scholarly journals Human Body Mixed Motion Pattern Recognition Method Based on Multi-Source Feature Parameter Fusion

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 537 ◽  
Author(s):  
Jiyuan Song ◽  
Aibin Zhu ◽  
Yao Tu ◽  
Yingxu Wang ◽  
Muhammad Affan Arif ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Lower Extremity ◽  
Human Body ◽  
Recognition Accuracy ◽  
Screening Method ◽  
Motion Pattern ◽  
Recognition Method ◽  
Motion State ◽  
Motion Mode

Aiming at the requirement of rapid recognition of the wearer’s gait stage in the process of intelligent hybrid control of an exoskeleton, this paper studies the human body mixed motion pattern recognition technology based on multi-source feature parameters. We obtain information on human lower extremity acceleration and plantar analyze the relationship between these parameters and gait cycle studying the motion state recognition method based on feature evaluation and neural network. Based on the actual requirements of exoskeleton per use, 15 common gait patterns were determined. Using this, the studies were carried out on the time domain, frequency domain, and energy feature extraction of multi-source lower extremity motion information. The distance-based feature screening method was used to extract the optimal features. Finally, based on the multi-layer BP (back propagation) neural network, a nonlinear mapping model between feature quantity and motion state was established. The experimental results showed that the recognition accuracy in single motion mode can reach up to 98.28%, while the recognition accuracy of the two groups of experiments in mixed motion mode was found to be 92.7% and 97.4%, respectively. The feasibility and effectiveness of the model were verified.

scholarly journals Cable Incipient Fault Identification with a Sparse Autoencoder and a Deep Belief Network

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3424 ◽  
Author(s):  
Ning Liu ◽  
Bo Fan ◽  
Xianyong Xiao ◽  
Xiaomei Yang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Recognition Accuracy ◽  
Power Cables ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Belief Network ◽  
Incipient Faults ◽  
Sparse Autoencoder ◽  
Traditional Pattern

Incipient faults in power cables are a serious threat to power safety and are difficult to accurately identify. The traditional pattern recognition method based on feature extraction and feature selection has strong subjectivity. If the key feature information cannot be extracted accurately, the recognition accuracy will directly decrease. To accurately identify incipient faults in power cables, this paper combines a sparse autoencoder and a deep belief network to form a deep neural network, which relies on the powerful learning ability of the neural network to classify and identify various cable fault signals, without requiring preprocessing operations for the fault signals. The experimental results demonstrate that the proposed approach can effectively identify cable incipient faults from other disturbances with a similar overcurrent phenomenon and has a higher recognition accuracy and reliability than the traditional pattern recognition method.

Research on fault diagnosis of automobile engines based on the deep learning 1D-CNN method

2022 ◽  
Author(s):  
Canyi Du ◽  
Rui Zhong ◽  
Yishen Zhuo ◽  
Xinyu Zhang ◽  
Feifei Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Vibration Signals ◽  
One Dimensional ◽  
Sample Set

Abstract Traditional engine fault diagnosis methods usually need to extract the features manually before classifying them by the pattern recognition method, which makes it difficult to solve the end-to-end fault diagnosis problem. In recent years, deep learning has been applied in different fields, bringing considerable convenience to technological change, and its application in the automotive field also has many applications, such as image recognition, language processing, and assisted driving. In this paper, a one-dimensional convolutional neural network (1D-CNN) in deep learning is used to process vibration signals to achieve fault diagnosis and classification. By collecting the vibration signal data of different engine working conditions, the collected data are organized into several sets of data in a working cycle, which are divided into a training sample set and a test sample set. Then, a one-dimensional convolutional neural network model is built in Python to allow the feature filter (convolution kernel) to learn the data from the training set and these convolution checks process the input data of the test set. Convolution and pooling extract features to output to a new space, which is characterized by learning features directly from the original vibration signals and completing fault diagnosis. The experimental results show that the pattern recognition method based on a one-dimensional convolutional neural network can be effectively applied to engine fault diagnosis and has higher diagnostic accuracy than traditional methods.

scholarly journals Impact of Synaptic Device Variations on Pattern Recognition Accuracy in a Hardware Neural Network

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungho Kim ◽  
Meehyun Lim ◽  
Yeamin Kim ◽  
Hee-Dong Kim ◽  
Sung-Jin Choi
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Recognition Accuracy ◽  
Hardware Neural Network

scholarly journals Fast identification method for express end sorting label code based on convolutional recurrent neural network

2020 ◽  
Vol 14 (8) ◽  
pp. 1689-1697
Author(s):  
Haiyan Du ◽  
Chunxue Wu ◽  
Yan Wu ◽  
Ren Han ◽  
Xiao Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Processing Speed ◽  
Recognition Accuracy ◽  
Code Region ◽  
Digital Information ◽  
Coding Region ◽  
Recognition Method ◽  
Sequence Recognition ◽  
Sorting Process

Abstract In the automatic sorting process of express, the express end sorting label code is used to indicate that the express is dispatched to a specific address by a specific courier. Since there are many areas on the express bill containing digital information, some areas may be improperly photographed, etc. The difficulty in positioning and recognizing the express end sorting label code region is increased. To solve this problem, this paper proposes an express end sorting label code recognition method with convolutional recurrent neural network for the code specification, which has certain versatility. In order to improve the overall code recognition speed, this paper optimizes the traditional digital recognition method, removes the original segmentation operation of the character and recognizes the code as sequence recognition. Firstly, the coding region is located, and then, the express end sorting label code is recognized by the convolutional recurrent neural network. In order to test the experimental performance, this paper tests on Free-Type dataset and SUN-synthesized dataset. The experimental results show that the proposed method improves the recognition accuracy and processing speed of the express end sorting label code.

scholarly journals Impact of RTN on Pattern Recognition Accuracy of RRAM-Based Synaptic Neural Network

2018 ◽  
Vol 39 (11) ◽  
pp. 1652-1655 ◽  
Author(s):  
Zheng Chai ◽  
Pedro Freitas ◽  
Weidong Zhang ◽  
Firas Hatem ◽  
Jian Fu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Recognition Accuracy

scholarly journals Effects of Different Feature Parameters of sEMG on Human Motion Pattern Recognition Using Multilayer Perceptrons and LSTM Neural Networks

2020 ◽  
Vol 10 (10) ◽  
pp. 3358 ◽  
Author(s):  
Jiyuan Song ◽  
Aibin Zhu ◽  
Yao Tu ◽  
Hu Huang ◽  
Muhammad Affan Arif ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Human Motion ◽  
Control Mode ◽  
Multilayer Perceptrons ◽  
Model Parameters ◽  
Motion Pattern ◽  
Accuracy Rate ◽  
Feature Parameters

In response to the need for an exoskeleton to quickly identify the wearer’s movement mode in the mixed control mode, this paper studies the impact of different feature parameters of the surface electromyography (sEMG) signal on the accuracy of human motion pattern recognition using multilayer perceptrons and long short-term memory (LSTM) neural networks. The sEMG signals are extracted from the seven common human motion patterns in daily life, and the time domain and frequency domain features are extracted to build a feature parameter dataset for training the classifier. Recognition of human lower extremity movement patterns based on multilayer perceptrons and the LSTM neural network were carried out, and the final recognition accuracy rates of different feature parameters and different classifier model parameters were compared in the process of establishing the dataset. The experimental results show that the best accuracy rate of human motion pattern recognition using multilayer perceptrons is 95.53%, and the best accuracy rate of human motion pattern recognition using the LSTM neural network is 96.57%.

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