circuit simulation model (MathWorks Inc)
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Circuit Simulation Model, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/simulated+circuit+model/pmc11437159-323-1-7
Average 90 stars, based on 1 article reviews
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Article Snippet: The voltage is one of limited reliable information for battery management system, and the faults of voltage sampling will result in adverse effects and lead to potential risks for operation, which emphasize the importance for investigating the failure modes of voltage sampling and diagnosis algorithm.. In this article, a knowledge-data driven sampling diagnosis algorithm is established and an online intelligent diagnosis algorithm is proposed accordingly based on outlier detection with fuzzy entropy.. The fault diagnosis algorithm is established and evaluated under positive exploitation, where the knowledge-base of failure mode based on equivalent simulating models is firstly constructed. Battery:Article Title: Knowledge-data driven sampling diagnosis algorithm for lithium batteries on electric vehicles. Article Snippet: The voltage is one of limited reliable information for battery management system, and the faults of voltage sampling will result in adverse effects and lead to potential risks for operation, which emphasize the importance for investigating the failure modes of voltage sampling and diagnosis algorithm.. In this article, a knowledge-data driven sampling diagnosis algorithm is established and an online intelligent diagnosis algorithm is proposed accordingly based on outlier detection with fuzzy entropy.. The fault diagnosis algorithm is established and evaluated under positive exploitation, where the knowledge-base of failure mode based on equivalent simulating models is firstly constructed. Construct:Article Title: Knowledge-data driven sampling diagnosis algorithm for lithium batteries on electric vehicles. Article Snippet: The voltage is one of limited reliable information for battery management system, and the faults of voltage sampling will result in adverse effects and lead to potential risks for operation, which emphasize the importance for investigating the failure modes of voltage sampling and diagnosis algorithm.. In this article, a knowledge-data driven sampling diagnosis algorithm is established and an online intelligent diagnosis algorithm is proposed accordingly based on outlier detection with fuzzy entropy.. The fault diagnosis algorithm is established and evaluated under positive exploitation, where the knowledge-base of failure mode based on equivalent simulating models is firstly constructed. Article Title: Characterization of Battery‐Powered Portable Ar Plasma Jets Contacting Human Impedance Model and Its Safety Assessment for Direct Human Treatment Article Snippet: A portable Ar plasma jet (size: 347 × 300 × 145mm, weight: 6 kg including the gas bottle) powered by a battery for direct human treatment is developed, and the discharge characteristics are investigated with the human equivalent circuit model in this study.. The root‐mean‐square value and the specific single pulse energy of the discharge current are calculated as a combination to estimate human contact safety from the perspectives of average effects and instantaneous effects.. The equivalent circuit model built at the end figures out the essential parameter, plasma plume resistance, which mainly affects the electrical safety for human contact. Injection:Article Title: Knowledge-data driven sampling diagnosis algorithm for lithium batteries on electric vehicles. Article Snippet: The voltage is one of limited reliable information for battery management system, and the faults of voltage sampling will result in adverse effects and lead to potential risks for operation, which emphasize the importance for investigating the failure modes of voltage sampling and diagnosis algorithm.. In this article, a knowledge-data driven sampling diagnosis algorithm is established and an online intelligent diagnosis algorithm is proposed accordingly based on outlier detection with fuzzy entropy.. The fault diagnosis algorithm is established and evaluated under positive exploitation, where the knowledge-base of failure mode based on equivalent simulating models is firstly constructed. |
