Environmental noise classifier using a new set of feature parameters based on pitch range

Automatic Noise Recognition was performed in two stages: (1) feature extraction based on the pitch range, found by analyzing the autocorrelation function and (2) classification using a classifier trained on the extracted features. Since most environmental noise types change their acoustical characteristics over time, we focused on the “pitch range” of the sounds in order to extract features. Two different classifiers, Support Vector Machines (SVM) and k-means clustering, were performed and compared using the proposed features. The SVM and k-means clustering classifiers achieve recognition rates up to 95.4% and 92.8%, respectively. Although both classifiers provided high accuracy, the SVM-based classifier outperformed the k-means clustering classifier by approximately 7.4%.     

构造性算法的神经网络集成在近红外光谱分析中的应用

针对传统的近红外数据分析方法精度较低、,应用的局限性问题,本文提出了一种基于构造性算法的神经网络集成方法,由一个构造性算法决定个体网络中隐层节点的数量以保证个体网络的精确性,运用负相关学习算法和网络个体训练次数不同保证了网络个体的多样性。这种方法在近红外光谱分析中得到了成功的应用。     

Process development for isolation of dietary eugenol from leaves of basil (Ocimum sanctum) in combination of optimization of process variables and modeling by artificial neural network

Dietary eugenol helps prevent free radical-induced and lifestyle-related chronic illnesses such as cancer, autoimmune disorders, cardiovascular disease, and aging. A technique for extracting eugenol from green basil (Ocimum sanctum) leaves is created using a combination of extraction variable optimization and the organization of an artificial neural network (ANN) model. For thermally degradable bioactive eugenol, solvent extraction is the recommended separation method. With the following optimum variables: polarity of the solvent of 0.009, the solid-solvent ratio of 1.0 ?g/20 ?mL, stirring speed of 200 ?rpm, extraction temperature of 40 ?°C, and extraction duration of 40 ?min, a yield of 5.39 × ?10^?3 ?kg eugenol per kilogram dried leaves of basil was found. At 10 ?min of batch extraction, the highest throughput of eugenol was found to be 5.4 ?× ?10^?3 ?kg ?m^?3 ?s^?1. Additionally, experimental data are used to construct the yield prediction model. The statistical parameters that are obtained in model evaluation encourage the use of the predicted model for the commercialization of eugenol isolation.     

基于神经网络的机械磨损故障光谱定位诊断法

在分析常用光谱定位诊断方法的基础上提出了基于神经网络的光谱定位诊断法；将机械摩擦副材质的元素含量作为神经网络输入，将材质所对应的部件作为神经网络输出，建立了相应的神经网络训练样本；通过整理训练样本和训练神经网络，利用神经网络超强的非线性映射能力和容错性实现了磨损故障部位诊断；通过算例分析验证了所提出的诊断方法的可行性和准确性．结果表明，所建立的方法简洁有效，并具有很高的诊断精度．     

Review of validation and reporting of non-targeted fingerprinting approaches for food authentication

Food fingerprinting approaches are expected to become a very potent tool in authentication processes aiming at a comprehensive characterization of complex food matrices. By non-targeted spectrometric or spectroscopic chemical analysis with a subsequent (multivariate) statistical evaluation of acquired data, food matrices can be investigated in terms of their geographical origin, species variety or possible adulterations. Although many successful research projects have already demonstrated the feasibility of non-targeted fingerprinting approaches, their uptake and implementation into routine analysis and food surveillance is still limited. In many proof-of-principle studies, the prediction ability of only one data set was explored, measured within a limited period of time using one instrument within one laboratory. Thorough validation strategies that guarantee reliability of the respective data basis and that allow conclusion on the applicability of the respective approaches for its fit-for-purpose have not yet been proposed. Within this review, critical steps of the fingerprinting workflow were explored to develop a generic scheme for multivariate model validation. As a result, a proposed scheme for “good practice” shall guide users through validation and reporting of non-targeted fingerprinting results. Furthermore, food fingerprinting studies were selected by a systematic search approach and reviewed with regard to (a) transparency of data processing and (b) validity of study results. Subsequently, the studies were inspected for measures of statistical model validation, analytical method validation and quality assurance measures. In this context, issues and recommendations were found that might be considered as an actual starting point for developing validation standards of non-targeted metabolomics approaches for food authentication in the future. Hence, this review intends to contribute to the harmonization and standardization of food fingerprinting, both required as a prior condition for the authentication of food in routine analysis and official control.     

基于BP神经网络建立姬松茸多糖超滤分离模型

姬松茸（Agaricus Blazei Murill）是食药两用真菌,原产于北美南部和巴西、秘鲁等地,其主要活性物质多糖已被证实具有抑制肿瘤、抗HIV、降低血压、血糖、胆固醇等作用。目前多糖主要采用热水溶解加酒精沉淀法提取,此法提取的多糖粗液通常是多分散性的混合物。而膜分离技术可以按一定分子量要求分离多糖,且具有常温操作、无相态变化,可以防止杂菌污染和热敏性物质失活等特点,因此在多糖分离中的应用研究越来越受到重视。     

Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection

Methodologies for automatic non-rapid eye movement and cyclic alternating pattern analysis were proposed to examine the signal from one electroencephalogram monopolar derivation for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments. A population composed of subjects free of neurological disorders and subjects diagnosed with sleep-disordered breathing was studied. Parallel classifications were performed for non-rapid eye movement and A phase estimations, examining a one-dimension convolutional neural network (fed with the electroencephalogram signal), a long short-term memory (fed with the electroencephalogram signal or with proposed features), and a feed-forward neural network (fed with proposed features), along with a finite state machine for the cyclic alternating pattern cycle scoring. Two hyper-parameter tuning algorithms were developed to optimize the classifiers. The model with long short-term memory fed with proposed features was found to be the best, with accuracy and area under the receiver operating characteristic curve of 83% and 0.88, respectively, for the A phase classification, while for the non-rapid eye movement estimation, the results were 88% and 0.95, respectively. The cyclic alternating pattern cycle classification accuracy was 79% for the same model, while the cyclic alternating pattern rate percentage error was 22%.     

径向基神经网络模型在滇东南金矿潜力预测中的应用

采用人工网络神经法（Artificial Neural Network,ANN）有助于理解成矿系统的非线性动力学行为和对矿产资源进行预测.其中的径向基神经网络（Radial Basis Function Neural Network,RBFNN）具有优秀的逼近特性,优化过程简单,训练速度快,适合于需要大量数据综合的矿产预测.采用RBFNN方法对成矿地质条件复杂的中国滇东南地区开展金矿成矿预测.研究结果表明,该模型能快速获取成矿潜力信息.通过采用受试者工作特征（Re-ceiver Operating Characteristic,ROC）曲线进行精度验证,表明该模型具有优越的预测能力.