Готові списки джерел за темами / Robust Long-Short Term Memory (RoLSTM)

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Добірка наукової літератури з теми "Robust Long-Short Term Memory (RoLSTM)"

Автор: Grafiati

Опубліковано: 28 вересня 2022

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Статті в журналах з теми "Robust Long-Short Term Memory (RoLSTM)"

Javid, Gelareh, Djaffar Ould Abdeslam, and Michel Basset. "Adaptive Online State of Charge Estimation of EVs Lithium-Ion Batteries with Deep Recurrent Neural Networks." Energies 14, no. 3 (February 1, 2021): 758. http://dx.doi.org/10.3390/en14030758.

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The State of Charge (SOC) estimation is a significant issue for safe performance and the lifespan of Lithium-ion (Li-ion) batteries. In this paper, a Robust Adaptive Online Long Short-Term Memory (RoLSTM) method is proposed to extract SOC estimation for Li-ion Batteries in Electric Vehicles (EVs). This real-time, as its name suggests, method is based on a Recurrent Neural Network (RNN) containing Long Short-Term Memory (LSTM) units and using the Robust and Adaptive online gradient learning method (RoAdam) for optimization. In the proposed architecture, one sequential model is defined for each of the three inputs: voltage, current, and temperature of the battery. Therefore, the three networks work in parallel. With this approach, the number of LSTM units are reduced. Using this suggested method, one is not dependent on precise battery models and can avoid complicated mathematical methods. In addition, unlike the traditional recursive neural network where content is re-written at any time, the LSTM network can decide on preserving the current memory through the proposed gateways. In that case, it can easily transfer this information over long paths to receive and maintain long-term dependencies. Using real databases, the experiment results illustrate the better performance of RoLSTM applied to SOC estimation of Li-Ion batteries in comparison with a neural network modeling and unscented Kalman filter method that have been used thus far.

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Fister, Dušan, Matjaž Perc, and Timotej Jagrič. "Two robust long short-term memory frameworks for trading stocks." Applied Intelligence 51, no. 10 (February 27, 2021): 7177–95. http://dx.doi.org/10.1007/s10489-021-02249-x.

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Liu, Yong, Xin Hao, Biling Zhang, and Yuyan Zhang. "Simplified long short-term memory model for robust and fast prediction." Pattern Recognition Letters 136 (August 2020): 81–86. http://dx.doi.org/10.1016/j.patrec.2020.05.033.

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Yang, Haimin, Zhisong Pan, and Qing Tao. "Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory." Computational Intelligence and Neuroscience 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/9478952.

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Online time series prediction is the mainstream method in a wide range of fields, ranging from speech analysis and noise cancelation to stock market analysis. However, the data often contains many outliers with the increasing length of time series in real world. These outliers can mislead the learned model if treated as normal points in the process of prediction. To address this issue, in this paper, we propose a robust and adaptive online gradient learning method, RoAdam (Robust Adam), for long short-term memory (LSTM) to predict time series with outliers. This method tunes the learning rate of the stochastic gradient algorithm adaptively in the process of prediction, which reduces the adverse effect of outliers. It tracks the relative prediction error of the loss function with a weighted average through modifying Adam, a popular stochastic gradient method algorithm for training deep neural networks. In our algorithm, the large value of the relative prediction error corresponds to a small learning rate, and vice versa. The experiments on both synthetic data and real time series show that our method achieves better performance compared to the existing methods based on LSTM.

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Son, Namrye, Seunghak Yang, and Jeongseung Na. "Hybrid Forecasting Model for Short-Term Wind Power Prediction Using Modified Long Short-Term Memory." Energies 12, no. 20 (October 15, 2019): 3901. http://dx.doi.org/10.3390/en12203901.

