scholarly journals Development of the Artificial Intelligence and Optical Sensing Methods for Oil Pollution Monitoring of the Sea by Drones

2021 ◽  
Vol 11 (8) ◽  
pp. 3642
Author(s):  
Oleg Bukin ◽  
Dmitry Proschenko ◽  
Denis Korovetskiy ◽  
Alexey Chekhlenok ◽  
Viktoria Yurchik ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Navigation ◽  
Oil Pollution ◽  
Optical Sensing ◽  
Movement Control ◽  
Oil Products ◽  
Sea Surface ◽  
Pollution Monitoring ◽  
Monitoring Methods ◽  
Marine Fuel

The oil pollution of seas is increasing, especially in local areas, such as ports, roadsteads of the vessels, and bunkering zones. Today, methods of monitoring seawater are costly and applicable only in the case of big ecology disasters. The development of an operative and reasonable project for monitoring the sea surface for oil slick detection is described in this article using drones equipped with optical sensing and artificial intelligence. The monitoring system is implemented in the form of separate hard and soft frameworks (HSFWs) that combine monitoring methods, hardware, and software. Three frameworks are combined to fulfill the entire monitoring mission. HSFW1 performs the function of autonomous monitoring of thin oil slicks on the sea surface, using computer vision with AI elements for detection, segmentation, and classification of thin slicks. HSFW2 is based on the use of laser-induced fluorescence (LIF) to identify types of oil products that form a slick or that are in a dissolved state, as well as measure their concentration in solution. HSFW3 is designed for autonomous navigation and drone movement control. This article describes AI elements and hardware complexes of the three separate frameworks designed to solve the problems with monitoring slicks of oil products on the sea surface and oil products dissolved in seawater. The results of testing the HSFWs for the detection of pollution caused by marine fuel slicks are described.

scholarly journals Remotely Piloted Aircraft route optimization when performing oil pollution monitoring of the sea aquatorium

Aviation ◽  
2018 ◽  
Vol 21 (2) ◽  
pp. 70-74 ◽  
Author(s):  
Aleksandrs URBAHS ◽  
Vladislavs ŽAVTKĒVIČS
Keyword(s):  
Oil Pollution ◽  
Route Optimization ◽  
Sea Surface ◽  
Pollution Monitoring ◽  
Remotely Piloted Aircraft

The main problem of sea aquatorium monitoring is the surveillance of large areas of the sea surface and the presence of many moving ships with different parameters. A methodology for the optimization of a Remotely Piloted Aircraft’s (RPA) route is presented.

scholarly journals New Solutions of Laser-Induced Fluorescence for Oil Pollution Monitoring at Sea

Photonics ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. 36
Author(s):  
Oleg Bukin ◽  
Dmitry Proschenko ◽  
Chekhlenok Alexey ◽  
Denis Korovetskiy ◽  
Ilya Bukin ◽  
...  
Keyword(s):  
Oil Pollution ◽  
Limit Of Detection ◽  
Laser Induced Fluorescence ◽  
Oil Products ◽  
Pollution Monitoring ◽  
Time Dynamics ◽  
Ocean Pollution ◽  
Method Of Solution ◽  
Aerial Vehicle ◽  
Water Disposal

Laser-induced fluorescence (LIF) spectral features for oil products of different states (solutions in the seawater and thin slicks) are discussed in this article. This research was done to evaluate LIF application for the identification of oil products and the measurement of the volume of ocean pollution by bilge water disposal. It was found out that the form of LIF spectral distribution was changed depending on the oil product state (pure fuel, slick or solution). The LIF method was calibrated for the most common types of heavy and light marine fuels at the standard measurement method of solution concentrations and limit of detection (LoD) values were established for each type. The time dynamics of the solution spectra were researched, and the time change features were determined. The smallsized LIF sensor for the unmanned aerial vehicle (UAV) is described and aims to investigate the LIF for oil pollution at sea.

scholarly journals Assessing autonomous ship navigation using bridge simulators enhanced by cycle-consistent adversarial networks

2021 ◽  
pp. 1748006X2110210
Author(s):  
Andreas Brandsæter ◽  
Ottar L Osen
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Decision Support ◽  
Object Detection ◽  
Autonomous Navigation ◽  
Adversarial Networks ◽  
Maritime Navigation ◽  
Ship Navigation ◽  
Autonomous Ship ◽  
Ship Operation

