早龄期混凝土分数阶Burgers徐变模型研究

早龄期混凝土徐变对工程结构体安全设计至关重要,研究针对早龄期混凝土徐变是控制和保证混凝土工程安全施工和正常运行的关键.基于粘弹塑性流变理论、Riemann-Liouville理论和Burgers组合模型理论,利用Able粘壶重构分数阶软体组合元件,提出分数阶Burgers模型的应力-应变关系表达式,构建基于分数微积分理论的早龄期混凝土徐变本构模型.通过早龄期混凝土压缩徐变试验数据进行验证,所构建模型能够对早龄期混凝土徐变过程保持较高的一致性,并通过分数阶阶数n的敏感性分析,可以实现对实际工程中早龄期混凝土徐变程度的有效控制.基于分数阶的Burgers模型可为早龄期混凝土徐变模型提供一种新的思路.

A New Model Using Multiple Feature Clustering and Neural Networks for Forecasting Hourly PM2.5 Concentrations,and Its Applications in China

Particulate matter with an aerodynamic diameter no greater than 2.5 lm(PM2.5)concentration forecasting is desirable for air pollution early warning.This study proposes an improved hybrid model,named multi-feature clustering decomposition(MCD)–echo state network(ESN)–particle swarm optimization(PSO),for multi-step PM2.5 concentration forecasting.The proposed model includes decomposition and optimized forecasting components.In the decomposition component,an MCD method consisting of rough sets attribute reduction(RSAR),k-means clustering(KC),and the empirical wavelet transform(EWT)is proposed for feature selection and data classification.Within the MCD,the RSAR algorithm is adopted to select significant air pollutant variables,which are then clustered by the KC algorithm.The clustered results of the PM2.5 concentration series are decomposed into several sublayers by the EWT algorithm.In the optimized forecasting component,an ESN-based predictor is built for each decomposed sublayer to complete the multi-step forecasting computation.The PSO algorithm is utilized to optimize the initial parameters of the ESN-based predictor.Real PM2.5 concentration data from four cities located in different zones in China are utilized to verify the effectiveness of the proposed model.The experimental results indicate that the proposed forecasting model is suitable for the multi-step high-precision forecasting of PM2.5 concentrations and has better performance than the benchmark models.

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble

Dissolved oxygen(DO)is an important indicator of aquaculture,and its accurate forecasting can effectively improve the quality of aquatic products.In this paper,a new DO hybrid forecasting model is proposed that includes three stages:multi-factor analysis,adaptive decomposition,and an optimizationbased ensemble.First,considering the complex factors affecting DO,the grey relational(GR)degree method is used to screen out the environmental factors most closely related to DO.The consideration of multiple factors makes model fusion more effective.Second,the series of DO,water temperature,salinity,and oxygen saturation are decomposed adaptively into sub-series by means of the empirical wavelet transform(EWT)method.Then,five benchmark models are utilized to forecast the sub-series of EWT decomposition.The ensemble weights of these five sub-forecasting models are calculated by particle swarm optimization and gravitational search algorithm(PSOGSA).Finally,a multi-factor ensemble model for DO is obtained by weighted allocation.The performance of the proposed model is verified by timeseries data collected by the pacific islands ocean observing system(PacIOOS)from the WQB04 station at Hilo.The evaluation indicators involved in the experiment include the Nash–Sutcliffe efficiency(NSE),Kling–Gupta efficiency(KGE),mean absolute percent error(MAPE),standard deviation of error(SDE),and coefficient of determination(R^(2)).Example analysis demonstrates that:①The proposed model can obtain excellent DO forecasting results;②the proposed model is superior to other comparison models;and③the forecasting model can be used to analyze the trend of DO and enable managers to make better management decisions.

误诊为慢性阻塞性肺疾病的气管鳞癌1例

1病例资料,患者,女,70岁,因“咳嗽、活动后气促1年余,加重4月余”入院。患者于2017年4月无明显诱因出现活动后气促,伴干咳,无发热、盗汗、咯血、夜间阵发性呼吸困难等症状。患者自行口服头孢类药物治疗,症状未见缓解且反复发作。2018年3月活动后气促加重到外院就诊,行胸片检查未见明显异常,肺功能检查提示重度混合性通气功能障碍,大小气道气流重度受限,弥散功能正常,气道阻力增高,支气管舒张试验阴性,考虑为支气管哮喘合并COPD,予以沙美特罗氟替卡松粉吸入剂治疗3个月,症状未见缓解并加重,于2018年7月入我院治疗。

