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| 1 | Bias correction of sea surface temperature retrospective forecasts in the South China Sea显示文摘Offline bias correction of numerical marine forecast products is an effective post-processing means to improve forecast accuracy. Two offline bias correction methods for sea surface temperature(SST) forecasts have been developed in this study: a backpropagation neural network(BPNN) algorithm, and a hybrid algorithm of empirical orthogonal function(EOF) analysis and BPNN(named EOF-BPNN). The performances of these two methods are validated using bias correction experiments implemented in the South China Sea(SCS), in which the target dataset is a six-year(2003–2008) daily mean time series of SST retrospective forecasts for one-day in advance, obtained from a regional ocean forecast and analysis system called the China Ocean Reanalysis(CORA),and the reference time series is the gridded satellite-based SST. The bias-correction results show that the two methods have similar good skills;however, the EOF-BPNN method is more than five times faster than the BPNN method. Before applying the bias correction, the basin-wide climatological error of the daily mean CORA SST retrospective forecasts in the SCS is up to-3°C;now, it is minimized substantially, falling within the error range(±0.5°C) of the satellite SST data. | Guijun Han Jianfeng Zhou Qi Shao Wei Li Chaoliang Li Xiaobo Wu Lige Cao Haowen Wu Yundong Li Gongfu Zhou | 2022 | Acta Oceanologica Sinica2022，41，2： | 2 |
| 2 | Numerical analysis on the interaction of arc and flow field for SF6 circuit breaker 显示文摘 | LIU Xiaoming CAO Yundong WANG Erzhi | 2006 | 1EEE Transactions on Magneties2006，，42： | 1 |
| 3 | High‑Performance Flexible Microneedle Array as a Low‑Impedance Surface Biopotential Dry Electrode for Wearable Electrophysiological Recording and Polysomnography显示文摘Microneedle array(MNA)electrodes are an effective solution to achieve high-quality surface biopotential recording without the coordination of conductive gel and are thus very suitable for long-term wearable applications.Existing schemes are limited by flexibility,biosafety,and manufacturing costs,which create large barriers for wider applications.Here,we present a novel flexible MNA electrode that can simultaneously achieve flexibility of the substrate to fit a curved body surface,robustness of microneedles to penetrate the skin without fracture,and a simplified process to allow mass production.The compatibility with wearable wireless systems and the short preparation time of the electrodes significantly improves the comfort and convenience of electrophysiological recording.The normalized electrode–skin contact impedance reaches 0.98 kΩcm^(2)at 1 kHz and 1.50 kΩcm^(2)at 10 Hz,a record low value compared to previous reports and approximately 1/250 of the standard electrodes.The morphology,biosafety,and electrical/mechanical properties are fully characterized,and wearable recordings with a high signal-to-noise ratio and low motion artifacts are realized.The first reported clinical study of microneedle electrodes for surface electrophysiological monitoring was conducted in tens of healthy and sleep-disordered subjects with 44 nights of recording(over 8 h per night),providing substantial evidence that the electrodes can be leveraged to substitute for clinical standard electrodes. | Junshi Li Yundong Ma Dong Huang Zhongyan Wang Zhitong Zhang Yingjie Ren Mengyue Hong Yufeng Chen Tingyu Li Xiaoyi Shi Lu Cao Jiayan Zhang Bingli Jiao Junhua Liu Hongqiang Sun Zhihong Li | 2022 | Nano-Micro Letters2022，14，8： | 1 |
| 4 | Electric field optimization design of a vacuum interrupter based on the Tabu search algorithm显示文摘 | Cao Yundong Liu Xiaoming Wang Erzhi | 2002 | IEEE Transactions on Dielectrics and Electrical Insulation2002，9，2： | 1 |
| 5 | Dynamic arc modeling based on computation of couple electric field and flow field for high voltage SF6 interrupter显示文摘 | Liu Xiaoming Wang Erzhi Cao Yundong | 2004 | IEEE Transactions on Magnetics2004，40，2： | 1 |
| 6 | Optical Nonlinearities and Optical Limiting Properties of Pbs Semieonduetor Nanobelts显示文摘 | Bei Cao Yundong Zhang Honggang Zhang | 2005 | Chinese Optics Letters (supplement)2005，3，： | 1 |
| 7 | Optimization of SF6 circuit breaker based on chaotic neural network显示文摘 | Cao Yundong | 2006 | IEEE Transactions on Magnetics2006，42，4： | 1 |
