169 research outputs found
Intelligent Fault Analysis in Electrical Power Grids
Power grids are one of the most important components of infrastructure in
today's world. Every nation is dependent on the security and stability of its
own power grid to provide electricity to the households and industries. A
malfunction of even a small part of a power grid can cause loss of
productivity, revenue and in some cases even life. Thus, it is imperative to
design a system which can detect the health of the power grid and take
protective measures accordingly even before a serious anomaly takes place. To
achieve this objective, we have set out to create an artificially intelligent
system which can analyze the grid information at any given time and determine
the health of the grid through the usage of sophisticated formal models and
novel machine learning techniques like recurrent neural networks. Our system
simulates grid conditions including stimuli like faults, generator output
fluctuations, load fluctuations using Siemens PSS/E software and this data is
trained using various classifiers like SVM, LSTM and subsequently tested. The
results are excellent with our methods giving very high accuracy for the data.
This model can easily be scaled to handle larger and more complex grid
architectures.Comment: In proceedings of the 29th IEEE International Conference on Tools
with Artificial Intelligence (ICTAI) 2017 (full paper); 6 pages; 13 figure
Intent-Aware Contextual Recommendation System
Recommender systems take inputs from user history, use an internal ranking
algorithm to generate results and possibly optimize this ranking based on
feedback. However, often the recommender system is unaware of the actual intent
of the user and simply provides recommendations dynamically without properly
understanding the thought process of the user. An intelligent recommender
system is not only useful for the user but also for businesses which want to
learn the tendencies of their users. Finding out tendencies or intents of a
user is a difficult problem to solve.
Keeping this in mind, we sought out to create an intelligent system which
will keep track of the user's activity on a web-application as well as
determine the intent of the user in each session. We devised a way to encode
the user's activity through the sessions. Then, we have represented the
information seen by the user in a high dimensional format which is reduced to
lower dimensions using tensor factorization techniques. The aspect of intent
awareness (or scoring) is dealt with at this stage. Finally, combining the user
activity data with the contextual information gives the recommendation score.
The final recommendations are then ranked using filtering and collaborative
recommendation techniques to show the top-k recommendations to the user. A
provision for feedback is also envisioned in the current system which informs
the model to update the various weights in the recommender system. Our overall
model aims to combine both frequency-based and context-based recommendation
systems and quantify the intent of a user to provide better recommendations.
We ran experiments on real-world timestamped user activity data, in the
setting of recommending reports to the users of a business analytics tool and
the results are better than the baselines. We also tuned certain aspects of our
model to arrive at optimized results.Comment: Presented at the 5th International Workshop on Data Science and Big
Data Analytics (DSBDA), 17th IEEE International Conference on Data Mining
(ICDM) 2017; 8 pages; 4 figures; Due to the limitation "The abstract field
cannot be longer than 1,920 characters," the abstract appearing here is
slightly shorter than the one in the PDF fil
Probing the light radion through diphotons at the Large Hadron Collider
A radion in a scenario with a warped extra dimension can be lighter than the
Higgs boson, even if the Kaluza-Klein excitation modes of the graviton turn out
to be in the multi-TeV region. The discovery of such a light radion would be
gateway to new physics. We show how the two-photon mode of decay can enable us
to probe a radion in the mass range 60 - 110 GeV. We take into account the
diphoton background, including fragmentation effects, and include cuts designed
to suppress the background to the maximum possible extent. Our conclusion is
that, with an integrated luminosity of 3000 or less, the next run
of the Large Hadron Collider should be able to detect a radion in this mass
range, with a significance of 5 standard deviations or more.Comment: 24 pages, 4 figures, Version published in Phys. Rev.
Mono-X signal and two component dark matter: new distinction criteria
The identification and isolation of two WIMP dark matter (DM) components at
colliders is of wide interest on the one hand but extremely challenging on the
other, especially when the dominant signal of both DM components is of the
mono-X type (). After emphasizing that an collider is
more suitable for this goal, we first identify the theoretical principles that
govern the occurrence of two peaks in missing energy (ME) distribution, in a
double-DM scenario. We then identify a variable that rather spectacularly
elicits the double-peaking behaviour, namely, the plot of bin-wise statistical
significance () against ME. Using Gaussian fits of the histograms,
we apply a set of criteria developed by us, to illustrate the above points
numerically for suitable benchmarks.Comment: 5 pages, 7 figures, 2 table
Unitarity violation in sequential neutrino mixing in a model of extra dimensions
We investigate the possibility of unitarity violation in the sequential
neutrino mixing matrix in a scenario with extra compact spacelike dimensions.
Gauge singlet neutrinos are assumed to propagate in one extra dimension, giving
rise to an infinite tower of states in the effective four-dimensional theory.
It is shown that this leads to small lepton-number violating entries in the
neutrino mass matrix, which can violate unitarity on the order of one per cent.Comment: 16 pages, 2 table
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