Deep learning provide successful applications in many fields. Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneously observing the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine reflectance.By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc…, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West Africa. These data fusion take into account the bias between case water and instruments.We construct several chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low and vary between 0,07 to 0,13 mg/m3 .
Nonlinear filtering approaches to field mapping by sampling using mobile sensorsijassn
This work proposes a novel application of existing powerful nonlinear filters, such as the standard
Extended Kalman Filter (EKF), some of its variants and the standard Unscented Kalman Filter (UKF), to
the estimation of a continuous spatio-temporal field that is spread over a wide area, and hence represented
by a large number of parameters when parameterized. We couple these filters with the powerful scheme of
adaptive sampling performed by a single mobile sensor, and investigate their performances with a view to
significantly improving the speed and accuracy of the overall field estimation. An extensive simulation work
was carried out to show that different variants of the standard EKF and the standard UKF can be used to
improve the accuracy of the field estimate. This paper also aims to provide some guideline for the user of
these filters in reaching a practical trade-off between the desired field estimation accuracy and the
required computational load.
Nonlinear filtering approaches to field mapping by sampling using mobile sensorsijassn
This work proposes a novel application of existing powerful nonlinear filters, such as the standard
Extended Kalman Filter (EKF), some of its variants and the standard Unscented Kalman Filter (UKF), to
the estimation of a continuous spatio-temporal field that is spread over a wide area, and hence represented
by a large number of parameters when parameterized. We couple these filters with the powerful scheme of
adaptive sampling performed by a single mobile sensor, and investigate their performances with a view to
significantly improving the speed and accuracy of the overall field estimation. An extensive simulation work
was carried out to show that different variants of the standard EKF and the standard UKF can be used to
improve the accuracy of the field estimate. This paper also aims to provide some guideline for the user of
these filters in reaching a practical trade-off between the desired field estimation accuracy and the
required computational load.
The Copernicus land monitoring service provides geographical information on land cover and on variables related, for instance, to the vegetation state or the water cycle. It supports applications in a variety of domains such as spatial planning, forest management, water management, agriculture and food security, etc.
The service became operational in 2012.
It consists of three main components:
◾A global component;
◾A Pan-European component;
◾A local component.
Spectroscopic confirmation of an ultra-faint galaxy at the epoch of reionizationSérgio Sacani
Within one billion years of the Big Bang, intergalactic hydrogen
was ionized by sources emitting ultraviolet and higher energy
photons. This was the final phenomenon to globally affect all
the baryons (visible matter) in the Universe. It is referred to
as cosmic reionization and is an integral component of cosmology.
It is broadly expected that intrinsically faint galaxies
were the primary ionizing sources due to their abundance
in this epoch1,2. However, at the highest redshifts (z > 7.5;
lookback time 13.1 Gyr), all galaxies with spectroscopic confirmations
to date are intrinsically bright and, therefore, not
necessarily representative of the general population3. Here,
we report the unequivocal spectroscopic detection of a low
luminosity galaxy at z > 7.5. We detected the Lyman-α emission
line at ∼10,504 Å in two separate observations with
MOSFIRE4 on the Keck I Telescope and independently with
the Hubble Space Telescope’s slitless grism spectrograph,
implying a source redshift of z = 7.640 ± 0.001. The galaxy
is gravitationally magnified by the massive galaxy cluster
MACS J1423.8+2404 (z = 0.545), with an estimated intrinsic
luminosity of MAB = −19.6 ± 0.2 mag and a stellar mass of
☆ = × − +
M 3.0 0.8 10
1.5 8 solar masses. Both are an order of magnitude
lower than the four other Lyman-α emitters currently
known at z > 7.5, making it probably the most distant representative
source of reionization found to date.
