With the development of robotics, intelligent neuroprosthesis for amputees is more concerned. Research of robot controlling based on electrocardiogram, electromyography, and electroencephalogram is a hot spot. In medical research, electrode arrays are commonly used as sensors for surface electromyograms. Although these sensors collect more accurate data and sampling at higher frequencies, they have no advantage in terms of portability and ease of use. In recent years, there are also some small surface electromyography sensors for research. The portability of the sensor and the calculation speed of the calculation method directly affect the development of the bionic prosthesis. A consumer-grade surface electromyography device is selected as surface electromyography sensor in this study. We first proposed a data structure to convert raw surface electromyography signals from an array structure into a matrix structure (we called it surface electromyography graph). Then, a convolutional neural network was used to classify it. Discrete surface electromyography signals recorded from three persons 14 gestures (widely used in other research to evaluate the performance of classifier) have been applied to train the classifier and we get an accuracy of 97.27%. The impacts of different components used in convolutional neural network were tested with this data, and subsequently, the best results were selected to build the classifier used in this article. The NinaPro database 5 (one of the biggest surface electromyography data sets) was also used to evaluate our method, which comprises of hand movement data of 10 intact subjects with two myo armbands as sensors, and the classification accuracy increased by 13.76% on average when using double myo armbands and increased by 18.92% on average when using single myo armband. In order to driving the robot hand (bionic manipulator), a group of continuous surface electromyography signals was recorded to train the classifier, and an accuracy of 91.72% was acquired. We also used the same method to collect a set of surface electromyography data from a disabled with hand lost, then classified it using the abovementioned network and achieved an accuracy of 89.37%. Finally, the classifier was deployed to the microcontroller to drive the bionic manipulator, and the full video URL is given in the conclusion, with both the healthy man and the disabled tested with the bionic manipulator. The abovementioned results suggest that this method will help to facilitate the development and application of surface electromyography neuroprosthesis.
Regeneration of spent caustic from liquefied petroleum gas sweetening in refineries is significant to save resources and reduce solid waste emissions. Conventional regeneration technologies have been widely used, but these technologies usually have a reactor and a separator in series which occupies a large space. In this work, simultaneous reaction and separation processes were proposed to be conducted in a rotating packed bed (RPB), aiming for the process intensification of spent caustic regeneration. Experimental results of laboratory tests show that the regeneration processes of mercaptide oxidation and disulfide separation were efficiently enhanced in the RPB. The side-line test, treating the actual spent caustic from a refinery, could achieve good performance for a long-time running. An artificial neural network model was applied for parameters analysis and prediction of the regeneration performance. This work demonstrated the feasibility of spent caustic regeneration in only one RPB unit, which displays bright prospects for industrial application.
In this work, an
artificial neural network was first achieved and
optimized for evaluating product distribution and studying the octane
number of the sulfuric acid-catalyzed C4 alkylation process in the
stirred tank and rotating packed bed. The feedstock compositions,
operating conditions, and reactor types were considered as input parameters
into the artificial neural network model. Algorithm, transfer function,
and framework were investigated to select the optimal artificial neural
network model. The optimal artificial neural network model was confirmed
as a network topology of 10-20-30-5 with Bayesian Regularization backpropagation
and tan-sigmoid transfer function. Research octane number and product
distribution were specified as output parameters. The artificial neural
network model was examined, and 5.8 × 10
–4
training
mean square error, 8.66 × 10
–3
testing mean
square error, and ±22% deviation were obtained. The correlation
coefficient was 0.9997, and the standard deviation of error was 0.5592.
Parameter analysis of the artificial neural network model was employed
to investigate the influence of operating conditions on the research
octane number and product distribution. It displays a bright prospect
for evaluating complex systems with an artificial neural network model
in different reactors.
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