The Installations and Systems in Intelligent Buildings as an Element of Smart Cities Concept

2018 ◽  
Author(s):  
Miroslaw Parol ◽  
Magdalena Stepaniuk
Keyword(s):  
Smart Cities ◽  
Intelligent Buildings

scholarly journals Big data technology in construction

2019 ◽  
Vol 97 ◽  
pp. 01032 ◽  
Author(s):  
Nikolay Garyaev ◽  
Venera Garyaeva
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Life Cycle ◽  
Construction Industry ◽  
Smart Cities ◽  
Entire Life ◽  
Entire Life Cycle ◽  
Intelligent Buildings ◽  
Big Data Technology

The article presents the results of the analysis of the use of large amounts of data in the construction industry, new trends such as BIM, IOT, cloud computing, intelligent buildings and smart cities with great prospects for application. These problems are related to the presence of huge amounts of data produced by the construction industry during the entire life cycle of a building, which are not fully used for optimizing processes and making decisions in construction.

scholarly journals Deep Reinforcement Learning Algorithms in Intelligent Infrastructure

2019 ◽  
Vol 4 (3) ◽  
pp. 52 ◽  
Author(s):  
Serrano
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Capital Expenditure ◽  
Smart Cities ◽  
Time Data ◽  
Intelligent Buildings ◽  
Operational Expenditure ◽  
Biological Algorithm ◽  
Biological Organisms

Intelligent infrastructure, including smart cities and intelligent buildings, must learn and adapt to the variable needs and requirements of users, owners and operators in order to be future proof and to provide a return on investment based on Operational Expenditure (OPEX) and Capital Expenditure (CAPEX). To address this challenge, this article presents a biological algorithm based on neural networks and deep reinforcement learning that enables infrastructure to be intelligent by making predictions about its different variables. In addition, the proposed method makes decisions based on real time data. Intelligent infrastructure must be able to proactively monitor, protect and repair itself: this includes independent components and assets working the same way any autonomous biological organisms would. Neurons of artificial neural networks are associated with a prediction or decision layer based on a deep reinforcement learning algorithm that takes into consideration all of its previous learning. The proposed method was validated against an intelligent infrastructure dataset with outstanding results: the intelligent infrastructure was able to learn, predict and adapt to its variables, and components could make relevant decisions autonomously, emulating a living biological organism in which data flow exhaustively.

scholarly journals Analysis of the Social Aspect of Smart Cities Development for the Example of Smart Sustainable Buildings

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4330
Author(s):  
Aleksandra Radziejowska ◽  
Bartosz Sobotka
Keyword(s):  
Urban Space ◽  
Smart City ◽  
Smart Cities ◽  
Social Aspect ◽  
Sustainable Buildings ◽  
Effect Analysis ◽  
Intelligent Buildings ◽  
The Social ◽  
Swot Matrix ◽  
The Impact

The concept of a smart city is assumed to use resources more efficiently and in an innovative, creative, and intelligent manner. Initial experience with implementing this concept relates primarily to investments in technology and infrastructure using smart solutions, particularly technical urban infrastructure. An important social aspect of a smart city—people—cannot not go unnoticed. The inhabitants of a smart city are not only beneficiaries; they participate in its co-creation, initiate activities and are creative. This paper focuses on one of the smart city’s technical infrastructure components, which are intelligent sustainable buildings. This article aims to analyze the factors characterizing smart sustainable buildings (SSB) and the possibilities for their development. For this purpose, a SWOT matrix was developed. The factors of this matrix were subjected to a cause-effect analysis using the DEMATEL method to establish relationships between them. The results of the analysis allowed us to examine the social aspect, i.e., the impact of creators and users of sustainable, intelligent buildings on their development and possibilities for their creative and innovative use in an urban space.

Technologies Disability and Inclusion

2018 ◽  
Author(s):  
Tomas Brusell
Keyword(s):  
Social Media ◽  
Augmented Reality ◽  
Information And Communication Technologies ◽  
Future Development ◽  
Smart Cities ◽  
Communication Technologies ◽  
Modern Technology ◽  
The Social ◽  
The Disabled ◽  
Information And Communication

When modern technology permeates every corner of life, there are ignited more and more hopes among the disabled to be compensated for the loss of mobility and participation in normal life, and with Information and Communication Technologies (ICT), Exoskeleton Technologies and truly hands free technologies (HMI), it's possible for the disabled to be included in the social and pedagogic spheres, especially via computers and smartphones with social media apps and digital instruments for Augmented Reality (AR) .In this paper a nouvel HMI technology is presented with relevance for the inclusion of disabled in every day life with specific focus on the future development of "smart cities" and "smart homes".

SenCity – Evaluating Users’ Experiences of Intelligent Lighting for Well-Being in Smart Cities

2018 ◽  
pp. 60-67
Author(s):  
Henrika Pihlajaniemi ◽  
Anna Luusua ◽  
Eveliina Juntunen
Keyword(s):  
Case Studies ◽  
Smart City ◽  
Smart Cities ◽  
Well Being ◽  
Evaluation Methods ◽  
Data Analyses ◽  
Sense Of Security ◽  
System Data

This paper presents the evaluation of usersХ experiences in three intelligent lighting pilots in Finland. Two of the case studies are related to the use of intelligent lighting in different kinds of traffic areas, having emphasis on aspects of visibility, traffic and movement safety, and sense of security. The last case study presents a more complex view to the experience of intelligent lighting in smart city contexts. The evaluation methods, tailored to each pilot context, include questionnaires, an urban dashboard, in-situ interviews and observations, evaluation probes, and system data analyses. The applicability of the selected and tested methods is discussed reflecting the process and achieved results.

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