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您的检索式：作者名="ZHAO Chendi"
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| 1 | Life Prediction Model of Machine Tool based on Deep Learning显示文摘In view of the shortage of traditional life prediction methods for machine tools,such as low accuracy of life prediction and few samples basis attributes,a life prediction model of machine tools combined with machine tool attributes is proposed.The life prediction model of machine tool adopts KL dispersion distribution theory,uses modal superposition method to carry out machine tool life analysis,calculates the theoretical life of machine tool,and then carries on the simulation,obtains the machine tool life prediction value.Compared with the traditional method of machine tool life prediction,the model is based on the application life fatigue damage model,which superimposes the service times and maintenance cycle of the machine tool,derives the influence factor of machine tool life,and obtains the linear relationship between the influence factor of machine tool life and the life of machine tool.The influence factor of machine tool life is introduced as the life prediction parameter of machine tool.The data transformation relationship of HT300 parts is constructed.The original part data is enhanced.The effective training set is obtained.The life prediction model of machine tool based on deep learning is completed.The quantitative analysis of machine tool life is carried out.The experiment of machine tool life prediction using training data set proves the validity of the model.Regression test was carried out on the training data set to reflect the robustness of the model.The prediction accuracy of the model is further verified by Weibull test. | HE Jiawei ZHAO Chendi GAO Ruiyu LIU Xuehui WANG Xue | 2021 | International Journal of Plant Engineering and Management2021，26，1： | 2 |
| 2 | Electrochemical Properties of Spinel LiMn_2O_(4-δ)F_δ for Cathode Materials of Secondary Lithium-ion Battery显示文摘The spinel LiMn2O4-δFδ cathode materials were synthesized by solid-state reaction, With calculated amounts of LiOH·H2O, MnO2(EMD). LiF. The results of electrochemical test demonstrated that these new materials exhibited excellent electrochemical properties.Its initial capacity reached -115 mAb·g-1 and reversible efficiency is about 100%. After 60 cycles. its capacity was still around 110 mAh· g-1, with nearly 100% reversible efficiency, | Zhao Yong CHEN Xing Quan LIU Zuo Long YU (Chengdu Institute of Organic Chemistry, The Chinese Academy of Sciences, Chendy 610041) | 2000 | Chinese Chemical Letters2000，11，5： | 0 |
| 3 | CSELM-QE:A Composite Semi-supervised Extreme Learning Machine with Unlabeled RSS Quality Estimation for Radio Map Construction显示文摘Wireless local area network(WLAN)fingerprint-based localization has become the most attractive and popular approach for indoor localization.However,the primary concern for its practical implementation is the laborious manual effort of calibrating sufficient location-labeled fingerprints.The Semi-supervised extreme learning machine(SELM)performs well in reducing calibration effort.Traditional SELM methods only use Received signal strength(RSS)information to construct the neighbor graph and ignores location information,which helps recognizing prior information for manifold alignments.We propose Composite SELM(CSELM)method by using both RSS signals and location information to construct composite graph.Besides,the issue of unlabeled RSS data quality has not been solved.We propose a novel approach called Composite semisupervised extreme learning machine with unlabeled RSS Quality estimation(CSELM-QE)that takes into account the quality of unlabeled RSS data and combines the composite neighbor graph,which considers location information in the semi-supervised extreme learning machine.Experimental results show that the CSELM-QE could construct a precise localization model,reduce the calibration effort for radio map construction and improve localization accuracy.Our quality estimation method can be applied to other methods that need to retain high quality unlabeled Received signal strength data to improve model accuracy. | ZHAO Jianli WANG Wei SUN Qiuxia HUO Huan SUN Guoqiang GAO Xiang ZHU Chendi | 2020 | Chinese Journal of Electronics2020，29，6： | 0 |
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