[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Hsu et al., 2019 - Google Patents

Self-supervised learning of appliance usage

Hsu et al., 2019

View PDF
Document ID
1376874240572891039
Author
Hsu C
Zeitoun A
Lee G
Katabi D
Jaakkola T
Publication year
Publication venue
International Conference on Learning Representations

External Links

Snippet

Learning home appliance usage is important for understanding people's activities and optimizing energy consumption. The problem is modeled as an event detection task, where the objective is to learn when a user turns an appliance on, and which appliance it is …
Continue reading at openreview.net (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/00771—Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6279—Classification techniques relating to the number of classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce, e.g. shopping or e-commerce
    • G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0202—Market predictions or demand forecasting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00335—Recognising movements or behaviour, e.g. recognition of gestures, dynamic facial expressions; Lip-reading
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks

Similar Documents

Publication Publication Date Title
Djenouri et al. Machine learning for smart building applications: Review and taxonomy
Raykov et al. Predicting room occupancy with a single passive infrared (PIR) sensor through behavior extraction
Vafeiadis et al. Machine learning based occupancy detection via the use of smart meters
Zoha et al. Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey
Jazizadeh et al. An unsupervised hierarchical clustering based heuristic algorithm for facilitated training of electricity consumption disaggregation systems
Arrotta et al. Dexar: Deep explainable sensor-based activity recognition in smart-home environments
Hsu et al. Self-supervised learning of appliance usage
Clement et al. Detecting activities of daily living with smart meters
Pauwels et al. Sensor networks for ambient intelligence
Patel et al. Proactive robot assistance via spatio-temporal object modeling
Coppola et al. Learning temporal context for activity recognition
Tundis et al. A feature-based model for the identification of electrical devices in smart environments
Rieping et al. Behavior analysis of elderly using topic models
Shokrollahi et al. Passive infrared sensor-based occupancy monitoring in smart buildings: a review of methodologies and machine learning approaches
Yin et al. Unsupervised daily routine and activity discovery in smart homes
Adams et al. Hierarchical span-based conditional random fields for labeling and segmenting events in wearable sensor data streams
Fahim et al. ApplianceNet: a neural network based framework to recognize daily life activities and behavior in smart home using smart plugs
Tominaga et al. A unified framework for modeling and predicting going-out behavior
Xie et al. Efficient deep learning models for privacy-preserving people counting on low-resolution infrared arrays
Xiao et al. I know your intent: Graph-enhanced intent-aware user device interaction prediction via contrastive learning
Stamatescu et al. Privacy‐Preserving Sensing and Two‐Stage Building Occupancy Prediction Using Random Forest Learning
Kalra et al. Detection of daily living activities using a two-stage Markov model
Vanus et al. A innovative wavelet transformation method optimization in the noise-canceling application within intelligent building occupancy detection monitoring
Saqaeeyan et al. A novel probabilistic hybrid model to detect anomaly in smart homes
Wong et al. Extracting patterns of behavior from a network of binary sensors