Infrared Spectroscopy-Based Chemometric Analysis for Lard Differentiation in Meat Samples

One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness. The rapid and accurate identification mechanism for lard adulteration in meat products is highly necessary, for developing a mechanism trusted by consumers and that can be used to make a definitive diagnosis. Fourier Transform Infrared Spectroscopy (FTIR) is used in this work to identify lard adulteration in cow, lamb, and chicken samples. A simplified extraction method was implied to obtain the lipids from pure and adulterated meat. Adulterated samples were obtained by mixing lard with chicken, lamb, and beef with different concentrations (10%–50% v/v). Principal component analysis (PCA) and partial least square (PLS) were used to develop a calibration model at 800–3500 cm⁻¹. Three-dimension PCA was successfully used by dividing the spectrum in three regions to classify lard meat adulteration in chicken, lamb, and beef samples. The corresponding FTIR peaks for the lard have been observed at 1159.6, 1743.4, 2853.1, and 2922.5 cm⁻¹, which differentiate chicken, lamb, and beef samples. The wavenumbers offer the highest determination coefficient R² value of 0.846 and lowest root mean square error of calibration (RMSEC) and root mean square error prediction (RMSEP) with an accuracy of 84.6%. Even the tiniest fat adulteration up to 10% can be reliably discovered using this methodology.     

Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and non-seizure. In addition, a fast Walsh-Hadamard Transform (FWHT) technique is implemented to analyze the EEG signals within the recurrence space of the brain. Thus, Hadamard coefficients of the EEG signals are obtained via the FWHT. Moreover, the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings. Also, a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers. The LSTM classifier provides the best performance, with a testing accuracy of 99.00%. The training and testing loss rates for the LSTM are 0.0029 and 0.0602, respectively, while the weighted average precision, recall, and F1-score for the LSTM are 99.00%. The results of the SVM classifier in terms of accuracy, sensitivity, and specificity reached 91%, 93.52%, and 91.3%, respectively. The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s, respectively. The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals. Eventually, the proposed classifiers provide high classification accuracy compared to previously published classifiers.     

Stochastic simulation of velocity pulses of near-fault ground motions based on multivariate copula modeling

Due to the featured high-amplitude, long-period pulses in velocity or displacement time–history, the near-fault ground motion may have a stronger destructive effect on engineering structures. Therefore, studying velocity pulses modeling of near-fault ground motions has been an important topic in the earthquake engineering community. However, the variability and probabilistic correlation of pulse model parameters have not received enough attention. To this end, an elegant method for stochastic simulation of velocity pulses of near-fault ground motions is proposed based on the classical Gabor wavelet modeling and probabilistic correlation analysis of associated model parameters by multivariate copula modeling. Especially, an efficient scheme of iteratively determining the optimal copulas pertinent to edges of a complex vine structure is developed, and two parameters relevant to the near-fault ground motion records, i.e., the rupture distance and the strongest pulse orientation, are introduced which provides a physical basis for addressing the dependence structure among variables. For illustrative purposes, the ground accelerograms recorded by near-fault instruments in the 1999 Chi-Chi earthquake are used. It is shown that the multivariate copula modeling enables the efficient representation of probabilistic correlation between model parameters which is consistent with their physical mechanism. The time histories and spectral characteristics of velocity pulses of near-fault ground motions and their stochastic fluctuations with respect to the rupture distance are well simulated. While the empirical linear regression model does not meet the requirements of random vibration analysis and thus cannot guide the safety design of seismic structures.     

基于小波变换和人工神经网络的保护模型

采用一种结合小波变换和神经网络原理的模型,来识别电力系统短路故障．用小波变换提取测量信号的特征量,作为多层前向神经网络的输入．对不同的输出要求,采用不同的神经网络,判断出发生故障的相别、性质和位置．实验结果表明,该模型是有效、可行的．     

卷积积分和拉普拉斯变换在一阶电路中的应用

电路的任意激励的零状态响应,可以用卷积积分和拉普拉斯交换的方法求解。本文通过两个一阶电路的实例,阐述了两种方法的应用特点。     

用模糊集合理论识别电力系统振荡中的短路的研究

本文通过对电力系统振荡和短路所呈出的不同特征的分析,从故障特征入手,利用模糊集合理论来识别振荡过程中发生的短路,对于振荡中的三相短路的识别,本文提出一种基于振荡中心电压的波形跟踪识别方法,对于振荡中的不对称故障的识别,本文选取模变换中的Ｃｌａｒｋ变换作为数学工具,与对称分子量变换相结合,构造了Ｉα／Ｉβ判据,Ｉａ－Ｉβ与序分量组合判据,文章给出了相应的模糊数学模型,并经大量的仿真试验,获得良好的仿     

由文本至口形的媒体变换技术的研究

本文根据汉语的发音特点,提出了由基本音素口形参数和预先建立的口形模型来生成各种音节相应的正面口形图象的方法,从而完成由文本至口形的媒体变换,首先我们建立变形物体运动模型,然后由此构造一个口唇的动态发音模型,最后采用纹理映射技术,由基本音素口形参数和动态发音模型合成正面发音口形的图象。     

奇异值分解视频水印嵌入与检测及安全性测试

文章针对目前现有的基于小波变换和奇异值分解相结合的数字水印算法存在的缺陷,提出了一种新的视频水印算法:以二值图像为水印的混合整数小波变换和奇异值分解的视频水印盲提取算法,重点论述了奇异值分解的视频水印嵌入与检测以及安全性的测试.实验表明,提出的新算法具有较好的高透明性和鲁棒性.     

基于暂态零序分量的配电网选线方法研究

针对小电流接地系统通过装设消弧线圈来减小单相接地短路电流的方法可能造成已有选线装置失效的问题,介绍了一种基于暂态零序分量的配电网选线方法。该方法采用db10小波包对故障时刻流经每条馈线的暂态零序电流进行分解,找到能量最大和次大的两个特征频带,并对两个频带上小波包分解系数进行相关分析,从而选出发生故障的线路。以中性点经消弧线圈接地系统为例建立了仿真模型,并分别对架空线接地故障和电缆线接地故障进行了仿真实验。实验结果证明了该方法的有效性。     

图像处理在矿井瓦斯监控中的应用研究

针对目前煤矿采掘工作面便携式瓦斯报警仪监控过程中存在繁琐、枯燥的现场报数问题,提出了采用摄像机对便携式瓦斯报警仪进行实时监控并对监控视频图像序列进行图像处理和识别的方法;详细介绍了瓦斯浓度自动识别过程中的图像预处理、边缘检测和瓦斯浓度示数区域分割方法,即采用图像灰度化法和中值滤波法对监控图像进行预处理,分别使用Roberts边缘算子、Prewitt边缘算子、Sobel边缘算子对瓦斯浓度示数区域的图像边缘进行提取,通过Hough变换对瓦斯浓度示数区域附近的边缘直线进行检测,实现对目标图像的分割。