Improved Pattern Recognition Accuracy of Hardware Neural Network: Deactivating Short Failed Synapse Device by Adopting Ovonic Threshold Switching (OTS)-Based Fuse Device

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.

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

IEEE Electron Device Letters ◽

10.1109/led.2018.2869072 ◽

2018 ◽

Vol 39 (11) ◽

pp. 1652-1655 ◽

Cited By ~ 8

Author(s):

Zheng Chai ◽

Pedro Freitas ◽

Weidong Zhang ◽

Firas Hatem ◽

Jian Fu Zhang ◽

...

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Recognition Accuracy

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Recognition of Crop Diseases Based on Depthwise Separable Convolution in Edge Computing

Sensors ◽

10.3390/s20154091 ◽

2020 ◽

Vol 20 (15) ◽

pp. 4091

Author(s):

Musong Gu ◽

Kuan-Ching Li ◽

Zhongwen Li ◽

Qiyi Han ◽

Wenjie Fan

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Recognition Accuracy ◽

Recognition Algorithm ◽

Edge Computing ◽

Recognition Model ◽

Fast Recognition ◽

Proposed Model ◽

Crop Diseases ◽

High Recognition Accuracy

The original pattern recognition and classification of crop diseases needs to collect a large amount of data in the field and send them next to a computer server through the network for recognition and classification. This method usually takes a long time, is expensive, and is difficult to carry out for timely monitoring of crop diseases, causing delays to diagnosis and treatment. With the emergence of edge computing, one can attempt to deploy the pattern recognition algorithm to the farmland environment and monitor the growth of crops promptly. However, due to the limited resources of the edge device, the original deep recognition model is challenging to apply. Due to this, in this article, a recognition model based on a depthwise separable convolutional neural network (DSCNN) is proposed, which operation particularities include a significant reduction in the number of parameters and the amount of computation, making the proposed design well suited for the edge. To show its effectiveness, simulation results are compared with the main convolution neural network (CNN) models LeNet and Visual Geometry Group Network (VGGNet) and show that, based on high recognition accuracy, the recognition time of the proposed model is reduced by 80.9% and 94.4%, respectively. Given its fast recognition speed and high recognition accuracy, the model is suitable for the real-time monitoring and recognition of crop diseases by provisioning remote embedded equipment and deploying the proposed model using edge computing.

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Partial Discharge Pattern Recognition of Transformers Based on MobileNets Convolutional Neural Network

Applied Sciences ◽

10.3390/app11156984 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6984

Author(s):

Yuanyuan Sun ◽

Shuo Ma ◽

Shengya Sun ◽

Ping Liu ◽

Lina Zhang ◽

...

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Power System ◽

Convolutional Neural Network ◽

Oil And Gas ◽

Recognition Accuracy ◽

Partial Discharge ◽

Nonlinear Function ◽

Classification Performance ◽

Feature Expression

The power system on the offshore platform is of great importance since it is the power source for oil and gas exploitation, procession and transportation. Transformers constitute key equipment in the power system, and partial discharge (PD) is its most common fault that should be monitored and identified ın a timely and accurate manner. However, the existing PD classifiers cannot meet the demand for real-time online monitoring due to their disadvantages of high memory consumption and poor timeliness. Therefore, a new MobileNets convolutional neural network (MCNN) model is proposed to identify the PD pattern of transformers based on the phase resolved partial discharge (PRPD) spectrum. The model has the advantages of low computational complexity, fast reasoning speed and excellent classification performance. Firstly, we make four typical defect models of PD and conduct a test in a laboratory to collect the PRPD spectra as the data sample. In order to further improve the feature expression ability and recognition accuracy of the model, the lightweight attention mechanism Squeeze-and-Excitation (SE) module and the nonlinear function hard-swish (h-swish) are added after constructing the MCNN model to eliminate the potential accuracy loss in PD pattern recognition. The MCNN model is trained and tested with the pre-processed PRPD spectrum, and a variety of methods are used to visualize the model to verify the effectiveness of the model. Finally, the performance of MCNN is compared with many existing PD pattern recognition models based on convolutional neural network (CNN), the results show that the proposed MCNN can further reduce the number of parameters of the model and improve the calculation speed to achieve the best performance on the premise of good recognition accuracy.

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

Energies ◽

10.3390/en12183424 ◽

2019 ◽

Vol 12 (18) ◽

pp. 3424 ◽

Cited By ~ 1

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.

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Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network

Energies ◽

10.3390/en12244674 ◽

2019 ◽

Vol 12 (24) ◽

pp. 4674 ◽

Cited By ~ 4

Author(s):

Yanxin Wang ◽

Jing Yan ◽

Zhou Yang ◽

Tingliang Liu ◽

Yiming Zhao ◽

...

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Convolutional Neural Network ◽

Recognition Accuracy ◽

Partial Discharge ◽

Feature Engineering ◽

Deep Convolutional Neural Networks ◽

Pattern Recognition Methods ◽

Gas Insulated Switchgear ◽

Model Visualization

Partial discharge (PD) is one of the major form expressions of gas-insulated switchgear (GIS) insulation defects. Because PD will accelerate equipment aging, online monitoring and fault diagnosis plays a significant role in ensuring safe and reliable operation of the power system. Owing to feature engineering or vanishing gradients, however, existing pattern recognition methods for GIS PD are complex and inefficient. To improve recognition accuracy, a novel GIS PD pattern recognition method based on a light-scale convolutional neural network (LCNN) without artificial feature engineering is proposed. Firstly, GIS PD data are obtained through experiments and finite-difference time-domain simulations. Secondly, data enhancement is reinforced by a conditional variation auto-encoder. Thirdly, the LCNN structure is applied for GIS PD pattern recognition while the deconvolution neural network is used for model visualization. The recognition accuracy of the LCNN was 98.13%. Compared with traditional machine learning and other deep convolutional neural networks, the proposed method can effectively improve recognition accuracy and shorten calculation time, thus making it much more suitable for the ubiquitous-power Internet of Things and big data.

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