ECG heart beat classification method based on modified ABC algorithm

Electrocardiogram is the most commonly used tool for the diagnosis of cardiologic diseases. In order to help cardiologists to diagnose the arrhythmias automatically, new methods for automated, computer aided ECG analysis are being developed. In this paper, a Modified Artificial Bee Colony (MABC) algorithm for ECG heart beat classification is introduced. It is applied to ECG data set which is obtained from MITBIH database and the result of MABC is compared with seventeen other classifier's accuracy.In classification problem, some features have higher distinctiveness than others. In this study, in order to find higher distinctive features, a detailed analysis has been done on time domain features. By using the right features in MABC algorithm, high classification success rate (99.30%) is obtained. Other methods generally have high classification accuracy on examined data set, but they have relatively low or even poor sensitivities for some beat types. Different data sets, unbalanced sample numbers in different classes have effect on classification result. When a balanced data set is used, MABC provided the best result as 97.96% among all classifiers.Not only part of the records from examined MITBIH database, but also all data from selected records are used to be able to use developed algorithm on a real time system in the future by using additional software modules and making adaptation on a specific hardware.     

F206钢的热处理工艺及其力学性能研究

研究了Ｆ２０６钢的时效处理工艺及其显微组织与性能。试验结果表明，经８３０－８６０℃加热空冷后，在５１０℃时效５小时，产生明显的二次硬化效应，含碳从０．１４％增至０．１８％，钢的抗张强度１６３５和１８３０ＭＰａ，当晶粒尺雨粗化到３４μｍ时，钢的强度和韧性均有明显的降低。     

Classification of cardiac sound signals using constrained tunable-Q wavelet transform

The features extracted from the cardiac sound signals are commonly used for detection and identification of heart valve disorders. In this paper, we present a new method for classification of cardiac sound signals using constrained tunable-Q wavelet transform (TQWT). The proposed method begins with a constrained TQWT based segmentation of cardiac sound signals into heart beat cycles. The features obtained from heart beat cycles of separately reconstructed heart sounds and murmur can better represent the various types of cardiac sound signals than that from containing both. Therefore, heart sounds and murmur have been separated using constrained TQWT. Then the proposed novel raw feature set has been created by the parameters that have been optimized while constraining the output of TQWT together with that of extracted by using time-domain representation and Fourier–Bessel (FB) expansion of separated heart sounds and murmur. However, the adaptively selected features have been used to obtain the final feature set for subsequent classification of cardiac sound signals using least squares support vector machine (LS-SVM) with various kernel functions. The performance of the proposed method has been validated with publicly available datasets and the results have been compared with the existing short-time Fourier transform (STFT) based method. The proposed method shows higher percentage classification accuracy of 94.01 as compared to 93.53 of STFT based method. In comparison with STFT based method, it is noteworthy that the proposed method uses well defined and lower dimensionality of feature vector that can reduce the computational complexity.     

Application of InP Neural Network to ECG BeatClassification

This paper presents an application of a hybrid neural network structure to the classification of the electrocardiogram (ECG) beats. Three different feature extraction methods are comparatively examined: discrete cosine transform, wavelet transform and a direct method. Classification performances, training times and the numbers of nodes of Kohonen network, Restricted Coulomb Energy (RCE) network and the hybrid neural network are presented. To increase the classification performance and to decrease the number of nodes, the hybrid neural network is trained by Genetic Algorithms (GAs). Ten types of ECG beats obtained from the MIT-BIH database and from a real-time ECG measurement system are classified with a success of 98% by using the hybrid neural network structure and discrete cosine transform together.     

数控自动成形压机的设计

本文介绍了数控自动成形压机的设计．由于采用数控系统的控制，使国产的小吨位摩擦压力机成为全自动压机．这一设计，满足了国内陶瓷制品工厂对压机发展的要求，为数控系统在轻工企业中的应用开辟了新的市场．     

目标函数优化的有源滤波器差拍控制算法

以并联型有源滤波器为例,以降低差拍控制算法的复杂度为目的,通过优化目标函数提出了3种差拍控制算法。其中一范数差拍控制算法和无穷范数差拍控制算法通过构造新目标函数对算法进行化简,更适于控制三相有源滤波器,其新目标函数与传统差拍控制的二范数目标函数等价。通过优化传统差拍控制的目标函数得到改进二范数差拍控制算法,该算法的运算量比一范数算法和无穷范数算法少,适用于控制三相及单相有源滤波器。仿真结果表明上述3种算法均具有与传统差拍算法十分接近的控制精度,对控制周期变化的适应性相同,可完全取代传统差拍算法。考虑到算法的复杂度,改进二范数差拍控制算法最具应用价值。     

基于差频检测技术的高速AD单粒子翻转评估方法研究

本文基于差频检测的原理,提出一种在高频动态输入模式下,对高速高精度模数转换器（AD）的抗单粒子翻转效应进行评估的测试方法,并以一款8位3 GSPS高速AD为测试对象,设计开发了一套高速AD单粒子翻转效应测试系统,对目标器件进行了重离子试验。通过对试验结果的图像和错误数据进行分析,评估参试器件的抗辐照性能参数,为抗辐照高速高精度AD的加固设计提供数据支撑。     

一起热备用中500kV SF_6断路器爆炸原因探讨

通过对一起处于热备用中的500 kV SF6断路器,因加上工频与拍频电压的矢量差而爆炸的原因分析,提出在500 kV电网的规划设计中,应增加拍频过电压的计算内容。对现有同类500 kV线路,采用先将断路器转为冷备用后,再操作线路对侧断路器的方法,以防止该类事故的发生。     

基于LabVIEW的光拍频噪声的特性分析

为了提高低相干干涉技术(LCI)的测量精度,满足实际中高精度测量的要求,本文对LCI中的光拍频噪声进行了详细的理论研究、推导出拍频噪声的基本公式,并通过LabVIEW对噪声的性质进行仿真分析。结果表明:拍频噪声大小与光源功率的平方成正比,信噪比随光源功率线性增加并达到饱和值。     

带并联电抗器输电线路拍频电流幅值分析

目前在自适应单相重合闸中缺乏对拍频电流表达式、拍频电流是否一定存在及其幅值范围的研究。单相瞬时性故障消失后,拍频电流不存在或过小,都会影响重合闸判定的可靠性。拍频电流受健全相电压和故障消失后线路中元器件初始状态的影响,采用叠加原理得到完整的拍频电流表达式;通过分析表达式得出最小拍频电流及其出现的条件,分析出拍频电流为零属于小概率事件;拍频电流受故障时刻、故障持续时间、电弧电阻、电源相角差及故障位置等共同影响,使得不同工况下的拍频电流差异明显,若幅值过小,将会影响重合闸判定的可靠性。文中利用正交实验的方法给出某500kV输电线路拍频电流幅值大小的范围,证明了在工程实践中可以用拍频电流实现自适应重合闸。