of steel billets according to the production plan. This issue is related to the trend of digitization of individual technological processes in metallurgy and also to the social sustainability of processes, which means the elimination of human errors in the management of the billet rolling process.

scholarly journals Automated identification of cephalometric landmarks: Part 1—Comparisons between the latest deep-learning methods YOLOV3 and SSD

2019 ◽  
Vol 89 (6) ◽  
pp. 903-909 ◽  
Author(s):  
Ji-Hoon Park ◽  
Hye-Won Hwang ◽  
Jun-Ho Moon ◽  
Youngsung Yu ◽  
Hansuk Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computational Time ◽  
Automatic Identification ◽  
Identification System ◽  
Single Shot ◽  
Learning Methods ◽  
Radiographic Images ◽  
Data Set ◽  
Landmark Identification ◽  
Significant Difference

ABSTRACT Objective: To compare the accuracy and computational efficiency of two of the latest deep-learning algorithms for automatic identification of cephalometric landmarks. Materials and Methods: A total of 1028 cephalometric radiographic images were selected as learning data that trained You-Only-Look-Once version 3 (YOLOv3) and Single Shot Multibox Detector (SSD) methods. The number of target labeling was 80 landmarks. After the deep-learning process, the algorithms were tested using a new test data set composed of 283 images. Accuracy was determined by measuring the point-to-point error and success detection rate and was visualized by drawing scattergrams. The computational time of both algorithms was also recorded. Results: The YOLOv3 algorithm outperformed SSD in accuracy for 38 of 80 landmarks. The other 42 of 80 landmarks did not show a statistically significant difference between YOLOv3 and SSD. Error plots of YOLOv3 showed not only a smaller error range but also a more isotropic tendency. The mean computational time spent per image was 0.05 seconds and 2.89 seconds for YOLOv3 and SSD, respectively. YOLOv3 showed approximately 5% higher accuracy compared with the top benchmarks in the literature. Conclusions: Between the two latest deep-learning methods applied, YOLOv3 seemed to be more promising as a fully automated cephalometric landmark identification system for use in clinical practice.

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.

Measuring Port Disruptions with Automatic Identification System Data

Ports 2019 ◽  
2019 ◽  
Author(s):  
Brandan M. Scully ◽  
Katherine F. Chambers
Keyword(s):  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
System Data

Vessel Detection and Automatic Identification System

2020 ◽  
Author(s):  
Malika Pagare ◽  
Hemlata Ahire ◽  
Akshada Shitole
Keyword(s):  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Vessel Detection

Prediction of aircraft estimated time of arrival using machine learning methods

2021 ◽  
pp. 1-15
Author(s):  
O. Basturk ◽  
C. Cetek
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Time Of Arrival ◽  
Learning Models ◽  
Trajectory Data ◽  
Different Sources ◽  
Machine Learning Models

ABSTRACT In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.

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