Landslide displacement prediction based on a novel hybrid model and convolutional neural network considering time-varying factors

The convolutional neural network (CNN) has made great strides in the area of voiceprint recognition; but it needs a huge number of data samples to train a deep neural network. In practice, it is too difficult to get a large number of training samples, and it cannot achieve a better convergence state due to the limited dataset. In order to solve this question, a new method using a deep migration hybrid model is put forward, which makes it easier to realize voiceprint recognition for small samples. Firstly, it uses Transfer Learning to transfer the trained network from the big sample voiceprint dataset to our limited voiceprint dataset for the further training. Fully-connected layers of a pre-training model are replaced by restricted Boltzmann machine layers. Secondly, the approach of Data Augmentation is adopted to increase the number of voiceprint datasets. Finally, we introduce fast batch normalization algorithms to improve the speed of the network convergence and shorten the training time. Our new voiceprint recognition approach uses the TLCNN-RBM (convolutional neural network mixed restricted Boltzmann machine based on transfer learning) model, which is the deep migration hybrid model that is used to achieve an average accuracy of over 97%, which is higher than that when using either CNN or the TL-CNN network (convolutional neural network based on transfer learning). Thus, an effective method for a small sample of voiceprint recognition has been provided.

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Landslide Displacement Prediction Based on Extended Escendant Strategy PSO Neural Network

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/750/1/012141 ◽

2020 ◽

Vol 750 ◽

pp. 012141

Author(s):

Yuqiu Lin ◽

Wenxing Jian ◽

Chang Xu ◽

Yongliang Pan ◽

Linjun Li

Keyword(s):

Neural Network ◽

Displacement Prediction ◽

Landslide Displacement Prediction

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Writer Identification with Hybrid Model using Hybrid HMM and ANN

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4435.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 1656-1661

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Hybrid Model ◽

Optimum Number ◽

Multi Layer Perceptron ◽

Writer Identification ◽

Mlp Model

In this paper, writer identification is performed with three models, namely, HMMBW, HMMMLP and HMMCNN. The features are extracted from the HMM and are classified using Baum Welch algorithm (BW), Multi layer perceptron (MLP) model and Convolutional neural network (CNN) model. A dataset, namely, VTU-WRITER dataset is created for the experiential purpose and the performance of the models were tested. The test train ratio was varied to derive its relation to accuracy. Also the number of states was varied to determine the optimum number of states to be considered in the HMM model. Finally the performance of all the three models is compared

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Wireless Indoor Localization Using Convolutional Neural Network and Gaussian Process Regression

Sensors ◽

10.3390/s19112508 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2508 ◽

Cited By ~ 8

Author(s):

Guolong Zhang ◽

Ping Wang ◽

Haibing Chen ◽

Lan Zhang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Gaussian Process ◽

Hybrid Model ◽

Nearest Neighbor ◽

Gaussian Process Regression ◽

Kernel Functions ◽

Training Dataset ◽

K Nearest Neighbor ◽

Positive Effects

This paper presents a localization model employing convolutional neural network (CNN) and Gaussian process regression (GPR) based on Wi-Fi received signal strength indication (RSSI) fingerprinting data. In the proposed scheme, the CNN model is trained by a training dataset. The trained model adapts to complex scenes with multipath effects or many access points (APs). More specifically, the pre-processing algorithm makes the RSSI vector which is formed by considerable RSSI values from different APs readable by the CNN algorithm. The trained CNN model improves the positioning performance by taking a series of RSSI vectors into account and extracting local features. In this design, however, the performance is to be further improved by applying the GPR algorithm to adjust the coordinates of target points and offset the over-fitting problem of CNN. After implementing the hybrid model, the model is experimented with a public database that was collected from a library of Jaume I University in Spain. The results show that the hybrid model has outperformed the model using k-nearest neighbor (KNN) by 61.8%. While the CNN model improves the performance by 45.8%, the GPR algorithm further enhances the localization accuracy. In addition, the paper has also experimented with the three kernel functions, all of which have been demonstrated to have positive effects on GPR.

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