The Experts below are selected from a list of 174 Experts worldwide ranked by ideXlab platform

Xiaofei Mi - One of the best experts on this subject based on the ideXlab platform.

  • Comparative Analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD Sensor Data for Grassland Monitoring Applications
    Remote Sensing, 2015
    Co-Authors: Lei Wang, Yanjun Yang, Ranran Yang, Qingjiu Tian, Yang Zhou, Xiaofei Mi
    Abstract:

    The increasing number of Chinese sensor types used for terrestrial remote sensing has necessitated an additional effort to evaluate and standardize the data they acquire. In this study, we assessed the potential use of GF-1 WFV (Wild Field Camera), ZY-3 MUX (Multi-spectral camera), and HJ-1 CCD (Charge Coupled Device) sensor data for grassland monitoring by comparing spectral field measurements, vegetation coverage, and the leaf area index (LAI) of grassland stands with reflectance in the red and near-infrared bands and the Normalized Difference Vegetation Index (NDVI). Based on spectral field measurements, the characteristic differences of spectral response functions of the sensors were analyzed. Based on simulations using the SAIL bidirectional canopy reflectance model coupled with the PROSPECT leaf optical properties model (PROSAIL), we investigated the effects of changes in the sensors’ zenith angle caused by Side Sway. The following conclusions were drawn. (1) Differences in the adjusted coefficients of determination (R2) exist when comparing correlations between the reflectances from the three sensor types in different bands. The values of R2 are 0.556–0.893 and 0.819–0.850 for the infrared and red bands, respectively, and these data show a better correlation for the red band than for the infrared band. Fitted slope equations revealed inconsistencies in the data between the different sensor types. In the red band, GF-1 WFV and HJ-1 CCD data are the most consistent, but in the near-infrared band, GF-1 WFV and ZY-3 MUX data are the most consistent; (2) The correlation of NDVIs obtained from the different sensor types is high (R2 between 0.758 and 0.852); however, the consistency is low in that the NDVI based on GF-1 WFV data is significantly higher than that based on ZY-3 MUX and HJ-1 CCD data. In contrast, the mean difference is small between the NDVIs based on ZY-3 MUX and HJ-1 CCD; (3) Correlation analysis between ground grass-coverage and measured LAI data shows that the three sensor types are better at estimating coverage than the LAI, and that the GF-1 WFV sensor gave the best performance; (4) Changes in the sensors’ zenith angle caused by Side Sway were proven to have greater impact on reflectance and NDVI than the spectral response function; (5) For LAI values of 0–3, the NDVI changes significantly with increasing LAI, and differences between the three sensor types are obvious. For LAI > 3.5, the NDVI appears to experience a saturated tendency, which greatly reduces the differences between the sensors.

  • Comparative analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD sensor data for grassland monitoring applications
    Remote Sensing, 2015
    Co-Authors: Lei Wang, Yanjun Yang, Ranran Yang, Yuan Sun, Qingjiu Tian, Yang Zhou, Xiaofei Mi
    Abstract:

    The increasing number of Chinese sensor types used for terrestrial remote sensing has necessitated an additional effort to evaluate and standardize the data they acquire. In this study, we assessed the potential use of GF-1 WFV (Wild Field Camera), ZY-3 MUX (Multi-spectral camera), and HJ-1 CCD (Charge Coupled Device) sensor data for grassland monitoring by comparing spectral field measurements, vegetation coverage, and the leaf area index (LAI) of grassland stands with reflectance in the red and near-infrared bands and the Normalized Difference Vegetation Index (NDVI). Based on spectral field measurements, the characteristic differences of spectral response functions of the sensors were analyzed. Based on simulations using the SAIL bidirectional canopy reflectance model coupled with the PROSPECT leaf optical properties model (PROSAIL), we investigated the effects of changes in the sensors’ zenith angle caused by Side Sway. The following conclusions were drawn. (1) Differences in the adjusted coefficients of determination (R2) exist when comparing correlations between the reflectances from the three sensor types in different bands. The values of R2 are 0.556–0.893 and 0.819–0.850 for the infrared and red bands, respectively, and these data show a better correlation for the red band than for the infrared band. Fitted slope equations revealed inconsistencies in the data between the different sensor types. In the red band, GF-1 WFV and HJ-1 CCD data are the most consistent, but in the near-infrared band, GF-1 WFV and ZY-3 MUX data are the most consistent; (2) The correlation of NDVIs obtained from the different sensor types is high (R2 between 0.758 and 0.852); however, the consistency is low in that the NDVI based on GF-1 WFV data is significantly higher than that based on ZY-3 MUX and HJ-1 CCD data. In contrast, the mean difference is small between the NDVIs based on ZY-3 MUX and HJ-1 CCD; (3) Correlation analysis between ground grass-coverage and measured LAI data

