scholarly journals Deep Learning Approach for Estimating Workability of Self-Compacting Concrete from Mixing Image Sequences

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Zhongcong Ding ◽  
Xuehui An
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Image Sequences ◽  
Learning Approach ◽  
Self Compacting Concrete ◽  
Short Term ◽  
Slump Flow ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Funnel Flow

We propose a deep learning approach to better utilize the spatial and temporal information obtained from image sequences of the self-compacting concrete- (SCC-) mixing process to recover SCC characteristics in terms of the predicted slump flow value (SF) and V-funnel flow time (VF). The proposed model integrates features of the convolutional neural network and long short-term memory and is trained to extract features and compute an estimate. The performance of the method is evaluated using the testing set. The results indicate that the proposed method could potentially be used to automatically estimate SCC workability.

scholarly journals Forecasting Day-Ahead Hourly Photovoltaic Power Generation Using Convolutional Self-Attention Based Long Short-Term Memory

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 4017 ◽  
Author(s):  
Dukhwan Yu ◽  
Wonik Choi ◽  
Myoungsoo Kim ◽  
Ling Liu
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Local Context ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Pv Power Generation

The problem of Photovoltaic (PV) power generation forecasting is becoming crucial as the penetration level of Distributed Energy Resources (DERs) increases in microgrids and Virtual Power Plants (VPPs). In order to improve the stability of power systems, a fair amount of research has been proposed for increasing prediction performance in practical environments through statistical, machine learning, deep learning, and hybrid approaches. Despite these efforts, the problem of forecasting PV power generation remains to be challenging in power system operations since existing methods show limited accuracy and thus are not sufficiently practical enough to be widely deployed. Many existing methods using long historical data suffer from the long-term dependency problem and are not able to produce high prediction accuracy due to their failure to fully utilize all features of long sequence inputs. To address this problem, we propose a deep learning-based PV power generation forecasting model called Convolutional Self-Attention based Long Short-Term Memory (LSTM). By using the convolutional self-attention mechanism, we can significantly improve prediction accuracy by capturing the local context of the data and generating keys and queries that fit the local context. To validate the applicability of the proposed model, we conduct extensive experiments on both PV power generation forecasting using a real world dataset and power consumption forecasting. The experimental results of power generation forecasting using the real world datasets show that the MAPEs of the proposed model are much lower, in fact by 7.7%, 6%, 3.9% compared to the Deep Neural Network (DNN), LSTM and LSTM with the canonical self-attention, respectively. As for power consumption forecasting, the proposed model exhibits 32%, 17% and 44% lower Mean Absolute Percentage Error (MAPE) than the DNN, LSTM and LSTM with the canonical self-attention, respectively.

scholarly journals Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Mamunur Rashid ◽  
Minarul Islam ◽  
Norizam Sulaiman ◽  
Bifta Sama Bari ◽  
Ripon Kumar Saha ◽  
...  
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Short Term Memory ◽  
Learning Approach ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Malicious Text Identification: Deep Learning from Public Comments and Emails

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 312 ◽  
Author(s):  
Asma Baccouche ◽  
Sadaf Ahmed ◽  
Daniel Sierra-Sosa ◽  
Adel Elmaghraby
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Language Processing ◽  
Short Term Memory ◽  
Good Alternative ◽  
Classification Problems ◽  
Short Term ◽  
Independent Dataset ◽  
Proposed Model ◽  
Long Short Term Memory

Identifying internet spam has been a challenging problem for decades. Several solutions have succeeded to detect spam comments in social media or fraudulent emails. However, an adequate strategy for filtering messages is difficult to achieve, as these messages resemble real communications. From the Natural Language Processing (NLP) perspective, Deep Learning models are a good alternative for classifying text after being preprocessed. In particular, Long Short-Term Memory (LSTM) networks are one of the models that perform well for the binary and multi-label text classification problems. In this paper, an approach merging two different data sources, one intended for Spam in social media posts and the other for Fraud classification in emails, is presented. We designed a multi-label LSTM model and trained it on the joint datasets including text with common bigrams, extracted from each independent dataset. The experiment results show that our proposed model is capable of identifying malicious text regardless of the source. The LSTM model trained with the merged dataset outperforms the models trained independently on each dataset.

CONVOLUTIONAL LONG-SHORT TERM MEMORY NETWORKS MODEL FOR LONG DURATION EEG SIGNAL CLASSIFICATION

2019 ◽  
Vol 19 (01) ◽  
pp. 1940005 ◽  
Author(s):  
ULAS BARAN BALOGLU ◽  
ÖZAL YILDIRIM
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Learning Algorithms ◽  
Eeg Signals ◽  
Short Term ◽  
Term Memory ◽  
Long Duration ◽  
Proposed Model ◽  
Clinical Scenarios ◽  
Long Short Term Memory

Background and objective: Deep learning structures have recently achieved remarkable success in the field of machine learning. Convolutional neural networks (CNN) in image processing and long-short term memory (LSTM) in the time-series analysis are commonly used deep learning algorithms. Healthcare applications of deep learning algorithms provide important contributions for computer-aided diagnosis research. In this study, convolutional long-short term memory (CLSTM) network was used for automatic classification of EEG signals and automatic seizure detection. Methods: A new nine-layer deep network model consisting of convolutional and LSTM layers was designed. The signals processed in the convolutional layers were given as an input to the LSTM network whose outputs were processed in densely connected neural network layers. The EEG data is appropriate for a model having 1-D convolution layers. A bidirectional model was employed in the LSTM layer. Results: Bonn University EEG database with five different datasets was used for experimental studies. In this database, each dataset contains 23.6[Formula: see text]s duration 100 single channel EEG segments which consist of 4097 dimensional samples (173.61[Formula: see text]Hz). Eight two-class and three three-class clinical scenarios were examined. When the experimental results were evaluated, it was seen that the proposed model had high accuracy on both binary and ternary classification tasks. Conclusions: The proposed end-to-end learning structure showed a good performance without using any hand-crafted feature extraction or shallow classifiers to detect the seizures. The model does not require filtering, and also automatically learns to filter the input as well. As a result, the proposed model can process long duration EEG signals without applying segmentation, and can detect epileptic seizures automatically by using the correlation of ictal and interictal signals of raw data.

