scholarly journals Detection, classification, and superposition resolution of action potentials in multiunit single-channel recordings by an on-line real-time neural network

1997 ◽  
Vol 44 (5) ◽  
pp. 403-412 ◽  
Author(s):  
R. Chandra ◽  
L.M. Optican
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Action Potentials ◽  
Single Channel ◽  
Single Channel Recordings ◽  
On Line

scholarly journals Automatic Implementation of a Self-Adaption Non-Intrusive Load Monitoring Method Based on the Convolutional Neural Network

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 704 ◽  
Author(s):  
Xin Wu ◽  
Dian Jiao ◽  
Yu Du
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Load Identification ◽  
Neural Network Models ◽  
Monitoring Method ◽  
Line Load ◽  
The Real ◽  
Load Monitoring ◽  
On Line

Non-intrusive load monitoring (NILM) is an effective way to achieve demand-side measurement and energy efficiency optimization. This paper studies a method of non-intrusive on-line load monitoring under a high-frequency mode of electric data acquisition, which enables the NILM to be automated and in real-time, including the short-term construction of a dynamic signature library and continuous on-line load identification. Firstly, in the short initial operation phase, load separation and category determination are carried out to construct the load waveform library of the monitoring user. Then, the continuous load monitoring phase begins. Based on the data of each user’s signature library, the decomposition waveforms are classified by convolutional neural network models that are constructed to be suitable for each signature library in order to realize load identification. The real-time power consumption status of the load can be obtained continuously. In this paper, the electricity data of actual users are collected and used to perform the experiments, which show that the proposed method can construct the load signature library adaptively for different users. Meanwhile, the classification of the convolutional neural network model based on a library constructed in actual operation ensures the real-time and accuracy of load monitoring.

scholarly journals An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mohammadali Sharifshazileh ◽  
Karla Burelo ◽  
Johannes Sarnthein ◽  
Giacomo Indiveri
Keyword(s):  
Neural Network ◽  
Real Time ◽  
High Frequency ◽  
Embedded Devices ◽  
Neuromorphic Circuits ◽  
Intracranial Eeg ◽  
Mixed Signal ◽  
High Frequency Oscillations ◽  
On Line ◽  
Neuromorphic System

AbstractThe analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies.

scholarly journals SUBSTRUCTURE ON-LINE TEST TECHNIQUE USING REAL-TIME HYSTERESIS MODELING WITH NEURAL NETWORK

2005 ◽  
Vol 70 (589) ◽  
pp. 89-96
Author(s):  
Wonjik YANG ◽  
Yoshiaki NAKANO ◽  
Naruhito YAMAUCHI ◽  
Yasushi SANADA
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Test Technique ◽  
Hysteresis Modeling ◽  
On Line ◽  
Line Test
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