Lei Wang - One of the best experts on this subject based on the ideXlab platform.

  • Comparative Analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD Sensor Data for Grassland Monitoring Applications
    Remote Sensing, 2015
    Co-Authors: Lei Wang, Yanjun Yang, Ranran Yang, Qingjiu Tian, Yang Zhou, Xiaofei Mi
    Abstract:

    The increasing number of Chinese sensor types used for terrestrial remote sensing has necessitated an additional effort to evaluate and standardize the data they acquire. In this study, we assessed the potential use of GF-1 WFV (Wild Field Camera), ZY-3 MUX (Multi-spectral camera), and HJ-1 CCD (Charge Coupled Device) sensor data for grassland monitoring by comparing spectral field measurements, vegetation coverage, and the leaf area index (LAI) of grassland stands with reflectance in the red and near-infrared bands and the Normalized Difference Vegetation Index (NDVI). Based on spectral field measurements, the characteristic differences of spectral response functions of the sensors were analyzed. Based on simulations using the SAIL bidirectional canopy reflectance model coupled with the PROSPECT leaf optical properties model (PROSAIL), we investigated the effects of changes in the sensors’ zenith angle caused by Side Sway. The following conclusions were drawn. (1) Differences in the adjusted coefficients of determination (R2) exist when comparing correlations between the reflectances from the three sensor types in different bands. The values of R2 are 0.556–0.893 and 0.819–0.850 for the infrared and red bands, respectively, and these data show a better correlation for the red band than for the infrared band. Fitted slope equations revealed inconsistencies in the data between the different sensor types. In the red band, GF-1 WFV and HJ-1 CCD data are the most consistent, but in the near-infrared band, GF-1 WFV and ZY-3 MUX data are the most consistent; (2) The correlation of NDVIs obtained from the different sensor types is high (R2 between 0.758 and 0.852); however, the consistency is low in that the NDVI based on GF-1 WFV data is significantly higher than that based on ZY-3 MUX and HJ-1 CCD data. In contrast, the mean difference is small between the NDVIs based on ZY-3 MUX and HJ-1 CCD; (3) Correlation analysis between ground grass-coverage and measured LAI data shows that the three sensor types are better at estimating coverage than the LAI, and that the GF-1 WFV sensor gave the best performance; (4) Changes in the sensors’ zenith angle caused by Side Sway were proven to have greater impact on reflectance and NDVI than the spectral response function; (5) For LAI values of 0–3, the NDVI changes significantly with increasing LAI, and differences between the three sensor types are obvious. For LAI > 3.5, the NDVI appears to experience a saturated tendency, which greatly reduces the differences between the sensors.

  • Comparative analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD sensor data for grassland monitoring applications
    Remote Sensing, 2015
    Co-Authors: Lei Wang, Yanjun Yang, Ranran Yang, Yuan Sun, Qingjiu Tian, Yang Zhou, Xiaofei Mi
    Abstract:

