Design and Application of a Slow Feature Algorithm Coupling Visual Selectivity and Multiple Long Short-Term Memory Networks

In the traditional slow feature analysis (SFA), the expansion of polynomial basis function lacks the support of visual computing theories for primates, and cannot learn the uniform, continuous long short-term features through selective visual mechanism. To solve the defects, this paper designs and implements a slow feature algorithm coupling visual selectivity and multiple long short-term memory networks (LSTMs). Inspired by the visual invariance theory of natural images, this paper replaces the principal component analysis (PCA) of traditional SFA algorithm with myTICA (TICA: topologically independent component analysis) to extract image invariant Gabor basis functions, and initialize the space and series of basis functions. In view of the ability of the LSTM to learn long and short-term features, four LSTM algorithms were constructed to separately predict the long and short-term visual selectivity features of Gabor basis functions from the basis function series, and combine the functions into a new basis function, thereby solving the defect of polynomial prediction algorithms. In addition, a Lipschitz consistency condition was designed, and used to develop an approximate orthogonal pruning technique, which optimizes the prediction basis functions, and constructs a hyper-complete space for the basis function. The performance of our algorithm was evaluated by three metrics and mySFA’s classification method. The experimental results show that our algorithm achieved a good prediction effect on INRIA Holidays dataset, and outshined SFA, graph-based SFA (SFA), TICA, and myTICA in accuracy and feasibility; when the threshold was 6, the recognition rate of our algorithm was 99.98%, and the false accept rate (FAR) and false reject rate (FRR) were both smaller than 0.02%, indicating the strong classification ability of our approach.

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Human Gait Recognition Based on Frame-by-Frame Gait Energy Images and Convolutional Long Short-Term Memory

International Journal of Neural Systems ◽

10.1142/s0129065719500278 ◽

2019 ◽

Vol 30 (01) ◽

pp. 1950027 ◽

Cited By ~ 8

Author(s):

Xiuhui Wang ◽

Wei Qi Yan

Keyword(s):

Short Term Memory ◽

Gait Recognition ◽

Large Population ◽

Recognition Rate ◽

Human Gait ◽

Short Term ◽

Term Memory ◽

Cross Covariance ◽

Novel Method ◽

Long Short Term Memory

Human gait recognition is one of the most promising biometric technologies, especially for unobtrusive video surveillance and human identification from a distance. Aiming at improving recognition rate, in this paper we study gait recognition using deep learning and propose a novel method based on convolutional Long Short-Term Memory (Conv-LSTM). First, we present a variation of Gait Energy Images, i.e. frame-by-frame GEI (ff-GEI), to expand the volume of available Gait Energy Images (GEI) data and relax the constraints of gait cycle segmentation required by existing gait recognition methods. Second, we demonstrate the effectiveness of ff-GEI by analyzing the cross-covariance of one person’s gait data. Then, making use of the temporality of our human gait, we design a novel gait recognition model using Conv-LSTM. Finally, the proposed method is evaluated extensively based on the CASIA Dataset B for cross-view gait recognition, furthermore the OU-ISIR Large Population Dataset is employed to verify its generalization ability. Our experimental results show that the proposed method outperforms other algorithms based on these two datasets. The results indicate that the proposed ff-GEI model using Conv-LSTM, coupled with the new gait representation, can effectively solve the problems related to cross-view gait recognition.

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Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

Sustainability ◽

10.3390/su12187529 ◽

2020 ◽

Vol 12 (18) ◽

pp. 7529 ◽

Cited By ~ 4

Author(s):

Marek Vochozka ◽

Jaromir Vrbka ◽

Petr Suler

Keyword(s):

Short Term Memory ◽

Manufacturing Sector ◽

Good Prediction ◽

Manufacturing Companies ◽

Financial Health ◽

Short Term ◽

The Czech Republic ◽

Term Memory ◽

Long Short Term Memory ◽

A Company

There is no doubt that the issue of making a good prediction about a company’s possible failure is very important, as well as complicated. A number of models have been created for this very purpose, of which one, the long short-term memory (LSTM) model, holds a unique position in that it generates very good results. The objective of this contribution is to create a methodology for the identification of a company failure (bankruptcy) using artificial neural networks (hereinafter referred to as “NN”) with at least one long short-term memory (LSTM) layer. A bankruptcy model was created using deep learning, for which at least one layer of LSTM was used for the construction of the NN. For the purposes of this contribution, Wolfram’s Mathematica 13 (Wolfram Research, Champaign, Illinois) software was used. The research results show that LSTM NN can be used as a tool for predicting company failure. The objective of the contribution was achieved, since the model of a NN was developed, which is able to predict the future development of a company operating in the manufacturing sector in the Czech Republic. It can be applied to small, medium-sized and manufacturing companies alike, as well as used by financial institutions, investors, or auditors as an alternative for evaluating the financial health of companies in a given field. The model is flexible and can therefore be trained according to a different dataset or environment.

