{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T19:13:08Z","timestamp":1779217988709,"version":"3.51.4"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CMMI-2000156"],"award-info":[{"award-number":["CMMI-2000156"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Instrum. Meas."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tim.2023.3236321","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T21:16:26Z","timestamp":1673558186000},"page":"1-9","source":"Crossref","is-referenced-by-count":15,"title":["PDP-CNN: A Deep Learning Model for Post-Hurricane Reconnaissance of Electricity Infrastructure on Resource-Constrained Embedded Systems at the Edge"],"prefix":"10.1109","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7268-1405","authenticated-orcid":false,"given":"Ashkan B.","family":"Jeddi","sequence":"first","affiliation":[{"name":"Department of Civil, Environmental and Geodetic Engineering, Ohio State University, Columbus, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6768-8522","authenticated-orcid":false,"given":"Abdollah","family":"Shafieezadeh","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Geodetic Engineering, Ohio State University, Columbus, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4569-9233","authenticated-orcid":false,"given":"Roshanak","family":"Nateghi","sequence":"additional","affiliation":[{"name":"Division of Environmental Engineering, School of Industrial Engineering, Purdue University, West Lafayette, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"Economic Benefits of Increasing Electric Grid Resilience to Weather Outages","year":"2013"},{"key":"ref2","volume-title":"Power OFF: Extreme Weather and Power Outages Bar Climate Central","year":"2020"},{"key":"ref3","first-page":"22","volume-title":"Florida Power & Light Company Grid Hardening and Hurricane Response","author":"Gwaltney","year":"2018"},{"issue":"GAO-21-423T","key":"ref4","volume-title":"Electricity and Grid Resilience: Climate Change is Expected to Have Far-Reaching Effects and DOE and FERC Should Take Actions","year":"2021"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JPETS.2019.2900293"},{"key":"ref6","author":"Hoffman","year":"2013","journal-title":"Comparing the Impacts of Northeast Hurricanes on Energy Infrastructure"},{"key":"ref7","volume-title":"Biden Administration Launches $2.3 Billion Program to Strengthen and Modernize America\u2019s Power Grid","year":"2022"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2022.3170533"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.116355"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA52953.2021.00261"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3162615"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JPETS.2018.2881429"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2020.2970156"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CO.1943-7862.0002153"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3120796"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3106112"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s13753-020-00254-1"},{"key":"ref18","first-page":"1","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"28","author":"Ren"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2876865"},{"key":"ref20","article-title":"Ultralytics\/yolov5: V6.1 TensorRT, TensorFlow edge TPU and OpenVINO Export and inference","author":"Jocher","year":"2022"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.l007\/978-3-319-46448-0_2"},{"key":"ref22","article-title":"YOLOv3: An incremental improvement","author":"Redmon","year":"2018","journal-title":"arXiv:1804.02767"},{"key":"ref23","article-title":"YOLOX: Exceeding YOLO series in 2021","author":"Ge","year":"2021","journal-title":"arXiv:2107.08430"},{"key":"ref24","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref28","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017","journal-title":"arXiv:1704.04861"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref30","article-title":"Multi-scale dense networks for resource efficient image classification","author":"Huang","year":"2017","journal-title":"arXiv:1703.09844"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/WF-IoT48130.2020.9221150"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2915404"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref34","volume-title":"Home |StEER Website"},{"key":"ref35","volume-title":"StEER Hurricane Dorian: Field Assessment Structural Team (FAST-1) Early Access Reconnaissance Report (EARR)","author":"Salman","year":"2019"},{"key":"ref36","volume-title":"StEER Hurricane Michael: Field Assessment Team 1 (FAT-1) Early Access Reconnaissance Report (EARR)","author":"Roueche","year":"2018"},{"key":"ref37","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tan"}],"container-title":["IEEE Transactions on Instrumentation and Measurement"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/19\/10012124\/10015862-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/19\/10012124\/10015862.pdf?arnumber=10015862","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T07:14:13Z","timestamp":1707808453000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10015862\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/tim.2023.3236321","relation":{},"ISSN":["0018-9456","1557-9662"],"issn-type":[{"value":"0018-9456","type":"print"},{"value":"1557-9662","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}