    The increasing number of Chinese sensor types used for terrestrial remote sensing has necessitated an additional effort to evaluate and standardize the data they acquire. In this study, we assessed the potential use of GF-1 WFV (Wild Field Camera), ZY-3 MUX (Multi-spectral camera), and HJ-1 CCD (Charge Coupled Device) sensor data for grassland monitoring by comparing spectral field measurements, vegetation coverage, and the leaf area index (LAI) of grassland stands with reflectance in the red and near-infrared bands and the Normalized Difference Vegetation Index (NDVI). Based on spectral field measurements, the characteristic differences of spectral response functions of the sensors were analyzed. Based on simulations using the SAIL bidirectional canopy reflectance model coupled with the PROSPECT leaf optical properties model (PROSAIL), we investigated the effects of changes in the sensors’ zenith angle caused by Side Sway. The following conclusions were drawn. (1) Differences in the adjusted coefficients of determination (R2) exist when comparing correlations between the reflectances from the three sensor types in different bands. The values of R2 are 0.556–0.893 and 0.819–0.850 for the infrared and red bands, respectively, and these data show a better correlation for the red band than for the infrared band. Fitted slope equations revealed inconsistencies in the data between the different sensor types. In the red band, GF-1 WFV and HJ-1 CCD data are the most consistent, but in the near-infrared band, GF-1 WFV and ZY-3 MUX data are the most consistent; (2) The correlation of NDVIs obtained from the different sensor types is high (R2 between 0.758 and 0.852); however, the consistency is low in that the NDVI based on GF-1 WFV data is significantly higher than that based on ZY-3 MUX and HJ-1 CCD data. In contrast, the mean difference is small between the NDVIs based on ZY-3 MUX and HJ-1 CCD; (3) Correlation analysis between ground grass-coverage and measured LAI data

Libin Zhang - One of the best experts on this subject based on the ideXlab platform.

  • ROBIO - Flexible pneumatic robotic actuator FPA and its applications
    2013 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2013
    Co-Authors: Zhiheng Wang, Pengcheng Huang, Qinghua Yang, Sheng Xu, Fang Xu, Libin Zhang
    Abstract:

    Totally different from conventional rigid robot, bioinspired soft robot has characteristics of deformable body, flexibility, continuous changeability and high active adaptability. It has become a new evolution in robotics. In this context, a flexible pneumatic robotic actuator FPA was proposed to act as both driver and executor for soft robot configration. The structure, principle and mathematic model of FPA are described. Based on FPA, a series of soft joints, such as soft bending joint, link-embedded soft bending joint, hybrid bending joint, torsion joint, spherical joint and Side-Sway joint, are developed. Then soft robots based FPA are reported, including climbing robot, cucumber gripper, end-effector for cone, multi-fingered dexterous hand and hand rehabilitator. All the structures, working principles and experiments of the soft joints and robots are elaborated.

  • Flexible pneumatic robotic actuator FPA and its applications
    2013 IEEE International Conference on Robotics and Biomimetics ROBIO 2013, 2013
    Co-Authors: Guanjun Bao, Shibo Cai, Pengcheng Huang, Zhiheng Wang, Qinghua Yang, Sheng Xu, Fang Xu, Libin Zhang
    Abstract:

    Totally different from conventional rigid robot, bioinspired soft robot has characteristics of deformable body, flexibility, continuous changeability and high active adaptability. It has become a new evolution in robotics. In this context, a flexible pneumatic robotic actuator FPA was proposed to act as both driver and executor for soft robot configration. The structure, principle and mathematic model of FPA are described. Based on FPA, a series of soft joints, such as soft bending joint, link-embedded soft bending joint, hybrid bending joint, torsion joint, spherical joint and Side-Sway joint, are developed. Then soft robots based FPA are reported, including climbing robot, cucumber gripper, end-effector for cone, multi-fingered dexterous hand and hand rehabilitator. All the structures, working principles and experiments of the soft joints and robots are elaborated.

  • Design and control of integrated pneumatic dexterous robot finger
    Journal of Central South University of Technology, 2011
    Co-Authors: Zhiheng Wang, Shaoming Qian, Libin Zhang, Qinghua Yang
    Abstract:

    Based on flexible pneumatic actuator (FPA), bending joint and Side-Sway joint, a new kind of pneumatic dexterous robot finger was developed. The finger is equipped with one five-component force sensor and four contactless magnetic rotary encoders. Mechanical parts and FPAs are integrated, which reduces the overall size of the finger. Driven by FPA directly, the joint output torque is more accurate and the friction and vibration can be effectively reduced. An improved adaptive genetic algorithm (IAGA) was adopted to solve the inverse kinematics problem of the redundant finger. The statics of the finger was analyzed and the relation between fingertip force and joint torque was built. Finally, the finger force/position control principle was introduced. Tracking experiments of fingertip force/position were carried out. The experimental results show that the fingertip position tracking error is within ±1 mm and the fingertip force tracking error is within ±0.4 N. It is also concluded from the theoretical and experimental results that the finger can be controlled and it has a good application prospect.

