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719 results on '"Swagatam Das"'

1. Two-Phase Evolutionary Convolutional Neural Network Architecture Search for Medical Image Classification

Author

Arjun Ghosh, Nanda Dulal Jana, Swagatam Das, and Rammohan Mallipeddi

Subjects
Convolutional neural network, differential evolution, genetic algorithm, medical imaging, neural architecture search, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Recently, convolutional neural networks (CNNs) have shown promising achievements in various computer vision tasks. However, designing a CNN model architecture necessitates a high-domain knowledge expert, which can be difficult for new researchers while solving real-world problems like medical image diagnosis. Neural architecture search (NAS) is an approach to reduce human intervention by automatically designing CNN architecture. This study proposes a two-phase evolutionary framework to design a suitable CNN model for medical image classification named TPEvo-CNN. The proposed framework mainly focuses on architectural depth search and hyper-parameter settings of the layered architecture for the CNN model. In the first phase, differential evolution (DE) is applied to determine the optimal number of layers for a CNN architecture, which enhances faster convergence to achieve CNN model architectures. In the second phase, the genetic algorithm (GA) is used to fine-tune the hyper-parameter settings of the generated CNN layer architecture in the first phase. Crossover and mutation operations of GA are devised to explore the hyper-parameter search space. Also, an elitism selection strategy is introduced to select the potential hyper-parameters of the CNN architecture for the next generation. The suggested approach is experimented on six medical image datasets, including pneumonia, skin cancer, and four COVID-19 datasets, which are categorized based on image types and class numbers. The experimental findings demonstrate the superiority of the proposed TPEvo-CNN model compared to existing hand-crafted, pre-trained, and NAS-based CNN models in terms of classification metrics, confusion matrix, radar plots, and statistical analysis.

Published
2023
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2. Digital Restoration of Cultural Heritage With Data-Driven Computing: A Survey

Author

Arkaprabha Basu, Sandip Paul, Sreeya Ghosh, Swagatam Das, Bhabatosh Chanda, Chakravarthy Bhagvati, and Vaclav Snasel

Subjects
Cultural heritage, 3D reconstruction, classification, generative adversarial network, building information modeling, inpainting, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Digitized methodologies in the recent era contribute to various fields of automation that used to hold different interests and meanings of human life. Buildings with historical significance, cultural values, and beliefs are becoming an interdisciplinary field of interest, engaging more computer scientists nowadays. Such structures need more attention towards reconstructing their values using a flavor of computerized tools instead of brickwork directly. Due to the wear of time, the tiles and engravings of most of the historical monuments are on the verge of ruin, endangering significant historical values. In this survey, we rebuild the values by delving deep into the device and methodologies by providing a comprehensive understanding of emerging fields and some experimental decisions. We discuss heritage restoration from some essential papers on 3D reconstruction, image inpainting, IoT-based methods, genetic algorithms, and image processing. The survey explains Machine Learning, Deep Learning, and Computer Vision-based methods for various restoration tasks in the related field. We divide this into certain parts contributing to different fields that restore cultural heritage. Moreover, we infer that the techniques will be faster, cheaper, and more beneficial to the context of image reconstruction in the near future.

Published
2023
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3. Power Flow Analysis of Islanded Microgrids: A Differential Evolution Approach

Author

Abhishek Kumar, Bablesh Kumar Jha, Swagatam Das, and Rammohan Mallipeddi

Subjects
Constrained optimization problem, differential evolution, islanded microgrid, power flow, distributed generation, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Power flow (PF) analysis of microgrids (MGs) has been gaining a lot of attention due to the evolution of islanded MGs. To calculate islanded MGs’ PF solution, a globally convergent technique is proposed using Differential Evolution (DE)- a popular optimization algorithm for global non-convex optimization. This paper formulates the PF problem as a constrained optimization problem (COP) considering all the operating conditions of the Droop Controlled Islanded MGs (DCIMGs). To solve the proposed COP, $\epsilon $ DE-NGM, (Epsilon based Differential Evolution with Newton-Gauss-based mutation) is proposed. The proposed algorithm, $\epsilon $ DE-NGM, is a novel variant of DE since it comprises a novel mutation operator, Newton-Gauss-based mutation (NGM). NGM includes all the important features of DE’s mutation strategies as well as reduces the constraint violation by utilizing the information of constraint-space. Numerical experiments validate that the global convergence ability of proposed algorithms in solving COPs than existing state-of-the-art algorithms. Furthermore, the proposed algorithm as a PF tool has better robustness than existing tools on ill- and well-conditioned systems with heavy loads, different limit violations, and inappropriate final solutions (far from the flat start). The performed comparative analysis confirms good agreement of accuracy and efficacy with the existing method for islanded MG’s PF.

