東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Redefining Interactive Space Through...

Aboutorabi, Seyedehsan.

FindBook

Google Book

Amazon

博客來

Redefining Interactive Space Through Physical Computing and Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Redefining Interactive Space Through Physical Computing and Machine Learning./
作者:	Aboutorabi, Seyedehsan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	60 p.
附註:	Source: Masters Abstracts International, Volume: 81-11.
Contained By:	Masters Abstracts International81-11.
標題:	Artificial intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27963891
ISBN:	9798644904464

Redefining Interactive Space Through Physical Computing and Machine Learning.
Aboutorabi, Seyedehsan.

Redefining Interactive Space Through Physical Computing and Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 60 p.

Source: Masters Abstracts International, Volume: 81-11.

Thesis (M.S.)--The University of North Carolina at Charlotte, 2020.

This item must not be sold to any third party vendors.

Making of physical systems that integrate software and hardware to sense their surroundings and respond to have gone through a lot of transformations with development in Artificial intelligence. These systems can blend digital and physical processes to form interactive experiences that seem more intuitive and predictive .This form of interactions between people and built-environment would enable space to be more responsive and adaptable. Such qualities would accommodate the environment for us to thrive, and help us have more meaningful connections.This thesis proposes a form of physical system that help us design an environment which uses machine learning to learn from us and to explore different possible interaction in order to provide appropriate physical responses by changing its shape through flexible move-able parts. The question as to what form of physicality would provide enough solution space is explored through physical computing. Different fabrication technologies have been explored in order to find the proper materials and assembly methods.As a result an interactive system which uses the proximity sensors as an input data is proposed. Then the data would be processed through a set of machine learning modules to provide feedback data for actuators. This system is responsible for developing a human-device relation that would learn and evolve through time. These characteristics of the system would create a unique user experience for each individual based on their interactions with built-environment.

ISBN: 9798644904464Subjects--Topical Terms:

516317
Artificial intelligence.
Subjects--Index Terms:

Adaptive environments

Redefining Interactive Space Through Physical Computing and Machine Learning.
LDR:02727nmm a2200373 4500 001 2271354
005 20201007134705.5
008 220629s2020 ||||||||||||||||| ||eng d
020 $a 9798644904464
035 $a (MiAaPQ)AAI27963891
035 $a AAI27963891
040 $a MiAaPQ $c MiAaPQ
100 1 $a Aboutorabi, Seyedehsan. $3 3548767
245 1 0 $a Redefining Interactive Space Through Physical Computing and Machine Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 60 p.
500 $a Source: Masters Abstracts International, Volume: 81-11.
500 $a Advisor: Sauda, Eric.
502 $a Thesis (M.S.)--The University of North Carolina at Charlotte, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Making of physical systems that integrate software and hardware to sense their surroundings and respond to have gone through a lot of transformations with development in Artificial intelligence. These systems can blend digital and physical processes to form interactive experiences that seem more intuitive and predictive .This form of interactions between people and built-environment would enable space to be more responsive and adaptable. Such qualities would accommodate the environment for us to thrive, and help us have more meaningful connections.This thesis proposes a form of physical system that help us design an environment which uses machine learning to learn from us and to explore different possible interaction in order to provide appropriate physical responses by changing its shape through flexible move-able parts. The question as to what form of physicality would provide enough solution space is explored through physical computing. Different fabrication technologies have been explored in order to find the proper materials and assembly methods.As a result an interactive system which uses the proximity sensors as an input data is proposed. Then the data would be processed through a set of machine learning modules to provide feedback data for actuators. This system is responsible for developing a human-device relation that would learn and evolve through time. These characteristics of the system would create a unique user experience for each individual based on their interactions with built-environment.
590 $a School code: 0694.
650 4 $a Artificial intelligence. $3 516317
650 4 $a Architectural engineering. $3 3174102
653 $a Adaptive environments
653 $a Artificial intelligence
653 $a Fabrication technologies
653 $a Interactive surfaces
653 $a Physical computing
653 $a Physical systems
690 $a 0800
690 $a 0462
710 2 $a The University of North Carolina at Charlotte. $b Architecture. $3 3347805
773 0 $t Masters Abstracts International $g 81-11.
790 $a 0694
791 $a M.S.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27963891