The advent of artificial intelligence and deep learning has provided sophisticated functionality for sensor fusion and object detection and classification which have accelerated the development of highly automated and autonomous ships as well as decision support systems for maritime navigation. It is, however, challenging to assess how the implementation of these systems affects the safety of ship operation. We propose to utilize marine training simulators to conduct controlled, repeated experiments allowing us to compare and assess how functionality for autonomous navigation and decision support affects navigation performance and safety. However, although marine training simulators are realistic to human navigators, it cannot be assumed that the simulators are sufficiently realistic for testing the object detection and classification functionality, and hence this functionality cannot be directly implemented in the simulators. We propose to overcome this challenge by utilizing Cycle-Consistent Adversarial Networks (Cycle-GANs) to transform the simulator data before object detection and classification is performed. Once object detection and classification are completed, the result is transferred back to the simulator environment. Based on this result, decision support functionality with realistic accuracy and robustness can be presented and autonomous ships can make decisions and navigate in the simulator environment.

scholarly journals Bioindication of the Influence of Oil Production on Sphagnum Bogs in the Khanty-Mansiysk Autonomous Okrug–Yugra, Russia

Diversity ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 207
Author(s):  
Olga Skorobogatova ◽  
Elvira Yumagulova ◽  
Tatiana Storchak ◽  
Sophia Barinova
Keyword(s):  
Species Richness ◽  
Oil Pollution ◽  
Organic Pollution ◽  
Control Site ◽  
Oil Field ◽  
Pollution Monitoring ◽  
Rehabilitation Period ◽  
Scientific Results ◽  
First Time ◽  
Contaminated Area

Algal diversity in the bogs of the Ershov oil field of the Khanty-Mansiysk Autonomous Okrug–Yugra (KMAO-Yugra) with the gradient of oil pollution between 255 and 16,893 mg kg−1 has been studied with the help of bioindication methods and ecological mapping. Altogether 91 species, varieties, and forms of algae and cyanobacteria from seven divisions have been revealed for the first time from seven studied sites on the bogs. Charophyta algae prevail followed by diatoms, cyanobacteria, and euglenoids. The species richness and abundance of algae were maximal at the control site, with charophytic algae prevailing. The species richness of diatoms decreased in the contaminated area, but cyanobacteria were tolerated in a pH which varied between 4.0 and 5.4. Euglenoid algae survived under the influence of oil and organic pollution. Bioindication revealed a salinity influence in the oil-contaminated sites. A comparative floristic analysis shows a similarity in communities at sites surrounding the contaminated area, the ecosystems of which have a long-term rehabilitation period. The percent of unique species was maximal in the control site. Bioindication results were implemented for the first time in assessing the oil-polluted bogs and can be recommended as a method to obtain scientific results visualization for decision-makers and for future pollution monitoring.

Acoustic, Electromagnetic and Optical Sensing and Monitoring Methods

2018 ◽  
pp. 43-139 ◽  
Author(s):  
Wen Xiao ◽  
Xiaosu Yi ◽  
Feng Pan ◽  
Rui Li ◽  
Tian Xia
Keyword(s):  
Optical Sensing ◽  
Monitoring Methods

Research on Technology and Application of Multi-Sensor Data Fusion for Indoor Service Robots

2014 ◽  
Vol 651-653 ◽  
pp. 831-834
Author(s):  
Xi Pei Ma ◽  
Bing Feng Qian ◽  
Song Jie Zhang ◽  
Ye Wang
Keyword(s):  
Data Fusion ◽  
Autonomous Navigation ◽  
Control Method ◽  
Movement Control ◽  
Sensor Data ◽  
Service Robots ◽  
Service Robot ◽  
Mobile Service ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion

The autonomous navigation process of a mobile service robot is usually in uncertain environment. The information only given by sensors has been unable to meet the demand of the modern mobile robots, so multi-sensor data fusion has been widely used in the field of robots. The platform of this project is the achievement of the important 863 Program national research project-a prototype nursing robot. The aim is to study a mobile service robot’s multi-sensor information fusion, path planning and movement control method. It can provide a basis and practical use’s reference for the study of an indoor robot’s localization.

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