Visualization of activated muscle area based on sEMG

Self Cite

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: Methodsmentioning

confidence: 99%

Gesture recognition based on surface electromyography‐featureimage

Cheng

et al. 2020

Self Cite

“…Choosing appropriate initial learning rate is the key to whether and when the model converges. Of course, the smaller the learning rate is, the easier it is to make the model converge, but the training time will be multiplied, so it is necessary to choose the appropriate initial learning rate 57,58 . After the parameters in the configuration file are correctly configured, the training process file will be generated from step 0 to step 200,000 in the training process.…”

Section: Methodsmentioning

confidence: 99%

“…Of course, the smaller the learning rate is, the easier it is to make the model converge, but the training time will be multiplied, so it is necessary to choose the appropriate initial learning rate. 57,58 After the parameters in the configuration file are correctly configured, the training process file will be generated from step 0 to step 200,000 in the training process. After completing the whole training process, the model file which iterates to 200,000 steps is transformed into pb model file and saved to get the two hand gesture recognition model under the color background.…”

Section: Two-hand Gesture Recognition Model Based On 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.

“…It can be concluded from Table 5 that the stress level of designed ladle and the traditional ladle in the working layer and the permanent layer is not much different, but the range of the stress level of designed ladle shell is obviously narrowed by about 25 MPa compared with the traditional ladle, which shows that the stress distribution is more uniform. Although the minimum stress of designed ladle is close to that of the traditional ladle, the maximum stress is 30 MPa less than traditional ladle, so that the ladle has enough stress margin, and its safety is further improved 63 . This shows that the new ladle has the superior performance of super insulation and heat insulation, which makes the temperature distribution more uniform, the thermal stress reduced, and the stress distribution uniform, thus prolonging the work time of in‐service of the ladle lining material, achieving the longevity of ladle.…”

Section: Stress Field Analysis Of Designed Ladlementioning

confidence: 96%

“…Although the minimum stress of designed ladle is close to that of the traditional ladle, the maximum stress is 30 MPa less than traditional ladle, so that the ladle has enough stress margin, and its safety is further improved. 63 This shows that the new ladle has the superior performance of super insulation and heat insulation, which makes the temperature distribution more uniform, the thermal stress reduced, and the stress distribution uniform, thus prolonging the work time of in-service of the ladle lining material, achieving the longevity of ladle.…”

Section: Boundary Conditions and Solutionmentioning

confidence: 96%

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

Sun

Tian

et al. 2020

Self Cite

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.