新型BOD微生物传感流动注射检测仪的研制

研制采用微生物传感技术和流动分析技术来快速测定BOD的仪器。主要技术性能:测量周期为15min;误差率<5%;偏差<5%。通过对国家标准样品和不同水质污水的测试表明,其具有自动化程度高,测量周期短的优点;准确度、稳定性均达到国家标准;且具有良好的适应性。     

快速检测痕量铜的光电型传感器的研制

利用铜对葡萄糖氧化酶的抑制作用,以及葡萄糖氧化酶-辣根过氧化物酶-葡萄糖(Glu)-邻联甲苯胺(OT)偶联反应体系生成蓝色底物的原理,将Glu与OT固定化制成试纸条,插入自制的便携式单色光反射计,组装出检测铜的光电型传感器。当铜的质量浓度范围为1 mg/L～100 mg/L时,光反射强度值与铜的质量浓度呈良好的线性关系。在优化的试验条件下,该方法的检测限为0.064 mg/L,检测时间为5 min。     

基于大数据分析的大气网格化监测质控技术研究

基于对现阶段中国环境空气的污染特征及监测手段进行分析,研究了如何采用传感器技术进行大气污染防治网格化监测。对标物校准、训化校准、自适应校准、传递校准等校准质控技术进行了充分研究并基于此建立了自主学习神经网络算法的校准体系。为解决传感器应用过程中零点漂移、温度和湿度漂移、时间漂移等问题,利用大数据、基因算法成功开发出了智能数据修正模型,实现了低成本、高稳定、具有相当精度且可自动化运行的网格化监测体系。     

绿僵菌钙传感蛋白基因克隆及其与致病性的关系

通过筛选文库获得绿僵菌钙传感蛋白基因MaNcs1基因全长cDNA序列,并对其在致病过程中的作用进行了分析.结果显示,MaNcs1基因cDNA全长792 bp,开放阅读框为573 bp,编码190个氨基酸;采用RNA干扰技术下调该基因的表达后,对东亚飞蝗的生测实验表明,绿僵菌毒力显著下降,说明该基因与绿僵菌的致病性有关,为以后深入研究该基因的致病机制奠定了基础.     

基于物联网林区火险监警系统方案的探讨

利用传感技术、卫星定位、地理信息、人工智能等物联网相关技术,设计适用于城市森林、自然保护区、旅游景区等环境防火监测系统,系统可对监测区域环境因素尤其火情信息实现区域覆盖、全天候、数字化监测,构建起数字化森林物联网络,可为森林防火提供实时、准确、科学的依据。     

基于数据融合的风机风量值无线监测技术研究

为了解决主要通风机在测风量过程中在线实时监测等问题,建立了以无线传输技术为基础的三层传输系统框架,并将改进的数据融合算法引入到风量值数据处理当中,通过选取阙值区间利用二次曲线函数来消除阙值点处相互支持的模糊性,取得最优融合传感器组,并对实测数据进行抗干扰实验分析。实验结果表明,无线传输方式有利于风量数据的在线实时监测;改进的数据融合算法能及时有效地消除传感器的测量偏差、提高风量数据的测量精确度。研究结果对煤矿主要通风机风量在线远程监测具一定的理论价值和实用意义。     

岩石变形破裂过程中电荷感应信号的检测

设计了采用非接触方式、高放大倍数、高效率的电荷传感器,并给出了电荷传感器的设计原理和技术指标。利用研制的电荷传感器对岩石变形破裂过程中产生的电荷感应信号进行了检测试验。结果表明,岩石在变形破裂过程中会产生电荷感应信号,并能够被研制的电荷传感器检测到;电荷感应信号是瞬时脉冲的;随着加载应力水平的增加,电荷感应信号强度增强,在峰值应力前,电荷感应信号强度达到最大值;在破裂面接收到的电荷感应信号较强,主破裂面接收到的信号最强,因而电荷的产生和岩石的破裂有很大的关系。作为正在探索的检测岩石破坏过程的手段之一,电荷感应方法应是一种很具潜力的方法,值得深入研究。     

Pressure Sensitive Mats as Safety Devices in Danger Zones

Developing prototypes of pressure sensitive mats and testing their practical application were the aims of this study. Two contact plate mats were designed and constructed: rubber-rubber (R) and metal-metal (M). A recipe for rubber mixes and the production technology were prepared. Two laboratory test stands for measuring the actuating force, response time, static pressure resistance, and the durability of the mats were constructed. Computer software was written to control the operation of those test stands. Methods of testing pressure sensitive mats were based on PrDIN 31 006 (Deutsches Institut fur Normung [DIN], 1990) and EN 1760–1 (Comite Europeen de Normalisation [CEN], 1997). Both prototypes of contact plate mats were tested under laboratory and industrial conditions.

The test results proved that the design was correct, the setup requirements were fulfilled, and the mats were efficient and reliable in the industrial environment.     

Enhancing sensitivity of microbial fuel cell sensors for low concentration biodegradable organic matter detection: Regulation of substrate concentration, anode area and external resistance

The relatively low sensitivity is an important reason for restricting the microbial fuel cell (MFC) sensors’ application in low concentration biodegradable organic matter (BOM) detection. The startup parameters, including substrate concentration, anode area and external resistance, were regulated to enhance the sensitivity of MFC sensors. The results demonstrated that both the substrate concentration and anode area were positively correlated with the sensitivity of MFC sensors, and an external resistance of 210 Ω was found to be optimal in terms of sensitivity of MFC sensors. Optimized MFC sensors had lower detection limit (1 mg/L) and higher sensitivity (Slope value of the linear regression curve was 1.02), which effectively overcome the limitation of low concentration BOM detection. The essential reason is that optimized MFC sensors had higher coulombic efficiency, which was beneficial to improve the sensitivity of MFC sensors. The main impact of the substrate concentration and anode area was to regulate the proportion between electrogens and nonelectrogens, biomass and living cells of the anode biofilm. The external resistance mainly affected the morphology structure and the proportion of living cells of the anode. This study demonstrated an effective way to improve the sensitivity of MFC sensors for low concentration BOM detection.     

基于智能手机采集数据的施工活动识别

施工人员的活动监控对施工安全管理及预防职业疾病至关重要。为提升施工安全的智能化管理水平,对楼板钢筋工程施工中的8个主要活动进行识别。为弥补单一传感技术采集维度不足的缺陷和集成传感技术对系统灵活性的限制,采用智能手机内置加速度传感器和陀螺仪采集试验人员模拟施工人员活动时的加速度和倾角数据,并提取平均值、标准差、协方差、四分位距(IQR)为活动的特征矢量。通过决策树中的CART算法建立分类训练模型,采用“交叉验证法”对模型进行评估和验证。测试结果表明:对于样本个体的平均分类准确率为95.28%,预测准确率为92.86%;样本总体的分类准确率为89.67%,预测准确率为94.82%。研究表明,基于智能手机采集数据的决策树模型可以用于施工人员的活动识别。