Prediction of cold rolling gas based on EEMD-LSTM deep learning technology

Purpose The prediction of by-product gas is an important guarantee for the full utilization of resources. The purpose of this research is to predict gas consumption to provide a basis for gas dispatch and reduce the production cost of enterprises. Design/methodology/approach In this paper, a new method using the ensemble empirical mode decomposition (EEMD) and the back propagation neural network is proposed. Unfortunately, this method does not achieve the ideal prediction. Further, using the advantages of long short-term memory (LSTM) neural network for long-term dependence, a prediction method based on EEMD and LSTM is proposed. In this model, the gas consumption series is decomposed into several intrinsic mode functions and a residual term (r(t)) by EEMD. Second, each component is predicted by LSTM. The predicted values of all components are added together to get the final prediction result. Findings The results show that the root mean square error is reduced to 0.35%, the average absolute error is reduced to 1.852 and the R-squared is reached to 0.963. Originality/value A new gas consumption prediction method is proposed in this paper. The production data collected in the actual production process is non-linear, unstable and contains a lot of noise. But the EEMD method has the unique superiority in the analysis data aspect and may solve these questions well. The prediction of gas consumption is the result of long-term training and needs a lot of prior knowledge. Relying on LSTM can solve the problem of long-term dependence.

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Machine learning-based prediction of component self-alignment in vapour phase and infrared soldering

Soldering & Surface Mount Technology ◽

10.1108/ssmt-11-2018-0045 ◽

2019 ◽

Vol 31 (3) ◽

pp. 163-168 ◽

Cited By ~ 2

Author(s):

Oliver Krammer ◽

Péter Martinek ◽

Balazs Illes ◽

László Jakab

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Prediction Method ◽

Absolute Error ◽

Vapour Phase ◽

The Self ◽

Phase Method ◽

Content Type ◽

Artificial Neural

Purpose This paper aims to investigate the self-alignment of 0603 size (1.5 × 0.75 mm) chip resistors, which were soldered by infrared or vapour phase soldering. The results were used for establishing an artificial neural network for predicting the component movement during the soldering. Design/methodology/approach The components were soldered onto an FR4 testboard, which was designed to facilitate the measuring of the position of the components both prior to and after the soldering. A semi-automatic placement machine misplaced the components intentionally, and the self-alignment ability was determined for soldering techniques of both infrared and vapour phase soldering. An artificial neural network-based prediction method was established, which is able to predict the position of chip resistors after soldering as a function of component misplacement prior to soldering. Findings The results showed that the component can self-align from farer distances by using vapour phase method, even from relative misplacement of 50 per cent parallel to the shorter side of the component. Components can self-align from a relative misplacement only of 30 per cent by using infrared soldering method. The established artificial neural network can predict the component self-alignment with an approximately 10-20 per cent mean absolute error. Originality/value It was proven that the vapour phase soldering method is more stable from the component’s self-alignment point of view. Furthermore, machine learning-based predictors can be applied in the field of reflow soldering technology, and artificial neural networks can predict the component self-alignment with an appropriately low error.

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Based on improved deep convolutional neural network model pneumonia image classification

PLoS ONE ◽

10.1371/journal.pone.0258804 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0258804

Author(s):

Lingzhi Kong ◽

Jinyong Cheng

Keyword(s):

Neural Network ◽

Short Term Memory ◽

Characteristic Curve ◽

Recall Rate ◽

Receiver Operator Characteristic Curve ◽

Learning Technology ◽

Accuracy Rate ◽

X Ray ◽

Lung Abnormalities

Pneumonia remains the leading infectious cause of death in children under the age of five, killing about 700,000 children each year and affecting 7% of the world’s population. X-ray images of lung become the key to the diagnosis of this disease, skilled doctors in the diagnosis of a certain degree of subjectivity, if the use of computer-aided medical diagnosis to automatically detect lung abnormalities, will improve the accuracy of diagnosis. This research aims to introduce a deep learning technology based on the combination of Xception neural network and long-term short-term memory (LSTM), which can realize automatic diagnosis of patients with pneumonia in X-ray images. First, the model uses the Xception network to extract the deep features of the data, passes the extracted features to the LSTM, and then the LSTM detects the extracted features, and finally selects the most needed features. Secondly, in the training set samples, the traditional cross-entropy loss cannot more balance the mismatch between categories. Therefore, this research combines Pearson’s feature selection ideas, fusion of the correlation between the two loss functions, and optimizes the problem. The experimental results show that the accuracy rate of this paper is 96%, the receiver operator characteristic curve accuracy rate is 99%, the precision rate is 98%, the recall rate is 91%, and the F1 score accuracy rate is 94%. Compared with the existing technical methods, the research has achieved expected results on the currently available datasets. And assist doctors to provide higher reliability in the classification task of childhood pneumonia.

