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

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.

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

Facilitating Human Activity Data Annotation via Context-Aware Change Detection on Smartwatches

2021 ◽  
Vol 20 (2) ◽  
pp. 1-20
Author(s):  
Ali Akbari ◽  
Jonathan Martinez ◽  
Roozbeh Jafari
Keyword(s):  
Cognitive Load ◽  
Real World ◽  
Change Point ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Motion Sensors ◽  
Context Aware ◽  
Human Errors ◽  
Data Annotation ◽  
Point Detection

Annotating activities of daily living (ADL) is vital for developing machine learning models for activity recognition. In addition, it is critical for self-reporting purposes such as in assisted living where the users are asked to log their ADLs. However, data annotation becomes extremely challenging in real-world data collection scenarios, where the users have to provide annotations and labels on their own. Methods such as self-reports that rely on users’ memory and compliance are prone to human errors and become burdensome since they increase users’ cognitive load. In this article, we propose a light yet effective context-aware change point detection algorithm that is implemented and run on a smartwatch for facilitating data annotation for high-level ADLs. The proposed system detects the moments of transition from one to another activity and prompts the users to annotate their data. We leverage freely available Bluetooth low energy (BLE) information broadcasted by various devices to detect changes in environmental context. This contextual information is combined with a motion-based change point detection algorithm, which utilizes data from wearable motion sensors, to reduce the false positives and enhance the system's accuracy. Through real-world experiments, we show that the proposed system improves the quality and quantity of labels collected from users by reducing human errors while eliminating users’ cognitive load and facilitating the data annotation process.

scholarly journals Construction of a Power Quality Monitoring System Based on the Wavelet Transform Change-Point Detection Algorithm on the Virtual Instrument Platform

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Wavelet Transform ◽  
Power Quality ◽  
Monitoring System ◽  
Virtual Instrument ◽  
Change Point ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Quality Monitoring ◽  
Power Quality Monitoring ◽  
Point Detection

It is aimed to carry out the investigation on power quality detection, promote the realization of efficient transmission of network data, and expand the application of wavelet transform change-point detection algorithm in the monitoring system. The voltage deviation is used as a starting point to explore the detection of power quality. First, it describes the harmonics of the public power grid and the limits of harmonic voltage. Second, based on the virtual instrument platform, the power quality monitoring system based on wavelet transform change-point detection algorithm is completed. Finally, by adding a monitoring terminal and a service terminal, the design of the monitoring system server is completed. Through the analysis of the experimental results, it is found that in the monitoring system, the current waveform and the three-phase voltage can be accurately displayed. The combined design of the networked monitoring system and the system server enables the system to complete the rapid transmission of data related to power quality, while having a good monitoring effect. For the optimization of networked monitoring experienceof the server, the application of wavelet transform in power quality measurement is realized. The power quality monitoring system proposed has a strong practicality in power quality monitoring.

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