| 8 | Numerical analyses on the interaction of arc and flow field for SF6 circuit breaker 显示文摘 | Xiaoming Liu Yundong Cao Erzhi Wang | 2006 | IEEE Transactions on Magnetics2006，，42： | 1 |
| 9 | A homologous and molecular dual-targeted biomimetic nanocarrier for EGFR-related non-small cell lung cancer therapy显示文摘The abnormal activation of epidermal growth factor receptor(EGFR)drives the development of non-small cell lung cancer(NSCLC).The EGFR-targeting tyrosine kinase inhibitor osimertinib is frequently used to clinically treat NSCLC and exhibits marked efficacy in patients with NSCLC who have an EGFR mutation.However,free osimertinib administration exhibits an inadequate response in vivo,with only~3%patients demonstrating a complete clinical response.Consequently,we designed a biomimetic nanoparticle(CMNP^(@Osi))comprising a polymeric nanoparticle core and tumor cell-derived membrane-coated shell that combines membrane-mediated homologous and molecular targeting for targeted drug delivery,thereby supporting a dual-target strategy for enhancing osimertinib efficacy.After intravenous injection,CMNP^(@Osi)accumulates at tumor sites and displays enhanced uptake into cancer cells based on homologous targeting.Osimertinib is subsequently released into the cytoplasm,where it suppresses the phosphorylation of upstream EGFR and the downstream AKT signaling pathway and inhibits the proliferation of NSCLC cells.Thus,this dual-targeting strategy using a biomimetic nanocarrier can enhance molecular-targeted drug delivery and improve clinical efficacy. | Bin Xu Fanjun Zeng Jialong Deng Lintong Yao Shengbo Liu Hengliang Hou Yucheng Huang Hongyuan Zhu Shaowei Wu Qiaxuan Li Weijie Zhan Hongrui Qiu Huili Wang Yundong Li Xianzhu Yang Ziyang Cao Yu Zhang Haiyu Zhou | 2023 | Bioactive Materials2023，，9： | 1 |
| 10 | Dynamicarc modeling based on computation of couple electric fieldand flow field for high voltage SF6 interrupter 显示文摘 | Liu Xiaoming Wang Erzhi Cao Yundong | 2004 | IEEETransactions on Magnetics2004，40，2： | 1 |
| 11 | Dynamic arc modeling based on computation of couple electric field and flow field for high voltage SF6 interrupter显示文摘 | Liu Xiaoming Wang Erzhi Cao Yundong | 2004 | IEEE Transactions on Magnetics2004，40，2： | 1 |
| 12 | Numerical analyses on the interaction of arc and flow field for SF6 circuit breaker显示文摘 | LIU Xiaoming CAO Yundong WANG Erzhi | 2006 | IEEE Transactions on Magnetics2006，，42： | 1 |
| 13 | SNES: Social-Network-Oriented Public Opinion Monitoring Platform Based on ElasticSearch显示文摘With the rapid development of social network,public opinion monitoring based on social networks is becoming more and more important.Many platforms have achieved some success in public opinion monitoring.However,these platforms cannot perform well in scalability,fault tolerance,and real-time performance.In this paper,we propose a novel social-network-oriented public opinion monitoring platform based on ElasticSearch(SNES).Firstly,SNES integrates the module of distributed crawler cluster,which provides real-time social media data access.Secondly,SNES integrates ElasticSearch which can store and retrieve massive unstructured data in near real time.Finally,we design subscription module based on Apache Kafka to connect the modules of the platform together in the form of message push and consumption,improving message throughput and the ability of dynamic horizontal scaling.A great number of empirical experiments prove that the platform can adapt well to the social network with highly real-time data and has good performance in public opinion monitoring. | Chuiju You Dongjie Zhu Yundong Sun Anshan Ye Gangshan Wu Ning Cao Jinming Qiu Helen Min Zhou | 2019 | Computers, Materials & Continua2019，，9： | 1 |
| 14 | Hybrid finite element-charge simulation method for SF6 tank type circuit breakerwith double break显示文摘 | Cao Yundong Liu Xiaoming Wang Erzhi | 2001 | IEEE Transactions on Magnetics2001，37，5： | 1 |