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Renewable energy has recently gained considerable attention. In particular, the interest in wind energy is rapidly growing globally. However, the characteristics of instability and volatility in wind energy systems also affect power systems significantly. To address these issues, many studies have been carried out to predict wind speed and power. Methods of predicting wind energy are divided into four categories: physical methods, statistical methods, artificial intelligence methods, and hybrid methods. In this study, we proposed a hybrid model using modified LSTM (Long short-term Memory) to predict short-term wind power. The data adopted by modified LSTM use the current observation data (wind power, wind direction, and wind speed) rather than previous data, which are prediction factors of wind power. The performance of modified LSTM was compared among four multivariate models, which are derived from combining the current observation data. Among multivariable models, the proposed hybrid method showed good performance in the initial stage with Model 1 (wind power) and excellent performance in the middle to late stages with Model 3 (wind power, wind speed) in the estimation of short-term wind power. The experiment results showed that the proposed model is more robust and accurate in forecasting short-term wind power than the other models.

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Ngoc-Lan Huynh, Anh, Ravinesh C. Deo, Mumtaz Ali, Shahab Abdulla, and Nawin Raj. "Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition." Applied Energy 298 (September 2021): 117193. http://dx.doi.org/10.1016/j.apenergy.2021.117193.

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Darling, Stephen, Richard J. Allen, and Jelena Havelka. "Visuospatial Bootstrapping." Current Directions in Psychological Science 26, no. 1 (February 2017): 3–9. http://dx.doi.org/10.1177/0963721416665342.

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Visuospatial bootstrapping is the name given to a phenomenon whereby performance on visually presented verbal serial-recall tasks is better when stimuli are presented in a spatial array rather than a single location. However, the display used has to be a familiar one. This phenomenon implies communication between cognitive systems involved in storing short-term memory for verbal and visual information, alongside connections to and from knowledge held in long-term memory. Bootstrapping is a robust, replicable phenomenon that should be incorporated in theories of working memory and its interaction with long-term memory. This article provides an overview of bootstrapping, contextualizes it within research on links between long-term knowledge and short-term memory, and addresses how it can help inform current working memory theory.

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Avci, Gunes, Steven P. Woods, Marizela Verduzco, David P. Sheppard, James F. Sumowski, Nancy D. Chiaravalloti, and John DeLuca. "Effect of Retrieval Practice on Short-Term and Long-Term Retention in HIV+ Individuals." Journal of the International Neuropsychological Society 23, no. 3 (January 9, 2017): 214–22. http://dx.doi.org/10.1017/s1355617716001089.

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AbstractObjectives: Episodic memory deficits are both common and impactful among persons infected with HIV; however, we know little about how to improve such deficits in the laboratory or in real life. Retrieval practice, by which retrieval of newly learned material improves subsequent recall more than simple restudy, is a robust memory boosting strategy that is effective in both healthy and clinical populations. In this study, we investigated the benefits of retrieval practice in 52 people living with HIV and 21 seronegatives. Methods: In a within-subjects design, all participants studied 48 verbal paired associates in 3 learning conditions: Massed-Restudy, Spaced-Restudy, and Spaced-Testing. Retention of verbal paired associates was assessed after short- (30 min) and long- (30 days) delay intervals. Results: After a short delay, both HIV+ persons and seronegatives benefited from retrieval practice more so than massed and spaced restudy. The same pattern of results was observed specifically for HIV+ persons with clinical levels of memory impairment. The long-term retention interval data evidenced a floor effect that precluded further analysis. Conclusions: This study provides evidence that retrieval practice improves verbal episodic memory more than some other mnemonic strategies among HIV+ persons. (JINS, 2017, 23, 214–222)

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Bukhari, Syed Basit Ali, Khawaja Khalid Mehmood, Abdul Wadood, and Herie Park. "Intelligent Islanding Detection of Microgrids Using Long Short-Term Memory Networks." Energies 14, no. 18 (September 13, 2021): 5762. http://dx.doi.org/10.3390/en14185762.

