scholarly journals Atmospheric Duct Estimation Using Radar Sea Clutter Returns by the Adjoint Method with Regularization Technique

2014 ◽  
Vol 31 (6) ◽  
pp. 1250-1262 ◽  
Author(s):  
Xiaofeng Zhao ◽  
Sixun Huang
Keyword(s):  
Genetic Algorithm ◽  
Parabolic Equation ◽  
Adjoint Method ◽  
Regularization Parameter ◽  
Background Field ◽  
Sea Clutter ◽  
Regularization Technique ◽  
Atmospheric Duct ◽  
Adjoint Approach ◽  
Space Range

Abstract This paper focuses on retrieving the atmospheric duct structure from radar sea clutter returns by the adjoint approach with the regularization technique. The adjoint is derived from the split-step Fourier parabolic equation method, and the regularization term is constructed by the background refractivity field. To ensure successful implementations of the regularization, the L-curve criterion is used to find the optimal regularization parameter. The feasibility of the proposed method is validated by the numerical simulations of different noise-level clutter returns, as well as a real clutter profile measured by the S-Band Space Range Radar located in Wallops Island. In the process of inversions, the refractivity profile is first obtained by genetic algorithm, and then it is used as the background field for the adjoint method. The retrieved results indicate that, with an appropriate regularization parameter, the structure of the background refractivity profile can be improved by the proposed method.

scholarly journals Estimation of Atmospheric Duct Structure Using Radar Sea Clutter

2012 ◽  
Vol 69 (9) ◽  
pp. 2808-2818 ◽  
Author(s):  
Xiaofeng Zhao ◽  
Sixun Huang
Keyword(s):  
Approximate Solutions ◽  
Propagation Loss ◽  
Sea Surface ◽  
Model Parameters ◽  
Sea Clutter ◽  
Radar Clutter ◽  
Atmospheric Duct ◽  
Analytical Transformation ◽  
Ill Posed ◽  
Adjoint Approach

Abstract Retrieving atmospheric refractivity profiles from the sea surface backscattered radar clutter is known as the refractivity-from-clutter (RFC) technique. Because the relationship between refractivity and radar sea clutter is clearly nonlinear and ill posed, it is difficult to get analytical solutions according to current theories. Previous works treat this problem as a model parameter estimation issue and some optimization algorithms are selected to get approximate solutions. Two main factors that limit the accuracy of the estimation are that 1) the refractive environments are described by using some idealized refractivity parameter models that cannot describe the exact information of the refractivity profile, and 2) accurate modeling of the sea surface radar cross section (RCS) is very difficult. Rather than estimating a few model parameters, this paper puts forward possibilities of using the variational adjoint approach to jointly retrieve the every-height refractivity values and sea surface RCS using radar clutter data. The derivation of the adjoint model is accomplished by an analytical transformation of the parabolic equation (PE) in the continuous domain. Numerical simulations including range-independent and range-dependent RCS cases are presented to demonstrate the ability of this method for RFC estimations. Making use of the refractivity retrievals, propagation loss predictions are also presented.

scholarly journals Optimization of RBF-SVM hyperparameters using genetic algorithm for face recognit

2021 ◽  
Vol 39 (4) ◽  
pp. 1190-1197
Author(s):  
Y. Ibrahim ◽  
E. Okafor ◽  
B. Yahaya
Keyword(s):  
Genetic Algorithm ◽  
Deep Learning ◽  
Face Recognition ◽  
Regularization Parameter ◽  
Local Binary Patterns ◽  
Classification Performance ◽  
Support Vector ◽  
Vector Machines ◽  
Linear Svm ◽  
Learning Architectures

Manual grid-search tuning of machine learning hyperparameters is very time-consuming. Hence, to curb this problem, we propose the use of a genetic algorithm (GA) for the selection of optimal radial-basis-function based support vector machine (RBF-SVM) hyperparameters; regularization parameter C and cost-factor γ. The resulting optimal parameters were used during the training of face recognition models. To train the models, we independently extracted features from the ORL face image dataset using local binary patterns (handcrafted) and deep learning architectures (pretrained variants of VGGNet). The resulting features were passed as input to either linear-SVM or optimized RBF-SVM. The results show that the models from optimized RBFSVM combined with deep learning or hand-crafted features yielded performances that surpass models obtained from Linear-SVM combined with the aforementioned features in most of the data splits. The study demonstrated that it is profitable to optimize the hyperparameters of an SVM to obtain the best classification performance. Keywords: Face Recognition, Feature Extraction, Local Binary Patterns, Transfer Learning, Genetic Algorithm and Support Vector  Machines.

