数据挖掘技术浅析

本文主要概述了数据挖掘理论知识。首先从数据挖掘与知识发现的交叉关系角度讨论了数据挖掘的概念;然后,在对数据挖掘功能作了简单介绍的基础上,分析了基于统计学、基于机器学习、基于数据仓库和基于神经网络的四类数据挖掘方法的基本原理、特点。最后,给出了比较和评价挖掘方法的参考标准。     

多功能机床电机保护器的研制

多功能机床电机保护器,对供电电源的过电压、断相,对电动机的过载、过热、绕组短路与断相,对机床电气设备的漏电,都能进行自动检测,并及时切断电源,因此,它能有效地保护机床及其供电线路。该保护器已投入使用,并取得很好效果。     

数控设备通信连接——现场信息管理系统的研究

针对江西工业职业技术学院原数控设备利用效率低的现状,进行了一系列的研究,利用现代的通信管理技术,实现数控机床的共享管理,使有限的数控设备与众多的计算机有机地连接起来,达到数控教学改革的目的.     

饲料中水溶性氯化物的测定方法的讨论

在食品化工检验工作中,公式的合理性至关重要。本文按照《饲料中水溶性氯化物的测定》（GB/T 6439—2007/ISO 6495：1999）进行公式讨论和实际试验,修改了结果公式的表述,使得表述更合逻辑,更易理解。     

Code2vect: An efficient heterogenous data classifier and nonlinear regression technique

The aim of this paper is to present a new classification and regression algorithm based on Artificial Intelligence. The main feature of this algorithm, which will be called Code2Vect, is the nature of the data to treat: qualitative or quantitative and continuous or discrete. Contrary to other artificial intelligence techniques based on the “Big-Data,” this new approach will enable working with a reduced amount of data, within the so-called “Smart Data” paradigm. Moreover, the main purpose of this algorithm is to enable the representation of high-dimensional data and more specifically grouping and visualizing this data according to a given target. For that purpose, the data will be projected into a vectorial space equipped with an appropriate metric, able to group data according to their affinity (with respect to a given output of interest). Furthermore, another application of this algorithm lies on its prediction capability. As it occurs with most common data-mining techniques such as regression trees, by giving an input the output will be inferred, in this case considering the nature of the data formerly described. In order to illustrate its potentialities, two different applications will be addressed, one concerning the representation of high-dimensional and categorical data and another featuring the prediction capabilities of the algorithm.     

Nonintrusive data-based learning of a switched control heating system using POD,DMD and ANN

The aim of this work is to derive an accurate model of two-dimensional switched control heating system from data generated by a Finite Element solver. The nonintrusive approach should be able to capture both temperature fields, dynamics and the underlying switching control rule. To achieve this goal, the algorithm proposed in this paper will make use of three main ingredients: proper orthogonal decomposition (POD), dynamic mode decomposition (DMD) and artificial neural networks (ANN). Some numerical results will be presented and compared to the high-fidelity numerical solutions to demonstrate the capability of the method to reproduce the dynamics.     

A machine learning efficient frontier

We propose a simple approach to bridge between portfolio theory and machine learning. The outcome is an out-of-sample machine learning efficient frontier based on two assets, high risk and low risk. By rotating between the two assets, we show that the proposed frontier dominates the mean–variance efficient frontier out-of-sample. Our results, therefore, shed important light on the appeal of machine learning into portfolio selection under estimation risk.     

Evaluating continuous chirality measure as a 3D descriptor in chemoinformatics applied to asymmetric catalysis

Continuous Chirality Measure (CCM) is a computational metric by which to quantify the chirality of a compound. In enantioselective catalysis, prior work has postulated that CCM is correlated to selectivity and can be used to understand which structural features dictate catalyst efficacy. Herein, the investigation of CCM as a metric capable of guiding catalyst optimization is explored. Conformer-dependent CCM is also explored. Finally, CCM is used with Sterimol parameters to significantly improve the performance of Random Forest models.