  • Characteristics of Side-Sway joint based on flexible pneumatic actuator
    Nongye Gongcheng Xuebao Transactions of the Chinese Society of Agricultural Engineering, 2009
    Co-Authors: Libin Zhang, Shaoming Qian, Guanjun Bao, Zhiheng Wang, Qinghua Yang
    Abstract:

    Based on flexible pneumatic actuator (FPA), a new kind of flexible pneumatic Side-Sway joint was proposed. The Side-Sway joint is used for designing agricultural dexterous robot multi-fingered hand. The Side-Sway joint is basically composed of two FPAs. When the two FPAs were inflated with different compressed air pressure, the Side-Sway joint could realize left and right movement. The force equilibrium equation of the FPA free end was established. The static model of the Side-Sway joint was obtained, and the static model was also simplified. Experiments were carried out to verify the static model. Experimental results show that the experimental curve matched well with the simulated curve, and there was certain error between them (maximal static error is 0.035 rad). The error cause was analyzed. Then the experiments were carried out to measure the dynamic performances of the Side-Sway joint. Experimental results show that the step response time of the joint was about 2 s (steady value's tolerance zone Δ=5%) respected to different expected angle step signals. The Side-Sway joint can significantly increase the workspace and further improve the adaptability and dexterity of the agricultural dexterous robot hand.

  • Development and simulation of ZJUT hand based on flexible pneumatic actuator FPA
    2009 International Conference on Mechatronics and Automation, 2009
    Co-Authors: Libin Zhang, Zhiheng Wang, Qinghua Yang, Shaoming Qian
    Abstract:

    Based on flexible pneumatic actuator FPA, two new kinds of pneumatic rigid-flexible joints are proposed. Three bending joints and one Side-Sway joints are used for designing two kinds of 4-DOF robot fingers. When inflating the FPA of each joint of 4-DOF fingers with different compressed air pressure, the fingers can simulate human fingers and realize bending movement and Side-Sway movement. Then the 20-DOF robot multi-fingered dexterous hand composed five fingers and a palm is designed which is named ZJUT Hand. Driving directly by FPA is ZJUT Hand's most notable characteristic. Because of this, ZJUT Hand has many advantages such as good adaptability, simple structure, small size and little energy loss and so on. At last, simulation experiments are carried to verify the grasping ability of ZJUT Hand. Simulation experimental results show that ZJUT Hand can complete power grasp and precision grasp for objects with different size and shape.

Ranran Yang - One of the best experts on this subject based on the ideXlab platform.

  • Comparative Analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD Sensor Data for Grassland Monitoring Applications
    Remote Sensing, 2015
    Co-Authors: Lei Wang, Yanjun Yang, Ranran Yang, Qingjiu Tian, Yang Zhou, Xiaofei Mi
    Abstract:

