The random subspace concept is widely used in decision forests. However, there is not a reasonable approach to specify the appropriate number of randomly selected features. Previous random subspace decision forests si...
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A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcom...
A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario (< 1.70% difference between shift mitigation and No shift scenarios). These outcomes suggest that our method can provide a practical solution for adaptation to electrode shift, thus improving the robustness of the EMG pattern recognition systems in both clinical and commercial settings.
Soft grippers are receiving growing attention due to their compliance-based interactive safety and dexterity. Hybrid gripper (soft actuators enhanced by rigid constraints) is a new trend in soft gripper design. With r...
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Envisioned as one of the most promising technologies, holographic multiple-input multiple-output (H-MIMO) recently attracts notable research interests for its great potential in expanding wireless possibilities and ac...
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In the motion control system, input shaper is often used to suppress the residual oscillation of high-precision positioning system, but the selection of input shaper parameters is difficult. In view of the difficulty ...
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ISBN:
(纸本)9781665493970
In the motion control system, input shaper is often used to suppress the residual oscillation of high-precision positioning system, but the selection of input shaper parameters is difficult. In view of the difficulty of selecting input shaper parameters, this paper proposes to use genetic algorithm to select the parameters of input shaper. Firstly, the input shaper is designed in the light of the three-loop mathematical model of motor; Secondly, because the genetic algorithm directly optimizes the output of the motor and the algorithm can reduce the influence of the deviation of generator parameters on the parameter selection of the input shaper to a certain extent, the parameters of the input shaper are selected by the genetic algorithm to optimize the output of the motor; In the finish, a comparative experiment between a system with input shaper and a system with simple PID control is carried out. The results show that the optimized system can obtain higher robustness and smaller tracking deviation.
Wind power is a clean and sustainable source of electricity, but its intermittent nature creates challenges for its grid integration. Utility-scale batteries are a popular and effective solution to make wind power dis...
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Wind power is a clean and sustainable source of electricity, but its intermittent nature creates challenges for its grid integration. Utility-scale batteries are a popular and effective solution to make wind power dispatchable, however, batteries are expensive and increase the overall cost of operation. In this paper, we propose utilizing the flexibility in the consumption of smart buildings to reduce the size of the utility-scale batteries needed for the dispatchability of wind energy. A method based on stochastic optimization is proposed to compute the dispatch plan required to participate in the day-ahead energy market. A stochastic model predictive controller is proposed for the control of the buildings and batteries to track the dispatch plan in real-time. Simulations are carried out with realistic building models and real weather, wind power production, and forecast data. Results demonstrate that utilizing the flexibility of building thermodynamics can significantly reduce the size (and usage) of the required battery for making the wind power production dispatchable.
This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different ...
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Processing of hard-to-cut materials is always challenging, justifying the need of the adoption of efficient non-conventional cutting processes, such as Abrasive Waterjet (AWJ) cutting. In the present study, an experim...
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Titanium alloys find extensive applications in the modern aerospace and offshore industry, as well in the field of biomedical implants. Nevertheless, the titanium inherent thermo-physical properties (low thermal condu...
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Biomimetic flexible tactile sensors endow prosthetics with the ability to manipulate objects,similar to human ***,it is still a great challenge to selectively respond to static and sliding friction forces,which is cru...
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Biomimetic flexible tactile sensors endow prosthetics with the ability to manipulate objects,similar to human ***,it is still a great challenge to selectively respond to static and sliding friction forces,which is crucial tactile information relevant to the perception of weight and slippage during ***,inspired by the structure of fingerprints and the selective response of Ruffini endings to friction forces,we developed a biomimetic flexible capacitive sensor to selectively detect static and sliding friction *** sensor is designed as a novel plane-parallel capacitor,in which silver nanowire-3D polydimethylsiloxane(PDMS)electrodes are placed in a spiral configuration and set perpendicular to the *** nanowires are uniformly distributed on the surfaces of 3D polydimethylsiloxane microcolumns,and silicon rubber(Ecoflex^(■))acts as the dielectric *** capacitance of the sensor remains nearly constant under different applied normal forces but increases with the static friction force and decreases when sliding ***,aiming at the slippage perception of neuroprosthetics,a custom-designed signal encoding circuit was designed to transform the capacitance signal into a bionic pulsed signal modulated by the applied sliding friction *** results demonstrate the great potential of the novel biomimetic flexible sensors with directional and dynamic sensitivity of haptic force for smart neuroprosthetics.
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