Triboelectric nanogenerators (TENGs), as a new energy technology for distributed power, are used widely in the field of the natural environment energy harvesting. Because the natural energy is random and unstable, dyn...
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Aiming at the existing single image haze removal algorithms, which are based on prior knowledge and assumptions, subject to many limitations in practical applications, and could suffer from noise and halo amplificatio...
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Amorphous zero-valent iron (AZVI) is a promising material for the treatment of heavy metal pollution due to its special crystal structure. However, little was known about their electron transport properties, and the i...
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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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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 experimental work on AWJ cutting of a titanium alloy is performed and analysis of the results is conducted in two steps; after the correlation between process parameters and slot depth and width is established, the correlation between cutting head vibrations and kerf characteristics is investigated, in order to determine whether it is possible to use vibration values for the monitoring of the outcome of the AWJ cutting process.
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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Oysters are ecologically and commercially important species that require frequent monitoring to track population demographics (e.g. abundance, growth, mortality). Current methods of monitoring oyster reefs often requi...
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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.
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