Supervisory control of the hybrid off-highway vehicle for fuel economy improvement using predictive double Q-learning with backup models

This paper studied a supervisory control system for a hybrid off-highway electric vehicle under the chargesustaining(CS)condition.A new predictive double Q-learning with backup models(PDQL)scheme is proposed to optimize the engine fuel in real-world driving and improve energy efficiency with a faster and more robust learning process.Unlike the existing“model-free”methods,which solely follow on-policy and off-policy to update knowledge bases(Q-tables),the PDQL is developed with the capability to merge both on-policy and off-policy learning by introducing a backup model(Q-table).Experimental evaluations are conducted based on software-in-the-loop(SiL)and hardware-in-the-loop(HiL)test platforms based on real-time modelling of the studied vehicle.Compared to the standard double Q-learning(SDQL),the PDQL only needs half of the learning iterations to achieve better energy efficiency than the SDQL at the end learning process.In the SiL under 35 rounds of learning,the results show that the PDQL can improve the vehicle energy efficiency by 1.75%higher than SDQL.By implementing the PDQL in HiL under four predefined real-world conditions,the PDQL can robustly save more than 5.03%energy than the SDQL scheme.

H.264中全零块预判的新方法

为了避免全零DCT（离散余弦变换）系数进行变换与量化产生的冗余计算，从H．264中的DCT公式与量化原理出发，结合视频图像残差系数的分布规律，在分析现有算法存在的误判与漏判的基础上，提出了一种预先判断全零DCT系数块的方法。实验结果表明，与文献中的经典算法相比较，新提出的方法应用在中低比特率的视频序列中可以减少12％～37％的平均编码时间，平均压缩率提高了6％～10％。

Chemical comparison of Semen Euphorbiae and Semen Euphorbiae Pulveratum by UPLC-Q-TOF/MS coupled with multivariate statistical techniques

In the present study,we aimed to assess the chemical composition changes of Semen Euphorbiae(SE)and Semen Euphorbiae Pulveratum(SEP)by UPLC-Q-TOF/MS and multivariate statistical methods.The UPLC-Q-TOF/MS method and SIMCA-P software were used to analyze the changes of chemical components of SE and SEP based on PCA and PLS-DA multivariate statistical methods.A"component-target-disease"network model was constructed by Intelligent Platform for Life Sciences of traditional Chinese medicine(TCM)to predict potential related diseases.The differences of chemical composition were significant between SE and SEP.Under positive ion mode,the amounts of Euphorbia factor L2,L3,L7a,L8,L9 and lathyrol were obviously decreased after processing.Under negative ion mode,the amounts of aesculetin,bisaesculetin,ingenol and cetylic acid were increased obviously,while Euphorbia factor L1,L4 and L5 were decreased obviously after processing,and the components of euphobiasteroid,aesculetin,lathyrol and linoleic acid among the 14 differentiated compounds were closely related to the SE-related diseases through the"component-target-disease"network model.UPLC-Q-TOF/MS technology in combination with multivariate statistical methods had certain advantages in studying the complex changes of chemical composition before and after manufacturing pulveratum of SE.It provided a basis for clarifying the toxicity-attenuated mechanisms of SE manufacturing pulveratum,and laid the foundation for its further development and utilization.