车装焊复合加工装备的服役可靠性评估

目的评估某型航天用车装焊复合加工装备在服役过程中的服役可靠性。方法首先分析装备常见的故障模式,统计装备在使用过程中的故障数据,由于装备故障数据样本量较少,所以提出基于贝叶斯理论的小样本车装焊复合加工装备可靠性评估方法,其中通过马尔科夫链蒙特卡罗(MCMC)法抽样解决贝叶斯理论中后验积分复杂的问题。结果确定了车装焊复合加工装备的寿命威布尔分布模型,并运用贝叶斯方法,计算出该车装焊复合加工装备的平均无故障工作时间(MTBF)。结论评估结果略低于设计要求,原因是目前该装备处于服役初期,服役初期故障数据较多。待进入稳定服役期时,故障率会有一定程度的降低后趋于稳定,MTBF会有一定幅度的增加,所以该车装焊复合加工装备MTBF基本满足设计要求。     

基于T-S模糊神经网络的大沽河水质变化特征分析研究

基于T-S模糊神经网络,利用大沽河2010年-2015年水质监测数据,选取溶解氧、化学需氧量、高锰酸盐指数、氨氮、总氮、总磷对水质具有重要影响的6项指标,建立适用的水质评价模型,对大沽河水质变化特征进行分析.结果显示:上游水质评价结果明显优于中游、下游水质评价结果,网络评价水质等级变化趋势同真实指标数据变化趋势一致.验证结果充分表明了T-S模糊神经网络用于水质变化特征分析是可行、有效的.     

基于WebGIS的水环境模拟关键技术研究

近年来随着水环境模拟领域的发展出现了较多水环境数学模型,目前日益成熟的WebGIS技术是使模型使用变得简单化和直观化的一大途径.是在通过Flex和ArcGIS Server搭建WebGIS系统平台的基础上,利用PHP进行WebGIS和水环境模拟模型的数据交互,实现系统平台和数值模型的松散耦合.用户在浏览器端进行模型输入条件的设置,模型在远程服务器端进行运行计算,反馈的模拟结果通过RIA在WebGIS中进行多元化动态展示.该方法对一般模型具有适用性,可降低模型使用门槛,提高模拟结果展示和分析能力.     

Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data-A case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake

Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.     

基于机器学习算法的新冠疫情管控对河南省空气质量影响的模拟分析

全国各地为了减少新冠疫情对社会和人民生活的影响,采取了必要的防疫防控措施,这些措施对空气质量的变化产生了重要的影响,此外空气质量的变化与气象条件也存在很大的关系.通过对河南省疫情前（1月1~26日）和疫情管控期（1月27日~2月29日）这两阶段的空气质量分析对比发现,整个河南省除了O₃浓度上升了69.64%外,PM_2.5、PM₁₀、SO₂、CO和NO₂分别降低了36.89%、34.18%、19.43%、29.85%和58.51%;通过机器学习算法中的长短期记忆型网络（LSTM）模拟显示,气象条件引起污染物浓度的降幅大部分在15%~30%之间;人为排放减少引起的污染物浓度的降幅大部分在6%~40%之间.O₃在疫情期间上升过程中,气象条件和人为排放两种因素分别占了34.84%和34.81%.结果表明,疫情管控期间,河南省空气质量总体上有所改善,但是也有重污染发生,其中O₃的浓度对于疫情管控减排的影响不明显,呈负相关,需要进一步探索引起臭氧浓度上升的原因,以此帮助政府合理控制臭氧等前体污染物的减排比例.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

BP 神经网络模型在环境气象灾害预测中的应用研究--以湖南省为例

依据环境气象数据与自然灾害统计数据,建立BP神经网络模型,对湖南主要气象灾害（洪灾、旱灾、冰冻灾）及受灾经济损失进行实例预测,将在MATLAB7软件中的仿真结果与传统的多元线性回归模型分析结果进行比较和误差分析。结果表明,BP神经网络模型在洪灾、旱灾受灾率方面的预测效果和精度优于多元回归模型,而由于冰灾训练样本不足及经济损失与输入因子的线性相关程度高,在冰灾与受灾经济损失率方面稍逊于多元回归模型。     

GIS支持下的水文生态效应评价混合型专家系统设计初探

文章从人工神经网络(ANN)、专家系统(ES)各自的优势和不足出发，结合地理信息系统(GIS)技术的特点，介绍了水文生态效应评价的混合型专家系统(HEMES)的基本结构模式和各模块的功能。然后抓住ANN、ES、GIS的共同作用因子一专家知识，从数据库结构、C 类方面探讨了三者集成的问题，并根据集成的思想探讨了相关功能设计。指出HEMES是一种更准确、易操作的评价技术，可作为相关管理、决策者的辅助工具。     

基于遗传算法的人工神经网络大气环境评价

人工神经网络模拟大脑的认知功能,通过改变连接点的权值和阈值来训练神经网络完成特定的功能。运用基于遗传算法的BP网络模型,以国家大气质量标准作为网络的输入样本对网络进行训练,对某地区大气质量进行评价,结果与综合指数法比较吻合。     

基于BP神经网络的安全评价方法研究

阐述了BP神经网络的基本原理,利用机会约束的思想建立了综合安全评价模型．运用反向传播算法和遗传算法对神经元网络进行训练,在此分析基础上对系统综合安全评价模型进行求解,并对运用神经元网络进行综合安全评价的优点进行了分析。