15 research outputs found

    Higher level modeling of analog integrated circuits

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    Ph.D.Phillip E. Alle

    High Performance Silicon Carbide Power Packaging—Past Trends, Present Practices, and Future Directions

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    This paper presents a vision for the future of 3D packaging and integration of silicon carbide (SiC) power modules. Several major achievements and novel architectures in SiC modules from the past and present have been highlighted. Having considered these advancements, the major technology barriers preventing SiC power devices from performing to their fullest ability were identified. 3D wire bondless approaches adopted for enhancing the performance of silicon power modules were surveyed, and their merits were assessed to serve as a vision for the future of SiC power packaging. Current efforts pursuing 3D wire bondless SiC power modules were described, and the concept for a novel SiC power module was discussed

    A Complete Step-by-Step Optimal Design for LLC

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    A Variable Inductor Based LCL

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    555-Timer and Comparators Operational at 500 °C

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    Detection and Diagnosis of Data Integrity Attacks in Solar Farms Based on Multilayer Long Short-Term Memory Network

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    Photovoltaic (PV) systems are becoming more vulnerable to cyber threats. In response to this emerging concern, developing cyber-secure power electronics converters has received increased attention from the IEEE Power Electronics Society that recently launched a cyber-physical-security initiative. This letter proposes a deep sequence learning based diagnosis solution for data integrity attacks on PV systems in smart grids, including dc-dc and dc-ac converters. The multilayer long short-term memory networks are used to leverage time-series electric waveform data from current and voltage sensors in PV systems. The proposed method has been evaluated in a PV smart grid benchmark model with extensive quantitative analysis. For comparison, we have evaluated classic data-driven methods, including KK-nearest neighbor, decision tree, support vector machine, artificial neural network, and convolutional neural network. Comparison results verify performances of the proposed method for detection and diagnosis of various data integrity attacks on PV systems
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