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Wei Chen - One of the best experts on this subject based on the ideXlab platform.
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A Global Optimal Solution With Higher Order Continuity for the Estimation of Surface Velocity From Infrared Images
IEEE Transactions on Geoscience and Remote Sensing, 2010Co-Authors: Wei ChenAbstract:A Global Optimal Solution (GOS) provides surface velocities from Advanced Very High ReSolution Radiometer (AVHRR) remote image sequences using bilinear interpolation algorithms. Although an accurate velocity field can be estimated by GOS from a sequence of infrared images, the field has only first-order continuity. Because an actual coastal ocean has a complex irregular coastland and some ocean studies need vorticity and divergence analysis, which must be extracted from the velocity field, the development of generic GOS algorithms with higher order continuity and smoothed cutouts around these edges is very important. This paper addresses the issues of higher order continuity and smoothed cutouts around coastland edges for using GOS to estimate surface velocities. GOS bilinear polynomials, previously applied to square tiles with first-order continuity, are replaced by surface B-spline functions. The new GOS algorithms can be applied to AVHRR images containing complicated coastal land boundaries, even clouds, to yield smooth velocity fields next to land and higher order continuity velocity fields. The velocity fields obtained through the applications of the first- and higher order GOS techniques to a sequence of two National Oceanic Atmospheric Administration AVHRR images, which were taken from the New York Bight fields, are compared with those measured with the CODAR array. The retrieved velocity fields are used directly to calculate the surface divergence and vorticity. It is found that the angular and magnitude errors of the velocity by the first- and third-order GOSs are quite close for both numerical model data and AVHRR image sequences, and the velocity field estimated by the third-order GOS is Globally smoothed.
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Near-surface ocean velocity from infrared images: Global Optimal Solution to an inverse model
Journal of Geophysical Research, 2008Co-Authors: Wei Chen, R P Mied, Colin Y ShenAbstract:[1] We address the problem of obtaining ocean surface velocities from sequences of thermal (AVHRR) space-borne images by inverting the heat conservation equation (including sources of surface heat fluxes and vertical entrainment). We demonstrate the utility of the technique by deriving surface velocities from (1) The motion of a synthetic surface tracer in a numerical model and (2) a sequence of five actual AVHRR images from 1 day. Typical formulations of this tracer inversion problem yield too few equations at each pixel, which is often remedied by imposing additional constraints (e.g., horizontal divergence, vorticity, and energy). In contrast, we propose an alternate strategy to convert the underdetermined equation set to an overdetermined one. We divide the image scene into many subarrays and define velocities and sources within each subarray using bilinear expressions in terms of the corner points (called knots). In turn, all velocities and sources on the knots can be determined by seeking an optimum Solution to these linear equations over the large scale, which we call the Global Optimal Solution (GOS). We test the accuracy of the GOS by contaminating the model output with up to 10% white noise but find that filtering the data with a Gaussian convolution filter yields velocities nearly indistinguishable from those without the added noise. We compare the GOS velocity fields with those from the numerical model and from the Maximum Cross Correlation (MCC) technique. A histogram of the difference between GOS and numerical model velocities is narrower and more peaked than the similar comparison with MCC, irrespective of the time interval (Δt = 2 or 4 h) between images. The calculation of the root mean square error difference between the GOS (and MCC) results and the model velocities indicates that the GOS/model error is only half that of the MCC/model error irrespective of the time interval (Δt = 2 or 4 h) between images. Finally, the application of the technique to a sequence of five NOAA AVHRR images yields a velocity field, which we compare with that from a Coastal Ocean Dynamics Radar (CODAR) array. We find that the GOS velocities generally agree more closely with those from the CODAR than they do with those from the MCC. Specifically, the root mean square error obtained by differencing GOS and CODAR velocities is smaller than that from the similar calculation with MCC velocities. The magnitude of the complex correlation between GOS and CODAR is larger than that between MCC and CODAR. The phase of the complex correlation indicates that both MCC and GOS on average yield velocity vectors biased in the clockwise direction relative to the CODAR vectors for the period examined.
