Prediction of Protein B-Factor Profiles Based on Bidirectional Long Short-Term Memory Network.

Hydrate plugging and pipeline leak can impair the normal operation of natural gas pipeline and may lead to serious accidents. Since natural gas pipeline safety monitoring based on active acoustic excitation can detect and locate not only the two abnormal events but also normal components such as valves and pipeline elbows, recognition and classification of these events are of great importance to provide maintenance guidance for the pipeline operators and avoid false alarm. In this article, long short-term memory (LSTM) network is introduced and applied to classify detection signals of hydrate plugging, pipeline leak, and elbow. Adaptive moment estimation (Adam) algorithm is introduced and utilized to accelerate the long short-term memory network convergence in training. Experimental results demonstrate that the network with three layers and 64 units per cell performs the best. The cross-entropy loss in training is 0.0005, and classification accuracies are all 100% in training, validation, and testing which verify the validity of the long short-term memory network. Therefore, the method based on the long short-term memory network and adaptive moment estimation algorithm can work efficiently on pipeline events classification and has great guiding significance for safety assurance of natural gas transmission.

In human-machine interaction systems, continuous movement estimation methods occupy an important position because they are more natural and intuitive than pattern-recognition methods. Essentially, arm position is decided by the shoulder and elbow joint angles. However, the various deformations of muscles around the shoulder and elbow often lead to difficulties in sensor fixation, which results in a loss of synchronization between the surface electromyography (sEMG) signals and joint angles. In order to accurately estimate movement angles using sEMG in situations where the sEMG is not synchronized with joint angles, we utilized a bi-directional long short-term memory (Bi-LSTM) network rather than other deep learning methods to estimate non-dominant arm movements, based on the sEMG signal from the dominant arm. This estimation protocol was designed to avoid a multiplicity of sensors and to simulate more complicated loss of synchronization problems). The performance of the Bi-LSTM was compared with multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and a long short-term memory network (LSTM). The Pearson correlation coefficient (cc) between the estimated and target joint angle sequences was calculated to evaluate the performance of each neural network. The Wilcoxon signed-rank results showed that the Bi-LSTM model significantly outperformed the MLP, CNN, and LSTM models (tested with completely untrained newly recorded free movements).

Modern decision-making in fixed income asset management benefits from intelligent systems, which involve the use of state-of-the-art machine learning models and appropriate methodologies. We conduct the first study of bond yield forecasting using long short-term memory (LSTM) networks, validating its potential and identifying its memory advantage. Specifically, we model the 10-year bond yield using univariate LSTMs with three input sequences and five forecasting horizons. We compare those with multilayer perceptrons (MLP), univariate and with the most relevant features. To demystify the notion of black box associated with LSTMs, we conduct the first internal study of the model. To this end, we calculate the LSTM signals through time, at selected locations in the memory cell, using sequence-to-sequence architectures, uni and multivariate. We then proceed to explain the states’ signals using exogenous information, for what we develop the LSTM-LagLasso methodology. The results show that the univariate LSTM model with additional memory is capable of achieving similar results as the multivariate MLP using macroeconomic and market information. Furthermore, shorter forecasting horizons require smaller input sequences and vice-versa. The most remarkable property found consistently in the LSTM signals, is the activation/deactivation of units through time, and the specialisation of units by yield range or feature. Those signals are complex but can be explained by exogenous variables. Additionally, some of the relevant features identified via LSTM-LagLasso are not commonly used in forecasting models. In conclusion, our work validates the potential of LSTMs and methodologies for bonds, providing additional tools for financial practitioners.