Performance improvement of a Rainfall Prediction Model using Particle Swarm O...ijceronline
The performances of the statistical methods of time series forecast can be improved by precise selection of their parameters. Various techniques are being applied to improve the modeling accuracy of these models. Particle swarm optimization is one such technique which can be conveniently used to determine the model parameters accurately. This robust optimization technique has already been applied to improve the performance of artificial neural networks for time series prediction. This study uses particle swarm optimization technique to determine the parameters of an exponential autoregressive model for time series prediction. The model is applied for annual rainfall prediction and it shows a fairly good performance in comparison to the statistical ARIMA model
Radar reflectance model for the extraction of height from shape from shading ...eSAT Journals
Abstract
The shape-from-shading (SFS) technique deals with the recovery of shape of an object through a gradual variation of shading
encoded in the image. Most SFS approaches have assumed Lambertian surface to extract DEM from individual images. The
quality of the derived DEM from radar SFS in particular, depends on the appropriate radar reflectance model, which relates the
radar backscatter to the surface normal.This paper will focus on a new reflectance model for relatingthe radar SAR backscatter
coefficient values to surface normal orientation. An iterative minimization SFS algorithm was implemented using this radar
reflectance model to derive the height measurements.The most important key of derivation of the surface height using this model is
forward and inverse Fast Fourier Transform (FFT). The model performance was evaluated on RADARSAT-1 image using both
graphical and statistical analysis. Root mean square error (RMSE) and coefficient of determination (R2) were used as evaluation
criteria for the model performance. The model has shown good performance in reconstructing surface heights from RADARSAT-1
imagery. It gave 17.47m and 97.2% for RMSE and R2, respectively.
Keywords:3-D, SFS, Remote Sensing, Radar Remote Sensing, Satellite Images, SAR Imageries.
Computer model simulations are widely used in the investigation of complex hydrological systems. In particular, hydrological models are tools that help both to better understand hydrological processes and to predict extreme events such as floods and droughts. Usually, model parameters need to be estimated through calibration, in order to constrain model outputs to observed variables.
Relevant model parameters used for calibration are usually selected based on expert knowledge of the modeller or by using a local one-at-a-time (OAT) sensitivity analysis (SA). However, in case of complex models those approaches may not result in proper identification of the most sensitive parameters for model calibration. In particular local OAT SA methods are only effective for assessing the relative importance of input factors when the model is linear, monotonic, and additive, which is rarely the case for complex environmental models. In contrast Global Sensitivity Analysis (GSA)
is a formal method for statistical evaluation of relevant parameters that contribute significantly to model performance. GSA techniques explore the entire feasible space of each model parameter, and they do not require any assumptions on the model nature (such as linearity or additivity).
In this work we apply the GSA to LISFLOOD, a fully-distributed hydrological model used for flood forecasting at Pan-European scale within the European Flood Awareness System (EFAS). Two case studies are considered, snowmelt- and evapotranspiration-driven catchments, to identify sensitive parameters for both types of hydrological regimes. Results of the GSA will then be used for selecting parameters that need to be estimated during model calibration. Considering the large
number of parameters of a fully-distributed model, a two-step GSA framework is applied. First, we implement the computationally efficient screening method of Morris. This method requires a limited number of simulations and produces a qualitative ranking and selection of important factors. As a second step, we apply the variance-based method of Sobol, only to the subset of factors determined as important during the previous screening. The method of Sobol provides quantitative estimates for first order and total order sensitivity indexes of input factors.
The calibration results after the GSA will be described for both case studies and compared against those obtained by using only prior expert knowledge
Dr Jerome O Connell - presentation made at various conferences throughout Europe as part of PhD which was funded by the EPA under the STRIVE Research Programme 2007-2013 (2007-PhD-ET-2)
Modeling the Chlorophyll-a from Sea Surface Reflectance in West Africa by Dee...gerogepatton
Deep learning provide successful applications in many fields. Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators
for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneously observing the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine eflectance.By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc…, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West
Africa. These data fusion take into account the bias between case water and instruments. We construct several chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low and vary between 0,07 to 0,13 mg/m3.
DEEP LEARNING BASED MULTIPLE REGRESSION TO PREDICT TOTAL COLUMN WATER VAPOR (...IJDKP
Total column water vapor is an important factor for the weather and climate. This study apply
deep learning based multiple regression to map the TCWV with elements that can improve
spatiotemporal prediction. In this study, we predict the TCWV with the use of ERA5 that is the
fifth generation ECMWF atmospheric reanalysis of the global climate. We use an appropriate
deep learning based multiple regression algorithm using Keras library to improve nonlinear
prediction between Total Column water vapor and predictors as Mean sea level pressure, Surface
pressure, Sea surface temperature, 100 metre U wind component, 100 metre V wind component,
10 metre U wind component, 10 metre V wind component, 2 metre dew point temperature, 2
metre temperature.
