When linear programming is used to decode low-density parity-check (LDPC) codes, the outcome is a codeword or a pseudocodeword that contains fractional symbol values. It is possible to make pseudocodewords infeasible ...
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When linear programming is used to decode low-density parity-check (LDPC) codes, the outcome is a codeword or a pseudocodeword that contains fractional symbol values. It is possible to make pseudocodewords infeasible and increase the performance of linear programming decoders by generating redundant parity check equations. In this paper, redundant parity check equations that can eliminate pseudocodewords are searched using an integer programming based optimization approach. We show that the generated parity check equations increase the performance of the adaptive linear programming decoder and that its performance can converge that of a maximum-likelihood decoder.
The aim of this paper is to present a simple new class of recurrent neural networks, which solves linear programming. It is considered as a sliding mode control problem, where the network structure is based on the Kar...
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The aim of this paper is to present a simple new class of recurrent neural networks, which solves linear programming. It is considered as a sliding mode control problem, where the network structure is based on the Karush-Kuhn-Tucker (KKT) optimality conditions, and the KKT multipliers are the control inputs to be implemented with finite time stabilizing terms based on the unit control, instead of common used activation functions. Thus, the main feature of the proposed network is the fixed number of parameters despite of the optimization problem dimension, which means, the network can be easily scaled from a small to a higher dimension problem. The applicability of the proposed scheme is tested on real-time optimization of an electrical Microgrid prototype.
In this paper, we propose a linear programming based interactive method for multiobjective linear programming problems, in which fuzzy coefficients and random variable coefficients are involved in the objective functi...
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In this paper, we propose a linear programming based interactive method for multiobjective linear programming problems, in which fuzzy coefficients and random variable coefficients are involved in the objective functions simultaneously. In the proposed method, it is assumed that the decision maker has a fuzzy goal for each objective function, and such a fuzzy goal can be quantified by eliciting the membership function. Through the possibility measure and a fractile optimization model, the original problem is transformed to the well-defined multiobjective programming problem. Then, a generalized Pareto optimal concept is defined, and a linear programming based interactive algorithm is proposed to obtain a satisfactory solution from among a generalized Pareto optimal solution set.
This paper exploits the control algorithm design of fuel-optimal satellite formation keeping strategy using linear programming *** the study,a fuel-optimal control problem is converted into a linear programming proble...
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ISBN:
(纸本)9781479946983
This paper exploits the control algorithm design of fuel-optimal satellite formation keeping strategy using linear programming *** the study,a fuel-optimal control problem is converted into a linear programming problem by means of an approximate discretization ***,model predictive control approach is adopted to realize the fuel-optimal *** last,the designed control algorithm is applied to a satellite which maneuvers from an initial orbit to a passive and periodic relative orbit with minimal fuel *** results demonstrate the efficiency and rapidity of the proposed algorithm.
Despite the great potential of hybrid wind-diesel system in supplying energy to remote or island communities, sizing the system components have been a challenging problem for many project managers due to the reliance ...
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ISBN:
(纸本)9781632666390
Despite the great potential of hybrid wind-diesel system in supplying energy to remote or island communities, sizing the system components have been a challenging problem for many project managers due to the reliance on various factors. This work considers utilising a fixed speed wind turbine (induction generator) in the hybrid system. It requires energy for start-up operation and this work takes into account for sizing the battery storage. In addition, the trade-off between the number of batteries and diesel generator fuel usage in a system is studied. linear programming for optimal sizing of batteries needed in a hybrid wind-diesel system, in the context of minimum diesel fuel usage is reported in this paper. Finally, this paper also shows that the storage capacity required in a hybrid system for various wind and load conditions can be computed in a systematic manner.
One of the most critical goals in the operation and planning of distribution networks is the creation of networks with sufficient reliability. Existing models are often simulation-based or try to introduce topology-in...
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One of the most critical goals in the operation and planning of distribution networks is the creation of networks with sufficient reliability. Existing models are often simulation-based or try to introduce topology-independent algebraic reliability measures with simplifications or extensive computations. This paper presents new and efficient topology-variable-based linear expressions that can evaluate the reliability indices of practical radial distribution networks. Furthermore, the model is extended to consider the inclusion of renewable distributed generation (DG) units to restore part of restorable loads in the islanded mode of operation. Also, the stochastic nature of renewable generation, as well as load demand, is considered. Therefore, the proposed model can be readily used in various optimization models to operate and plan distribution networks with reliability concerns. The application of the proposed method to several small- to large-scale test cases ranging from standard 37-node up to practical 1080-node benchmarks shows the proposed model's accuracy and computational effectiveness compared to the state-of-the-art conventional simulation-based topology-variable-based approaches. The impact of the system's islanded operation on the reliability indices is also evaluated on the modified DG-enhanced 37node test system.