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Coal Thickness Prediction Method Based on VMD and LSTM

Electronics ◽

10.3390/electronics11020232 ◽

2022 ◽

Vol 11 (2) ◽

pp. 232

Author(s):

Yaping Huang ◽

Lei Yan ◽

Yan Cheng ◽

Xuemei Qi ◽

Zhixiong Li

Keyword(s):

Neural Network ◽

Coal Seam ◽

Bp Neural Network ◽

Short Term Memory ◽

Prediction Method ◽

Absolute Error ◽

High Accuracy ◽

Mine Safety ◽

Mode Decomposition ◽

Thickness Prediction

The change in coal seam thickness has an important influence on coal mine safety and efficient mining. It is very important to predict coal thickness accurately. However, the accuracy of borehole interpolation and BP neural network is not high. Variational mode decomposition (VMD) has strong denoising ability, and the long short-term memory neural network (LSTM) is especially suitable for the prediction of complex sequences. This paper presents a method of coal thickness prediction using VMD and LSTM. Firstly, empirical mode decomposition (EMD) and VMD methods are used to denoise simple signals, and the denoising effect of the VMD method is verified. Then, the wedge-shaped coal thickness model is constructed, and the seismic forward modeling and analysis are carried out. The results show that the coal thickness prediction based on seismic attributes is feasible. On the basis of VMD denoising of the original 3D seismic data, VMD-LSTM is used to predict coal thickness and compared with the prediction results of the traditional BP neural network. The coal thickness prediction method proposed in this paper has high accuracy and basically coincides with the coal seam information exposed by existing boreholes. The minimum absolute error of the predicted coal thickness is only 0.08 m, and the maximum absolute error is 0.48 m. This indicates that VMD-LSTM has high accuracy in predicting coal thickness. The proposed coal thickness prediction method can be used as a new method for coal thickness prediction.

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A stock price prediction method based on deep learning technology

International Journal of Crowd Science ◽

10.1108/ijcs-05-2020-0012 ◽

2021 ◽

Vol 5 (1) ◽

pp. 55-72

Author(s):

Xuan Ji ◽

Jiachen Wang ◽

Zhijun Yan

Keyword(s):

Social Media ◽

Deep Learning ◽

Stock Price ◽

Prediction Method ◽

Series Data ◽

Learning Technology ◽

Content Type ◽

Stock Price Prediction ◽

Price Prediction ◽

Text Feature

Purpose Stock price prediction is a hot topic and traditional prediction methods are usually based on statistical and econometric models. However, these models are difficult to deal with nonstationary time series data. With the rapid development of the internet and the increasing popularity of social media, online news and comments often reflect investors’ emotions and attitudes toward stocks, which contains a lot of important information for predicting stock price. This paper aims to develop a stock price prediction method by taking full advantage of social media data. Design/methodology/approach This study proposes a new prediction method based on deep learning technology, which integrates traditional stock financial index variables and social media text features as inputs of the prediction model. This study uses Doc2Vec to build long text feature vectors from social media and then reduce the dimensions of the text feature vectors by stacked auto-encoder to balance the dimensions between text feature variables and stock financial index variables. Meanwhile, based on wavelet transform, the time series data of stock price is decomposed to eliminate the random noise caused by stock market fluctuation. Finally, this study uses long short-term memory model to predict the stock price. Findings The experiment results show that the method performs better than all three benchmark models in all kinds of evaluation indicators and can effectively predict stock price. Originality/value In this paper, this study proposes a new stock price prediction model that incorporates traditional financial features and social media text features which are derived from social media based on deep learning technology.

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Flutter speed prediction by using deep learning

Advances in Mechanical Engineering ◽

10.1177/16878140211062275 ◽

2021 ◽

Vol 13 (11) ◽

pp. 168781402110622

Author(s):

Yi-Ren Wang ◽

Yi-Jyun Wang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Short Term Memory ◽

Learning Technology ◽

Memory Space ◽

Flutter Speed ◽

Aeroelastic Flutter ◽

Speed Prediction ◽

Computational Fluid Dynamics Cfd ◽

Cfd Method

Deep learning technology has been widely used in various field in recent years. This study intends to use deep learning algorithms to analyze the aeroelastic phenomenon and compare the differences between Deep Neural Network (DNN) and Long Short-term Memory (LSTM) applied on the flutter speed prediction. In this present work, DNN and LSTM are used to address complex aeroelastic systems by superimposing multi-layer Artificial Neural Network. Under such an architecture, the neurons in neural network can extract features from various flight data. Instead of time-consuming high-fidelity computational fluid dynamics (CFD) method, this study uses the K method to build the aeroelastic flutter speed big data for different flight conditions. The flutter speeds for various flight conditions are predicted by the deep learning methods and verified by the K method. The detailed physical meaning of aerodynamics and aeroelasticity of the prediction results are studied. The LSTM model has a cyclic architecture, which enables it to store information and update it with the latest information at the same time. Although the training of the model is more time-consuming than DNN, this method can increase the memory space. The results of this work show that the LSTM model established in this study can provide more accurate flutter speed prediction than the DNN algorithm.

