铝箔腐蚀用大功率变频电源

研制了用于电解电容器阳极铝箔腐蚀的变频电源.设计了一种应用单片机控制的高频开关电源和用电子开关桥换相的低频变频、多波形大功率电源.该电源不需要庞大、笨重的变压器,而且频率下限不受限制,甚至可低至1Hz以下.     

用高场非对称波形离子迁移谱技术快速检测二乙醇胺

采用高场非对称波形离子迁移谱(FAIMS)对二乙醇胺(DEA)进行快速检测分析, 以热解析法进样, 确定了二乙醇胺的离子特征信号, 并与气相色谱-质谱联用仪的检测结果进行了比较. 用聚四氟乙烯(PTFE)扩散管进样, 控制二乙醇胺样品气(DEA与空气的混合气)浓度, 利用FAIMS对不同浓度的二乙醇胺样品气进行检测. 通过对离子特征信号进行量化和重复性分析, 确定了二乙醇胺样品气的检出限为0.02 μg/L, 并建立了FAIMS检测二乙醇胺样品气的离子电流强度积分面积与样品气浓度关系曲线. 为FAIMS应用于现场快速检测二乙醇胺提供了一定的依据.     

波长校准用低压石英汞灯驱动电源的研制

研制波长校准用低压石英汞灯电源,用于驱动汞灯起辉并维持稳态发光。电路由EMI滤波电路、桥式整流电路、LCC半桥谐振逆变电路、控制电路和保护电路5部分组成。采用系统建模与仿真验证电路的可行性并计算电路参数理论值,利用示波器和功率表验证电源输出参数,功率输出稳定性不大于0.03 W。根据紫外可见近红外分光光度计国防最高计量标准对研制的电源驱动汞灯的工作效果进行评价。结果表明,汞灯能够输出系列特征谱线,谱线相对强度稳定性不大于5.16%;汞灯工作3 h后,灯管外壳表面温度为43℃。该驱动电源的性能参数满足JJG112–2015《低压石英汞灯波长标准器检定规程》的要求。     

Smith－Hieftje背景校正法空心阴极灯供电条件的选择

本文提出在原子吸收光谱测定中，采用Ｓｍｉｔｈ－Ｈｉｅｆｔｊｅ法校正背景时，空心阴极灯供电条件（即最佳宽窄脉冲电流）的选择依据和选择方法；并给出２４个常见元素的宽窄脉冲电流的选定结果。     

原子吸收光谱仪石墨炉电源的故障分析与维修

在对原子吸收光谱仪石墨炉电源电路板测绘的基础上,分析了该电源主电路的故障原因,并给出了维修过程。     

双模高压电源在填充型电渗泵流量控制中的应用

研制了两台填充型电渗泵,利用双模对接高压电源可以产生0～60kV超高压,通过提高操作电压,增加了泵系统产生的流量,给出了电渗泵在高电压范围内电压与流量的关系曲线。实验结果表明,电压与流量在高压范围内仍呈线性关系,线性相关系数达到0.9996。同时,通过条件优化,克服了焦耳热,使电渗泵在高电压范围内稳定运行。     

Study on Comprehensive Protection Device of Underground Electric Grid Based on DSP

采用数字信号处理器（DSP）作为控制核心,设计了基于DSP的井下供电网智能保护装置,该综合保护装置重点解决了井下相敏过电流短路保护、热过载反时限保护、功率方向型漏电保护等长期困扰井下电网可靠性运行的问题,实验证明该综合保护装置反应灵敏、运行可靠。     

A Variable Selection Method of Near Infrared Spectroscopy Based on Automatic Weighting Variable Combination Population Analysis

