Yangetal Efficient Letkf
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1) The document proposes using weight interpolation for the Local Ensemble Transform Kalman Filter (LETKF) data assimilation method to improve computational efficiency. 2) In LETKF, weights are computed on coarse grids and then interpolated to high-resolution grids, rather than computing the analysis directly on the high-resolution grids. 3) Experiments with a quasi-geostrophic model show that interpolating the weights produces analysis accuracy comparable to computing the analysis on full grids, while greatly reducing computational cost. Weight interpolation allows analysis to be performed in regions without local observations.
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Abstract: Thermoluminescent dosimeters (TLDs) are small crystalline materials that measure ionizing radiation dose to a person or the environment. When heated after receiving a radiation dose, TLDs emit light proportional to the dose received. The light signal is recorded as a function of temperature known as a glow curve, which consists of several peaks corresponding to different electron trap state energies. The area under the curve is directly proportional to the radiation dose. Various dosimetry applications require the individual glow peak areas. In order to determine these, a glow curve analysis (GCA) program was written to deconstruct the glow curve into its individual peaks. If individual glow peak areas are not required, a simpler region of interest (ROI) analysis program was written to determine the area under the glow curve while ignoring any noise appearing in the high temperature region. A comparison of using ROIs versus GCA will be made.
This comparison will be made based on 10 TLD readings each containing the same 100 TLD chips all of which are type TLD-100 (LiF doped with Mg and Ti) irradiated at 4.4 mGy. The ROI and GCA programs will both be run over this data, and statistical analysis will be used to determine if the GCA program increases the standard deviation of glow curve areas when it generates a line of best fit to approximate the glow curve. Results show that the GCA program does not add additional variation when used to calculate the area under the glow curve.
Artificial Intelligence Applications For Building Energy Management Applicati...
Artificial Intelligence Applications For Building Energy Management Applicati...University of the Highlands and Islands
This document summarizes a PhD candidate's research using artificial intelligence to automate building energy management. The research aims to integrate a building energy management system with micro-generation systems on a single platform to achieve zero energy building status. The candidate is developing a cost function controller that considers energy costs, thermal comfort, and energy production. Preliminary MATLAB modeling shows a neural network model can accurately predict and optimize a simulated heat plant to track temperature references. Further work is needed to optimize plant tracking and include integral or derivative controls. Overall, the research aims to make buildings more adaptive and energy-productive entities.Application of cgpann in solar irradiance
A Neuro-evolutionary approach for extracting trend ensembles in the solar irradiance patterns for renewable electric power generation.
Cartesian Genetic Programming Evolved Artificial Neural Network (CGPANN) is developed and trained for hourly and 24-hourly prediction.
The System takes Solar Irradiance data as its only Input parameter And it is 95.48% accurate in solar irradiance prediction
An Efficient Cluster Tree Based Data Collection Scheme for Large Mobile With ...
This document describes a proposed efficient cluster tree based data collection scheme for large mobile wireless sensor networks (WSNs) with polling points. The objectives are to reduce the workload of cluster heads and increase throughput while decreasing energy consumption. It involves defining data gathering nodes and using a spanning tree covering algorithm to select polling points for data collection from sensor nodes. Performance is evaluated showing this approach reduces energy usage, end-to-end delay, and traffic to cluster heads compared to existing methods. Applications discussed include wildfire monitoring and preventative maintenance on oil tankers.
Detecting stable phase structures in eeg signals to classify brain activity a...
These patterns are described as stable frames with carrier frequencies in the beta or gamma band that recur at similar rates in the theta or alpha band
QUERY AND NETWORK ANALYSIS IN GIS
This document discusses geospatial techniques for query and network analysis in GIS. It introduces key concepts like attribute queries, spatial queries, and network analysis. Network analysis allows solving problems like finding optimal routes and closest facilities. Common network analysis techniques discussed include shortest path, closest facility, and OD cost matrix analysis. Applications mentioned are routing, resource allocation, and traveling salesman problems. The document concludes that query and network analysis techniques are useful for obtaining detailed spatial and attribute information in urban planning.
Determination of wind energy potential of campus area of siirt university
In this study, wind energy potential of Siirt
University campus area is statistically examined by using the mean hourly wind speed data between 2014
and 2015 years which are measured by Vantage Pro2 device, located at the roof of the Engineering Faculty building with 6 m altitude. Weibull distribution
function and Rayleigh distribution function are used
as statistical approach to evaluate the wind data. Weibull distribution function is examined by using two different methods that are maximum likelihood estimation and Rayleigh method. The determination
coefficient (R2) and Root Mean Square Error (RMSE) values of these methods are compared. According the error analysis, it is indicated that the Rayleigh method
gives better results. Wind speed and wind power density are calculated in pursuance of Weibull distribution parameters. The results are evaluated as
monthly and annually. Hence, this preliminary study is made to determine the wind energy potential of Siirt University campus area.