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Long-Term Wind Power Prediction Based on Rough Set

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.329.411 ◽

2013 ◽

Vol 329 ◽

pp. 411-415 ◽

Cited By ~ 1

Author(s):

Shuang Gao ◽

Lei Dong ◽

Xiao Zhong Liao ◽

Yang Gao

Keyword(s):

Neural Network ◽

Wind Power ◽

Rough Set ◽

Wind Farm ◽

Rough Set Theory ◽

Prediction Method ◽

Key Factors ◽

Power Prediction ◽

Wind Power Prediction

In long-term wind power prediction, dealing with the relevant factors correctly is the key point to improve the prediction accuracy. This paper presents a prediction method with rough set analysis. The key factors that affect the wind power prediction are identified by rough set theory. The chaotic characteristics of wind speed time series are analyzed. The rough set neural network prediction model is built by adding the key factors as the additional inputs to the chaotic neural network model. Data of Fujin wind farm are used for this paper to verify the new method of long-term wind power prediction. The results show that rough set method is a useful tool in long-term prediction of wind power.

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Forecasting the momentum using customised loss function for financial series

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-05-2021-0098 ◽

2021 ◽

Vol 14 (4) ◽

pp. 702-713

Author(s):

N. Prabakaran ◽

Rajasekaran Palaniappan ◽

R. Kannadasan ◽

Satya Vinay Dudi ◽

V. Sasidhar

Keyword(s):

Neural Network ◽

Time Series ◽

Loss Function ◽

Short Term Memory ◽

Learning Algorithm ◽

Financial Time Series ◽

Storage Unit ◽

Content Type ◽

Financial Time ◽

Unit Structure

PurposeWe propose a Machine Learning (ML) approach that will be trained from the available financial data and is able to gain the trends over the data and then uses the acquired knowledge for a more accurate forecasting of financial series. This work will provide a more precise results when weighed up to aged financial series forecasting algorithms. The LSTM Classic will be used to forecast the momentum of the Financial Series Index and also applied to its commodities. The network will be trained and evaluated for accuracy with various sizes of data sets, i.e. weekly historical data of MCX, GOLD, COPPER and the results will be calculated.Design/methodology/approachDesirable LSTM model for script price forecasting from the perspective of minimizing MSE. The approach which we have followed is shown below. (1) Acquire the Dataset. (2) Define your training and testing columns in the dataset. (3) Transform the input value using scalar. (4) Define the custom loss function. (5) Build and Compile the model. (6) Visualise the improvements in results.FindingsFinancial series is one of the very aged techniques where a commerce person would commerce financial scripts, make business and earn some wealth from these companies that vend a part of their business on trading manifesto. Forecasting financial script prices is complex tasks that consider extensive human–computer interaction. Due to the correlated nature of financial series prices, conventional batch processing methods like an artificial neural network, convolutional neural network, cannot be utilised efficiently for financial market analysis. We propose an online learning algorithm that utilises an upgraded of recurrent neural networks called long short-term memory Classic (LSTM). The LSTM Classic is quite different from normal LSTM as it has customised loss function in it. This LSTM Classic avoids long-term dependence on its metrics issues because of its unique internal storage unit structure, and it helps forecast financial time series. Financial Series Index is the combination of various commodities (time series). This makes Financial Index more reliable than the financial time series as it does not show a drastic change in its value even some of its commodities are affected. This work will provide a more precise results when weighed up to aged financial series forecasting algorithms.Originality/valueWe had built the customised loss function model by using LSTM scheme and have experimented on MCX index and as well as on its commodities and improvements in results are calculated for every epoch that we run for the whole rows present in the dataset. For every epoch we can visualise the improvements in loss. One more improvement that can be done to our model that the relationship between price difference and directional loss is specific to other financial scripts. Deep evaluations can be done to identify the best combination of these for a particular stock to obtain better results.