scholarly journals A complexity-driven framework for waveform tomography with discrete adjoints

2020 ◽  
Vol 223 (2) ◽  
pp. 1247-1264
Author(s):  
Alexandre Szenicer ◽  
Kuangdai Leng ◽  
Tarje Nissen-Meyer
Keyword(s):  
Adjoint Method ◽  
Waveform Inversion ◽  
Spectral Element ◽  
Frequency Scaling ◽  
Seismic Waveform ◽  
Discrete Adjoint ◽  
Continuous Adjoint Method ◽  
Adjoint Approach ◽  
Pseudo Spectral Method ◽  
Continuous Adjoint

Summary We develop a new approach for computing Fréchet sensitivity kernels in full waveform inversion by using the discrete adjoint approach in addition to the widely used continuous adjoint approach for seismic waveform inversion. This method is particularly well suited for the forward solver AxiSEM3D, a combination of the spectral-element method (SEM) and a Fourier pseudo-spectral method, which allows for a sparse azimuthal wavefield parametrization adaptive to wavefield complexity, leading to lower computational costs and better frequency scaling than conventional 3-D solvers. We implement the continuous adjoint method to serve as a benchmark, additionally allowing for simulating off-axis sources in axisymmetric or 3-D models. The kernels generated by both methods are compared to each other, and benchmarked against theoretical predictions based on linearized Born theory, providing an excellent fit to this independent reference solution. Our verification benchmarks show that the discrete adjoint method can produce exact kernels, largely identical to continuous kernels. While using the continuous adjoint method we lose the computational advantage and fall back on a full-3-D frequency scaling, using the discrete adjoint retains the speedup offered by AxiSEM3D. We also discuss the creation of a data-coverage based mesh to run the simulations on during the inversion process, which would allow to exploit the flexibility of the Fourier parametrization and thus the speedup offered by our method.

Optimizing Feature Subset and Parameters for Support Vector Machine Using Multiobjective Genetic Algorithm

2015 ◽  
Vol 24 (2) ◽  
pp. 145-160 ◽  
Author(s):  
Jyoti Ahuja ◽  
Saroj Ratnoo
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Regularization Parameter ◽  
Kernel Functions ◽  
Support Vector ◽  
Model Parameters ◽  
Feature Subset ◽  
Nsga Ii ◽  
Multiobjective Genetic Algorithm ◽  
Grid Algorithm

AbstractThe well-known classifier support vector machine has many parameters associated with its various kernel functions. The radial basis function kernel, being the most preferred kernel, has two parameters (namely, regularization parameter C and γ) to be optimized. The problem of optimizing these parameter values is called model selection in the literature, and its results strongly influence the performance of the classifier. Another factor that affects the classification performance of a classifier is the feature subset. Both these factors are interdependent and must be dealt with simultaneously. Following the multiobjective definition of feature selection, we have applied a multiobjective genetic algorithm (MOGA), NSGA II, to optimize the feature subset and model parameters simultaneously. Comparison of the proposed approach with the grid algorithm and GA-based method suggests that the MOGA-based approach performs better than the grid algorithm and is as good as the GA-based approach. Moreover, it provides multiple solutions instead of a single solution. The users can prefer one feature subset over the other as per their requirement and available resources.

Inferring the atmospheric duct from radar sea clutter using the improved artificial bee colony algorithm

2018 ◽  
Vol 10 (4) ◽  
pp. 437-445 ◽  
Author(s):  
Chao Yang ◽  
Lixin Guo
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Orthogonal Experimental Design ◽  
Sea Clutter ◽  
Particle Swarm Optimizer ◽  
Bee Colony ◽  
Opposition Based Learning ◽  
Atmospheric Duct ◽  
Comparison Results ◽  
Better Than

AbstractIn this paper, an orthogonal crossover artificial bee colony (OCABC) algorithm based on orthogonal experimental design is presented and applied to infer the marine atmospheric duct using the refractivity from clutter technique, and the radar sea clutter power is simulated by the commonly used parabolic equation method. In order to test the accuracy of the OCABC algorithm, the measured data and the simulated clutter power with different noise levels are, respectively, utilized to estimate the evaporation duct and surface duct. The estimation results obtained by the proposed algorithm are also compared with those of the comprehensive learning particle swarm optimizer and the artificial bee colony algorithm combined with opposition-based learning and global best search equation. The comparison results demonstrate that the performance of proposed algorithm is better than those of the compared algorithms for the marine atmospheric duct estimation.

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