scholarly journals Vector Magnetic Anomaly Detection via an Attention Mechanism Deep-Learning Model

2021 ◽  
Vol 11 (23) ◽  
pp. 11533
Author(s):  
Xueshan Wu ◽  
Song Huang ◽  
Min Li ◽  
Yufeng Deng
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Magnetic Anomaly ◽  
Learning Model ◽  
Attention Mechanism ◽  
Detection Capability ◽  
Power Spectral ◽  
Gaussian Colored Noise ◽  
Long Time ◽  
Deep Learning Model

Magnetic anomaly detection (MAD) is used for detecting moving ferromagnetic targets. In this study, we present an end-to-end deep-learning model for magnetic anomaly detection on data recorded by a single static three-axis magnetometer. We incorporate an attention mechanism into our network to improve the detection capability of long time-series signals. Our model has good performance under the Gaussian colored noise with the power spectral density of 1/fα, which is similar to the field magnetic noise. Our method does not require another magnetometer to eliminate the effects of the Earth’s magnetic field or external interferences. We evaluate the network’s performance through computer simulations and real-world experiments. The high detection performance and the single magnetometer implementation show great potential for real-time detection and edge computing.

scholarly journals Which Attributes Matter the Most for Loan Origination? A Neural Attention Approach

2020 ◽  
Author(s):  
Antonios Alexos ◽  
Sotirios Chatzis
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Individual Case ◽  
Learning Model ◽  
Attention Mechanism ◽  
Novel Approach ◽  
Specific Individual ◽  
Real World Datasets ◽  
Deep Learning Model

In this paper we address the understanding of the problem, of why a deep learning model decides that an individual is eligible for a loan or not. Here we propose a novel approach for inferring, which attributes matter the most, for making a decision in each specific individual case. Specifically we leverage concepts from neural attention to devise a novel feature wise attention mechanism. As we show, using real world datasets, our approach offers unique insights into the importance of various features, by producing a decision explanation for each specific loan case. At the same time, we observe that our novel mechanism, generates decisions which are much closer to the decisions generated by human experts, compared to the existent competitors.

scholarly journals Multi-Task Deep Learning Model with an Attention Mechanism for Ship Accident Sentence Prediction

2021 ◽  
Vol 12 (1) ◽  
pp. 233
Author(s):  
Ho-Min Park ◽  
Jae-Hoon Kim
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Attention Mechanism ◽  
Maritime Safety ◽  
Proposed Model ◽  
Deep Learning Model

The number of ship accidents occurring in the Korean ocean has been steadily increasing year by year. The Korea Maritime Safety Tribunal (KMST) has published verdicts to ensure that the relevant personnel can share judgment on these accidents. As of 2020, there have been 3156 ship accidents; thus, it is difficult for the relevant personnel to study these various accidents by only reading the verdicts. Therefore, in this study, we propose a multi-task deep learning model with an attention mechanism for predicting the sentencing of ship accidents. The tasks are accident types, applied articles, and the sentencing of ship accidents. The proposed model was tested under verdicts published by the KMST between 2010 and 2019. Through experiments, we show that the proposed model can improve the performance of sentence prediction and can assist the relevant personnel to study these accidents.

scholarly journals Prediction of pandemic risk for animal-origin coronavirus using a deep learning method

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Zheng Kou ◽  
Yi-Fan Huang ◽  
Ao Shen ◽  
Saeed Kosari ◽  
Xiang-Rong Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
General Model ◽  
Characteristic Curve ◽  
Learning Model ◽  
Attention Mechanism ◽  
Animal Origin ◽  
Coding Region ◽  
Species Barrier ◽  
Potential Pathogen ◽  
Deep Learning Model

Abstract Background Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animal-origin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes. Methods A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models. Results The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5–25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor. Conclusions Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic. Graphical Abstract

scholarly journals An Unsupervised Deep Learning Model for Early Network Traffic Anomaly Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 30387-30399 ◽  
Author(s):  
Ren-Hung Hwang ◽  
Min-Chun Peng ◽  
Chien-Wei Huang ◽  
Po-Ching Lin ◽  
Van-Linh Nguyen
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Network Traffic ◽  
Learning Model ◽  
Unsupervised Deep Learning ◽  
Traffic Anomaly ◽  
Deep Learning Model ◽  
Traffic Anomaly Detection

scholarly journals Dynamic Pollution Emission Prediction Method of a Combined Heat and Power System Based on the Hybrid CNN-LSTM Model and Attention Mechanism

2021 ◽  
Author(s):  
Anping Wan ◽  
Jie Yang ◽  
Ting Chen ◽  
Yang Jinxing ◽  
Ke Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power System ◽  
Prediction Method ◽  
Learning Model ◽  
Combined Heat And Power ◽  
Attention Mechanism ◽  
Feature Engineering ◽  
Pollution Emission ◽  
Highly Nonlinear ◽  
Deep Learning Model

Abstract The prediction of pollution emission from a combined heat and power (CHP) system is very important for the production regulation and emergency response of a power system. The composition and structure of the CHP equipment are complex, and the production process is cumbersome. The fuel chemical reaction of the pulverized coal in the boiler represents a highly nonlinear and strongly interrelated process that is strongly affected by external environmental factors, which causes a certain level of volatility and uncertainty. In this study, a pollution emission prediction method of CHP systems based on feature engineering and a hybrid deep learning model is proposed. Feature engineering performs multi-step preprocessing on the original data, refines the correlation factors, and removes redundant variables. The hybrid deep learning model has a multi-variable input and is established by combining the convolutional neural network-long short-term memory network with the attention mechanism. The case study is conducted on the collected actual dataset. The influence of the prediction target periodicity on the prediction results is analyzed seasonally to verify the effectiveness of the hybrid model. The results show that the root mean square error of the proposed method is less than one, and the error is reduced compared to the other basic methods, which proves the superiority of the proposed pollution emission prediction method over the existing methods.

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