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This paper proposes a direct model predictive current control (MPC) strategy for matrix converters (MCs). The proposed method aims to regulate the output current of the MC while minimizing the input reactive power at the same time. In addition, in order to reduce the switching losses, the MPC adjusts the average switching frequency of the converter. The MPC scheme uses the discrete-time model of the...
In many spectral estimation and array processing problems, the process of finding estimates of model parameters often involves the optimisation of a cost function containing multiple peaks and dips. Such non-convex problems are hard to solve using traditional optimisation algorithms developed for convex problems, and computationally intensive grid searches are therefore often used instead. In this...
Visual mapping is a required capability for practical autonomous mobile robots where there exists a growing industry with applications ranging from the service to industrial sectors. Prior to map building, Visual Odometry(VO) is an essential step required in the process of pose graph construction. In this work, we first propose to tackle the pose estimation problem by using both photometric and geometric...
The conditioning technique has been conceived and must always be considered as an a posteriori anti-windup method (the a priori knowledge of the non-linearity acting on the desired control variable has not to be known, provided a post-measurement of the actual control variable is furnished). And in this sense this kind of "backward" anti-windup should always be used because it is never possible...
The use of indoor wireless communication systems is continuously increasing while the main concern is how to provide nonstop high quality service. This requires positioning the base stations in a way that optimise the quality measures such as the path loss in addition to minimising the transmitted power in order to reduce the power consumption as well as the interference with neighbour base stations...
This paper presents an Improved Parallel Differential Evolution (IPDE) optimization algorithm based dynamic decomposed strategy to solving large economic dispatch (ED) with consideration of practical generators constraints. The migration operation inspired from Biogeography-based Optimization algorithm (BBO) is newly introduced in the parallel DE approach, thereby can effectively explore and exploit...
Generation scheduling is an important problem for power system operation and management. Results of scheduling are used by generation companies, electricity consumers and market regulators. The electricity market creates new environment for generation scheduling. New models and methods are needed for adequate solution of the problem. The paper presents a two-level optimization model that helps to...
In order to provide safety against high sea water levels, in many low-lying countries on the one hand dunes are maintained at a certain safety level and dikes are built, while on the other hand large control structures that can be controlled dynamically are constructed. Currently, these structures are often operated purely locally, without coordination on actions between different structures. Automatically...
Model predictive control (MPC) is an advanced technique for process control. It is based on iterative, finite horizon optimization of a cost function associated with a plant model. Neural network is an effective approach for on-line optimization problems. In this paper, we apply the general projection neural network for MPC of nonlinear affine systems. Continuous stirred tank reactor (CSTR) system...
The H.264 encoder has input parameters that determine the bit rate and distortion of the compressed video and the encoding complexity. These encoding parameters may significantly affect not only the encoding performance, but also affect the cross-layer design optimization for the video streaming transmission over wireless networks. However, it is very difficult to derive the impact factors of these...
A common practical problem when implementing signal model-fitting procedures is that of the frequency local minima. Unfortunately, conventional optimization methods, like Steepest Descent, Newton's Method and Conjugate Gradients (CG) are subject to this problem. What is worse, if the estimated frequency is not correct, the estimated signal amplitude and decay rate will be incorrect. In this paper,...
One of the challenges in routing for dense Wireless Sensor Networks (WSN) is balancing the traffic over different paths. Load-balancing methods inspired by physical phenomena such as electrostatics and optics have been studied in recent years, and the proposed methods are promising in energy-critical routing applications. Such methods model information flow in continuous domain in order to benefit...
We consider the Noisy MIMO Interference Channel (IFC) with linear transmitters and receivers and full CSI. The maximization of the Weighted Sum Rate (WSR) or transceiver design for Interference Alignment (IA) lead to cost functions with many local optima. Deterministic annealing is an approach that allows to track the variation of the known solution of one version of the problem into the unknown solution...
Optical network design problems fall in the broad category of network optimization problems. We give a short introduction on network optimization and general algorithmic techniques that can be used to solve complex and difficult network design problems. We apply these techniques to address the static Routing and Wavelength Assignment problem that is related to planning phase of a WDM optical network...
Inter-picture prediction uses a local decoded picture for the reference, in order to avoid a mismatch between encoding and decoding. However, this scheme does not necessarily result in optimal coding efficiency since it requires encoding the processing altogether. Therefore, we study the use of the original picture as the reference. In this case, although the mismatch causes degradation of the picture...
We present a novel method for designing controllers for robots with variable impedance actuators. We take an imitation learning approach, whereby we learn impedance modulation strategies from observations of behaviour (for example, that of humans) and transfer these to a robotic plant with very different actuators and dynamics. In contrast to previous approaches where impedance characteristics are...
Modern signal processing applications invoke various evolutionary algorithms in different areas such as aerodynamic shape optimization, pattern recognition, digital filter design, automated mirror design etc. Presently, Differential Evolution (DE) algorithm has proved to be quite efficient in these areas. Digital filters of various kinds can be designed using this particular evolutionary technique...
Solving the vision problem using convex optimization theory is now a focus in computer vision and robot communities. Second Order Cone Programming (SOCP) is especially effective in these methods. This paper discusses homography estimation in omnidirectional vision under the L∞-norm, which provides a theoretical guarantee of global optimality and a wide field of view. We give three different kinds...
We consider the problem faced by a passive receiver in determining the symbol stream that has been output from a wireless transmitter array encoded as Orthogonal Space-Time Block Codes (OSTBC). An informed receiver will usually be able to perform maximum likelihood channel estimation using knowledge of a symbol training sequence embedded in the transmitted data. An uninformed receiver or eavesdropper...
Switched dynamical systems have shown great utility in modeling a variety of systems. Unfortunately, the determination of a numerical solution for the optimal control of such systems has proven difficult, since it demands optimal mode scheduling. Recently, we constructed an optimization algorithm to calculate a numerical solution to the problem subject to a running and final cost. In this paper, we...
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