scholarly journals Semantic Segmentation of SLAR Imagery with Convolutional LSTM Selectional AutoEncoders

2019 ◽  
Vol 11 (12) ◽  
pp. 1402 ◽  
Author(s):  
Antonio-Javier Gallego ◽  
Pablo Gil ◽  
Antonio Pertusa ◽  
Robert B. Fisher
Keyword(s):  
Oil Spill ◽  
Emergency Services ◽  
Short Term Memory ◽  
Oil Spills ◽  
Semantic Segmentation ◽  
Neural Architecture ◽  
Learning Classifier ◽  
Previous State ◽  
New Type ◽  
Long Short Term Memory

We present a method to detect maritime oil spills from Side-Looking Airborne Radar (SLAR) sensors mounted on aircraft in order to enable a quick response of emergency services when an oil spill occurs. The proposed approach introduces a new type of neural architecture named Convolutional Long Short Term Memory Selectional AutoEncoders (CMSAE) which allows the simultaneous segmentation of multiple classes such as coast, oil spill and ships. Unlike previous works using full SLAR images, in this work only a few scanlines from the beam-scanning of radar are needed to perform the detection. The main objective is to develop a method that performs accurate segmentation using only the current and previous sensor information, in order to return a real-time response during the flight. The proposed architecture uses a series of CMSAE networks to process in parallel each of the objectives defined as different classes. The output of these networks are given to a machine learning classifier to perform the final detection. Results show that the proposed approach can reliably detect oil spills and other maritime objects in SLAR sequences, outperforming the accuracy of previous state-of-the-art methods and with a response time of only 0.76 s.

scholarly journals 2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2392
Author(s):  
Antonello Rosato ◽  
Rodolfo Araneo ◽  
Amedeo Andreotti ◽  
Federico Succetti ◽  
Massimo Panella
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Short Term ◽  
Neural Architecture ◽  
Network Layers ◽  
Learning Scheme ◽  
World Energy ◽  
Multivariate Prediction ◽  
Long Short Term Memory

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.

scholarly journals Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation

2016 ◽  
Vol 4 ◽  
pp. 371-383 ◽  
Author(s):  
Jie Zhou ◽  
Ying Cao ◽  
Xuguang Wang ◽  
Peng Li ◽  
Wei Xu
Keyword(s):  
Machine Translation ◽  
Short Term Memory ◽  
Short Term ◽  
Neural Machine Translation ◽  
Attention Model ◽  
Linear Connections ◽  
New Type ◽  
Long Short Term Memory ◽  
Unknown Words ◽  
First Time

Neural machine translation (NMT) aims at solving machine translation (MT) problems using neural networks and has exhibited promising results in recent years. However, most of the existing NMT models are shallow and there is still a performance gap between a single NMT model and the best conventional MT system. In this work, we introduce a new type of linear connections, named fast-forward connections, based on deep Long Short-Term Memory (LSTM) networks, and an interleaved bi-directional architecture for stacking the LSTM layers. Fast-forward connections play an essential role in propagating the gradients and building a deep topology of depth 16. On the WMT’14 English-to-French task, we achieve BLEU=37.7 with a single attention model, which outperforms the corresponding single shallow model by 6.2 BLEU points. This is the first time that a single NMT model achieves state-of-the-art performance and outperforms the best conventional model by 0.7 BLEU points. We can still achieve BLEU=36.3 even without using an attention mechanism. After special handling of unknown words and model ensembling, we obtain the best score reported to date on this task with BLEU=40.4. Our models are also validated on the more difficult WMT’14 English-to-German task.

scholarly journals SEMANTIC SEGMENTATION OF MOBILE LASER SCANNING POINT CLOUDS WITH LONG SHORT-TERM MEMORY NETWORKS: PRELIMINARY RESULTS

