Temporal-spatial Filtering in the Preprocessing of Clinical EEG Signals

EEG spot-recording for clinical patients in the bed is always advocated due to easing patients and making them comfortable. But EEG signals recorded in the ward exposed to various noisesand interference is less effective in the EEG analysis. Atemporal-spatial filter is designed for noise removing of clinical EEG signals, the first filtering is temporal filtering using band-pass filter, and the second filtering is spatial filtering with ICA-based spatial filter. Experimental results indicate that various noises and interferences, such as power interference, blinking, eyes movements, muscle moments, ECG artifacts, etc., are removed effectively, individually or simultaneously.     

THE STUDY OF SINGLE CHIP COMPUTER BASED SYMMETRICAL COMPONENT DISTANCE PROTECTION

In this paper, a new microcomputer distance protection scheme with high property-cost ratio has been approached. The paper presents the performance equations based on the symmetrical components of current and voltage, proposes a new (1,a) coordinate plane and derives a Fourier symmetrical component algorithem with high accuracy and less calculation amount on the (1,a) plane. The protection program is debugged on the developing system of single chip computer. The digital imitation tests indicate that the proposed relaying system is satisfactory.     

Application of Principal Component Analysis on Workable Competition Evaluation

The effective competition is an effective organizational form of the industry in modem market economy. It is a form accompanied with large - scale production and vigor competition. The analysis of effective competition has been the focus that people have paid close attention to it very much all the time. It is a basis of industrial policy, on the basis of the former research, the aufhors bring out 9 indexes according to the actual situation of our country, then take automobile trade as an example, we analyze the automobile trade with principal component.     

基于KPCA-WLSSVM的建筑能耗预测模型

为降低建筑能耗影响因素间复杂相关性对模型性能的影响,建立了一种基于KPCA-WLSSVM的建筑能耗预测模型。利用核主元分析(KPCA)对输入变量进行数据压缩,消除变量之间的相关性,简化模型结构;进一步采用加权最小二乘支持向量机(WLSSVM)方法建立建筑能耗预测模型,同时结合一种新型混沌粒子群-模拟退火混合优化(CPSO-SA)算法对模型参数进行优化,以提高模型的预测性能及泛化能力。通过将KPCA-WLSSVM模型方法应用于某办公建筑能耗的预测中,并与WLSSVM、LSSVM及RBFNN模型相比,实验结果表明,KPCA-WLSSVM模型方法能有效提高建筑能耗预测精度。     

厌氧水解-移动床生物膜反应器处理香精调料废水的研究

我国的香精调料行业发展很快,废水的成分复杂,浓度高,必须采用多种技术联合处理。针对某食用香精调料企业的废水特点,采用化学混凝预处理、厌氧水解和好氧移动床(MBBR)组合工艺处理,生产性装置的运行效果表明,COD,BOD5,SS和NH3-N等去除效率突出,完全可以达到排放标准,TN和TP的去除效率可分别达到66.3%~69.7%和83.0%~90.2%,其中,TN是由厌氧段反硝化和MBBR同步硝化反硝化实现,TP则通过化学混凝而去除。厌氧水解和MBBR是去除有机物和生物硝化的核心,曝气生物活性炭滤池进一步保证了出水的水质。     

新疆阿瓦提灌区土壤盐渍化状况的

采用主成分分析方法对阿瓦提灌区土壤盐渍化状况进行了研究，结果表明，该地属于中重度硫酸盐盐渍地，0～30cm耕作层含盐量在0.5%-4%之间，在灌区内部土壤盐分存在多次积盐和脱盐过程，耕作层第一、二主成分显著，累计贡献率高达90%，第一主成分综合反映灌区内土壤盐渍化程度，第二、第三主成分联合反映了土壤的碱性特征。     

玉米籽粒内部组分低对比度透射图像精确分割研究

为解决玉米籽粒透射图像由于对比度较低造成内部组分提取不精确的问题,提出一种基于色彩通道非线性变换的多通道重叠区域分割方法,对玉米籽粒图像在灰度、R通道及b通道下得到的二值图像使用重叠区域原理,实现玉米籽粒组分的精确分割。首先,采集不同玉米品种的籽粒透射图像,提取单粒玉米籽粒;其次,采用多通道重叠区域法分割单粒籽粒图像,得到玉米籽粒胚部、角质胚乳和粉质胚乳3部分的图像;最后,以查全率和查准率评价多通道重叠区域法与传统图像分割方法对不同品种玉米籽粒的分割效果。不同品种玉米籽粒的分割试验表明:多通道重叠区域分割方法的查全率、查准率及综合评价指标均达到98%以上,分割效果优于传统的图像分割方法,能够实现不同品种透明角质玉米籽粒透射图像的精确组分分割。     

基于PCA-GA-SVM的甘蔗收获机运行状态识别

由于甘蔗收获机在收获过程中智能化水平较低,依靠人工操作很容易对甘蔗收获机的运行状态产生误判,从而造成物流通道堵塞、能源浪费、收割效率低。针对这些问题,提出一种基于主成分分析(PCA)、遗传算法(GA)和支持向量机(SVM)状态识别模型。首先,通过实地采集甘蔗收获机刀盘轴、行走轴、切段轴和风机轴扭矩和行驶速度特征信息,然后通过PCA进行数据降维,最后利用GA优化参数C、γ,使用每个特性信息来训练SVM,对甘蔗收获机运行状态进行分类。结果表明:PCA-GA-SVM状态识别模型对甘蔗收获机运行状态的识别准确率为93.75%,建模时间为3.688 s,与SVM(81.25%,9.487 s)、PCA-SVM(87.5%,5.817 s)和GA-SVM(90%,8.969 s)进行对比,该模型具有最高准确识别率和最快建模速度,具有较大的应用价值。     

小球藻多糖的分离纯化和组成分析

采用冻结-融化方法,用冷水提取海水小球藻Chlorellasp.多糖,多糖经除蛋白、乙醇分级沉淀、柱层析等一系列步骤处理后,得到组分均一的小球藻多糖(简称CPSB-1)。将CPSB-1完全酸水解,用高效阴离子液相色谱对其溶液进行分析,结果表明:组成多糖CPSB-1的单糖有D-岩藻糖、D-鼠李糖、D-2-氨基葡萄糖、D-半乳糖、D-葡萄糖和几种未知的单糖;各已知糖基的摩尔比为D-岩藻糖∶D-鼠李糖∶D-2-氨基葡萄糖∶D-半乳糖∶D-葡萄糖=1.3∶1.0∶1.4∶1.2∶3.1。     

微量元素对烤烟品质效应的影响

为了提高贵州烤烟的品质,通过大田小区试验,以施用Zn、Mo、B、Cu 4种微量元素肥料为材料,研究微量元素对烤烟品质效应的影响,结果表明:不同微量元素影响烤烟不同的化学成分。微量元素配施可以促进烟碱的合成,Mo、B、Cu与Zn肥配施,消除锌的负面影响,保持烟叶有良好的化学成分,有利于提高烟叶品质。根据贵州省烤烟状况,施用微量元素可采取促控结合的调施技术,保障和改善烟叶产量和品质。