{"data":{"id":"10.21227/7c6h-5m58","type":"dois","attributes":{"doi":"10.21227/7c6h-5m58","prefix":"10.21227","suffix":"7c6h-5m58","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Het  Bhalja","affiliation":[],"nameIdentifiers":[]},{"name":"Bhaveshkumar  Bhalja","affiliation":[],"nameIdentifiers":[]},{"name":"Pramod  Agarwal","affiliation":[],"nameIdentifiers":[]},{"name":"Om P. Malik","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"\"Walsh Matrix Analysis and Feed Forward Neural NEtowrk based Fault Detection Technqiue\""}],"publisher":"IEEE DataPort","container":{},"publicationYear":2025,"subjects":[],"contributors":[],"dates":[],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"\"Raw electrical current fault data, which can be useful to develop fault detection techniques based on artificial intelligence, machine learning and deep learning. The data consist of simulation and experimental studies considered. Different operating scenarios are simulated based on the different normal, abnormal and faulty operations. The raw data can be useful for the training of the artificial intelligence, machine learning and deep learning based techniques. Further, as part of data-driven approaches, it can be useful for the model-based and waveshape-based approaches as well.\"","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://ieee-dataport.org/documents/walsh-matrix-analysis-and-feed-forward-neural-netowrk-based-fault-detection-technqiue","contentUrl":null,"metadataVersion":0,"schemaVersion":null,"source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":0,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2025-06-25T20:39:36.000Z","registered":"2025-06-25T20:39:37.000Z","published":"2025","updated":"2025-06-25T20:39:37.000Z"},"relationships":{"client":{"data":{"id":"ieee.dataport","type":"clients"}},"provider":{"data":{"id":"ieee","type":"providers"}},"media":{"data":{"id":"10.21227/7c6h-5m58","type":"media"}},"references":{"data":[]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}