| 15 | DCRL-KG: Distributed Multi-Modal Knowledge Graph Retrieval Platform Based on Collaborative Representation Learning显示文摘The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval platforms.Image and text descriptions added to the knowledge graph enrich the node information,which accounts for the advantage of the multi-modal knowledge graph.In the field of cross-modal retrieval platforms,multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational infor-mation provided by knowledge graphs.The representation learning method is sig-nificant to the application of multi-modal knowledge graphs.This paper proposes a distributed collaborative vector retrieval platform(DCRL-KG)using the multi-modal knowledge graph VisualSem as the foundation to achieve efficient and high-precision multimodal data retrieval.Firstly,use distributed technology to classify and store the data in the knowledge graph to improve retrieval efficiency.Secondly,this paper uses BabelNet to expand the knowledge graph through multi-ple filtering processes and increase the diversification of information.Finally,this paper builds a variety of retrieval models to achieve the fusion of retrieval results through linear combination methods to achieve high-precision language retrieval and image retrieval.The paper uses sentence retrieval and image retrieval experi-ments to prove that the platform can optimize the storage structure of the multi-modal knowledge graph and have good performance in multi-modal space. | Leilei Li Yansheng Fu Dongjie Zhu Xiaofang Li Yundong Sun Jianrui Ding Mingrui Wu Ning Cao Russell Higgs | 2023 | Intelligent Automation & Soft Computing2023，，6： | 0 |
| 16 | Massive Files Prefetching Model Based on LSTM Neural Network with Cache Transaction Strategy显示文摘In distributed storage systems,file access efficiency has an important impact on the real-time nature of information forensics.As a popular approach to improve file accessing efficiency,prefetching model can fetches data before it is needed according to the file access pattern,which can reduce the I/O waiting time and increase the system concurrency.However,prefetching model needs to mine the degree of association between files to ensure the accuracy of prefetching.In the massive small file situation,the sheer volume of files poses a challenge to the efficiency and accuracy of relevance mining.In this paper,we propose a massive files prefetching model based on LSTM neural network with cache transaction strategy to improve file access efficiency.Firstly,we propose a file clustering algorithm based on temporal locality and spatial locality to reduce the computational complexity.Secondly,we propose a definition of cache transaction according to files occurrence in cache instead of time-offset distance based methods to extract file block feature accurately.Lastly,we innovatively propose a file access prediction algorithm based on LSTM neural network which predict the file that have high possibility to be accessed.Experiments show that compared with the traditional LRU and the plain grouping methods,the proposed model notably increase the cache hit rate and effectively reduces the I/O wait time. | Dongjie Zhu Haiwen Du Yundong Sun Xiaofang Li Rongning Qu Hao Hu Shuangshuang Dong Helen Min Zhou Ning Cao | 2020 | Computers, Materials & Continua2020，，5： | 0 |
| 17 | Sensor Network Structure Recognition Based on P-law显示文摘A sensor graph network is a sensor network model organized according to graph network structure.Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks.In sensor networks,network structure recognition is the basis for accurate identification and effective prediction and control of node states.Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks,based on the characteristics of sensor networks,a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal transmission path of the core node.This method which builds on unit patulousness and core node signal propagation(called p-law)can rapidly and effectively achieve the global structure identification of a sensor graph network.Different from the traditional graph network structure recognition algorithms such as modularity maximization and spectral clustering,the proposed method reveals the natural evolution process and law of graph network subgroup generation.Experimental results confirm the effectiveness,accuracy and rationality of the proposed method and suggest that our method can be a new approach for graph network global structure recognition. | Chuiju You Guanjun Lin Jinming Qiu Ning Cao Yundong Sun Russell Higgs | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