Published
2021
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4. A Deep Learning Framework Integrating the Spectral and Spatial Features for Image-Assisted Medical Diagnostics

Author

Susmita Ghosh, Swagatam Das, and Rammohan Mallipeddi

Subjects
Medical imaging, diagnostic solution, COVID-19 detection, discrete cosine transform, discrete wavelet transform, deep learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The development of a computer-aided disease detection system to ease the long and arduous manual diagnostic process is an emerging research interest. Living through the recent outbreak of the COVID-19 virus, we propose a machine learning and computer vision algorithms-based automatic diagnostic solution for detecting the COVID-19 infection. Our proposed method applies to chest radiograph that uses readily available infrastructure. No studies in this direction have considered the spatial aspect of the medical images. This motivates us to investigate the role of spectral-domain information of medical images along with the spatial content towards improved disease detection ability. Successful integration of spatial and spectral features is demonstrated on the COVID-19 infection detection task. Our proposed method comprises three stages – Feature extraction, Dimensionality reduction via projection, and prediction. At first, images are transformed into spectral and spatio-spectral domains by using Discrete cosine transform (DCT) and Discrete Wavelet transform (DWT), two powerful image processing algorithms. Next, features from spatial, spectral, and spatio-spectral domains are projected into a lower dimension through the Convolutional Neural Network (CNN), and those three types of projected features are then fed to Multilayer Perceptron (MLP) for final prediction. The combination of the three types of features yielded superior performance than any of the features when used individually. This indicates the presence of complementary information in the spectral domain of the chest radiograph to characterize the considered medical condition. Moreover, saliency maps corresponding to classes representing different medical conditions demonstrate the reliability of the proposed method. The study is further extended to identify different medical conditions using diverse medical image datasets and shows the efficiency of leveraging the combined features. Altogether, the proposed method exhibits potential as a generalized and robust medical image-assisted diagnostic solution.

Published
2021
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5. D-Matrix: A Novel Ranking Procedure for Prioritizing Data Items

Author

Lingping Kong, Vaclav Snasel, and Swagatam Das

Subjects
Ranking, matrices, possibility theory, Markov processes, Google, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

In this article, we propose a ranking method based on a matrix, called D-matrix, with the special identical diagonal values. This ranking system has five properties: (1) it can provide both biased and bias-free ranking, and except for that, the working matrix can be built in two ways: results merging and results separating for both biased and bias-free matrices. (2) it can perform the webpage ranking with a sparse matrix to generate ratings for pages instead of constructing complicated, irreducible, and stochastic matrices as the Google PageRank matrix does, thereby accelerating the computation speed. (3) this D-matrix has a solution no matter how much data is selected. If there are no comparisons among items, then all the items end up with the same equal ratings. (4) the ranking system has the least effects on data variation. If one item changes, only those connecting to it get different ratings, those without connection retain the same ratings. (5) this D-matrix has a $\ddot {R}$ support matrix with a delicate diagonal value which may or may not appear crucial. These five features are illustrated with five different and comprehensive examples. Besides that, a 2017 game of the National Football League data is tested where the D-matrix generates a reasonable result. Furthermore, we introduce a new approximate non-dominated sorting method based on D-matrix and thereby put forth a new algorithm for solving the multi-objective optimization problems. Experimental results indicate that our algorithm can maintain a better spread of solutions on many standard test functions.

Published
2020
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6. Coronal Heating as Determined by the Solar Flare Frequency Distribution Obtained by Aggregating Case Studies

