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。     

Particular behavior of spindle thermal deformation by thermal bending

Thermally induced errors reduce the accuracy in precision machining, and a great deal of research has been presented on compensation for these errors in machine tools. However, during the transition period after commencing or stopping spindle rotation, thermal deformation behavior is very complex. In particular, the y-directional movement of the vertical machining center cannot be explained by thermal expansion alone because of the relationship between deformation and temperature. Thermal bending that is generated from the thermal gradient in the structure causes this movement. In the research described in this paper, a theoretical explanation and an experimental verification is given for the particular behavior of spindle thermal deformation. As it is not easy to map the relationship of the compensation model, separation of the steady from the non-steady state in the mapping process is strongly recommended.     

A review of machine learning applications in IoT-integrated modern power systems

This paper aims to provide a systematic and comprehensive survey of state-of-the-art machine learning techniques and their potential applications in IoT-integrated power systems.     

Machine-Learning based methods in short-term load forecasting

Short-term load forecasting is of great significance to the secure and efficient operation of power systems. However, loads can be affected by a variety of external impact factors and thus involve high levels of uncertainties. So it is a challenging task to achieve an accurate load forecast. This paper discusses three commonly-used machine-learning methods used for load forecasting, i.e., the support vector machine method, the random forest regression method, and the long short-term memory neural network method. The features and applications of these methods are analyzed and compared. By integrating the advantages of these methods, a fusion forecasting approach and a data preprocessing technique are proposed for improving the forecasting accuracy. A comparative study based on real load data is performed to verify that the proposed approach is capable of achieving a relatively higher forecasting accuracy.     

LX—2型变频式喂线设备的研制及应用

根据炼钢生产的实际需要,分析了喂线技术的工艺要求,介绍了所研制的LX－2型变频式喂线机的结构特点,指出了影响喂线机使用效果的因素,并提出了若干措施。     

支持向量机及其在径流预测中的应用

给出了支持向量机方法(SVM)的思路、特点及关键之处,探讨了SVM在径流预测中的可能性,并与基于遗传算法的门限回归模型(TR) 进行了对比分析。径流预测实例分析表明,在拟合阶段,SVM模型要好于TR模型;在预留检验阶段,SVM模型与TR模型接近。同时SVM模型适合于小样本情况且能达到全局最优。SVM模型用于径流预测是可行的、优越的。     

晶闸管式等离子弧切割机的使用与维修

本文介绍了晶闸管式等离子弧切割机的操作原理、使用和维修技术。     

矢功率谱与RBF网络结合在旋转机械故障诊断中的应用

与传统功率谱相比,矢功率谱融合了多通道的能量信息,反映的信息更全面.径向基函数网络具有良好的推广能力和分类能力.基于此,本文结合矢功率谱和径向基函数网络,提出了一种新的故障识别方法,该方法以矢功率谱为特征向量,径向基函数网络为分类器.并以转子裂纹、转子不对中、油膜涡动为例进行实验研究,实验结果表明,该方法是有效的.     

机床液压系统故障树分析

应用故障树分析法对某厂一台机床液压系统的典型故障进行了故障树分析，取得了满意的结果。实践证明，将故障树分析法应用于机床液压系统故障诊断中是非常有效的。     

纺机织轴边盘的金属型低压铸造

介绍了采用金属型低压铸造生产边盘所涉及的设备、模具、工艺 ,并对产品质量和经济效益进行了分析