Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities
The vast proliferation of sensor devices and Internet of Things enables the applications of
sensor-based activity recognition. However, there exist substantial challenges that could …
sensor-based activity recognition. However, there exist substantial challenges that could …
Deep learning for time series classification: a review
Abstract Time Series Classification (TSC) is an important and challenging problem in data
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
[PDF][PDF] 卷积神经网络研究综述
周飞燕， 金林鹏， 董军 - 计算机学报, 2017 - cjc.ict.ac.cn
摘要作为一个十余年来快速发展的崭新领域, 深度学习受到了越来越多研究者的关注,
它在特征提取和模型拟合上都有着相较于浅层模型显然的优势. 深度学习善于从原始输入数据中 …
它在特征提取和模型拟合上都有着相较于浅层模型显然的优势. 深度学习善于从原始输入数据中 …
LSTM-CNN architecture for human activity recognition
K Xia, J Huang, H Wang - IEEE Access, 2020 - ieeexplore.ieee.org
In the past years, traditional pattern recognition methods have made great progress.
However, these methods rely heavily on manual feature extraction, which may hinder the …
However, these methods rely heavily on manual feature extraction, which may hinder the …
Electricity price forecasting on the day-ahead market using machine learning
The price of electricity on the European market is very volatile. This is due both to its mode of
production by different sources, each with its own constraints (volume of production …
production by different sources, each with its own constraints (volume of production …
[HTML][HTML] Deep reinforcement learning for predictive aircraft maintenance using probabilistic remaining-useful-life prognostics
The increasing availability of sensor monitoring data has stimulated the development of
Remaining-Useful-Life (RUL) prognostics and maintenance planning models. However …
Remaining-Useful-Life (RUL) prognostics and maintenance planning models. However …
Deep learning for healthcare applications based on physiological signals: A review
Background and objective: We have cast the net into the ocean of knowledge to retrieve the
latest scientific research on deep learning methods for physiological signals. We found 53 …
latest scientific research on deep learning methods for physiological signals. We found 53 …
DeepAnT: A deep learning approach for unsupervised anomaly detection in time series
Traditional distance and density-based anomaly detection techniques are unable to detect
periodic and seasonality related point anomalies which occur commonly in streaming data …
periodic and seasonality related point anomalies which occur commonly in streaming data …
Anomaly detection in univariate time-series: A survey on the state-of-the-art
M Braei, S Wagner - arXiv preprint arXiv:2004.00433, 2020 - arxiv.org
Anomaly detection for time-series data has been an important research field for a long time.
Seminal work on anomaly detection methods has been focussing on statistical approaches …
Seminal work on anomaly detection methods has been focussing on statistical approaches …
Multivariate time series forecasting via attention-based encoder–decoder framework
Time series forecasting is an important technique to study the behavior of temporal data and
forecast future values, which is widely applied in many fields, eg air quality forecasting …
forecast future values, which is widely applied in many fields, eg air quality forecasting …