Algorithms for generating random variables with a rational probability-generating function

Two algorithms for generating random variables with a rational probability-generating function are presented. One of them implements the recently developed general range reduction method, and the other is an extension of the alias method designed for generating discrete finite-valued random variables to the case where the generated random variable is infinite-valued. An example of a random variable which was efficiently generated by random number generators implementing the presented algorithms is given. Possible ways of improving the complexity of the presented algorithms are discussed.     

Numerical solution of nonlinear system of Klein–Gordon equations by cubic B-spline collocation method

A technique to approximate the solutions of nonlinear Klein–Gordon equation and Klein–Gordon-Schrödinger equations is presented separately. The approach is based on collocation of cubic B-spline functions. The above-mentioned equations are decomposed into a system of partial differential equations, which are further converted to an amenable system of ODEs. The obtained system has been solved by SSP-RK54 scheme. Numerical solutions are presented for five examples, to show the accuracy and usefulness of proposed approach. The approximate solutions of both the equations are computed without using any transformation and linearization. The technique can be applied with ease to solve linear and nonlinear PDEs and also reduces the computational work.     

Debugging mixed‐environment programs with Blink

Programmers build large‐scale systems with multiple languages to leverage legacy code and languages best suited to their problems. For instance, the same program may use Java for ease of programming and C to interface with the operating system. These programs pose significant debugging challenges, because programmers need to understand and control code across languages, which often execute in different environments. Unfortunately, traditional multilingual debuggers require a single execution environment. This paper presents a novel composition approach to building portable mixed‐environment debuggers, in which an intermediate agent interposes on language transitions, controlling and reusing single‐environment debuggers. We implement debugger composition in Blink, a debugger for Java, C, and the Jeannie programming language. We show that Blink is (i) simple: it requires modest amounts of new code; (ii) portable: it supports multiple Java virtual machines, C compilers, operating systems, and component debuggers; and (iii) powerful: composition eases debugging, while supporting new mixed‐language expression evaluation and Java native interface bug diagnostics. To demonstrate the generality of interposition, we build prototypes and demonstrate debugger language transitions with C for five of six other languages (Caml, Common Lisp, C#, Perl 5, Python, and Ruby) without modifications to their debuggers. Using real‐world case studies, we show that diagnosing language interface errors require prior single‐environment debuggers to restart execution multiple times, whereas Blink directly diagnoses them with one execution. Copyright © 2014 John Wiley & Sons, Ltd.     

鲁棒的单类协同排序算法

单类协同过滤(One-class collaborative filtering, OCCF)问题是当前的一大研究热点.之前的研究所提出的算法对噪声数据很敏感,因为训练数据中的噪声数据将给训练过程带来巨大影响,从而导致算法的不准确性.文中引入了Sigmoid成对损失函数和Fidelity成对损失函数,这两个函数具有很好的灵活性,能够和当前最流行的基于矩阵分解(Matrix factorization, MF)的协同过滤算法和基于最近邻(K-nearest neighbor, KNN)的协同过滤算法很好地融合在一起,进而提出了两个鲁棒的单类协同排序算法,解决了之前此类算法对噪声数据的敏感性问题.基于Bootstrap抽样的随机梯度下降法用于优化学习过程.在包含有大量噪声数据点的实际数据集上实验验证,本文提出的算法在各个评价指标下均优于当前最新的单类协同排序算法.     

基于二重积分滑动面的Buck变换器滑模研究

利用状态空间平均方法建立了Buck变换器的数学模型,该模型考虑了电容和电感的串联等效电阻,仿真结果与电路模型的仿真结果基本一致,其准确性较高;基于该数学模型,分别设计了PID控制和PWM滑模控制,滑模控制基于二重积分滑动面,并利用了电流信息。仿真结果表明,在负载突变情况下,基于二重积分滑动面的PWM滑模电流控制具有更好的动态响应特性和稳态误差调节特性。     

具备反向学习和局部学习能力的粒子群算法

为解决粒子群优化（Particle Swarm Optimization,PSO）算法中存在的种群多样性和收敛性之间的矛盾,该文提出了一种具备反向学习和局部学习能力的粒子群优化算法（Reverse-learning and Local-learning PSO,RLPSO）。该算法保留了初始种群中满足排异距离要求的多个较差粒子以及每个粒子的历史最差位置。当检测到算法陷入局部最优时,利用这些较差粒子的位置信息指导部分粒子以较快飞行速度进行反向学习,将其迅速牵引出局部最优区域。反向学习过程可改善粒子种群的多样性,保证了算法的全局探测能力;同时,利用较优粒子间的差分结果指导最优粒子进行局部学习与搜索,该过程可与粒子群的飞行过程并行执行,且局部学习的缩放因子可随进化过程动态调节。局部学习可提高算法的求解精度,保证算法的迅速收敛。实验结果表明,RLPSO 算法同其他 PSO 算法相比,在高维函数优化中具有收敛速度快、求解精度高的特点。