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 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 A Simple And Novel Algorithm For State Estimation Of Continuous Time Linear Stochastic Dynamic Systems Excited By Random Inputs

2019 ◽  
Vol 8 (10) ◽  
pp. 3559-3563
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Continuous Time ◽  
Stochastic Dynamic ◽  
Time Dynamic ◽  
Time Dynamics ◽  
Hardware Realization ◽  
Aerial Vehicle ◽  
Non Linear System ◽  
Stochastic Dynamic Systems

The major goal of this paper is to explore the effective state estimation algorithm for continuous time dynamic system under the lossy environment without increasing the complexity of hardware realization. Though the existing methods of state estimation of continuous time system provides effective estimation with data loss, the real time hardware realization is difficult due to the complexity and multiple processing. Kalman Filter and Particle Filer are fundamental algorithms for state estimation of any linear and non-linear system respectively, but both have its limitation. The approach adopted here, detect the expected state value and covariance, existed by random input at each stage and filtered the noisy measurement and replace it with predicted modified value for the effective state estimation. To demonstrate the performance of the results, the continuous time dynamics of position of the Aerial Vehicle is used with proposed algorithm under the lossy measurements scenario and compared with standard Kalman filter and smoothed filter. The results show that the proposed method can effectively estimate the position of Aerial Vehicle compared to standard Kalman and smoothed filter under the non-reliable sensor measurements with less hardware realization complexity.

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.

scholarly journals Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation

2019 ◽  
Vol 11 (9) ◽  
pp. 1046 ◽  
Author(s):  
Heming Jia ◽  
Zhikai Xing ◽  
Wenlong Song
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Optimization Algorithm ◽  
Oil Pollution ◽  
Three Dimensional ◽  
Oil Field ◽  
Segmentation Method ◽  
Pulse Coupled Neural Network ◽  
Aerial Vehicle ◽  
Pollution Area

This paper proposes a three dimensional pulse coupled neural network (3DPCNN) image segmentation method based on a hybrid seagull optimization algorithm (HSOA) to solve the oil pollution image. The image of oil pollution is taken by the unmanned aerial vehicle (UAV) in the oil field area. The UAV is good at shooting the ground area, but its ability to identify the oil pollution area is poor. In order to solve this problem, a 3DPCNN-HSOA algorithm is proposed to segment the oil pollution image, and the oil pollution area is segmented to identify the dirty oil area and improve the inspection of environmental pollution. The 3DPCNN image segmentation method has simple structure and good segmentation effect, but it has many parameters and poor segmentation effect for complex oil images. Therefore, we apply HSOA algorithm to optimize the parameters of 3DPCNN algorithm, so as to improve the segmentation accuracy and solve the segmentation of oil pollution images. The experimental results show that the 3DPCNN-HSOA model can separate the oil pollution area from the complex background.

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