The Copernicus land monitoring service provides geographical information on land cover and on variables related, for instance, to the vegetation state or the water cycle. It supports applications in a variety of domains such as spatial planning, forest management, water management, agriculture and food security, etc.
The service became operational in 2012.
It consists of three main components:
◾A global component;
◾A Pan-European component;
◾A local component.
Spectroscopic confirmation of an ultra-faint galaxy at the epoch of reionizationSérgio Sacani
Within one billion years of the Big Bang, intergalactic hydrogen
was ionized by sources emitting ultraviolet and higher energy
photons. This was the final phenomenon to globally affect all
the baryons (visible matter) in the Universe. It is referred to
as cosmic reionization and is an integral component of cosmology.
It is broadly expected that intrinsically faint galaxies
were the primary ionizing sources due to their abundance
in this epoch1,2. However, at the highest redshifts (z > 7.5;
lookback time 13.1 Gyr), all galaxies with spectroscopic confirmations
to date are intrinsically bright and, therefore, not
necessarily representative of the general population3. Here,
we report the unequivocal spectroscopic detection of a low
luminosity galaxy at z > 7.5. We detected the Lyman-α emission
line at ∼10,504 Å in two separate observations with
MOSFIRE4 on the Keck I Telescope and independently with
the Hubble Space Telescope’s slitless grism spectrograph,
implying a source redshift of z = 7.640 ± 0.001. The galaxy
is gravitationally magnified by the massive galaxy cluster
MACS J1423.8+2404 (z = 0.545), with an estimated intrinsic
luminosity of MAB = −19.6 ± 0.2 mag and a stellar mass of
☆ = × − +
M 3.0 0.8 10
1.5 8 solar masses. Both are an order of magnitude
lower than the four other Lyman-α emitters currently
known at z > 7.5, making it probably the most distant representative
source of reionization found to date.
Performance improvement of a Rainfall Prediction Model using Particle Swarm O...ijceronline
The performances of the statistical methods of time series forecast can be improved by precise selection of their parameters. Various techniques are being applied to improve the modeling accuracy of these models. Particle swarm optimization is one such technique which can be conveniently used to determine the model parameters accurately. This robust optimization technique has already been applied to improve the performance of artificial neural networks for time series prediction. This study uses particle swarm optimization technique to determine the parameters of an exponential autoregressive model for time series prediction. The model is applied for annual rainfall prediction and it shows a fairly good performance in comparison to the statistical ARIMA model
Radar reflectance model for the extraction of height from shape from shading ...eSAT Journals
Abstract
The shape-from-shading (SFS) technique deals with the recovery of shape of an object through a gradual variation of shading
encoded in the image. Most SFS approaches have assumed Lambertian surface to extract DEM from individual images. The
quality of the derived DEM from radar SFS in particular, depends on the appropriate radar reflectance model, which relates the
radar backscatter to the surface normal.This paper will focus on a new reflectance model for relatingthe radar SAR backscatter
coefficient values to surface normal orientation. An iterative minimization SFS algorithm was implemented using this radar
reflectance model to derive the height measurements.The most important key of derivation of the surface height using this model is
forward and inverse Fast Fourier Transform (FFT). The model performance was evaluated on RADARSAT-1 image using both
graphical and statistical analysis. Root mean square error (RMSE) and coefficient of determination (R2) were used as evaluation
criteria for the model performance. The model has shown good performance in reconstructing surface heights from RADARSAT-1
imagery. It gave 17.47m and 97.2% for RMSE and R2, respectively.
Keywords:3-D, SFS, Remote Sensing, Radar Remote Sensing, Satellite Images, SAR Imageries.
Computer model simulations are widely used in the investigation of complex hydrological systems. In particular, hydrological models are tools that help both to better understand hydrological processes and to predict extreme events such as floods and droughts. Usually, model parameters need to be estimated through calibration, in order to constrain model outputs to observed variables.
