Journal ArticleDOI
Building occupancy estimation and detection: A review
TLDR
A comprehensive review on building occupancy estimation and detection is presented and some potential future research directions are indicated based on current progresses of the systems.About:
This article is published in Energy and Buildings.The article was published on 2018-06-15. It has received 162 citations till now. The article focuses on the topics: Occupancy.read more
Citations
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[ACM Press the 2nd ACM Workshop - Zurich, Switzerland (2010.11.02-2010.11.02)] Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building - BuildSys \'10 - Occupancy-driven energy management for smart building automation
TL;DR: In this article, the authors present the design and implementation of a presence sensor platform that can be used for accurate occupancy detection at the level of individual offices, which is low-cost, wireless, and incrementally deployable within existing buildings.
Journal ArticleDOI
A survey of data fusion in smart city applications
Billy Pik Lik Lau,Sumudu Hasala Marakkalage,Yuren Zhou,Naveed Ul Hassan,Naveed Ul Hassan,Chau Yuen,Meng Zhang,U-Xuan Tan +7 more
TL;DR: In this article, a multi-perspectives classification of the data fusion to evaluate the smart city applications is presented, where the proposed classification is applied to evaluate selected applications in each domain of smart city and the potential future direction and challenges of data fusion integration are discussed.
Journal ArticleDOI
Human-in-the-loop HVAC operations: A quantitative review on occupancy, comfort, and energy-efficiency dimensions
Wooyoung Jung,Farrokh Jazizadeh +1 more
TL;DR: A five-tier hierarchical taxonomy of studies based on their contributions to occupancy- and comfort-driven human-in-the-loop HVAC operations is proposed and categorization for techniques and their quantitative performance assessment is presented.
Journal ArticleDOI
Modeling occupant behavior in buildings
Salvatore Carlucci,Salvatore Carlucci,Marilena De Simone,Steven K. Firth,Mikkel Baun Kjærgaard,Romana Markovic,Mohammad Saiedur Rahaman,Masab Khalid Annaqeeb,Silvia Biandrate,Silvia Biandrate,Anooshmita Das,Jakub Dziedzic,Gianmarco Fajilla,Matteo Favero,Martina Ferrando,Jakob Hahn,Mengjie Han,Yuzhen Peng,Flora D. Salim,Arno Schlüter,Christoph van Treeck +20 more
TL;DR: This study reviews approaches, methods and key findings related to OPA modeling in buildings and identifies machine learning and deep learning are emerging in recent years as promising methods to address OPA modeled in real-world applications.
Journal ArticleDOI
A Survey of Data Fusion in Smart City Applications
Billy Pik Lik Lau,Sumudu Hasala Marakkalage,Yuren Zhou,Naveed Ul Hassan,Naveed Ul Hassan,Chau Yuen,Meng Zhang,U-Xuan Tan +7 more
TL;DR: A multi-perspectives classification of the data fusion is introduced and applied to evaluate selected applications in each domain of the smart city applications to discuss potential future direction and challenges of data fusion integration.
References
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Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Proceedings ArticleDOI
A unified architecture for natural language processing: deep neural networks with multitask learning
Ronan Collobert,Jason Weston +1 more
TL;DR: This work describes a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense using a language model.
Proceedings ArticleDOI
Multi-column deep neural networks for image classification
TL;DR: In this paper, a biologically plausible, wide and deep artificial neural network architectures was proposed to match human performance on tasks such as the recognition of handwritten digits or traffic signs, achieving near-human performance.