TDLAS湿度传感器与维萨拉湿度传感器性能对比研究

水汽含量对于诸如文物保护、气象、工业生产等领域都有明确标准并需要严格控制,因此能够精确和快速的实现环境湿度监测已成为一大研究热点.现在常用的湿度传感器为芬兰VAISALA生产的湿度传感器系列,但是其测湿部分采用聚合物高分子薄膜电容传感器,存在响应时间长,褪湿慢等缺点,已经不能满足用户需求.基于TDLAS技术的湿度传感器使用半导体激光器作为光源,参考气室提供光谱调节反馈,构成了一种高精度水汽含量检测系统,响应速度快,灵敏度高,可以克服传统湿度传感器的不足.该文以米歇尔露点仪为标准,对比测试了TDLAS湿度传感器与传统的VAISALA电容传感器响应时间、测量精度等性能.证明了TDLAS湿度传感器具有更好的测量准确度与灵敏度.     

PRICE AND SERVICE COMPETITION WITH MAINTENANCE SERVICE BUNDLING

In many equipment manufacturing industries,firms compete with each other not only on products price,but also on maintenance service.More and more traditional products oriented firms are offering their customers products bundled with maintenance service(P&S bundles).In this study,we examine firms’ incentive to offer customers products bundling with long-term maintenance or repair support service in a duopoly competitive environment.When providing P&S bundles,a firm need to determine the service level(in terms of average response time guarantee for the service in this paper) to offer and needs to build a service facility to handle the maintenance service requirements.Based on the analysis of three sub-game models,we characterize the market conditions in which only one firm,both firms or neither firm will offer P&S bundles.Finally,we analyze the affects of serval market factors on firms’ strategy choices.     

基于PEST分析法的网络教育新环境研究

我国网络教育经过十多年的发展，取得了巨大的成就。随着经济社会的发展，支撑网络教育发展的环境也发生了深刻的变化。本文运用PEST分析法，深入分析了当前我国网络教育环境：我国的网络教育正在得到政策、经济、社会和技术条件的大力支持，处于一个最佳的发展环境中，但依然存在着学生支持薄弱、网络教育基础设施和学习资源大量重复建设、学习成本居高不下等问题。分析网络教育新环境，并提出网络教育的努力方向，有助于推动我国全民学习、终身学习体系的构建。     

非均匀介质下的可调光流控器件及其生化应用

 探讨了非均匀介质下不同光流控器件及其生化应用。在微流控芯片上，主要从2个方面开发和实现各类具有光波导、透镜、细胞计数、化学检测等功能的新器件。首先，在液液非均匀介质下，通过调节沟道内液体流速，控制液体间的对流扩散来实现液体在微腔内的渐变折射率分布和阶跃分布，从而得到其特殊的光学特性，例如光束分离、弯曲、自聚焦等。其次，在固体非均匀介质下，利用特殊的微流结构与液体相结合，实现更加灵敏可调的新型探测手段。这些新型技术手段分别在生物传感、能源生产、细胞探测及海水检测等诸多应用中可发挥关键性作用。     

一类具有转移和治疗机制的随机结核病模型的平稳分布与灭绝

考虑一类具有转移和治疗机制的肺结核传播模型在随机扰动下的动力学行为. 首先, 通过Lyapunov分析方法得到该模型正解的存在和唯一性； 然后利用Has’minskii理论证明该模型存在唯一的平稳分布； 最后通过随机分析方法得到该模型疾病消失的条件.     

Mean Square Average Generalized Consensus of Multi-Agent Systems Under Time-Delays and Stochastic Disturbances

Compared with the traditional consensus problem, this paper deals with the mean square average generalized consensus(MSAGC) of multi-agent systems under fixed directed topology, where all agents are affected by stochastic disturbances. Distributed protocol depending on delayed time information from neighbors is designed. Based on Lyapunov stability theory, together with results from matrix theory and It o s derivation theory, the linear matrix inequalities approach is used to establish sufficient conditions to ensure MSAGC of multi-agent systems. Finally, numerical simulations are provided to illustrate the theoretical results.     

Improved hemimorphite flotation using xanthate as a collector with S(Ⅱ) and Pb(Ⅱ) activation

The flotation of hemimorphite using the S(Ⅱ)–Pb(Ⅱ)–xanthate process,which includes sulfidization with sodium sulfide,activation by lead cations,and subsequent flotation with xanthate,was investigated.The flotation results indicated that hemimorphite floats when the S(Ⅱ)–Pb(Ⅱ)–xanthate process is used; a maximum recovery of approximately 90% was obtained.Zeta-potential,contact-angle,scanning electron microscopy–energy-dispersive spectrometry(SEM–EDS),and diffuse-reflectance infrared Fourier transform spectroscopy(DRIFTS) measurements were used to characterize the activation products on the hemimorphite surface and their subsequent interaction with sodium butyl xanthate(SBX).The results showed that a Zn S coating formed on the hemimorphite surface after the sample was conditioned in an Na2 S solution.However,the formation of a Zn S coating on the hemimorphite surface did not improve hemimorphite flotation.With the subsequent addition of lead cations,Pb S species formed on the mineral surface.The formation of the Pb S species on the surface of hemimorphite significantly increased the adsorption capacity of SBX,forming lead xanthate(referred to as chemical adsorption) and leading to a substantial improvement in hemimorphite flotation.Our results indicate that the addition of lead cations is a critical step in the successful flotation of hemimorphite using the sulfidization–lead ion activation–xanthate process.     

基于改进匹配网络的单样本学习

当前深度学习是基于大量标注数据样本通过多层网络实现模型自动识别。然而,在很多特殊场景下,难以获取大量标注样本数据,小样本物体识别仍是深度学习下关键性的难题。针对这一问题,首先利用4层深度卷积神经网络(deep convolution neural network,DCNN)提取训练样本和测试样本的高层语义特征,然后基于改进的匹配网络分别采用双向LSTM和attLSTM算法对训练样本和测试样本深入提取更加关键和有用特征并进行编码,最后在平方欧氏距离上利用softmax非线性分类器对测试样本进行分类识别。实验通过Omniglot数据集对提出的改进模型进行测试,取得了非常好的效果。改进的模型即使在最复杂的20 way 1 shot情况下,依然能够达到93.2%的识别率,Vinyals的原创匹配网络模型在20 way 1 shot的情况下只能达到88.2%的识别率,与原创匹配网络模型相比,改进的模型在类别数更多而样本数较少的复杂场景下具有更好的识别效果。