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Articles 1 - 5 of 5
Full-Text Articles in Engineering
Effect Of Label Noise On The Machine-Learned Classification Of Earthquake Damage, Jared Frank, Umaa Rebbapragada, James Bialas, Thomas Oommen, Timothy C. Havens
Effect Of Label Noise On The Machine-Learned Classification Of Earthquake Damage, Jared Frank, Umaa Rebbapragada, James Bialas, Thomas Oommen, Timothy C. Havens
Michigan Tech Publications
Automated classification of earthquake damage in remotely-sensed imagery using machine learning techniques depends on training data, or data examples that are labeled correctly by a human expert as containing damage or not. Mislabeled training data are a major source of classifier error due to the use of imprecise digital labeling tools and crowdsourced volunteers who are not adequately trained on or invested in the task. The spatial nature of remote sensing classification leads to the consistent mislabeling of classes that occur in close proximity to rubble, which is a major byproduct of earthquake damage in urban areas. In this study, …
Harmonic Distortion Minimization In Power Grids With Wind And Electric Vehicles, Ritam Misra, Sumit Paudyal, Oguzhan Ceylan, Paras Mandal
Harmonic Distortion Minimization In Power Grids With Wind And Electric Vehicles, Ritam Misra, Sumit Paudyal, Oguzhan Ceylan, Paras Mandal
Michigan Tech Publications
Power-electronic interfacing based devices such as wind generators (WGs) and electrical vehicles (EVs) cause harmonic distortions on the power grid. Higher penetration and uncoordinated operation of WGs and EVs can lead to voltage and current harmonic distortions, which may exceed IEEE limits. It is interesting to note that WGs and EVs have some common harmonic profiles. Therefore, when EVs are connected to the grid, the harmonic pollution EVs impart onto the grid can be reduced to some extent by the amount of wind power injecting into the grid and vice versa. In this context, this work studies the impact of …
Off-Line Handwritten Signature Recognition By Wavelet Entropy And Neural Network, Khaled Daqrouq, Husam Sweidan, Ahmad Balamesh, Mohammed N. Ajour
Off-Line Handwritten Signature Recognition By Wavelet Entropy And Neural Network, Khaled Daqrouq, Husam Sweidan, Ahmad Balamesh, Mohammed N. Ajour
Michigan Tech Publications
Handwritten signatures are widely utilized as a form of personal recognition. However, they have the unfortunate shortcoming of being easily abused by those who would fake the identification or intent of an individual which might be very harmful. Therefore, the need for an automatic signature recognition system is crucial. In this paper, a signature recognition approach based on a probabilistic neural network (PNN) and wavelet transform average framing entropy (AFE) is proposed. The system was tested with a wavelet packet (WP) entropy denoted as a WP entropy neural network system (WPENN) and with a discrete wavelet transform (DWT) entropy denoted …
The Influence Of Bmss On The Characterization And Modeling Of Series And Parallel Li-Ion Packs, Sandra Castano-Solis, Daniel Serrano-Jimenez, Lucia Gauchia, Javier Sanz
The Influence Of Bmss On The Characterization And Modeling Of Series And Parallel Li-Ion Packs, Sandra Castano-Solis, Daniel Serrano-Jimenez, Lucia Gauchia, Javier Sanz
Michigan Tech Publications
This work analyzes the effects of a BMS (battery management system) on the characterization and modeling of series and parallel connections of Li-ion cell packs. The Li-ion pack studied consists of four series modules connected in parallel. This pack has been characterized by means of charge, discharge and frequency tests. As a result of these tests, series and parallel influence on battery parameters have been determined. A model considering the effects of a BMS is established and compared with a model based on a single-cell approach. Experimental validations show that the single cell based approach gives poor results in comparison …
Multi-Time Scale Control Of Demand Flexibility In Smart Distribution Networks, Bishnu P. Bhattarai, Kurt S. Myers, Birgitte Bak-Jensen, Sumit Paudyal
Multi-Time Scale Control Of Demand Flexibility In Smart Distribution Networks, Bishnu P. Bhattarai, Kurt S. Myers, Birgitte Bak-Jensen, Sumit Paudyal
Michigan Tech Publications
This paper presents a multi-timescale control strategy to deploy electric vehicle (EV) demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator’s perspectives. A hierarchical control architecture (HCA) comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating multi-time scale controls that work from a day-ahead scheduling up to real-time adaptive control. The performance of the developed method is investigated with high EV penetration in a typical residential distribution grid. The …