Data Embedding is an art of concealing information of high significance or secrecy inside an image or video sequence. Numerous techniques are being researched to develop a strong and optimized algorithm to embed the d...
详细信息
ISBN:
(纸本)9781424458561
Data Embedding is an art of concealing information of high significance or secrecy inside an image or video sequence. Numerous techniques are being researched to develop a strong and optimized algorithm to embed the data so as to withstand aggressive operations done on it and to extract the vital piece of information embedded into it. The proposed work is focused towards studying the behavior of multiple data embedding techniques subjected to noisy channels thus providing the user to select an optimal embedding technique to establish a tradeoff between the level of invisibility and robustness to external attacks. The invisibility criteria are determined in terms of embedding and scaling parameters and the robustness of the watermark is tested with respect to its ability to withstand channel noise.
optimization of discrete event systems conventionally uses simulation as a black-box oracle to estimate performance at design points generated by a separate optimization algorithm. This decoupled approach fails to exp...
详细信息
ISBN:
(纸本)9781467397414
optimization of discrete event systems conventionally uses simulation as a black-box oracle to estimate performance at design points generated by a separate optimization algorithm. This decoupled approach fails to exploit an important advantage: simulation codes are white-boxes, at least to their creators. In fact, the full integration of the simulation model and the optimization algorithm is possible in many situations. In this contribution, a framework previously proposed by the authors, based on the mathematical programming methodology, is presented under a wider perspective. We show how to derive mathematical models for solving optimization problems while simultaneously considering the dynamics of the system to be optimized. Concerning the solution methodology, we refer back to retrospective optimization (RO) and sample path optimization (SPO) settings. Advantages and drawbacks deriving from the use of mathematical programming as work models within the RO (SPO) framework will be analyzed and its convergence properties will be discussed.
This paper presents a case study for multi-variable and multimodal design optimisation of a doubly fed induction generator (DFIG) based on surrogate-model optimisation algorithm. The DFIG's winding of stator and r...
详细信息
ISBN:
(纸本)9781510825666
This paper presents a case study for multi-variable and multimodal design optimisation of a doubly fed induction generator (DFIG) based on surrogate-model optimisation algorithm. The DFIG's winding of stator and rotor are optimised to obtain higher efficiency for rewinding purposes. First, a Latin hypercube design is selected as the design of experiments to obtain sampling points. Then, the surrogate model is constructed using Kriging Model (KRG) method based on the Latin hypercube design. Finally, the particle swarm optimisation algorithm is applied in conjunction with the finite element method to achieve the machine design optimisation.
We study the implicit bias of the general family of steepest descent algorithms, which includes gradient descent, sign descent and coordinate descent, in deep homogeneous neural networks. We prove that an algorithm-de...
详细信息
Bayesian modelling allows for the quantification of predictive uncertainty which is crucial in safety-critical applications. Yet for many machine learning algorithms, it is difficult to construct or implement their Ba...
详细信息
Thompson Sampling has been demonstrated in many complex bandit models, however the theoretical guarantees available for the parametric multi-armed bandit are still limited to the Bernoulli case. Here we extend them by...
详细信息
ISBN:
(纸本)9781632660244
Thompson Sampling has been demonstrated in many complex bandit models, however the theoretical guarantees available for the parametric multi-armed bandit are still limited to the Bernoulli case. Here we extend them by proving asymptotic optimality of the algorithm using the Jeffreys prior for 1-dimensional exponential family bandits. Our proof builds on previous work, but also makes extensive use of closed forms for Kullback-Leibler divergence and Fisher information (through the Jeffreys prior) available in an exponential family. This allow us to give a finite time exponential concentration inequality for posterior distributions on exponential families that may be of interest in its own right. Moreover our analysis covers some distributions for which no optimistic algorithm has yet been proposed, including heavy-tailed exponential families.
In this paper, the power allocation problem for Orthogonal frequency division multiplexing (OFDM)-based relay cognitive radio systems is investigated. An optimization power allocation algorithm is developed. In the al...
详细信息
ISBN:
(纸本)9781467384155
In this paper, the power allocation problem for Orthogonal frequency division multiplexing (OFDM)-based relay cognitive radio systems is investigated. An optimization power allocation algorithm is developed. In the algorithm, objective function is studied and simplified. The channel capacity of secondary users is maximized while keeping the total interference introduced to primary user less than a given threshold with a specified power. Compared to the optimal algorithm and the average power allocation algorithm, the proposed algorithm is close to the optimal algorithm and has a better performance than average power allocation algorithm, and has low complexity.
For statistical modeling wherein the data regime is unfavorable in terms of dimensionality relative to the sample size, finding hidden sparsity in the ground truth can be critical in formulating an accurate statistica...
详细信息
The paper develops a Model Predictive Controller for constrained control of spacecraft attitude with reaction wheel actuators. The controller exploits a special formulation of the cost with the reference governor like...
详细信息
ISBN:
(纸本)9781479917730
The paper develops a Model Predictive Controller for constrained control of spacecraft attitude with reaction wheel actuators. The controller exploits a special formulation of the cost with the reference governor like term, a low complexity addition of integral action to guarantee offset-free tracking of attitude set points, and an online optimization algorithm for the solution of the Quadratic Programming problem which is tailored to run in fixed-point arithmetic. Simulations on a nonlinear space-craft model demonstrate that the MPC controller achieves good tracking performance while satisfying reaction wheel torque constraints. The controller also has low computational complexity and is suitable for implementation in spacecrafts with fixed-point processors.
In optimization algorithms used for on-line Model Predictive Control (MPC), the main computational effort is spent while solving linear systems of equations to obtain search directions. Hence, it is of greatest intere...
详细信息
ISBN:
(纸本)9781467357159
In optimization algorithms used for on-line Model Predictive Control (MPC), the main computational effort is spent while solving linear systems of equations to obtain search directions. Hence, it is of greatest interest to solve them efficiently, which commonly is performed using Riccati recursions or generic sparsity exploiting algorithms. The focus in this work is efficient search direction computation for activeset methods. In these methods, the system of equations to be solved in each iteration is only changed by a low-rank modification of the previous one. This highly structured change of the system of equations from one iteration to the next one is an important ingredient in the performance of active-set solvers. It seems very appealing to try to make a structured update of the Riccati factorization, which has not been presented in the literature so far. The main objective of this paper is to present such an algorithm for how to update the Riccati factorization in a structured way in an active-set solver. The result of the work is that the computational complexity of the step direction computation can be significantly reduced for problems with bound constraints on the control signal. This in turn has important implications for the computational performance of active-set solvers used for linear, nonlinear as well as hybrid MPC.
暂无评论