大学生体质健康评价模型及不同评分标准的相通性

为更准确、客观的测定每个大学生体质健康等级,利用分段拟合方法分别构建大学男、女生体质健康评价模型,并得到体质测试分及各项测试项目基本得分间的具体表达式．后又具体论析了模型求解过程中,所涉及四个测试项目均占0．25的权重值与《国家体质健康标准》中公布的权重值之间是相通的．     

社会主义核心价值观引领大众文化建设探析——马克思主义人学视角

科学构建社会主义核心价值引领大众文化建设方案,应准确把握社会主义核心价值观的科学内涵与精神实质;夯实社会主义核心价值观引领大众文化建设的马克思主义人学基础,科学把握三者的互动品质;从马克思主义人学视角重视社会主义核心价值观引领大众文化建设的功能和价值,促进人的本质的自由全面发展。     

Modeling the Direct Synthesis of Dimethyl Ether using Artificial Neural Networks

Artificial neural networks (ANNs) are designed and implemented to model the direct synthesis of dimethyl ether (DME) from syngas over a commercial catalyst system. The predictive power of the ANNs is assessed by comparison with the predictions of a lumped model parameterized to fit the same data used for ANN training. The ANN training converges much faster than the parameter estimation of the lumped model, and the predictions show a higher degree of accuracy under all conditions. Furthermore, the simulations show that the ANN predictions are also accurate even at some conditions beyond the validity range.     

一种改进的BP神经网络在故障诊断中的应用研究

人工神经网络作为一个具有高度非线性映射能力的计算模型,在工程中具有广泛的应用前景.它不需要预选确定样本的数学模型,仅通过学习样本数据即可以进行故障诊断.本文结合计算机综合业务的一些实际问题,探讨了利用改进的BP神经网络进行故障诊断的方法和应该注意的问题,在分析神经网络的基础上提出了基于改进的BP模型神经网络的故障诊断推理方法.结果表明,基于BP神经网络的故障诊断方法是行之有效的.     

板料拉深成形过程中的变压边力技术

压边力是板料成形过程中的重要参数之一。在概述压边力在生产中的主要用途和意义的基础上，从压边力的加载模式、理论模型、实验研究变压边力技术与拉延筋、人工智能技术的交叉应用等方面，比较全面的综述了压边力技术的研究现状及存在问题，并探讨压边力技术的研究方向。     

基于响应面法-改进人工蜂群算法的绞车滚筒多目标优化设计北大核心

为提高绞车滚筒使用性能、减轻质量、降低耗能,提出一种基于响应面法-改进人工蜂群算法的方法对滚筒进行结构优化。利用有限元对绞车滚筒静强度和刚度、疲劳寿命进行仿真分析;建立以滚筒内部材料参数为设计变量,刚度为约束,最小疲劳寿命、最大静强度、质量为目标的响应面模型。在传统人工蜂群基础上,改进蜜源判断准则,有利于算法跳出局部最优、保留潜在最优。在搜索策略的算法中引入变异的思想,提高收敛速度;利用改进后的人工蜂群算法进行多目标求解,获得绞车滚筒的Pareto前沿解集。结果表明:优化后的滚筒质量减小350.1 kg,最大应力减小20 MPa,最小疲劳寿命增加5.888 7×10~5次,最大变形减小0.37 mm,达到了优化设计的目的。     

XPS characteristics of sulfur of bio-oxidized arsenic-bearing gold concentrate and changes of surface nature of bio-oxidation residue

During bio-oxidation of sulfides, the chemical state change of sulfur is a complex and key factor. It is not only an indicator of the extent and intensity of the bio-oxidation, but also controls the property of bio-leaching medium and the period of oxidation. The chemical state of sulfur in sulfides oxidized by leaching bacteria was studied with XPS. Sulfide minerals in the arsenic-bearing gold concentrate consist of pyrite, arsenopyrite, chalcopyrite, galena, sphalerite and so on. In order to probe the pattern of the chemical state change of sulfur in the bio-oxidation residue of arsenic-bearing gold concentrate, the structure of the grains, and the surface nature of the residue, XPS test was carried out through different sputtering duration. The study of XPS clearly shows that: sulfides is progressively oxidized from the surface of minerals to the core by leaching bacteria; the chemical valence of sulfur changes from S^2- or [S2]^2- to [SO4]^2- ; sulfur in the core is in a reduction state, S^2- or [S2]^2- , but exists in an oxidation state S^6 on the surface; due to the chemical state change of sulfur, mineral phase of the bio-oxidation residue is also changed(sulfides inside, while sulfates outside); the layered structure is found in the grains of the bio-oxidation residue.     

基于遗传算法的Al-5%Cu合金电脉冲孕育处理参数优化

以实验为基础,利用神经网络和遗传算法优化Al-5%Cu合金的电脉冲孕育处理工艺参数。神经网络的输入参数为脉冲电压、脉冲时间和电脉冲孕育处理时熔体温度,输出参数是合金凝固组织的晶粒度。在神经网络训练的基础上,采用遗传算法优化神经网络的输入参数。结果表明,神经网络和遗传算法的组合建模获得了较好的优化结果。     

控制技术及模糊控制在冲天炉熔炼中的应用

介绍了国内外冲天炉熔炼控制技术的发展状况。在分析国内铸造业具体情况后，提出了采用人工智能控制技术中的模糊控制理论对冲天炉熔炼控制的可行性和发展方向。     

Modeling of tool wear in drilling by statistical analysis and artificial neural network

The useful life of a cutting tool and its operating conditions largely control the economics of the machining operations. Hence, it is imperative that the condition of the cutting tool, particularly some indication as to when it requires changing, to be monitored. The drilling operation is frequently used as a preliminary step for many operations like boring, reaming and tapping, however, the operation itself is complex and demanding.

Back propagation neural networks were used for detection of drill wear. The neural network consisted of three layers input, hidden and output. Drill size, feed, spindle speed, torque, machining time and thrust force are given as inputs to the ANN and the flank wear was estimated. Drilling experiments with 8 mm drill size were performed by changing the cutting speed and feed at two different levels. The number of neurons in the hidden layer were selected from 1, 2, 3, …, 20. The learning rate was selected as 0.01 and no smoothing factor was used. The estimated values of tool wear were obtained by statistical analysis and by various neural network structures. Comparative analysis has been done between statistical analysis, neural network structures and the actual values of tool wear obtained by experimentation.