A Multirate Sensor Information Fusion Strategy for Multitask Fault Diagnosis Based on Convolutional Neural Network

Ma, Xiangkai; Wang, Pei; Zhang, Bozhou; Sun, Ming

doi:10.1155/2021/9952450

Cited by 10 publications

(3 citation statements)

References 34 publications

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“…e optimal kernel functions corresponding to different features may also be different, so the performance of a single core for complex face image recognition is not high, and it becomes meaningful for multi-core learning and research [11,12]. Support vector machines have great advantages in dealing with small sample learning problems and have a solid theoretical foundation [13].…”

Section: Application Of Support Vector Machine Face Recognitionmentioning

confidence: 99%

Convolutional Neural Network Face Recognition Method Using Fisher’s Criterion

Wang

2022

Mobile Information Systems

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In order to further improve the ability of automatic feature extraction of deep learning technology and better solve the problem of system recognition ability declining when the number of training samples is reduced or the number of iterations is reduced, this paper proposes a fisher-based convolutional neural network algorithm, which automatically obtains the structural feature information of a face image with the help of a deep learning algorithm network and then reduces the number of weights with the help of a convolutional neural network to further reduce the complexity of the face recognition model. Through the system test, the verification data shows that the recognition error rate can be controlled at about 10% by extracting 10 of the 14 pictures of each type of face for training. When the training images are reduced, the recognition rate can be further improved.

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Section: Application Of Support Vector Machine Face Recognitionmentioning

confidence: 99%

Convolutional Neural Network Face Recognition Method Using Fisher’s Criterion

Wang

2022

Mobile Information Systems

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“…Reference [11] uses a convolutional LSTM to identify the original time series data of the signal, and its model performance exceeds the simple LSTM model in terms of recognition ability. Reference [12] used the deep network built by CNN and LSTM to classify and identify vibration signals. On the basis of previous research, this paper proposes a mental health state prediction method based on ResNet combined with LSTM [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Mental Health Education in Primary and Secondary Schools Based on Deep Learning

Wang¹,

An²

2022

Journal of Sensors

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Strengthening the mental health education of primary and middle school students is a key measure to lead their healthy growth. This paper takes mental health early warning as the starting point to carry out research on mental health education in primary and secondary schools. Aiming at the defect that the existing methods cannot effectively warn the mental health of primary and secondary school students, this paper proposes a mental health early warning research method based on the deep learning models. The method firstly obtains the mental health data of college students through the symptom self-rating scale. Then, a mental health prediction model combining convolutional neural networks (CNN), deep residual networks (ResNet), and long short-term memory (LSTM) is adopted. Through in-depth processing and analysis of mental health data, the mental health status of students can be determined. Experiments show that this method can effectively improve the accuracy of mental health early warning compared with the currently commonly used mental health early warning methods.

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“…It is mostly used to find early faults [ 17 ]. Frequency domain analysis can obtain rich and effective characteristic information to realize fault identification, but it is limited to stationary signal analysis [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

Fault Diagnosis of Wind Turbine Based on Convolution Neural Network Algorithm

Xiao

Wang

2022

Computational Intelligence and Neuroscience

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Relying on expert diagnosis, it solves the problem of fan failure efficiency and meets the needs of automatic inspection and intelligent operation monitoring of fans. In order to make up for the deficiency of intelligent diagnosis of bearing fault based on vibration signal detection, signal transformation, and convolution neural network identification and improve the ability of intelligent diagnosis, this study designs a deep convolution neural network model and diagnosis algorithm with three pairs of convolution pooling layers and two full connection layers. The experimental verification of the proposed method is carried out based on the public data set, and the effects of three different signal transformation methods based on vibration signal through vibration gray map, short-time Fourier transform time-frequency map, and continuous wavelet transform time-frequency map on the accuracy of diagnosis model are compared and analyzed. A very accurate guarantee is received, close to 100%. The final experimental results demonstrate the effectiveness of the information on the accuracy of diagnostic testing and provide new ideas for the verification and testing of wind turbine wind energy. Compared with other machine learning algorithms, the real-time recognition of machine learning based on time-domain statistical features is lower than that of convolutional neural network methods. The effect of the scale of the trained model on the accuracy of the algorithm is discussed. A sample ratio of 50% and 70% was found to be appropriate.

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A Multirate Sensor Information Fusion Strategy for Multitask Fault Diagnosis Based on Convolutional Neural Network

Cited by 10 publications

References 34 publications

Convolutional Neural Network Face Recognition Method Using Fisher’s Criterion

Convolutional Neural Network Face Recognition Method Using Fisher’s Criterion

Mental Health Education in Primary and Secondary Schools Based on Deep Learning

Fault Diagnosis of Wind Turbine Based on Convolution Neural Network Algorithm

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