基于支持向量机的代谢网络特征分析

把细胞内所有生化反应表示为一个网络指为代谢网络,是所有参与代谢过程的化合物之间以及所有催化酶之间的相互作用的反映,是抽象表达对细胞的代谢。在不同的物种中都含有大量的代谢翻译,却代谢网络是高度保守的。要了解包括代谢系统在内的许多自然、社会系统都起着重要的作用,所以要对于复杂网络进行研究并掌握它们的规律意义很大。利用代谢网络对微生物的耐热性进行分类研究对认识和利用细胞代谢过程有很大的帮助,从而促进发酵工程、制药工业等产业的发展。     

A hybrid intelligent system for medical data classification

In this paper, a hybrid intelligent system that consists of the Fuzzy Min–Max neural network, the Classification and Regression Tree, and the Random Forest model is proposed, and its efficacy as a decision support tool for medical data classification is examined. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. It is able to learn incrementally from data samples (owing to Fuzzy Min–Max neural network), explain its predicted outputs (owing to the Classification and Regression Tree), and achieve high classification performances (owing to Random Forest). To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Breast Cancer Wisconsin, Pima Indians Diabetes, and Liver Disorders from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity, as well as the area under the Receiver Operating Characteristic curve are computed. The results are analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system is effective in undertaking medical data classification tasks. More importantly, the hybrid intelligent system not only is able to produce good results but also to elucidate its knowledge base with a decision tree. As a result, domain users (i.e., medical practitioners) are able to comprehend the prediction given by the hybrid intelligent system; hence accepting its role as a useful medical decision support tool.     

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.     

乳山硫铁矿破碎工艺的改进

原矿含泥量大造成设备的堵塞。根据现场生产实际改用粉碎后的矿粒直接给入扇形孔条筛，筛下可调刮板使细粒均匀分布在皮带下面，粗粒复盖其上。物料到达皮带首轮处，使其呈抛掷状态，然后利用分离挡板除去细粒。首轮的直径和转速是按粒度分离的决定因素。分离细粒级后使中碎机生产能力增大，降低能耗，减少磨损，提高了设备运转率。     

基于模拟退火的粒子群算法优化的二次回归在矿井通风机故障诊断中的应用

针对矿井通风机故障诊断过程中样本数据有限的特点,提出了一种经模拟退火的粒子群算法优化的二次回归诊断方法。将样本数据分为建模数据和测试数据,测试结果表明该方法具有适用性强、操作简单、精准度高,且无需太多样本数据等特点,值得推广。     

KAFD Arabic font database

Font recognition is useful for improving optical text recognition systems’ accuracy and time, and to restore the documents’ original formats. This paper addresses a need for Arabic font recognition research by introducing an Arabic font recognition database consisting of 40 fonts, 10 sizes (ranging from 8 to 24 points) and 4 styles (viz. normal, bold, italic, and bold–italic). The database is split into three sets (viz. training, validation, and testing). The database is freely available to researchers.¹ Moreover, we introduce a baseline font recognition system for benchmarking purposes, and report identification rates on our KAFD database and the Arabic Printed Text Image (APTI) database with 20 and 10 fonts, respectively. The best recognition rates are achieved using log-Gabor filters.     

Random Forests with ensemble of feature spaces

Random Forests receive much attention from researchers because of their excellent performance. As Breiman suggested, the performance of Random Forests depends on the strength of the weak learners in the forests and the diversity among them. However, in the literature, many researchers only considered pre-processing of the data or post-processing of the Random Forests models. In this paper, we propose a new method to increase the diversity of each tree in the forests and thereby improve the overall accuracy. During the training process of each individual tree in the forest, different rotation spaces are concatenated into a higher space at the root node. Then the best split is exhaustively searched within this higher space. The location where the best split lies decides which rotation method to be used for all subsequent nodes. The performance of the proposed method here is evaluated on 42 benchmark data sets from various research fields and compared with the standard Random Forests. The results show that the proposed method improves the performance of the Random Forests in most cases.     

The C-loss function for pattern classification

This paper presents a new loss function for neural network classification, inspired by the recently proposed similarity measure called Correntropy. We show that this function essentially behaves like the conventional square loss for samples that are well within the decision boundary and have small errors, and L₀ or counting norm for samples that are outliers or are difficult to classify. Depending on the value of the kernel size parameter, the proposed loss function moves smoothly from convex to non-convex and becomes a close approximation to the misclassification loss (ideal 0–1 loss). We show that the discriminant function obtained by optimizing the proposed loss function in the neighborhood of the ideal 0–1 loss function to train a neural network is immune to overfitting, more robust to outliers, and has consistent and better generalization performance as compared to other commonly used loss functions, even after prolonged training. The results also show that it is a close competitor to the SVM. Since the proposed method is compatible with simple gradient based online learning, it is a practical way of improving the performance of neural network classifiers.     

化工生产装置工艺外管架的设计

随着化工事业的迅速发展,外管架的设计工作增多,在如此众多的管架中,选用经济合理的材质和形式,设计成各种不同类型的管架,以满足生产装置的需求和确保生产装置安全正常运行。介绍了化工生产装置工艺外管架的布置原则、分类、设计、向土建专业提供的要求及设计中存在的问题和改正建议,供设计人员参考。     

基于多特征融合和SVM分类器的植物病虫害检测方法

针对农业领域植物病虫害检测问题,提出一种基于高清视频图像融合特征的支持向量机(SVM)的检测方法,实现农业生产中植物病虫害的快速检测。对每幅植物叶片图像的颜色、HSV、纹理和方向梯度直方图四种特征采用基于特征包的多特征融合方法,形成特征向量,并利用SVM分类器进行训练分类。对单特征与融合特征的SVM分类器性能进行试验比较,所提出的方法具有较高的准确率。