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 $\rm fb^{-1}$ 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 ($X=\gamma, Z, H$). After emphasizing that an $e^+e^-$ 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 ($S/\sqrt{B}$) 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