An automated approach to the collection of 1 H NMR (nuclear magnetic resonance) spectra using a benchtop NMR spectrometer and the subsequent analysis, processing, and elucidation of components present in seized drug samples are reported. An algorithm is developed to compare spectral data to a reference library of over 300 1 H NMR spectra, ranking matches by a correlation-based score. A threshold for identification was set at 0.838, below which identification of the component present was deemed unreliable. Using this system, 432 samples were surveyed and validated against contemporaneously acquired GC–MS (gas chromatography–mass spectrometry) data. Following removal of samples which possessed no peaks in the GC–MS trace or in both the 1 H NMR spectrum and GC–MS trace, the remaining 416 samples matched in 93% of cases. Thirteen of these samples were binary mixtures. A partial match (one component not identified) was obtained for 6% of samples surveyed whilst only 1% of samples did not match at all.
In this paper, we present a novel approach for bipedal walking pattern generation. The proposed method is designed based on 2D inverted pendulum model. All control variables are optimized for an energy efficient gait. To obviate the need of solving non-linear dynamics on-line, a deep neural network is adopted for fast non-linear mapping from desired states to control variables. Normalized dimensionless data is generated to train the neural network, therefore, the trained neural network can be applied to bipedal robots of any size, without any specific modification. The proposed method is later verified through numerical simulations. Simulation results demonstrated that the proposed approach can generate feasible walking motions, and regulate robot's walking velocity successfully. Its disturbance rejection capability was also validated.
Falling is an unavoidable problem for humanoid robots due to the inherent instability of bipedal locomotion. In this paper, we present a novel strategy for humanoid fall prevention by using environmental contacts. Humans favour to contact using the upper limbs with the proximate environmental object to prevent falling and subliminally or consciously select a pose that can generate suitable Cartesian stiffness of the arm end-effector. Inspired by this intuitive human interaction, we design a configuration optimization method to choose a well thought pose of the arm as it approaches the long axis of the stiffness ellipsoid, with the displacement direction of the end-effector to utilize the joint torques. In order to validate the proposed strategy, we perform several simulations in MATLAB & Simulink, in which this strategy proves to be effective and feasible.
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