Relevant model parameters used for calibration are usually selected based on expert knowledge of the modeller or by using a local one-at-a-time (OAT) sensitivity analysis (SA). However, in case of complex models those approaches may not result in proper identification of the most sensitive parameters for model calibration. In particular local OAT SA methods are only effective for assessing the relative importance of input factors when the model is linear, monotonic, and additive, which is rarely the case for complex environmental models. In contrast Global Sensitivity Analysis (GSA)
is a formal method for statistical evaluation of relevant parameters that contribute significantly to model performance. GSA techniques explore the entire feasible space of each model parameter, and they do not require any assumptions on the model nature (such as linearity or additivity).
In this work we apply the GSA to LISFLOOD, a fully-distributed hydrological model used for flood forecasting at Pan-European scale within the European Flood Awareness System (EFAS). Two case studies are considered, snowmelt- and evapotranspiration-driven catchments, to identify sensitive parameters for both types of hydrological regimes. Results of the GSA will then be used for selecting parameters that need to be estimated during model calibration. Considering the large
number of parameters of a fully-distributed model, a two-step GSA framework is applied. First, we implement the computationally efficient screening method of Morris. This method requires a limited number of simulations and produces a qualitative ranking and selection of important factors. As a second step, we apply the variance-based method of Sobol, only to the subset of factors determined as important during the previous screening. The method of Sobol provides quantitative estimates for first order and total order sensitivity indexes of input factors.
The calibration results after the GSA will be described for both case studies and compared against those obtained by using only prior expert knowledge
Dr Jerome O Connell - presentation made at various conferences throughout Europe as part of PhD which was funded by the EPA under the STRIVE Research Programme 2007-2013 (2007-PhD-ET-2)
Modeling the Chlorophyll-a from Sea Surface Reflectance in West Africa by Dee...gerogepatton
Deep learning provide successful applications in many fields. Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators
for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneously observing the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine eflectance.By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc…, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West
Africa. These data fusion take into account the bias between case water and instruments. We construct several chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low and vary between 0,07 to 0,13 mg/m3.
DEEP LEARNING BASED MULTIPLE REGRESSION TO PREDICT TOTAL COLUMN WATER VAPOR (...IJDKP
Total column water vapor is an important factor for the weather and climate. This study apply
deep learning based multiple regression to map the TCWV with elements that can improve
spatiotemporal prediction. In this study, we predict the TCWV with the use of ERA5 that is the
fifth generation ECMWF atmospheric reanalysis of the global climate. We use an appropriate
deep learning based multiple regression algorithm using Keras library to improve nonlinear
prediction between Total Column water vapor and predictors as Mean sea level pressure, Surface
pressure, Sea surface temperature, 100 metre U wind component, 100 metre V wind component,
10 metre U wind component, 10 metre V wind component, 2 metre dew point temperature, 2
metre temperature.
Boosting CED Using Robust Orientation Estimationijma
n this paper, Coherence Enhancement Diffusion (CED) is boosted feeding external orientation using new
robust orientation estimation. In CED, proper scale selection is very important as the gradient vector at
that scale reflects the orientation of local ridge. For this purpose a new scheme is proposed in which pre
calculated orientation, by using local and integration scales. From the experiments it is found the proposed
scheme is working much better in noisy environment as compared to the traditional Coherence
Enhancement Diffusion
Calculation of solar radiation by using regression methodsmehmet şahin
Abstract. In this study, solar radiation was estimated at 53 location over Turkey with
varying climatic conditions using the Linear, Ridge, Lasso, Smoother, Partial least, KNN
and Gaussian process regression methods. The data of 2002 and 2003 years were used to
obtain regression coefficients of relevant methods. The coefficients were obtained based on
the input parameters. Input parameters were month, altitude, latitude, longitude and landsurface
temperature (LST).The values for LST were obtained from the data of the National
Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer
(NOAA-AVHRR) satellite. Solar radiation was calculated using obtained coefficients in
regression methods for 2004 year. The results were compared statistically. The most
successful method was Gaussian process regression method. The most unsuccessful method
was lasso regression method. While means bias error (MBE) value of Gaussian process
regression method was 0,274 MJ/m2, root mean square error (RMSE) value of method was
calculated as 2,260 MJ/m2. The correlation coefficient of related method was calculated as
0,941. Statistical results are consistent with the literature. Used the Gaussian process
regression method is recommended for other studies.
