Detection proposal method based on shallow feature constraints

2018 ◽  
Author(s):  
Hao Chen ◽  
Hong Zheng ◽  
Ying Deng
Keyword(s):  
Feature Constraints

Is it possible that playing a video game might help you improve your computational thinking abilities?

2022 ◽  
Vol 1 (2) ◽  
Author(s):  
Ceylan Zhao ◽  
Timothy Burgess
Keyword(s):  
Middle School ◽  
Computer Science ◽  
Video Game ◽  
Detrimental Effect ◽  
Computational Thinking ◽  
Behavioral Effects ◽  
Future Research ◽  
Students Attitudes ◽  
Feature Constraints

In this research, we looked at the cognitive and behavioral effects of playing Penguin Go, a video game that was created to help middle school kids improve their computational thinking (CT) abilities. Apart from the general efficacy of the game, we looked at the effects of a single game feature—constraints on the amount of blocks that may be used in a solution. Students' CT abilities increased dramatically after playing Penguin Go for fewer than two hours, according to the findings, but the extra limits had no meaningful effect on learning. Furthermore, although the game as a whole had no effect on students' views toward computer science, the limitations condition of the game had a detrimental effect on students' attitudes toward computer science. The outcomes of this study, as well as suggested possibilities for future research in the area of employing these sorts of games to build computational thinking abilities, are reviewed.

Local terrain modification method considering physical feature constraints for vector elements

2020 ◽  
Vol 47 (5) ◽  
pp. 452-470
Author(s):  
Jiangfeng She ◽  
Junyan Liu ◽  
Junzhong Tan ◽  
JiWei Dong ◽  
Wang Biao
Keyword(s):  
Physical Feature ◽  
Modification Method ◽  
Feature Constraints

scholarly journals Automatic sensor orientation using horizontal and vertical line feature constraints

2019 ◽  
Vol 150 ◽  
pp. 172-184
Author(s):  
Yanbiao Sun ◽  
Stuart Robson ◽  
Daniel Scott ◽  
Jan Boehm ◽  
Qiang Wang
Keyword(s):  
Vertical Line ◽  
Line Feature ◽  
Sensor Orientation ◽  
Feature Constraints

Learning feature constraints in a chaotic neural memory

1997 ◽  
Vol 55 (1) ◽  
pp. 826-830 ◽  
Author(s):  
Shigetoshi Nara ◽  
Peter Davis
Keyword(s):  
Feature Constraints

TYPE INFERENCE FOR FIRST-CLASS MESSAGES WITH FEATURE CONSTRAINTS

2000 ◽  
Vol 11 (01) ◽  
pp. 29-63
Author(s):  
MARTIN MÜLLER ◽  
SUSUMU NISHIMURA
Keyword(s):  
Polynomial Time ◽  
Type System ◽  
Type Inference ◽  
Satisfiability Problem ◽  
Recent Proposal ◽  
Inference Problem ◽  
Np Complete ◽  
Feature Constraints ◽  
Feature Trees ◽  
Selection Constraint

We present a constraint system, OF, of feature trees that is appropriate to specify and implement type inference for first-class messages. OF extends traditional systems of feature constraints by a selection constraint x <y> z, "by first-class feature tree" y, which is in contrast to the standard selection constraint x[f]y, "by fixed feature" f. We investigate the satisfiability problem of OF and show that it can be solved in polynomial time, and even in quadratic time if the number of features is bounded. We compare OF with Treinen's system EF of feature constraints with first-class features, which has an NP-complete satisfiability problem. This comparison yields that the satisfiability problem for OF with negation is NP-hard. Further we obtain NP-completeness, for a specific subclass of OF with negation that is useful for a related type inference problem. Based on OF we give a simple account of type inference for first-class messages in the spirit of Nishimura's recent proposal, and we show that it has polynomial time complexity: We also highlight an immediate extension of this type system that appears to be desirable but makes type inference NP-complete.

A small displacement torsor model for tolerance analysis in a workpiece-fixture assembly

2009 ◽  
Vol 223 (8) ◽  
pp. 1005-1020 ◽  
Author(s):  
J N Asante
Keyword(s):  
Effective Means ◽  
Experimental System ◽  
Tolerance Analysis ◽  
Geometric Error ◽  
Small Displacement ◽  
Small Displacement Torsor ◽  
Set Up ◽  
Workpiece Setup ◽  
Feature Constraints

Workpiece geometric error, locator geometric error, and clamping error are factors that influence workpiece setup in workpiece fixturing. These errors accumulate and propagate during fixturing. They may be the reason for a machined feature being out of tolerance after machining. This paper presents a methodology for modelling and analysing the combined effect of these errors on a machined feature. Deviation of a machined feature due to the combined errors is expressed in terms of the small displacement torsor parameters. Given a tolerance on the machined feature, constraints are specified for that feature to establish a relationship between the tolerance zone of the feature and the torsor parameters. These constraints provide boundaries within which the machined feature must lie. This is used for tolerance analysis of the machined feature. A case study example was used to illustrate the approach. An experimental system was also set up to verify the analytical model. The results show that this approach offers an effective means for fixturing tolerance analysis.

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