Author

James Paul Mason, Alexandra Werth, Colin G. West, Allison Youngblood, Donald L. Woodraska, Courtney L. Peck, Arvind J. Aradhya, Yijian Cai, David Chaparro, James W. Erikson, Koushik Ganesan, T. R. Geerdts, Thi D Hoang, Thomas M. Horning, Yan Jin, Haixin Liu, Noah Lordi, Zheng Luo, Thanmay S. Menon, Josephine C. Meyer, Emma E Nelson, Kristin A. Oliver, Jorge L Ramirez Ortiz, Andrew Osborne, Alyx Patterson, Nick Pellatz, John Pitten, Nanako Shitara, Daniel Steckhahn, Aseem Visal, Hongda Wang, Chaoran Wang, Evan Wickenden, John Wilson, Mengyu Wu, Nikolay Yegovtsev, Ingrid H Zimmermann, James Holland Aaron, Jumana T. Abdullah, Jonathan M. Abrams, Riley Abrashoff, Andres B. Acevedo, Iker Acha, Daniela M. Meza Acosta, Megan M. Adam, Dante Q. Adams, Kalvyn N Adams, Elena R Adams, Zainab A. Akbar, Ushmi H. Akruwala, Adel Al-Ghazwi, Batool H. Alabbas, Areej A. Alawadhi, Yazeed A. Alharbi, Mohammed S. Alahmed, Mohammed A. Albakr, Yusef J. Albalushi, Jonathan Albaum, Ahmed Aldhamen, Nolan Ales, Mohammad Alesmail, Abdulelah Alhabeeb, Dania Alhamli, Isehaq Alhuseini, Suhail Alkaabi, Tameem Alkhezzi, Mohamed Alkubaisi, Nasser Allanqawi, Martin Allsbrook, Yousef A. Almohsen, Justin Thomas Almquist, Teeb Alnaji, Yousef A Alnasrallah, Nicholas Alonzi, Meshal Alosaimi, Emeen Alqabani, Mohammad Alrubaie, Reema A. Alsinan, Ava L. Altenbern, Abdullah Altokhais, Saleh A. Alyami, Federico Ameijenda, Hamzi Amer, Meggan Amos, Hunter J. Anderson, Carter Andrew, Jesse C Andringa, Abigail Angwin, Gabreece Van Anne, Andrew Aramians, Camila Villamil Arango, Jack. W. Archibald, Brian A. Arias-Robles, Maryam Aryan, Kevin Ash, Justin Astalos, N. S. Atchley-Rivers, Dakota N. Augenstein, Bryce W. Austin, Abhinav Avula, Matthew C. Aycock, Abdulrahman A. Baflah, Sahana Balaji, Brian Balajonda, Leo M Balcer, James O. Baldwin, David J Banda, Titus Bard, Abby Barmore, Grant M. Barnes, Logan D. W. Barnhart, Kevin M. Barone, Jessica L. Bartman, Claire Bassel, Catalina S Bastias, Batchimeg Bat-Ulzii, Jasleen Batra, Lexi Battist, Joshua Bay, Simone Beach, Sara Beard, Quinn I Beato, Ryan Beattie, Thomas Beatty, Tristan De La Beaujardiere, Jacob N. Beauprez, M. G. Beck, Lily Beck, Simone E. Becker, Braden Behr, Timothy A. Behrer, Joshua Beijer, Brennan J. Belei, Annelene L. Belknap, Aislyn Bell, Caden Bence, Evan Benke, Naomi Berhanu, Zachary D. Berriman-Rozen, Chrisanna Bertuccio, Owen A. Berv, Blaine B. Biediger, Samuel J Biehle, Brennen Billig, Jacob Billingsley, Jayce A. Billman, Connor J. Biron, Gabrielle E. Bisacca, Cassidy A. Blake, Guillermo Blandon, Olivia Blevins, Ethan Blouin, Michal Bodzianowski, Taylor A. Boeyink, Matthew Bondar, Lauren Bone, Alberto Espinosa De Los Monteros Bonilla, William T Borelli, Luke R. Borgerding, Troy Bowen, Christine Boyer, Aidan Boyer, Aidan P. Boyle, Tom Boyne, Donovan Branch, Ariana E. Brecl, David J. Brennan, Alexander J Brimhall, Jennifer L. Brockman, Sarah Brookins, Gabriel T. Brown, Cameron L. Brown, Ryan Brown, Jordi Brownlow, Grant Brumage-Heller, Preston J. Brumley, Samuel Bryan, A. Brzostowicz, Maryam Buhamad, Gigi Bullard-Connor, J. R. Ramirez Bunsow, Annemarie C. Burns, John J. Burritt, Nicholas David Burton, Taylor Burton, Celeste Busch, Dylan R. Butler, B. W. Buxton, Malena C. Toups, Carter C. Cabbage, Breonna Cage, Jackson R. Cahn, Andrew J Campbell, Braden P. Canales, Alejandro R. Cancio, Luke Carey, Emma L. Carillion, Michael Andrew Carpender, Emily Carpenter, Shivank Chadda, Paige Chambers, Jasey Chanders, Olivia M. Chandler, Ethan C. Chang, Mitchell G. Chapman, Logan T. Chapman, S. Chavali, Luis Chavez, Kevin Chen, Lily Chen, Sam Chen, Judy Chen, Jenisha Chhetri, Bradyn Chiles, Kayla M. Chizmar, Katherine E Christiansen, Nicholas A. Cisne, Alexis Cisneros, David B. Clark, Evelyn Clarke, Peter C Clarkson, Alexis R. Clausi, Brooke Cochran, Ryan W. Coe, Aislinn Coleman-Plante, Jake R. Colleran, Zachary Colleran, Curran Collier, Nathaniel A. Collins, Sarah Collins, Jack C. Collins, Michael Colozzi, Aurora Colter, Rebecca A. Cone, Thomas C. Conroy, Reese Conti, Charles J. Contizano, Destiny J. Cool, Nicholas M. Cooper, Jessica S Corbitt, Jonas Courtney, Olivia Courtney, Corben L. Cox, Wilmsen B. Craig, Joshua B. Creany, Anastasia