    The increasing number of Chinese sensor types used for terrestrial remote sensing has necessitated an additional effort to evaluate and standardize the data they acquire. In this study, we assessed the potential use of GF-1 WFV (Wild Field Camera), ZY-3 MUX (Multi-spectral camera), and HJ-1 CCD (Charge Coupled Device) sensor data for grassland monitoring by comparing spectral field measurements, vegetation coverage, and the leaf area index (LAI) of grassland stands with reflectance in the red and near-infrared bands and the Normalized Difference Vegetation Index (NDVI). Based on spectral field measurements, the characteristic differences of spectral response functions of the sensors were analyzed. Based on simulations using the SAIL bidirectional canopy reflectance model coupled with the PROSPECT leaf optical properties model (PROSAIL), we investigated the effects of changes in the sensors’ zenith angle caused by Side Sway. The following conclusions were drawn. (1) Differences in the adjusted coefficients of determination (R2) exist when comparing correlations between the reflectances from the three sensor types in different bands. The values of R2 are 0.556–0.893 and 0.819–0.850 for the infrared and red bands, respectively, and these data show a better correlation for the red band than for the infrared band. Fitted slope equations revealed inconsistencies in the data between the different sensor types. In the red band, GF-1 WFV and HJ-1 CCD data are the most consistent, but in the near-infrared band, GF-1 WFV and ZY-3 MUX data are the most consistent; (2) The correlation of NDVIs obtained from the different sensor types is high (R2 between 0.758 and 0.852); however, the consistency is low in that the NDVI based on GF-1 WFV data is significantly higher than that based on ZY-3 MUX and HJ-1 CCD data. In contrast, the mean difference is small between the NDVIs based on ZY-3 MUX and HJ-1 CCD; (3) Correlation analysis between ground grass-coverage and measured LAI data shows that the three sensor types are better at estimating coverage than the LAI, and that the GF-1 WFV sensor gave the best performance; (4) Changes in the sensors’ zenith angle caused by Side Sway were proven to have greater impact on reflectance and NDVI than the spectral response function; (5) For LAI values of 0–3, the NDVI changes significantly with increasing LAI, and differences between the three sensor types are obvious. For LAI > 3.5, the NDVI appears to experience a saturated tendency, which greatly reduces the differences between the sensors.

  • Comparative analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD sensor data for grassland monitoring applications
    Remote Sensing, 2015
    Co-Authors: Lei Wang, Yanjun Yang, Ranran Yang, Yuan Sun, Qingjiu Tian, Yang Zhou, Xiaofei Mi
    Abstract:

    The increasing number of Chinese sensor types used for terrestrial remote sensing has necessitated an additional effort to evaluate and standardize the data they acquire. In this study, we assessed the potential use of GF-1 WFV (Wild Field Camera), ZY-3 MUX (Multi-spectral camera), and HJ-1 CCD (Charge Coupled Device) sensor data for grassland monitoring by comparing spectral field measurements, vegetation coverage, and the leaf area index (LAI) of grassland stands with reflectance in the red and near-infrared bands and the Normalized Difference Vegetation Index (NDVI). Based on spectral field measurements, the characteristic differences of spectral response functions of the sensors were analyzed. Based on simulations using the SAIL bidirectional canopy reflectance model coupled with the PROSPECT leaf optical properties model (PROSAIL), we investigated the effects of changes in the sensors’ zenith angle caused by Side Sway. The following conclusions were drawn. (1) Differences in the adjusted coefficients of determination (R2) exist when comparing correlations between the reflectances from the three sensor types in different bands. The values of R2 are 0.556–0.893 and 0.819–0.850 for the infrared and red bands, respectively, and these data show a better correlation for the red band than for the infrared band. Fitted slope equations revealed inconsistencies in the data between the different sensor types. In the red band, GF-1 WFV and HJ-1 CCD data are the most consistent, but in the near-infrared band, GF-1 WFV and ZY-3 MUX data are the most consistent; (2) The correlation of NDVIs obtained from the different sensor types is high (R2 between 0.758 and 0.852); however, the consistency is low in that the NDVI based on GF-1 WFV data is significantly higher than that based on ZY-3 MUX and HJ-1 CCD data. In contrast, the mean difference is small between the NDVIs based on ZY-3 MUX and HJ-1 CCD; (3) Correlation analysis between ground grass-coverage and measured LAI data

Qingjiu Tian - One of the best experts on this subject based on the ideXlab platform.