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Wind Power Prediction Based on Principal Component Analysis and Long Short-Term Memory Networks

2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia) ◽

10.1109/isgt-asia.2019.8881062 ◽

2019 ◽

Author(s):

Weiming Lang ◽

Xiangjin Ma ◽

Yanhong Luo ◽

Bowen Zhou ◽

Dongsheng Yang

Keyword(s):

Principal Component Analysis ◽

Wind Power ◽

Short Term Memory ◽

Principal Component ◽

Component Analysis ◽

Power Prediction ◽

Short Term ◽

Term Memory ◽

Wind Power Prediction ◽

Long Short Term Memory

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Using Stacked Long Short Term Memory with Principal Component Analysis for Short Term Prediction of Solar Irradiance based on Weather Patterns

2020 IEEE REGION 10 CONFERENCE (TENCON) ◽

10.1109/tencon50793.2020.9293719 ◽

2020 ◽

Author(s):

Justin D. de Guia ◽

Ronnie S. Concepcion ◽

Hilario A. Calinao ◽

Jonnel Alejandrino ◽

Elmer P. Dadios ◽

...

Keyword(s):

Principal Component Analysis ◽

Solar Irradiance ◽

Short Term Memory ◽

Principal Component ◽

Component Analysis ◽

Short Term ◽

Term Memory ◽

Weather Patterns ◽

Long Short Term Memory ◽

Short Term Prediction

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Automatic Modulation Recognition Based on a DCN-BiLSTM Network

Sensors ◽

10.3390/s21051577 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1577

Author(s):

Kai Liu ◽

Wanjun Gao ◽

Qinghua Huang

Keyword(s):

Neural Network ◽

Complex Network ◽

Communication Systems ◽

Short Term Memory ◽

Recognition Rate ◽

Modulation Recognition ◽

Short Term ◽

Term Memory ◽

Automatic Modulation Recognition ◽

Long Short Term Memory

Automatic modulation recognition (AMR) is a significant technology in noncooperative wireless communication systems. This paper proposes a deep complex network that cascades the bidirectional long short-term memory network (DCN-BiLSTM) for AMR. In view of the fact that the convolution operation of the traditional convolutional neural network (CNN) loses the partial phase information of the modulated signal, resulting in low recognition accuracy, we first apply a deep complex network (DCN) to extract the features of the modulated signal containing phase and amplitude information. Then, we cascade bidirectional long short-term memory (BiLSTM) layers to build a bidirectional long short-term memory model according to the extracted features. The BiLSTM layers can extract the contextual information of signals well and address the long-term dependence problems. Next, we feed the features into a fully connected layer. Finally, a softmax classifier is used to perform classification. Simulation experiments show that the performance of our proposed algorithm is better than that of other neural network recognition algorithms. When the signal-to-noise ratio (SNR) exceeds 4 dB, our model’s recognition rate for the 11 modulation signals can reach 90%.

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Long Short-Term Memory Projection Recurrent Neural Network Architectures for Piano’s Continuous Note Recognition

Journal of Robotics ◽

10.1155/2017/2061827 ◽

2017 ◽

Vol 2017 ◽

pp. 1-7 ◽

Cited By ~ 3

Author(s):

YuKang Jia ◽

Zhicheng Wu ◽

Yanyan Xu ◽

Dengfeng Ke ◽

Kaile Su

Keyword(s):