Retraining maximum likelihood classifiers using low-rank model.ppt
The document proposes a low-rank parameter modeling method to retrain maximum likelihood classifiers and address dataset shift between training and test data. It models test data using a low-rank approach with unknown parameter vectors estimated from the data. The method is evaluated on cloud detection using satellite images and tree cover mapping with Landsat images, showing improved classification performance over not retraining.
Analysis of Time Diversity Gain for Satellite Communication Link based on Ku-...
This paper reports a study on mitigation of propagation impairments on Earth–space communication links. The study uses time diversity as a technique for mitigating rain propagation impairment in order to rectify rain fade. Rain attenuation time series along earth-to-satellite link were measured for two years period at 12.255 GHz in Malaysia. The time diversity technique was applied on measured rain fade to investigate the level of possible improvement in system. Time diversity gain from measured oneminute rain attenuation for two years period was estimated and significant improvement was observed with different delays of time. These findings will be utilized as a useful tool for link designers to apply time diversity as a rain fade mitigation technique in Earth-satellite communications systems.
ECCV 2010
The document proposes a method to reduce the number of levels in image segmentation hierarchies using a modified form of Laplacian graph energy as a filter. The filter works across different hierarchy types and can reduce levels by an order of magnitude without losing semantic content. It is valuable for applications like tracking where representation size impacts efficiency. The method looks for minima in the modified graph energy as a graph is built from bottom to top levels, and uses this to identify optimal levels that balance connectivity and complexity. Evaluation on standard datasets shows the approach effectively identifies key semantic levels.
A multi sensor-information_fusion_method_based_on_factor_graph_for_integrated...
The document proposes a multi-sensor information fusion method based on factor graph for integrated navigation systems. Key points:
- It constructs a factor graph framework where sensor measurements are factor nodes and navigation states are variable nodes, allowing for efficient fusion of asynchronous sensor data.
- The method formulates the optimal navigation solution as the maximum a posteriori estimate of the navigation state probability distribution based on all available sensor measurements.
- It is experimentally validated on two real-world datasets, demonstrating effectiveness compared to the widely used Federated Filter approach. Analysis of navigation with simulated data loss also verifies the method's ability to achieve sensor "plug and play" in software.
The effect of microscale spatial variability of wind on estimation of technic...
This document summarizes a study that estimated wind energy potential using high-resolution microscale wind data compared to typical mesoscale data. The study found that using microscale wind climate data from the Global Wind Atlas project resulted in:
1) Over twice as high of an estimated economic wind energy potential compared to using simulated mesoscale data for the same region of Washington State.
2) Microscale data showed a more varied annual energy production between grid cells from 0 to 14.9 GWh, while mesoscale data had lower variation between 6.4 to 9.6 GWh.
3) The maximum, average, and binary integer programming approaches for estimating net potential from microscale data all resulted in higher potential than estimates
Optimal combinaison of CFD modeling and statistical learning for short-term w...
Optimal combinaison of CFD modeling and statistical learning for short-term w...Jean-Claude Meteodyn
After almost three decades of active research, short-term wind power forecasting is now considered as a mature field. It has been widely and successfully put into operation within the past ten years. Meteodyn with over a decade of experience in wind engineering has contributed to this spread with tens of wind farm equipped with forecast solutions around the world. Our next-generation short-term forecasting solution has been designed to makes the most of both a tailored micro-scale CFD modeling and advanced statistical learning. In the frame of our model design, various options have been considered and evaluated taking into account both model performance and operational constraints. Two main approaches for wind power forecasting are usually considered in the literature (and sometimes opposed): “physical” and “statistical”. It is widely admitted that an optimal combination of both is necessary to build a high performance forecasting system. However, behind "optimal combination" resides a wide variety of design options. We propose here to shed some light on what performances one should expect from several modeling options for combining physics (mesoscale/CFD modeling) and statistics (grey/black box statistical learning, phase/magnitude correction, data filtering). Case studies are taken from real wind farms in various climate and terrain conditions.Kalman filtering and it's applications
We can know the information about kalman filtering & it's history of invention and applications of that kalman filtering
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Calculation of solar radiation by using regression methods
Calculation of solar radiation by using regression methods
A Comparison of Different Methods of Glow Curve Analysis for Thermoluminescen...
A Comparison of Different Methods of Glow Curve Analysis for Thermoluminescen...
Artificial Intelligence Applications For Building Energy Management Applicati...
