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Добірка наукової літератури з теми "LSTM. ESN"

Автор: Grafiati
Опубліковано: 14 березня 2023

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Статті в журналах з теми "LSTM. ESN"

1

Chattopadhyay, Ashesh, Pedram Hassanzadeh, and Devika Subramanian. "Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network." Nonlinear Processes in Geophysics 27, no. 3 (July 2, 2020): 373–89. http://dx.doi.org/10.5194/npg-27-373-2020.

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Анотація:
Abstract. In this paper, the performance of three machine-learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multiscale spatiotemporal Lorenz 96 system is examined. The methods are an echo state network (ESN, which is a type of reservoir computing; hereafter RC–ESN), a deep feed-forward artificial neural network (ANN), and a recurrent neural network (RNN) with long short-term memory (LSTM; hereafter RNN–LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale (X), intermediate (Y), and fast/small-scale (Z) processes. For training or testing, only X is available; Y and Z are never known or used. We show that RC–ESN substantially outperforms ANN and RNN–LSTM for short-term predictions, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps equivalent to several Lyapunov timescales. The RNN–LSTM outperforms ANN, and both methods show some prediction skills too. Furthermore, even after losing the trajectory, data predicted by RC–ESN and RNN–LSTM have probability density functions (pdf's) that closely match the true pdf – even at the tails. The pdf of the data predicted using ANN, however, deviates from the true pdf. Implications, caveats, and applications to data-driven and data-assisted surrogate modeling of complex nonlinear dynamical systems, such as weather and climate, are discussed.
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Mirza, Sami F., and Abdulbasit K. Al-Talabani. "Efficient Kinect Sensor-based Kurdish Sign Language Recognition Using Echo System Network." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 9, no. 2 (October 13, 2021): 1–9. http://dx.doi.org/10.14500/aro.10827.

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Sign language assists in building communication and bridging gaps in understanding. Automatic sign language recognition (ASLR) is a field that has recently been studied for various sign languages. However, Kurdish sign language (KuSL) is relatively new and therefore researches and designed datasets on it are limited. This paper has proposed a model to translate KuSL into text and has designed a dataset using Kinect V2 sensor. The computation complexity of feature extraction and classification steps, which are serious problems for ASLR, has been investigated in this paper. The paper proposed a feature engineering approach on the skeleton position alone to provide a better representation of the features and avoid the use of all of the image information. In addition, the paper proposed model makes use of recurrent neural networks (RNNs)-based models. Training RNNs is inherently difficult, and consequently, motivates to investigate alternatives. Besides the trainable long short-term memory (LSTM), this study has proposed the untrained low complexity echo system network (ESN) classifier. The accuracy of both LSTM and ESN indicates they can outperform those in state-of-the-art studies. In addition, ESN which has not been proposed thus far for ASLT exhibits comparable accuracy to the LSTM with a significantly lower training time.
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3

Pei, Jiaxin, and Jian Wang. "Multisensor Prognostic of RUL Based on EMD-ESN." Mathematical Problems in Engineering 2020 (November 24, 2020): 1–12. http://dx.doi.org/10.1155/2020/6639171.

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This paper presents a prognostic method for RUL (remaining useful life) prediction based on EMD (empirical mode decomposition)-ESN (echo state network). The combination method adopts EMD to decompose the multisensor time series into a bunch of IMFs (intrinsic mode functions), which are then predicted by ESNs, and the outputs of each ESN are summarized to obtain the final prediction value. The EMD can decompose the original data into simpler portions and during the decomposition process, much noise is filtered out and the subsequent prediction is much easier. The ESN is a relatively new type of RNN (recurrent neural network), which substitutes the hidden layers with a reservoir remaining unchanged during the training phase. The characteristic makes the training time of ESN is much shorter than traditional RNN. The proposed method is applied to the turbofan engine datasets and is compared with LSTM (Long Short-Term Memory) and ESN. Extensive experimental results show that the prediction performance and efficiency are much improved by the proposed method.
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Chen, Xiaojuan, Haiyang Zhang, and Hongwu Qin. "Lowering Nitrogen Oxide Emissions in a Coal-Powered 1000-MW Boiler." Journal of Sensors 2021 (August 8, 2021): 1–11. http://dx.doi.org/10.1155/2021/9958972.

