Characterizing Smart Environments as Interactive and Collective Platforms: A Review of the Key Behaviors of Responsive Architecture

Lee, Ju Hyun; Ostwald, Michael J.; Kim, Mi Jeong

doi:10.3390/s21103417

Cited by 15 publications

(8 citation statements)

References 152 publications

(375 reference statements)

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“…In another seminal research work, Lee et al reviewed the major concepts of responsive architecture that can be evaluated within the scope of energy-efficient buildings and environments with smart systems [23]. Comprehensive surveys on smart building technologies, systems, and sensing technologies also aim to discover new approaches in human-building interactions with regard to energy use and occupation, as well as the features of buildings and components [2,14].…”

Section: Related Workmentioning

confidence: 99%

A Systematic Approach to Optimizing Energy-Efficient Automated Systems with Learning Models for Thermal Comfort Control in Indoor Spaces

Erişen

2023

Buildings

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Energy-efficient automated systems for thermal comfort control in buildings is an emerging research area that has the potential to be considered through a combination of smart solutions. This research aims to explore and optimize energy-efficient automated systems with regard to thermal comfort parameters, energy use, workloads, and their operation for thermal comfort control in indoor spaces. In this research, a systematic approach is deployed, and building information modeling (BIM) software and energy optimization algorithms are applied at first to thermal comfort parameters, such as natural ventilation, to derive the contextual information and compute the building performance of an indoor environment with Internet of Things (IoT) technologies installed. The open-source dataset from the experiment environment is also applied in training and testing unique black box models, which are examined through the users’ voting data acquired via the personal comfort systems (PCS), thus revealing the significance of Fanger’s approach and the relationship between people and their surroundings in developing the learning models. The contextual information obtained via BIM simulations, the IoT-based data, and the building performance evaluations indicated the critical levels of energy use and the capacities of the thermal comfort control systems. Machine learning models were found to be significant in optimizing the operation of the automated systems, and deep learning models were momentous in understanding and predicting user activities and thermal comfort levels for well-being; this can optimize energy use in smart buildings.

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Section: Related Workmentioning

confidence: 99%

A Systematic Approach to Optimizing Energy-Efficient Automated Systems with Learning Models for Thermal Comfort Control in Indoor Spaces

Erişen

2023

Buildings

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“…In the field of architecture, smart spaces from homes and offices to smart cities are often linked to energy, sustainability, comfort, and convenience. A smart building operates through automation of operations in order to maintain its occupants' comfort, and it is a way of minimizing energy consumption (Lee et al, 2021).…”

Section: Smart Space and Sentient Spacementioning

confidence: 99%

“…The architecture framework for the behaviour of the adaptive system enables occupants to sense, experience and understand smart spaces (Lee et al, 2021). Nowadays, a number of adaptive prototypes have developed, such as the physiologically-driven "ExoBuilding" that can change shape in real time and provide immersive experience (Schnädelbach et al, 2016).…”

Section: Smart Space and Sentient Spacementioning

confidence: 99%

Searchlight Space- A Sentient Space Having Exploratory Behaviour

Shiung,

Hou

2023

CAADRIA Proceedings

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If a space is a sentient being, how would it recognize the environment to establish interactive relationships with people and objects? The modern living environment is full of ubiquitous computing and data flows, the smart space is equipped with rich technologies allowing data exchange between devices for the purpose of meeting human demands. However, the lack of development for a basic environment with cognitive abilities presents a challenge for creating an adaptable space, which can grow and control our surroundings to enhance our behaviour. Thus, the goal of this study is to propose a basic cognition of a sentient space. Firstly, the author designed the methodology for a sensorimotor through learning biological mechanism. Three adaptive strategies of exploratory behaviour are then developed to operate the cognitive map of the space. Lastly, the process is visualized to enable human interaction. Through the on-site installation, usability of the prototype is verified and positive user feedbacks were obtained. In conclusion, the ultimate goal of the research is to develop a space with the characteristic of a sentient being through a prototype, also, it allows users to understand how human activities affect the cognition embodied in the space.

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“…The intelligent environment scene not only provides convenient life services but also provides people with a more comfortable and intelligent experience. In order to better understand and describe intelligent environment scenarios, Lee et al [7] described them. The interactive platform in intelligent environment scenarios refers to the use of human-machine interaction technology to achieve interaction between humans and the environment.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Automatic Generation Algorithm for Advertising Artistic Design Based on Neural Networks

Li,

Zhou

2024

CAD&A

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The traditional computer-aided design (CAD) software is often not realistic in rendering scenes, such as light, materials, shadows, and other details, which leads designers to spend a lot of energy on manual adjustment. The rendering algorithm based on deep learning (DL) can automatically learn the complex output, and the effect is usually better than traditional methods. In this article, a convolutional neural network (GA-CNN) based on genetic algorithm (GA) optimization is proposed, and it is combined with CAD data and applied to the rendering of environmental art design scenes. This method uses GA to optimize the parameters of CNN to improve its learning effect and generalization ability. From the overall trend, the response speed of GA-CNN algorithm and support vector machine (SVM). The response speed of the traditional CNN method is relatively slow, and the fluctuation range is large. This is because traditional CNN needs a lot of convolution operations and feature extraction when dealing with artistic scenes in complex environments, which leads to a long calculation time. In practical application, it is necessary to select appropriate algorithms and parameter settings according to specific task requirements to achieve the best results.

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Characterizing Smart Environments as Interactive and Collective Platforms: A Review of the Key Behaviors of Responsive Architecture

Cited by 15 publications

References 152 publications

A Systematic Approach to Optimizing Energy-Efficient Automated Systems with Learning Models for Thermal Comfort Control in Indoor Spaces

A Systematic Approach to Optimizing Energy-Efficient Automated Systems with Learning Models for Thermal Comfort Control in Indoor Spaces

Searchlight Space- A Sentient Space Having Exploratory Behaviour

Design and Implementation of Automatic Generation Algorithm for Advertising Artistic Design Based on Neural Networks

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