Crews, K. A. Crocker, A. J. Croteau, Christian J. Crow, Zoe Cruse, Avril Cruz, Tyler L. Curnow, Hayden Current, Riley T. Curry, Libby Cutler, Aidan St. Cyr, Frederick M. Dabberdt, Johnston Daboub, Olivia Damgaard, Swagatam Das, Emma A. B. Davis, Elyse Debarros, Sean Deel, Megan E. Delasantos, Tianyue Deng, Zachary Derwin, Om Desai, Kai Dewey, John S. Dias, Kenzie A. Dice, R. Dick, Cyrus A. Dicken, Henry Dietrick, Alexis M. Dinser, Alyssa M. Dixon, Thomas J. Dixon, Helen C. Do, Chris H Doan, Connor Doane, Joshua Dodrill, Timothy Doermer, Lizbeth Montoya Dominguez, J. Dominguez, Emerson N. Domke, Caroline R. Doran, Jackson A. Dorr, Philip Dorricott, Danielle C. Dresdner, Michael Driscoll, Kailer H. Driscoll, Sheridan J. Duncan, Christian Dunlap, Gabrielle M. Dunn, Tien Q. Duong, Tomi Oshima Dupeyron, Peter Dvorak, Andrew East, Andrew N. East, Bree Edwards, Lauren Ehrlich, Sara I. Elbashir, Rasce Engelhardt, Jacob Engelstad, Colin England, Andrew Enrich, Abbey Erickson, Benjamin Erickson, Nathan Evans, Calvin A Ewing, Elizabeth A. Eyeson, Ian Faber, Avery M. Fails, John T Fauntleroy, Kevin Fell, Zitian Feng, Logan D. Fenwick, Nikita Feoktistov, Ryann Fife, John Alfred D. Figueirinhas, Jean-Paul Fisch, Emmalee Fischer, Jules Fischer-White, Aidan F. Fitton, Alexander Fix, Lydia Flackett, Fernando Flores, Aidan Floyd, Leonardo Del Foco, Adeduni Folarin, Aidan E. Forbes, Elise Fortino, Benjamin L. Fougere, Alexandra A. Fowler, Margaret Fox, James M. French, Katherine V. French, Florian G. Frick, Calvin R. Fuchs, Bethany E. S. Fuhrman, Sebastian Furney, Moutamen Gabir, Gabriela Galarraga, Skylar Gale, Keala C. Gapin, A. J. Garscadden, Rachel Gasser, Lily Gayou, Emily E. Gearhart, Jane Geisman, Julianne R. Geneser, Sl Genne, Julia G Gentile, Eleanor Gentry, Jacob D. George, Nathaniel James Georgiades, Phillip Gerhardstein, Clint Gersabeck, Bandar Abu Ghaith, Dorsa Ghiassi, B. C. Giebner, Dalton Gilmartin, Connor B. Gilpatrick, Michael Gjini, Olivia Golden, Nathan T. Golding, C. A. Goldsberry, Angel R. Gomez, Angel A. Gomez, Sean Gopalakrishnan, Mariam Gopalani, Nicholas Gotlib, Alaina S. Graham, Michael J Gray, Alannah H. Gregory, Joshua A. Gregory, Kristyn Grell, Justus Griego, Nicholas F. Griffin, Kyle J. Griffin, Matt Guerrero, Nicole Gunderson, Mutian Guo, E. R. Gustavsson, Grace K. Hach, L. N. Haile, Jessica Haines, Jack J. Mc Hale, Ryder Buchanan Hales, Mark S. Haley, Jacqueline L. Hall, Sean R. Hamilton, Soonhee Han, Tyler Hand, Luke C. Hanley, Connor M Hansen, Joshua A. Hansen, Jonathan Hansson, Tony Yunfei Hao, Nicholas Haratsaris, Isabelle Hardie, Dillon F. Hardwick, Cameron T. Hares, Logan Swous Harris, Coyle M. Harris, Omer Hart, Kyle Hashiro, Elsie Hattendorf, Calder Haubrich, Elijah Hawat, Griffin A. Hayrynen, Danielle A. Heintz, Tim Hellweg, Angel Hernandez, Emanuel Herrera, Robert N. Herrington, Tim Herwig, Troy M. Hesse, Quinn Hiatt, Lea Pearl Hibbard, Imari R. Hicks, Andrew J. Hicks, Nigel Highhouse, Annalise K. Hildebrand, Paula Hill, Hallie Hill, Evan Hintsa, Anna E. Hirschmann, Travis Hitt, Ella Ho, Isabelle J. Hoff, Alex Hoffman, Blake A. Hogen, Linda Horne, Timothy J Houck, Noah H. Howell, E. M. Hrudka, J. Hu, Jianyang Huang, Chenqi Huang, Shancheng Huang, Zachary A. Hudson, Nathan C. Hudson, Tyler J. Huebsch, Owen Hull, Samuel C Hunter, Troy Husted, Abigail P. Hutabarat, Leslie Huynh, Antonio E. Samour Ii, Yolande Idoine, Julia A. Ingram, Taro Iovan, Samuel A. Isert, Antonio Salcido-Alcontar Jr, Thomas Jacobsen, Alan A Jaimes, Connor Jameson, J. R. Jarriel, Sam Jarvis, Josh Jenkins, Alexander V. Jensen, Jacob Jeong, Luke A. Jeseritz, Trevor Jesse, Soo Yeun Ji, Yufan Jiang, Owen Johnson, Matthew Johnson, Sawyer Johnson, Julia Johnston, Braedon Y. Johnston, Olivia M. Jones, M. R. Jones, Tara Jourabchi, Tony A. House Jr., Parker Juels, Sabrina J. H. T. Kainz, Emily Kaiser, Nicolas Ian Kallemeyn, Madison H. Kalmus, Etash Kalra, Margaret Kamenetskiy, Jeerakit Kanokthippayakun, Shaun D. Kapla, Brennan J. Karsh, Caden J. Keating, Morgan A. Kelley, Michael P. Kelley, Nicholas Kelly, James Kelly, Teagan Kelly, Christopher M Kelly, Kellen Kennedy, Cayla J. Kennedy, Forrest Kennedy, Abigail Kennedy, Liana Kerr-Layton, Marilyn Ketterer, Ibraheem