A Real-Time Integrated Hierarchical Temporal Memory Network for the Real-Time Continuous Multi-Interval Prediction of Data Streams

2014 ◽  
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Temporal Memory ◽  
The Real ◽  
Hierarchical Temporal Memory ◽  
Interval Prediction ◽  
Memory Network

scholarly journals Temporal Memory Network Towards Real-Time Video Understanding

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 223837-223847
Author(s):  
Ziming Liu ◽  
Jinyang Li ◽  
Guangyu Gao ◽  
Alex K. Qin
Keyword(s):  
Real Time ◽  
Temporal Memory ◽  
Video Understanding ◽  
Memory Network

scholarly journals Multivariate-Time-Series-Driven Real-time Anomaly Detection Based on Bayesian Network

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3367 ◽  
Author(s):  
Nan Ding ◽  
Huanbo Gao ◽  
Hongyu Bu ◽  
Haoxuan Ma ◽  
Huaiwei Si
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Bayesian Network ◽  
Real Time ◽  
Multivariate Time Series ◽  
Research Direction ◽  
Detection Algorithm ◽  
The Real ◽  
Hierarchical Temporal Memory ◽  
Sensing Time

Anomaly detection is an important research direction, which takes the real-time information system from different sensors and conditional information sources into consideration. Based on this, we can detect possible anomalies expected of the devices and components. One of the challenges is anomaly detection in multivariate-sensing time-series in this paper. Based on this situation, we propose RADM, a real-time anomaly detection algorithm based on Hierarchical Temporal Memory (HTM) and Bayesian Network (BN). First of all, we use HTM model to evaluate the real-time anomalies of each univariate-sensing time-series. Secondly, a model of anomalous state detection in multivariate-sensing time-series based on Naive Bayesian is designed to analyze the validity of the above time-series. Lastly, considering the real-time monitoring cases of the system states of terminal nodes in Cloud Platform, the effectiveness of the methodology is demonstrated using a simulated example. Extensive simulation results show that using RADM in multivariate-sensing time-series is able to detect more abnormal, and thus can remarkably improve the performance of real-time anomaly detection.

Multi-object recognition by optimized hierarchical temporal memory network

Optik ◽  
2016 ◽  
Vol 127 (19) ◽  
pp. 7594-7601 ◽  
Author(s):  
Liming Xie ◽  
Kai Yang ◽  
Xiaorong Gao
Keyword(s):  
Object Recognition ◽  
Temporal Memory ◽  
Hierarchical Temporal Memory ◽  
Memory Network

scholarly journals Motion-Print: A Biometric for Real-Time Pilot Identification using Hierarchical Temporal Memory

2020 ◽  
Author(s):  
Sam Heiserman ◽  
Kirill Zaychik ◽  
Timothy Miller
Keyword(s):  
Real Time ◽  
Superior Performance ◽  
Time Warping ◽  
Control Task ◽  
Time Data ◽  
Temporal Memory ◽  
Hierarchical Temporal Memory ◽  
Motion Simulator ◽  
Monitoring Applications ◽  
Dynamic Time

<p>This study presents a novel biometric approach to identify operators, given only streams of their control movements within a manual control task setting. In the present task subjects control a simulated, remotely operated robotic arm, attempting to dock onto a satellite in orbit. The proposed methodology utilizes the Hierarchical Temporal Memory (HTM) algorithm to distinguish operators by their unique control behaviors. Results presented compare the identification performance of HTM with Dynamic Time Warping (DTW) and Edit Distance on Real Sequences (EDR), in both static and real-time data settings. The HTM method outperformed both DTW and EDR in the real- time setting, and matched DTW in the static setting. Observed superior performance of the HTM algorithm lays the foundation for the extension of the proposed methodology to other motion- monitoring applications, such as real-time workload assessment, motion/simulator sickness onset or distraction detection.</p><p><br></p><p>The data gathered in the study was posted to IEEE-dataport, DOI: <a href="http://dx.doi.org/10.21227/wpyf-r927" target="_blank">10.21227/wpyf-r927</a><br></p><div><br></div>

Digital Data Streams: Creating Value from the Real-Time Flow of Big Data

2016 ◽  
Vol 58 (3) ◽  
pp. 5-25 ◽  
Author(s):  
Federico Pigni ◽  
Gabriele Piccoli ◽  
Richard Watson
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Streams ◽  
Digital Data ◽  
The Real ◽  
Time Flow

scholarly journals Motion-Print: A Biometric for Real-Time Pilot Identification using Hierarchical Temporal Memory

2020 ◽  
Author(s):  
Sam Heiserman ◽  
Kirill Zaychik ◽  
Timothy Miller
Keyword(s):  
Real Time ◽  
Superior Performance ◽  
Time Warping ◽  
Control Task ◽  
Time Data ◽  
Temporal Memory ◽  
Hierarchical Temporal Memory ◽  
Motion Simulator ◽  
Monitoring Applications ◽  
Dynamic Time

<p>This study presents a novel biometric approach to identify operators, given only streams of their control movements within a manual control task setting. In the present task subjects control a simulated, remotely operated robotic arm, attempting to dock onto a satellite in orbit. The proposed methodology utilizes the Hierarchical Temporal Memory (HTM) algorithm to distinguish operators by their unique control behaviors. Results presented compare the identification performance of HTM with Dynamic Time Warping (DTW) and Edit Distance on Real Sequences (EDR), in both static and real-time data settings. The HTM method outperformed both DTW and EDR in the real- time setting, and matched DTW in the static setting. Observed superior performance of the HTM algorithm lays the foundation for the extension of the proposed methodology to other motion- monitoring applications, such as real-time workload assessment, motion/simulator sickness onset or distraction detection.</p><p><br></p><p>The data gathered in the study was posted to IEEE-dataport, DOI: <a href="http://dx.doi.org/10.21227/wpyf-r927" target="_blank">10.21227/wpyf-r927</a><br></p><div><br></div>

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