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.     

混凝土成层结构的流激振动特性

泄流激励引起水工建筑物振动的现象时有发生且形式多样,严重的还可能引起结构的破坏。对于混凝土成层结构而言,由振动引起破坏的案例不多,对其振动响应机制的研究也不够深入。以混凝土拱坝为例,通过数值计算及模型试验对比分析了整体与成层两种不同结构在相同泄流荷载作用下结构的振动响应,探讨了响应与结构自身的关系,并结合"拍"的概念,对混凝土成层结构振动响应特性与泄流荷载之间的关系进行了研究。结果表明,成层结构频率相近的前两阶振动是拍振形成的主要原因。此外,即使不满足拍振形成的条件,振动曲线同样可以表现为"拍"的形式,且振动量较受迫振动大幅增加,实质为"水力共振"现象。     

基于稀疏表示分类器的和弦识别研究

和弦识别作为音乐信息标注的基础,在分析音乐结构和旋律方面具有非常重要的作用.结合音乐理论知识,提出一种基于稀疏表示分类器的和弦识别方法.与传统的基于帧的识别方法不同,以节拍作为和弦变化的最小时间间隔,利用CQT (Constant-Q Transform)变换对音乐信号进行时频分析,提取PCP (Pitch Class Profile)特征,采用稀疏表示分类器(Sparse Representation-based Classification,SRC)进行和弦识别.实验结果表明,提出的特征和识别方法在识别率上均高于传统的方法.     

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。     

Buck变换器的两种不同数字控制器设计方法

根据Buck变换器的小信号模型,用两种设计方法设计了平均电流控制的Buck型双环开关调压系统的数字双环控制器;然后给出了仿真分析;最后以TMS320F28335为DSP芯片搭建实验电路,通过实验对两种控制方式的控制效果进行了比较.仿真及实验结果表明,将频域法设计的连续域的补偿网络传递函数离散化得到离散域的补偿网络传递函数,平均电流控制的Buck型双环开关调压系统具有更好的响应特性.     

Proposal of a New Method for Measuring F?rster Resonance Energy Transfer (FRET) Rapidly,Quantitatively and Non-Destructively

The process of radiationless energy transfer from a chromophore in an excited electronic state (the “donor”) to another chromophore (an “acceptor”), in which the energy released by the donor effects an electronic transition, is known as “Förster Resonance Energy Transfer” (FRET). The rate of energy transfer is dependent on the sixth power of the distance between donor and acceptor. Determining FRET efficiencies is tantamount to measuring distances between molecules. A new method is proposed for determining FRET efficiencies rapidly, quantitatively, and non-destructively on ensembles containing donor acceptor pairs: at wavelengths suitable for mutually exclusive excitations of donors and acceptors, two laser beams are intensity-modulated in rectangular patterns at duty cycle ½ and frequencies f₁ and f₂ by electro-optic modulators. In an ensemble exposed to these laser beams, the donor excitation is modulated at f₁, and the acceptor excitation, and therefore the degree of saturation of the excited electronic state of the acceptors, is modulated at f₂. Since the ensemble contains donor acceptor pairs engaged in FRET, the released donor fluorescence is modulated not only at f₁ but also at the beat frequency Δf: = |f₁ − f₂|. The depth of the latter modulation, detectable via a lock-in amplifier, quantitatively indicates the FRET efficiency.