Advanced computing methods and related technologies for constructing smart cities

2022 ◽  
Vol 51 ◽  
pp. 101524
Author(s):  
Ming-Fung Francis Siu ◽  
Sik-Wah Patrick Fong ◽  
Hongqin Fan ◽  
JoonOh Seo ◽  
Vineet Kamat
Keyword(s):  
Smart Cities ◽  
Computing Methods ◽  
Advanced Computing

scholarly journals Cognitive Computing and its Relationship to Computing Methods and Advanced Computing from a Functional Modeling Perspective

2020 ◽  
Author(s):  
Andy E Williams
Keyword(s):  
Problem Solving ◽  
General Problem ◽  
Collective Intelligence ◽  
Computing Methods ◽  
Modeling Technique ◽  
Human Cognition ◽  
Cognitive Computing ◽  
Functional Modeling ◽  
Semantic Modeling ◽  
Advanced Computing

Recent advances in modeling human cognition have resulted in what is suggested to be the first model of Artificial General Intelligence (AGI) with the potential capacity for human-like general problem-solving ability, as well as a model for a General Collective Intelligence or GCI, which has been described as software that organizes a group into a single collective intelligence with the potential for vastly greater general problem-solving ability than any individual in the group. Both this model for GCI and this model for AGI require functional modeling of concepts that is complete in terms of meaning being self-contained in the model and not requiring interpretation based on information outside the model. This definition of a model for cognition has also been suggested to implicitly provide a semantic interpretation of functional models created within the functional modeling technique defined to meet the data format requirements of this AGI and GCI, so that the combination of the model of cognition to define an interpretation of meaning, and the functional modeling technique, together result in fully self-contained definitions of meaning that are suggested to be the first complete implementation of semantic modeling. With this semantic modeling, and with these models for AGI and GCI, cognitive computing is far better defined. This paper explores the various computing methods and advanced computing paradigms from the perspective of this cognitive computing.

scholarly journals Necessary Tools

2001 ◽  
Vol 123 (11) ◽  
pp. 60-62
Author(s):  
Jean Thilmany
Keyword(s):  
Virtual Reality ◽  
Parallel Computing ◽  
Computer Aided Design ◽  
High Performance ◽  
Three Dimensional ◽  
Computing Methods ◽  
Desktop Computer ◽  
Current State ◽  
Advanced Computing ◽  
Aided Design

This article reviews that the rate of discovery obtained from an experiment or a computational model is enhanced and accelerated by using parallel computing techniques, visualization algorithms, and advanced visualization hardware. The National Institute of Standards and Technology (NIST) in Gaithersburg, MD, team believe that high-performance computing speeds discovery within the sciences. It defines advanced computing methods as those technologies that possess capabilities beyond current state-of-the-art desktop computing. Visualization tools, for example, now extend beyond the three-dimensional computer-aided design model viewable on a desktop computer to include virtual reality software and hardware. A cave automatic virtual environment, called a CAVE, features four walls onto which an image is projected in 3D so that engineers feel they are standing in front of an object. Researchers at Iowa State and NIST’s engineers both say the future of technology won't happen without advanced computing methods, including visualization, virtual reality, and parallel computing.

scholarly journals Advanced Computing Methods

1973 ◽  
Vol 4 (10) ◽  
pp. 7-7 ◽  
Author(s):  
A.C. Hearn
Keyword(s):  
Computing Methods ◽  
Advanced Computing

Cognitive Computing and its Relationship to Computing Methods and Advanced Computing from a Human-Centric Functional Modeling Perspective

2021 ◽  
Author(s):  
Andy E. Williams
Keyword(s):  
Problem Solving ◽  
General Problem ◽  
Collective Intelligence ◽  
Semantic Representation ◽  
Computing Methods ◽  
Cognitive Computing ◽  
Functional Modeling ◽  
Semantic Modeling ◽  
Problems And Solutions ◽  
Advanced Computing

Recent advances in modeling human cognition have resulted in what is suggested to be the first model of Artificial General Intelligence (AGI) with the potential capacity for human-like general problem-solving ability, as well as a model for a General Collective Intelligence or GCI, which has been described as software that organizes a group into a single collective intelligence with the potential for vastly greater general problem-solving ability than any individual in the group. Both this model for GCI and this model for AGI require functional modeling of concepts that is complete in terms of meaning being self-contained in the model and not requiring interpretation based on information outside the model. The combination of a model of cognition to define an interpretation of meaning, and this functional modeling technique to represent information that way together results in fully self-contained definitions of meaning that are suggested to be the first complete implementation of semantic modeling. With this semantic modeling, and with these models for AGI and GCI, cognitive computing and its capacity for general problem-solving ability become far better defined. However, semantic representation of problems and of the details of solutions, as well general problem-solving ability in navigating those problems and solutions is not required in all cases. This paper attempts to explore the cases in which it is, and how the various computing methods and advanced computing paradigms are best utilized in each case from the perspective of cognitive computing.

The High Performance Computing of Inversion Algorithms

2013 ◽  
Vol 791-793 ◽  
pp. 1145-1148
Author(s):  
Wen Dong ◽  
Shi Qiao ◽  
Jia Li Mao ◽  
Miao Yue
Keyword(s):  
Image Processing ◽  
Inverse Problem ◽  
High Performance Computing ◽  
High Performance ◽  
Information Science ◽  
Computing Methods ◽  
Natural Sciences ◽  
Advanced Computing ◽  
Performance Computing ◽  
Science Information

The inverse problem is an important interdisciplinary subject, which receives more and more attention in the fields of mathematics, computer science, information science and other applied natural sciences in recent years. Nowadays, the inverse problem is more and more commonly applied than before, e.g., in image processing and geophysics. This trend promotes the development of both the advanced computing methods and high performance computing techniques. The high performance of computing problems for inverse algorithms is discussed in this paper, which is meaningful for the research of applied inversion subjects.

A Novel Heuristic Genetic Algorithm Based on Simulated Annealing Strategy for Intelligent Computing

2010 ◽  
Vol 143-144 ◽  
pp. 619-623
Author(s):  
Hong Xiang Diao ◽  
Jian Xiao
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Simulated Annealing ◽  
Computing Methods ◽  
Experimental Results ◽  
Improve Performance ◽  
Intelligent Computing ◽  
Advanced Computing

Intelligent computing is the application of advanced computing methods to improve performance in areas such as complex representations that are clear to users and easily modifiable. The goal is to make decision making more reliable, spontaneous and creative. In this paper, we put forwards a novel heuristic genetic algorithm based on simulated annealing strategy. Experimental results show the effectiveness of the proposed algorithm.

Sign in / Sign up

Export Citation Format

Share Document