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Анотація:
Burning of coal in power plants produces excessive nitrogen oxide (NOx) emissions, which endanger people’s health. Proven and effective methods are highly needed to reduce NOx emissions. This paper constructs an echo state network (ESN) model of the interaction between NOx emissions and the operational parameters in terms of real historical data. The grey wolf optimization (GWO) algorithm is employed to improve the ESN model accuracy. The operational parameters are subsequently optimized via the GWO algorithm to finally cut down the NOx emissions. The experimental results show that the ESN model of the NOx emissions is more accurate than both of the LSTM and ELM models. The simulation results show NOx emission reduction in three selected cases by 16.5%, 15.6%, and 10.2%, respectively.
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Sheng, Hui, Min Liu, Jiyong Hu, Ping Li, Yali Peng, and Yugen Yi. "LA-ESN: A Novel Method for Time Series Classification." Information 14, no. 2 (January 26, 2023): 67. http://dx.doi.org/10.3390/info14020067.

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Time-series data is an appealing study topic in data mining and has a broad range of applications. Many approaches have been employed to handle time series classification (TSC) challenges with promising results, among which deep neural network methods have become mainstream. Echo State Networks (ESN) and Convolutional Neural Networks (CNN) are commonly utilized as deep neural network methods in TSC research. However, ESN and CNN can only extract local dependencies relations of time series, resulting in long-term temporal data dependence needing to be more challenging to capture. As a result, an encoder and decoder architecture named LA-ESN is proposed for TSC tasks. In LA-ESN, the encoder is composed of ESN, which is utilized to obtain the time series matrix representation. Meanwhile, the decoder consists of a one-dimensional CNN (1D CNN), a Long Short-Term Memory network (LSTM) and an Attention Mechanism (AM), which can extract local information and global dependencies from the representation. Finally, many comparative experimental studies were conducted on 128 univariate datasets from different domains, and three evaluation metrics including classification accuracy, mean error and mean rank were exploited to evaluate the performance. In comparison to other approaches, LA-ESN produced good results.
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Li, Xin, Fengrong Bi, Lipeng Zhang, Xiao Yang, and Guichang Zhang. "An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer." Energies 15, no. 3 (February 7, 2022): 1205. http://dx.doi.org/10.3390/en15031205.

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This paper aims to develop an efficient pattern recognition method for engine fault end-to-end detection based on the echo state network (ESN) and multi-verse optimizer (MVO). Bispectrum is employed to transform the one-dimensional time-dependent vibration signal into a two-dimensional matrix with more impact features. A sparse input weight-generating algorithm is designed for the ESN. Furthermore, a deep ESN model is built by fusing fixed convolution kernels and an autoencoder (AE). A novel traveling distance rate (TDR) and collapse mechanism are studied to optimize the local search of the MVO and speed it up. The improved MVO is employed to optimize the hyper-parameters of the deep ESN for the two-dimensional matrix recognition. The experiment result shows that the proposed method can obtain a recognition rate of 93.10% in complex engine faults. Compared with traditional deep belief networks (DBNs), convolutional neural networks (CNNs), the long short-term memory (LSTM) network, and the gated recurrent unit (GRU), this novel method displays superior performance and could benefit the fault end-to-end detection of rotating machinery.
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Tian, Ye, Yue-Ping Xu, Zongliang Yang, Guoqing Wang, and Qian Zhu. "Integration of a Parsimonious Hydrological Model with Recurrent Neural Networks for Improved Streamflow Forecasting." Water 10, no. 11 (November 14, 2018): 1655. http://dx.doi.org/10.3390/w10111655.

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This study applied a GR4J model in the Xiangjiang and Qujiang River basins for rainfall-runoff simulation. Four recurrent neural networks (RNNs)—the Elman recurrent neural network (ERNN), echo state network (ESN), nonlinear autoregressive exogenous inputs neural network (NARX), and long short-term memory (LSTM) network—were applied in predicting discharges. The performances of models were compared and assessed, and the best two RNNs were selected and integrated with the lumped hydrological model GR4J to forecast the discharges; meanwhile, uncertainties of the simulated discharges were estimated. The generalized likelihood uncertainty estimation method was applied to quantify the uncertainties. The results show that the LSTM and NARX better captured the time-series dynamics than the other RNNs. The hybrid models improved the prediction of high, median, and low flows, particularly in reducing the bias of underestimation of high flows in the Xiangjiang River basin. The hybrid models reduced the uncertainty intervals by more than 50% for median and low flows, and increased the cover ratios for observations. The integration of a hydrological model with a recurrent neural network considering long-term dependencies is recommended in discharge forecasting.
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8

Youssef, Samuel M., MennaAllah Soliman, Mahmood A. Saleh, Mostafa A. Mousa, Mahmoud Elsamanty, and Ahmed G. Radwan. "Modeling of Soft Pneumatic Actuators with Different Orientation Angles Using Echo State Networks for Irregular Time Series Data." Micromachines 13, no. 2 (January 29, 2022): 216. http://dx.doi.org/10.3390/mi13020216.