Artificial Intelligence Applications For Building Energy Management Applicati...
An Efficient Cluster Tree Based Data Collection Scheme for Large Mobile With ...
An Efficient Cluster Tree Based Data Collection Scheme for Large Mobile With ...
Detecting stable phase structures in eeg signals to classify brain activity a...
Detecting stable phase structures in eeg signals to classify brain activity a...
Determination of wind energy potential of campus area of siirt university
Determination of wind energy potential of campus area of siirt university
Retraining maximum likelihood classifiers using low-rank model.ppt
Retraining maximum likelihood classifiers using low-rank model.ppt
Analysis of Time Diversity Gain for Satellite Communication Link based on Ku-...
Analysis of Time Diversity Gain for Satellite Communication Link based on Ku-...
A multi sensor-information_fusion_method_based_on_factor_graph_for_integrated...
A multi sensor-information_fusion_method_based_on_factor_graph_for_integrated...
The effect of microscale spatial variability of wind on estimation of technic...
The effect of microscale spatial variability of wind on estimation of technic...
Optimal combinaison of CFD modeling and statistical learning for short-term w...
Optimal combinaison of CFD modeling and statistical learning for short-term w...
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The super resolution technology 2016
The document discusses superresolution technology that can improve the resolution of infrared camera images. It begins by explaining the basic problem that small objects may be invisible or measured incorrectly in infrared images due to pixel size limitations. It then describes how superresolution works by using multiple images and deconvolution algorithms to effectively decrease pixel pitch by 1.6x and increase usable resolution also by 1.6x compared to normal images. Experimental results show that superresolution detects spatial frequencies about 50% higher than the camera's detector cutoff and improves temperature measurement accuracy compared to interpolation. The technology will be available as a software update for all current Testo infrared cameras.
WE1.L10 - USE OF NASA DATA IN THE JOINT CENTER FOR SATELLITE DATA ASSIMILATION
The document discusses the use of NASA satellite data in weather and environmental analysis by the Joint Center for Satellite Data Assimilation (JCSDA). The JCSDA is an interagency partnership that works to improve forecast models through better use of satellite observations. It assimilates many NASA sensors operationally, including MODIS, AIRS, and Jason altimetry, and is working to prepare other sensors like SMAP for assimilation testing. Highlights are presented on atmospheric, ocean, and land data assimilation using NASA data to improve analysis and forecasts.
GCCOM\_DART: Ensemble Data Assimilation Analysis System for Sub-mesoscale Pr...
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Our research demonstrates how data assimilation can be used, with a non-hydrostatic coastal ocean model, to study sub-mesoscale processes and accurately estimate the state variables. The implementation is non trivial for physical ocean models which are highly nonlinear, sensitive to perturbations, and require a dense spatial discretization in order to correctly reproduce the dynamics. A major challenge of this approach is the high computational cost incurred by a high-resolution numerical model with a three-dimensional data assimilation scheme in a complicated stratified system. Interfacing the General Curvilinear Coastal Ocean Model (GCCOM) with the faster data assimilation framework, NCAR Data Assimilation Research Testbed (DART), allowed us to assimilate very high resolution observations into the system. Observing System Simulation Experiments (OSSEs) in very steep seamount test cases are presented. These were used to explore the proper initial ensemble members for the model, estimate the observation error variance needed to reproduce the dynamics in a turbulent flow experiment, and to analyze the impact of localization in such small processes. Our results demonstrate that the DART-GCCOM model can assimilate high resolution observations (tenths of meters) using as few as 30 ensemble members.
Introduction_to_dataassimilation
This document provides an introduction to data assimilation and the OpenDA framework. It discusses how data assimilation combines models and observations to improve model predictions while accounting for uncertainties. Several examples of data assimilation applications are provided, such as for storm surge forecasting and calibrating wave models. The motivation for developing the OpenDA framework is explained, as it aims to provide reusable data assimilation algorithms and components that can be easily implemented and tested across different models.
Data assimilation with OpenDA
1) OpenDA is a data assimilation toolbox that allows for both data assimilation and model calibration in a generic way.
2) It has an object oriented design that allows components like models and algorithms to be easily exchanged.
3) OpenDA supports parallel computing concepts and various ways of integrating models, including keeping models as "black boxes".
Hidro Modelamiento
This document describes the different categories of diagnostic output from the DART data assimilation system: state space, observation space, regression confidence factors, and program diagnostics. It provides details on the format and contents of files for each category, and discusses tools for viewing the output files, including MATLAB diagnostics, netCDF viewers, and NCO utilities.
Damian Barrett_Improved soil moisture and canopy conductance data products fo...
