Practical applications of data mining in plant monitoring and diagnostics

Using available expert knowledge in conjunction with a structured process of data mining, characteristics observed in captured condition monitoring data, representing characteristics of plant operation may be understood, explained and quantified. Knowledge and understanding of satisfactory and unsatisfactory plant condition can be gained and made explicit from the analysis of data observations and subsequently used to form the basis of condition assessment and diagnostic rules/models implemented in decision support systems supporting plant maintenance. This paper proposes a data mining method for the analysis of condition monitoring data, and demonstrates this method in its discovery of useful knowledge from trip coil data captured from a population of in-service distribution circuit breakers and empirical UHF data captured from laboratory experiments simulating partial discharge defects typically found in HV transformers. This discovered knowledge then forms the basis of two separate decision support systems for the condition assessment/defect clasification of these respective plant items

Examining the Scope of Channel Expansion: A Test of Channel Expansion Theory with New and Traditional Communication Media

This article draws on channel expansion theory to explore the selection and use of communication media by organizational members. Channel expansion theory scholars posit that media richness perceptions are dependent on experiences with communication partners, the message topic, and the communication media utilized. This study tests channel expansion theory in the context of new and traditional communication media. Respondents (N = 269) completed questionnaires regarding their use and perceptions of face-to-face, telephone, e-mail, or instant-messaging interactions. Results indicate that experience with channel, topic, partner, and social influence are all significant predictors of richness perceptions, when controlling for age and media characteristics. Findings also suggest that the richness of a medium is not fixed and may be shaped by interpersonal factors, including one’s relevant experiences

Agnostic Learning of Geometric Patterns

Goldberg, Goldman, and Scott demonstrated how the problem of recognizing a landmark from a one-dimensional visual image can be mapped to that of learning a one-dimensional geometric pattern and gave a PAC algorithm to learn that class. In this paper, we present an efficient on-line agnostic learning algorithm for learning the class of constant-dimension geometric patterns. Our algorithm can tolerate both classification and attribute noise. By working in higher dimensional spaces we can represent more features from the visual image in the geometric pattern. Our mapping of the data to a geometric pattern, and hence our learning algorithm, is applicable to any data representable as a constant-dimensional array of values, e.g. sonar data, temporal difference information, or amplitudes of a waveform. To our knowledge, these classes of patterns are more complex than any class of geometric patterns previously studied. Also, our results are easily adapted to learn the union of fixed-dimensional boxes from multiple-instance examples. Finally, our algorithms are tolerant of concept shift

Learning from Examples with Unspecified Attribute Values

We introduce the UAV learning model in which some of the attributes in the examples are unspecified. In our model, an example x is classified positive (resp., negative) if all possible assignments for the unspecified attributes result in a positive (resp., negative) classification. Otherwise the classificatoin given to x is ? (for unknown). Given an example x in which some attributes are unspecified, the oracle UAV-MQ responds with the classification of x. Given a hypothesis h, the oracle UAV-EQ returns an example x (that could have unspecified attributes) for which h(x) is incorrect. We show that any class learnable in the exact model using the MQ and EQ oracles is also learnable in the UAV model using the MQ and UAV-EQ oracles as long as the counterexamples provided by the UAV-EQ oracle have a logarithmic number of unspecified attributes. We also show that any class learnable in the exact model using the MQ and EQ oracles is also learnable in the UAV model using the UAV-MQ and UAV-EQ oracles as well as an oracle to evaluate a given boolean formula on an example with unspecified attributes. (For some hypothesis classes such as decision trees and unate formulas the evaluation can be done in polynomial time without an oracle.) We also study the learnability of a universal class of decision trees under the UAV model and of DNF formulas under a representation-dependent variation of the UAV model

Characterizing 15 Years of Saharan-like, Dry, Well-Mixed Air Layers in North Africa

The Saharan Air Layer (SAL) is a dry, well-mixed layer (WML) of warm and sometimes dusty air of nearly constant water vapor mixing ratio generated by the intense surface heating and strong, dry convection in the Sahara Desert, which has notable downstream impacts on the surface energy balance, organized convective system development, seasonal precipitation, and air quality. Characterizing both WMLs and SALs from the existing rawinsonde network has proven challenging because of its sparseness and inconsistent data reporting. Spurred on by this challenge, we previously created a detection methodology and supporting software to automate the identification and characterization of WMLs from multiple data sources including rawinsondes, remote sensing platforms, and model products. We applied our algorithm to each dataset at both its native and at a common (most coarse data product) vertical resolution to detect WMLs and their characteristics (temperature, mixing ratio, AOD, etc.) at each of the 53 rawinsonde launch sites in north Africa

The West: slowing, but still strong

Federal Reserve District, 12th