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Анотація:
Modeling of soft robotics systems proves to be an extremely difficult task, due to the large deformation of the soft materials used to make such robots. Reliable and accurate models are necessary for the control task of these soft robots. In this paper, a data-driven approach using machine learning is presented to model the kinematics of Soft Pneumatic Actuators (SPAs). An Echo State Network (ESN) architecture is used to predict the SPA’s tip position in 3 axes. Initially, data from actual 3D printed SPAs is obtained to build a training dataset for the network. Irregular-intervals pressure inputs are used to drive the SPA in different actuation sequences. The network is then iteratively trained and optimized. The demonstrated method is shown to successfully model the complex non-linear behavior of the SPA, using only the control input without any feedback sensory data as additional input to the network. In addition, the ability of the network to estimate the kinematics of SPAs with different orientation angles θ is achieved. The ESN is compared to a Long Short-Term Memory (LSTM) network that is trained on the interpolated experimental data. Both networks are then tested on Finite Element Analysis (FEA) data for other θ angle SPAs not included in the training data. This methodology could offer a general approach to modeling SPAs with varying design parameters.
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9

Feitosa, Allan Rivalles Souza, Henrique Figuerôa Lacerda, Wellington Pinheiro dos Santos, and Abel Guilhermino da Silva Filho. "Household appliance usage recommendation based on demand forecasting and multi­objective optimization." Research, Society and Development 11, no. 1 (January 3, 2022): e13411124515. http://dx.doi.org/10.33448/rsd-v11i1.24515.

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Accelerated population growth in the 21st century and increased demand for energy sources, associated with climate change, have resulted in two main challenges: the search for sustainable energy sources and the need to find more efficient ways to use extant sustainable sources. The forecasting module provides an estimate of the future usage of these appliances and it is the source of the recommended module’s suggestion. Time Series Forecasting techniques, such as Autoregressive Integrated Moving Average, Long­Short Term Memory (LSTM), Gated Recurrent Units, Echo State Networks (ESN), and Support Vector Regression, were tested for the predictive module. Multi­objective optimization techniques such as Non­Sorted Genetic Algorithm II (NSGA II), Multi­Objective Particle Swarm Optimization (MOPSO), Speed constrained Multi-­objective Particle Swarm Optimization (SMOPSO), and Strength Pareto Evolutionary Algorithm two (SPEA2), for example, were tested for the Recommendation Module. The Forecasting and Recommendation module experiments were performed independently. In the Forecasting Module, the results and statistical tests revealed LSTM as the best­ suited technique for forecasting the loads of the majority of the appliances tested (in this case seven) in terms of root mean square error. In the experiments performed for the recommendation module, NSGA II showed a higher overall performance compared to other metrics in terms of hyper volume of the Pareto Front generated. This work presents the potential of using both Predictive Models and Multi­Objective Optimization Techniques combined to reduce energy usage in household environments.
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10

Rajamoorthy, Rajasekaran, Hemachandira V. Saraswathi, Jayanthi Devaraj, Padmanathan Kasinathan, Rajvikram Madurai Elavarasan, Gokulalakshmi Arunachalam, Tarek M. Mostafa, and Lucian Mihet-Popa. "A Hybrid Sailfish Whale Optimization and Deep Long Short-Term Memory (SWO-DLSTM) Model for Energy Efficient Autonomy in India by 2048." Sustainability 14, no. 3 (January 25, 2022): 1355. http://dx.doi.org/10.3390/su14031355.

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In order to formulate the long-term and short-term development plans to meet the energy needs, there is a great demand for accurate energy forecasting. Energy autonomy helps to decompose a large-scale grid control into a small sized decisions to attain robustness and scalability through energy independence level of a country. Most of the existing energy demand forecasting models predict the amount of energy at a regional or national scale and failed to forecast the demand for power generation for small-scale decentralized energy systems, like micro grids, buildings, and energy communities. A novel model called Sailfish Whale Optimization-based Deep Long Short- Term memory (SWO-based Deep LSTM) to forecast electricity demand in the distribution systems is proposed. The proposed SWO is designed by integrating the Sailfish Optimizer (SO) with the Whale Optimization Algorithm (WOA). The Hilbert-Schmidt Independence Criterion (HSIC) is applied on the dataset, which is collected from the Central electricity authority, Government of India, for selecting the optimal features using the technical indicators. The proposed algorithm is implemented in MATLAB software package and the study was done using real-time data. The optimal features are trained using Deep LSTM model. The results of the proposed model in terms of install capacity prediction, village electrified prediction, length of R & D lines prediction, hydro, coal, diesel, nuclear prediction, etc. are compared with the existing models. The proposed model achieves percentage improvements of 10%, 9.5%,6%, 4% and 3% in terms of Mean Squared Error (MSE) and 26%, 21%, 16%, 12% and 6% in terms of Root Mean Square Error (RMSE) for Bootstrap-based Extreme Learning Machine approach (BELM), Direct Quantile Regression (DQR), Temporally Local Gaussian Process (TLGP), Deep Echo State Network (Deep ESN) and Deep LSTM respectively. The hybrid approach using the optimization algorithm with the deep learning model leads to faster convergence rate during the training process and enables the small-scale decentralized systems to address the challenges of distributed energy resources. The time series datasets of different utilities are trained using the hybrid model and the temporal dependencies in the sequence of data are predicted with point of interval as 5 years-head. Energy autonomy of the country till the year 2048 is assessed and compared.
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Дисертації з теми "LSTM. ESN"