1. Model data assimilation provides a framework to combine multiple datasets and biophysical models to generate synthesis products, reducing bias compared to traditional modeling.
2. It allows diagnosis of canopy conductance from satellite-observed land surface temperature and soil moisture from microwave radiometer data.
3. Linking TERN and NCRIS infrastructure enables the computationally intensive processing needed to assimilate high-volume satellite datasets.
Damiano Pasetto
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D ecimation and interpolation
This document discusses decimation and interpolation using polyphase filters. It begins by defining decimation and interpolation and describing how decimators use anti-aliasing filters followed by downsamplers, while interpolators use upsamplers followed by anti-imaging filters. It then presents the principles of polyphase decimation and interpolation, showing how they can be represented in both the time and z-transform domains. This allows implementing decimators and interpolators more efficiently using fewer filter operations and less memory than traditional implementations.
Super Resolution of Image
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Inverse distance weighting
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Super resolution from a single image
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DSD-INT 2016 Data assimilation to improve volcanic ash forecasts using LOTOS-...
This document summarizes research on improving volcanic ash forecasts using an ensemble Kalman filter data assimilation method with the LOTOS-EUROS model and OpenDA software. Aircraft measurements of volcanic ash are assimilated to improve forecast accuracy compared to models alone. A two-way tracking localized ensemble Kalman filter is developed to reduce sampling errors from small ensembles. A mask-state ensemble Kalman filter is also created to accelerate volcanic ash data assimilation by focusing computations only on relevant grid points containing ash. Validation shows both methods successfully improve volcanic ash concentration forecasts.
Spatial interpolation techniques
Spatial interpolation techniques are used to estimate values at unsampled locations based on known sample points. There are two main types of interpolation methods: global methods which apply a single mathematical function to all points, and local methods which apply functions to subsets of points. Specific interpolation methods covered include Thiessen polygons, triangulated irregular networks, spatial moving average, trend surfaces, inverse distance weighting, and Kriging. Kriging is similar to inverse distance weighting but uses minimum variance to calculate weights.
Datta-Barua, URSI AT-RASC, 2015, Canary Islands, Ionospheric-Thermospheric St...
This document summarizes research that assimilated new neutral wind measurements from Fabry-Perot interferometers (FPI) into an ionospheric data assimilation model for the first time. The results showed reduced errors in estimated neutral wind velocities near the location where FPI data was ingested. This suggests an overall improvement to measurements in that region when more direct neutral wind observations are included. Assimilating data from additional FPI instruments in the future could further refine horizontal wind maps and ionosphere understanding.
Super Resolution in Digital Image processing
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This document discusses the use of satellite soil moisture data for hydrological applications. It summarizes research validating satellite soil moisture products against in situ observations across different scales. It also describes a method called SM2RAIN that estimates rainfall from satellite soil moisture observations by inverting the soil water balance equation. Initial tests of SM2RAIN show good agreement between estimated and observed rainfall.
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WE1.L10 - USE OF NASA DATA IN THE JOINT CENTER FOR SATELLITE DATA ASSIMILATION
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Damian Barrett_Improved soil moisture and canopy conductance data products fo...
Damian Barrett_Improved soil moisture and canopy conductance data products fo...
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Uncertainty Analysis and Data Assimilation of Remote Sensing Data for the Cal...
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1-A Fault Tolerant Integrated Navigation Scheme Realized through Online Tunin...
This document presents a fault tolerant integrated navigation scheme using an adaptive federated Kalman filter. The scheme integrates strapdown inertial navigation system (SINS) measurements with GPS and celestial navigation system (CNS) measurements. It uses a federated Kalman filter architecture with two local filters (one for SINS/GPS and one for SINS/CNS) and a master filter. Weighting factors are introduced and adapted online to tune the contribution of each local filter in the final data fusion, making the scheme nearly optimal and fault tolerant. The measurement noise covariance is also made adaptive using a fuzzy inference system based on the relative degree of mismatch between actual and theoretical innovation covariances.
6-A robust data fusion scheme for integrated navigation systems employing fau...
This document describes a data fusion scheme for integrated navigation systems that employs fault detection and fuzzy adaptive filtering. It uses a federated Kalman filter (FKF) approach where local filters process sensor data in parallel and provide estimates to a master filter. An adaptive Kalman filter using fuzzy inference adapts the statistical features of sensors online based on real dynamics and varying noise. Faults are detected using a Chi Square test. The scheme was implemented on a system integrating strapdown inertial navigation, celestial navigation, GPS, and Doppler radar. Simulation results validated its effectiveness in improving precision, reliability, and fault tolerance compared to standard centralized and federated Kalman filters.
