Traffic flow prediction based on BILSTM model and data denoising scheme

Accurate prediction of road traffic flow is a significant part in the intelligent transportation systems.Accurate prediction can alleviate traffic congestion,and reduce environmental pollution.For the management department,it can make effective use of road resources.For individuals,it can help people plan their own travel paths,avoid congestion,and save time.Owing to complex factors on the road,such as damage to the detector and disturbances from environment,the measured traffic volume can contain noise.Reducing the influence of noise on traffic flow prediction is a piece of very important work.Therefore,in this paper we propose a combination algorithm of denoising and BILSTM to effectively improve the performance of traffic flow prediction.At the same time,three denoising algorithms are compared to find the best combination mode.In this paper,the wavelet(WL) denoising scheme,the empirical mode decomposition(EMD) denoising scheme,and the ensemble empirical mode decomposition(EEMD) denoising scheme are all introduced to suppress outliers in traffic flow data.In addition,we combine the denoising schemes with bidirectional long short-term memory(BILSTM)network to predict the traffic flow.The data in this paper are cited from performance measurement system(PeMS).We choose three kinds of road data(mainline,off ramp,on ramp) to predict traffic flow.The results for mainline show that data denoising can improve prediction accuracy.Moreover,prediction accuracy of BILSTM+EEMD scheme is the highest in the three methods(BILSTM+WL,BILSTM+EMD,BILSTM+EEMD).The results for off ramp and on ramp show the same performance as the results for mainline.It is indicated that this model is suitable for different road sections and long-term prediction.

The invention of an automatic measurement and data recording method of a fluorescence spectrophotometer

1.Objectives The laboratory of Xi’an Center,China Geological Survey has long been engaged in the testing of oil and gas geochemical samples.Among the different indexes,the measurement of the polycyclic aromatic hydrocarbon needs to be completed by the fluorescence spectrophotometer which is made in PE Company.According to the standard method,the fluorescence intensity values of three emission wavelengths under a certain excitation wavelength should be provided.

Electrolytes additives for Zn metal anodes:regulation mechanism and current perspectives

With distinct advantages such as high gravimetric and volumetric capacity(5855 mAh·cm^(-3)and 820mAh·g^(-1)),low redox potential(-0.762 V vs.standard hydrogen electrode(SHE)),high abundance,low toxicity and intrinsic safety of Zn metal anode,Zn-ion batteries have become a potential alternative to Li-ion batteries.However,several challenges still need to be addressed prior to the practical applications of Zn-ion batteries,such as dendrite growth during Zn plating/stripping and interfacial side reactions on the Zn surface.Such issues can be addressed by introducing additives to regulate the components and structures of the electrolyte.In this review,we systematically discussed the core issues of metallic Zn anodes and comprehensively summarized a novel perspective of the regulation mechanism of inhibiting dendrite growth or interfacial side reactions in Zn anodes by introducing additives into aqueous electrolytes.Furthermore,some discussions and prospects for aqueous Zn ion batteries(AZIBs)are presented for future research.

高效绿色的微波热解废旧轮胎方法（英文）

目的:废旧轮胎正成为一个日益严峻的环境问题。本文旨在研究发掘一个高效绿色的处理废旧轮胎的方法。本文探讨微波热解废旧轮胎的可能性,同时研究不同环境温度、微波频率以及样本大小对微波热解废旧轮胎效率的影响。方法:1.通过实验分析,验证影响微波热解废旧轮胎的关键因素,同时找出微波热解废旧轮胎的最佳条件。2.理论设计多个与分解效率相关的变量,推导其计算公式,从而证明提高热解效率的关键因素。3.分析热解后产物,证明该方法是高效绿色的。结论:1.微波热解废旧轮胎的最佳条件是:轮胎样品厚度为5 mm、微波频率为915 MHz以及环境温度为592.1°C。2.分解产物中包含35%的炭黑、40%的原油以及25%的可燃性气体;这证明微波热解法是有效的。3.所有产物皆可重复利用,证明该方法是高效绿色的。