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Baker, Ryan. "IMAGING AND ANALYSIS OF LARVAL ZEBRAFISH GUT MOTILITY, AND AUTOMATED TOOLS FOR 3D MICROSCOPY." Thesis, University of Oregon, 2018. http://hdl.handle.net/1794/23133.

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Nearly all individual members of the animal kingdom have gastrointestinal tracts which feature unique cellular compositions, geometries, and temporal dynamics. These guts are distinct enough from one another, even across siblings or even across the same individual at different points in space and time, that defining meaningful scientific representations of those features is difficult. Studying these guts is also innately challenging as it requires accessing to the insides of the enclosed 3D volumes. The work presented here describes tools and methodologies designed to address these difficulties. To investigate gut motility, we constructed a combined light sheet fluorescence and differential interference contrast microscope to obtain videos of larval zebrafish (Danio rerio) gut motility and to obtain 3D information about nearby fluorescently tagged cells. Using advanced computer vision algorithms, we quantified aspects of zebrafish gut motility which have never before been characterized, then used that information to identify the effects of different genetic, chemical, and physiological states of zebrafish gut motility. Finally, we designed and constructed an instrument for automating 3D microscopy for future studies. This dissertation includes previously published and unpublished co-authored material.
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2

Elkandari, Bader M. H. M. "Excimer laser surface melting treatment on 7075-T6 aluminium alloy for improved corrosion resistance." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/excimer-laser-surface-melting-treatment-on-7075t6-aluminium-alloy-for-improved-corrosion-resistance(c2da3b82-eeb5-4eae-a1dc-e4aefba18c62).html.

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High strength 7xxx aluminium alloys are used extensively in the aerospace industry because the alloys offer excellent mechanical properties. Unfortunately, the alloys can suffer localised corrosion due to the presence of large intermetallic particles at the alloy surface that are aligned in the rolling direction. Laser surface melting (LSM) techniques offer the potential to reduce and/or to eliminate the intermetallic phases from the surface of the alloy without affecting the alloy matrix.The present study concerns the application of LSM using an excimer laser to enhance the corrosion resistance of AA 7075-T6 aluminium alloy. The initial stage of the project was aimed at optimising the laser conditions for production of a uniform microstructure, with the increase in the corrosion resistance of the alloy being determined by potentiodynamic polarization measurements in sodium chloride solution. Low and high laser energy densities were used with a different number of pulses per unit area to treat the alloy surface, which were achieved by changing both the laser fluence and the pulse repetition frequency. A laser fluence of 3.3 J/cm2 with 80 pulses was subsequently selected as the optimum condition to treat the surface of the alloy. The composition and microstructure of the alloy before and after LSM treatment, and following corrosion tests, were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray diffraction (XRD).After the laser treatment, the surface and the cross-sections of the alloy showed a significant reduction in the number of large intermetallic particles and a relatively homogenous melted layer was generated that provided significant improvement in the resistance of the alloy against corrosion, as assessed by several corrosion test methods, including exfoliation corrosion (EXCO) tests. However, delamination of the melted layer was observed after extended testing in the EXCO solution which is possibly related to the formation of bands of fine magnesium and zinc-rich precipitates within the melted layer. Therefore, anodising in sulphuric acid was applied to the LSM alloy, in order to further increase the corrosion resistance and to protect the laser treated layer from delamination by generating a thin oxide film over the LSM layer. The results revealed that the anodic treatment increased the resistance of the alloy to exfoliation attack.
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Книги з теми "LSTM. ESN"

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Lorenzana Molina, Daniela, and Miroslava Cruz-Aldrete. Xochicalco / Xochelkalhme. Universidad Autónoma del Estado de Morelos, 2020. http://dx.doi.org/10.30973/2020/xochicalco-xochelkalhme.