Estimation of global solar radiation by using machine learning methodsmehmet şahin
In this study, global solar radiation (GSR) was estimated based on 53 locations by using ELM, SVR, KNN, LR and NU-SVR methods. Methods were trained with a two-year data set and accuracy of the mentioned methods was tested with a one-year data set. The data set of each year was consisting of 12 months. Whereas the values of month, altitude, latitude, longitude, vapour pressure deficit and land surface temperature were used as input for developing models, GSR was obtained as output. Values of vapour pressure deficit and land surface temperature were taken from radiometry of NOAA-AVHRR satellite. Estimated solar radiation data were compared with actual data that were obtained from meteorological stations. According to statistical results, most successful method was NU-SVR method. The RMSE and MBE values of NU-SVR method were found to be 1,4972 MJ/m2 and 0,2652 MJ/m2, respectively. R value was 0,9728. Furthermore, worst prediction method was LR. For other methods, RMSE values were changing between 1,7746 MJ/m2 and 2,4546 MJ/m2. It can be seen from the statistical results that ELM, SVR, k-NN and NU-SVR methods can be used for estimation of GSR.
Ill-posedness formulation of the emission source localization in the radio- d...Ahmed Ammar Rebai PhD
To contact the authors : tarek.salhi@gmail.com and ahmed.rebai2@gmail.com
In the field of radio detection in astroparticle physics, many studies have shown the strong dependence of the solution of the radio-transient sources localization problem (the radio-shower time of arrival on antennas) such solutions are purely numerical artifacts. Based on a detailed analysis of some already published results of radio-detection experiments like : CODALEMA 3 in France, AERA in Argentina and TREND in China, we demonstrate the ill-posed character of this problem in the sens of Hadamard. Two approaches have been used as the existence of solutions degeneration and the bad conditioning of the mathematical formulation problem. A comparison between experimental results and simulations have been made, to highlight the mathematical studies. Many properties of the non-linear least square function are discussed such as the configuration of the set of solutions and the bias.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Path Loss Prediction by Robust Regression Methodsijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
The dosimetry was carried out for radiotherapy patients, and measurements were performed using LiF and thermoluminescent dosimeters (TLDs). Evaluations were done for water-equivalent (effective) thicknesses and target dose with transmission data. Considerations were made for the accuracy of the parameter for the ratio of measured to expected value for each quantity. The entrance dose was estimated as 1.01 ± 0.07. The mean ratio of effective to contour depth was 1.00 ± 0.13, showing a wide distribution reflecting the influence of contour inaccuracies. The mean ratio of the measured contour dose prescription was 1.00 ± 0.07. The difference in depths that is patient and effective depth is a reflection of target dose discrepancies. Graphical simulations were done using Monte-Carlo Simulations and presented.
Using Subspace Pursuit Algorithm to Improve Performance of the Distributed Co...Polytechnique Montreal
This paper applies a compressed algorithm to improve the spectrum sensing performance of cognitive radio technology.
At the fusion center, the recovery error in the analog to information converter (AIC) when reconstructing the
transmit signal from the received time-discrete signal causes degradation of the detection performance. Therefore, we
propose a subspace pursuit (SP) algorithm to reduce the recovery error and thereby enhance the detection performance.
In this study, we employ a wide-band, low SNR, distributed compressed sensing regime to analyze and evaluate the
proposed approach. Simulations are provided to demonstrate the performance of the proposed algorithm.
3 d single gaas co axial nanowire solar cell for nanopillar-array photovoltai...ijcsa
Nanopillar array photovoltaics give unique advantages over today’s planar thin films in the areas of
optical properties and carrier collection, arising from their 3D geometry. The choice of the material
system, however, is essential in order to gain the advantage of the large surface/interface area associated
with nanopillars. Therefore, a well known Si and GaAs material are used in the design and studied in this
nanowire application. This work calculates and analyses the performance of the coaxial GaAs nanowire
and compared with that of Si nanowire using a semi-classical method. The current-voltage characteristics
are investigated for both under dark and AM1.5G illumination. It is found that GaAs nanowire gives almost
double efficiency with its counterpart Si nanowire. Their TCAD simulations can be validated reasonably
with that of published experimental result.
