1,281 research outputs found
Tracking Performance of Online Stochastic Learners
The utilization of online stochastic algorithms is popular in large-scale
learning settings due to their ability to compute updates on the fly, without
the need to store and process data in large batches. When a constant step-size
is used, these algorithms also have the ability to adapt to drifts in problem
parameters, such as data or model properties, and track the optimal solution
with reasonable accuracy. Building on analogies with the study of adaptive
filters, we establish a link between steady-state performance derived under
stationarity assumptions and the tracking performance of online learners under
random walk models. The link allows us to infer the tracking performance from
steady-state expressions directly and almost by inspection
Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Drift
Federated Learning (FL) is an emerging domain in the broader context of
artificial intelligence research. Methodologies pertaining to FL assume
distributed model training, consisting of a collection of clients and a server,
with the main goal of achieving optimal global model with restrictions on data
sharing due to privacy concerns. It is worth highlighting that the diverse
existing literature in FL mostly assume stationary data generation processes;
such an assumption is unrealistic in real-world conditions where concept drift
occurs due to, for instance, seasonal or period observations, faults in sensor
measurements. In this paper, we introduce a multiscale algorithmic framework
which combines theoretical guarantees of \textit{FedAvg} and \textit{FedOMD}
algorithms in near stationary settings with a non-stationary detection and
adaptation technique to ameliorate FL generalization performance in the
presence of model/concept drifts. We present a multi-scale algorithmic
framework leading to \Tilde{\mathcal{O}} ( \min \{ \sqrt{LT} ,
\Delta^{\frac{1}{3}}T^{\frac{2}{3}} + \sqrt{T} \}) \textit{dynamic regret} for
rounds with an underlying general convex loss function, where is the
number of times non-stationary drifts occured and is the cumulative
magnitude of drift experienced within rounds
Sensor based real-time process monitoring for ultra-precision manufacturing processes with non-linearity and non-stationarity
This research investigates methodologies for real-time process monitoring in ultra-precision manufacturing processes, specifically, chemical mechanical planarization (CMP) and ultra-precision machining (UPM), are investigated in this dissertation.The three main components of this research are as follows: (1) developing a predictive modeling approaches for early detection of process anomalies/change points, (2) devising approaches that can capture the non-Gaussian and non-stationary characteristics of CMP and UPM processes, and (3) integrating multiple sensor data to make more reliable process related decisions in real-time.In the first part, we establish a quantitative relationship between CMP process performance, such as material removal rate (MRR) and data acquired from wireless vibration sensors. Subsequently, a non-linear sequential Bayesian analysis is integrated with decision theoretic concepts for detection of CMP process end-point for blanket copper wafers. Using this approach, CMP polishing end-point was detected within a 5% error rate.Next, a non-parametric Bayesian analytical approach is utilized to capture the inherently complex, non-Gaussian, and non-stationary sensor signal patterns observed in CMP process. An evolutionary clustering analysis, called Recurrent Nested Dirichlet Process (RNDP) approach is developed for monitoring CMP process changes using MEMS vibration signals. Using this novel signal analysis approach, process drifts are detected within 20 milliseconds and is assessed to be 3-7 times faster than traditional SPC charts. This is very beneficial to the industry from an application standpoint, because, wafer yield losses will be mitigated to a great extent, if the onset of CMP process drifts can be detected timely and accurately.Lastly, a non-parametric Bayesian modeling approach, termed Dirichlet Process (DP) is combined with a multi-level hierarchical information fusion technique for monitoring of surface finish in UPM process. Using this approach, signal patterns from six different sensors (three axis vibration and force) are integrated based on information fusion theory. It was observed that using experimental UPM sensor data that process decisions based on the multiple sensor information fusion approach were 15%-30% more accurate than the decisions from individual sensors. This will enable more accurate and reliable estimation of process conditions in ultra-precision manufacturing applications
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