BackgroundFor the functional control of prosthetic hand, it is insufficient to obtain only the motion pattern information. As far as practicality is concerned, the control of the prosthetic hand force is indispensable. The application value of prosthetic hand will be greatly improved if the stable grip of prosthetic hand can be achieved. To address this problem, in this study, a bio-signal control method for grasping control of a prosthetic hand is proposed to improve patient’s sense of using prosthetic hand and the thus improving the quality of life.MethodsA MYO gesture control armband is used to collect the surface electromyographic (sEMG) signals from the upper limb. The overlapping sliding window scheme are applied for data segmentation and the correlated features are extracted from each segmented data. Principal component analysis (PCA) methods are then deployed for dimension reduction. Deep neural network is used to generate sEMG-force regression model for force prediction at different levels. The predicted force values are input to a fuzzy controller for the grasping control of a prosthetic hand. A vibration feedback device is used to feed grasping force value back to patient’s arm to improve patient’s sense of using prosthetic hand and realize accurate grasping. To test the effectiveness of the scheme, 15 able-bodied subjects participated in the experiments.ResultsThe classification results indicated that 8-channel sEMG applying all four time-domain features, with PCA reduction from 32 to 8 dimensions results in the highest classification accuracy. Based on the experimental results from 15 participants, the average recognition rate is over 95%. On the other hand, from the statistical results of standard deviation, the between-subject variations ranges from 3.58 to 1.25%, proving that the robustness and stability of the proposed approach.ConclusionsThe method proposed hereto control grasping power through the patient’s own sEMG signal, which achieves a high recognition rate to improve the success rate of grip and increases the sense of operation and also brings the gospel for upper extremity amputation patients.
Unsteady cavitation flows in a centrifugal pump operating under off-design conditions are investigated by using a numerical framework combining the re-normalization group k–ɛ turbulence model and the transport equation-based cavitation model. The reliability and accuracy of the numerical model are demonstrated by the satisfactory agreement between the experimental and numerical values of the pump performance. Under partial discharge, the frequency spectra of the pressure fluctuation at the impeller inlet become more complex as the pump inlet pressure decreases. The maximum amplitude of pressure fluctuation at the blade leading edge for cavitation flow is 2.54 times larger than that for non-cavitation flow because of the violent disturbances caused by cavitation shedding and explosion. Under large discharge, the magnification on the maximum pressure amplitude is 1.6. This finding indicates that cavitation has less influence on pressure fluctuations in the impeller under large discharge than under partial discharge. This numerical simulation demonstrates the evolution of cavitation structure inside the impeller.
Coalbed methane (CBM) is a relatively
common unconventional natural
gas, which has great exploitation value. Coal permeability is an important
parameter that affects the production and production efficiency of
CBM, which is mainly controlled by the sorption expansion/contraction
strain and effective stress. To study the seepage characteristics
of coal in the process of CBM production, we have used CH4 and CO2 as test gases separately and conducted comparative
seepage tests of different gases under constant pore pressure conditions.
At the same time, the elastic modulus reduction coefficient R
m has been introduced to characterize the sorption
strain of coal, following which the permeability models suitable for
different boundary conditions were derived according to the stress–strain
relationship. Under the two gases, the new model could not only better
reflect the law of coal sorption strain but also better reflect the
relationship among effective stress, pore pressure, and coal permeability.
Under the conditions of constant pore pressure, coal permeability
was mainly controlled by effective stress; with the increase of effective
stress, permeability decreased sharply initially and then gradually.
Under the conditions of uniaxial strain and constant external stress,
with an increase of pore pressure and R
m, the matrix sorption expansion strain increased, resulting in a
narrowing of the seepage channel, and R
m indirectly inhibited permeability. At this point, coal permeability
was mainly controlled by sorption expansion/contraction strain and
effective stress. In addition, compared with other permeability models,
the new permeability model possesses higher applicability both in
theoretical mechanism and in data matching. The general change trend
concerning coal permeability, determined by rebound pressure p
rb, was consistent with the test results, which
further verified the applicability of the model. It is believed that
the results of this study could provide a basis for subsequent research
on the stress–strain–permeability relationship and for
the study of efficient development of CBM.
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