AN IMPEDANCE TUBE FOR THE IN-SITU CLASSIFICATION OF BUBBLY LIQUIDS

It is well documented that the presence of even a few air bubbles in water can signifi- cantly alter the propagation and scattering of sound. Air bubbles are both naturally and artificially generated in all marine environments, especially near the sea surface. The abil- ity to measure the acoustic propagation parameters of bubbly liquids in situ has long been a goal of the underwater acoustics community. One promising solution is a submersible, thick-walled, liquid-filled impedance tube. Recent water-filled impedance tube work was successful at characterizing low void fraction bubbly liquids in the laboratory [1]. This work details the modifications made to the existing impedance tube design to allow for submersed deployment in a controlled environment, such as a large tank or a test pond. As well as being submersible, the useable frequency range of the device is increased from 5 - 9 kHz to 1 - 16 kHz and it does not require any form of calibration. The opening of the new impedance tube is fitted with a large stainless steel flange to better define the boundary condition on the plane of the tube opening. The new device was validated against the classic theoretical result for the complex reflection coefficient of a tube opening fitted with an infinite flange. The complex reflection coefficient was then measured with a bubbly liquid (order 250 micron radius and 0.1 - 0.5 % void fraction) outside the tube opening. Results from the bubbly liquid experiments were inconsistent with flanged tube theory using current bubbly liquid models. The results were more closely matched to unflanged tube theory, suggesting that the high attenuation and phase speeds in the bubbly liquid made the tube opening appear as if it were radiating into free space.

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.

Fuzzy ART: An Adaptive Resonance Algorithm for Rapid, Stable Classification of Analog Patterns

The Fuzzy ART system introduced herein incorporates computations from fuzzy set theory into ART 1. For example, the intersection (n) operator used in ART 1 learning is replaced by the MIN operator (A) of fuzzy set theory. Fuzzy ART reduces to ART 1 in response to binary input vectors, but can also learn stable categories in response to analog input vectors. In particular, the MIN operator reduces to the intersection operator in the binary case. Learning is stable because all adaptive weights can only decrease in time. A preprocessing step, called complement coding, uses on-cell and off-cell responses to prevent category proliferation. Complement coding normalizes input vectors while preserving the amplitudes of individual feature activations.