Research in gum, dust and rust filtering.

cover-title

Aspects of optimum nonlinear filtering using nonlinear zero-memory elements.

"Cornell Aeronautical Laboratory internal research."

Approximate solutions for nonequilibrium airflow in hypersonic nozzles,

Contract no. AF40(600)-804. AEDC TN-60-181. Arnold Engineering Development Center, Arnold Air Force Station, Tennessee. Air Research and Development Command, United States Air Force."

Portraits + personalities : a self-guided tour for children and their families in the west building of the National Gallery of Art : suggested for ages 8 to 10 : approximate-- 45 minutes.

Mode of access: Internet.

Organizational, direct and general support, maintenance repair parts and special tools list : container, service, ward, rigid construction, approximate expanded dimension, 10 ft, 10 3/4 inches long, 12 ft wide, 7 ft, 3 inches high, FSN 5410-809-6634.

Includes index.

The approximate numerical solution of boundary value problems by relaxation methods,

"Contract N7 onr-358, T. O. I., NR-041-032."

Exact and approximate Bayesian solutions for inference about variance components and multivariate inadmissibility : final report for period 1 May 1972-1 January 1976 /

Performing organization: Dept. of Statistics, University of Michigan.

Scheme for the examination of urine for clinical indications. Approximate determinations.

Mode of access: Internet.

Principles of coding, filtering, and information theory.

Includes bibliography.

Approximate solutions to the optimum climbing trajectory for a rocket-powered aircraft,

"Purdue Research Foundation. Research project no.1255. Project Ae-25. This research was supported by the National Advisory Committee for Aeronautics, Washington, D.C., under Contract no. NAW-6465."

Approximate processing of massive continuous quantile queries over high-speed data streams

Quantile computation has many applications including data mining and financial data analysis. It has been shown that an is an element of-approximate summary can be maintained so that, given a quantile query d (phi, is an element of), the data item at rank [phi N] may be approximately obtained within the rank error precision is an element of N over all N data items in a data stream or in a sliding window. However, scalable online processing of massive continuous quantile queries with different phi and is an element of poses a new challenge because the summary is continuously updated with new arrivals of data items. In this paper, first we aim to dramatically reduce the number of distinct query results by grouping a set of different queries into a cluster so that they can be processed virtually as a single query while the precision requirements from users can be retained. Second, we aim to minimize the total query processing costs. Efficient algorithms are developed to minimize the total number of times for reprocessing clusters and to produce the minimum number of clusters, respectively. The techniques are extended to maintain near-optimal clustering when queries are registered and removed in an arbitrary fashion against whole data streams or sliding windows. In addition to theoretical analysis, our performance study indicates that the proposed techniques are indeed scalable with respect to the number of input queries as well as the number of items and the item arrival rate in a data stream.

Tyre-force estimation by Kalman inverse filtering: applications to off-highway mining trucks

Kalman inverse filtering is used to develop a methodology for real-time estimation of forces acting at the interface between tyre and road on large off-highway mining trucks. The system model formulated is capable of estimating the three components of tyre-force at each wheel of the truck using a practical set of measurements and inputs. Good tracking is obtained by the estimated tyre-forces when compared with those simulated by an ADAMS virtual-truck model. A sensitivity analysis determines the susceptibility of the tyre-force estimates to uncertainties in the truck's parameters.

Incremental learning for interactive e-mail filtering

In this article, we propose a framework, namely, Prediction-Learning-Distillation (PLD) for interactive document classification and distilling misclassified documents. Whenever a user points out misclassified documents, the PLD learns from the mistakes and identifies the same mistakes from all other classified documents. The PLD then enforces this learning for future classifications. If the classifier fails to accept relevant documents or reject irrelevant documents on certain categories, then PLD will assign those documents as new positive/negative training instances. The classifier can then strengthen its weakness by learning from these new training instances. Our experiments’ results have demonstrated that the proposed algorithm can learn from user-identified misclassified documents, and then distil the rest successfully.

Interactive email filtering: Learning from misclassified examples

Learning from mistakes has proven to be an effective way of learning in the interactive document classifications. In this paper we propose an approach to effectively learning from mistakes in the email filtering process. Our system has employed both SVM and Winnow machine learning algorithms to learn from misclassified email documents and refine the email filtering process accordingly. Our experiments have shown that the training of an email filter becomes much effective and faster