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estimation of surface velocity from infrared image using the Global Optimal Solution to an inverse model
International Geoscience and Remote Sensing Symposium, 2008Co-Authors: Wei Chen, R P Mied, Colin Y ShenAbstract:We address the problem of obtaining ocean surface velocities from sequences of thermal (AVHRR) space-borne images by inverting the heat conservation equation (including sources of surface heat fluxes and vertical entrainment). We demonstrate the utility of the technique by deriving surface velocities from actual AVHRR images from one day. Typical formulations of this tracer inversion problem yield too few equations at each pixel, which is often remedied by imposing additional constraints (e.g., horizontal divergence, vorticity, and energy). In contrast, we propose an alternate strategy to convert the under-determined equation set to an over- determined one. We divide the image scene into many sub-arrays, and define velocities and sources within each sub-array using bilinear expressions in terms of the corner points (called knots). In turn, all velocities and sources on the knots can be determined by seeking an optimum Solution to these linear equations over the large-scale, which we call the Global Optimal Solution (QOS). We test the accuracy of the GOS by contaminating the model output with up to 10% white noise, but find that filtering the data with a Gaussian convolution filter yields velocities nearly indistinguishable from those without the added noise. Application of the technique to a sequence of five NOAA AVHRR images yields a velocity field, which we compare with that from a Coastal Ocean Dynamics Radar (CODAR) array. We find that the GOS velocities generally agree more closely with those from the CODAR than they do with those from the MCC. Specifically, the root mean square error obtained by differencing GOS and CODAR velocities is smaller than that from the similar calculation with MCC velocities. The magnitude of the complex correlation between GOS and CODAR is larger than that between MCC and CODAR. The phase of the complex correlation indicates that both MCC and GOS on average yield velocity vectors biased in the clockwise direction relative to the CODAR vectors for the period examined.
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IGARSS (1) - Estimation of Surface Velocity from Infrared Image Using the Global Optimal Solution to an Inverse Model
IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008Co-Authors: Wei Chen, R P Mied, Colin Y ShenAbstract:We address the problem of obtaining ocean surface velocities from sequences of thermal (AVHRR) space-borne images by inverting the heat conservation equation (including sources of surface heat fluxes and vertical entrainment). We demonstrate the utility of the technique by deriving surface velocities from actual AVHRR images from one day. Typical formulations of this tracer inversion problem yield too few equations at each pixel, which is often remedied by imposing additional constraints (e.g., horizontal divergence, vorticity, and energy). In contrast, we propose an alternate strategy to convert the under-determined equation set to an over- determined one. We divide the image scene into many sub-arrays, and define velocities and sources within each sub-array using bilinear expressions in terms of the corner points (called knots). In turn, all velocities and sources on the knots can be determined by seeking an optimum Solution to these linear equations over the large-scale, which we call the Global Optimal Solution (QOS). We test the accuracy of the GOS by contaminating the model output with up to 10% white noise, but find that filtering the data with a Gaussian convolution filter yields velocities nearly indistinguishable from those without the added noise. Application of the technique to a sequence of five NOAA AVHRR images yields a velocity field, which we compare with that from a Coastal Ocean Dynamics Radar (CODAR) array. We find that the GOS velocities generally agree more closely with those from the CODAR than they do with those from the MCC. Specifically, the root mean square error obtained by differencing GOS and CODAR velocities is smaller than that from the similar calculation with MCC velocities. The magnitude of the complex correlation between GOS and CODAR is larger than that between MCC and CODAR. The phase of the complex correlation indicates that both MCC and GOS on average yield velocity vectors biased in the clockwise direction relative to the CODAR vectors for the period examined.
Colin Y Shen - One of the best experts on this subject based on the ideXlab platform.
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Near-surface ocean velocity from infrared images: Global Optimal Solution to an inverse model
Journal of Geophysical Research, 2008Co-Authors: Wei Chen, R P Mied, Colin Y ShenAbstract:[1] We address the problem of obtaining ocean surface velocities from sequences of thermal (AVHRR) space-borne images by inverting the heat conservation equation (including sources of surface heat fluxes and vertical entrainment). We demonstrate the utility of the technique by deriving surface velocities from (1) The motion of a synthetic surface tracer in a numerical model and (2) a sequence of five actual AVHRR images from 1 day. Typical formulations of this tracer inversion problem yield too few equations at each pixel, which is often remedied by imposing additional constraints (e.g., horizontal divergence, vorticity, and energy). In contrast, we propose an alternate strategy to convert the underdetermined equation set to an overdetermined one. We divide the image scene into many subarrays and define velocities and sources within each subarray using bilinear expressions in terms of the corner points (called knots). In turn, all velocities and sources on the knots can be determined by seeking an optimum Solution to these linear equations over the large scale, which we call the Global Optimal Solution (GOS). We test the accuracy of the GOS by contaminating the model output with up to 10% white noise but find that filtering the data with a Gaussian convolution filter yields velocities nearly indistinguishable from those without the added noise. We compare the GOS velocity fields with those from the numerical model and from the Maximum Cross Correlation (MCC) technique. A histogram of the difference between GOS and numerical model velocities is narrower and more peaked than the similar comparison with MCC, irrespective of the time interval (Δt = 2 or 4 h) between images. The calculation of the root mean square error difference between the GOS (and MCC) results and the model velocities indicates that the GOS/model error is only half that of the MCC/model error irrespective of the time interval (Δt = 2 or 4 h) between images. Finally, the application of the technique to a sequence of five NOAA AVHRR images yields a velocity field, which we compare with that from a Coastal Ocean Dynamics Radar (CODAR) array. We find that the GOS velocities generally agree more closely with those from the CODAR than they do with those from the MCC. Specifically, the root mean square error obtained by differencing GOS and CODAR velocities is smaller than that from the similar calculation with MCC velocities. The magnitude of the complex correlation between GOS and CODAR is larger than that between MCC and CODAR. The phase of the complex correlation indicates that both MCC and GOS on average yield velocity vectors biased in the clockwise direction relative to the CODAR vectors for the period examined.
