Grasping force prediction based on sEMG signals

Ma, Ruyi; Zhang, Leilei; Li, Gongfa; Du, Jiang; Xu, Shuang; Chen, Disi

doi:10.1016/j.aej.2020.01.007

Cited by 118 publications

(86 citation statements)

References 55 publications

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“…When the torque classification granularity was very fine (such as 20), the independent CNN network had the worst training effect and the training process did not converge Figure 8 , with an estimated accuracy of only 66.25%. This was because the Myo armband used in this study had a larger measurement error than the professional sEMG testing equipment used in other studies [ 38 ]. Therefore, too fine classification granularity would increase the training difficulty of the independent CNN model.…”

Section: Experiments and Results Analysismentioning

confidence: 98%

Multi-Segmentation Parallel CNN Model for Estimating Assembly Torque Using Surface Electromyography Signals

Chen

Huang

et al. 2020

Sensors

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The precise application of tightening torque is one of the important measures to ensure accurate bolt connection and improvement in product assembly quality. Currently, due to the limited assembly space and efficiency, a wrench without the function of torque measurement is still an extensively used assembly tool. Therefore, wrench torque monitoring is one of the urgent problems that needs to be solved. This study proposes a multi-segmentation parallel convolution neural network (MSP-CNN) model for estimating assembly torque using surface electromyography (sEMG) signals, which is a method of torque monitoring through classification methods. The MSP-CNN model contains two independent CNN models with different or offset torque granularities, and their outputs are fused to obtain a finer classification granularity, thus improving the accuracy of torque estimation. First, a bolt tightening test bench is established to collect sEMG signals and tightening torque signals generated when the operator tightens various bolts using a wrench. Second, the sEMG and torque signals are preprocessed to generate the sEMG signal graphs. The range of the torque transducer is divided into several equal subdivision ranges according to different or offset granularities, and each subdivision range is used as a torque label for each torque signal. Then, the training set, verification set, and test set are established for torque monitoring to train the MSP-CNN model. The effects of different signal preprocessing methods, torque subdivision granularities, and pooling methods on the recognition accuracy and torque monitoring accuracy of a single CNN network are compared experimentally. The results show that compared to maximum pooling, average pooling can improve the accuracy of CNN torque classification and recognition. Moreover, the MSP-CNN model can improve the accuracy of torque monitoring as well as solve the problems of non-convergence and slow convergence of independent CNN network models.

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Section: Experiments and Results Analysismentioning

confidence: 98%

Multi-Segmentation Parallel CNN Model for Estimating Assembly Torque Using Surface Electromyography Signals

Chen

Huang

et al. 2020

Sensors

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“…27,28 The specific raw materials used, as well as the contents of the required micronized powder, aggregate and bleaching beads in the raw materials can be seen in Table 1. 29 The thermal conductivity of inorganic nano -SiO 2 is smaller than that of static air, which can effectively improve the thermal insulation of ladle. 30 Hollow floating bead is selected from the fly ash of power plant.…”

Section: Selection Of Refractory Materials For Ladle Liningmentioning

confidence: 99%

“…High alumina cement and various fine powders and aggregates were added, optimized the proportion of various fine powders and aggregates to make thermal insulation bricks and the ladle was simulated under the conditions of baking 27,28 . The specific raw materials used, as well as the contents of the required micronized powder, aggregate and bleaching beads in the raw materials can be seen in Table 1 29 …”

Section: New Lightweight Ladlementioning

confidence: 99%

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

Sun

Tian

et al. 2020

Concurrency and Computation

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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.

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“…Applying computer-aided diagnosis technology to intestinal cancer is a relatively new topic both domestically and internationally, not only because of its concealed location, but also as a narrow and long tubular structure, and because of its complicated adjacent relationship and strong adhesion to other organs, which have brought great difficulties to the early diagnosis of intestinal cancer. Currently there is no general digital image processing method [39]. The purpose of this study is to find a universal, rapid and accurate method for the detection of intestinal cancer.…”

Section: Related Workmentioning

confidence: 99%

Research on Recognition of Medical Image Detection Based on Neural Network

Wang

et al. 2020

IEEE Access

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Bowel cancer, which is easily affected by diet and drugs, has some restrictive factors such as the fecal occult blood test (FOBT) in the routine detection and the high cost and inconvenience of microscopy. In order to break through these restrictive factors, a possible alternative method of FOBT is sought. In this paper, error back propagation neural network (BPNN) algorithm is used, and expression spectrum is used as an auxiliary method to detect medical images, and a colorectal cancer (CRC) diagnosis model based on neural network is constructed. The results show that the accuracy of the model on the training set and the test set are 0.943 and 0.935, respectively, the AUC reaches more than 0.95. Therefore, the CRC diagnosis model based on neural network provides a possible alternative method of FOBT. Experimental results show that the proposed algorithm have high robustness and accuracy, which meets the current clinical needs. INDEX TERMS Neural network, fecal occult blood detection, error back propagation neural network, medical image.

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Grasping force prediction based on sEMG signals

Cited by 118 publications

References 55 publications

Multi-Segmentation Parallel CNN Model for Estimating Assembly Torque Using Surface Electromyography Signals

Multi-Segmentation Parallel CNN Model for Estimating Assembly Torque Using Surface Electromyography Signals

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

Research on Recognition of Medical Image Detection Based on Neural Network

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