Streamflow Hydrology Estimate Using Machine Learning (SHEM)

Continuity and accuracy of near real‐time streamflow gauge (streamgage) data are critical for flood forecasting, assessing imminent risk, and implementing flood mitigation activities. Without these data, decision makers and first responders are limited in their ability to effectively allocate resources, implement evacuations to save lives, and reduce property losses. The Streamflow Hydrology Estimate using Machine Learning (SHEM) is a new predictive model for providing accurate and timely proxy streamflow data for inoperative streamgages. SHEM relies on machine learning (“training”) to process and interpret large volumes (“big data”) of historic complex hydrologic information. Continually updated with real‐time streamflow data, the model constructs a virtual dataset index of correlations and groups (clusters) of relationship correlations between selected streamgages in a watershed and under differing flow conditions. Using these datasets, SHEM interpolates estimated discharge and time data for any indexed streamgage that stops transmitting data. These estimates are continuously tested, scored, and revised using multiple regression analysis processes and methodologies. The SHEM model was tested in Idaho and Washington in four diverse watersheds, and the model's estimates were then compared to the actual recorded data for the same time period. Results from all watersheds revealed a high correlation, validating both the degree of accuracy and reliability of the model.     

基于三坐标测量仪测量角度及不确定度分析

通过三坐标测量仪对机械加工零件的外锥和内锥进行测量分析。对一些通用量具无法测量的角度提供准确的数值。利用不确定度评定标准公式计算出三坐标测量角度的不确定度分析。经过三坐标测量仪的先进技术改变现代通用量具的测量方法,提高通用量具无法达到的高精度测量时代,为高精度测量提供了一种新的测量手段。     

天津钢铁行业烧结烟气脱硫治理工程实践

天津两大钢铁企业分别采用LJS-FGD技术和石灰-石膏湿法工艺进行烧结烟气脱硫。通过分析两种脱硫方法的工程实践数据,从其脱硫效果、除尘脱硝效果、能耗技术指标等方面对两种脱硫方法进行比较:LJS-FGD技术在保证脱硫脱硝效果的基础上,具有总投资低、占地面积小、能耗低、污染小、运行成本低等特点;石灰-石膏湿法脱硫脱硝效果好,但能耗高,占地面积大,设备易于腐蚀。     

基于KPCA-LSSVM的矿井工作面动力环境安全评价模型

为实现对采前工作面所处动力环境的客观、准确评价,选取9个直接影响工作面 动力环境的指标因素构建安全评价指标体系,建立基于核主成分分析(KPCA)和最小二乘 支持向量机(LSSVM)的工作面动力环境多因素耦合安全评价智能模型。首先根据KPCA理 论对评价指标施行简约化处理,剔除冗余信息,得出6个简约后的评价指标并输入LSSVM 模型中训练学习,最后得到评价模型。选取从平顶山矿区和大同矿区搜集到的30组工作 面历史数据,按照20∶10的比例对模型进行训练和测试,并将测试结果与其他四种模型 结果进行了对比,结果表明:KPCA方法可有效减少数据信息冗余,利用KPCA优化的 LSSVM模型可准确评价工作面动力环境,误判率为0。     

烧结机分导式半干法脱硫技术研究

介绍了烧结烟气的特性,列举了目前烧结脱硫存在的主要问题,并对常用的烧结脱硫技术进行分析。提出一种经济性更好的烧结机分导式半干法脱硫技术。     

表面处理行业废水治理及中水回用工程实践

针对电镀等表面处理行业废水的水质特征,以浙江台州某表面处理有限公司为例,介绍了高压脉冲电凝机处理电镀综合废水与膜法反渗透中水回用工艺,经过近年的运行及实践证明,该工艺先进可行,效果好,不仅确保出水达标,而且75%中水回用,操作性强,工程投资和运行费用较低,无二次污染,是一种较实用的治理电镀综合废水与中水回用新技术。     

基于支持向量机的采空区稳定性分级

针对采空区稳定性分级的影响因素众多且关系复杂的特点,提出采用支持向量机理论对采空区稳定性进行分级。根据分级评价指标选取原则,选取岩体结构、地质构造、岩石的质量指标、地下可见水、地下水体、周边开采的影响、相邻空区的情况、工程布置、跨度、面积、高度、矿柱的尺寸及布置、埋藏深度和采空区的规格14个影响因子,建立了采空区稳定性评价指标体系,引入支持向量机理论,选择有向无环图方式构造多类分类器,得到采空区稳定性分级的支持向量机模型。将该模型用于山东黄金矿业西山矿区的25个采空区进行分级,并与未确知测度方法的分级情况     

应用支持向量机评价土壤环境质量

基于野外采样和室内分析相结合的方法,采用电感耦合等离子体质谱法(ICP-MS)对羊草沟煤矿研究区表层土壤样品中的Cd、Cr、Zn、Pb和Cu含量进行测定,应用非线性支持向量机模型中的分类支持向量机,选用sigmoid核函数,利用MATLAB编写程序,进行土壤环境质量评价,并利用模糊综合评判法对评价结果进行验证.在此基础上,运用对应分析方法对样品和变量进行了关联分析,进一步了解重金属污染特征.评价结果表明,研究区土壤环境质量多为Ⅰ类,与模糊综合评判法的相同率达到91.67%,将支持向量机用于土壤环境质量评价是可行的.相比于传统的评价方法,支持向量机采用结构风险最小化原则,将复杂的非线性问题转化为线性问题,成功的解决了多分类、高维运算等问题.     

全封闭设备制冷剂回收及相关用途研究

介绍了制冷剂回收器技术原理、回收实验装置．分析了回收过程的安全性、回收的制冷剂的质量及该技术对其他非封闭式制冷设备的适用性等问题。     

Automated conservation assessment of the orchid family with deep learning

International Union for Conservation of Nature (IUCN) Red List assessments are essential for prioritizing conservation needs but are resource intensive and therefore available only for a fraction of global species richness. Automated conservation assessments based on digitally available geographic occurrence records can be a rapid alternative, but it is unclear how reliable these assessments are. We conducted automated conservation assessments for 13,910 species (47.3% of the known species in the family) of the diverse and globally distributed orchid family (Orchidaceae), for which most species (13,049) were previously unassessed by IUCN. We used a novel method based on a deep neural network (IUC-NN). We identified 4,342 orchid species (31.2% of the evaluated species) as possibly threatened with extinction (equivalent to IUCN categories critically endangered [CR], endangered [EN], or vulnerable [VU]) and Madagascar, East Africa, Southeast Asia, and several oceanic islands as priority areas for orchid conservation. Orchidaceae provided a model with which to test the sensitivity of automated assessment methods to problems with data availability, data quality, and geographic sampling bias. The IUC-NN identified possibly threatened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias relative to the IUCN Red List and was robust even when data availability was low and there were geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in identifying species at the greatest risk of extinction.