Spatio-temporal predictions of SST time series in China’s offshore waters using a regional convolution long short-term memory (RC-LSTM) network

An enhanced architecture of recurrent neural network based on Long Short-Term Memory (LSTM) is suggested in this paper for predicting the microclimate inside the greenhouse through its time series data. The microclimate inside the greenhouse largely affected by the external weather variations and it has a great impact on the greenhouse crops and its production. Therefore, it is a massive importance to predict the microclimate inside greenhouse as a preceding stage for accurate design of a control system that could fulfill the requirements of suitable environment for the plants and crop managing. The LSTM network is trained and tested by the temperatures and relative humidity data measured inside the greenhouse utilizing the mathematical greenhouse model with the outside weather data over 27 days. To evaluate the prediction accuracy of the suggested LSTM network, different measurements, such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), are calculated and compared to those of conventional networks in references. The simulation results of LSTM network for forecasting the temperature and relative humidity inside greenhouse outperform over those of the traditional methods. The prediction results of temperature and humidity inside greenhouse in terms of RMSE approximately are 0.16 and 0.62 and in terms of MAE are 0.11 and 0.4, respectively, for both of them.

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Time series analysis and long short-term memory (LSTM) network prediction of BPV current density

Energy & Environmental Science ◽

10.1039/d0ee02970j ◽

2021 ◽

Vol 14 (4) ◽

pp. 2408-2418 ◽

Cited By ~ 1

Author(s):

Tonny I. Okedi ◽

Adrian C. Fisher

Keyword(s):

Time Series ◽

Current Density ◽

Short Term Memory ◽

High Accuracy ◽

Seasonal Component ◽

Short Term ◽

Term Memory ◽

Network Prediction ◽

Long Short Term Memory ◽

Lstm Network

LSTM networks are shown to predict the seasonal component of biophotovoltaic current density and photoresponse to high accuracy.

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Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network

Sensors ◽

10.3390/s19214612 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4612 ◽

Cited By ~ 19

Author(s):

Pangun Park ◽

Piergiuseppe Di Marco ◽

Hyejeon Shin ◽

Junseong Bang

Keyword(s):

Time Series ◽

Fault Detection ◽

Short Term Memory ◽

Fault Detection And Diagnosis ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Detection And Diagnosis ◽

Lstm Network ◽

Types Of Faults

Fault detection and diagnosis is one of the most critical components of preventing accidents and ensuring the system safety of industrial processes. In this paper, we propose an integrated learning approach for jointly achieving fault detection and fault diagnosis of rare events in multivariate time series data. The proposed approach combines an autoencoder to detect a rare fault event and a long short-term memory (LSTM) network to classify different types of faults. The autoencoder is trained with offline normal data, which is then used as the anomaly detection. The predicted faulty data, captured by autoencoder, are put into the LSTM network to identify the types of faults. It basically combines the strong low-dimensional nonlinear representations of the autoencoder for the rare event detection and the strong time series learning ability of LSTM for the fault diagnosis. The proposed approach is compared with a deep convolutional neural network approach for fault detection and identification on the Tennessee Eastman process. Experimental results show that the combined approach accurately detects deviations from normal behaviour and identifies the types of faults within the useful time.

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Long-Short Term Memory (LSTM) Networks with Time Series and Spatio-Temporal Approaches Applied in Forecasting Earthquakes in the Philippines

Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval ◽

10.1145/3443279.3443288 ◽

2020 ◽

Author(s):

Aleta C. Fabregas ◽

Patrick Brian V. Arellano ◽

Andrea Nicole D. Pinili

Keyword(s):

Time Series ◽

Short Term Memory ◽

The Philippines ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Spatio Temporal

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Convolutional Long Short-Term Memory Hybrid Networks for Skeletal Based Human Action Recognition

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.c8085.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 955-961

Keyword(s):

Time Series ◽

Action Recognition ◽

Short Term Memory ◽

Human Action Recognition ◽

Human Action ◽

Short Term ◽

Term Memory ◽

Temporal Modeling ◽

Long Short Term Memory ◽

Spatio Temporal

The objective is to develop a time series image representation of the skeletal action data and use it for recognition through a convolutional long short-term deep learning framework. Consequently, Kinect captured human skeletal data is transformed into a Joint Change Distance Image (JCDI) descriptor which maps the time changes in the joints. Subsequently, JCDIs are decoded spatially well with a Convolutional (CNN). Temporal decomposition is executed on long short term memory (LSTM) with data changes along x , y and z position vectors of the skeleton. We propose a combination of CNN and LSTM which maps the spatio temporal information to generate a generalized time series features for recognition. Finally, scores are fused from spatially vibrant CNNs and temporally sound LSTMs for action recognition. Publicly available action datasets such as NTU RGBD, MSR Action, UTKinect and G3D were used as test inputs for experimentation. The results showed a better performance due to spatio temporal modeling at both the representation and the recognition stages when compared to other state-of-the-arts

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Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life

Sensors ◽

10.3390/s20010166 ◽

2019 ◽

Vol 20 (1) ◽

pp. 166 ◽

Cited By ~ 4

Author(s):

Jehn-Ruey Jiang ◽

Juei-En Lee ◽

Yi-Ming Zeng

Keyword(s):

