scholarly journals Adaptive change point detection algorithm for heart rate in children

2005 ◽  
Vol 52 (S1) ◽  
pp. A172-A172
Author(s):  
Mark Ansermino ◽  
Ping Yang ◽  
Joanne Lim ◽  
Guy Dumont ◽  
Craig R Ries
Keyword(s):  
Heart Rate ◽  
Change Point ◽  
Detection Algorithm ◽  
Adaptive Change ◽  
Change Point Detection ◽  
Point Detection

scholarly journals A Doubly Stochastic Change Point Detection Algorithm for Noisy Biological Signals

2018 ◽  
Vol 8 ◽  
Author(s):  
Nathan Gold ◽  
Martin G. Frasch ◽  
Christophe L. Herry ◽  
Bryan S. Richardson ◽  
Xiaogang Wang
Keyword(s):  
Change Point ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Doubly Stochastic ◽  
Biological Signals ◽  
Point Detection

Adaptive Change-Point Detection for Studying Human Locomotion

2021 ◽  
Author(s):  
Sylvain Jung ◽  
Laurent Oudre ◽  
Charles Truong ◽  
Eric Dorveaux ◽  
Louis Gorintin ◽  
...  
Keyword(s):  
Change Point ◽  
Human Locomotion ◽  
Adaptive Change ◽  
Change Point Detection ◽  
Point Detection

scholarly journals Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data

Smart Cities ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 1-16
Author(s):  
Haoran Niu ◽  
Olufemi A. Omitaomu ◽  
Qing C. Cao
Keyword(s):  
Event Detection ◽  
Change Point ◽  
Power Grid ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Disturbance Analysis ◽  
Events Detection ◽  
Point Detection

Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios.

A cusum change-point detection algorithm for non-stationary sequences with application to data network surveillance

2010 ◽  
Vol 83 (7) ◽  
pp. 1288-1297 ◽  
Author(s):  
Veronica Montes De Oca ◽  
Daniel R. Jeske ◽  
Qi Zhang ◽  
Carlos Rendon ◽  
Mazda Marvasti
Keyword(s):  
Change Point ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Stationary Sequences ◽  
Data Network ◽  
Network Surveillance ◽  
Point Detection

scholarly journals Examining nonstationarity in the recruitment dynamics of fishes using Bayesian change point analysis

2017 ◽  
Vol 74 (5) ◽  
pp. 751-765 ◽  
Author(s):  
Tommi A. Perälä ◽  
Douglas P. Swain ◽  
Anna Kuparinen
Keyword(s):  
Change Point ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Change Point Analysis ◽  
Model Parameters ◽  
Point Analysis ◽  
Stock Recruitment ◽  
Novel Method ◽  
Point Detection ◽  
Time Invariant

Marine ecosystems can undergo regime shifts, which result in nonstationarity in the dynamics of the fish populations inhabiting them. The assumption of time-invariant parameters in stock–recruitment models can lead to severe errors when forecasting renewal ability of stocks that experience shifts in their recruitment dynamics. We present a novel method for fitting stock–recruitment models using the Bayesian online change point detection algorithm, which is able to cope with sudden changes in the model parameters. We validate our method using simulations and apply it to empirical data of four demersal fishes in the southern Gulf of St. Lawrence. We show that all of the stocks have experienced shifts in their recruitment dynamics that cannot be captured by a model that assumes time-invariant parameters. The detected shifts in the recruitment dynamics result in clearly different parameter distributions and recruitment predictions between the regimes. This study illustrates how stock–recruitment relationships can experience shifts, which, if not accounted for, can lead to false predictions about a stock’s recovery ability and resilience to fishing.

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