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estimation of surface velocity from infrared image using the Global Optimal Solution to an inverse model
International Geoscience and Remote Sensing Symposium, 2008Co-Authors: Wei Chen, R P Mied, Colin Y ShenAbstract:We address the problem of obtaining ocean surface velocities from sequences of thermal (AVHRR) space-borne images by inverting the heat conservation equation (including sources of surface heat fluxes and vertical entrainment). We demonstrate the utility of the technique by deriving surface velocities from actual AVHRR images from one day. Typical formulations of this tracer inversion problem yield too few equations at each pixel, which is often remedied by imposing additional constraints (e.g., horizontal divergence, vorticity, and energy). In contrast, we propose an alternate strategy to convert the under-determined equation set to an over- determined one. We divide the image scene into many sub-arrays, and define velocities and sources within each sub-array using bilinear expressions in terms of the corner points (called knots). In turn, all velocities and sources on the knots can be determined by seeking an optimum Solution to these linear equations over the large-scale, which we call the Global Optimal Solution (QOS). We test the accuracy of the GOS by contaminating the model output with up to 10% white noise, but find that filtering the data with a Gaussian convolution filter yields velocities nearly indistinguishable from those without the added noise. Application of the technique to a sequence of five NOAA AVHRR images yields a velocity field, which we compare with that from a Coastal Ocean Dynamics Radar (CODAR) array. We find that the GOS velocities generally agree more closely with those from the CODAR than they do with those from the MCC. Specifically, the root mean square error obtained by differencing GOS and CODAR velocities is smaller than that from the similar calculation with MCC velocities. The magnitude of the complex correlation between GOS and CODAR is larger than that between MCC and CODAR. The phase of the complex correlation indicates that both MCC and GOS on average yield velocity vectors biased in the clockwise direction relative to the CODAR vectors for the period examined.
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IGARSS (1) - Estimation of Surface Velocity from Infrared Image Using the Global Optimal Solution to an Inverse Model
IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008Co-Authors: Wei Chen, R P Mied, Colin Y ShenAbstract:We address the problem of obtaining ocean surface velocities from sequences of thermal (AVHRR) space-borne images by inverting the heat conservation equation (including sources of surface heat fluxes and vertical entrainment). We demonstrate the utility of the technique by deriving surface velocities from actual AVHRR images from one day. Typical formulations of this tracer inversion problem yield too few equations at each pixel, which is often remedied by imposing additional constraints (e.g., horizontal divergence, vorticity, and energy). In contrast, we propose an alternate strategy to convert the under-determined equation set to an over- determined one. We divide the image scene into many sub-arrays, and define velocities and sources within each sub-array using bilinear expressions in terms of the corner points (called knots). In turn, all velocities and sources on the knots can be determined by seeking an optimum Solution to these linear equations over the large-scale, which we call the Global Optimal Solution (QOS). We test the accuracy of the GOS by contaminating the model output with up to 10% white noise, but find that filtering the data with a Gaussian convolution filter yields velocities nearly indistinguishable from those without the added noise. Application of the technique to a sequence of five NOAA AVHRR images yields a velocity field, which we compare with that from a Coastal Ocean Dynamics Radar (CODAR) array. We find that the GOS velocities generally agree more closely with those from the CODAR than they do with those from the MCC. Specifically, the root mean square error obtained by differencing GOS and CODAR velocities is smaller than that from the similar calculation with MCC velocities. The magnitude of the complex correlation between GOS and CODAR is larger than that between MCC and CODAR. The phase of the complex correlation indicates that both MCC and GOS on average yield velocity vectors biased in the clockwise direction relative to the CODAR vectors for the period examined.
Kevin S Tickle - One of the best experts on this subject based on the ideXlab platform.
