In order to meet increasing traffic demands, future generations of cellular networks are characterized by decreasing cell sizes at full frequency reuse. Due to inevitable inter-cell interference, load conditions in ne...
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ISBN:
(纸本)9781479922390
In order to meet increasing traffic demands, future generations of cellular networks are characterized by decreasing cell sizes at full frequency reuse. Due to inevitable inter-cell interference, load conditions in neighboring cells can no longer be considered independent. This paper provides a flow level modeling framework for cellular networks, where the coupling of flow level dynamics due to intercell interference is specifically taken into account. Since an adequate queueing model renders analytically intractable, we review different methods from the literature to bound and approximate the stationary behavior of network performance measures. Numerical investigations of a typical wireless scenario reveal, that in high and low load regimes first as well as second order bounds may be quite loose, depending on the type of bound. Especially for design of network optimization algorithms, bounds do not appear to suitably reflect network performance, and approximation techniques must be considered instead. In this regard, a suitable tradeoff between computational complexity and accuracy over the whole traffic range is provided by a model based on the notion of average interference.
This paper develops an iterative optimization technique that can be applied to mode scheduling. The algorithm provides both a mode schedule and timing of that mode schedule with convergence guarantees. Moreover, the a...
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ISBN:
(纸本)9781467357159
This paper develops an iterative optimization technique that can be applied to mode scheduling. The algorithm provides both a mode schedule and timing of that mode schedule with convergence guarantees. Moreover, the algorithm takes advantage of a line search, and the number of iterations in the line search is bounded. There are two key ingredients in the algorithm. First, a projection operation is used that takes arbitrary curves and maps them to feasible switching controls. Second, a descent direction that incorporates the projection is calculated using the mode insertion gradient. Similar to derivative-based finite dimensional optimization, the convergence guarantees and sufficient decrease criteria follow from a local approximation of the cost in the direction of the search direction, but this local approximation is not the standard quadratic approximation. An example demonstrates the steps to implement the optimization algorithm and illustrates convergence.
This paper is concerned with the inference of marginal densities based on MRF models. The optimization algorithms for continuous variables are only applicable to a limited number of problems, whereas those for discret...
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ISBN:
(纸本)9781467364102
This paper is concerned with the inference of marginal densities based on MRF models. The optimization algorithms for continuous variables are only applicable to a limited number of problems, whereas those for discrete variables are versatile. Thus, it is quite common to convert the continuous variables into discrete ones for the problems that ideally should be solved in the continuous domain, such as stereo matching and optical flow estimation. In this paper, we show a novel formulation for this continuous-discrete conversion. The key idea is to estimate the marginal densities in the continuous domain by approximating them with mixtures of rectangular densities. Based on this formulation, we derive a mean field (MF) algorithm and a belief propagation (BP) algorithm. These algorithms can correctly handle the case where the variable space is discretized in a non-uniform manner. By intentionally using such a non-uniform discretization, a higher balance between computational efficiency and accuracy of marginal density estimates could be achieved. We present a method for actually doing this, which dynamically discretizes the variable space in a coarse-to-fine manner in the course of the computation. Experimental results show the effectiveness of our approach.
Here, we give an algorithm for deciding if the nonnegative rank of a matrix M of dimension m × n is at most r which runs in time (nm)~(O(r~2)). This is the first exact algorithm that runs in time singly-exponenti...
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ISBN:
(纸本)9781611972511
Here, we give an algorithm for deciding if the nonnegative rank of a matrix M of dimension m × n is at most r which runs in time (nm)~(O(r~2)). This is the first exact algorithm that runs in time singly-exponential in r. This algorithm (and earlier algorithms) are built on methods for finding a solution to a system of polynomial inequalities (if one exists). Notably, the best algorithms for this task run in time exponential in the number of variables but polynomial in all of the other parameters (the number of inequalities and the maximum degree). Hence these algorithms motivate natural algebraic questions whose solution have immediate algorithmic implications: How many variables do we need to represent the decision problem, does M have nonnegative rank at most r? A naive formulation uses nr + mr variables and yields an algorithm that is exponential in n and m even for constant r. (Arora, Ge, Kannan, Moitra, STOC 2012) recently reduced the number of variables to 2r~2 2~r, and here we exponentially reduce the number of variables to 2r~2 and this yields our main algorithm. In fact, the algorithm that we obtain is nearly-optimal (under the Exponential Time Hypothesis) since an algorithm that runs in time (nm)~(o(r)) would yield a subexponential algorithm for 3-SAT. Our main result is based on establishing a normal form for nonnegative matrix factorization - which in turn allows us to exploit algebraic dependence among a large collection of linear transformations with variable entries. Additionally, we also demonstrate that nonnegative rank cannot be certified by even a very large submatrix of M, and this property also follows from the intuition gained from viewing nonnegative rank through the lens of systems of polynomial inequalities.
