We present a fast, efficient algorithm for learning an overcomplete dictionary for sparse representation of signals. The whole problem is considered as a minimization of the approximation error function with a coheren...
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We present a fast, efficient algorithm for learning an overcomplete dictionary for sparse representation of signals. The whole problem is considered as a minimization of the approximation error function with a coherence penalty for the dictionary atoms and with the sparsity regularization of the coefficient matrix. Because the problem is nonconvex and nonsmooth, this minimization problem cannot be solved efficiently by an ordinary optimization method. We propose a decomposition scheme and an alternating optimization that can turn the problem into a set of minimizations of piecewise quadratic and univariate subproblems, each of which is a single variable vector problem, of either one dictionary atom or one coefficient vector. Although the subproblems are still nonsmooth, remarkably they become much simpler so that we can find a closed-form solution by introducing a proximal operator. This leads to an efficient algorithm for sparse representation. To our knowledge, applying the proximal operator to the problem with an incoherence term and obtaining the optimal dictionary atoms in closed form with a proximal operator technique have not previously been studied. The main advantages of the proposed algorithm are that, as suggested by our analysis and simulation study, it has lower computational complexity and a higher convergence rate than state-of-the-art algorithms. In addition, for real applications, it shows good performance and significant reductions in computational time.
The efficiency of the wind power conversions systems can be greatly improved using an appropriate control algorithm. In this work, an adaptive robust control for a variable speed wind power generation is described. A ...
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ISBN:
(纸本)9781467379298
The efficiency of the wind power conversions systems can be greatly improved using an appropriate control algorithm. In this work, an adaptive robust control for a variable speed wind power generation is described. A robust aerodynamic torque observer is also designed in order to avoid the wind speed sensors. The proposed adaptive robust control law is based on a sliding mode control theory, that presents a good performance under system uncertainties. The stability analysis of the proposed controller under disturbances and parameter uncertainties is provided using the Lyapunov stability theory. Finally simulated results show, on the one hand that the proposed controller provides high-performance dynamic characteristics, and on the other hand that this scheme is robust with respect to plant parameter variations and external disturbances.
Adapting Classical computer Science algorithms for Biological Reality To evaluate the feasibility of microbiome-based identifiability, Franzosa et al. (4) intertwine microbial ecological theories with computer science...
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Adapting Classical computer Science algorithms for Biological Reality To evaluate the feasibility of microbiome-based identifiability, Franzosa et al. (4) intertwine microbial ecological theories with computer science algorithms. These two fields are elegantly blended throughout the manuscript as the authors walk readers through algorithm development, taking time to justify each decision with a biological reality. This care in explaining the details ensures the story is accessible to scientists with mixed backgrounds and yields an excellent “teaching” paper as well as research study. Multidisciplinary appeal is particularly important as more and more research involves collaborations spanning different fields of expertise.
The automated steering and manipulation of multiple nanowires independently would enable the potentially scalable assembly of nanodevices for a variety of applications. We present an electric-field-based design for si...
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ISBN:
(纸本)9781467381833
The automated steering and manipulation of multiple nanowires independently would enable the potentially scalable assembly of nanodevices for a variety of applications. We present an electric-field-based design for simultaneous motion planning and manipulation of multiple nanowires in liquid suspension. The design is built on a micro-fluidic device that is actuated by a simple, generic set of electrodes. We first present a motion-control algorithm to simultaneously steer multiple nanowires along desired trajectories under controlled electrophoretic forces, while compensating for background electro-osmotic flow. A two-stage motion-planning algorithm is then presented to generate the desired trajectory for each individual nanowire. Numerical simulations and experimental results confirm and demonstrate the performance of the proposed motion planning and control design.
This paper presents an estimation and control algorithm for an aerial manipulator using a hexacopter with a 2-DOF robotic arm. The unknown parameters of a payload are estimated by an on-line estimator based on paramet...
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ISBN:
(纸本)9781467381833
This paper presents an estimation and control algorithm for an aerial manipulator using a hexacopter with a 2-DOF robotic arm. The unknown parameters of a payload are estimated by an on-line estimator based on parametrization of the aerial manipulator dynamics. With the estimated mass information and the augmented passivity-based controller, the aerial manipulator can fly with the unknown object. Simulation for an aerial manipulator is performed to compare estimation performance between the proposed control algorithm and conventional adaptive sliding mode controller. Experimental results show a successful flight of a custom-made aerial manipulator while the unknown parameters related to an additional payload were estimated satisfactorily.
The article focuses on the paper 'A primal-dual aggregation algorihtm for minimizing conditional value-at-risk in linear programs,' by Daniel Espinoza and Eduardo Moreno which won the Computational Optimizatio...
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The article focuses on the paper 'A primal-dual aggregation algorihtm for minimizing conditional value-at-risk in linear programs,' by Daniel Espinoza and Eduardo Moreno which won the Computational Optimization and Applications (COAP) Best Paper Award. Topics discussed include the approximation method for stochastic optimization problems, the conditional value at risk (CvaR), and production scheduling problems.
Wireless Networks have become ubiquitous and dense to support the growing demand from mobile users. To improve the performance of these networks, new approaches are required, such as context and service aware control ...
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ISBN:
(纸本)9783901882760
Wireless Networks have become ubiquitous and dense to support the growing demand from mobile users. To improve the performance of these networks, new approaches are required, such as context and service aware control algorithms, which are not possible on today's closed proprietary WLAN controllers. In this work, we propose Ethanol, a software-defined networking architecture for 802.11 dense WLANs. This paper describes the benefits of programmable APs, and proposes Ethanol, an architecture for network-wide control of QoS, user mobility, AP virtualization, and security on 802.11 APs. The proposal is evaluated on a prototype using off-the-shelf APs over three use cases.
This paper presents an integrated control framework for the simulation and visualization of cooperative missions for unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The X-Plane simulator is utiliz...
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ISBN:
(纸本)9781479959273
This paper presents an integrated control framework for the simulation and visualization of cooperative missions for unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The X-Plane simulator is utilized to simulate vehicle dynamics and visualize experiments in realistic environments, whereas the control algorithms are executed and validated in Matlab/Simulink. A novel approach to integrate ground vehicles in X-Plane is presented and an overall open source framework is developed to facilitate the interaction and usability of the two software programs used. The framework facilitates research in cooperative vehicle control, path planning, formation control, and centralized control topologies through straightforward and cost effective system simulation, visualization and evaluation.
Reliable subsea communication is the principal technical challenge that limits the ability of underwater vehicles to collaboratively perform tasks. The subsea communication channel is extremely low bandwidth and suffe...
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ISBN:
(纸本)9781467376488
Reliable subsea communication is the principal technical challenge that limits the ability of underwater vehicles to collaboratively perform tasks. The subsea communication channel is extremely low bandwidth and suffers from latencies and frequent dropouts. We present fundamental advances in network theory and corresponding distributed estimation and control algorithms that enable autonomous underwater vehicles to cooperate despite infrequent communication between vehicles. We describe the practical challenges of subsea operations and how our approach distributed data fusion and decentralized control addresses these challenges. We also discuss advances in stochastic graph theory that enable us to model the sparse, time-varying network topology available underwater in a manner suitable for control design. The practical utility of our approach has been verified during field trials in which a team of autonomous underwater vehicles cooperatively localized the source of an acoustic signal.
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