78 research outputs found
A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication
Semiconductor lasers, one of the key components for optical communication
systems, have been rapidly evolving to meet the requirements of next generation
optical networks with respect to high speed, low power consumption, small form
factor etc. However, these demands have brought severe challenges to the
semiconductor laser reliability. Therefore, a great deal of attention has been
devoted to improving it and thereby ensuring reliable transmission. In this
paper, a predictive maintenance framework using machine learning techniques is
proposed for real-time heath monitoring and prognosis of semiconductor laser
and thus enhancing its reliability. The proposed approach is composed of three
stages: i) real-time performance degradation prediction, ii) degradation
detection, and iii) remaining useful life (RUL) prediction. First of all, an
attention based gated recurrent unit (GRU) model is adopted for real-time
prediction of performance degradation. Then, a convolutional autoencoder is
used to detect the degradation or abnormal behavior of a laser, given the
predicted degradation performance values. Once an abnormal state is detected, a
RUL prediction model based on attention-based deep learning is utilized.
Afterwards, the estimated RUL is input for decision making and maintenance
planning. The proposed framework is validated using experimental data derived
from accelerated aging tests conducted for semiconductor tunable lasers. The
proposed approach achieves a very good degradation performance prediction
capability with a small root mean square error (RMSE) of 0.01, a good anomaly
detection accuracy of 94.24% and a better RUL estimation capability compared to
the existing ML-based laser RUL prediction models.Comment: Published in Journal of Lightwave Technology (Volume: 40, Issue: 14,
15 July 2022
Fault Monitoring in Passive Optical Networks using Machine Learning Techniques
Passive optical network (PON) systems are vulnerable to a variety of
failures, including fiber cuts and optical network unit (ONU)
transmitter/receiver failures. Any service interruption caused by a fiber cut
can result in huge financial losses for service providers or operators.
Identifying the faulty ONU becomes difficult in the case of nearly equidistant
branch terminations because the reflections from the branches overlap, making
it difficult to distinguish the faulty branch given the global backscattering
signal. With increasing network size, the complexity of fault monitoring in PON
systems increases, resulting in less reliable monitoring. To address these
challenges, we propose in this paper various machine learning (ML) approaches
for fault monitoring in PON systems, and we validate them using experimental
optical time domain reflectometry (OTDR) data.Comment: ICTON 202
Degradation Prediction of Semiconductor Lasers using Conditional Variational Autoencoder
Semiconductor lasers have been rapidly evolving to meet the demands of
next-generation optical networks. This imposes much more stringent requirements
on the laser reliability, which are dominated by degradation mechanisms (e.g.,
sudden degradation) limiting the semiconductor laser lifetime. Physics-based
approaches are often used to characterize the degradation behavior
analytically, yet explicit domain knowledge and accurate mathematical models
are required. Building such models can be very challenging due to a lack of a
full understanding of the complex physical processes inducing the degradation
under various operating conditions. To overcome the aforementioned limitations,
we propose a new data-driven approach, extracting useful insights from the
operational monitored data to predict the degradation trend without requiring
any specific knowledge or using any physical model. The proposed approach is
based on an unsupervised technique, a conditional variational autoencoder, and
validated using vertical-cavity surface-emitting laser (VCSEL) and tunable edge
emitting laser reliability data. The experimental results confirm that our
model (i) achieves a good degradation prediction and generalization performance
by yielding an F1 score of 95.3%, (ii) outperforms several baseline ML based
anomaly detection techniques, and (iii) helps to shorten the aging tests by
early predicting the failed devices before the end of the test and thereby
saving costsComment: Published in: Journal of Lightwave Technology (Volume: 40, Issue: 18,
15 September 2022
Re-telling, Re-cognition, Re-stitution: Sikh Heritagization in Canada
In Canada, the language and techniques of museums and heritage sites have been adopted and adapted by some immigrant communities to make sense of their place within their new country. For some groups, âheritagizationâ is a new value, mobilized for diverse purposes. New museums and heritage sites serve as a form of ethnic media, becoming community gathering points, taking on pedagogical roles, enacting citizenship, and enabling strategic assertion of identity in the public sphere. This article explores this enactment of heritage and citizen-membership through a case study, the Sikh Heritage Museum, developed in Abbotsford by Indo-Canadians. Established in 2011 in an historic and still-functioning gurdwara, the museum is an example of a communityâs desire to balance inward-looking historical consciousness and community belonging, with outward-looking voice, recognition and acceptance by mainstream Canadian society. The museum has also become a site of tension between top-down and bottom-up initiatives, where amateur and local expressions butt up against professionalized government activities such as the Canadian Historical Recognition Program that seek to insert formal recognition and social inclusion policies. The article considers the effects of this resource and power differential on the museumâs development, and on the sensibilities and practices of immigrant âheritageâ and âcitizenshipâ in Canada
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