Boosting ced using robust orientation estimationijma
In this paper, Coherence Enhancement Diffusion (CED) is boosted feeding external orientation using new
robust orientation estimation. In CED, proper scale selection is very important as the gradient vector at
that scale reflects the orientation of local ridge. For this purpose a new scheme is proposed in which pre
calculated orientation, by using local and integration scales. From the experiments it is found the proposed
scheme is working much better in noisy environment as compared to the traditional Coherence
Enhancement Diffusion
Atmospheric Correction of Remote Sensing Data_RamaRao.pptxssusercd49c0
Atmospheric correction of remote sensing data. This PPT describes development of a region sensitive atmospheric correction method for hyperspectral image processing
Similar to MODELING THE CHLOROPHYLL-A FROM SEA SURFACE REFLECTANCE IN WEST AFRICA BY DEEP LEARNING METHODS: A COMPARISON OF MULTIPLE ALGORITHMS (20)
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...
MODELING THE CHLOROPHYLL-A FROM SEA SURFACE REFLECTANCE IN WEST AFRICA BY DEEP LEARNING METHODS: A COMPARISON OF MULTIPLE ALGORITHMS
1. International Journal of Artificial Intelligence & Applications (IJAIA) Vol.10, No.6, November 2019
DOI: 10.5121/ijaia.2019.10603 33
MODELING THE CHLOROPHYLL-A FROM SEA
SURFACE REFLECTANCE IN WEST AFRICA BY DEEP
LEARNING METHODS: A COMPARISON OF MULTIPLE
ALGORITHMS
Daouda DIOUF and Djibril Seck
Laboratoire de Traitement de l’Information (LTI) – ESP –
Université Cheikh Anta Diop de Dakar
BP: 5085 Dakar-Fann (Sénégal)
ABSTRACT
Deep learning provide successful applications in many fields. Recently, machines learning are involved for
oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators
for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments
simultaneously observing the earth on a variety of platforms, several algorithm are used to estimate the
chlolophyll-a from marine reflectance.By using a long-term multi-sensor time-series of satellite ocean-colour
data, as MODIS, SeaWifs, VIIRS, MERIS, etc…, we make a unique deep network model able to establish a
relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West
Africa. These data fusion take into account the bias between case water and instruments.We construct several
chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for
our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low
and vary between 0,07 to 0,13 mg/m3
.
KEYWORDS
deep learning estimators; remote sensing; chlorophyll-a
1. INTRODUCTION
The sensor ocean color provides measures ρtoa multispectral Top Of Atmosphere (TOA) reflectance (λ)
of ocean-atmosphere system in the visible and near infrared since many decades.
Reflectance ρcor measured by the radiometer, corrected to Rayleigh scattering contribution, specular
reflection and absorption gas is expressed as follows:
ρcor (λ) = ρA (λ) + t.ρw (λ)
where ρA is the atmospheric contribution and ρw the contribution due to the ocean, t the atmospheric
transmittance.
Atmospheric correction algorithm can be used [1] to estimate ρA and and determined chlorophyll-a
from the remaining ρw.
Phytoplankton are important to marine ecosystems. Its play great role in the food web and
biogeochemical cycles. Chlorophyll represents mainly the phytoplankton.
2. International Journal of Artificial Intelligence & Applications (IJAIA) Vol.10, No.6, November 2019
34
To retrieved chlorophyll-a concentration from ρw, SeaWifS sensor use OC4V4 algorithm that compute
the rapports of ρw at wavelengths 443nm, 490nm, 510nm and 555nm.
)532.1049.6930.1067.3366.0( 432
10 RRRR
achl
where ))
)555(
)510(
,
)555(
)490(
,
)555(
)443(
(max(log10
w
w
w
w
w
w
R
This mean that the maximum rapport value is taken.
The machine learning show strong computing power of classification and fitting capability to big data
and multi-feature data [2]. In many fields as image recognition [3], search engines [4], stock price
predictions [5], accurate results are obtained.
Machine learning found many application on earth remote sensing [6] especially for ocean data
products.
Due to the non-linearity and complexity of the measurements made by ocean colour sensors, and
taking advantage of the high-dimensional data reduction technique for the construction of high-
dimensional predictors in input-output models of deep learning, we aim, with these last ones, to
construct and optimize a chlorophyll-a concentration prediction model.
