No abstract

Representation-based classification (RBC) has been attracting a great deal of attention in pattern recognition. As a typical extension to RBC, collaborative representation-based classification (CRC) has demonstrated its superior performance in various image classification tasks. Ideally, we expect that the learned class-specific representations for a testing sample are discriminative, and the representation computed for the true class dominates the final representation of the testing sample. Most existing CRC-based methods can learn pattern discrimination, but cannot differentiate the contribution of class-specific representations to the classification of each testing sample. It is challenging for a representation-based classifier to retain both properties. To address this challenge and further improve CRC's classification performance, we propose a novel CRC-based method, class mean-weighted discriminative collaborative representation-based classifier (CMW-DCRC). Its objective function penalises the standard l 2 -norm residuals with two discriminative regularisation terms. A decorrelating term makes the class-specific representations more discriminative, and a newly designed class meanweighted term that promotes the training samples from individual classes to competitively reconstruct the testing sample while boosting the contribution of the true class. To further enhance the robustness of CRC, we extend CMW-DCRC by replacing the l 2 -norm coding residual with a l 1 -norm coding residual, and solve the optimisation problem with an iteratively reweighted least square algorithm. Extensive experimental results on nine image data sets have shown that our methods outperform the state-of-the-art RBC-based methods.

In order to improve the horizontal moving acceleration of stackers, the paper proposes a horizontal moving mechanism called "side-wheel operating simultaneously" and presents the calculating method of its main parameters. The FEM model of improved stacker’s rail system is established by using software ANSYS and the analyzing results show that the design meets the requirement of high-speed & high-acceleration for stackers.

Due to the influence of robot arm’s and patient’s cantilever’s weight a drive motor with large drive torque was required in the design of upper-limb rehabilitation robot. In order to solve this problem, a new gravity-supporting system that combines gas spring and tension spring to provide supporting force was presented. Due to the use of this device in Upper Limb rehabilitation robot, the power and torque fluctuation of the driving motor can be reduced and the security and stability of robot can be increased. First of all, the movement mechanism was designed and the theoretical analysis is given to prove that it can reduce the driving torque of motor effectively. Three dimensional model of the device was created by Pro/E and was imported into ADAMS/view for the dynamic simulation and got the change curves of driving torque on the condition of the supporting device or not . In the end, the experimental data verify the application of the device that extreme driving torque of robot was decreased over 50%.

In order to solve the problems that the functions of exiting upper-limbed rehabilitation robot are single and the structure is complicated, a 6-DOF exoskeleton upper-limbed rehabilitation robot is put forward. The kinematics model of the rehabilitation robot is established and kinematics positive and inverse solutions are solved using D-H transformation method. Three dimensional model of the rehabilitation robot is created by Pro/E and imported into ADAMS/view for the kinematics simulation by Mechanism/Pro. The gotten simulation results of the simulation verify the correctness of theoretical derivation and prove the smooth movement Characteristics of the scheme.