A. Khan, Usman Khan, Sapriya Khanal, Jack L. Kiechlin, Dominic Killian, Kevin Kim, Brian T. Kim, Matthew M. Kim, Jake Kim, Aspen Kimlicko, Isabel M Kipp, Hunter B. Kirkpatrick, Natalie Kissner, Emily R. Kite, Olivia R. Kleinhaus, Philip Whiting Knott, Will Koch, Greta Koenig, Emily Koke, Thomas Kokes, Yash S. Kothamdi, Zack Krajnak, Zoe M. Kresek, Dylan Kriegman, Jake E. Kritzberg, Davis J. Krueger, Bartlomiej Kubiak, Kirsten Kuehl, Chrisanne Kuester, Nicolas A. Kuiper, Aman Priyadarshi Kumar, Connor Kuybus, Daniel Kwiatkowski, Quintin Y. Lafemina, Kevin Lacjak, Kyle Lahmers, Antonia Lam, Kalin Landrey, Maxwell B. Lantz, Zachary Larter, Benjamin P. Lau, Megan Lauzon, Rian Lawlor, Tyler Learned, E. C. Lee, Junwon Lee, Adrianna J. Lee, Justin Lee, Alexis Ying-Shan Lee, Christian J Lee, Nathaniel F. Lee, Linzhi Leiker, Dylan Lengerich, Cecilia Leoni, Adrienne R. Lezak, David Y. Li, Isaac Li, Ryan Z. Liao, Bridget Linders, Morgan I Linger, Katherine B. Linnane, Sam Lippincott, Barrett Lister, Shelby D Litton, Nianzi Liu, Steven Y. Liu, Timothy W. Logan, Nathan Londres, Mia C. Lonergan, Emily Lookhoff, N. E. Loomis, Christian Lopez, Justin Loring, Jeffrey Lucca, Dax Lukianow, Nathan M. Cheang, William Macdonald, Claire A. Madonna, Kasey O. Madsen, Tiffany E. Maksimuk, Macguire Mallory, Ryan A. Malone, Blake Maly, Xander R. Manzanares, Aimee S. Maravi, Serafima M. Marcus, Nasreen Marikar, Josie A. Marquez, Mathew J. Marquez, Lauren Marsh, Toni Marsh, Logan S. Martin, Alexa M. Martinez, Jose R. Martinez, Hazelia K. Martinez, Cara Martyr, Mirna Masri, Giorgio Matessi, Adam Izz Khan Mohd Reduan Mathavan, Randi M. Mathieson, Kabir P. Mathur, Graham Mauer, Victoria A. Mayer, Liam Mazzotta, Glen S. Mccammon, Rowan Mcconvey, Tyler Mccormick, Andrew Mccoy, Kelleen Mcentee, Meaghan V. Mcgarvey, Riley M. Mcgill, James K. Mcintyre, Finbar K. Mckemey, Zane Mcmorris, Jesse J. Mcmullan, Ella Mcquaid, Caden Mcvey, Kyle Mccurry, Mateo M. Medellin, Melissa Medialdea, Amar Mehidic, Stella Meillon, Jonah B. Meiselman-Ashen, Sarah Mellett, Dominic Menassa, Citlali Mendez, Patricia Mendoza-Anselmi, Riley Menke, Sarah Mesgina, William J. Mewhirter, Ethan Meyer, Aya M. Miften, Ethan J. Miles, Andrew Miller, Joshua B. Miller, Emily B. Millican, Sarah J. Millican, Dylan P. Mills, Josh Minimo, Jay H. Misener, Alexander J. Mitchell, Alexander Z. Mizzi, Luis Molina-Saenz, Tyler S Moll, Hayden Moll, Maximus Montano, Michael Montoya, Eli Monyek, Jacqueline Rodriguez Mora, Gavin Morales, Genaro Morales, Annalise M. Morelock, Cora Morency, Angel J. Moreno, Remy Morgan, Alexander P. Moss, Brandon A. Muckenthaler, Alexander Mueller, Owen T. Mulcahy, Aria T. Mundy, Alexis A. Muniz, Maxwell J. Murphy, Madalyn C. Murphy, Ryan C. Murphy, Tyler Murrel, Andrew J. Musgrave, Michael S. Myer, Kshmya Nandu, Elena R. Napoletano, Abdulaziz Naqi, Anoothi Narayan, Liebe Nasser, Brenna K Neeland, Molly Nehring, Maya Li Nelson, Lena P. Nguyen, Lena Nguyen, Leonardo Nguyen, Valerie A. Nguyen, Khoa D Nguyen, Kelso Norden, Cooper Norris, Dario Nuñez, Rosemary O. Nussbaum, Cian O’Sullivan, Ian O’Neill, S. H. Oakes, Anand Odbayar, Caleb Ogle, Sean Oishi-Holder, Nicholas Olguin, Nathaniel P. Olson, Jason Ong, Elena N. Opp, Dan Orbidan, Ryan Oros, Althea E. Ort, Matthew Osborn, Austin Osogwin, Grant Otto, Jessica Oudakker, Igor Overchuk, Hannah M. Padgette, Jacqueline Padilla, Mallory Palizzi, Madeleine L. Palmgren, Adler Palos, Luke J. Pan, Nathan L. Parker, Sasha R. Parker, Evan J. Parkinson, Anish Parulekar, Paige J. Pastor, Kajal Patel, Akhil Patel, Neil S. Patel, Samuel Patti, Catherine Patton, Genevieve K. Payne, Matthew P. Payne, Harrison M. Pearl, Charles B. Beck Von Peccoz, Alexander J. Pedersen, Lily M. Pelster, Munisettha E. Peou, J. S. Perez, Freddy Perez, Anneliese Pesce, Audrey J. Petersen, B. Peterson, Romeo S. L. Petric, Joshua Pettine, Ethan J. Phalen, Alexander V. Pham, Denise M. Phan, Callie C Pherigo, Lance Phillips, Justin Phillips, Krista Phommatha, Alex Pietras, Tawanchai P. Pine, Sedique Pitsuean-Meier, Andrew M. Pixley, Will Plantz, William C. Plummer, Kaitlyn E. Plutt, Audrey E. Plzak, Kyle Pohle, Hyden Polikoff, Matthew Pollard, Madelyn Polly, Trevor J. Porter, David Price, Nicholas