A RRAY F ACTOR O PTIMIZATION OF AN A CTIVE P LANAR P HASED A RRAY USING...
Evolutionary algorithms (EAs) have the potential to
handle complex, multi-dimensional optimization
problems in the field of phased array. Out of diffe
rent EAs, particle swarm optimization (PSO) is a po
pular
choice. In a phased array, antenna element failure
is a common phenomenon and this leads to degradatio
n
of the array factor (AF) pattern, primarily in term
s of increased side lobe levels (SLLs), displacemen
t of
nulls and reduction in the null depths. The recover
y of a degraded pattern using a cost and time-effec
tive
approach is on demand. In this context, an attempt
made to obtain an optimized AF pattern after fault
in a
49 elements quasi-circular aperture equilateral tri
angular grid active planar phased array using PSO.
In
the paper, multiple cases on recovery are discussed
having a maximum 20% element failure. Each recover
y
is also further evaluated by different statistical
analyses. A dedicated software tool was developed t
o carry
out the work presented in this paper.
Project doc
This document proposes an Adaptive Resolution Enhancement Algorithm (AREA) using an Edge Targeted Filter (ETF) and Dual Tree Complex Wavelet Transform (DTCWT) to enhance the resolution and image quality of low-quality, low-resolution images. The ETF first detects edges in an input image and generates a mask. It then focuses on improving edge reconstruction. The output is input to DTCWT, which analyzes subband coefficients to detect flaws and interpolates coefficients to enhance spectral resolution. The inverse DTCWT produces a spatially enhanced output image with improved resolution up to 17% compared to existing techniques. Simulation results demonstrate the effectiveness of the proposed algorithm.
ARRAY FACTOR OPTIMIZATION OF AN ACTIVE PLANAR PHASED ARRAY USING EVOLUTIONARY...
Evolutionary algorithms (EAs) have the potential to handle complex, multi-dimensional optimization problems in the field of phased array. Out of different EAs, particle swarm optimization (PSO) is a popular choice. In a phased array, antenna element failure is a common phenomenon and this leads to degradation of the array factor (AF) pattern, primarily in terms of increased side lobe levels (SLLs), displacement of nulls and reduction in the null depths. The recovery of a degraded pattern using a cost and time-effective approach is on demand. In this context, an attempt made to obtain an optimized AF pattern after fault in a 49 elements quasi-circular aperture equilateral triangular grid active planar phased array using PSO. In the paper, multiple cases on recovery are discussed having a maximum 20% element failure. Each recovery is also further evaluated by different statistical analyses. A dedicated software tool was developed to carry out the work presented in this paper.
ARRAY FACTOR OPTIMIZATION OF AN ACTIVE PLANAR PHASED ARRAY USING EVOLUTIONARY...
Evolutionary algorithms (EAs) have the potential to handle complex, multi-dimensional optimization
problems in the field of phased array. Out of different EAs, particle swarm optimization (PSO) is a popular
choice. In a phased array, antenna element failure is a common phenomenon and this leads to degradation
of the array factor (AF) pattern, primarily in terms of increased side lobe levels (SLLs), displacement of
nulls and reduction in the null depths. The recovery of a degraded pattern using a cost and time-effective
approach is on demand. In this context, an attempt made to obtain an optimized AF pattern after fault in a
49 elements quasi-circular aperture equilateral triangular grid active planar phased array using PSO. In
the paper, multiple cases on recovery are discussed having a maximum 20% element failure. Each recovery
is also further evaluated by different statistical analyses. A dedicated software tool was developed to carry
out the work presented in this paper.
ARRAY FACTOR OPTIMIZATION OF AN ACTIVE PLANAR PHASED ARRAY USING EVOLUTIONARY...
Evolutionary algorithms (EAs) have the potential to handle complex, multi-dimensional optimization problems in the field of phased array. Out of different EAs, particle swarm optimization (PSO) is a popular choice. In a phased array, antenna element failure is a common phenomenon and this leads to degradation of the array factor (AF) pattern, primarily in terms of increased side lobe levels (SLLs), displacement of nulls and reduction in the null depths. The recovery of a degraded pattern using a cost and time-effective approach is on demand. In this context, an attempt made to obtain an optimized AF pattern after fault in a 49 elements quasi-circular aperture equilateral triangular grid active planar phased array using PSO. In the paper, multiple cases on recovery are discussed having a maximum 20% element failure. Each recovery is also further evaluated by different statistical analyses. A dedicated software tool was developed to carry out the work presented in this paper.
ARRAY FACTOR OPTIMIZATION OF AN ACTIVE PLANAR PHASED ARRAY USING EVOLUTIONARY...
