Partial redundancy elimination for access expressions by speculative code motion

We present a new memory access optimization for Java to perform aggressive code motion for speculatively optimizing memory accesses by applying partial redundancy elimination (PRE) techniques. First, to reduce as many barriers as possible and to enhance code motion, we perform alias analysis to identify all the regions in which each object reference is not aliased. Secondly, we find all the possible barriers. Finally, we perform code motions in three steps. For the first step, we apply a non‐speculative PRE algorithm to move load instructions and their following instructions in the backwards direction of the control flow graph. For the second step, we apply a speculative PRE algorithm to move some of them aggressively before the conditional branches. For the third step, we apply our modified version of a non‐speculative PRE algorithm to move store instructions in the forward direction of the control flow graph and to even move some of them after the merge points. We implemented our new algorithm in our production‐level Java just‐in‐time compiler. Our experimental results show that our speculative algorithm improves the average (maximum) performance by 13.1% (90.7%) for jBYTEmark and 1.4% (4.4%) for SPECjvm98 over the fastest algorithm previously described, while it increases the average (maximum) compilation time by 0.9% (2.9%) for both benchmark suites. Copyright © 2004 John Wiley & Sons, Ltd.     

基于神经网络和对称秩的特定谐波消除开关角生成算法

利用查表法实现三相级联11电平逆变器特定谐波消除面临存储开关角多、占用内存大的问题;而利用小规模神经网络拟合则面临生成开关角精度低的问题。针对这些问题,文中提出基于小规模神经网络和对称秩迭代相结合的特定谐波消除开关角生成算法。首先,利用小规模神经网络预测开关角的迭代初值,降低对神经网络训练精度的要求;其次,利用对称秩算法迭代开关角初值得到精确解。与查表法和Hopfield神经网络算法相比,所提算法可以减少数据存储空间,同时生成精度较高的开关角。仿真和实验结果表明:所生成的开关角能有效地消除5次、7次、11次、13次谐波,同时保证了期望的基波幅值。     

线性方程组的消元和多维几何的投影

对多维几何中的正投影、次投影给出一个新的统一的定义，并在此概念下，对线性方程组的消元赋予新的几何解释。     

On the position analysis of a new spherical parallel robot with orientation applications

In this paper, we propose a new spherical parallel robot for celestial orientation, and rehabilitation applications (TV satellite dish, tracking systems, solar panels, cameras, telescopes, table of the machine tools, ankle, shoulder, wrist and etc.). The proposed robot can completely rotate about an axis. After describing the robot and its inverse position analysis, using the genetic algorithm, the dimensional optimization to maximize the workspace of the robot is performed. The workspace analysis shows that the proposed robot has a relatively large workspace. Also, singularity analysis represents that the manipulator is a singularity-free workspace. It is a great advantage of the proposed robot. Next, an optimal approach is proposed for solving the direct position problem of the robot. According to the geometry of the robot, two coupled trigonometric equations are obtained through using a special form of Rodrigues' rotation formula. Next, the two coupled equations are transformed to a 8-degrees polynomial using the Sylvester's Dialytic elimination method. Finally, a numerical example for the robot with an asymmetric structure is given with eight real solutions. Therefore, the polynomial being minimal and the proposed approach is optimal. This greatly decreases computational time, which is necessary for dynamics, control and simulation.     

Cross-task emotion recognition using eeg measures: first step towards practical application

Electroencephalographic (EEG)-based emotion recognition has received increasing attention in the field of human-computer interaction (HCI) recently, there however remains a number of challenges in building a generalized emotion recognition model, one of which includes the difficulty of an EEG-based emotion classifier trained on a specific task to handle other tasks. Little attention has been paid to this issue. The current study is to determine the feasibility of coping with this challenge using feature selection. 12 healthy volunteers were emotionally elicited when conducting picture induced and video induced tasks. Firstly, support vector machine (SVM) classifier was examined under within-task conditions (trained and tested on the same task) and cross-task conditions (trained on one task and tested on another task) for picture induced and video induced tasks. The within-task classification performed fairly well (classification accuracy: 51.6% for picture task and 94.4% for video task). Cross-task classification, however, deteriorated to low levels (around 44%). Trained and tested with the most robust feature subset selected by SVM-recursive feature elimination (RFE), the performance of cross-task classifier was significantly improved to above 68%. These results suggest that cross-task emotion recognition is feasible with proper methods and bring EEG-based emotion recognition models closer to being able to discriminate emotion states for any tasks.     

A comparison of the computatlonal complexities of the stride reduction methods

In this paper the 'Stride of 3' reduction method is compared with the wtfell known cyclic reduction method for solving tridiagonal systems derived from the discretiped steady state convection diffusion equation. The Stride of 3 algorithm is shown to be superior for moderate to large linear systems (e.g., of order > 20).     

A mixture of experts for classifying sleep apneas

This paper presents a novel approach for classifying sleep apneas into one of the three basic types: obstructive, central and mixed. The goal is to overcome the problems encountered in previous work and improve classification accuracy. The proposed model uses a new classification approach based on the characteristics that each type of apnea presents in different segments of the signal. The model is based on the error correcting output code and it is formed by a combination of artificial neural networks experts where their inputs are the coefficients obtained by a discrete wavelet decomposition applied to the raw samples of the apnea in the thoracic effort signal. The input coefficients received for each network were determined by a feature selection method (support vector machine recursive feature elimination). In order to train and test the systems, 120 events from six different patients were used. The true error rate was estimated using a 10-fold cross validation. The results presented in this work were averaged over 10 different simulations and a multiple comparison procedure was used for model selection. The mean test accuracy obtained was 90.27% ± 0.79, and the values for each class apnea were 94.62% (obstructive), 95.47% (central) and 90.45% (mixed). Up to the authors’ knowledge, the proposed classifier surpasses all previous results.     

采煤工作面消突综合评价技术体系

在消突评价机理及现状研究的基础上,依据相关法律法规,提出了一套采煤工作面消突综合评价技术体系,补充了目前消突评价技术在低指标突出方面验证的缺陷,简化了评价程序,消除了评价指标重复考察。通过现场应用实践,验证了该技术的可行性及准确性。     

一种基于施密特触发器原理的直流电机位置控制及消抖算法

有刷直流电机的位置控制是一种常用的工作场景，对系统的响应速度、调节速度和控制精度等都有一定要求。经典的有刷直流电机位置控制算法一般都较理论，算法编写和调试较为复杂。基于现实应用需求，提出了一种新型的控制算法。该算法基于电子电路中施密特触发器的工作原理，实测具有响应速度快、控制精度较高、无抖动、适用范围广和编程简易等特点，可供相关技术人员参考借鉴。