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 123-130
Author(s):  
J. Balado ◽  
P. van Oosterom ◽  
L. Díaz-Vilariño ◽  
P. Arias
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Short Term Memory ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Time Signal ◽  
Success Rates ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract. Although point clouds are characterized as a type of unstructured data, timestamp attribute can structure point clouds into scanlines and shape them into a time signal. The present work studies the transformation of the street point cloud into a time signal based on the Z component for the semantic segmentation using Long Short-Term Memory (LSTM) networks. The experiment was conducted on the point cloud of a real case study. Several training sessions were performed changing the Level of Detail of the classification (coarse level with 3 classes and fine level with 11 classes), two levels of network depth and the use of weighting for the improvement of classes with low number of points. The results showed high accuracy, reaching at best 97.3% in the classification with 3 classes (ground, buildings, and objects) and 95.7% with 11 classes. The distribution of the success rates was not the same for all classes. The classes with the highest number of points obtained better results than the others. The application of weighting improved the classes with few points at the expense of the classes with more points. Increasing the number of hidden layers was shown as a preferable alternative to weighting. Given the high success rates and a behaviour of the LSTM consistent with other Neural Networks in point cloud processing, it is concluded that the LSTM is a feasible alternative for the semantic segmentation of point clouds transformed into time signals.

scholarly journals Jamming Prediction for Radar Signals Using Machine Learning Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Gyeong-Hoon Lee ◽  
Jeil Jo ◽  
Cheong Hee Park
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Learning Model ◽  
Training Data ◽  
Electronic Warfare ◽  
New Type ◽  
Long Short Term Memory ◽  
Radar Signals ◽  
Feature Values ◽  
Deep Learning Model

Jamming is a form of electronic warfare where jammers radiate interfering signals toward an enemy radar, disrupting the receiver. The conventional method for determining an effective jamming technique corresponding to a threat signal is based on the library which stores the appropriate jamming method for signal types. However, there is a limit to the use of a library when a threat signal of a new type or a threat signal that has been altered differently from existing types is received. In this paper, we study two methods of predicting the appropriate jamming technique for a received threat signal using deep learning: using a deep neural network on feature values extracted manually from the PDW list and using long short-term memory (LSTM) which takes the PDW list as input. Using training data consisting of pairs of threat signals and corresponding jamming techniques, a deep learning model is trained which outputs jamming techniques for threat signal inputs. Training data are constructed based on the information in the library, but the trained deep learning model is used to predict jamming techniques for received threat signals without using the library. The prediction performance and time complexity of two proposed methods are compared. In particular, the ability to predict jamming techniques for unknown types of radar signals which are not used in the stage of training the model is analyzed.

Semantic Segmentation of QRS Complex in Single Channel ECG with Bidirectional LSTM Networks

2020 ◽  
Vol 10 (3) ◽  
pp. 758-762 ◽  
Author(s):  
Lingfeng Liu ◽  
Baodan Bai ◽  
Xinrong Chen ◽  
Qin Xia
Keyword(s):  
Short Term Memory ◽  
Single Channel ◽  
Semantic Segmentation ◽  
Time Interval ◽  
Qrs Complex ◽  
Short Term ◽  
Attention Model ◽  
Interval Prediction ◽  
Long Short Term Memory ◽  
Electrocardiogram Ecg

In this paper, bidirectional Long Short-Term Memory (BiLSTM) networks are designed to realize the semantic segmentation of QRS complex in single channel electrocardiogram (ECG) for the tasks of R peak detection and heart rate estimation. Three types of seq2seq BiLSTM networks are introduced, including the densely connected BiLSTM with attention model, the BiLSTM U-Net, and the BiLSTM U-Net++. To alleviate the sparse problem of the QRS labels, symmetric label expansion is applied by extending the single R peak into a time interval of fixed length. Linear ensemble method is introduced that averages the outputs of different BiLSTM networks. The cross-validation results show significant increase of the accuracy and decrease of discontinuous gaps in the QRS interval prediction by the ensembling over singular neural networks.

