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Anastasios D. Doulamis

6 accepted papers

2021

A Robust to Noise Adversarial Recurrent Model for Non-Intrusive Load Monitoring

Maria Kaselimi, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios D. Doulamis, Eftychios Protopapadakis
ICASSP 2021accepted

The problem of separating the household aggregated power signal into its additive sub-components, called energy (power) disaggregation or Non-Intrusive Load Monitoring (NILM) can play an instrumental role as a driver towards consumer energy consumption awareness and behavioral change. In this paper,…

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2020

EnerGAN: A GENERATIVE ADVERSARIAL NETWORK FOR ENERGY DISAGGREGATION

Maria Kaselimi, Athanasios Voulodimos, Eftychios Protopapadakis, Nikolaos Doulamis, Anastasios D. Doulamis
ICASSP 2020accepted

An efficient, appliance-level approach for energy disaggregation, exploiting the benefits of Generative Adversarial Networks, is presented. The concept of adversarial training supports the creation of fine tuned dissagregators, which produce more detailed load estimations for a specific appliance, c…

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2019

A Deep-narma Filter for Unusual Behavior Detection from Visual, Thermal and Wireless Signals

Nikolaos Bakalos, Athanasios Voulodimos, Anastasios D. Doulamis, Nikolaos Doulamis
ICASSP 2019accepted

Detection of unusual behavior is an important topic in signal and image processing. Because of the topic's complexity, addressing it as a solely RGB video analysis problem raises significant challenges. This has resulted in approaches that aim at exploiting different data modalities that can overcom…

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2019

Bayesian-optimized Bidirectional LSTM Regression Model for Non-intrusive Load Monitoring

Maria Kaselimi, Nikolaos Doulamis, Anastasios D. Doulamis, Athanasios Voulodimos, Eftychios Protopapadakis
ICASSP 2019accepted

In this paper, a Bayesian-optimized bidirectional Long Short -Term Memory (LSTM) method for energy disaggregation, is introduced. Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), is a process aiming to identify the individual contribution of appliances in the aggregate electricity loa…

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2019

Common Mode Patterns for Supervised Tensor Subspace Learning

Konstantinos Makantasis, Anastasios D. Doulamis, Nikolaos Doulamis, Athanasios Voulodimos
ICASSP 2019accepted

In this work we propose a method for reducing the dimensionality of tensor objects in a binary classification framework. The proposed Common Mode Patterns method takes into consideration the labels' information, and ensures that tensor objects that belong to different classes do not share common fea…

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2018

Tensor-Based Nonlinear Classifier for High-Order Data Analysis

Konstantinos Makantasis, Anastasios D. Doulamis, Nikolaos Doulamis, Antonis Nikitakis, Athanasios Voulodimos
ICASSP 2018accepted

In this paper we propose a tensor-based nonlinear model for high-order data classification. The advantages of the proposed scheme are that (i) it significantly reduces the number of weight parameters, and hence of required training samples, and (ii) it retains the spatial structure of the input samp…

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