scholarly journals Anomaly Detection for Smart Lighting Infrastructure with the Use of Time Series Analysis

2020 ◽  
Vol 26 (4) ◽  
pp. 508-527
Author(s):  
Tomasz Andrysiak ◽  
Łukasz Saganowski
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Anomaly Detection ◽  
Network Traffic ◽  
Short Term Memory ◽  
Abnormal Behavior ◽  
Short Term ◽  
Term Memory ◽  
Smart Lighting ◽  
Informative Content

One of the basic elements of every Smart City is currently a system of managing urban infrastructure, in particular, smart systems controlling street lighting. Ensuring proper level of security, continuity and failure-free operation of such systems, in practice, seems not yet a solved problem. In this article we present proposals of a system allowing to detect different types of anomalies in network traffic for Smart Lighting critical infrastructure realized with the use of Power Line Communication technology. Furthermore, there is proposed and described the structure of the examined Smart Lighting Communications Network along with its particular elements. We discuss key security aspects which affect proper operation of advance communication infrastructure, i.e. possibility of occurrence of abuse connected both to activity of external factors which could disturb transmission of steering signals, as well as active forms of attack aiming at influencing the informative content of the transmitted data. In the article, there is also presented an effective and quick anomaly detection method in the tested network traffic represented by suitable time series. At the initial stage of the method, the process of detection and elimination of potential outlying observations was realized by one-dimensional quartile criterion. Data prepared in this manner was used for learning recurrent neural networks, i.e. Long and Short-Term Memory types, in order to predict values of the analyzed time series. Further, tests were performed on relations between the forecasted network traffic and its real variability in order to detect abnormal behavior which could mean an attempt of an attack or abuse. Due to a possibility of occurrence of significant fluctuations in real network traffic of the tested Smart Lighting infrastructure, we propose a procedure of recurrent learning with the use of neural networks to obtain more accurate forecasting. The results achieved by means of the performed experiments confirmed effectiveness of the presented method and proper choice of the Long Short-Term Memory neural network for forecasting the analyzed time series.

scholarly journals Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series

2018 ◽  
Vol 10 (3) ◽  
pp. 452 ◽  
Author(s):  
Yun-Long Kong ◽  
Qingqing Huang ◽  
Chengyi Wang ◽  
Jingbo Chen ◽  
Jiansheng Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Short Term Memory ◽  
Satellite Image ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Disturbance Detection

Deep machine learning for detection of acoustic wave reflections

2019 ◽  
Vol 19 (5) ◽  
pp. 1340-1350
Author(s):  
Mulugeta A Haile ◽  
Edward Zhu ◽  
Christopher Hsu ◽  
Natasha Bradley
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Acoustic Emission ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Propagating Waves ◽  
Long Short Term Memory

Acoustic emission signals are information rich and can be used to estimate the size and location of damage in structures. However, many existing algorithms may be deceived by indirectly propagated acoustic emission waves which are modulated by reflection boundaries within the structures. We propose two deep learning models to identify such waves such that existing algorithms for damage detection and localization may be used. The first approach uses long short-term memory recurrent neural networks to learn distinct patterns directly from the time-series data. In the second approach, we transform the time-series data into spectrograms and utilize convolutional neural networks to perform binary classification by leveraging spectro-temporal features. We achieved 80% classification accuracy using long short-term memory and near-perfect accuracy using convolutional neural networks on a dataset of acoustic emission signals generated by the Hsu-Nielsen sources. Both long short-term memory and convolutional neural network models were able to learn general and context-specific features of the direct and reflected acoustic emission waves. Once accurately identified, the indirectly propagating waves are filtered out while the directly propagating waves are used for source location using existing methods.

scholarly journals LSTM-Based VAE-GAN for Time-Series Anomaly Detection

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3738
Author(s):  
Zijian Niu ◽  
Ke Yu ◽  
Xiaofei Wu
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Short Term Memory ◽  
Generative Adversarial Networks ◽  
Short Term ◽  
Term Memory ◽  
Adversarial Networks ◽  
Time Space ◽  
Detection Stage ◽  
Long Short Term Memory

Time series anomaly detection is widely used to monitor the equipment sates through the data collected in the form of time series. At present, the deep learning method based on generative adversarial networks (GAN) has emerged for time series anomaly detection. However, this method needs to find the best mapping from real-time space to the latent space at the anomaly detection stage, which brings new errors and takes a long time. In this paper, we propose a long short-term memory-based variational autoencoder generation adversarial networks (LSTM-based VAE-GAN) method for time series anomaly detection, which effectively solves the above problems. Our method jointly trains the encoder, the generator and the discriminator to take advantage of the mapping ability of the encoder and the discrimination ability of the discriminator simultaneously. The long short-term memory (LSTM) networks are used as the encoder, the generator and the discriminator. At the anomaly detection stage, anomalies are detected based on reconstruction difference and discrimination results. Experimental results show that the proposed method can quickly and accurately detect anomalies.

scholarly journals Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Xiaolu Wei ◽  
Binbin Lei ◽  
Hongbing Ouyang ◽  
Qiufeng Wu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Temporal Pattern ◽  
Short Term Memory ◽  
Stock Index ◽  
Support Vector ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

This study attempts to predict stock index prices using multivariate time series analysis. The study’s motivation is based on the notion that datasets of stock index prices involve weak periodic patterns, long-term and short-term information, for which traditional approaches and current neural networks such as Autoregressive models and Support Vector Machine (SVM) may fail. This study applied Temporal Pattern Attention and Long-Short-Term Memory (TPA-LSTM) for prediction to overcome the issue. The results show that stock index prices prediction through the TPA-LSTM algorithm could achieve better prediction performance over traditional deep neural networks, such as recurrent neural network (RNN), convolutional neural network (CNN), and long and short-term time series network (LSTNet).

Sign in / Sign up

Export Citation Format

Share Document