K. Price, Gale H. Prinster, Henry Austin Propper, Josh Quarderer, Megan S. Quinn, Oliver Quinonez, Devon Quispe, Cameron Ragsdale, Anna L. Rahn, M. Rakhmonova, Anoush K Ralapanawe, Nidhi Ramachandra, Nathaniel Ramirez, Ariana C. Ramirez, Sacha Ramirez, Parker Randolph, Anurag Ranjan, Frederick C Rankin, Sarah Grace Rapaport, Nicholas O Ratajczyk, Mia G. V. Ray, Brian D. Reagan, John C. Recchia, Brooklyn J. Reddy, Joseph Reed, Charlie Reed, Justin Reeves, Eileen N. Reh, Ferin J. Von Reich, Andrea B. Reyna, Alexander Reynolds, Hope Reynolds, Matthew Rippel, Guillermo A. Rivas, Anna Linnea Rives, Amanda M. Robert, Samuel M. Robertson, Maeve Rodgers, Stewart Rojec, Andres C. Romero, Ryan Rosasco, Beth Rossman, Michael Rotter, Tyndall Rounsefell, Charlotte Rouse, Allie C. Routledge, Marc G. Roy, Zoe A. Roy, Ryan Ruger, Kendall Ruggles-Delgado, Ian C. Rule, Madigan Rumley, Brenton M. Runyon, Collin Ruprecht, Bowman Russell, Sloan Russell, Diana Ryder, David Saeb, J. Salazar, Violeta Salazar, Maxwell Saldi, Jose A. Salgado, Adam D. Salindeho, Ethan S. Sanchez, Gustavo Sanchez-Sanchez, Darian Sarfaraz, Sucheta Sarkar, Ginn A. Sato, Carl Savage, Marcus T. Schaller, Benjamin T. Scheck, Jared A. W. Schlenker, Matthew J Schofer, Stephanie H. Schubert, Courtney Schultze, Grace K Schumacher, Kasper Seglem, Lauren Serio, Octave Seux, Hannan Shahba, Callie D. Shannahan, Shajesh Sharma, Nathan Shaver, Timothy Shaw, Arlee K. Shelby, Emma Shelby, Grace Shelchuk, Tucker Sheldrake, Daniel P. Sherry, Kyle Z. Shi, Amanda M. Shields, Kyungeun Shin, Michael C. Shockley, Dominick Shoha, Jadon Shortman, Mitchell Shuttleworth, Lisa Sibrell, Molly G. Sickler, Nathan Siles, H. K. Silvester, Conor Simmons, Dylan M. Simone, Anna Simone, Savi Singh, Maya A. Singh, Madeline Sinkovic, Leo Sipowicz, Chris Sjoroos, Ryan Slocum, Colin Slyne, Korben Smart, Alexandra N. Smith, Kelly Smith, Corey Smith, Elena K. Smith, Samantha M. Smith, Percy Smith, Trevor J Smith, G. L. Snyder, Daniel A. Soby, Arman S. Sohail, William J. Solorio, Lincoln Solt, Caitlin Soon, Ava A Spangler, Benjamin C. Spicer, Ashish Srivastava, Emily Stamos, Peter Starbuck, Ethan K. Stark, Travis Starling, Caitlyn Staudenmier, Sheen L. Steinbarth, Christopher H. Steinsberger, Tyler Stepaniak, Ellie N. Steward, Trey Stewart, T. C. Stewart, Cooper N. Stratmeyer, Grant L. Stratton, Jordin L. Stribling, S. A Sulaiman, Brandon J Sullivan, M. E. Sundell, Sohan N. Sur, Rohan Suri, Jason R. Swartz, Joshua D. Sweeney, Konner Syed, Emi Szabo, Philip Szeremeta, Michael-Tan D. Ta, Nolan C. Tanguma, Kyle Taulman, Nicole Taylor, Eleanor Taylor, Liam C. Taylor, K. E. Tayman, Yesica Tellez, Richard Terrile, Corey D Tesdahl, Quinn N. Thielmann, Gerig Thoman, Daniel Thomas, Jeffrey J. Thomas, William N. Thompson, Noah R. Thornally, Darien P. Tobin, Kelly Ton, Nathaniel J. Toon, Kevin Tran, Bryn Tran, Maedee Trank-Greene, Emily D. Trautwein, Robert B. Traxler, Judah Tressler, Tyson R. Trofino, Thomas Troisi, Benjamin L. Trunko, Joshua K. Truong, Julia Tucker, Thomas D Umbricht, C. H. Uphoff, Zachary T. Upthegrove, Shreenija Vadayar, Whitney Valencia, Mia M. Vallery, Eleanor Vanetten, John D. Vann, Ilian Varela, Alexandr Vassilyev, Nicholas J. Vaver, Anjali A. Velamala, Evan Vendetti, Nancy Ortiz Venegas, Aditya V. Vepa, Marcus T. Vess, Jenna S. Veta, Andrew Victory, Jessica Vinson, Connor Maklain Vogel, Michaela Wagoner, Steven P. Wallace, Logan Wallace, Caroline Waller, Jiawei Wang, Keenan Warble, N. R. D. Ward-Chene, James Adam Watson, Robert J. Weber, Aidan B. Wegner, Anthony A Weigand, Amanda M. Weiner, Ayana West, Ethan Benjamin Wexler, Nicola H. Wheeler, Jamison R. White, Zachary White, Oliver S. White, Lloyd C. Whittall, Isaac Wilcove, Blake C. Wilkinson, John S. Willard, Abigail K. Williams, Sajan Williams, Orion K. Wilson, Evan M. Wilson, Timothy R. Wilson, Connor B. Wilson, Briahn Witkoff, Aubrey M. Wolfe, Jackson R. Wolle, Travis M. Wood, Aiden L. Woodard, Katelynn Wootten, Catherine Xiao, Jianing Yang, Zhanchao Yang, Trenton J. Young, Isabel Young, Thomas Zenner, Jiaqi Zhang, Tianwei Zhao, Tiannie Zhao, Noah Y. Zhao, Chongrui Zhou, Josh J Ziebold, Lucas J. Ziegler, James C. Zygmunt, Jinhua Zhang, and H. J. Lewandowski

