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| 1 | Ensemble deep transfer learning driven by multisensor signals for the fault diagnosis of bevel-gear cross-operation conditions显示文摘The existing intelligent fault diagnosis techniques of bevel gear focus on single-sensor signal analysis under the steady operation condition.In this study,a new method is proposed based on ensemble deep transfer learning and multisensor signals to enhance the fault diagnosis adaptability and reliability of bevel gear under various operation conditions.First,a novel stacked autoencoder(NSAE)is constructed using a denoising autoencoder,batch normalization,and the Swish activation function.Second,a series of source-domain NSAEs with multisensor vibration signals is pretrained.Third,the good model parameters provided by the source-domain NSAEs are transferred to initialize the corresponding target-domain NSAEs.Finally,a modified voting fusion strategy is designed to obtain a comprehensive result.The multisensor signals collected under the different operation conditions of bevel gear are used to verify the proposed method.The comparison results show that the proposed method can diagnose different faults in an accurate and stable manner using only one target-domain sample,thereby outperforming the existing methods. | DI ZiYang SHAO HaiDong XIANG JiaWei | 2021 | Science China(Technological Sciences)2021，64，3： | 5 |
| 2 | Electrochemical Proton Storage:From Fundamental Understanding to Materials to Devices显示文摘Simultaneously improving the energy density and power density of electrochemical energy storage systems is the ultimate goal of electrochemical energy storage technology.An effective strategy to achieve this goal is to take advantage of the high capacity and rapid kinetics of electrochemical proton storage to break through the power limit of batteries and the energy limit of capacitors.This article aims to review the research progress on the physicochemical properties,electrochemical performance,and reaction mechanisms of electrode materials for electrochemical proton storage.According to the different charge storage mechanisms,the surface redox,intercalation,and conversion materials are classified and introduced in detail,where the influence of crystal water and other nanostructures on the migration kinetics of protons is clarified.Several reported advanced full cell devices are summarized to promote the commercialization of electrochemical proton storage.Finally,this review provides a framework for research directions of charge storage mechanism,basic principles of material structure design,construction strategies of full cell device,and goals of practical application for electrochemical proton storage. | Tiezhu Xu Di Wang Zhiwei Li Ziyang Chen Jinhui Zhang Tingsong Hu Xiaogang Zhang Laifa Shen | 2022 | Nano-Micro Letters2022，14，8： | 1 |
| 3 | Preparation of heteropolyacid/ TiOJfly-ash-cenosphere photocalalyst for the degradation of ciprofloxa- cin from aqueous solutions 显示文摘 | Wu Di Huo Pengwei Lu Ziyang | 2012 | Applied Supplied Surface Science2012，258，18： | 1 |
| 4 | A multipath routing algorithm for satellite networksbased on service demand and traffic awareness显示文摘With the reduction in manufacturing and launch costs of low Earth orbit satellites and the advantages of large coverage and high data transmission rates,satellites have become an important part of data transmission in air-ground networks.However,due to the factors such as geographical location and people’s living habits,the differences in user’demand for multimedia data will result in unbalanced network traffic,which may lead to network congestion and affect data transmission.In addition,in traditional satellite network transmission,the convergence of network information acquisition is slow and global network information cannot be collected in a fine-grained manner,which is not conducive to calculating optimal routes.The service quality requirements cannot be satisfied when multiple service requests are made.Based on the above,in this paper artificial intelligence technology is applied to the satellite network,and a software-defined network is used to obtain the global network information,perceive network traffic,develop comprehensive decisions online through reinforcement learning,and update the optimal routing strategy in real time.Simulation results show that the proposed reinforcement learning algorithm has good convergence performance and strong generalizability.Compared with traditional routing,the throughput is 8%higher,and the proposed method has load balancing characteristics. | Ziyang XING Hui QI Xiaoqiang DI Jinyao LIU Rui XU Jing CHEN Ligang CONG | 2023 | Frontiers of Information Technology & Electronic Engineering2023，24，6： | 0 |
| 5 | Insight into the efficiency-stability-cost balanced organic solar cell based on a polymerized nonfused-ring electron acceptor显示文摘Organic solar cells(OSCs)have attracted extensive attention from both academia and industry in recent years due to their remarkable improvement in power conversion efficiency(PCE).However,the Golden Triangle(the balance of efficiency-stability-cost)required for large-scale industrialization of OSCs still remains a great challenge.Here,a new nonfused-ring electron acceptor(NFREA)BF and its polymerized counterpart PBF were designed and synthesized,and their photovoltaic performance,storage stability and material cost were systematically investigated.When blended with a widely-used polymer donor PBDB-T,the PBFbased all-polymer solar cell(all-PSC)displayed a record high PCE of 12.61%for polymerized NFREAs(PNFREAs)with an excellent stability(95.2%of initial PCE after 800 h storage),superior to the BF counterpart.Impressively,PBF-based allPSC possesses the highest industrial figure-of-merit(i-FOM)value of 0.309 based on an efficiency-stability-cost evaluation,in comparison to several representative OSC systems(such as PM6:Y6 and PBDB-T:PZ1).This work provides an insight into the balance of efficiency,stability,and cost,and also indicates that the PNFREAs are promising materials toward the commercial application of OSCs. | Xiaobin Gu Yanan Wei Guanyu Lu Ziyang Han Di Zheng Guanghao Lu Jianqi Zhang Zhixiang Wei Yunhao Cai Xin Zhang Hui Huang | 2023 | Aggregate2023，4，6： | 0 |
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