2. DATASET
Data are from ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Version 3.1 Data.
This collection contains version 3.1 datasets produced by the Ocean Colour project of the ESA
Climate Change Inititative (CCI). The Ocean Colour CCI is producing long-term multi-sensor time-
series of satellite ocean-colour data with a particular focus for use in climate studies.
Data products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing
reflectance at six wavelengths (412 nm, 443 nm, 490 nm, 510 nm, 555 nm, 670 nm)
Datasets are 5 days composite images of year 2014 and are taken in an area off the West Africa,
between 6°N and 30°N and - 34°W and -8°W. This area is very important. It contains the senegalo-
mauritanian upwelling zone.
Datasets of year 2014 are about 26 686 250 pixels. We random these datasets to avoid the overfitting
and take the 5 % for train data and 1 % for test data which is used to test the prediction accuracy of the
model.
We use also independent dataset from MODIS sensor for validation.
3. METHODS
We implemented ours models in Keras and sklearn libraries and we used many deep learning regressor
methods to establish deep network relationship between sea surface reflectance. The aims is, first to
shown the feasibility because of its multivalued of ocean color data, and second, to choose the best
one and further explored it and their hyperparameters tuned. For each model, input are the six spectral
sea surface reflectance and output is the chlorophyll-a.
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35
3.1. Generalized Linear Models
Linear models are simple way to predict output using a linear function of input features.
The spectral sea surface reflectance represents the input features and are notes by nxxxX ,..., 21 ,
output by y and therefore yˆ the predicted output.
Linear Regression: The library of sklearn allow us to parameterize a linear regression. It fits a linear
model with coefficients w=(w1...wn) to minimize the residual sum of squares between the spectral sea
surface reflectance X, and the chlorophyll-a concentration yˆ by the linear approximation:
nn xwxwxwwXwy ...),(ˆ 22110
Linear regression optimization is to minimize the cost function written as:
M
i
M
i
n
j
ijjiii xwyyy
0 0 0
22
)()ˆ(
Ridge regression: The cost function is altered by imposing a penalty equivalent to square of the
magnitude of the coefficients. The cost function to minimize is:
M
i
M
i
n
j
n
j
jijjiii wxwyyy
0 0 0 0
222
)()ˆ(
Ridge Regression is an optimization of Ordinary Least Squares Regression.
3.2. Generalized Ensemble Methods
Ensemble methods are algorithms that combine multiple algorithms into a single predictive model in
order to decrease variance, decrease bias, or improve predictions. In others words, they are a sets of
machine learning techniques whose decisions are combined to improve the performances of the overall
system.
Bagging regressor: Bagging methods are aggregation methods. The bagging approach consists of
trying to reduce the dependency between the estimators that are aggregated by building them on
bootstrap samples. The algorithm is simple to implement: it is necessary to build n estimators on
bootstrap samples and to aggregate them.
Decision trees regressor: Classification and regression trees models, or CART models, were
introduced by [7]. A top down approach is applied to dataset. The complexity of the model is managed
by two parameters: max_depth, which determines the max number of leaves in the trees, and the
minimum number min_samples_split of dataset required to search for a dichotomy.
Random forest regressor: A random forest is only an aggregation trees dependent on random
variables. For example, bagging trees (building trees on bootstrap samples) defines a random forest.
The Random Forest allows to improve the predictive accuracy and to control over-fitting. [8].
More than the number of trees n_estimators, the parameter to be optimized is the number of variables
randomly drawn for the search for the optimal division of a node: max_features. Maximizing the
max_features parameter can be achieved by minimizing the out-of-bag forecast error.
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36
Extra Trees regressor: This class implements a meta-estimator that fits a number of randomized
decision trees on various sub-samples of the dataset and uses averaging to improve the predictive
accuracy and control over-fitting.
3.3. Support Vector Regressor Model
SVR algorithm use RBF as a kernel function. SVR minimizes the generalization error bound so as to
achieve generalized, instead of minimizing the observed training error [9].
3.4. K-Neighbors Regressor Model
The k-nearest neighbors algorithm (k-NN) is a non-parametric. For regression, the output is based on
the mean or median of the k-nearest neighbors in the feature space. The parameter to optimize to
control the complexity of the model is the number of neighbor K.
