Research on the Air-Fuel Ratio Intelligent Control Method for Coke Oven Combustion Energy Saving

103

In the current development and design of sports rehabilitation equipment or biomimetic prostheses, in addition to pay attention to the development and design of the structure, the more core is how to realize the accurate and effective control of the rehabilitation equipment or intelligent prosthesis, and the current research is based on data process and pattern recognition. This paper designs 9 kinds of actions that can react effectively to the function of the hand and extracts the original EMG signals, which are based on the sEMG of the forearm muscles of human hand movement, and uses the 20 order comb filter and wavelet threshold to preprocess the signal, and uses the root mean square, wavelength and nonlinear characteristics sample entropy in time domain as three eigenvalues to construct the input feature vectors of the subsequent action classifier. Finally, the recognition of the hand movements is realized successfully through GRNN and SVM. The recognition rate is 98.64% in SVM classifier and 96.27% in GRNN classifier. Experimental results show that the SVM classifier is better than the GRNN classifier.

Section: Wavelet Threshold Noise Reduction Of Surface Emg Signalmentioning

confidence: 99%

Surface EMG data aggregation processing for intelligent prosthetic action recognition

Jiang

et al. 2018

103

“…The main reason is that there are too many noise signals in the sample, which greatly increases the difficulty of network identification. A more skillful training strategy is needed to achieve a higher recognition rate [49][50][51]. First of all, four different training methods are used to train the DBNs network with two hidden layers, and the bottom unit of DBNs uses GB_RBM, as shown in Fig.…”

Section: Complex Background Of Gesture Recognition Results and Analysismentioning

confidence: 99%

Jointly network: a network based on CNN and RBM for gesture recognition

Cheng

Sun

et al. 2018

Hand belongs to non-rigid objects and is rich in variety, making gesture recognition more difficult. The essence of dynamic gesture recognition is the classification and recognition of single-frame still images. Therefore, this paper mainly focuses on static gesture recognition. At present, there are some problems in gesture recognition, such as accuracy, real-time or poor robustness. To solve the above problems, in this paper, the Kinect sensor is used to obtain the color and depth gesture samples, and the gesture samples are processed. On this basis, a jointly network of CNN and RBM is proposed for gesture recognition. It mainly uses superposed network of multiple RBMs to carry out unsupervised feature extraction and combined with supervised feature extraction of CNN. Finally, these two features are combined to classify them. The simulation results show that the proposed jointly network has a better performance in identifying simple background gesture samples, and the recognition capability of gesture samples in complex background needs to be improved.

“…A large number of Statistics found on the skin color in the YCbCr color space, the distribution of the ellipse in the coordinates to fit the color is more appropriate [25], and obtains the calculation formula of elliptical model:…”

Section: Selection Of Skin Color Modelmentioning

confidence: 99%

Research on gesture recognition of smart data fusion features in the IoT

Tan

Sun

et al. 2019

With the rapid development of Internet of Things (IoT) technology, the interaction between people and things has become increasingly frequent. Use simple gestures instead of complex operations to interact with the machine, the fusion of smart data feature information and so on has gradually become a research hotspot. Considering that the depth image of the Kinect sensor lacks color information and is susceptible to depth thresholds, this paper proposes a gesture segmentation method based on the fusion of color information and depth information, in order to ensure the complete information of the segmentation image, a gesture feature extraction method based on Hu invariant moment and HOG feature fusion is proposed, by determining the optimal weight parameters, the global and local features are effectively fused. Finally, the SVM classifier is used to classify and identify gestures. The experimental results show that the proposed fusion features method has a higher gesture recognition rate and better robustness than the traditional method.