Abstract: Machining instability, or chatter, can impose an important limitation to discrete part machining. In this work, a networked implementation of milling stability optimization with Bayesian learning is presented. The milling process was monitored with a wireless sensory tool holder instrumented with an accelerometer at the TU Wien, Vienna, Austria. The recorded data from a milling test cut were used to classify the cut as stable or unstable based on a frequency analysis. The test cut result was used in a Bayesian stability learning algorithm at the University of Tennessee, Knoxville, Tennessee, USA. The algorithm calculated the probability of stability as a function of axial depth of cut and spindle speed based on the test result and recommended parameters for the next test cut. The iterative process between two transatlantic locations was repeated until convergence to a stable optimal process parameter set was achieved.
Abstract: We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.
Abstract: The near-field synthetic aperture radar (SAR) imaging
is an advanced nondestructive testing and evaluation (NDT&E)
technique. This paper investigates the complex-valued signal
processing related to the near-field SAR imaging system, where
the measurement data turns out to be noncircular and improper,
meaning that the complex-valued data is correlated to its complex
conjugate. Furthermore, we discover that the degree of impropriety
of the measurement data and that of the target image can be highly
correlated in near-field SAR imaging. Based on these observations, A
modified generalized sparse Bayesian learning algorithm is proposed,
taking impropriety and noncircularity into account. Numerical results
show that the proposed algorithm provides performance gain, with the
help of noncircular assumption on the signals.