Multi-object intergroup gesture recognition combined with fusion feature and KNN algorithm

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For the problem of surface electromyography (sEMG) gesture recognition, considering the fact that the traditional machine learning model is susceptible to the sEMG feature extraction method, it is difficult to distinguish the subtle differences between similar gestures. The NinaPro DB1 dataset is used as the research object, and the sEMG feature image and the Convolutional Neural Network (CNN) are combined to recognize 52 gesture movements. The CNN model effectively solves the limitations of traditional machine learning in sEMG gesture recognition, and combines 1-dim convolution kernel to extract deep abstract features to improve the recognition effect. Finally, the simulation experiment shows that compared with the accuracy of the raw-sEMG images based on the CNN and the sEMG-feature-images based on the CNN and sEMG based on the traditional machine learning, the multi-sEMG-features image based on the CNN is the highest, which coming up to 82.54%.

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Gesture recognition based on surface electromyography‐featureimage

Cheng

et al. 2020

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“…Roughly speaking, we can reduce the dimension of the activation space by reducing the layer dimension. Mobilenets network uses this point to reduce the dimension of the activation space by the wide high multiplier until the manifold of interest spans the whole space 32‐35 . But when the depth neural network makes nonlinear transformation to each coordinate, this method fails.…”

Section: Improved Ssd Networkmentioning

confidence: 99%

Occlusion gesture recognition based on improvedSSD

Liao

et al. 2020

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Gesture recognition has always been a research hotspot in the field of human-computer interaction. Its purpose is to realize the natural interaction with the machine by recognizing the semantics expressed by gesture. In the process of gesture recognition, the occlusion of gesture is an inevitable problem. In the process of gesture recognition, some or even all of the gesture features will be lost due to the occlusion of the gesture, resulting in the wrong recognition or even unrecognizability of the gesture. Therefore, it is of great significance to study gesture recognition under occlusion. The single shot multibox detector (SSD) algorithm is analyzed, and the front-end network is compared. Mobilenets is selected as the front-end network, and the Mobilenets-SSD network is improved. In tensorflow environment, based on the improved network model, the self-occlusion gesture and object occluding gesture are trained in color map, depth map, and color and depth fusion respectively. The recognition models of self-occlusion gestures and object-occlusion gestures in color map, depth map, and color and depth fusion are obtained. And compare and analyze the learning rate, loss function, and average accuracy of various models obtained for occlusion gesture recognition.

“…The thermal insulation performance of ladle mainly depends on the ladle shell temperature and the speed of temperature drop of liquid steel. 37 In this study, the ladle shell temperature is taken as the main reference value of the thermal insulation performance of ladle. Table 2 shows the thickness design of each layer of different schemes and Table 3 shows the physical parameters of various layers in steel ladle.…”

Section: Ladle Lining Structure Designmentioning

confidence: 99%

Numerical simulation of thermal insulation and longevity performance in new lightweight ladle

Sun

Tian

et al. 2020

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For meeting the comprehensive requirements of "super insulation," "lightweight," and "longevity" of the contemporary ladle, this article designs a new kind of lightweight ladle with heat preservation and longevity performance, and based on steady-state analysis method and numerical simulation technology, the comparison of temperature distribution between new lightweight and traditional ladle under typical operating modes is made and analyzed. The simulation results of temperature field prove that the performance of heat preservation in new kind of lightweight ladle has been improved obviously from the two aspects of ladle shell temperature and molten steel file rate. At the same time, the simulation results of stress field indicate that the stress of designed lightweight ladle reduced and distributed more evenly, which is conducive to prolonging the work time in-service of the ladle. Finally, based on the field test, the simulation is proved to be effective, and the designed ladle structure achieves the expected purpose.