Traditional methods for flexible capacity allocation do not take into account the actual operation status of resources, and this can lead to redundancy of allocation results in a high renewable penetration power syste...
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Traditional methods for flexible capacity allocation do not take into account the actual operation status of resources, and this can lead to redundancy of allocation results in a high renewable penetration power system. Using collaborative optimization during the flexibility resource planning stage can significantly improve the overall economics and flexibility. Therefore, a bilevel operation-planning joint optimization model for flexible capacity allocation is proposed in this paper. The aim is to optimize the annual total cost and flexibility of the system. The upper planning level introduces the economic costs, flexibility resource capacity, and flexibility index which are used as the evaluation index of system flexibility, while in the lower operation level, a morphological clustering algorithm based on the multiscale and entropy weight method is proposed for obtaining typical scenarios of flexibility demand. On this basis, the lower level simulates production to estimate daily operating costs. In addition, the model is solved iteratively using the nondominated sorting genetic algorithm-II (NSGA-II) and the linear programming method to obtain the Pareto solutions. Case studies are carried out based on a practical town area, and the results verify the validity and rationality of the proposed bilevel capacity allocation model.
Based on the Delsarte-Yudin linear programming approach, we extend Levenshtein's framework to obtain lower bounds for the minimum henergy of spherical codes of prescribed dimension and cardinality, and upper bound...
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Based on the Delsarte-Yudin linear programming approach, we extend Levenshtein's framework to obtain lower bounds for the minimum henergy of spherical codes of prescribed dimension and cardinality, and upper bounds on the maximal cardinality of spherical codes of prescribed dimension and minimum separation. These bounds are universal in the sense that they hold for a large class of potentials h and in the sense of Levenshtein. Moreover, codes attaining the bounds are universally optimal in the sense of Cohn-Kumar. Referring to Levenshtein bounds and the energy bounds of the authors as "first level", our results can be considered as "next level" universal bounds as they have the same general nature and imply necessary and sufficient conditions for their local and global optimality. For this purpose, we introduce the notion of Universal Lower Bound space (ULB-space), a space that satisfies certain quadrature and interpolation properties. While there are numerous cases for which our method applies, we will emphasize the model examples of 24 points (24-cell) and 120 points (600-cell) on S-3 . In particular, we provide a new proof that the 600-cell is universally optimal, and in so doing, we derive optimality of the 600-cell on a class larger than the absolutely monotone potentials considered by Cohn-Kumar.
The aim of this paper is to show a novel floorplanner based on Mixed-Integer linear programming (MILP), providing a suitable formulation that makes the problem tractable using state-of-the-art solvers. The proposed me...
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The aim of this paper is to show a novel floorplanner based on Mixed-Integer linear programming (MILP), providing a suitable formulation that makes the problem tractable using state-of-the-art solvers. The proposed method takes into account an accurate description of heterogeneous resources and partially reconfigurable constraints of recent FPGAs. A global optimum can be found for small instances in a small amount of time. For large instances, with a time limited search, a 20% average improvement can be achieved over floorplanners based on simulated annealing. Our approach allows the designer to customize the objective function to be minimized, so that different weights can be assigned to a linear combination of metrics such as total wire length, aspect ratio and area occupancy.
It is of great importance to calculate PV access capacity on different scene and different time division. Kmeans clustering method was used to cluster the scene, time division is carried out. Genetic algorithm is used...
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ISBN:
(数字)9798350389579
ISBN:
(纸本)9798350389586
It is of great importance to calculate PV access capacity on different scene and different time division. Kmeans clustering method was used to cluster the scene, time division is carried out. Genetic algorithm is used to solve the PV access capacity problem. Without network reconfiguration, there are three time divisions that exceed the constraints. Constraint overlimit for three exceedance period can be eliminated by network reconfiguration. The total PV access capacity increases and the active power loss decreases compared with optimization results without network reconfiguration.
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