Embodied empathy: Using affective computing to incarnate human emotion and cognition in architecture

2021 ◽  
pp. 147807712110395
Author(s):  
Mona Ghandi ◽  
Marcus Blaisdell ◽  
Mohamed Ismail
Keyword(s):  
Real Time ◽  
Emotional Expression ◽  
Feedback Loop ◽  
Affective Computing ◽  
Current Theory ◽  
Theory And Practice ◽  
Interactive Installation ◽  
Adaptive Architecture ◽  
Color And Light

This research aims to develop a cyber-physical adaptive architectural space capable of real-time responses to people’s emotions, based on biological and neurological data. To achieve this goal, we integrated artificial intelligence (AI), wearable technology, sensory environments, and adaptive architecture to create an emotional bond between a space and its occupants and encourage affective emotional interactions between the two. The project’s objectives were to (1) measure and analyze biological and neurological data to detect emotions, (2) map and illustrate that emotional data, and (3) link occupants’ emotions and cognition to a built environment through a real-time emotive feedback loop. Using an interactive installation as a case study, this work examines the cognition-emotion-space interaction through changes in volume, color, and light as a means of emotional expression. It contributes to the current theory and practice of cyber-physical design and the role AI plays, as well as the interaction of technology and empathy.

Dynamic Leadership Process

2012 ◽  
pp. 33-64
Keyword(s):  
Leadership Style ◽  
Current Theory ◽  
Theory And Practice ◽  
Assessment Instrument ◽  
Leadership Roles ◽  
Dynamic Complexity ◽  
Dynamic Leadership ◽  
Leadership Process ◽  
Personal Assessment

Based on a wealth of experience in leadership roles and development, and enhanced by a broad literature review, the authors present an enabling framework for individuals at all levels of an organization independent of gender, industry, discipline, and leadership style, that will enhance their ability to carry out their leadership roles, particularly in digitally connected environments where dynamic complexity is typically most challenging. Leadership is treated as emergent, being co-developed with the context in which the leadership is taking place. To this end the authors detail, and relate to current theory and practice, a four step incremental leadership process cycle together with a personal assessment instrument. A case study is provided that illustrates application of the approach.

Pollution based real time control strategies for combined sewer systems

1997 ◽  
Vol 36 (8-9) ◽  
pp. 331-336 ◽  
Author(s):  
Gabriela Weinreich ◽  
Wolfgang Schilling ◽  
Ane Birkely ◽  
Tallak Moland
Keyword(s):  
Real Time ◽  
Control Strategies ◽  
Ammonia Nitrogen ◽  
Simple Extension ◽  
Real Time Control ◽  
Time Control ◽  
Sewer Systems ◽  
Receiving Waters ◽  
Tunnel System

This paper presents results from an application of a newly developed simulation tool for pollution based real time control (PBRTC) of urban drainage systems. The Oslo interceptor tunnel is used as a case study. The paper focuses on the reduction of total phosphorus Ptot and ammonia-nitrogen NH4-N overflow loads into the receiving waters by means of optimized operation of the tunnel system. With PBRTC the total reduction of the Ptot load is 48% and of the NH4-N load 51%. Compared to the volume based RTC scenario the reductions are 11% and 15%, respectively. These further reductions could be achieved with a relatively simple extension of the operation strategy.

scholarly journals Understanding why impact assessment fails; a case study of theory and practice from Wafi-Golpu, Papua New Guinea

2021 ◽  
Vol 89 ◽  
pp. 106582
Author(s):  
Charles Roche ◽  
Martin Brueckner ◽  
Nawasio Walim ◽  
Howard Sindana ◽  
Eugene John
Keyword(s):  
Impact Assessment ◽  
Papua New Guinea ◽  
New Guinea ◽  
Theory And Practice

scholarly journals A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 156
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Calautit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Energy Savings ◽  
Internal Heat ◽  
Electrical Equipment ◽  
Building Energy ◽  
Hvac Systems ◽  
Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

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