scholarly journals A Deep Learning Approach for the Romanized Tunisian Dialect Identification

2020 ◽  
Vol 17 (6) ◽  
pp. 935-946
Author(s):  
Jihene Younes ◽  
Hadhemi Achour ◽  
Emna Souissi ◽  
Ahmed Ferchichi
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Conditional Random Fields ◽  
Short Term Memory ◽  
Learning Approach ◽  
Short Term ◽  
The Social ◽  
Long Short Term Memory ◽  
N Gram ◽  
Textual Content

Language identification is an important task in natural language processing that consists in determining the language of a given text. It has increasingly picked the interest of researchers for the past few years, especially for code-switching informal textual content. In this paper, we focus on the identification of the Romanized user-generated Tunisian dialect on the social web. We segment and annotate a corpus extracted from social media and propose a deep learning approach for the identification task. We use a Bidirectional Long Short-Term Memory neural network with Conditional Random Fields decoding (BLSTM-CRF). For word embeddings, we combine word-character BLSTM vector representation and Fast Text embeddings that takes into consideration character n-gram features. The overall accuracy obtained is 98.65%.

scholarly journals Lane-Changing Behavior Prediction Based on Game Theory and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shuo Jia ◽  
Fei Hui ◽  
Cheng Wei ◽  
Xiangmo Zhao ◽  
Jianbei Liu
Keyword(s):  
Game Theory ◽  
Deep Learning ◽  
Short Term Memory ◽  
Accurate Prediction ◽  
Behavior Prediction ◽  
Short Term ◽  
Lane Changing ◽  
Proposed Model ◽  
The Game Theory ◽  
Long Short Term Memory

Lane changing is an important scenario in traffic environments, and accurate prediction of lane-changing behavior is essential to ensure traffic and driver safety. To achieve this goal, a vehicle lane-changing prediction model based on game theory and deep learning is developed. In the game theory component, the interaction between vehicles during lane changing is analyzed according to the running state of the vehicle, with the probability of lane changing as its output. For the deep-learning component, long short-term memory and a convolutional neural network are used to extract and learn data features during the lane-changing process as well as combine the output of the game theory component to obtain the prediction result of whether the vehicle will change lanes. By using an open-source traffic dataset to train and verify the proposed model, the verification results show that the prediction accuracy can reach 94.56% within 0.4 s of lane-changing operation and that the model can achieve timely and accurate prediction of the lane-changing behavior of vehicles.

scholarly journals Pay attention and you won’t lose it: a deep learning approach to sequence imputation

2019 ◽  
Vol 5 ◽  
pp. e210
Author(s):  
Ilia Sucholutsky ◽  
Apurva Narayan ◽  
Matthias Schonlau ◽  
Sebastian Fischmeister
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Learning Approach ◽  
Short Term ◽  
Fold Reduction ◽  
Data Restoration ◽  
Major Bottleneck ◽  
Long Short Term Memory ◽  
End Stage

In most areas of machine learning, it is assumed that data quality is fairly consistent between training and inference. Unfortunately, in real systems, data are plagued by noise, loss, and various other quality reducing factors. While a number of deep learning algorithms solve end-stage problems of prediction and classification, very few aim to solve the intermediate problems of data pre-processing, cleaning, and restoration. Long Short-Term Memory (LSTM) networks have previously been proposed as a solution for data restoration, but they suffer from a major bottleneck: a large number of sequential operations. We propose using attention mechanisms to entirely replace the recurrent components of these data-restoration networks. We demonstrate that such an approach leads to reduced model sizes by as many as two orders of magnitude, a 2-fold to 4-fold reduction in training times, and 95% accuracy for automotive data restoration. We also show in a case study that this approach improves the performance of downstream algorithms reliant on clean data.

scholarly journals Non-Intrusive Load Identification Method Based on Improved Long Short Term Memory Network

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 684
Author(s):  
Jiateng Song ◽  
Hongbin Wang ◽  
Mingxing Du ◽  
Lei Peng ◽  
Shuai Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Rapid Development ◽  
Series Data ◽  
Load Identification ◽  
Short Term ◽  
Term Memory ◽  
Identification Method ◽  
Proposed Model ◽  
Long Short Term Memory

Non-intrusive load monitoring (NILM) is an important research direction and development goal on the distribution side of smart grid, which can significantly improve the timeliness of demand side response and users’ awareness of load. Due to rapid development, deep learning becomes an effective way to optimize NILM. In this paper, we propose a novel load identification method based on long short term memory (LSTM) on deep learning. Sequence-to-point (seq2point) learning is introduced into LSTM. The innovative combination of the LSTM and the seq2point brings their respective advantages together, so that the proposed model can accurately identify the load in process of time series data. In this paper, we proved the feature of reducing identification error in the experimental data, from three datasets, UK-DALE dataset, REDD dataset, and REFIT dataset. In terms of mean absolute error (MAE), the three datasets have increased by 15%, 14%, and 18% respectively; in terms of normalized signal aggregate error (SAE), the three datasets have increased by 21%, 24%, and 30% respectively. Compared with the existing models, the proposed model has better accuracy and generalization in identifying three open source datasets.

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