42,641 research outputs found
Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels
Developing data-driven fault detection systems for chemical plants requires managing uncertain data labels and dynamic attributes due to operator-process interactions. Mislabeled data is a known problem in computer science that has received scarce attention from the process systems community. This work introduces and examines the effects of operator actions in records and labels, and the consequences in the development of detection models. Using a state space model, this work proposes an iterative relabeling scheme for retraining classifiers that continuously refines dynamic attributes and labels. Three case studies are presented: a reactor as a motivating example, flooding in a simulated de-Butanizer column, as a complex case, and foaming in an absorber as an industrial challenge. For the first case, detection accuracy is shown to increase by 14% while operating costs are reduced by 20%. Moreover, regarding the de-Butanizer column, the performance of the proposed strategy is shown to be 10% higher than the filtering strategy. Promising results are finally reported in regard of efficient strategies to deal with the presented problemPeer ReviewedPostprint (author's final draft
Discriminatively Trained Latent Ordinal Model for Video Classification
We study the problem of video classification for facial analysis and human
action recognition. We propose a novel weakly supervised learning method that
models the video as a sequence of automatically mined, discriminative
sub-events (eg. onset and offset phase for "smile", running and jumping for
"highjump"). The proposed model is inspired by the recent works on Multiple
Instance Learning and latent SVM/HCRF -- it extends such frameworks to model
the ordinal aspect in the videos, approximately. We obtain consistent
improvements over relevant competitive baselines on four challenging and
publicly available video based facial analysis datasets for prediction of
expression, clinical pain and intent in dyadic conversations and on three
challenging human action datasets. We also validate the method with qualitative
results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text
overlap with arXiv:1604.0150
Structural Results for Decentralized Stochastic Control with a Word-of-Mouth Communication
In this paper, we analyze a network of agents that communicate through the
``word of mouth," in which, every agent communicates only with its neighbors.
We introduce the prescription approach, present some of its properties and show
that it leads to a new information state. We also state preliminary structural
results for optimal control strategies in systems that evolve using
word-of-mouth communication. The proposed approach can be generalized to
analyze several decentralized systems
Electromagnetic and thermal responses in topological matter: topological terms, quantum anomalies and D-branes
We discuss the thermal (or gravitational) responses in topological
superconductors and in topological phases in general. Such thermal responses
(as well as electromagnetic responses for conserved charge) provide a
definition of topological insulators and superconductors beyond the
single-particle picture. In two-dimensional topological phases, the Str\v{e}da
formula for the electric Hall conductivity is generalized to the thermal Hall
conductivity. Applying this formula to the Majorana surface states of
three-dimensional topological superconductors predicts cross-correlated
responses between the angular momentum and thermal polarization (entropy
polarization). We also discuss a use of D-branes in string theory as a
systematic tool to derive all such topological terms and topological responses.
In particular, we relate the index of topological insulators
introduced by Kane and Mele (and its generalization to other symmetry classes
and to arbitrary dimensions) to the K-theory charge of non-BPS D-branes, and
vice versa. We thus establish a link between the stability of non-BPS D-branes
and the topological stability of topological insulators.Comment: 16 pages, 2 figures; Submitted to a topical issue of the Comptes
Rendus de l Academie des Sciences (CRAS
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Accurate diagnosis of tool wear in metal turning process remains an open
challenge for both scientists and industrial practitioners because of
inhomogeneities in workpiece material, nonstationary machining settings to suit
production requirements, and nonlinear relations between measured variables and
tool wear. Common methodologies for tool condition monitoring still rely on
batch approaches which cannot cope with a fast sampling rate of metal cutting
process. Furthermore they require a retraining process to be completed from
scratch when dealing with a new set of machining parameters. This paper
presents an online tool condition monitoring approach based on Parsimonious
Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly
flexible principle where both ensemble structure and base-classifier structure
can automatically grow and shrink on the fly based on the characteristics of
data streams. Moreover, the online feature selection scenario is integrated to
actively sample relevant input attributes. The paper presents advancement of a
newly developed ensemble learning algorithm, pENsemble+, where online active
learning scenario is incorporated to reduce operator labelling effort. The
ensemble merging scenario is proposed which allows reduction of ensemble
complexity while retaining its diversity. Experimental studies utilising
real-world manufacturing data streams and comparisons with well known
algorithms were carried out. Furthermore, the efficacy of pENsemble was
examined using benchmark concept drift data streams. It has been found that
pENsemble+ incurs low structural complexity and results in a significant
reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic
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