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

To solve the problem that offshore wind power generation is susceptible to extreme weather conditions that result in low accuracy of monitoring data, this work proposes a method for predicting offshore wind power generation output by combining convolution and attention mechanisms. The method combines bidirectional long and short-term memory (BiLSTM) network with attention mechanism (AM). The input data are first weighted with the AM for reducing the predictive weight of the interference data, and then the attention mechanism-weighted data are fed into the BiLSTM network. The bi-directional propagation neural network can effectively utilize all the input information, resulting in higher prediction accuracy. The method of combining BiLSTM network with AM is compared with the method employing long and short-term memory network, gated recurrent unit, and bidirectional long and short-term memory network alone through simulation. The mean square error (MSE) of the BiLSTM combined with AM method is 41.28% smaller than the MSE of the best LSTM method among the compared methods and 58.43% smaller than the average of the compared methods. The R2-R-Square is 14.21% larger than the R2-R-Square of the LSTM and 32.91% larger than the average of the compared methods. The results show that the proposed method of combining convolution and attention mechanism for offshore wind power generation output prediction has higher prediction accuracy.

This paper aims to predict the occurrence of pests and diseases for cotton based on long short term memory (LSTM) network. First, the problem of occurrence of pests and diseases was formulated as time series prediction. Then LSTM was adopted to solve the problem. LSTM is a special kind of recurrent neutral network (RNN), which introduces gate mechanism to prevent the vanished or exploding gradient problem. It has been shown good performance in solving time series problem and can handle the long-term dependency problem, as mentioned in many literatures. The experimental results showed that LSTM performed good on the prediction of occurrence of pests and diseases in cotton fields, and yielded an Area Under the Curve (AUC) of 0.97. The paper further verified that the weather factors indeed have strong impact on the occurrence of pests and diseases, and the LSTM network has great advantage on solving the long-term dependency problem.

Aim and Background: In recent periods, micro-array data analysis using soft computing and machine learning techniques gained more interest among researchers to detect prostate cancer. Due to the small sample size of micro-array data with a larger number of attributes, traditional machine learning techniques face difficulty detecting prostate cancer. Methodology: The selection of relevant genes exploits useful information about micro-array data, which enhances the accuracy of detection. In this research, the samples are acquired from the gene expression omnibus database, particularly related to the prostate cancer GEO IDs such as GSE 21034, GSE 15484 and GSE 3325/GSE 3998. In addition, ensemble feature optimization technique and Bidirectional Long Short Term Memory (Bi-LSTM) network are employed for detecting prostate cancer from the microarray data of gene expression. Results: The ensemble feature optimization technique includes 4 metaheuristic optimizers that select the top 2000 genes from each GEO IDs, which are relevant to prostate cancer. Next, the selected genes are given to the Bi-LSTM network for classifying the normal and prostate cancer subjects. Conclusion: The simulation analysis revealed that the ensemble based Bi-LSTM network obtained 99.13%, 98.97%, and 94.12% of accuracy on the GEO IDs like GSE 3325/GSE 3998, GSE 21034, and GSE 15484.

As living standards improve, people's demand for appreciation and learning of art is growing gradually. Unlike the traditional learning model, art teaching requires a specific understanding of learners' psychology and controlling what they have learned so that they can create new ideas. This article combines the current deep learning technology with heart rate to complete the action recognition of art dance teaching. The video data processing and recognition are conducted through the Openpose network and graph convolution network. The heart rate data recognition is completed through the Long Short-Term Memory (LSTM) network. The optimal recognition model is established through the data fusion of the two decision levels through the adaptive weight analysis method. The experimental results show that the accuracy of the classification fusion model is better than that of the single-mode recognition method, which is improved from 85.0% to 97.5%. The proposed method can evaluate the heart rate while ensuring high accuracy recognition. The proposed research can help analyze dance teaching and provide a new idea for future combined research on teaching interaction.

Correcting mean areal precipitation forecasts to improve urban flooding predictions by using long short-term memory network

As a kind of continuous time series, plantar pressure data contains rich contact of time information which has not been fully utilized in existing gait recognition methods. In this paper, we proposed a new gait recognition method based on plantar pressure data with a Long Short-Term Memory (LSTM) network. By normalization and dimensionality reduction, the raw pressure data was converted to feature tensor. Then we feed the LSTM network with the feature tensors and implement classification recognition. We collected data from 93 subjects of different age groups, and each subjects was collected 10 sets of pressure data. The experiment results turn out that our LSTM network can get high classification accuracy and performs better than CNN model and many traditional methods.