Forecasting wind speed using a reinforcement learning hybrid ensemble model:a high-speed railways strong wind signal prediction study in Xinjiang,China

Considering the application of wind-forecasting technology along the railway,it becomes an effective means to reduce the risk of tain more reliable wind-speed prediction results,this study proposes an intelligent ensemble forecasting method for strong winds train derailment and overturning.Accurate prediction of crosswinds can provide scientific guidance for safe train operation.To obalong the high-speed railway.The method consists of three parts:the data preprocessing module,the hybrid prediction module and original wind speed data.Then,Broyden-Fletcher-Goldfarb-Shanno(BFGS)method,non-linear autoregressive network with exoge-the reinforcement learing ensemble module.First,fast ensemble empirical model decomposition(FEEMD)is used to process the prediction models for all the sublayers of decomposition.Finally,Q-learning is utilized to iteratively calculate the combined weights nous inputs(NARX)and deep belief network(DBN),three benchmark predictors with different characteristics are employed to build of the three models,and the prediction results of each sublayer are superimposed to obtain the model output.The real wind speed data of two railway stations in Xinjiang are used for experimental comparison.Experiments show that compared with the single benchmark model,the hybrid ensemble model has better accumacy and robustness for wind speed prediction along the railway.The 1-step forecasting results mean absolute error(MAE),mean absolute percentage error(MAPE)and root mean square error(RMSE)of Q-leaming-FEEMD-BFGS-NARX-DBN in site #1 and site #2 are 0.0894 m/s,0.6509%,0.1146 m/s,and 0.0458 m/s.0.2709%,0.0616 m/s.respectively.The proposed ensemble model is a promising method for railway wind speed prediction.

A hybrid ensemble deep reinforcement learning model for locomotive axle temperature using the deterministic and probabilistic strategy

This paper proposes a hybrid deep reinforcement learning framework for locomotive axle temperature by combining the wavelet packet decomposition(WPD),long short-term memory(LSTM),gated recurrent unit(GRU)reinforcement learning and generalized autoregressive conditional heteroskedasticity(GARCH)algorithms.The WPD is utilized to decompose the raw nonlinear series into subseries.Then the deep learning predictors LSTM and GRU are established to predict the future axle temperatures in each subseries.The Q-learning could generate optimal ensembleweights to integrate the predictors to finish the deterministic forecasting and GARCH is used to conduct the deterministic forecasting based on the deterministic forecasting residual.These parts of the hybrid ensemble structure contributed to optimal modelling accuracy and provided effective support in the real-time monitoring and fault diagnosis of transportation.

Railway switch fault diagnosis based on Multi-heads Channel Self Attention,Residual Connection and Deep CNN

A novel switch diagnosis method based on self-attention and residual deep convolutional neural networks(CNNs)is proposed.Because of the imbalanced dataset,the K-means synthetic minority oversampling technique(SMOTE)is applied to balancing the dataset at first.Then,the deep CNN is utilized to extract local features from long power curves,and the residual connection is performed to handle the performance degeneration.In the end,the multi-heads channel self attention focuses on those important local features.The ablation and comparison experiments are applied to verifying the effectiveness of the proposed methods.With the residual connection and multi-heads channel self attention,the proposed method has achieved an impressive accuracy of 99.83%.The t-SNE based visualizations for features of the middle layers enhance the trustworthiness.

凝血功能异常对新冠肺炎重症患者的预后意义

目的分析新型冠状病毒肺炎(corona virus disease 2019,COVID-19)患者凝血功能的临床特征,及其对重症化的预测和预后意义。方法收集2020年2月至4月在武汉泰康同济医院确诊的COVID-19患者356例的凝血功能指标,分析其与COVID-19重症化、预后的关联。结果356例患者入院时,普通型200例,重型118例,危重38型例。出院时,普通型190例,重型107例,危重型59例。患者凝血酶原时间(prothrombin time,PT)越长、D二聚体越高(D-dimer,DD)D-二聚体越高(D-dimer,D-dimer)、血小板计数越低,诊断为重型和危重型的概率越高(P<0.05)。年龄显著影响重症化(P<0.05,OR=1.054),年龄每增加1,重症化概率提升0.054倍。PT能显著影响重症化(P<0.05,OR=1.245),PT每提升1,重症化概率提升0.245倍。结论高龄、心血管系统基础疾病、PT延长、D二聚体升高,是COVID-19患者重症化的高危因素和预测、预后重要因素。