  • Comparative Analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD Sensor Data for Grassland Monitoring Applications
    Remote Sensing, 2015
    Co-Authors: Lei Wang, Yanjun Yang, Ranran Yang, Qingjiu Tian, Yang Zhou, Xiaofei Mi
    Abstract:

    The increasing number of Chinese sensor types used for terrestrial remote sensing has necessitated an additional effort to evaluate and standardize the data they acquire. In this study, we assessed the potential use of GF-1 WFV (Wild Field Camera), ZY-3 MUX (Multi-spectral camera), and HJ-1 CCD (Charge Coupled Device) sensor data for grassland monitoring by comparing spectral field measurements, vegetation coverage, and the leaf area index (LAI) of grassland stands with reflectance in the red and near-infrared bands and the Normalized Difference Vegetation Index (NDVI). Based on spectral field measurements, the characteristic differences of spectral response functions of the sensors were analyzed. Based on simulations using the SAIL bidirectional canopy reflectance model coupled with the PROSPECT leaf optical properties model (PROSAIL), we investigated the effects of changes in the sensors’ zenith angle caused by Side Sway. The following conclusions were drawn. (1) Differences in the adjusted coefficients of determination (R2) exist when comparing correlations between the reflectances from the three sensor types in different bands. The values of R2 are 0.556–0.893 and 0.819–0.850 for the infrared and red bands, respectively, and these data show a better correlation for the red band than for the infrared band. Fitted slope equations revealed inconsistencies in the data between the different sensor types. In the red band, GF-1 WFV and HJ-1 CCD data are the most consistent, but in the near-infrared band, GF-1 WFV and ZY-3 MUX data are the most consistent; (2) The correlation of NDVIs obtained from the different sensor types is high (R2 between 0.758 and 0.852); however, the consistency is low in that the NDVI based on GF-1 WFV data is significantly higher than that based on ZY-3 MUX and HJ-1 CCD data. In contrast, the mean difference is small between the NDVIs based on ZY-3 MUX and HJ-1 CCD; (3) Correlation analysis between ground grass-coverage and measured LAI data shows that the three sensor types are better at estimating coverage than the LAI, and that the GF-1 WFV sensor gave the best performance; (4) Changes in the sensors’ zenith angle caused by Side Sway were proven to have greater impact on reflectance and NDVI than the spectral response function; (5) For LAI values of 0–3, the NDVI changes significantly with increasing LAI, and differences between the three sensor types are obvious. For LAI > 3.5, the NDVI appears to experience a saturated tendency, which greatly reduces the differences between the sensors.

  • Comparative analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD sensor data for grassland monitoring applications
    Remote Sensing, 2015
    Co-Authors: Lei Wang, Yanjun Yang, Ranran Yang, Yuan Sun, Qingjiu Tian, Yang Zhou, Xiaofei Mi
    Abstract:

    The increasing number of Chinese sensor types used for terrestrial remote sensing has necessitated an additional effort to evaluate and standardize the data they acquire. In this study, we assessed the potential use of GF-1 WFV (Wild Field Camera), ZY-3 MUX (Multi-spectral camera), and HJ-1 CCD (Charge Coupled Device) sensor data for grassland monitoring by comparing spectral field measurements, vegetation coverage, and the leaf area index (LAI) of grassland stands with reflectance in the red and near-infrared bands and the Normalized Difference Vegetation Index (NDVI). Based on spectral field measurements, the characteristic differences of spectral response functions of the sensors were analyzed. Based on simulations using the SAIL bidirectional canopy reflectance model coupled with the PROSPECT leaf optical properties model (PROSAIL), we investigated the effects of changes in the sensors’ zenith angle caused by Side Sway. The following conclusions were drawn. (1) Differences in the adjusted coefficients of determination (R2) exist when comparing correlations between the reflectances from the three sensor types in different bands. The values of R2 are 0.556–0.893 and 0.819–0.850 for the infrared and red bands, respectively, and these data show a better correlation for the red band than for the infrared band. Fitted slope equations revealed inconsistencies in the data between the different sensor types. In the red band, GF-1 WFV and HJ-1 CCD data are the most consistent, but in the near-infrared band, GF-1 WFV and ZY-3 MUX data are the most consistent; (2) The correlation of NDVIs obtained from the different sensor types is high (R2 between 0.758 and 0.852); however, the consistency is low in that the NDVI based on GF-1 WFV data is significantly higher than that based on ZY-3 MUX and HJ-1 CCD data. In contrast, the mean difference is small between the NDVIs based on ZY-3 MUX and HJ-1 CCD; (3) Correlation analysis between ground grass-coverage and measured LAI data