Long Short-term Memory Autoencoder Research Articles

Tracking of Flexible Brush Tip on Real Canvas: Silhouette-Based and Deep Ensemble Network-Based Approaches

Digital painting is a process of creating a digital artwork using modern human-computer interaction technologies. One of the core enabling technologies is the real-time tracking of user's strokes, which is generally supplied by a digital tablet with a stylus. While the digital tablet technology provides highly accurate tracking, the drawing should be done with a rigid stylus on a plastic surface. This sometimes destroys the realism of drawing, such as interaction with the digital tablet cannot provide the feedback of subtle texture, friction of the paper/fabric canvas and tension of soft painting brush. This becomes particularly problematic for traditional painting artists who are trained with and prefer real painting brush and paper/fabric canvas. Thus, the aim of this work is to present an alternative solution where the user's strokes can be tracked even when the actual brush and canvas are used. To this end, we proposed two approaches for digitally tracking the tip of flexible bristles of a soft brush, so that the painting can be created digitally on a computer. The first approach captures the silhouette of deforming bristles using a pair of well-aligned infra-red (IR) cameras, which extracts the tip from the silhouette, and reconstructs the 2D position of the tip. The second approach predicts the brush tip position through a deep ensemble network-based approach where the relationship between the brush tip position and brush handle pose are trained with our novel model comprising of Long-Short Term Memory Autoencoder and 1-D Convolutional Neural Network. The trained model is used to predict the brush tip position in realtime. Both approaches extensively evaluated through multiple tests. Furthermore, our model outperforms the state-of-the-art models.

Energy Disaggregation via Deep Temporal Dictionary Learning.

This paper presents a novel nonlinear dictionary learning (DL) model to address the energy disaggregation (ED) problem, i.e., decomposing the electricity signal of a home to its operating devices. First, ED is modeled as a new temporal DL problem where a set of dictionary atoms is learned to capture the most representative temporal features of electricity signals. The sparse codes corresponding to these atoms show the contribution of each device in the total electricity consumption. To learn powerful atoms, a novel deep temporal DL (DTDL) model is proposed that computes complex nonlinear dictionaries in the latent space of a long short-term memory autoencoder (LSTM-AE). While the LSTM-AE captures the deep temporal manifold of electricity signals, the DTDL model finds the most representative atoms inside this manifold. To simultaneously optimize the dictionary and the deep temporal manifold, a new optimization algorithm is proposed that alternates between finding the optimal LSTM-AE and the optimal dictionary. To the best of authors' knowledge, DTDL is the only DL model that understands the deep temporal structures of the data. Experiments on the Reference ED Data Set show an outstanding performance compared with the recent state-of-the-art algorithms in terms of precision, recall, accuracy, and F-score.

An AutoEncoder and LSTM-Based Traffic Flow Prediction Method.

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach.

BackgroundBleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with cardiovascular disease in patients on anticoagulant therapy. Prompt and accurate detection of bleeding events is essential to prevent serious consequences. As bleeding events are often described in clinical notes, automatic detection of bleeding events from electronic health record (EHR) notes may improve drug-safety surveillance and pharmacovigilance.ObjectiveWe aimed to develop a natural language processing (NLP) system to automatically classify whether an EHR note sentence contains a bleeding event.MethodsWe expert annotated 878 EHR notes (76,577 sentences and 562,630 word-tokens) to identify bleeding events at the sentence level. This annotated corpus was used to train and validate our NLP systems. We developed an innovative hybrid convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder (HCLA) model that integrates a CNN architecture with a bidirectional LSTM (BiLSTM) autoencoder model to leverage large unlabeled EHR data.ResultsHCLA achieved the best area under the receiver operating characteristic curve (0.957) and F1 score (0.938) to identify whether a sentence contains a bleeding event, thereby surpassing the strong baseline support vector machines and other CNN and autoencoder models.ConclusionsBy incorporating a supervised CNN model and a pretrained unsupervised BiLSTM autoencoder, the HCLA achieved high performance in detecting bleeding events.