Evolutionary algorithms (EAs) have the potential to handle complex, multi-dimensional optimization problems in the field of phased array. Out of different EAs, particle swarm optimization (PSO) is a popular choice. In a phased array, antenna element failure is a common phenomenon and this leads to degradation of the array factor (AF) pattern, primarily in terms of increased side lobe levels (SLLs), displacement of nulls and reduction in the null depths. The recovery of a degraded pattern using a cost and time-effective approach is on demand. In this context, an attempt made to obtain an optimized AF pattern after fault in a 49 elements quasi-circular aperture equilateral triangular grid active planar phased array using PSO. In the paper, multiple cases on recovery are discussed having a maximum 20% element failure. Each recovery is also further evaluated by different statistical analyses. A dedicated software tool was developed to carry out the work presented in this paper.
ARRAY FACTOR OPTIMIZATION OF AN ACTIVE PLANAR PHASED ARRAY USING EVOLUTIONARY...
This document discusses using particle swarm optimization (PSO) to optimize the array factor pattern of an active planar phased array after antenna element failures. It presents results from optimizing the pattern of a 49-element quasi-circular array with up to 20% element failure. PSO is used to calculate a new set of input amplitudes for the functional elements that recover the original pattern levels for the main beam, first side lobes, and first nulls. The results show PSO can effectively recover the pattern in cases with 1, 3, 5, and 10 non-functioning elements.
ARRAY FACTOR OPTIMIZATION OF AN ACTIVE PLANAR PHASED ARRAY USING EVOLUTIONARY...
Evolutionary algorithms (EAs) have the potential to handle complex, multi-dimensional optimization problems in the field of phased array. Out of different EAs, particle swarm optimization (PSO) is a popular choice. In a phased array, antenna element failure is a common phenomenon and this leads to degradation of the array factor (AF) pattern, primarily in terms of increased side lobe levels (SLLs), displacement of nulls and reduction in the null depths. The recovery of a degraded pattern using a cost and time-effective approach is on demand. In this context, an attempt made to obtain an optimized AF pattern after fault in a 49 elements quasi-circular aperture equilateral triangular grid active planar phased array using PSO. In the paper, multiple cases on recovery are discussed having a maximum 20% element failure. Each recovery is also further evaluated by different statistical analyses. A dedicated software tool was developed to carry out the work presented in this paper.
ARRAY FACTOR OPTIMIZATION OF AN ACTIVE PLANAR PHASED ARRAY USING EVOLUTIONARY...
Evolutionary algorithms (EAs) have the potential to handle complex, multi-dimensional optimization problems in the field of phased array. Out of different EAs, particle swarm optimization (PSO) is a popular choice. In a phased array, antenna element failure is a common phenomenon and this leads to degradation of the array factor (AF) pattern, primarily in terms of increased side lobe levels (SLLs), displacement of nulls and reduction in the null depths. The recovery of a degraded pattern using a cost and time-effective approach is on demand. In this context, an attempt made to obtain an optimized AF pattern after fault in a 49 elements quasi-circular aperture equilateral triangular grid active planar phased array using PSO. In the paper, multiple cases on recovery are discussed having a maximum 20% element failure. Each recovery is also further evaluated by different statistical analyses. A dedicated software tool was developed to carry out the work presented in this paper.
ARRAY FACTOR OPTIMIZATION OF AN ACTIVE PLANAR PHASED ARRAY USING EVOLUTIONARY...
Evolutionary algorithms (EAs) have the potential to handle complex, multi-dimensional optimization problems in the field of phased array. Out of different EAs, particle swarm optimization (PSO) is a popular choice. In a phased array, antenna element failure is a common phenomenon and this leads to degradation of the array factor (AF) pattern, primarily in terms of increased side lobe levels (SLLs), displacement of nulls and reduction in the null depths. The recovery of a degraded pattern using a cost and time-effective approach is on demand. In this context, an attempt made to obtain an optimized AF pattern after fault in a 49 elements quasi-circular aperture equilateral triangular grid active planar phased array using PSO. In the paper, multiple cases on recovery are discussed having a maximum 20% element failure. Each recovery is also further evaluated by different statistical analyses. A dedicated software tool was developed to carry out the work presented in this paper.
ARRAY FACTOR OPTIMIZATION OF AN ACTIVE PLANAR PHASED ARRAY USING EVOLUTIONARY...
