Of ants and academics: The computational power of external representation

1997 ◽  
Vol 20 (1) ◽  
pp. 78-79
Author(s):  
Jon Oberlander
Keyword(s):  
Real World ◽  
External Representation ◽  
External Representations ◽  
Computational Power ◽  
The Real

Clark & Thornton speculate that intervening in the real world might be a way of transforming type-2 problems into type-1, but they state that they are not aware of any definite cases. It is argued that the active construction of external representations often performs exactly this function, and that recoding via the real world is therefore common, if not ubiquitous.

126-LB: Glycemic Outcomes for People with Type 1 and Type 2 Diabetes Using Control-IQ Technology: Real-World Data from Early Adopters

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 126-LB ◽  
Author(s):  
STEPHANIE HABIF ◽  
ALEXANDRA CONSTANTIN ◽  
LARS MUELLER ◽  
HARSIMRAN SINGH
Keyword(s):  
Type 2 Diabetes ◽  
Real World ◽  
Real World Data ◽  
Early Adopters ◽  
World Data ◽  
Glycemic Outcomes

710-P: Real-World Evaluation of Glycemic Outcomes by Prior Therapy for People with Type 1 and Type 2 Diabetes Onboarding to Control-IQ Technology

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 710-P
Author(s):  
JOHN W. MORBERG ◽  
HARSIMRAN SINGH ◽  
MOLLY MCELWEE-MALLOY ◽  
STEPHANIE HABIF ◽  
ALEXANDRA CONSTANTIN
Keyword(s):  
Type 2 Diabetes ◽  
Real World ◽  
Prior Therapy ◽  
Glycemic Outcomes

scholarly journals Application of Type-1 and Type-2 Fuzzy Logic Controller for The Real Swarm Robot

2019 ◽  
Vol 15 (06) ◽  
pp. 83 ◽  
Author(s):  
Ade Silvia Handayani ◽  
Nyayu Latifah Husni ◽  
Siti Nurmaini ◽  
Irsyadi Yani
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Complex Problem ◽  
The Real ◽  
Advantages And Disadvantages ◽  
Path Navigation ◽  
Swarm Robot ◽  
Interval Type

Navigation is one of the typical problem domains occurred in studying swarm robot. This task needs a special ability in avoiding obstacles.  This research presents the navigation techniques using type 1 fuzzy logic and interval type 2 fuzzy logic. A comparison of those two fuzzy logic performances in controlling swarm robot as tools for complex problem modeling, especially for path navigation is presented in this paper.  Each hierarchical of fuzzy logic shows its advantages and disadvantages.  For testing the robustness of type-1 fuzzy logic and interval type-2 fuzzy logic algorithms, 3 robots for the real swarm robot experiment are used.  Each is equipped with one compass sensor, three distance sensors, and one X-Bee communication module.  The experimental results show that type-2 fuzzy logic has better performance than type-1 fuzzy logic.

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