| 18 | MINE:A Method of Multi-Interaction Heterogeneous Information Network Embedding显示文摘Interactivity is the most significant feature of network data,especially in social networks.Existing network embedding methods have achieved remarkable results in learning network structure and node attributes,but do not pay attention to the multi-interaction between nodes,which limits the extraction and mining of potential deep interactions between nodes.To tackle the problem,we propose a method called Multi-Interaction heterogeneous information Network Embedding(MINE).Firstly,we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm.Secondly,we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse multiple interactional relationships.Finally,applying a multitasking model makes the learned vector contain richer semantic relationships.A large number of practical experiments prove that our proposed method outperforms existing methods on multiple data sets. | Dongjie Zhu Yundong Sun Xiaofang Li Haiwen Du Rongning Qu Pingping Yu Xuefeng Piao Russell Higgs Ning Cao | 2020 | Computers, Materials & Continua2020，，6： | 0 |
| 19 | Surface modification of hollow capsule by Dawson-type polyoxometalate as sulfur hosts for ultralong-life lithium-sulfur batteries显示文摘Reasonable construction of sulfur host with high conductivity,large sulfur storage gap,strong chemical adsorption,and fast oxidation–reduction kinetics of polysulfide is very significant for its practical use in lithium-sulfur batteries(LSBs).In this paper,the surface modification of MIL-88A(Fe)is carried out by Dawson-type polyoxometalate(POM),and a hollow capsule shell material with P_(2)W_(18),Fe_(3)O_(4),and C components is synthesized by the subsequent carbonization process.When applied as the sulfur host,the hollow capsule shell material can efficiently improve the conductivity of sulfur electrode and restrain the volumetric change of active sulfur while charging and discharging.On this foundation,electrochemical analysis and density functional theory(DFT)calculation show that the P_(2)W_(18)on the outer layer of the capsule shell have effective electrocatalytic activity and potent chemical bond on the lithium polysulfides(LiPSs),which is helpful to block the shuttle effect.Therefore,the as-assembled LSBs display the outstanding specific capacity and prominent cycle stability.Specifically,it delivers an excellent reversible capacity of 1063 mAh/g after 100 cycles of charge–discharge at a rate of 0.5 C,accounting for a preservation by 96%in comparison to that of the initial cycle.Moreover,even after 2000 cycles at 1 C,the reversible specific capacity of 585 mAh/g can still be maintained with an average decay rate of only 0.021%. | Mingliang Wang Di Yin Yundong Cao Xinyang Dong Guanggang Gao Xun Hu Cheng Jin Linlin Fan Jian Yu Hong Liu | 2022 | Chinese Chemical Letters2022，33，9： | 0 |
| 20 | Fusion Recommendation System Based on Collaborative Filtering and Knowledge Graph显示文摘The recommendation algorithm based on collaborative filtering is currently the most successful recommendation method. It recommends items to theuser based on the known historical interaction data of the target user. Furthermore,the combination of the recommended algorithm based on collaborative filtrationand other auxiliary knowledge base is an effective way to improve the performance of the recommended system, of which the Co-Factorization Model(CoFM) is one representative research. CoFM, a fusion recommendation modelcombining the collaborative filtering model FM and the graph embeddingmodel TransE, introduces the information of many entities and their relationsin the knowledge graph into the recommendation system as effective auxiliaryinformation. It can effectively improve the accuracy of recommendations andalleviate the problem of sparse user historical interaction data. Unfortunately,the graph-embedded model TransE used in the CoFM model cannot solve the1-N, N-1, and N-N problems well. To tackle this problem, a novel fusion recommendation model Joint Factorization Machines and TransH Model (JFMH) isproposed, which improves CoFM by replacing the TransE model with TransHmodel. A large number of experiments on two widely used benchmark data setsshow that compared with CoFM, JFMH has improved performance in terms ofitem recommendation and knowledge graph completion, and is more competitivethan multiple baseline methods. | Donglei Lu Dongjie Zhu Haiwen Du Yundong Sun Yansong Wang Xiaofang Li Rongning Qu Ning Cao Russell Higgs | 2022 | Computer Systems Science & Engineering2022，42，9： | 0 |
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