Stochastic operator models for multiple target search tasks

2003 ◽  
Author(s):  
C. Schlick ◽  
C. Winkelholz ◽  
F. Motz ◽  
A. Kunzer ◽  
H. Luczak
Keyword(s):  
Multiple Target ◽  
Target Search ◽  
Search Tasks ◽  
Stochastic Operator

Effects of Natural Language on Multiple Letter Searches

1994 ◽  
Vol 79 (3_suppl) ◽  
pp. 1427-1433
Author(s):  
Masaru Miyao ◽  
John S. Allen ◽  
Selim S. Hacisalihzade ◽  
Lawrence W. Stark
Keyword(s):  
Natural Language ◽  
Target Letter ◽  
Multiple Target ◽  
Search Tasks ◽  
Significant Difference ◽  
Language Familiarity ◽  
Search Speed ◽  
Text Language ◽  
Letter Search

9 American and 9 Turkish subjects were surveyed on multiple letter-search tasks in English, Turkish, and a text of nonwords. The Americans could only understand English, while the Turkish subjects were fluent in both Turkish and English. The parameters measured were the letter-search speed and the number of identifications of two target letters. For searching speed, there was a significant difference for only the text language. The text of nonwords was searched at the lowest speed of all by both groups of subjects. In the case of the target-letter search, only fluency in a language was a significant factor. Also, when nonwords were used, performances declined on both tasks. We conclude that language familiarity is a more important factor than the language of the text when searching for multiple target letters.

scholarly journals Control over target selection determines switch costs in multiple-target search.

2016 ◽  
Vol 16 (12) ◽  
pp. 1287
Author(s):  
Eduard Ort ◽  
Johannes Fahrenfort ◽  
Christian Olivers
Keyword(s):  
Target Selection ◽  
Multiple Target ◽  
Target Search ◽  
Switch Costs

scholarly journals An Adaptive Prediction Target Search Algorithm for Multi-AUVs in an Unknown 3D Environment

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3853 ◽  
Author(s):  
Juan Li ◽  
Jianxin Zhang ◽  
Gengshi Zhang ◽  
Bingjian Zhang
Keyword(s):  
High Efficiency ◽  
Search Algorithm ◽  
Autonomous Underwater Vehicles ◽  
Environmental Information ◽  
Field Of View ◽  
Current Layer ◽  
Evaluation Function ◽  
Target Search ◽  
Search Tasks ◽  
Adaptive Prediction

For a target search of autonomous underwater vehicles (AUVs) in a completely unknown three-dimensional (3D) underwater environment, a multi-AUV collaborative target search algorithm based on adaptive prediction is proposed in this paper. The environmental information sensed by the forward-looking sonar is used to judge the current state of view, and the AUV system uses this environmental information to perform the target search task. If there is no target in the field of view, the AUV system will judge whether all sub-regions of the current layer have been searched or not. The next sub-region for searching is determined by the evaluation function and the task assignment strategy. If there are targets in the field of view, the evaluation function and the estimation function of the adaptive predictive optimization algorithm is used to estimate the location of the unknown target. At the same time, the algorithm also can reduce the positioning error caused by the noise of the sonar sensor. In this paper, the simulation results show that the proposed algorithm can not only deal with static targets and random dynamic interference target search tasks, but it can also perform target search tasks under some random AUV failure conditions. In this process, the underwater communication limits are also considered. Finally, simulation experiments indicate the high efficiency and great adaptability of the proposed algorithm.

scholarly journals Beta and theta oscillations differentially support free versus forced control over multiple-target search

2019 ◽  
pp. 2547-18 ◽  
Author(s):  
Joram van Driel ◽  
Eduard Ort ◽  
Johannes J. Fahrenfort ◽  
Christian N. L. Olivers
Keyword(s):  
Theta Oscillations ◽  
Multiple Target ◽  
Target Search
Sign in / Sign up

Export Citation Format

Share Document