Short Term Memory ◽

Deep Structure ◽

Recognition Rate ◽

Single Layer ◽

Network Architectures ◽

Short Term ◽

Term Memory ◽

Training Time ◽

Long Short Term Memory ◽

And Performance

Long Short-Term Memory (LSTM) is a kind of Recurrent Neural Networks (RNN) relating to time series, which has achieved good performance in speech recogniton and image recognition. Long Short-Term Memory Projection (LSTMP) is a variant of LSTM to further optimize speed and performance of LSTM by adding a projection layer. As LSTM and LSTMP have performed well in pattern recognition, in this paper, we combine them with Connectionist Temporal Classification (CTC) to study piano’s continuous note recognition for robotics. Based on the Beijing Forestry University music library, we conduct experiments to show recognition rates and numbers of iterations of LSTM with a single layer, LSTMP with a single layer, and Deep LSTM (DLSTM, LSTM with multilayers). As a result, the single layer LSTMP proves performing much better than the single layer LSTM in both time and the recognition rate; that is, LSTMP has fewer parameters and therefore reduces the training time, and, moreover, benefiting from the projection layer, LSTMP has better performance, too. The best recognition rate of LSTMP is 99.8%. As for DLSTM, the recognition rate can reach 100% because of the effectiveness of the deep structure, but compared with the single layer LSTMP, DLSTM needs more training time.

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Airfoil buffet aerodynamics at plunge and pitch excitation based on long short-term memory neural network prediction

CEAS Aeronautical Journal ◽

10.1007/s13272-021-00550-6 ◽

2021 ◽

Author(s):

R. Zahn ◽

C. Breitsamter

Keyword(s):

Neural Network ◽

Short Term Memory ◽

Training Data ◽

Good Prediction ◽

Short Term ◽

Data Set ◽

Term Memory ◽

Identification Approach ◽

Long Short Term Memory ◽

Transonic Buffet

AbstractIn the present study, a nonlinear system identification approach based on a long short-term memory (LSTM) neural network is applied for the prediction of transonic buffet aerodynamics. The identification approach is applied as a reduced-order modeling (ROM) technique for an efficient computation of time-varying integral quantities such as aerodynamic force and moment coefficients. Therefore, the nonlinear identification procedure as well as the generalization of the ROM are presented. The training data set for the LSTM–ROM is provided by performing forced-motion unsteady Reynolds-averaged Navier–Stokes simulations. Subsequent to the training process, the ROM is applied for the computation of the aerodynamic integral quantities associated with transonic buffet. The performance of the trained ROM is demonstrated by computing the aerodynamic loads of the NACA0012 airfoil investigated at transonic freestream conditions. In contrast to previous studies considering only a pitching excitation, both the pitch and plunge degrees of freedom of the airfoil are individually and simultaneously excited by means of an user-defined training signal. Therefore, strong nonlinear effects are considered for the training of the ROM. By comparing the results with a full-order computational fluid dynamics solution, a good prediction capability of the presented ROM method is indicated. However, compared to the results of previous studies including only the airfoil pitching excitation, a slightly reduced prediction performance is shown.

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iEEG based Epileptic Seizure Detection using Reconstruction Independent Component Analysis and Long Short Term Memory Network

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2021.5.4268 ◽

2021 ◽

Vol 16 (5) ◽

Author(s):

Praveena Hirald Dwaraka ◽

Subhas C. ◽

Rama Naidu K.

Keyword(s):

Independent Component Analysis ◽

Epileptic Seizure ◽

Short Term Memory ◽

Seizure Detection ◽

Component Analysis ◽

Independent Component ◽

Short Term ◽

Term Memory ◽

Epileptic Seizure Detection ◽

Long Short Term Memory

In recent decades, an epileptic seizure is a neurological disorder, which is commonly detected from intracranial Electroencephalogram (iEEG) signals. However, the visual interpretation and inspection of iEEG signal is subjective variability, a time-consuming mechanism, slow and vulnerable to errors. In this research article, an automated epileptic seizure detection model is proposed to highlight the above-mentioned concerns. The proposed automated model integrates the Reconstruction Independent Component Analysis (RICA) and Long Short Term Memory (LSTM) for seizure detection. In the proposed model, RICA is utilized to extract the features from the normalized iEEG signals, and then the obtained feature vectors are fed to the LSTM network for classification, which effectively classifies inter-ictal and ictal iEEG signals. This experimental outcome showed that the proposed RICA-LSTM model achieved an accuracy of 98.92%, sensitivity of 99.01%, specificity of 98.68%, balanced accuracy of 99.24%, and f-score of 98.25% in epileptic seizure recognition on the SWEC-ETHZ iEEG database, which is better compared to the conventional machine learning classifiers.

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