Near-infrared spectroscopy (NIR) is widely used in food quantitative and qualitative analysis. Variable selection technique is a critical step of the spectrum modeling with the development of chemometrics. In this study, a novel variable selection strategy, automatic weighting variable combination population analysis (AWVCPA), is proposed. Firstly, binary matrix sampling (BMS) strategy, which provides each variable the same chance to be selected and generates different variable combinations, is used to produce a population of subsets to construct a population of sub-models. Then, the variable frequency (Fre) and partial least squares regression (Reg), two kinds of information vector (IVs), are weighted to obtain the value of the contribution of each spectral variables, and the influence of two IVs of Rre and Reg is considered to each spectral variable. Finally, it uses the exponentially decreasing function (EDF) to remove the low contribution wavelengths so as to select the characteristic variables. In the case of near infrared spectra of beer and corn, yeast and oil concentration models based on partial least squares (PLS) of prediction are established. Compared with other variable selection methods, the research shows that AWVCPA is the best variable selection strategy in the same situation. It has 72.7% improvement comparing AWVCPA-PLS to PLS and the predicted root mean square error (RMSEP) decreases from 0.5348 to 0.1457 on beer dataset. Also it has 64.7% improvement comparing AWVCPA-PLS to PLS and the RMSEP decreases from 0.0702 to 0.0248 on corn dataset.     

Characterization of a Pesticide Formulation by Medium Wave Near-Infrared Spectroscopy with Uninformative Variable Elimination and Successive Projections Algorithm

Near-infrared (NIR) spectroscopy, a rapid and nondestructive analytical method, has been widely used in many fields. In this paper, medium wave near-infrared (MWNIR) was used to determine the active ingredient of a deltamethrin formulation. An uninformative variable elimination-successive projections algorithm (UVE-SPA) was employed to investigate effective variables and was compared with UVE, SPA, and full-spectrum partial least squares (PLS) regression. The results indicate that MWNIR was able to determine the pesticide active ingredient and that UVE-SPA was an efficient variable selection approach by eliminating spectral redundancy and colinearity. The developed method is a meaningful exploration in the application of near-infrared spectroscopy and provides a valuable reference on pesticide quality control.     

芯片毛细管电泳程控电源的研制

设计并制作了一种小型的、可与PC机相联的程序控制电源，其中有两路输出电压，用于芯片毛细管电泳的分离和进样；而且，可程序控制输出电压和运行时间，并在PC机上以图形方式显示是泳电流。该电源适用于芯片毛细管电泳的过程监控。     

基于灰狼算法的近红外光谱变量选择方法研究

基于群体智能的灰狼优化(GWO)算法具有参数少、结构简单、易于实现的优点,但在光谱领域的应用较少。该研究将GWO算法引入近红外光谱的变量筛选中,以玉米数据为例,考察了GWO算法中狼群性能、迭代次数、狼群数量及运算效率,并建立了偏最小二乘(PLS)模型对玉米样品中蛋白质、脂肪、水分以及淀粉含量的测定。结果显示,GWO算法运算效率很高,经过参数调优后建立PLS模型,其蛋白质、脂肪、水分及淀粉的保留变量数分别为19、19、14、34,预测均方根误差(RMSEP)从全波长PLS建模的0.245 8、0.122 4、0.339 8、1.105 8分别下降到0.147 7、0.080 1、0.176 2、0.739 8,分别下降了40%、35%、48%、33%,相关系数也相应地提高。因此,GWO算法不仅优化速度快,选择变量数少,还可以显著提高PLS模型的预测精度,是一种近红外光谱变量选择的有效方法。     

基于迭代缩减窗口自助软收缩算法的近红外光谱变量选择方法研究

该文针对近红外光谱因冗余变量导致的标定模型预测性能差的问题,提出了一种迭代缩减窗口自助软收缩（ISWBOSS）算法。该方法使用窗口对变量进行划分,随机抽取窗口并利用其中的变量建立子模型,计算窗口内变量回归系数的归一化并作为权重继续进行加权采样,从而逐步实现变量空间的软收缩。同时在迭代过程中不断缩减窗口大小对特征变量进行精确搜索。通过在玉米数据集上进行验证,并与全谱法、遗传算法、竞争自适应重加权采样法和自助软收缩法建立的偏最小二乘模型对比,结果表明,新方法不论在准确性还是稳定性上都具有显著优势。以玉米蛋白质含量预测为例,与自助软收缩算法相比,ISWBOSS的预测均方根误差从0.041 8降至0.010 3,且达到最优模型所需的迭代次数更少,运算效率更高。该方法对提高近红外光谱标定模型的性能具有一定的指导意义。