Reactive power planning using a new hybrid technique

Voltage deviation and stability constrained VAr planning or reactive power planning (RPP) is an important challenging issue in power systems. This paper presents a new hybrid technique for modeling and solving RPP problem taking into account the static voltage stability constraint. First, the uncertain fuzzy clustering theory is employed to select new candidate VAr source locations. Then, modified gray code is applied and used to represent a series of non-uniform VAr capacity intervals at different candidate buses. Based on the new ordering of the VAr capacity intervals, a simplified piecewise linear function between the total transfer capability and new VAr capacity is derived and applied as static voltage stability constraint in RPP problem. Last, the RPP optimization problem is solved by a self adaptive fuzzy chaotic interactive honey bee mating optimization (FCIHBMO) technique taking advantage of the modified gray code. In the FCIHBMO algorithm, a modified definition of the updating factors on generation solution is proposed. In the case study, uncertain fuzzy clustering mechanism, the modified gray code, and the modified HBMO are applied to the IEEE 118-bus and IEEE 300-bus systems. Test results conclude that the proposed hybrid technique is a simplified and effective approach for voltage stability constrained VAr planning with contingency considered.     

Neural network technique for fuzzy multiobjective linear programming

Neural Network(NN) is well-known as one of powerful computing tools to solve optimization problems. Due to the massive computing unit-neurons and parallel mechanism of neural network approach we can solve the large-scale problem efficiently and optimal solution can be gotten. In this paper, we intoroduce improvement of the two-phase approach for solving fuzzy multiobjectve linear programming problem with both fuzzy objectives and constraints and we propose a new neural network technique for solving fuzzy multiobjective linear programming problems. The procedure and efficiency of this approach are shown with numerical simulations.     

快速去块效应的线性规划方法

许多现有的图像压缩算法在高压缩比下会产生恼人的块效应,消除块效应的后处理方法一直以来都是图像处理领域的重要研究方向。消除块效应可以认为是从不准确的采样数据出发,尽可能恢复原始图像,这也是压缩传感理论所做的。因此利用压缩传感理论,给出了一种新的去块效应方法,将去块效应问题归结为一个无需调校任何其他参数的线性规划问题,最终采用GPU实现,得以快速求解。大量的实验结果表明,该方法能快速有效地去除块效应,改善了图像的视觉效果,同时提高了图像的PSNR。     

Lyapunov function construction by linear programming

In this paper the Lyapunov function given by the infinity norm is considered. A new proof of the theorem of Molchanov and Pyatintskii (1986) is given, The presented proof enables us to give an explicit calculation of the matrix which was only demonstrated to exist in the original paper. Combining the obtained result with the idea of scaling faces of a polytope gives the method of fitting the shape of level surfaces, of the Lyapunov function given by the infinity norm, to the specified demands     

Stand-alone renewable power system scheduling for a green data center using integer linear programming

Journal of Scheduling - In light of the rapid growth of data centers around the world and their huge energy consumption, several researchers have focused on the task scheduling and resource...     

Solving multiple objective quasi-convex goal programming problems by linear programming

This study presents a novel means of resolving multiple objective goal programming (GP) problems with quasi-convex linear penalty functions. The proposed method initially expresses a quasi-convex function by the maximum operator of two convex functions, then solves it via a linear programming technique. The proposed method does not contain any zero–one variables; nor does it require dividing the multi-objective quasi-convex GP problem into large sub-problems as in conventional methods. Some illustrative examples are provided.     

Towards a linear algebra of programming

The algebra of programming (AoP) is a discipline for programming from specifications using relation algebra. Specification vagueness and nondeterminism are captured by relations. (Final) implementations are functions. Probabilistic functions are half way between relations and functions: they express the propensity, or likelihood of ambiguous, multiple outputs. This paper puts forward a basis for a linear algebra of programming (LAoP) extending standard AoP towards probabilistic functions. Because of the quantitative essence of these functions, the allegory of binary relations which supports the AoP has to be extended. We show that, if one restricts to discrete probability spaces, categories of matrices provide adequate support for the extension, while preserving the pointfree reasoning style typical of the AoP.     

Verifying time Petri nets by linear programming

The paper proposes an approach to solving some verification problems of time petri nets using linear programming.The approach is based on the observation that for loop-closed time Ptri nets,it is only necessary to investigate a finite prefix of an untimed run of the underlying Petri net.Using the technique the paper gives solutions to reachabiltiy and bounded delay timing analysis problems.For both problems algorithms are given,that are decision procedures for loop-closed time Petri nets.and semi-decision procedures for general time Petri nets.     

Matching by linear programming and successive convexification

We present a novel convex programming scheme to solve matching problems, focusing on the challenging problem of matching in a large search range and with cluttered background. Matching is formulated as metric labeling with L₁ regularization terms, for which we propose a novel linear programming relaxation method and an efficient successive convexification implementation. The unique feature of the proposed relaxation scheme is that a much smaller set of basis labels is used to represent the original label space. This greatly reduces the size of the searching space. A successive convexification scheme solves the labeling problem in a coarse to fine manner. Importantly, the original cost function is reconvexified at each stage, in the new focus region only, and the focus region is updated so as to refine the searching result. This makes the method well-suited for large label set matching. Experiments demonstrate successful applications of the proposed matching scheme in object detection, motion estimation, and tracking     

Bicriterion linear programming

Basically leaning on the concept of “best” compromise, the technique seeks the optimal solution by fair relaxations of the objectives commensurate with their degrees of importance until the optimum feasible compromise is reached. The concept is made operational by deriving a linear constraint (referred to as the compromise constraint) to be added into the original set. An offspring of both objective functions, the compromise constraint cuts the original feasible region and forces both objectives to settle on a common point along this added restriction. The resulting singular equivalent of the bicriterion problem optimizes any one of the two objective functions and their equivalent sum (referred to as the supergoal) subject to the new set of constraints. Post optimality analysis is employed to minimize the computational effort usually done by a computer. A very attractive feature of this new practical technique is its ability to search for the optimum in any point in the feasible region, even other than the vertices of the convex set.     

