AUGMENTED EXCEPTION PROGNOSIS AND MANAGEMENT IN REAL TIME SAFETY CRITICAL EMBEDDED APPLICATIONS
A smart exception handler system for safety-critical real-time systems is provided. The system is configured to: receive a plurality of parameters at a plurality of nodal points in a real-time execution path; analyze the received parameters using a trained exception handling model, wherein the train...
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creator | Das, Prasun K, Sreenivasan Govindillam |
description | A smart exception handler system for safety-critical real-time systems is provided. The system is configured to: receive a plurality of parameters at a plurality of nodal points in a real-time execution path; analyze the received parameters using a trained exception handling model, wherein the trained exception handling model has been trained using machine learning techniques to learn the critical path of execution and/or critical range of parameters at critical nodes, wherein the critical range of parameters comprises a learned threshold at a node; compute, using the trained exception handling model, a probability of fault at the critical nodes; compare the probability of fault at a critical node against a learned threshold at the node; and take proactive action in real-time to avoid the occurrence of a fault when the probability of fault at the node is higher than the learned threshold at the node. |
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The system is configured to: receive a plurality of parameters at a plurality of nodal points in a real-time execution path; analyze the received parameters using a trained exception handling model, wherein the trained exception handling model has been trained using machine learning techniques to learn the critical path of execution and/or critical range of parameters at critical nodes, wherein the critical range of parameters comprises a learned threshold at a node; compute, using the trained exception handling model, a probability of fault at the critical nodes; compare the probability of fault at a critical node against a learned threshold at the node; and take proactive action in real-time to avoid the occurrence of a fault when the probability of fault at the node is higher than the learned threshold at the node.</description><language>eng</language><subject>AIRCRAFT ; ARRANGEMENTS OR MOUNTING OF POWER PLANTS OR PROPULSIONTRANSMISSIONS IN AIRCRAFT ; AVIATION ; CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COSMONAUTICS ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; EQUIPMENT FOR FITTING IN OR TO AIRCRAFT ; FLYING SUITS ; PARACHUTES ; PERFORMING OPERATIONS ; PHYSICS ; TRANSPORTING</subject><creationdate>2020</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20201001&DB=EPODOC&CC=US&NR=2020310907A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20201001&DB=EPODOC&CC=US&NR=2020310907A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Das, Prasun</creatorcontrib><creatorcontrib>K, Sreenivasan Govindillam</creatorcontrib><title>AUGMENTED EXCEPTION PROGNOSIS AND MANAGEMENT IN REAL TIME SAFETY CRITICAL EMBEDDED APPLICATIONS</title><description>A smart exception handler system for safety-critical real-time systems is provided. The system is configured to: receive a plurality of parameters at a plurality of nodal points in a real-time execution path; analyze the received parameters using a trained exception handling model, wherein the trained exception handling model has been trained using machine learning techniques to learn the critical path of execution and/or critical range of parameters at critical nodes, wherein the critical range of parameters comprises a learned threshold at a node; compute, using the trained exception handling model, a probability of fault at the critical nodes; compare the probability of fault at a critical node against a learned threshold at the node; and take proactive action in real-time to avoid the occurrence of a fault when the probability of fault at the node is higher than the learned threshold at the node.</description><subject>AIRCRAFT</subject><subject>ARRANGEMENTS OR MOUNTING OF POWER PLANTS OR PROPULSIONTRANSMISSIONS IN AIRCRAFT</subject><subject>AVIATION</subject><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COSMONAUTICS</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>EQUIPMENT FOR FITTING IN OR TO AIRCRAFT</subject><subject>FLYING SUITS</subject><subject>PARACHUTES</subject><subject>PERFORMING OPERATIONS</subject><subject>PHYSICS</subject><subject>TRANSPORTING</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjL0KwjAURrM4iPoOF5yFtB3E8ZpcY6D5oUmhTqFInEQL9f0xBR_A6YPD-c6aJeyVIRtJAg2CfNTOgu-csi7oAGglGLSoaJFAW-gIW4jaEAS8ULyB6HTUokAyZ5KyhND7tpAlFbZs9Rifc979dsP25SWuhzy9U56n8Z5f-ZP6UPOaNxU_8SNWzX_WFxaMM_k</recordid><startdate>20201001</startdate><enddate>20201001</enddate><creator>Das, Prasun</creator><creator>K, Sreenivasan Govindillam</creator><scope>EVB</scope></search><sort><creationdate>20201001</creationdate><title>AUGMENTED EXCEPTION PROGNOSIS AND MANAGEMENT IN REAL TIME SAFETY CRITICAL EMBEDDED APPLICATIONS</title><author>Das, Prasun ; K, Sreenivasan Govindillam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2020310907A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2020</creationdate><topic>AIRCRAFT</topic><topic>ARRANGEMENTS OR MOUNTING OF POWER PLANTS OR PROPULSIONTRANSMISSIONS IN AIRCRAFT</topic><topic>AVIATION</topic><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COSMONAUTICS</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>EQUIPMENT FOR FITTING IN OR TO AIRCRAFT</topic><topic>FLYING SUITS</topic><topic>PARACHUTES</topic><topic>PERFORMING OPERATIONS</topic><topic>PHYSICS</topic><topic>TRANSPORTING</topic><toplevel>online_resources</toplevel><creatorcontrib>Das, Prasun</creatorcontrib><creatorcontrib>K, Sreenivasan Govindillam</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Das, Prasun</au><au>K, Sreenivasan Govindillam</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>AUGMENTED EXCEPTION PROGNOSIS AND MANAGEMENT IN REAL TIME SAFETY CRITICAL EMBEDDED APPLICATIONS</title><date>2020-10-01</date><risdate>2020</risdate><abstract>A smart exception handler system for safety-critical real-time systems is provided. The system is configured to: receive a plurality of parameters at a plurality of nodal points in a real-time execution path; analyze the received parameters using a trained exception handling model, wherein the trained exception handling model has been trained using machine learning techniques to learn the critical path of execution and/or critical range of parameters at critical nodes, wherein the critical range of parameters comprises a learned threshold at a node; compute, using the trained exception handling model, a probability of fault at the critical nodes; compare the probability of fault at a critical node against a learned threshold at the node; and take proactive action in real-time to avoid the occurrence of a fault when the probability of fault at the node is higher than the learned threshold at the node.</abstract><oa>free_for_read</oa></addata></record> |
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subjects | AIRCRAFT ARRANGEMENTS OR MOUNTING OF POWER PLANTS OR PROPULSIONTRANSMISSIONS IN AIRCRAFT AVIATION CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COSMONAUTICS COUNTING ELECTRIC DIGITAL DATA PROCESSING EQUIPMENT FOR FITTING IN OR TO AIRCRAFT FLYING SUITS PARACHUTES PERFORMING OPERATIONS PHYSICS TRANSPORTING |
title | AUGMENTED EXCEPTION PROGNOSIS AND MANAGEMENT IN REAL TIME SAFETY CRITICAL EMBEDDED APPLICATIONS |
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