In this paper, we investigate energy-efficient hybrid precoding which consists of baseband precoding and radio frequency (RF) precoding for Millimeter Wave (mmWave) systems over multi-input multi-output (MIMO) interfe...
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ISBN:
(纸本)9781509016990
In this paper, we investigate energy-efficient hybrid precoding which consists of baseband precoding and radio frequency (RF) precoding for Millimeter Wave (mmWave) systems over multi-input multi-output (MIMO) interference channels. The considered optimization problem is intractable because both the object function and the constraint are non-convex. Instead of solving the multivariate optimization problem directly, we propose a near-optimal algorithm which includes two steps. First, the original problem is reformulated into an equivalent univariate optimization problem. Second, a near-optimal hybrid precoder is obtained via an orthogonal matching pursuit (OMP) based algorithm. Numerical results verify that the proposed hybrid precoding algorithm achieves a near-optimal EE performance. Moreover, the energy efficiency (EE) maximization algorithm outperforms the sum rate maximization algorithm in terms of the EE performance, especially at high transmit power.
An algorithm recommends a product to a buyer based on the product’s value to the buyer and its price. We characterize an algorithm that maximizes the buyer’s expected payoff and show that it strategically biases rec...
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A novel optimization algorithm by combining the artificial bee colony (ABC) algorithm and the sequential quadratic programming (SQP), that is the gradient based ABC algorithm, is presented to resolve the problems of g...
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ISBN:
(纸本)9781467350723
A novel optimization algorithm by combining the artificial bee colony (ABC) algorithm and the sequential quadratic programming (SQP), that is the gradient based ABC algorithm, is presented to resolve the problems of global optimization and inter-area oscillations damping in power system. The proposed algorithm merges the global exploration ability of the artificial bee colony to converge quickly to a near optimum resolution, and the correct local exploitation capacity of the sequential quadratic programming to accelerate the search process and discover a correct solution. To show the feasibility and efficiency of the new method, numerical result is investigated on the New England system by tuning a power system stabilizer and a controller for the static VAR compensator. The proposed gradient based ABC algorithm is compared with ABC. The simulations studies demonstrate that the proposed algorithm based designed damping controllers perform better than controller designed by ABC.
A novel approach is given to overcome the computational challenges of the full-matrix Adaptive Gradient algorithm (Full AdaGrad) in stochastic optimization. By developing a recursive method that estimates the inverse ...
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In a recent paper, the authors have proposed an extremum-seeking control strategy to single-input-single-output (SISO) uncertain nonlinear systems. Such proposed sliding mode based real-time optimization algorithm gua...
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ISBN:
(纸本)9781457710957
In a recent paper, the authors have proposed an extremum-seeking control strategy to single-input-single-output (SISO) uncertain nonlinear systems. Such proposed sliding mode based real-time optimization algorithm guaranteed global convergence properties to a small neighborhood of the optimal point by means of a periodic switching function. In this paper, we generalize our previous results to a multivariable framework and applies this new controller to the optimization of the output signal power spectrum of Raman optical amplifiers. The aim is to minimize the output signal power ripple and the deviation of the output signal power from a desired level, considering eventual inclusions/exclusions of signals in the fiber. The convergence analysis is developed in the presence of uncertainties and the control performance is evaluated via numerical simulations.
This paper proposes a decentralized continuous estimation of distribution algorithm to solve optimization problems in large scale networked systems which consist of many subsystems. The decentralized continuous estima...
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This paper proposes a decentralized continuous estimation of distribution algorithm to solve optimization problems in large scale networked systems which consist of many subsystems. The decentralized continuous estimation of distribution algorithm makes each subsystem cooperate with its neighbors to find good global solutions. Numerical examples illustrate the effectiveness of the algorithm.
We propose a method for the acceleration of the online linear model predictive control (MPC) calculations with partial information on the explicit solution. We highlight two properties of the proposed approach: (i) It...
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ISBN:
(纸本)9781467357159
We propose a method for the acceleration of the online linear model predictive control (MPC) calculations with partial information on the explicit solution. We highlight two properties of the proposed approach: (i) It does not require to calculate the explicit solution first, and its computational effort grows only polynomially in the number of the constraints of the problem. The proposed approach can therefore be applied to problems that are too large for today's explicit MPC methods. (ii) The method is not based on a specific type or implementation of the optimization algorithm and can therefore easily be combined with a variety of existing MPC implementations. The proposed approach is, to the knowledge of the authors, one of yet a few attempts to use the insight into the structure of the explicit MPC law in online MPC.
Most existing Multiple-Instance Learning (MIL) algorithms assume data instances and/or data bags are independently and identically distributed. But there often exists rich additional dependency/structure information b...
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ISBN:
(纸本)9781618395993
Most existing Multiple-Instance Learning (MIL) algorithms assume data instances and/or data bags are independently and identically distributed. But there often exists rich additional dependency/structure information between instances/bags within many applications of MIL. Ignoring this structure information limits the performance of existing MIL algorithms. This paper explores the research problem as multiple instance learning on structured data (MILSD) and formulates a novel framework that considers additional structure information. In particular, an effective and efficient optimization algorithm has been proposed to solve the original non-convex optimization problem by using a combination of Concave-Convex Constraint Programming (CCCP) method and an adapted Cutting Plane method, which deals with two sets of constraints caused by learning on instances within individual bags and learning on structured data. Our method has the nice convergence property, with specified precision on each set of constraints. Experimental results on three different applications, i.e., webpage classification, market targeting, and protein fold identification, clearly demonstrate the advantages of the proposed method over state-of-the-art methods.
In short term hydro scheduling and optimization in a pure hydro system, a viable objective function is to minimize the water drawdown from a long term reservoir, thereby utilizing the characteristics and shape of effi...
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ISBN:
(纸本)9781509041695
In short term hydro scheduling and optimization in a pure hydro system, a viable objective function is to minimize the water drawdown from a long term reservoir, thereby utilizing the characteristics and shape of efficiency curves for individual units, waterways and stations. This paper addresses the representation of key concepts related to these efficiencies, as represented by the generation and production functions in hydroelectric stations. To illustrate different aspects, data have been collected and analyzed for actual stations in the Icelandic hydro dominated power system and graphical results are presented for key concepts. These key results of the paper include marginal and average cost or objective function curves and definitions. The underlying objective could be to minimize water usage for all stations, as part of different possible objectives in short term hydro system optimization. The results should be helpful when designing optimization algorithms and interpreting their outputs, and in casting a light on what are important aspects in solving such practical optimization problems.
We present a monocular visual navigation methodology for autonomous orchard vehicles. Modern orchards are usually planted with straight and parallel tree rows that form a corridor-like environment. Our task consists o...
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ISBN:
(纸本)9781467317375
We present a monocular visual navigation methodology for autonomous orchard vehicles. Modern orchards are usually planted with straight and parallel tree rows that form a corridor-like environment. Our task consists of driving a vehicle autonomously along the tree rows. The original contributions of this paper are: 1) a method to recover vehicle rotation independently of translation by modeling the vehicle as a car-like robot driving on a 3D ground surface-the rotation is estimated from monocular images while the translation is measured by a wheel encoder;and 2) a method to fit the 3D points corresponding to the trees into straight lines via an optimization algorithm that minimizes the error variance on the robot lookahead point. Additionally, we use a simple vanishing point detection approach to find the ends of the tree rows. The vanishing point detection is integrated into the system via an extended Kalman filter. The methodology's robustness to environmental changes is validated in more than fifty experiments in research and commercial orchards, six of which are presented and discussed in detail.
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