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Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow

Water ◽

10.3390/w12102927 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2927

Author(s):

Jiyeong Hong ◽

Seoro Lee ◽

Joo Hyun Bae ◽

Jimin Lee ◽

Woon Ji Park ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Random Forest ◽

Multilayer Perceptron ◽

Short Term Memory ◽

Learning Algorithms ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Dam Inflow

Predicting dam inflow is necessary for effective water management. This study created machine learning algorithms to predict the amount of inflow into the Soyang River Dam in South Korea, using weather and dam inflow data for 40 years. A total of six algorithms were used, as follows: decision tree (DT), multilayer perceptron (MLP), random forest (RF), gradient boosting (GB), recurrent neural network–long short-term memory (RNN–LSTM), and convolutional neural network–LSTM (CNN–LSTM). Among these models, the multilayer perceptron model showed the best results in predicting dam inflow, with the Nash–Sutcliffe efficiency (NSE) value of 0.812, root mean squared errors (RMSE) of 77.218 m3/s, mean absolute error (MAE) of 29.034 m3/s, correlation coefficient (R) of 0.924, and determination coefficient (R2) of 0.817. However, when the amount of dam inflow is below 100 m3/s, the ensemble models (random forest and gradient boosting models) performed better than MLP for the prediction of dam inflow. Therefore, two combined machine learning (CombML) models (RF_MLP and GB_MLP) were developed for the prediction of the dam inflow using the ensemble methods (RF and GB) at precipitation below 16 mm, and the MLP at precipitation above 16 mm. The precipitation of 16 mm is the average daily precipitation at the inflow of 100 m3/s or more. The results show the accuracy verification results of NSE 0.857, RMSE 68.417 m3/s, MAE 18.063 m3/s, R 0.927, and R2 0.859 in RF_MLP, and NSE 0.829, RMSE 73.918 m3/s, MAE 18.093 m3/s, R 0.912, and R2 0.831 in GB_MLP, which infers that the combination of the models predicts the dam inflow the most accurately. CombML algorithms showed that it is possible to predict inflow through inflow learning, considering flow characteristics such as flow regimes, by combining several machine learning algorithms.

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Long Short Term Memory Recurrent Network for Standard and Poor’s 500 Index Modelling

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.15.21365 ◽

2018 ◽

Vol 7 (4.15) ◽

pp. 25 ◽

Cited By ~ 3

Author(s):

Said Jadid Abdulkadir ◽

Hitham Alhussian ◽

Muhammad Nazmi ◽

Asim A Elsheikh

Keyword(s):

Neural Network ◽

Time Series ◽

Recurrent Neural Network ◽

Time Series Data ◽

Short Term Memory ◽

Series Data ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Forecasting time-series data are imperative especially when planning is required through modelling using uncertain knowledge of future events. Recurrent neural network models have been applied in the industry and outperform standard artificial neural networks in forecasting, but fail in long term time-series forecasting due to the vanishing gradient problem. This study offers a robust solution that can be implemented for long-term forecasting using a special architecture of recurrent neural network known as Long Short Term Memory (LSTM) model to overcome the vanishing gradient problem. LSTM is specially designed to avoid the long-term dependency problem as their default behavior. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the forecasted outputs. An evaluation analysis is performed to validate that the LSTM model provides better forecasted outputs on Standard & Poor’s 500 Index (S&P 500) in terms of error metrics as compared to other forecasting models.

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A Heterogeneous Spiking Neural Network for Unsupervised Learning of Spatiotemporal Patterns

Frontiers in Neuroscience ◽

10.3389/fnins.2020.615756 ◽

2021 ◽

Vol 14 ◽

Author(s):

Xueyuan She ◽

Saurabh Dash ◽

Daehyun Kim ◽

Saibal Mukhopadhyay

Keyword(s):

Neural Network ◽

Moving Objects ◽

Short Term Memory ◽

Back Propagation ◽

Spike Timing ◽

Spatiotemporal Patterns ◽

Spiking Neural Network ◽

Spatial And Temporal Patterns ◽

Unsupervised Training ◽

Improved Performance

This paper introduces a heterogeneous spiking neural network (H-SNN) as a novel, feedforward SNN structure capable of learning complex spatiotemporal patterns with spike-timing-dependent plasticity (STDP) based unsupervised training. Within H-SNN, hierarchical spatial and temporal patterns are constructed with convolution connections and memory pathways containing spiking neurons with different dynamics. We demonstrate analytically the formation of long and short term memory in H-SNN and distinct response functions of memory pathways. In simulation, the network is tested on visual input of moving objects to simultaneously predict for object class and motion dynamics. Results show that H-SNN achieves prediction accuracy on similar or higher level than supervised deep neural networks (DNN). Compared to SNN trained with back-propagation, H-SNN effectively utilizes STDP to learn spatiotemporal patterns that have better generalizability to unknown motion and/or object classes encountered during inference. In addition, the improved performance is achieved with 6x fewer parameters than complex DNNs, showing H-SNN as an efficient approach for applications with constrained computation resources.

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