4. RESULTS
In this section, we verify the generalization capability of the constructed models. Therefore a set of
data were used for prediction experiments. Results are shown on table1. The ensemble models
regressor are more quite fitting data with respectively test accuracy and mean absolute error (mae)
predictions of 96,04% and 0,09 for bagging; 96,05% and 0,09 for random forest; 96,46% and 0,07 for
extra tree and 93,78% and 0,13 for decision trees.
The linear model give us a test accuracy of about 76,06 %. We get a mae prediction of about 0,43. The
K-neighbor model have a mae prediction of 0,09 and a test accuracy of 95,08.
The support vector and the ridge gave least good results with respectively test accuracy and mae
predictions of 5,05% and 0,67; 24,54% and 0,8.
The distribution with a kernel density estimate on figure below show it clearly.
Tab.1: Test of accuracy and error prediction
Regression Mean absolute error (mg/m3
) Accuracy (%)
Linear 0.43 76,06
Ridge 0,8 24,54
SVR 0,67 5,05
K-Neighbor 0,09 95,08
Extra Tree 0,07 96,46
Random Forest 0,09 96,05
Decision Trees 0,13 93,78
Bagging 0,09 96,04
With regard to Table 1, we find that all ensemble models regression work well with larger prediction
values.
In figure 1, we show kernel density estimation (KDE) to visualize frequency test data predicted by
each method. A KDE is used to get a smooth estimation of the probability density function. This curve
is estimated from the data, and the most widely used kernel is a Gaussian kernel. This is particularly
useful in looking for a cluster of analyses in spectra of data. We noted that extra tree, random forest,
decision trees and bagging method are more robust. For the following of this paper, we will work with
the extra trees regression model to predict the target.
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Fig. 1: Distribution with a kernel density estimation with a Gaussian kernel and a data set with 266862 sample
points from a combined normal density
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In figure 2 and figure 3, we plot the predicted chl-a concentration with extra trees model and the real
values measured respectively for the average of period from January 16, to January 30, 2014 and for
the average of period from from March 11, 2014, to March 30, 2014. For average value, we apply the
algorithms to each daily image and average this daily estimate for the climatology period under study.
The dataset we compare did not participate in learning phase.
The two images are very similar. Indeed, chl-a estimated for this average is equal to 95,05% with a
mae of 0,09 mg/m3
. We see that an abundance chlorophyll-a pattern is observed near the shore and this
rapidly decrease offshore. This is consistent with the literature because the upwelling area runs along
the West African coast from Guinea to Mauritania. [10] and more recently [11], [12] demonstrate that
the upwelling intensity is maximum in March–April.
Fig. 2: The average of estimate chlorophyll-a concentration from January 16, 2014, to January 30, 2014 in
mg/m3
for (left) the true CCI value and (right) Extra trees prediction value. The relative error between them is
represented in bottom.
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Fig. 3: The average of estimate chlorophyll-a concentration from March 11, 2014, to March 30, 2014 in mg/m3
for (left) the true CCI value and (right) Extra trees prediction value. The relative error between them is
represented in bottom.
By using independent dataset, comparison can be made with the chl-a predicted using from spectral
reflectance of MODIS sensor with the model and the standard chl-a estimate with standard algorithm,
OC3V3. This comparison shows that both methods bring out the patterns of chl-a. However the
intensity of is stronger in the standard retrieval. Figure 4.
This result is very significant because it mean that according to whether we use data from several
sensors, such as those used to build the different models of this work, or data from a single sensor
(MODIS sensor, figure 4), the modeling capacity remains good.
Fig.4: The average of estimate chlorophyll-a concentration from January 1, 2015, to January 16, 2015 in mg/m3
for (left) the standard MODIS value and (right) Extra trees prediction value.
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5. CONCLUSION
The high-dimensional predictors in input-output models offered by deep learning demonstrate in the
work the effectiveness of chlorophyll-a retrieval from sea surface reflectance.
High accuracy are obtained on both the training and test dataset with a low mean absolute error of 0,09
mg/m3
and correlation coefficient higher than 92%. The extra tree regression was the model we used.
Retrievals values of chlorophyll-a are in consistence with upwelling phenomena denoted on this area.
Comparison we independent value have shown satisfactory results.
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