50年来辽宁省不同等级舒适日数演变及空间区划分析

根据1964—2015年辽宁省23个气象站点的逐日平均气温、相对湿度及平均风速资料,采用人体舒适度指数法计算人体舒适度指数,在季尺度上分析辽宁省不同等级舒适日数演变情况及典型旅游城市最佳旅游期;运用旋转经验正交函数分解法(REOF),分析人体舒适度指数季尺度空间变化特征,并应用ArcGIS软件进行舒适区域划分。结果表明:①辽宁省多年平均冷不舒适日数呈降低趋势,舒适日数呈增加趋势,热不舒适日数也呈微弱增加趋势;舒适程度夏季＞秋季＞春季＞冬季;②除冬季外5座典型旅游城市均适宜旅游,夏季为最佳旅游期;其中,沈阳、本溪更适宜春季旅游,大连更适宜秋季旅游。③辽宁省四季舒适度变化情况可大致划分为辽西、辽中、辽东三个区域,具体划分情况随季节有相应变化,总体舒适度由辽宁东部向西部逐渐递减。     

A new approach to the epsilon expansion of generalized hypergeometric functions

Assuming that the parameters of a generalized hypergeometric function depend linearly on a small variable ε$\epsilon$, the successive derivatives of the function with respect to that small variable are evaluated at ε=0$\epsilon =0$ to obtain the coefficients of the ε$\epsilon$-expansion of the function. The procedure, which is quite naive, benefits from simple explicit expressions of the derivatives, to any order, of the Pochhammer and reciprocal Pochhammer symbols with respect to their argument. The algorithm may be used algebraically, irrespective of the values of the parameters. It reproduces the exact results obtained by other authors in cases of especially simple parameters. Implemented numerically, the procedure improves considerably, for higher orders in ε$\epsilon$, the numerical expansions given by other methods.     

带可变随机函数和变异算子的粒子群优化算法

标准粒子群优化算法的收敛分析表明,改变随机函数、个体历史最优,群体全局最优,有助于提高该算法的性能。为此,本文提出了一种带可变随机函数和变异算子的粒子群优化算法,即通过改变速度更新方程中的随机函数分布来调节粒子在迭代过程中飞向个体历史最优和群体全局最优的比重,通过对个体历史最优和群体全局最优进行变异来增强种群的搜索能力。实验结果证实了该算法的有效性。     

基于线性时序逻辑的最优巡回路径规划

基于线性时序逻辑(Linear temporal logic, LTL)的路径规划方法中, 多点巡回路径规划问题尚无有效解决方案. 为了在道路网络中实现最优巡回监测, 提出了基于LTL的最优巡回路径规划方法. 首先, 将环境建模成一个切换系统, 用LTL语言描述包含多个巡回点和障碍物的任务需求; 接着, 利用循环移位法构建能够融合任务需求和环境模型的扩展乘机自动机, 以建立路径信息完整的网络拓扑; 最后, 采用基于迪科斯彻法的最优综合算法搜索扩展乘机自动机网络上的最优路径, 从而获得能够满足复杂任务需求的最优巡回路径. 仿真结果表明, 该方法能够有效实现最优巡回路径规划.     

基于马尔可夫链的自适应性神经网络训练算法

提出一种基于马尔可夫链的自适应性神经网络训练方法,对传统的S型激励函数进行了改进,建立了自适应性的神经网络分类器。在假设样本中噪声服从于正态分布的情况下建立最大似然估计,通过后验概率建立马尔科夫链对样本进行训练,提高了神经网络训练速度。在轴承故障诊断中的测试结果表明,该算法可以迅速稳定地训练出神经网络,有效提高诊断的分类结果。     

Performance Histogram Curve: Abstractive Visualization and Analysis of NBA Games

NBA game is one of the world’s most exciting sports. Multiple datasets for a NBA game are available, e.g., play-by-play data, shot-position data, twitter and videos. Existing methods for visualizing game data commonly focus on the composition of multi-variate information of a game process. In this paper, we introduce a new parametric modeling approach, Performance Histogram Curve (PHC), that locally and adaptively encodes the game progression with game-related features derived from the play-by-play data. By transforming a PHC into the two-dimensional space with a two-phase projection technique, we create a unique 2D line representation. The 2D representation and auxiliary views abstracts the progress of a game and the performance of each team along the timeline. Our integrated system favor browsing a single play, analyzing game performance, and comparing multiple games. We conducted two case studies to demonstrate the effectiveness of our approach.     

Spline-based meshfree method with extended basis

In this work, an extension has been performed on the analysis basis of spline-based meshfree method (SBMFM) to stabilize its solution. The potential weakness of the SBMFM is its numerical instability from using regular grid background mesh. That is, if an extremely small trimmed element is produced by the trimming curves that represent boundaries of the analysis domain, it can induce an excessively large condition number in global system matrix. To resolve the instability problem, the extension technique of the weighted extended B-spline (WEB-spline) is implemented in the SBMFM. The basis functions with very small trimmed supports are extrapolated by neighboring basis functions with some special scheme so that those basis functions can be condensed in the solution process. In order to impose essential boundary conditions in the SBMFM with extended basis, Nitsche's method is implemented. Using numerical examples, the presented SBMFM with extended basis is shown to be valid and effective. Moreover, the condition number of the system is well-managed guaranteeing the stability of the numerical analysis.     

Nonlinear multiobjective model-predictive control scheme for wastewater treatment process

A nonlinear multiobjective model-predictive control (NMMPC) scheme, consisting of self-organizing radial basis function (SORBF) neural network prediction and multiobjective gradient optimization, is proposed for wastewater treatment process (WWTP) in this paper. The proposed NMMPC comprises a SORBF neural network identifier and a multiple objectives controller via the multi-gradient method (MGM). The SORBF neural network with concurrent structure and parameter learning is developed as a model identifier for approximating on-line the states of WWTP. Then, this NMMPC optimizes the multiple objectives under different operating functions, where all the objectives are minimized simultaneously. The solution of optimal control is based on the MGM which can shorten the solution time. Moreover, the stability and control performance of the closed-loop control system are well studied. Numerical simulations reveal that the proposed control strategy gives satisfactory tracking and disturbance rejection performance for WWTP. Experimental results show the efficacy of the proposed method.