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solving the traveling salesman problem using cooperative genetic ant systems
Expert Systems With Applications, 2012Co-Authors: Gaifang Dong, William W Guo, Kevin S TickleAbstract:The travelling salesman problem (TSP) is a classic problem of combinatorial optimization and has applications in planning, scheduling, and searching in many scientific and engineering fields. Ant colony optimization (ACO) has been successfully used to solve TSPs and many associated applications in the last two decades. However, ACO has problem in regularly reaching the Global Optimal Solutions for TSPs due to enormity of the search space and numerous local optima within the space. In this paper, we propose a new hybrid algorithm, cooperative genetic ant system (CGAS) to deal with this problem. Unlike other previous studies that regarded GA as a sequential part of the whole searching process and only used the result from GA as the input to subsequent ACO iterations, this new approach combines both GA and ACO together in a cooperative manner to improve the performance of ACO for solving TSPs. The mutual information exchange between ACO and GA in the end of the current iteration ensures the selection of the best Solutions for next iteration. This cooperative approach creates a better chance in reaching the Global Optimal Solution because independent running of GA maintains a high level of diversity in next generation of Solutions. Compared with results from other GA/ACO algorithms, our simulation shows that CGAS has superior performance over other GA and ACO algorithms for solving TSPs in terms of capability and consistency of achieving the Global Optimal Solution, and quality of average Optimal Solutions, particularly for small TSPs.
Hsiao-dong Chiang - One of the best experts on this subject based on the ideXlab platform.
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Toward a Long-Life Property of the Global Optimal Solution in OPF: Numerical Studies
2020 IEEE Power & Energy Society General Meeting (PESGM), 2020Co-Authors: Zi-shun Wang, Hsiao-dong ChiangAbstract:This paper discovers a long-life property of the Global AC Optimal power flow (OPF) Solution due to load variations. It is shown that the Global OPF Solution is always the Optimal Solution that remains until there is no other OPF Solution. This discovery of long-life property sheds some light on OPF Solutions and may lead to the development of computational methods to compute the Global Optimal Solution in a deterministic manner. This long-life property is numerically demonstrated on two representative systems—the 9- and 118-bus systems and other test systems via the OPF feasible region approach. Numerical results show the emergence and disappearance processes of all local OPF Solutions and also verify that the Global Optimal Solution is the OPF Solution with the longest lifespan. If this numerical property is well established for general OPF problems, one can explore it to develop a deterministic method for computing Global OPF Solution.
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A Novel Consensus-Based Particle Swarm Optimization-Assisted Trust-Tech Methodology for Large-Scale Global Optimization
IEEE transactions on cybernetics, 2016Co-Authors: Yong-feng Zhang, Hsiao-dong ChiangAbstract:A novel three-stage methodology, termed the “consensus-based particle swarm optimization (PSO)-assisted Trust-Tech methodology,” to find Global Optimal Solutions for nonlinear optimization problems is presented. It is composed of Trust-Tech methods, consensus-based PSO, and local optimization methods that are integrated to compute a set of high-quality local Optimal Solutions that can contain the Global Optimal Solution. The proposed methodology compares very favorably with several recently developed PSO algorithms based on a set of small-dimension benchmark optimization problems and 20 large-dimension test functions from the CEC 2010 competition. The analytical basis for the proposed methodology is also provided. Experimental results demonstrate that the proposed methodology can rapidly obtain high-quality Optimal Solutions that can contain the Global Optimal Solution. The scalability of the proposed methodology is promising.
K. Svanberg - One of the best experts on this subject based on the ideXlab platform.
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On the trajectories of the epsilon-relaxation approach for stress-constrained truss topology optimization
Structural and Multidisciplinary Optimization, 2001Co-Authors: M. Stolpe, K. SvanbergAbstract:We consider the nonconvex problem of minimizing the weight of a linearly elastic truss structure subject to stress constraints under multiple load conditions. The design variables are the cross-sectional areas of the elements, and the stress constraints are imposed only on elements with strictly positive areas. To avoid degenerate feasible domains, it has been suggested that the stress constraints of the original problem should be relaxed by a positive scalar ε, leading to the so-called ε-relaxed problem. In this paper, the trajectories associated with Optimal Solutions of the ε-relaxed problems, for continuously decreasing values of ε, are studied in detail on some carefully chosen examples. The Global trajectory is defined as the path followed by the Global Optimal Solution to the ε-relaxed problem, and we present two parameterized examples for which the Global trajectory is discontinuous for arbitrarily small values of ε>0. From that we conclude that, in practice, a sequence of Solutions to the ε-relaxed problem for decreasing values on ε may not converge to the Global Optimal Solution of the original problem, even if the starting point is on the Global trajectory.