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This paper presents a new intelligent islanding detection scheme (IIDS) based on empirical wavelet transform (EWT) and long short-term memory (LSTM) network to identify islanding events in microgrids. The concept of EWT is extended to extract features from three-phase signals. First, the three-phase voltage signals sampled at the terminal of targeted distributed energy resource (DER) or point of common coupling (PCC) are decomposed into empirical modes/frequency subbands using EWT. Then, instantaneous amplitudes and instantaneous frequencies of the three-phases at different frequency subbands are combined, and various statistical features are calculated. Finally, the EWT-based features along with the three-phase voltage signals are input to the LSTM network to differentiate between non-islanding and islanding events. To assess the efficacy of the proposed IIDS, extensive simulations are performed on an IEC microgrid and an IEEE 34-node system. The simulation results verify the effectiveness of the proposed IIDS in terms of non-detection zone (NDZ), computational time, detection accuracy, and robustness against noisy measurement. Furthermore, comparisons with existing intelligent methods and different LSTM architectures demonstrate that the proposed IIDS offers higher reliability by significantly reducing the NDZ and stands robust against measurements uncertainty.

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Baddar, Wissam J., and Yong Man Ro. "Encoding features robust to unseen modes of variation with attentive long short-term memory." Pattern Recognition 100 (April 2020): 107159. http://dx.doi.org/10.1016/j.patcog.2019.107159.

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Більше джерел

Дисертації з теми "Robust Long-Short Term Memory (RoLSTM)"

Javid, Gelareh. "Contribution à l’estimation de charge et à la gestion optimisée d’une batterie Lithium-ion : application au véhicule électrique." Thesis, Mulhouse, 2021. https://www.learning-center.uha.fr/.

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L'estimation de l'état de charge (SOC) est un point crucial pour la sécurité des performances et la durée de vie des batteries lithium-ion (Li-ion) utilisées pour alimenter les VE.Dans cette thèse, la précision de l'estimation de l'état de charge est étudiée à l'aide d'algorithmes de réseaux neuronaux récurrents profonds (DRNN). Pour ce faire, pour une cellule d’une batterie Li-ion, trois nouvelles méthodes sont proposées : une mémoire bidirectionnelle à long et court terme (BiLSTM), une mémoire robuste à long et court terme (RoLSTM) et une technique d'unités récurrentes à grille (GRU).En utilisant ces techniques, on ne dépend pas de modèles précis de la batterie et on peut éviter les méthodes mathématiques complexes, en particulier dans un bloc de batterie. En outre, ces modèles sont capables d'estimer précisément le SOC à des températures variables. En outre, contrairement au réseau de neurones récursif traditionnel dont le contenu est réécrit à chaque fois, ces réseaux peuvent décider de préserver la mémoire actuelle grâce aux passerelles proposées. Dans ce cas, il peut facilement transférer l'information sur de longs chemins pour recevoir et maintenir des dépendances à long terme.La comparaison des résultats indique que le réseau BiLSTM a de meilleures performances que les deux autres méthodes. De plus, le modèle BiLSTM peut travailler avec des séquences plus longues provenant de deux directions, le passé et le futur, sans problème de disparition du gradient. Cette caractéristique permet de sélectionner une longueur de séquence équivalente à une période de décharge dans un cycle de conduite, et d'obtenir une plus grande précision dans l'estimation. En outre, ce modèle s'est bien comporté face à une valeur initiale incorrecte du SOC.Enfin, une nouvelle méthode BiLSTM a été introduite pour estimer le SOC d'un pack de batteries dans un EV. Le logiciel IPG Carmaker a été utilisé pour collecter les données et tester le modèle en simulation. Les résultats ont montré que l'algorithme proposé peut fournir une bonne estimation du SOC sans utilisation de filtre dans le système de gestion de la batterie (BMS)
The State Of Charge (SOC) estimation is a significant issue for safe performance and the lifespan of Lithium-ion (Li-ion) batteries, which is used to power the Electric Vehicles (EVs). In this thesis, the accuracy of SOC estimation is investigated using Deep Recurrent Neural Network (DRNN) algorithms. To do this, for a one cell Li-ion battery, three new SOC estimator based on different DRNN algorithms are proposed: a Bidirectional LSTM (BiLSTM) method, Robust Long-Short Term Memory (RoLSTM) algorithm, and a Gated Recurrent Units (GRUs) technique. Using these, one is not dependent on precise battery models and can avoid complicated mathematical methods especially in a battery pack. In addition, these models are able to precisely estimate the SOC at varying temperature. Also, unlike the traditional recursive neural network where content is re-written at each time, these networks can decide on preserving the current memory through the proposed gateways. In such case, it can easily transfer the information over long paths to receive and maintain long-term dependencies. Comparing the results indicates the BiLSTM network has a better performance than the other two. Moreover, the BiLSTM model can work with longer sequences from two direction, the past and the future, without gradient vanishing problem. This feature helps to select a sequence length as much as a discharge period in one drive cycle, and to have more accuracy in the estimation. Also, this model well behaved against the incorrect initial value of SOC. Finally, a new BiLSTM method introduced to estimate the SOC of a pack of batteries in an Ev. IPG Carmaker software was used to collect data and test the model in the simulation. The results showed that the suggested algorithm can provide a good SOC estimation without using any filter in the Battery Management System (BMS)