Subjects
Solar physics, Solar flares, Astrostatistics distributions, Astrophysics, QB460-466
Abstract

Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counterintuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfvén waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, α = 2 as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed >600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: preflare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that α = 1.63 ± 0.03. This is below the critical threshold, suggesting that Alfvén waves are an important driver of coronal heating.

Published
2023
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7. Benchmarking Optimization-Based Energy Disaggregation Algorithms

Author

Oladayo S. Ajani, Abhishek Kumar, Rammohan Mallipeddi, Swagatam Das, and Ponnuthurai Nagaratnam Suganthan

Subjects
non-intrusive load monitoring, optimization-based energy disaggregation, benchmarking, evaluation metrics, Technology
Abstract

Energy disaggregation (ED), with minimal infrastructure, can create energy awareness and thus promote energy efficiency by providing appliance-level consumption information. However, ED is highly ill-posed and gets complicated with increase in number and type of devices, similarity between devices, measurement errors, etc. To design, test, and benchmark ED algorithms, the availability of open-access energy consumption datasets is crucial. Most datasets in the literature suit data-intensive pattern-based ED algorithms. Recently, optimization-based ED algorithms that only require information regarding the operational states of the devices are being developed. However, the lack of standard datasets and appropriate evaluation metrics is hindering the development of reproducible state-of-the-art optimization-based ED algorithms. Therefore, in this paper, we propose a dataset with multiple instances that are representative of the different challenges posed by ED in practice. Performance indicators to empirically evaluate different optimization-based ED algorithms are summarized. In addition, baseline simulation results of the state-of-the-art optimization-based ED algorithms are presented. The developed dataset, summarization of different metrics, and baseline results are expected to provide a platform for researchers to develop novel optimization-based frameworks, in general, and evolutionary computation-based frameworks in particular to solve ED.

