Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Dimitris Vrakas"'
Publikováno v:
EPJ Data Science, Vol 12, Iss 1, Pp 1-30 (2023)
Abstract Real estate markets depend on various methods to predict housing prices, including models that have been trained on datasets of residential or commercial properties. Most studies endeavor to create more accurate machine learning models by ut
Externí odkaz:
https://doaj.org/article/e0f3b2c065d343c2bacecc360988aa47
Publikováno v:
Smart Cities, Vol 6, Iss 4, Pp 1814-1831 (2023)
The automatic identification of various design elements in a floor-plan image has gained increasing attention in recent research. Emergency-evacuation applications can benefit greatly from automated floor-plan solutions, as they allow for the develop
Externí odkaz:
https://doaj.org/article/e603581b0fb4426db67a5dca61f18a5b
Publikováno v:
Sensors, Vol 23, Iss 4, p 2051 (2023)
Non-intrusive load monitoring systems that are based on deep learning methods produce high-accuracy end use detection; however, they are mainly designed with the one vs. one strategy. This strategy dictates that one model is trained to disaggregate o
Externí odkaz:
https://doaj.org/article/f12f827553504e1d90ede825844c7cd3
Publikováno v:
Energies, Vol 15, Iss 7, p 2647 (2022)
Non-intrusive load monitoring is a blind source separation task that has been attracting significant interest from researchers working in the field of energy informatics. However, despite the considerable progress, there are a very limited number of
Externí odkaz:
https://doaj.org/article/fe826462738b4e8abcc1f725c4e7db23
Publikováno v:
Sensors, Vol 22, Iss 6, p 2353 (2022)
As the world’s population is aging, and since access to ambient sensors has become easier over the past years, activity recognition in smart home installations has gained increased scientific interest. The majority of published papers in the litera
Externí odkaz:
https://doaj.org/article/0b0611c8d43c4ac083e68bee73e8d6b5
Publikováno v:
Sensors, Vol 22, Iss 2, p 473 (2022)
Deploying energy disaggregation models in the real-world is a challenging task. These models are usually deep neural networks and can be costly when running on a server or prohibitive when the target device has limited resources. Deep learning models
Externí odkaz:
https://doaj.org/article/88d282badfb34f299ec70d8cf888e70f
Publikováno v:
International Journal on Semantic Web and Information Systems. 18:1-34
The authors present a knowledge retrieval framework for the household domain enhanced with external knowledge sources that can argue over the information that it returns and learn new knowledge through an argumentation dialogue. The framework provide
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::35b22cd3b56ea5886ef0c9116e889395
https://doi.org/10.4018/ijswis.309421
https://doi.org/10.4018/ijswis.309421
Autor:
Christoforos Nalmpantis, Lazaros Vrysis, Danai Vlachava, Lefteris Papageorgiou, Dimitris Vrakas
Publikováno v:
Multimedia Tools and Applications. 81:32057-32072
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7643191b2170df4f76cf59420de18e78
https://doi.org/10.1007/s11042-022-12931-y
https://doi.org/10.1007/s11042-022-12931-y
Publikováno v:
2022 2nd International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::45e82d3cde4118ad426cff34ffbe7cb8
https://doi.org/10.1109/synergymed55767.2022.9941424
https://doi.org/10.1109/synergymed55767.2022.9941424
Publikováno v:
Neural Computing and Applications. 32:17275-17290
Given only the main power consumption of a household, a non-intrusive load monitoring (NILM) system identifies which appliances are operating. With the rise of Internet of things, running energy disaggregation models on the edge is more and more esse
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e84cd00c2c8045385d120d163ef66549
https://doi.org/10.1007/s00521-020-04916-5
https://doi.org/10.1007/s00521-020-04916-5