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Esta obra, dirigida al público infantil, es la propuesta de un grupo de profesionales provenientes de diversas disciplinas, entre ellas la lingüística y la literatura, para hacer partícipes a los usuarios de la lengua de señas mexicana (LSM), o de una lengua indígena, de la riqueza cultural de México, en particular, de su grandioso pasado prehispánico. Este propósito se concreta en este libro, que nos presenta una narración, en español y náhuatl, acompañada de cuidadas ilustraciones, y cuyo escenario es el sitio arqueológico de Xochicalco.
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2

Creation, Eabign. Ern�hrungstagebuch : Ein Ganz Pers�nliches Tagebuch F�r Die Ern�hrung Zum Eintragen Von Mahlzeiten: Fr�hst�ck, Mittagessen, Abendessen, Zwischenmahlzeiten - Damit Beh�lst du Die Kontrolle �ber Dein Essverhalten. Independently Published, 2019.

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Частини книг з теми "LSTM. ESN"

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Beiche, Hans-Peter. "Lst-1 — ein wissensbasiertes System zur Durchführung und Berechnung des Lohnsteuerjahresausgleichs." In 3. Österreichische Artificial-Intelligence-Tagung, 92–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-46620-5_9.

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Тези доповідей конференцій з теми "LSTM. ESN"

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Qazani, Mohammad Reza Chalak, Farzin Tabarsinezhad, Houshyar Asadi, Chee Peng Lim, Adetokunbo Arogbonlo, Shehab Alsanwy, Shadi Mohamed, Mehrdad Rostami, and Saeid Nahavandi. "A Prediction of Time Series Driving Motion Scenarios Using LSTM and ESN." In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2022. http://dx.doi.org/10.1109/smc53654.2022.9945220.

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2

Puriyanto, Riky, Supriyanto, and Anton Yudhana. "LSTM Based Prediction of Total Dissolved Solids in Hydroponic System." In Proceedings of the 2019 Ahmad Dahlan International Conference Series on Engineering and Science (ADICS-ES 2019). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/adics-es-19.2019.13.

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3

Devshali, Sagun, Shailesh Kumar Tripathi, Dhiraj Dodda, Manish Kumar, Rishabh Uniyal, M. Yadav, and S. Malhotra. "Predicting ESP failures Using Artificial Intelligence for Improved Production Performance in One of the Offshore Fields in India." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211031-ms.

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Abstract Field X is situated at a water depth of 90 meters in the western continental shelf at a distance of 200 Kilometers from Mumbai. It is one of the few fields in the world operating entirely on Electric Submersible Pumps with 36 wells in 5 wellhead platforms producing 62907 barrels of liquid per day with an average water cut of 68%. The performance of ESPs is being continuously monitored in the field. With continuous improvement, the run life of ESPs has increased from a few months to an average of 3 years. Despite the improvement in the run life, unexpected failures still occur from time to time. These unanticipated ESP failures cause substantial production deferment leading to considerable losses in terms of revenue and resources. This paper presents the findings of an Artificial Intelligence based model developed for failure prediction of ESPs aiming to minimize the unexpected production loss for Field X. From the historical data obtained, 47 instances of pump failure have been identified. One of the challenges encountered during Data Exploration was missing data which in many cases was due to downhole sensor failure before the pump failure. The missing values have been inferred and imputed from the known available parameters for each pump. Various machine learning algorithms including Random Forest Regressor, Xgboost Regression, Copula-based Outlier Detection, Scalable Unsupervised Outlier Detection and Long Short Term Memory (LSTM) Autoencoder have been applied on these failure instances to develop a model for predicting the run lives of ESPs. Out of all the methods, LSTM Autoencoder model has been found to be the best suited model for anomaly detection before failure of ESPs. Autoencoders learn patterns in data over long sequences which makes them suitable for anomaly detection before the actual pump failure. The pattern recognition algorithms of Autoencoders have been able to predict the anomaly at approximately ~60 days before failure in a number of pump failure instances. The paper discusses a proactive approach by building a predictive model for estimating ESP lifespan based on machine learning algorithms. The model's predictive accuracy can be improved over time by adding information and further improving the model components.
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4

Herreros de Tejada, A., D. De Frutos, J. Santiago, G. Vazquez, I. Gonzalez-Partida, B. Agudo, M. Gonzalez-Haba, et al. "ESD OF LST-NG TYPE WITH UNDERLYING SUBMUCOSAL TUMOR." In ESGE Days 2019. Georg Thieme Verlag KG, 2019. http://dx.doi.org/10.1055/s-0039-1681432.

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