Finite field computation technique for exact solution of systems of linear equations and interval linear programming problems

An error-free computational approach is employed for finding the integer solution to a system of linear equations, using finite-field arithmetic. This approach is also extended to find the optimum solution for linear inequalities such as those arising in interval linear programming probloms.     

Performance evaluation of marked graphs by linear programming

Performance evaluation is an important issue for discrete event dynamic systems, which in many cases can be described in terms of the language of Petri nets. In particular, for marked graphs, a subclass of Petri nets, a formula for performance evaluation is widely known. That is, the ratio of the total execution time to the number of tokens in a cycle gives a lower bound for the cycle time of the system, and the maximum of these ratios over all cycles determines the overall system performance. However, we need to enumerate all directed cycles in a graph to apply this formula of performance evaluation. Carrying out this enumeration for an actual system is often impractical, since the number of cycles in a graph usually grows exponentially with the size of a system. We give a linear algebraic characterization for directed cycles, and based on this result, transform the problem of performance evaluation into a simple linear programming problem. A few explanatory examples are also given.     

Optimal shakedown design of frames by linear programming

A mathematical model of flexural framed structures subjected to cyclic loadings is presented, and the problem of minimum-weight shakedown design of these structures is formulated as a pair of dual linear programming problems. Such a formulation reflects the duality between static and kinematic theorems of shakedown theory. An optimal solution for plastic moment distribution realizes a minimum volume of a structure and ensures a given safety factor against plastic collapse by inadaptation to a prescribed cyclic loading program. The reduced-size form of the problem is presented where the bending moments are expressed in terms of independent redundants. This form is more attractive from the computational point of view for the use of linear programming codes. Several examples illustrate the presented method.     

Solving parametric fuzzy systems of linear equations by a nonlinear programming method

Linear systems of equations, with uncertainty on the parameters, play a major role in various problems in economics and finance. In this paper parametric fuzzy linear systems of the general form A ₁ x + b ₁ = A ₂ x + b ₂, with A ₁, A ₂, b ₁ and b ₂ matrices with fuzzy elements, are solved by means of a nonlinear programming method. The relation between this methodology and the algorithm proposed in Muzzioli and Reynaerts [(2006) Fuzzy Sets and Systems, in press] is highlighted. The methodology is finally applied to an economic and a financial problem.     

Hysteresis compensation in a magnetostrictive linear position sensor

A new linear position sensor (Micrus) has been developed, based on the transmission of ultrasonic signals in a waveguide. Waves are generated at the cursor position by the magnetostrictive effect, and their times of flight to the ends of the waveguide are used to estimate the position of the mobile element. The choice of the generating/transmitting metal for this kind of sensor is discussed. We have found that the magnetic hysteresis inherent to the magnetostrictive phenomenon translates into measurement hysteresis, affecting the performance of the sensor. An explanation of the link between both effects is given, and a compensation technique based in focusing the ultrasonic generation is offered. This compensation technique is tested using an electromagnetic finite element method program and then empirically in the Micrus sensor, with satisfactory results.     

基于线性规划分类器的相关反馈技术

提出了一种基于线性规划分类器的相关反馈方法．所设计的线性规划分类器将特征选择和分类学习结合起来，使其不仅能在利用用户标注的小样本条件下进行实时训练，而且能根据样本对分类的贡献程度选择用户反馈中的敏感特征，从而能在相关反馈小样本训练条件下有效捕捉用户的反馈意图．针对草图检索的实验结果验证了所提出方法在相关反馈中的有效性．     

Breast cancer detection by Michaelis-Menten constants via linear programming

The Michaelis-Menten constants (K(m) and V(max)) operated by linear programming, were employed for detection of breast cancer. The rate of enzymatic hydrolysis of fluorescein diacetate (FDA) in living peripheral blood mononuclear cells (PBMC), derived from healthy subjects and breast cancer (BC) patients, was assessed by measuring the fluorescence intensity (FI) in individual cells under incubation with either the mitogen phytohemagglutinin (PHA) or with tumor tissue, as compared to control. The suggested model diagnoses three conditions: (1) the subject is diseased, (2) the diagnosis is uncertain, and (3) the subject is not diseased. Out of 50 subjects tested, 44 were diagnosed correctly, in 5 cases the diagnosis was not certain, and 1 subject was diagnosed incorrectly.     

Robust controller design by linear programming with application to a double-axis positioning system

A linear programming approach is proposed to tune fixed-order linearly parameterized controllers for stable LTI plants. The method is based on the shaping of the open-loop transfer function in the Nyquist diagram. A lower bound on the crossover frequency and a new linear stability margin which guarantees lower bounds for the classical robustness margins are defined. Two optimization problems are proposed and solved by linear programming. In the first one the robustness margin is maximized for a given lower bound on the crossover frequency, whereas in the second one the closed-loop performance in terms of the load disturbance rejection is optimized with constraints on the new stability margin. The method can directly consider multi-model as well as frequency-domain uncertainties. An application to a high-precision double-axis positioning system illustrates the effectiveness of the proposed approach.     

Nonlinear compensation of linear processes

The problem of designing stable nonlinear compensators for the purpose of making the process response suitably magnitude dependent is examined. Both ‘classical’ nonlinear lag-lead networks, and compensators involving nonlinear state feedback are considered.