scholarly journals A computational Bayesian approach for localizing an acoustic scatterer in a stratified ocean environment

2019 ◽  
Vol 146 (3) ◽  
pp. EL245-EL250 ◽  
Author(s):  
Abner C. Barros ◽  
Paul J. Gendron
Keyword(s):  
Bayesian Approach ◽  
Ocean Environment ◽  
Stratified Ocean

scholarly journals Concept of using and modeling of a marine autonomous smart profiler

2020 ◽  
pp. 106-113
Author(s):  
L.A. Krasnodubets ◽  
◽  
Keyword(s):  
High Speed ◽  
Inertial Sensors ◽  
Vertical Movement ◽  
Smart Structure ◽  
Sensor Data ◽  
Mobile Data Collection ◽  
Flexible Control ◽  
Ocean Environment ◽  
Stratified Ocean ◽  
Speed Mode

The article focuses on the development of technical support in terms of expanding the measuring base and improving marine profilers for operational observation systems as part of a new and developing scientific and applied field – operational oceanography. The concept of using a marine autonomous intelligent profiler for operational measurements of the thermohaline profile parameters of a stratified ocean environment with a significant reduction in the time to conduct an experiment using smart profiling is presented. At the same time, time savings are achieved due to the flexible control of the high-speed modes of vertical movement of the marine autonomous profiler with adjustable buoyancy. Low profiling speeds avoid significant dynamic distortions in the measurements obtained from inertial sensors. However, in a homogeneous environment, after taking measurements, the speed of the profiler can be significantly increased. The purpose of the smart profiler as a mobile data collection platform is to analyze its own motion and the properties of the surrounding ocean environment and choose, on this basis, a high-speed profiling mode that provides an acceptable level of dynamic distortion in the sensor data installed on board the measuring equipment. The results of computer simulation of the proposed smart structure in the MATLAB & Simulink environment based on the original mathematical models that make up its subsystems are presented. We studied the process of “smart” profiling during the transition of the profiler from a cruising speed mode (fast) to a working speed mode (slow), as well as its return to cruising speed in a stratified ocean environment. In this case, the behavior strategy of the smart profiler (ensuring the specified accuracy of thermohaline measurements) was implemented by choosing a speed mode based on the analysis of dynamic measurements of its motion parameters and stratification of the profile by density.

Verbal statistical learning: Prior knowledge impacts word identification but not metacognition

2020 ◽  
Author(s):  
Laetitia Zmuda ◽  
Charlotte Baey ◽  
Paolo Mairano ◽  
Anahita Basirat
Keyword(s):  
Task Performance ◽  
Prior Knowledge ◽  
Statistical Learning ◽  
Cognitive Processes ◽  
Bayesian Approach ◽  
Word Identification ◽  
Artificial Language ◽  
Artificial Languages ◽  
Transitional Probabilities ◽  
Fully Bayesian

It is well-known that individuals can identify novel words in a stream of an artificial language using statistical dependencies. While underlying computations are thought to be similar from one stream to another (e.g. transitional probabilities between syllables), performance are not similar. According to the “linguistic entrenchment” hypothesis, this would be due to the fact that individuals have some prior knowledge regarding co-occurrences of elements in speech which intervene during verbal statistical learning. The focus of previous studies was on task performance. The goal of the current study is to examine the extent to which prior knowledge impacts metacognition (i.e. ability to evaluate one’s own cognitive processes). Participants were exposed to two different artificial languages. Using a fully Bayesian approach, we estimated an unbiased measure of metacognitive efficiency and compared the two languages in terms of task performance and metacognition. While task performance was higher in one of the languages, the metacognitive efficiency was similar in both languages. In addition, a model assuming no correlation between the two languages better accounted for our results compared to a model where correlations were introduced. We discuss the implications of our findings regarding the computations which underlie the interaction between input and prior knowledge during verbal statistical learning.

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