Published
2022
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8. A Modified Differential Evolution With Distance-based Selection for Continuous Optimization in Presence of Noise

Author

Arka Ghosh, Swagatam Das, Rammohan Mallipeddi, Asit Kumar Das, and Subhransu Sekhar Dash

Subjects
Differential evolution, noisy optimization, selection, gaussian noise, poisson noise, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The performance of evolutionary algorithms (EAs), suitable for optimization on static functional landscapes, usually degrade in presence of noises with different statistical features. In this paper, we present a simple variant of the differential evolution (DE) algorithm, one of the most competitive EAs of recent interest, to tackle complex optimization problems in the presence of additive noise. The proposed DE variant is equipped with three new algorithmic components. A new population central tendency-based mutation scheme is proposed and it is switched in a probabilistic manner with the difference mean-based perturbation strategy in the mutation step. Instead of the regular binomial or exponential crossover of DE, we adopt a blending crossover during the recombination stage. Finally, a novel distance-based selection mechanism is incorporated to enable the occasional inclusion of a few inferior solutions to future generations, thus making the usual DE selection less greedy. Five different additive noise models namely Gaussian, Poisson, Rayleigh, Exponential, and Random are considered with a variety of noise amplitudes to simulate the noisy behavior of the objective functions. In total, 79 benchmark functions from traditional, as well as modern (IEEE CEC 2013 and 2017) test-suites, are used to extensively compare and contrast the proposed method with the other state-of-art evolutionary optimization algorithms, tailor-made for noisy function optimization. Experimental results, supported with the non-parametric statistical tests, indicate that our proposed method is very competitive against the noise-resilient variants of classical as well as very recent evolutionary optimizers, including the winners of the recent IEEE CEC competitions of real parameter optimization on the complex fitness landscapes.

Published
2017
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9. Self-Organizing Migrating Algorithm Pareto

Author

Quoc Bao Diep, Ivan Zelinka, and Swagatam Das

Subjects
self-organizing migrating algorithm, SOMA, single objective optimization, swarm intelligence, Electronic computers. Computer science, QA75.5-76.95
Abstract

In this paper, we propose a new method named Pareto-based self-organizing migrating algorithm (SOMA Pareto), in which the algorithm is divided into the Organization, Migration, and Update processes. The important key in the Organization process is the application of the Pareto Principle to select the Migrant and the Leader, increasing the performance of the algorithm. The adaptive PRT, Step, and PRTVector parameters are applied to enhance the ability to search for promising subspaces and then to focus on exploiting that subspaces. Based on the testing results on the well-known benchmark suites such as CEC'13, CEC'15, and CEC'17, the superior performance of the proposed algorithm compared to the SOMA family and the state-of-the-art algorithms such as variant DE and PSO are confirmed. These results demonstrate that SOMA Pareto is an effective, promising algorithm.

Published
2019
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10. Use of a multi-objective teaching-learning algorithm for reduction of power losses in a power test system

Author

Miguel A. Medina, Juan M. Ramirez, Carlos A. Coello, and Swagatam Das

Subjects
Technology, Mining engineering. Metallurgy, TN1-997
Abstract

Este artículo presenta un algoritmo de enseñanza-aprendizaje multi-objetivo basado en descomposición para resolver el problema del despacho óptimo de potencia reactiva (ORPD). La efectividad y el desempeño del algoritmo propuesto son comparados con respecto a un algoritmo evolutivo multi-objetivo basado en descomposición (MOEA/D) y con el NSGA-II. Un modelo de sistema de potencia de referencia se utiliza para probar el desempeño de los algoritmos. Los resultados de la reducción de las pérdidas de energía así como las métricas de desempeño indican que el algoritmo propuesto es una opción fiable para resolver el problema.

Published
2014

14. A Lyapunov-Based Extension to Particle Swarm Dynamics for Continuous Function Optimization

Author

Sang Yong Han, Swagatam Das, Sayantani Bhattacharya, and Amit Konar

Subjects
particle swarm dynamics, metaheuristics, continuous function optimization, stability, convergence, lyapunov stability theorem, Chemical technology, TP1-1185
Abstract

The paper proposes three alternative extensions to the classical global-best particle swarm optimization dynamics, and compares their relative performance with the standard particle swarm algorithm. The first extension, which readily follows from the well-known Lyapunov’s stability theorem, provides a mathematical basis of the particle dynamics with a guaranteed convergence at an optimum. The inclusion of local and global attractors to this dynamics leads to faster convergence speed and better accuracy than the classical one. The second extension augments the velocity adaptation equation by a negative randomly weighted positional term of individual particle, while the third extension considers the negative positional term in place of the inertial term. Computer simulations further reveal that the last two extensions outperform both the classical and the first extension in terms of convergence speed and accuracy.

Published
2009
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15. Multi-Objective Differential Evolution for Automatic Clustering with Application to Micro-Array Data Analysis

Author

Sang Yong Han, Swagatam Das, Ajith Abraham, Kaushik Suresh, Debarati Kundu, and Sayan Ghosh

Subjects
differential evolution, multi-objective optimization, fuzzy clustering, micro-array data clustering, Chemical technology, TP1-1185
Abstract

This paper applies the Differential Evolution (DE) algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK). Experimental results using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes.

Published
2009
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