Evolutionary algorithms (EAs) have the potential to handle complex, multi-dimensional optimization problems in the field of phased array. Out of different EAs, particle swarm optimization (PSO) is a popular choice. In a phased array, antenna element failure is a common phenomenon and this leads to degradation of the array factor (AF) pattern, primarily in terms of increased side lobe levels (SLLs), displacement of nulls and reduction in the null depths. The recovery of a degraded pattern using a cost and time-effective approach is on demand. In this context, an attempt made to obtain an optimized AF pattern after fault in a 49 elements quasi-circular aperture equilateral triangular grid active planar phased array using PSO. In the paper, multiple cases on recovery are discussed having a maximum 20% element failure. Each recovery is also further evaluated by different statistical analyses. A dedicated software tool was developed to carry out the work presented in this paper.
ARRAY FACTOR OPTIMIZATION OF AN ACTIVE PLANAR PHASED ARRAY USING EVOLUTIONARY...
Evolutionary algorithms (EAs) have the potential to handle complex, multi-dimensional optimization problems in the field of phased array. Out of different EAs, particle swarm optimization (PSO) is a popular choice. In a phased array, antenna element failure is a common phenomenon and this leads to degradation
of the array factor (AF) pattern, primarily in terms of increased side lobe levels (SLLs), displacement of nulls and reduction in the null depths. The recovery of a degraded pattern using a cost and time-effective approach is on demand. In this context, an attempt made to obtain an optimized AF pattern after fault in a
49 elements quasi-circular aperture equilateral triangular grid active planar phased array using PSO. In the paper, multiple cases on recovery are discussed having a maximum 20% element failure. Each recovery is also further evaluated by different statistical analyses. A dedicated software tool was developed to carry out the work presented in this paper.
ARRAY FACTOR OPTIMIZATION OF AN ACTIVE PLANAR PHASED ARRAY USING EVOLUTIONARY...
Evolutionary algorithms (EAs) have the potential to handle complex, multi-dimensional optimization problems in the field of phased array. Out of different EAs, particle swarm optimization (PSO) is a popular choice. In a phased array, antenna element failure is a common phenomenon and this leads to degradation
of the array factor (AF) pattern, primarily in terms of increased side lobe levels (SLLs), displacement of nulls and reduction in the null depths. The recovery of a degraded pattern using a cost and time-effective approach is on demand. In this context, an attempt made to obtain an optimized AF pattern after fault in a
49 elements quasi-circular aperture equilateral triangular grid active planar phased array using PSO. In the paper, multiple cases on recovery are discussed having a maximum 20% element failure. Each recovery is also further evaluated by different statistical analyses. A dedicated software tool was developed to carry out the work presented in this paper.
ARRAY FACTOR OPTIMIZATION OF AN ACTIVE PLANAR PHASED ARRAY USING EVOLUTIONARY...
Evolutionary algorithms (EAs) have the potential to handle complex, multi-dimensional optimization problems in the field of phased array. Out of different EAs, particle swarm optimization (PSO) is a popular choice. In a phased array, antenna element failure is a common phenomenon and this leads to degradation of the array factor (AF) pattern, primarily in terms of increased side lobe levels (SLLs), displacement of nulls and reduction in the null depths. The recovery of a degraded pattern using a cost and time-effective approach is on demand. In this context, an attempt made to obtain an optimized AF pattern after fault in a 49 elements quasi-circular aperture equilateral triangular grid active planar phased array using PSO. In the paper, multiple cases on recovery are discussed having a maximum 20% element failure. Each recovery is also further evaluated by different statistical analyses. A dedicated software tool was developed to carry out the work presented in this paper.
ARTIFICIAL NEURAL NETWORK APPROACH TO MODELING OF POLYPROPYLENE REACTOR
This paper shows modeling of highly nonlinear polymerization process using the artificial neural network approach for the model predictive purposes. Polymerization occurs in a fluidized bed polypropylene reactor using Ziegler - Natta catalyst and the main objective was modeling of the reactor production rate.
The data set used for an identification of the model is a real process data received from an existing polypropylene plant and the identified model is a nonlinear autoregressive neural network with the exogenous input. Performance of a trained network has been verified using the real process data and the
ability of the production rate prediction is shown in the conclusion.
Multisite UTE 31P Rosette MRSI(PETALUTE)
While Phosphorous (31P) MRS (I) has been promising in experimental and clinical settings since the early 70s, it has been beset by prohibitively lower sensitivity, limited spectral-spatial resolution, and prolonged acquisition. This manuscript and proceedings of the annual scientific meeting of ISMRM in 2022 (REF1) and 2023 (REF2) demonstrate that our novel acquisition strategy, the novel Rosette Trajectory for fast and flexible MR(S)I contrast (Shen et al. 2023 (REF3), later we renamed it as PETALUTE after the translation to the preclinical scanners of 7T and 9.4T), enables operator-independent (1) rapid acquisition (~7 minutes), (2) reconstruction, and (3) processing pipeline, resulting in phosphorous metabolite ratio maps (10 x 10 x 10 mm3) of the whole brain.
