Class (philosophy)

About: Class (philosophy) is a research topic. Over the lifetime, 821 publications have been published within this topic receiving 28000 citations.

A Multi-Source Weighted Deep Transfer Network for Open-Set Fault Diagnosis of Rotary Machinery

Abstract: In real industries, there often exist application scenarios where the target domain holds fault categories never observed in the source domain, which is an open-set domain adaptation (DA) diagnosis issue. Existing DA diagnosis methods under the assumption of sharing identical label space across domains fail to work. What is more, labeled samples can be collected from different sources, where multisource information fusion is rarely considered. To handle this issue, a multisource open-set DA diagnosis approach is developed. Specifically, multisource domain data of different operation conditions sharing partial classes are adopted to take advantage of fault information. Then, an open-set DA network is constructed to mitigate the domain gap across domains. Finally, a weighting learning strategy is introduced to adaptively weigh the importance on feature distribution alignment between known class and unknown class samples. Extensive experiments suggest that the proposed approach can substantially boost the performance of open-set diagnosis issues and outperform existing diagnosis approaches.

Projection, symmetry, and natural kinds

Abstract: Scientific practice involves two kinds of induction. In one, generalizations are drawn about the states of a particular system of variables. In the other, generalizations are drawn across systems in a class. We can discern two questions of correctness about both kinds of induction: (P1) what distinguishes those systems and classes of system that are ‘projectible’ in Goodman’s (Fact, fiction and forecast, 1955) sense from those that are not, and (P2) what are the methods by which we are able to identify kinds that are likely to be projectible? In answer to the first question, numerous theories of ‘natural kinds’ have been advanced, but none has satisfactorily addressed both questions simultaneously. I propose a shift in perspective. Both essentialist and cluster property theories have traditionally characterized kinds directly in terms of the causally salient properties their members possess. Instead, we should focus on ‘dynamical symmetries’, transformations of a system to which the causal structure of that system is indifferent. I suggest that to be a member of natural kind it is necessary and sufficient to possess a particular collection of dynamical symmetries. I show that membership in such a kind is in turn necessary and sufficient for the presence of the sort of causal structure that accounts for success in both kinds of induction, thus demonstrating that (P1) has been answered satisfactorily. More dramatically, I demonstrate that this new theory of ‘dynamical kinds’ provides an answer to (P2) with methodological implications concerning the discovery of projectible kinds.

Computation-Based Reliability Analysis

Abstract: A reliability analysis method for computing systems is considered in which the underlying criteria for "success" are based on the computations the system must perform in the use environment. Beginning with a general model of a "computer with faults," intermediate concepts of a "tolerance relation" and an "environment space" are introduced which account for the computational needs of the user and the probabilistic nature of the use environment. These concepts are then incorporated to obtain a precisely defined class of computation-based reliability measures. Formulation of a particular measure is illustrated and results, applying this measure, are compared with those of a typical structure-based analysis.