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Article
A scaled dirichlet-based predictive model for occupancy estimation in smart buildings
In this study, we introduce a predictive model leveraging the scaled Dirichlet mixture model (SDMM). This data-driven approach offers enhanced accuracy in predictions, especially with a limited training datase...
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Article
Non intrusive load monitoring using additive time series modeling via finite mixture models aggregation
Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), involves different methods aiming to distinguish the individual contribution of appliances, given the aggregated power signal. In this paper, the...
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Article
A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid
Non-intrusive load monitoring (NILM) is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or c...
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Article
Occupancy estimation in smart buildings using predictive modeling in imbalanced domains
This paper introduces a novel approach for occupancy estimation in smart buildings. In particular, we focus on the challenging yet common situation where the amount of training data is small and imbalanced (i....
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Article
Cross-collection latent Beta-Liouville allocation model training with privacy protection and applications
Cross-collection topic models extend previous single-collection topic models, such as Latent Dirichlet Allocation (LDA), to multiple collections. The purpose of cross-collection topic modeling is to model docu...
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Chapter and Conference Paper
Deep Learning Based Solution for Appliance Operational State Detection and Power Estimation in Non-intrusive Load Monitoring
This paper introduces a novel NILM algorithm that utilizes deep learning Temporal Convolutional Networks (TCN) for the regression and classification NILM tasks. The deep TCN layers in the proposed architecture...
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Chapter and Conference Paper
A Selective Supervised Latent Beta-Liouville Allocation for Document Classification
We propose a novel model, selective supervised Latent Beta-Liouville (ssLBLA), that improves the performance and generative process of supervised probabilistic topic models with a more flexible prior and simpl...
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Chapter and Conference Paper
Enhanced Energy Characterization and Feature Selection Using Explainable Non-parametric AGGMM
In this paper, we propose an asymmetric generalized Gaussian mixture model (AGGMM) with simultaneous feature selection for efficient and interpretable energy characterization in the context of demand response ...
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Chapter and Conference Paper
Novel Topic Models for Parallel Topics Extraction from Multilingual Text
In this work, we propose novel topic models to extract topics from multilingual documents. We add more flexibility to conventional LDA by relaxing some constraints in its prior. We apply other alternative prio...
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Chapter
A Novel Continuous Hidden Markov Model for Modeling Positive Sequential Data
As positive data are often encountered in a variety of real-life applications, research on modeling positive data vectors has increasingly drawn attention. The focus of this chapter is to tackle the problem of...
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Chapter
Bounded Asymmetric Gaussian Mixture-Based Hidden Markov Models
Hidden Markov models (HMMs) have been widely applied in machine learning to model diversified and heterogeneous time series data. In this chapter, integration of the bounded asymmetric Gaussian mixture model (...
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Chapter and Conference Paper
Stochastic Expectation Propagation Learning for Unsupervised Feature Selection
We introduce a statistical procedure for the simultaneous clustering and feature selection of positive vectors. The proposed method is based on well-principled infinite generalized inverted Dirichlet (GID) mix...
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Book
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Chapter and Conference Paper
A Generalized Inverted Dirichlet Predictive Model for Activity Recognition Using Small Training Data
In this paper, we develop the predictive distribution of the generalized inverted Dirichlet (GID) mixture model using local variational inference. The main goal is to be able to tackle classification problems ...
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Chapter
Multivariate Beta-Based Hidden Markov Models Applied to Human Activity Recognition
Over the past decades, human activity recognition has become an attention-grabbing topic of research. Various algorithms have been proposed and applied for activity recognition. Proposing a robust method is st...
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Chapter and Conference Paper
Interactive Generalized Dirichlet Mixture Allocation Model
A lot of efforts have been put in recent times for research in the field of natural language processing. Extracting topics is undoubtedly one of the most important tasks in this area of research. Latent Dirich...
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Article
Unsupervised Learning Using Variational Inference on Finite Inverted Dirichlet Mixture Models with Component Splitting
Unsupervised learning has been one of the essentials of pattern recognition and data mining. The role of Dirichlet family of mixture models in this field is inevitable. In this article, we propose a finite Inv...
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Chapter
Machine Learning for Activity Recognition in Smart Buildings: A Survey
Machine learning and data mining techniques have been widely used recently in several smart buildings applications. This is mainly due to the huge amount of data generated continuously by the smart sensors and...
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Book
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Chapter
Characterization of Energy Demand and Energy Services Using Model-Based and Data-Driven Approaches
This chapter describes the state-of-the-art methods to forecast energy consumption and energy services in residential buildings. The review spans from model-based approaches—like building thermal simulation to...