In response to the “Repeat it with Me” challenge organized by the Reproducible Research study group of ISMRM, we demonstrated the power of this technique in 5 healthy volunteers at three different institutions with different experimental setups (2nd Place: UTE 31P 3D Rosette MRSI Reproducibility Team, REF4). Since the proposed acquisition/reconstruction/processing pipeline was operator/scanner/coil-independent, the Reproducer sub-teams successfully replicated the findings of the original proceeding in 2022 (REF1). As part of this challenge, we provided some MATLAB scripts and k-space data to reproduce some of the results described in this manuscript. The software and data can be downloaded from https://purr.purdue.edu/projects/ismrm31pmrsi.
These results will likely be of broad interest across clinical settings since the proposed acquisition strategy is not specific to any region, nuclei, or magnetic field and is operator-independent. This study's resolution and signal-to-noise ratios permit the metabolite maps in an experimentally and clinically feasible timeframe at 3 Tesla and 7T.
REF1 Bozymski B, Shen X, Ozen AC, Ibey S, Chiew M, Thomas A, Dydak U, Emir UE. Ultra-Short Echo Time 31P 3D MRSI at 3T with Novel Rosette k-space Trajectory. Proceedings 30th Scientific Meeting, International Society for Magnetic Resonance in Medicine, 2022.
REF2 Farley N, Bozymski B, Dydak U, Emir UE*. Fast 3D 31P MRSI Using Novel Rosette Petal Trajectory at 3T with x4 Accelerated Compressed Sensing. Proceedings 31st Scientific Meeting, International Society for Magnetic Resonance in Medicine, 2023.
REF3 Shen X, Özen AC, Sunjar A, Ilbey S, Sawiak S, Shi R, Chiew M, Emir UE. Ultrashort T2 components imaging of the whole brain using 3D dual-echo UTE MRI with rosette k-space pattern. Magnetic Resonance in Medicine. 2023;89(2):508–521.
REF4 https://challenge.ismrm.org/2023-24-reproducibility-challenge/results-22-23/
J010116069
This document summarizes research on using particle swarm optimization (PSO) to enhance the radiation pattern of a phase array antenna. It begins by introducing the problem of high sidelobes in phase array antenna patterns. It then provides background on phased array antennas and array pattern modeling. A 24-element linear array modeled in MATLAB is used as a case study. Standard PSO and a modified PSO algorithm are applied to optimize the current excitations and minimize the sidelobe levels. Simulation results show that both PSO approaches reduce sidelobes compared to uniform excitation, with the modified PSO performing better. Overall, the document presents research on using computational optimization methods to improve phase array antenna radiation patterns by controlling sidelobe levels
Enhancing the Radiation Pattern of Phase Array Antenna Using Particle Swarm O...
The document describes a study that uses particle swarm optimization to enhance the radiation pattern of a phase array antenna by minimizing sidelobe levels. It first provides background on issues with high sidelobes in phase array antennas, such as power losses and interference. It then summarizes previous research using techniques like genetic algorithms for antenna array optimization. The study models the radiation pattern of linear arrays with different element numbers and calculates gain, finding that gain increases with more elements. However, sidelobe levels also increase relatively. Therefore, the study proposes using particle swarm optimization to optimize current excitation and control sidelobe levels while maintaining a narrow beamwidth.
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- 1. Weight interpolation for efficient data assimilation with the Local Ensemble Transform Kalman Filter Shu-Chih Yang 1,2 , Eugenia Kalnay 1,3 , Brian Hunt 1,3 and Neill E. Bowler 4 1 Department of Atmospheric and Oceanic Science, University of Maryland 2 Global Modeling and Assimilation Office, NASA/GSFC 3 Institute for Physical Science and Technology, University of Maryland 4 Met Office, FitzRoy Road, Exeter, UK
- 6. LETKF on coarse grids : coarse grids to perform LETKF analysis and calculate , : use the interpolated weights to compute the analysis Ex: 11% analysis coverage To ensure zero mean of X a Choose a linear interpolation scheme (bi-variate interpolation, Akima,1978)
- 9. Weights from different analysis grid coverage Full Analysis 11% analysis grids 4% analysis grids 2% analysis grids Based on dynamical evolution of the ensemble perturbations for analysis pert.1 from background pert.1 Full Analysis 11% analysis grids 4% analysis grids 2% analysis grids for analysis pert.1 from background pert.4
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- 11. Analysis accuracy ( weight-interpolation vs. increment-interpolation ) 11% analysis-grid coverage
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