Neuroevolution of a Modular Memory-Augmented Neural Network for Deep Memory Problems

2019 ◽  
Vol 27 (4) ◽  
pp. 639-664 ◽  
Author(s):  
Shauharda Khadka ◽  
Jen Jen Chung ◽  
Kagan Tumer
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Short Term Memory ◽  
Extended Period ◽  
New Class ◽  
Neural Architecture ◽  
Memory Block ◽  
Memory Problems ◽  
Long Short Term Memory ◽  
Gated Recurrent Units

We present Modular Memory Units (MMUs), a new class of memory-augmented neural network. MMU builds on the gated neural architecture of Gated Recurrent Units (GRUs) and Long Short Term Memory (LSTMs), to incorporate an external memory block, similar to a Neural Turing Machine (NTM). MMU interacts with the memory block using independent read and write gates that serve to decouple the memory from the central feedforward operation. This allows for regimented memory access and update, giving our network the ability to choose when to read from memory, update it, or simply ignore it. This capacity to act in detachment allows the network to shield the memory from noise and other distractions, while simultaneously using it to effectively retain and propagate information over an extended period of time. We train MMU using both neuroevolution and gradient descent, and perform experiments on two deep memory benchmarks. Results demonstrate that MMU performs significantly faster and more accurately than traditional LSTM-based methods, and is robust to dramatic increases in the sequence depth of these memory benchmarks.

scholarly journals AraSenCorpus: A Semi-Supervised Approach for Sentiment Annotation of a Large Arabic Text Corpus

2021 ◽  
Vol 11 (5) ◽  
pp. 2434
Author(s):  
Ali Al-Laith ◽  
Muhammad Shahbaz ◽  
Hind F. Alaskar ◽  
Asim Rehmat
Keyword(s):  
Short Term Memory ◽  
State Of The Art ◽  
Arabic Text ◽  
Short Term ◽  
Learning Classifier ◽  
Learning Technique ◽  
Benchmark Datasets ◽  
Long Short Term Memory ◽  
Self Learning ◽  
Modern Standard

At a time when research in the field of sentiment analysis tends to study advanced topics in languages, such as English, other languages such as Arabic still suffer from basic problems and challenges, most notably the availability of large corpora. Furthermore, manual annotation is time-consuming and difficult when the corpus is too large. This paper presents a semi-supervised self-learning technique, to extend an Arabic sentiment annotated corpus with unlabeled data, named AraSenCorpus. We use a neural network to train a set of models on a manually labeled dataset containing 15,000 tweets. We used these models to extend the corpus to a large Arabic sentiment corpus called “AraSenCorpus”. AraSenCorpus contains 4.5 million tweets and covers both modern standard Arabic and some of the Arabic dialects. The long-short term memory (LSTM) deep learning classifier is used to train and test the final corpus. We evaluate our proposed framework on two external benchmark datasets to ensure the improvement of the Arabic sentiment classification. The experimental results show that our corpus outperforms the existing state-of-the-art systems.

scholarly journals Improving sentiment analysis with multi-task learning of negation

2020 ◽  
pp. 1-21 ◽  
Author(s):  
Jeremy Barnes ◽  
Erik Velldal ◽  
Lilja Øvrelid
Keyword(s):  
Sentiment Analysis ◽  
English Language ◽  
Short Term Memory ◽  
Standard English ◽  
Data Sets ◽  
Main Task ◽  
Short Term ◽  
Neural Architecture ◽  
Language Data ◽  
Long Short Term Memory

Abstract Sentiment analysis is directly affected by compositional phenomena in language that act on the prior polarity of the words and phrases found in the text. Negation is the most prevalent of these phenomena, and in order to correctly predict sentiment, a classifier must be able to identify negation and disentangle the effect that its scope has on the final polarity of a text. This paper proposes a multi-task approach to explicitly incorporate information about negation in sentiment analysis, which we show outperforms learning negation implicitly in an end-to-end manner. We describe our approach, a cascading and hierarchical neural architecture with selective sharing of Long Short-term Memory layers, and show that explicitly training the model with negation as an auxiliary task helps improve the main task of sentiment analysis. The effect is demonstrated across several different standard English-language data sets for both tasks, and we analyze several aspects of our system related to its performance, varying types and amounts of input data and different multi-task setups.

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