Neural Network ◽

Time Series ◽

Convolutional Neural Network ◽

Short Term Memory ◽

Remaining Useful Life ◽

Multiple Channels ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network

This paper proposes two deep learning methods for remaining useful life (RUL) prediction of bearings. The methods have the advantageous end-to-end property that they take raw data as input and generate the predicted RUL directly. They are TSMC-CNN, which stands for the time series multiple channel convolutional neural network, and TSMC-CNN-ALSTM, which stands for the TSMC-CNN integrated with the attention-based long short-term memory (ALSTM) network. The proposed methods divide a time series into multiple channels and take advantage of the convolutional neural network (CNN), the long short-term memory (LSTM) network, and the attention-based mechanism for boosting performance. The CNN performs well for extracting features from data with multiple channels; dividing a time series into multiple channels helps the CNN extract relationship among far-apart data points. The LSTM network is excellent for processing temporal data; the attention-based mechanism allows the LSTM network to focus on different features at different time steps for better prediction accuracy. PRONOSTIA bearing operation datasets are applied to the proposed methods for the purpose of performance evaluation and comparison. The comparison results show that the proposed methods outperform the others in terms of the mean absolute error (MAE) and the root mean squared error (RMSE) of RUL prediction.

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Action Recognition Algorithm of Spatio–Temporal Differential LSTM Based on Feature Enhancement

Applied Sciences ◽

10.3390/app11177876 ◽

2021 ◽

Vol 11 (17) ◽

pp. 7876

Author(s):

Kai Hu ◽

Fei Zheng ◽

Liguo Weng ◽

Yiwu Ding ◽

Junlan Jin

Keyword(s):

Spatial Memory ◽

Action Recognition ◽

Short Term Memory ◽

Human Action ◽

Short Term ◽

Memory State ◽

Term Memory ◽

Long Short Term Memory ◽

Spatio Temporal ◽

Lstm Network

The Long Short-Term Memory (LSTM) network is a classic action recognition method because of its ability to extract time information. Researchers proposed many hybrid algorithms based on LSTM for human action recognition. In this paper, an improved Spatio–Temporal Differential Long Short-Term Memory (ST-D LSTM) network is proposed, an enhanced input differential feature module and a spatial memory state differential module are added to the network. Furthermore, a transmission mode of ST-D LSTM is proposed; this mode enables ST-D LSTM units to transmit the spatial memory state horizontally. Finally, these improvements are added into classical Long-term Recurrent Convolutional Networks (LRCN) to test the new network’s performance. Experimental results show that ST-D LSTM can effectively improve the accuracy of LRCN.

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Landslide Prediction Using Long Short Term Memory(LSTM)Neural Network on time series data in Pakistan

2021 International Conference on Artificial Intelligence (ICAI) ◽

10.1109/icai52203.2021.9445236 ◽

2021 ◽

Author(s):

Mehreen Mubashar ◽

Gul Muhammad Khan ◽

Ramla Khan

Keyword(s):

Neural Network ◽

Time Series ◽

Time Series Data ◽

Short Term Memory ◽

Series Data ◽

Short Term ◽

Landslide Prediction ◽

Term Memory ◽

Long Short Term Memory

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Prediction of InSAR deformation time-series using a long short-term memory neural network

International Journal of Remote Sensing ◽

10.1080/01431161.2021.1947540 ◽

2021 ◽

Vol 42 (18) ◽

pp. 6921-6944

Author(s):

Yi Chen ◽

Yi He ◽

Lifeng Zhang ◽

Youdong Chen ◽

Hongyu Pu ◽

...

Keyword(s):

Neural Network ◽

Time Series ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/09544062211016505 ◽

2021 ◽

pp. 095440622110165

Author(s):

Zhang Chao ◽

Wang Wei-zhi ◽

Zhang Chen ◽

Fan Bin ◽

Wang Jian-guo ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Condition Monitoring ◽

Short Term Memory ◽

Vibration Signal ◽

Short Term ◽

Global Features ◽

Term Memory ◽

Long Short Term Memory ◽

Lstm Network

Accurate and reliable fault diagnosis is one of the key and difficult issues in mechanical condition monitoring. In recent years, Convolutional Neural Network (CNN) has been widely used in mechanical condition monitoring, which is also a great breakthrough in the field of bearing fault diagnosis. However, CNN can only extract local features of signals. The model accuracy and generalization of the original vibration signals are very low in the process of vibration signal processing only by CNN. Based on the above problems, this paper improves the traditional convolution layer of CNN, and builds the learning module (local feature learning block, LFLB) of the local characteristics. At the same time, the Long Short-Term Memory (LSTM) is introduced into the network, which is used to extract the global features. This paper proposes the new neural network—improved CNN-LSTM network. The extracted deep feature is used for fault classification. The improved CNN-LSTM network is applied to the processing of the vibration signal of the faulty bearing collected by the bearing failure laboratory of Inner Mongolia University of science and technology. The results show that the accuracy of the improved CNN-LSTM network on the same batch test set is 98.75%, which is about 24% higher than that of the traditional CNN. The proposed network is applied to the bearing data collection of Western Reserve University under the condition that the network parameters remain unchanged. The experiment shows that the improved CNN-LSTM network has better generalization than the traditional CNN.

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