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Частини книг з теми "Robust Long-Short Term Memory (RoLSTM)"

Aung, Zaw Htet, and Panrasee Ritthipravat. "Robust Visual Voice Activity Detection Using Long Short-Term Memory Recurrent Neural Network." In Image and Video Technology, 380–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29451-3_31.

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Kiyani, Anum Tanveer, Aboubaker Lasebae, Kamran Ali, Ahmed Alkhayyat, Bushra Haq, and Bushra Naeem. "Robust Continuous User Authentication System Using Long Short Term Memory Network for Healthcare." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 295–307. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95593-9_22.

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Saideni, Wael, David Helbert, Fabien Courreges, and Jean Pierre Cances. "A Novel Video Prediction Algorithm Based on Robust Spatiotemporal Convolutional Long Short-Term Memory (Robust-ST-ConvLSTM)." In Proceedings of Seventh International Congress on Information and Communication Technology, 193–204. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1610-6_17.

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Sethi, Nishu, Shalini Bhaskar Bajaj, Jitendra Kumar Verma, and Utpal Shrivastava. "Google Stock Movement." In Multidisciplinary Functions of Blockchain Technology in AI and IoT Applications, 70–87. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5876-8.ch004.

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Human beings tend to make predictions about future events irrespective of probability of occurrence. We are fascinated to solve puzzles and patterns. One such area which intrigues many, full of complexity and unpredicted behavior, is the stock market. For the last decade or so, we have been trying to find patterns and understand the behavior of the stock market with the help of robust computation systems and new approaches to extract and analyze the huge amount of data. In this chapter, the authors have tried to understand stock price movement using a long short-term memory (LSTM) network and predict future behavior of stock price.

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David, Hepzibah Elizabeth, K. Ramalakshmi, R. Venkatesan, and G. Hemalatha. "Tomato Leaf Disease Detection Using Hybrid CNN-RNN Model." In Advances in Parallel Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210108.

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Tomato crops are infected with various diseases that impair tomato production. The recognition of the tomato leaf disease at an early stage protects the tomato crops from getting affected. In the present generation, the emerging deep learning techniques Convolutional Neural Network (CNNs), Recurrent Neural Network (RNNs), Long-Short Term Memory (LSTMs) has manifested significant progress in image classification, image identification, and Sequence Predictions. Thus by using these computer vision-based deep learning techniques, we developed a new method for automatic leaf disease detection. This proposed model is a robust technique for tomato leaf disease identification that gives accurate and better results than other traditional methods. Early tomato leaf disease detection is made possible by using the hybrid CNN-RNN architecture which utilizes less computational effort. In this paper, the required methods for implementing the disease recognition model with results are briefly explained. This paper also mentions the scope of developing more reliable and effective means of classifying and detecting all plant species.

