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Planar magnetic components have been widely used in high-density power converters and are suitable for various topologies. The application of planar inductors in LLC resonant converters can lead to parasitic capacitance, which causes current ringing and results in EMI issues. To mitigate the impact of current ringing, the parasitic capacitance of the planar inductor needs to be reduced. This paper proposes a new six-turn interleaved winding design. Compared to the previous four-turn interleaved winding design, it maintains low parasitic capacitance while positioning both the input and output terminals of the inductor on the outer turn, further enhancing the integration of high-density power converters. The parasitic capacitance was calculated using theoretical methods and verified through finite element simulations. Experimental validation was conducted using an LLC resonant converter test platform. Compared to the previous four-turn interleaved winding design, the new six-turn interleaved winding design satisfies both the input and output terminals, using an outer turn configuration. Additionally, the new design exhibits reduced parasitic capacitance and is suitable for use in LLC resonant converters, where it also minimizes current ringing. [ABSTRACT FROM AUTHOR]

The free energy principle and its corollary, the active inference framework, serve as theoretical foundations in the domain of neuroscience, explaining the genesis of intelligent behavior. This principle states that the processes of perception, learning, and decision making—within an agent—are all driven by the objective of "minimizing free energy," evincing the following behaviors: learning and employing a generative model of the environment to interpret observations, thereby achieving perception, and selecting actions to maintain a stable preferred state and minimize the uncertainty about the environment, thereby achieving decision making. This fundamental principle can be used to explain how the brain processes perceptual information, learns about the environment, and selects actions. Two pivotal tenets are that the agent employs a generative model for perception and planning and that interaction with the world (and other agents) enhances the performance of the generative model and augments perception. With the evolution of control theory and deep learning tools, agents based on the FEP have been instantiated in various ways across different domains, guiding the design of a multitude of generative models and decision-making algorithms. This letter first introduces the basic concepts of the FEP, followed by its historical development and connections with other theories of intelligence, and then delves into the specific application of the FEP to perception and decision making, encompassing both low-dimensional simple situations and high-dimensional complex situations. It compares the FEP with model-based reinforcement learning to show that the FEP provides a better objective function. We illustrate this using numerical studies of Dreamer3 by adding expected information gain into the standard objective function. In a complementary fashion, existing reinforcement learning, and deep learning algorithms can also help implement the FEP-based agents. Finally, we discuss the various capabilities that agents need to possess in complex environments and state that the FEP can aid agents in acquiring these capabilities. [ABSTRACT FROM AUTHOR]

Nuclear facilities generate large amounts of contaminated stainless steel metallic material during maintenance and decommissioning. As a new radioactive decontamination method, the self-brittle decontamination method has the advantages of fewer secondary contaminants and can be operated remotely mechanically. The addition of a certain amount of corrosive components to the self-brittlement composite decontaminant can achieve the dual function of self-brittlement and corrosion decontamination. The performance of the decontaminant was investigated by single-factor experiments using electrochemical tests, morphological observations, and weight loss tests. The results show that the decontaminant has good self-embrittlement. When the concentration of HCl is 2.50 mol/L, HNO3 is 3.50 mol/L, NaCl is 0.10 mol/L, and FeCl3 is 0.15 mol/L, the decontaminant is formulated to have the best corrosion and decontamination effect on stainless steel. The detergent will produce a uniform corrosion layer on the surface of stainless steel, and the average corrosion depth can reach 8.3268 μm. [ABSTRACT FROM AUTHOR]