基于电容电阻复合层析法测量糖膏结晶浓度

针对目前常规单测量点检测法在糖厂煮糖罐糖膏结晶浓度测量中精度低的不足,提出了基于电容电阻复合层析法对糖膏结晶浓度进行测量;首先采用主成分分析法消除传感器阵列测量数据集的冗余度,然后利用RBF神经网络法解决传感器检测场存在的非线性问题,同时预测出煮糖罐内糖膏结晶浓度,其中RBF神经网络法的参数学习采用遗传优化算法;对该过程进行理论分析和仿真测试,实验结果表明在低成本条件下,测量糖膏结晶浓度精度高,有很好的工业应用前景.     

新型中功率电法信号发送机设计与实现

针对传统频率域激电仪发送机的不足,利用CPLD及温补晶体产生高精度、高稳定激发极化矩形波驱动信号;采用新型IPM智能功率模块中的4个桥臂,组成H型桥式逆变开关,研究实现一种新型中功率频率域激电信号发送机;实验结果表明,该发送机不仅结构紧凑、携带方便、工作稳定可靠,而且输出功率、频率稳定度均有显著提高,应用效果很好.     

CDIO模式下高等工程教育的改革与探索

高等工程教育存在的一个共性问题即重视理论轻实践,强调个人的知识能力和学术能力,忽视团队协作能力,这与现今的国际人才培养模式不符。文章介绍了CDIO模式的标准及原理,并就此分析了CDIO模式给高等工程教育的改革与探索带来的启示。     

论手绘插画的价值和意义

手绘插画作为插画创作门类中最原始的一种,在进入数码社会后地位渐渐被数码绘画所超越。在传统与现代争论激烈的今天,在各种绘图软件完善自身功能的大环境下,手绘插画的地位、价值和意义应当如何进行定位?手绘插画的优势是否存在?文章将从手绘插画的现状出发,结合相应理论,对其价值和意义进行详细论述。     

数字与造型——论数字绘画技术对传统插画的冲击与渗透

数字绘画作为一种新兴的艺术形式。在艺术创作过程中,从单纯地模仿走向利用电脑程序在电脑中创作或自动生成图像,达到了一定的艺术效果。同时,它也在不知不觉中或多或少地影响了传统的插画艺术。这两种绘画方式具有各自独特的个性及美感,我们把更多的目光投向了这两种技术手段的相通性。探讨在这两者间相互影响的过程中,在超越这些"感觉材料"之上、之外,是否存在着一个共同体,构成了不同的艺术手段之间的作品。     

高密泥老虎

泥老虎是高密最有特色的民间艺术品,经过几百年的流传保持着它独特的艺术面貌,有着它独特的制作工艺,也是研究高密地区风土人情的重要资料。所以我们应该加强对它的认识与保护,促进它的传承与发展。     

基于WMI的计算机监控系统的设计与实现

无人值守监控系统在各种行业得到了广泛的应用,保证无人值守计算机的稳定运行对于监控系统具有重要的意义.WMI是Windows平台性能管理的事实标准,将WMI技术应用到无人值守计算机监控系统,设计并实现一个对无人值守计算机软硬件信息以及运行信息进行监视,对监视信息进行分析预警并对无人值守计算机实现远程控制的监控系统.系统无需安装客户端,相对于传统C/S模式的监控系统具有配置灵活方便、稳定性高、节约计算机资源等明显优势.     

Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes

Support vector machines (SVM) are an emerging data classification technique with many diverse applications. The feature subset selection, along with the parameter setting in the SVM training procedure significantly influences the classification accuracy. In this paper, the asymptotic behaviors of support vector machines are fused with genetic algorithm (GA) and the feature chromosomes are generated, which thereby directs the search of genetic algorithm to the straight line of optimal generalization error in the superparameter space. On this basis, a new approach based on genetic algorithm with feature chromosomes, termed GA with feature chromosomes, is proposed to simultaneously optimize the feature subset and the parameters for SVM.To evaluate the proposed approach, the experiment adopts several real world datasets from the UCI database and from the Benchmark database. Compared with the GA without feature chromosomes, the grid search, and other approaches, the proposed approach not only has higher classification accuracy and smaller feature subsets, but also has fewer processing time.     

A hybrid data-fusion system using modal data and probabilistic neural network for damage detection

This paper addresses a novel hybrid data-fusion system for damage detection by integrating the data fusion technique, probabilistic neural network (PNN) models and measured modal data. The hybrid system proposed consists of three models, i.e. a feature-level fusion model, a decision-level fusion model and a single PNN classifier model without data fusion. Underlying this system is the idea that we can choose any of these models for damage detection under different circumstances, i.e. the feature-level model is preferable to other models when enormous data are made available through multi-sensors, whereas the confidence level for each of multi-sensors must be determined (as a prerequisite) before the adoption of the decision-level model, and lastly, the single model is applicable only when data collected is somehow limited as in the cases when few sensors have been installed or are known to be functioning properly. The hybrid system is suitable for damage detection and identification of a complex structure, especially when a huge volume of measured data, often with uncertainties, are involved, such as the data available from a large-scale structural health monitoring system. The numerical simulations conducted by applying the proposed system to detect both single- and multi-damage patterns of a 7-storey steel frame show that the hybrid data-fusion system cannot only reliably identify damage with different noise levels, but also have excellent anti-noise capability and robustness.     

一种基于启发式奖赏函数的分层强化学习方法

针对强化学习在应用中经常出现的维数灾问题,即状态空间的大小随着特征数量的增加而发生指数级的增长,以及收敛速度过慢的问题,提出了一种基于启发式奖赏函数的分层强化学习方法.该方法不仅能够大幅度减少环境状态空间,还能加快学习的收敛速度.将此算法应用到俄罗斯方块的仿真平台中,通过对实验中的参数进行设置及对算法性能进行分析,结果表明:采用启发式奖赏函数的分层强化学习方法能在一定程度上解决维数灾问题,并具有很好的收敛速度.