Offshore wind turbines (OWTs) have complex operating conditions and are prone to accidents. Timely and accurate vibration prediction can reduce the probability of failure of OWTs. To address this issue, this study proposed a vibration prediction model based on long short-term memory (LSTM) network. The proposed prediction model was applied to an OWT in an offshore wind farm in China, and three evaluation indicators, i.e. mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE), were used to verify the vibration prediction performance of LSTM. The results demonstrated that the prediction accuracy of LSTM was better than that of back propagation (BP) neural network, and LSTM had optimum engineering practicability. The prediction accuracy of LSTM and BP neural networks decreased with an increase in prediction durations. However, the prediction accuracy of LSTM was higher. This study improves the online safety monitoring of OWTs.

Multi-fidelity surrogate modeling using long short-term memory networks

The increasingly severe state of coal burst disaster has emerged as a critical factor constraining coal mine safety production, and it has become a challenging task to enhance the accuracy of coal burst disaster prediction. To address the issue of insufficient exploration of the spatio-temporal characteristic of microseismic data and the challenging selection of the optimal time window size in spatio-temporal prediction, this paper integrates deep learning methods and theory to propose a novel coal burst spatio-temporal prediction method based on Bidirectional Long Short-Term Memory (Bi-LSTM) network. The method involves three main modules, including microseismic spatio-temporal characteristic indicators construction, temporal prediction model, and spatial prediction model. To validate the effectiveness of the proposed method, engineering application tests are conducted at a high-risk working face in the Ordos mining area of Inner Mongolia, focusing on 13 high-energy microseismic events with energy levels greater than 105 J. In terms of temporal prediction, the analysis indicates that the temporal prediction results consist of 10 strong predictions and 3 medium predictions, and there is no false alarm detected throughout the entire testing period. Moreover, compared to the traditional threshold-based coal burst temporal prediction method, the accuracy of the proposed method is increased by 38.5%. In terms of spatial prediction, the distribution of spatial prediction results for high-energy events comprises 6 strong hazard predictions, 3 medium hazard predictions, and 4 weak hazard predictions.

The failure of water chiller will cause a series of problems such as increasing energy consumption, decreasing comfort of users and decreasing life of equipment. The fault diagnosis of the chiller has an important role in the stable operation of the air conditioner. In order to improve the fault diagnosis level of water chillers and solve the problems of difficulty in determining the parameters of neural network and great randomness, this paper presents a fault diagnosis method of long short-term memory (LSTM) network based on whale optimization algorithm (WOA) optimization. The fault diagnosis performance was verified by RP-1043 dataset, and compared with the results of recurrent neural network (RNN) and BP neural network. The results show that the WOA-LSTM method can effectively identify five typical chillers, and the diagnostic accuracy is as high as 99.27%. Compared with RNN and BP neural network, the proposed method is helpful to detect faults as early as possible and reduce the loss. It is especially obvious to improve the detection efficiency of small faults and complex faults.

In this paper, we investigate how a deep learning machine learning model can be applied to improve Bitcoin price forecasting and trading by incorporating unstructured information from financial news. The two-stage model we propose that includes financial news significantly outperforms machine learning models without financial news. In the first stage, we leverage long short-term memory (LSTM) networks to extract structured information from financial news. In the second stage, we apply machine learning models with structured input from financial news to the prediction of Bitcoin prices. In addition to the superior performance relative to machine learning models without input from financial news, we find that the out-of-time rate of return attained with the proposed forecasting system is substantially higher than for a buy-and-hold strategy. Our study highlights how combining deep learning and financial news offers investors and traders support for the monetization of unstructured data in finance.