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Automation and Remote Control
Barabanov, “Algorithms for investigating the accuracy of nonstationary linear systems having continuous and discrete elements." ... Leibovich, "Optimal control of linear stochastic systems with sam- pling of data generated by restoration processes," considered the problems of controlling discrete-continuous linear stochastic systems ...
Summary: “A discrete-time stochastic optimal control model is considered. The traditional model assumes that all objects are ho- mogeneous in time. ... In addition, the differences of initial states of different systems for several initial discrete times are observed. ...
An algorithm for the solution of a linear problem of vector optimization. (Russian) Methods and software for optimization, modeling and construction of systems (Russian), 42-47, iv, Akad. ... We reduce the problem of linear placement to the problem of convex discrete optimization with polymatroid constraints.” 90C Mathematical programming 90n:90171 90h:90167 90C31 90C30 Mond, B. (5-LTRB); Husain ...
2006 International Conference on Computing & Informatics
In face recognition applications, the dimension of the sample space is usually larger than the number of the samples in a training set. ... No of components Recognition rate (%) KFDA PCA LDA KPCA Fig. 7: Recognition accuracy obtained using the combination of DCT+KFDA, DCT+LDA, DCT+PCA and DCT+KPCA. ... LDA is a linear algorithm; for solving non linear problems samples in the input space have to be transformed to a nonlinear high-dimensional feature space, demonstrated by ) ( , : x x F R d φ a → Φ and ...doi:10.1109/icoci.2006.5276535 fatcat:xotysufmqjhrnekdf7tehfwtsy
Bildverarbeitung für die Medizin 2013
This research was funded/supported by the Graduate School of Information Science in Health (GSISH) and the TUM Graduate School. Thanks for listening! ... Acknowledgements We gratefully acknowledge funding of the Erlangen Graduate School in Advanced Optical Technologies (SAOT) by the German Science (DFG) in the framework of the excellence initiative, as ... for unconstrained nonlinear optimization on the GPU Nonlinear Optimization • Objective function gradient nonlinear in HR image pixels → Iterative descent method for optimization • We chose L-BFGS (limited-memory ...doi:10.1007/978-3-642-36480-8_6 dblp:conf/bildmed/WetzlTHKKH13 fatcat:hja4vupb2fhhrp6nkl75vrjp6q
Object recognition and pose estimation are two fundamental problems in the field of computer vision. ... We compare our results with the state-of-the-art and present our increased category recognition and pose estimation accuracy. ... This model belongs to the category of limited-pose (discrete-pose) object recognition since it uses a classification approach to deal with pose estimation. ...doi:10.1109/wacv.2016.7477655 dblp:conf/wacv/BakryEEE16 fatcat:vspwy634cbgtfn7sqs4opo7aiy
EM is often called deformable template, flexible matching, or nonlinear template matching, and defined as the optimization problem of two-dimensional warping (2DW) which specifies the pixel-to-pixel correspondence ... The pattern distance evaluated under optimized 2DW is invariant to a certain range of geometric deformations. ... The optimization problem of GAT is approximated as a linear problem by fixing the parameters in the nonlinear part of an objective function at constant values. ...doi:10.1093/ietisy/e88-d.8.1781 fatcat:zlcm7bwznvgb7bw4jqbi44dzcq
Lecture Notes in Computer Science
Instead of attempting to segment an object from the background, we develop a novel successive convexification linear programming method to locate the target by searching for the best matching region based ... The linear programming based matching scheme generates relatively dense matching patterns and thus presents a key feature for robust object matching and human body gesture recognition. ... In general situations, the linear programming formulation is an approximation of the original nonlinear optimization problem. ...doi:10.1007/11564386_30 fatcat:oeaxnx2gazfbfgogyhxvfiteqe
(IAC) for nonlinear dynamic objects. ... In the second stage we use the formulas obtained for an optimal transformation of the system. We construct discrete optimization models of control of poorly formalized systems.” ...
Our main focus is on problems of recognition and deter- mination of the orientation of three-dimensional objects by means of information from photometric and telemetric transmitters. ... Summary: “The receding horizon control strategy provides a rela- tively simple method for determining feedback control for linear or nonlinear systems; the method is especially useful for the con- trol ...
., Reference Trajectory Reshaping Optimi-zation and Control of Robotic Exoskeletons for Human-Robot Co-Manipulation; TCYB Aug. 2020 3740-3751 Wu, X., Jiang, B., Yu, K., Miao, c., and Chen, H ... ., TCYB Oct. 2020 4370-4380 The Set-Based Hypervolume Newton Method for Bi-Objective Optimization. ... Tanabe, R., +, TCYB March 2020 mdpack sdgfdsf The Set-Based Hypervolume Newton Method for Bi-Objective Optimization. Ensemble Decision for Spam Detection Using Term Space Partition Approach. ...doi:10.1109/tcyb.2020.3047216 fatcat:5giw32c2u5h23fu4drupnh644a
The state variables and nonlinear output equations were obtained for a flying object at a fixed point position. ... This study present the design of extended Kalman filter (EKF) for object position tracking. It is required to accurately track the position of an object amidst noisy measurements. ... A Optimized Error Variance of 0.05 For the purpose of simulation of the system with the selected values for the optimized error variance for the system noise covariance matrix, the dynamic state of the ...doi:10.17577/ijertv7is070025 fatcat:5spndrhjgrh4vnc4lzfhkqkrie
On the other hand, heuristic methods can handle large and complex optimization problems while mathematical programming methods tend to fail as the size of the optimization problem increases. ... On the one hand, mathematical programming methods give us a guarantee of optimality while (meta)heuristic methods do not. ... Authors claim that their method can solve the problem of the lack of accuracy for the linear representation of the small sample size in face recognition. ...doi:10.1155/2018/3967457 fatcat:swfpmbdnfrgvbgfx2hhe55mwfm
The first step computes a discrete mapping called supervised Laplacian Eigenmap. The second step computes a continuous mapping from the discrete version through nonlinear regression. ... Recognizing the category of a visual object remains a challenging computer vision problem. ... Introduction Recognizing the category of a visual object is an important and challenging aspect of automatic object recognition. ...doi:10.1109/cvpr.2013.117 dblp:conf/cvpr/WuYW13 fatcat:qmu6gdcvfrhuppqulhajjnwfiy
For each object we give the probabilities of successful use of the designated resource, as well as the losses in the case when the object does not use or does not obtain resources. ... Fazylov, Optimization in pattern recognition problems (pp. 11-17, 118); Yu. G. Stoyan, Discrete random search algorithms (pp. 17-27, 118); V. V. ...
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