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Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine

Lei Jiang, Jiaming Li, Suhuai Luo, Sam West, Glenn Platt
2012 Applied Computational Intelligence and Soft Computing  
This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning.  ...  The load classification method is composed of two processes including frequency feature analysis and support vector machine.  ...  Power Load Events Detection and Classification It is known that although NIALM based on different techniques, it has several common principles [26] .  ... 
doi:10.1155/2012/742461 fatcat:mvkexb5aqvc77nti7jfdkip2ua

Trimming Feature Extraction and Inference for MCU-based Edge NILM: a Systematic Approach

Enrico Tabanelli, Davide Brunelli, Andrea Acquaviva, Luca Benini
2021 IEEE Transactions on Industrial Informatics  
State-of-the-Art approaches are based on Machine Learning methods and exploit the fusion of time- and frequency-domain features from current and voltage sensors.  ...  We compare four supervised learning techniques on different classification scenarios and characterize the overall NILM pipeline's implementation on a MCU-based Smart Measurement Node.  ...  We thus identified the most relevant time-and frequency-domain features in disaggregating load profiles depending on the classification scenarios.  ... 
doi:10.1109/tii.2021.3078186 fatcat:wkf2lj3itjh37paqbejrf5rsga

Semantic Analysis of Multimodal Sports Video Based on the Support Vector Machine and Mobile Edge Computing

Xu Chen
2022 Wireless Communications and Mobile Computing  
The semantic analysis of multimodal sports video using support vector machines and mobile edge computing is the subject of this study.  ...  The semantic analysis of multimodal sports video based on support vector machine method proposed in this study has a better prediction ability, and its theoretical prediction results are close to the actual  ...  of multimodal sports video based on support vector machine and mobile edge computing.  ... 
doi:10.1155/2022/3511535 doaj:938e6e95dce247529b6c418ae6a91e4c fatcat:mes6m4c6e5hxpoy3uspgwdnuma

Table of Contents

2021 2021 IEEE 18th India Council International Conference (INDICON)  
Perspective Automatic Kernel Selection of Support Vector Machine for Drug/Non-Drug Causal Connectivity based Classification of Functional MRI data Segmentation and Classification of Brain Tumors using  ...  Power Adaptive, Cooperative system using multivariate analysis Supervised Machine Learning Techniques Localization of Myocardial Infarction from Igniter tester for Pyro initiator health System based on  ... 
doi:10.1109/indicon52576.2021.9691710 fatcat:y24plnfg2bbmlb5cdstdrrh5qq

Comprehensive Analysis and Detection of Flash-Based Malware [chapter]

Christian Wressnegger, Fabian Yamaguchi, Daniel Arp, Konrad Rieck
2016 Lecture Notes in Computer Science  
As a remedy, we present Gordon, a method for the comprehensive analysis and detection of Flash-based malware.  ...  Adobe Flash is a popular platform for providing dynamic and multimedia content on web pages. Despite being declared dead for years, Flash is still deployed on millions of devices.  ...  Based on this vector space embedding, we apply a linear Support Vector Machine (SVM) for learning a classification between benign and malicious Flash animations.  ... 
doi:10.1007/978-3-319-40667-1_6 fatcat:hxjgye7flvgepjtzxcdcirktmq

Grant-Free Access: Machine Learning for Detection of Short Packets

Estefania Recayte, Andrea Munari, Federico Clazzer
2020 2020 10th Advanced Satellite Multimedia Systems Conference and the 16th Signal Processing for Space Communications Workshop (ASMS/SPSC)  
Targeting satellite-based massive machine type communications and internet of things scenarios, our focus is on a common channel shared among a large number of terminals via a fully asynchronous ALOHA  ...  The ability of machine learning to extract further information from incoming signals is also studied, discussing the possibility to classify detected preambles based on the level of interference they undergo  ...  The metric offers thus an estimate of the probability to correctly label an incoming vector, computed based on the available training and testing sets.  ... 
doi:10.1109/asms/spsc48805.2020.9268917 fatcat:dnaxwmj4end25l25wddehzxzu4

Grant-Free Access: Machine Learning for Detection of Short Packets [article]

Estefania Recayte and Andrea Munari and Federico Clazzer
2020 arXiv   pre-print
Targeting satellite-based massive machine type communications and internet of things scenarios, our focus is on a common channel shared among a large number of terminals via a fully asynchronous ALOHA  ...  The ability of machine learning to extract further information from incoming signals is also studied, discussing the possibility to classify detected preambles based on the level of interference they undergo  ...  The metric offers thus an estimate of the probability to correctly label an incoming vector, computed based on the available training and testing sets.  ... 
arXiv:2008.10956v1 fatcat:7dquoyegezh75njqratokumu7u

CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey

Xiang Fei, Nazaraf Shah, Nandor Verba, Kuo-Ming Chao, Victor Sanchez-Anguix, Jacek Lewandowski, Anne James, Zahid Usman
2019 Future generations computer systems  
There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems.  ...  One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core  ...  Acknowledgement This work is partially supported by EU H2020 programme (Project NOESIS under grant no 769980).  ... 
doi:10.1016/j.future.2018.06.042 fatcat:kj722esur5g5vinajrdanunjw4

Table of Contents

2021 IEEE Transactions on Industrial Informatics  
Hredzak 430 Pixel-Level Classification of Pollution Severity on Insulators Using Photothermal Radiometry and Multiclass Semisupervised Support Vector Machine. . . . . . . . . . . . . . . . . . . . . .  ...  Xu 627 Online Detection of Events With Low-Quality Synchrophasor Measurements Based on iForest. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tii.2020.3028538 fatcat:xnzo5ycjgbgv3ke5wqf6kuw2j4

Load recognition for automated demand response in microgrids

Adeel Abbas Zaidi, Friederich Kupzog, Tehseen Zia, Peter Palensky
2010 IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society  
In order to decide which loads are inessential and can be shedded, automated load recognition on the basis of measured power consumption profiles is needed.  ...  When the load of such an off-grid microgrid grows over the generation capacity and energy storage is not sufficient, demand has to be reduced to prevent a blackout.  ...  Based on these features, this paper presents a detailed analysis of two techniques applied for load identification; Dynamic Time Warping (DTW) [8] and Hidden Markov Model (HMM).  ... 
doi:10.1109/iecon.2010.5675022 fatcat:3ye4hhylanc2tp7gd5ikn4lui4

A Cloud-Fog-Edge Closed-loop Feedback Security Risk Prediction Method

Qianmu Li, Youhui Tian, Qiang Wu, Qi Cao, Haiyuan Shen, Huaqiu Long
2020 IEEE Access  
This paper designs a set of Cloud-Fog-Edge closed-loop feedback security risk prediction strategies for multi-task compound attacks based on the offensive and defensive ideas of intelligent games, combining  ...  INDEX TERMS Risk Prediction, classification deep Boltzmann machine, Markov time-varying model.  ...  SUPPORT VECTOR MACHINE Support vector machine (SVM) is a supervised learning method, which was proposed by Vapnik and Cortes in 1995.  ... 
doi:10.1109/access.2020.2972032 fatcat:72q4mvq7b5a6nbbjz63qjhtq6u

A Survey on Shadow Removal Techniques for Single Image

Saritha Murali, V.K. Govindan, Saidalavi Kalady
2016 International Journal of Image Graphics and Signal Processing  
In this paper, we propose a high accuracy human action classification and recognition method using hidden Markov model classifier.  ...  Video surveillance system especially for elderly care and their behavior analysis has an important role to take care of aged, impatient or bedridden persons.  ...  Extracted feature vectors are finally fed to a fuzzy multiclass support vector machine for precise classification of motions and the determination of a fall event.  ... 
doi:10.5815/ijigsp.2016.12.05 fatcat:fr6ppmnc4nbrbovvpyb4cibkra

A Survey on Shadow Removal Techniques for Single Image

Saritha Murali, V.K. Govindan, Saidalavi Kalady
2016 International Journal of Image Graphics and Signal Processing  
In this paper, we propose a high accuracy human action classification and recognition method using hidden Markov model classifier.  ...  Video surveillance system especially for elderly care and their behavior analysis has an important role to take care of aged, impatient or bedridden persons.  ...  Extracted feature vectors are finally fed to a fuzzy multiclass support vector machine for precise classification of motions and the determination of a fall event.  ... 
doi:10.5815/ijigsp.2015.12.05 fatcat:fbg2bzxwnfhxxnr7qol6shfwxe

The user side of sustainability: Modeling behavior and energy usage in the home

Chao Chen, Diane J. Cook, Aaron S. Crandall
2013 Pervasive and Mobile Computing  
Society is becoming increasingly aware of the impact that our lifestyle choices make on energy usage and the environment.  ...  It first demonstrates how data mining techniques may be used to find patterns and anomalies in smart home-based energy data.  ...  Support Vector Machines (SVMs) [28] are a class of training algorithms for data classification, which maximize the margin between the training examples and the class boundary.  ... 
doi:10.1016/j.pmcj.2012.10.004 fatcat:2whavevlbjbhrngvdoiubgrdo4

Artificial Intelligence based Sensor Data Analytics Framework for Remote Electricity Network Condition Monitoring [article]

Tharmakulasingam Sirojan
2021 arXiv   pre-print
An edge computing-based architecture is proposed to facilitate the high-frequency analysis for load identification.  ...  As the first contribution of this thesis, a distributed online monitoring platform is developed that incorporates power quality monitoring, real-time HIF identification and transient classification in  ...  Simple threshold based HIF detection [54] [67][68][69][70], support vector machines [71][72][73][74] and neural networks [75][76][77][78] demonstrate better performance on HIF detection compared to other  ... 
arXiv:2102.03356v1 fatcat:mb42q6i7craptoulvkt3cvy22e
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