Finding frequent itemsets is one of the most important fields of data mining and also finding the rules associated with the itemset is another important ground in association rule mining. The process of finding new te...
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ISBN:
(纸本)9781510804524
Finding frequent itemsets is one of the most important fields of data mining and also finding the rules associated with the itemset is another important ground in association rule mining. The process of finding new techniques to reduce candidate generation in order to generate frequent itemsets efficiently is a challenging task and generating rules in order to know the purchase behaviour of the customer to improve the business. In this paper, an efficient and optimized algorithm called Customer Purchase Behaviour (CPB) has been introduced for finding frequent itemsets using minimum scans, time and memory and the rules are generated. The subset invention process is used for the generation of frequent itemsets which reduces the intermediate tables. This approach reduces main memory requirement since it considers only a small cluster at a time. The purchase behaviour of the customer can be easily judged by using the Quine-McCluskey method. This algorithm is very efficient because of redundancy elimination and rule generation then compared with Apriori, Cluster-Based Bit Vector Mining for Association Rule Generation (CBVAR) and Improved Cluster-based Bit vector mining algorithm for Frequent Itemsets Generation (ICBV).
This paper presents general technique for implementing metaheuristic algorithms using S-Functions and its application in controller parameter optimization. The proposed method provides a suitable platform for paramete...
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ISBN:
(纸本)9781629939254
This paper presents general technique for implementing metaheuristic algorithms using S-Functions and its application in controller parameter optimization. The proposed method provides a suitable platform for parameter tuning in MATLAB/Simulink and PLECS software platforms. The proposed technique is suitable for exchanging optimization parameters when the objective function is not expressed in closed mathematical form. The S-Function based technique takes advantage of the software framework by embedding function calls of the optimization algorithm in callback methods which are executed at predefined time intervals. This leads to great improvement in algorithm efficiency in terms of simulation speed and ease of implementation. Validity of the proposed method is verified by implementing the particle swarm optimization algorithm (PSO) as an S-Function. The S-Function based PSO algorithm is applied to tune PI controllers for converter control in a doubly fed induction generator (DFIG) wind generation system.
We give highly efficient algorithms, and almost matching lower bounds, for a range of basic statistical problems that involve testing and estimating the L_1 (total variation) distance between two k-modal distributions...
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ISBN:
(纸本)9781611972511
We give highly efficient algorithms, and almost matching lower bounds, for a range of basic statistical problems that involve testing and estimating the L_1 (total variation) distance between two k-modal distributions p and q over the discrete domain {1,..., n}. More precisely, we consider the following four problems: given sample access to an unknown k-modal distribution p, TESTING IDENTITY TO A KNOWN OR UNKNOWN DISTRIBUTION: 1. Determine whether p = q (for an explicitly given k- modal distribution q) versus p is ε-far from q; 2. Determine whether p = q (where q is available via sample access) versus p is ε-far from q; ESTIMATING L_1 DISTANCE ("TOLERANT TESTING") AGAINST A KNOWN OR UNKNOWN DISTRIBUTION: 3. Approximate d_(TV) (p, q) to within additive ∈ where q is an explicitly given k-modal distribution q; 4. Approximate d_(TV) (p, q) to within additive ∈ where q is available via sample access.
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.
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.
This paper presents a new control strategy for a four-leg indirect matrix converter that effectively mitigates common-mode voltages and gives optimal control of source and load currents. This method uses the commutati...
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ISBN:
(纸本)9781479903375
This paper presents a new control strategy for a four-leg indirect matrix converter that effectively mitigates common-mode voltages and gives optimal control of source and load currents. This method uses the commutation state of the converter in the subsequent sampling time according to an optimization algorithm given by a simple cost function and a discrete system model. The control goals are the regulation of the output current according to an arbitrary reference and tracking of the source current reference, which is imposed in order to obtain sinusoidal waveforms with low distortion. The technique is enhanced by a reduction of the common-mode voltage using an extra term in the cost function to reduce early motor winding failure and bearing deterioration. Simulation results are presented to support the theoretical development.
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