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Тези доповідей конференцій з теми "Robust Long-Short Term Memory (RoLSTM)"

Grushin, Alexander, Derek D. Monner, James A. Reggia, and Ajay Mishra. "Robust human action recognition via long short-term memory." In 2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas). IEEE, 2013. http://dx.doi.org/10.1109/ijcnn.2013.6706797.

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Wöllmer, Martin, Yang Sun, Florian Eyben, and Björn Schuller. "Long short-term memory networks for noise robust speech recognition." In Interspeech 2010. ISCA: ISCA, 2010. http://dx.doi.org/10.21437/interspeech.2010-30.

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Meng, Zhong, Shinji Watanabe, John R. Hershey, and Hakan Erdogan. "Deep long short-term memory adaptive beamforming networks for multichannel robust speech recognition." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952160.

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Liu, Yuzhou, and DeLiang Wang. "Time and frequency domain long short-term memory for noise robust pitch tracking." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7953228.

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Mittal, Anant, Priya Aggarwal, Luiz Pessoa, and Anubha Gupta. "Robust brain state decoding using bidirectional long short term memory networks in functional MRI." In ICVGIP '21: Indian Conference on Computer Vision, Graphics and Image Processing. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3490035.3490269.

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C. Lemos Neto, Álvaro, Rodrigo A. Coelho, and Cristiano L. de Castro. "An Incremental Learning approach using Long Short-Term Memory Neural Networks." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1491.

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Due to Big Data and the Internet of Things, Machine Learning algorithms targeted specifically to model evolving data streams had gained attention from both academia and industry. Many Incremental Learning models had been successful in doing so, but most of them have one thing in common: they are complex variants of batch learning algorithms, which is a problem since, in a streaming setting, less complexity and more performance is desired. This paper proposes the Incremental LSTM model, which is a variant of the original LSTM with minor changes, that can tackle evolving data streams problems such as concept drift and the elasticity-plasticity dilemma without neither needing a dedicated drift detector nor a memory management system. It obtained great results that show it reacts fast to concept drifts and that is also robust to noise data.

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Geiger, Jürgen T., Zixing Zhang, Felix Weninger, Björn Schuller, and Gerhard Rigoll. "Robust speech recognition using long short-term memory recurrent neural networks for hybrid acoustic modelling." In Interspeech 2014. ISCA: ISCA, 2014. http://dx.doi.org/10.21437/interspeech.2014-151.

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Kolboek, Morten, Zheng-Hua Tan, and Jesper Jensen. "Speech enhancement using Long Short-Term Memory based recurrent Neural Networks for noise robust Speaker Verification." In 2016 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2016. http://dx.doi.org/10.1109/slt.2016.7846281.

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Okai, Jeremiah, Stylianos Paraschiakos, Marian Beekman, Arno Knobbe, and Claudio Rebelo de Sa. "Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and Long Short Term Memory Neural Networks." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8857288.

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Bryan, Kaylen J., Mitchell Solomon, Emily Jensen, Christina Coley, Kailas Rajan, Charlie Tian, Nenad Mijatovic, et al. "Classification of Rail Switch Data Using Machine Learning Techniques." In 2018 Joint Rail Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/jrc2018-6175.

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Rail switches are critical infrastructure components of a railroad network, that must maintain high-levels of reliable operation. Given the vast number and variety of switches that can exist across a rail network, there is an immediate need for robust automated methods of detecting switch degradations and failures without expensive add-on equipment. In this work, we explore two recent machine learning frameworks for classifying various switch degradation indicators: (1) a featureless recurrent neural network called a Long Short-Term Memory (LSTM) architecture, and (2), the Deep Wavelet Scattering Transform (DWST), which produces features that are locally time invariant and stable to time-warping deformations. We describe both methods as they apply to rail switch monitoring and demonstrate their feasibility on a dataset captured under the service conditions by Alstom Corporation. For multiple categories of degradation types, the baseline models consistently achieve near-perfect accuracies and are competitive with the manual analysis conducted by human switch-maintenance experts.

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