ExaGridPF: A Parallel Power Flow Solver for Transmission and Unbalanced Distribution Systems

This paper investigates parallelization strategies for solving power flow problems in both transmission and unbalanced, three-phase distribution systems by developing a scalable power flow solver, ExaGridPF, which is compatible with existing high-performance computing platforms. Newton-Raphson (NR) and Newton-Krylov (NK) algorithms have been implemented to verify the performance improvement over both standard IEEE test cases and synthesized grid topologies. For three-phase, unbalanced system, we adapt the current injection method (CIM) to model the power flow and utilize SuperLU to parallelize the computing load across multiple threads. The experimental results indicate that more than 5 times speedup ratio can be achieved for synthesized large-scale transmission topologies, and significant efficiency improvements are observed over existing methods for the distribution networks

Signal processing methods for EEG data classification

Imperial Users onl

Lithium industry in the behaviour of the mergers and acquisitions in the U.S. oil and gas industry.

Is lithium affecting the U.S. oil and gas industry strategies? Lithium has an increasingly strategic role as clean technologies emerge, affecting the strategies of oil and gas companies in response to energy trends. This paper contributes to this literature, studying the dynamics of lithium industry and mergers and acquisitions in the U.S. oil and gas industry in time-frequency domain. We use methodologies based on Continuous Wavelet Transform (CWT) and Vector AutoRegressive Models (VAR), and the results indicate that both time series are correlated in the long term, where M&A U.S. oil and gas industry dependence on lithium industry has increased, starting in the early 2014 until the end of the sample. Evidence of causality is not found between both time seriespre-print716 K

An Analysis Of Deconvolution: Modeling Reflectivity By Fractionally Integrated Noise

Reflection coefficients are observed in nature to have stochastic behavior that departs significantly from the white noise model. Conventional deconvolution methods, however, assume reflectivity to be a white noise process. In this paper we analyze the deconvolution process, study the implications of the assumption of white noise, and show that the conventional operator can recover only the white component of reflectivity. A new stochastic model, fractionally integrated noise, is proposed for modeling reflectivity. This model more closely approximates its spectral character and that encompasses white noise as a special case. We discuss different techniques to generalize the conventional deconvolution method based on the new model in order to handle reflectivity that is not white, and compare the results of the conventional and generalized filters using data derived from well logs.Massachusetts Institute of Technology. Borehole Acoustics and Logging ConsortiumMassachusetts Institute of Technology. Earth Resources Laboratory. Reservoir Delineation ConsortiumSaudi Aramc

Pattern identification of biomedical images with time series: contrasting THz pulse imaging with DCE-MRIs

Objective We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. Methods Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. Validation Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. Results Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. Conclusion The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community

Financial stress and crude oil implied volatility: New evidence from continuous wavelet transformation framework

This study explores the theoretical possibility of co-movement and causality between crude oil implied volatility (OVX) and financial stress in a wavelet framework. The paper contributes to the existing literature in at least three possible ways: (a) First, the study considers not only composite financial stress indicators but also uses the categorical stress components such as Credit, Equity Valuation, Funding, Safe Assets and Volatility. (b) Second, the study employs a wavelet-based approach in tracking the co-movement and causality between oil and financial stress in a continuous time-frequency space. Lastly, (c) while previous studies mainly use oil price changes to assess the relationship with financial stress, the present study evaluates the role of forward-looking (30-days ahead) oil price uncertainty (proxied by OVX). The findings indicate the existence of co-movement between oil volatility and financial stress, mainly around the phases of economic turbulence. The patterns and strength of such co-movements are time-variant. The direction of the relationship is mostly positive, and the lead-lag relationship reveals that OVX tends to drive the relationship. It is further observed that the causalities between the variables are mostly bi-directional. However, relatively stronger causalities are transmitted from OVX towards FSI. Furthermore, the association between OVX and stress indicators is assessed in two different states of the economy, i.e., state of distress and tranquillity. The findings suggest that the causal co-movement intensifies majorly during the state of distress. Overall, the outcome of this study could be useful to policymakers and investors to anticipate the impending changes in the relationship to mitigate its potential adverse impact.© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features