Large language models (LLMs) demonstrate advanced reasoning abilities, enabling robots to understand natural language instructions and generate high-level plans with appropriate grounding. However, LLM hallucinations present a significant challenge, often leading to overconfident yet potentially misaligned or unsafe plans. While researchers have explored uncertainty estimation to improve the reliability of LLM-based planning, existing studies have not sufficiently differentiated between epistemic and intrinsic uncertainty, limiting the effectiveness of uncertainty estimation. In this paper, we present Combined Uncertainty estimation for Reliable Embodied planning (CURE), which decomposes the uncertainty into epistemic and intrinsic uncertainty, each estimated separately. Furthermore, epistemic uncertainty is subdivided into task clarity and task familiarity for more accurate evaluation. The overall uncertainty assessments are obtained using random network distillation and multi-layer perceptron regression heads driven by LLM features. We validated our approach in two distinct experimental settings: kitchen manipulation and tabletop rearrangement experiments. The results show that, compared to existing methods, our approach yields uncertainty estimates that are more closely aligned with the actual execution outcomes. The code is at https://github.com/Firesuiry/CURE.

With the rapid development of distributed energy resources, the Isolated Modular Multilevel DC-DC (IMM DC-DC) converter is becoming a promising solution for the interconnection of Medium Voltage (MV) and/or High Voltage (HV) DC grids. In this paper, the detailed mathematical model of the IMM DC-DC converter based on harmonic analysis is firstly established by considering the magnetizing inductance of the high-frequency transformer. Then, the real and reactive powers, transformer primary and secondary side currents, arm currents, and capacitor voltage ripples can be accurately determined. Due to the existence of the large arm inductance, the magnetizing inductance of the transformer is no longer negligible. Comparisons between the study results confirm that the magnetizing inductance has a significant impact on each of the aforementioned variables. The proposed analytical model can help achieve accurate sizing of the major components of the IMM DC-DC converter. The presented analysis is validated by both simulation in the Matlab/Simulink environment and experiment.

Cryogenic operation of power electronics converters plays a key role in the development of the future electric aircraft systems. Characterization of semiconductor switches in the cryogenic environment is pivotal for implementation of the cryogenic power electronics converters. This paper characterizes two groups of Planar and Trench SiC MOSFETs from room temperature to –175°C. The objective is to explore the impact of the cryogenic environment on static and dynamic behavior of the SiC MOSFETs. The dynamic characteristics of these semiconductor switches are investigated by using a double pulse test circuit. The test circuit is placed inside a cryogenic chamber, which is able to accurately adjust the temperature. Important dynamic characteristics such as voltage and current rise and fall times, turn-on, turn-off, and total switching energy losses are investigated. It is shown that the impacts of the cryogenic environment on the two groups of SiC MOSFETs are not identical. Furthermore, the impact of gate driver output voltage on the switching energy losses is discussed. The static characteristics of the SiC MOSFETs are explored by using the same cryogenic chamber and a curve tracer. Breakdown voltage, gate-source threshold voltage, on-state resistance, body diode forward voltage, and average transconductance are the static parameters that are measured.