We report a new search for weakly interacting massive particles (WIMPs) using the combined low background data sets acquired in 2016 and 2017 from the PandaX-II experiment in China. The latest data set contains a new exposure of 77.1 live days, with the background reduced to a level of 0.8×10^{-3}  evt/kg/day, improved by a factor of 2.5 in comparison to the previous run in 2016. No excess events are found above the expected background. With a total exposure of 5.4×10^{4}  kg day, the most stringent upper limit on the spin-independent WIMP-nucleon cross section is set for a WIMP with mass larger than 100  GeV/c^{2}, with the lowest 90% C.L. exclusion at 8.6×10^{-47}  cm^{2} at 40  GeV/c^{2}.

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The multi-DOF prosthetic hand's myocontrol needs to recognize more hand gestures (or motions) based on myoelectric signals. This paper presents a classification method, which is based on the support vector machine (SVM), to classify 19 different hand gesture modes through electromyographic (EMG) signals acquired from six surface myoelectric electrodes. All hand gestures are based on a 3-DOF configuration, which makes the hand perform like three-fingered. The training performance is very high within each test session, but the cross-session validation is typically low. Acceptable cross-session performance can be achieved by training with more sessions or fewer gesture modes. A fast rhythm muscle contraction is suggested, which can make the training samples more resourceful and improve the prediction accuracy comparing with a relative slow muscle contraction method.For many precise grasp tasks, it is beneficial to the prosthetic hand's myocontrol if we can efficiently extract the grasp force directly from EMG signals. Through grasping a JR3 6 dimension force/torque sensor, the force signal applying to the sensor can be recorded synchronously with myoelectric signals. This paper uses three methods, local weighted projection regression (LWPR), artificial neural network (ANN) and SVM, to find the best regression relationship between these two kinds of signals. It reveals that the SVM method is better than ANN and LWPR, especially in the case of cross-session validation. Also, the performance of grasping force estimation based on specific hand gestures is superior to the performance of grasping with random fingers.978-1-4244-3804-4/09/$25.00 ©2009 IEEE

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In this paper, a novel adaptive control for dual-arm cooperative manipulators is proposed to accomplish the hybrid position/force tracking in the presence of dynamic and closed-chain kinematic uncertainties. Self-convergent parameter estimation of the grasped object's centre of mass and contact force estimation are incorporated into this systematic scheme. Moreover, internal force and contact force tracking objectives are achieved simultaneously by incorporating into the position tracking formula with proper null-space projection and rotation transformation. Noisy force derivative signals are not required. This adaptive controller is mathematically derived based on Lyapunov stability analysis. Three sets of simulations corresponding to three different situations are presented to verify the effectiveness and superiority of the proposed controller.