23 research outputs found

    Isoflavone Content of Soybean Cultivars from Maturity Group 0 to VI Grown in Northern and Southern China

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    Soybean isoflavone content has long been considered to be a desirable trait to target in selection programs for their contribution to human health and plant defense systems. The objective of this study was to determine isoflavone concentrations of various soybean cultivars from maturity groups 0 to VI grown in various environments and to analyze their relationship to other important seed characters. Forty soybean cultivars were grown in replicated trials at Wuhan and Beijing of China in 2009/2010 and their individual and total isoflavone concentrations were determined by HPLC. Their yield and quality traits were also concurrently analyzed. The isoflavone components had abundant genetic variation in soybean seed, with a range of coefficient variation from 45.01% to 69.61%. Moreover, individual and total isoflavone concentrations were significantly affected by cultivar, maturity group, site and year. Total isoflavone concentration ranged from 551.15 to 7584.07 μg g(−1), and averaged 2972.64 μg g(−1) across environments and cultivars. There was a similar trend regarding the isoflavone contents, in which a lower isoflavone concentration was generally presented in early rather than late maturing soybean cultivars. In spite of significant cultivar × year × site interactions, cultivars with consistently high or low isoflavone concentrations across environments were identified, indicating that a genetic factor plays the most important role for isoflavone accumulation. The total isoflavone concentration had significant positive correlations with plant height, effective branches, pods per plant, seeds per plant, linoleic acid and linolenic acid, while significant negative correlations with oleic acid and oil content, indicating that isoflavone concentration can be predicted as being associated with other desirable seed characteristics

    An Augmented Discrete-Time Approach for Human-Robot Collaboration

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    Human-robot collaboration (HRC) is a key feature to distinguish the new generation of robots from conventional robots. Relevant HRC topics have been extensively investigated recently in academic institutes and companies to improve human and robot interactive performance. Generally, human motor control regulates human motion adaptively to the external environment with safety, compliance, stability, and efficiency. Inspired by this, we propose an augmented approach to make a robot understand human motion behaviors based on human kinematics and human postural impedance adaptation. Human kinematics is identified by geometry kinematics approach to map human arm configuration as well as stiffness index controlled by hand gesture to anthropomorphic arm. While human arm postural stiffness is estimated and calibrated within robot empirical stability region, human motion is captured by employing a geometry vector approach based on Kinect. A biomimetic controller in discrete-time is employed to make Baxter robot arm imitate human arm behaviors based on Baxter robot dynamics. An object moving task is implemented to validate the performance of proposed methods based on Baxter robot simulator. Results show that the proposed approach to HRC is intuitive, stable, efficient, and compliant, which may have various applications in human-robot collaboration scenarios

    Target Tracking Algorithm Using Finite-time Convergence Smooth Second-order Sliding Mode Controller for Mobile Robots

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    Three-stream CNNs for action recognition

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    Head Pursuit Variable Structure Guidance Law for Three-dimensional Space Interception

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    AbstractThis article aims to develop a head pursuit (HP) guidance law for three-dimensional hypervelocity interception, so that the effect of the perturbation induced by seeker detection can be reduced. On the basis of a novel HP three-dimensional guidance model, a nonlinear variable structure guidance law is presented by using Lyapunov stability theory. The guidance law positions the interceptor ahead of the target on its flight trajectory, and the speed of the interceptor is required to be lower than that of the target. A numerical example of maneuvering ballistic target interception verifies the rightness of the guidance model and the effectiveness of the proposed method
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