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brussels,
belgium
Itt jelenleg ennyi az idő: 4:37 de.
Ekkor csatlakozott: május 27, 2013
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Utkarsh
@utkarshsingh1990
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brussels,
belgium
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Experienced professional, 5 years, various domains
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Értékelések
Változtatások elmentve
Nincsenek megtekinthető értékelések!
Tapasztalat
Energy Consultant
nov. 2022 - Jelenleg
Offered services in:
- Energy policy
- Grid resilience
- Renewable energy integration
- Monitoring systems
- AI solutions
R&D Scientist
okt. 2020 - okt. 2022 (2 év)
-Research & development for enhancement of GridEye power quality monitoring module.
-Data analysis for power quality check in low voltage distribution grids.
-Localization and root-cause analysis of power quality variations and events.
-Recommendations for ensuring compliance to European power quality standards.
-Communications and outreach activities
Postdoctoral Researcher
febr. 2020 - szept. 2020 (7 hónap, 2 nap)
- Steered 3 projects and wrote 4 research proposals for EU/ Belgian grants.
- Developed proof-of-concept and software architecture for context-aware machine learning pipeline.
- Jointly prepared training material for lifelong learning program, focused on promoting AI awareness.
- Analyzed COVID-19 data, in collaboration with University Hospital Brussels (UZB) to develop an AI based application.
- Managed/ assisted the hiring of research interns for the Lab.
Tanulmányok
Ph.D. (Electrical Engineering)
(4 év)
M.E. (Power Systems)
(2 év)
B.Tech. (Electrical & Electronics Engineering)
(4 év)
Végzettségek
Earthing Systems & Power Quality
EEP- Electrical Engineering Portal
2021
Online course
Exception Handling in Python
Coursera
2021
Online course
Testing & Debugging in Python
Coursera
2021
Online course
Publikációk
Near-Perfect Time–Frequency Analysis of Power Quality Disturbances
Taylor & Francis
Due to non-stationary nature of the disturbances and resolution restriction posed by the uncertainty principle, most signal processing techniques fail to single-handedly detect different types of disturbances with desired precision. As a solution to this problem, a near-perfect time-frequency analysis technique is presented for PQDs. It utilizes the concept of instantaneous frequency (IF)-based time–frequency representations.
Monitoring-based localization of unbalances and root cause analysis in LV distribution systems
IEEE
This article presents: a new unbalance index derived from feeder active power measurements, an algorithm for localization of unbalanced contributions, and an algorithm for root cause analysis that can distinguish whether the unbalance problem is loading, asymmetry, and/ or fault-oriented.
Time–frequency–scale transform for analysis of PQ disturbances
IET
This study presents a time–frequency–scale transform (TFST) based on chirplet transform (CT) for PQ analysis. CT is a multi‐domain transform with each domain having specific effect on time–frequency representations (TFRs). CT cannot be directly applied to PQ analysis, and therefore, TFST is derived from it which limits the TFR operations to shifting and scaling.
Monitoring Large Crowds With WiFi: A Privacy-Preserving Approach
IEEE
This article presents a crowd monitoring system based on the passive detection of probe requests. The system meets strict privacy requirements and is suited to monitoring events or buildings with a least a few hundred attendees.
Forecasting Crowd Counts With Wi-Fi Systems: Univariate, Non-Seasonal Models
IEEE
This paper presents a crowd monitoring system based on probe requests (PRs), which are Wi-Fi packets smartphones send periodically. By estimating the global rate at which nearby smartphones send PRs, Wi-Fi sensors can estimate crowd counts. The core contribution of this paper is a computationally tractable method that forecasts crowd counts based on ARIMA models.
Crowd Monitoring: State-of-the-Art and Future Directions
Taylor & Francis
This paper aims to serve as a single and sufficient source of information to the concerned researchers on various aspects of crowd monitoring and also provide future directions which can be helpful in developing advanced crowd monitoring solutions
Crowd Forecasting based on WiFi Sensors and LSTM Neural Networks
IEEE
This article presents a solution to the above problem in terms of WiFi-based crowd counting and long short-term memory (LSTM) neural network-based forecasting. Monitoring of an actual event organized in Brussels has been described, wherein crowd counts are obtained using WiFi sensors in a privacy-preserved manner. The time-stamped crowd counts are used to develop univariate time-series, which are in-turn utilized for forecasting.
Detection and Classification of PQ Disturbances based on Time-Frequency-Scale Transform
IET
In this study, a time–frequency‐scale transform is presented as a detection tool with high‐noise immunity. It is a variant of chirplet transform adapted for power quality studies, which incorporates a Hann window and is capable of shifting and scaling operations.
A new optimal feature selection scheme for classification of PQDs based on ant colony framework
Elsevier
A new multiobjective feature selection scheme is proposed which minimizes the product of feature set size and classification error. A feature set is declared feasible, if and only if it offers lesser error and features than other sets.
Optimal Feature Selection via NSGA-II for Power Quality Disturbances Classification
IEEE
This paper presents an application of nondominated sorting genetic algorithm II (NSGA-II) for multiobjective feature selection in power quality disturbances classification. Classification error and number of features are collectively minimized to ensure good accuracy and feasible computation time.
Application of fractional Fourier transform for classification of power quality disturbances
IET
This study, presents fractional Fourier transform (FRFT) based feature extraction as a new technique for classification of PQDs. FRFT can give time, frequency and intermediate time-frequency representations for a signal. The order control offers multi-domain feature extraction, such that most robust feature matrix is utilised for classification under any condition.
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