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| 1 | Magnetic-programmable organohydrogels with reconfigurable network for mechanical homeostasis显示文摘Synthetic materials with tunable mechanical properties have great potential in soft robotics and biomedical engineering.However,current materials are limited to the mechanical duality altering their mechanical properties only between soft and hard states and lack of consecutively programmable mechanics.Herein,the magnetic-programmable organohydrogels with heterogeneous dynamic architecture are designed by encasing oleophilic ferrofluid droplets into hydrogel matrix.As magnetic field increases,the mechanical properties of organohydrogels can be consecutively modulated owing to the gradual formation of chain-like assembly structures of nanoparticles.The storage modulus G'increases by 2.5 times when magnetic field goes up to 0.35 T.Small-Angle X-ray Scattering(SAXS)confirms the reconfigurable orientation of nanoparticles and the organohydrogels show reversible modulus switching.Besides,the materials also exhibit high stretchability,magnetic actuation behavior and effective self-healing capability.Furthermore,the organohydrogels are applied into the design of effectors with mechanical adaptivity.When subjected to serious external perturbations,the effector can maintain mechanical homeostasis by regulating modulus of organohydrogel under applied magnetic field.Such materials are applicable to homeostatic systems with mechanically adaptive behaviors and programmed responses to external force stimuli. | Yingchao Yang Qian Liu Tianyi Zhao Yunfei Ru Ruochen Fang Yichao Xu Jin Huang Mingjie Liu | 2021 | Nano Research2021，14，1： | 1 |
| 2 | A survey of data-driven approach on multimedia QoE evaluation显示文摘With the development of mobile communication technology and the growth of mobile device,the requirements for user quality of experience (QoE)become higher and higher.Network operators and content providers are interested in QoE evaluation for improving users'QoE.However,multimedia QoE evaluation faces severe challenges due to the subjective properties of the QoE.In this paper,we provide a survey of the state of the art about applying data-driven approach on QoE evaluation.Firstly,we describe the way to choose factors influencing QoE.Then we investigate and discuss the strengths and shortcomings of existing machine learning algorithms for modeling and predicting users'QoE. Finally,we describe our research work on how to evaluate QoE in imbalanced dataset. | Ruochen HUANG Xin WEI Liang ZHOU Chaoping LV Hao MENG Jiefeng JIN | 2018 | Frontiers of Computer Science2018，12，6： | 0 |
| 3 | Improved minimum variance distortionless response spectrum method for efficient and robust non-uniform undersampled frequency identification in blade tip timing显示文摘The noncontact blade tip timing(BTT)measurement has been an attractive technology for blade health monitoring(BHM).However,the severe undersampled BTT signal causes a significant challenge for blade vibration parameter identification and fault feature extraction.This study proposes a novel method based on the minimum variance distortionless response(MVDR)of the direction of arrival(DoA)estimation for blade natural frequency estimation from the non-uniformly undersampled BTT signals.First,based on the similarity between the general data acquisition model for BTT and the antenna array model in DoA estimation,the circumferentially arranged probes on the casing can be regarded as a non-uniform linear array.Thus,BTT signal reconstruction is converted into the DoA estimation problem of the non-uniform linear array signal.Second,MVDR is employed to address the severe undersampling issue and recover the BTT undersampled signal.In particular,spatial smoothing is innovatively utilized to enhance the estimation of covariance matrix of the BTT signal to avoid ill-condition or singularity,while improving efficiency and robustness.Lastly,numerical simulation and experimental testing are employed to verify the validity of the proposed method.Monte Carlo simulation results suggest that the proposed method behaves better than conventional methods,especially under a lower signal-to-noise ratio condition.Experimental results indicate that the proposed method can effectively overcome the severe undersampling problem of BTT signal induced by physical limitations,and has a strong potential in the field of BHM. | Ruochen JIN Laihao YANG Zhibo YANG Shaohua TIAN Guangrong TENG Xuefeng CHEN | 2023 | Frontiers of Mechanical Engineering2023，18，4： | 0 |
| 4 | Improving the Accuracy of Vegetation Index Retrieval for Biomass by Combining Ground-UAV Hyperspectral Data-A New Method for Inner Mongolia Typical Grasslands显示文摘Grassland biomass is an important parameter of grassland ecosystems.The complexity of the grassland canopy vegetation spectrum makes the long-term assessment of grassland growth a challenge.Few studies have explored the original spectral information of typical grasslands in Inner Mongolia and examined the influence of spectral information on aboveground biomass(AGB)estimation.In order to improve the accuracy of vegetation index inversion of grassland AGB,this study combined ground and Unmanned Aerial Vehicle(UAV)remote sensing technology and screened sensitive bands through ground hyperspectral data transformation and correlation analysis.The narrow band vegetation indices were calculated,and ground and airborne hyperspectral inversion models were established.Finally,the accuracy of the model was verified.The results showed that:(1)The vegetation indices constructed based on the ASD FieldSpec 4 and the UAV were significantly correlated with the dry and fresh weight of AGB.(2)The comparison between measured R^(2) with the prediction R^(2) indicated that the accuracy of the model was the best when using the Soil-Adjusted Vegetation Index(SAVI)as the independent variable in the analysis of AGB(fresh weight/dry weight)and four narrow-band vegetation indices.The SAVI vegetation index showed better applicability for biomass monitoring in typical grassland areas of Inner Mongolia.(3)The obtained ground and airborne hyperspectral data with the optimal vegetation index suggested that the dry weight of AGB has the best fitting effect with airborne hyperspectral data,where y=17.962e^(4.672x),the fitting R^(2) was 0.542,the prediction R^(2)was 0.424,and RMSE and REE were 57.03 and 0.65,respectively.Therefore,established vegetation indices by screening sensitive bands through hyperspectral feature analysis can significantly improve the inversion accuracy of typical grassland biomass in Inner Mongolia.Compared with ground monitoring,airborne hyperspectral monitoring better reflects the inversion of actual surface biomass.It provides a reliable modeling framework for grassland AGB monitoring and scientific and technological support for grazing management. | Ruochen Wang Jianjun Dong Lishan Jin Yuyan Sun Taogetao Baoyin Xiumei Wang | 2024 | Phyton-International Journal of Experimental Botany2024，93，2： | 0 |
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