###### Abstract
In reference [31], a method of extracting the rightmost bit using the fixed-point arithmetic mode yielded a type of uniformly distributed chaotic signal. Nevertheless, the diminishing usage of fixed-point arithmetic due to its low precision is gradually supplanted by floating-point arithmetic. In summary, although they are effective in obtaining uniformly distributed chaotic signals, these methods are incapable of generating chaotic signals with other probability distribution types. In the literature, several studies explore Gaussian distributed chaotic signals. Reference [32] suggests that within certain parameter ranges, the limit probability distribution of the logistic mapping resembles a Gaussian distribution. Reference [33] utilizes multi-value quantization to generate Gaussian distributed chaotic signals in binary-phase chaotic spread spectrum sequences. Reference [34], on the other hand, approximates the normal distribution using chaotic expressions with the Weierstrass function, resulting in an approximate Gaussian distributed chaotic signal. Reference [35] employs a conjugate mapping transformation to derive a Gaussian distributed chaotic signal from the tent map. Moreover, the research outlined in reference [36] demonstrates two methods: first, based on the central limit theorem, synthesizing Gaussian distributed chaotic signals within specific ranges using chaotic sequences; and second, utilizing the [PERSON] method to select samples from a uniformly distributed chaotic sequence to fit within a specific Gaussian distribution range. Although there has been some progress in the research on the probability density function of chaotic signals, there is still a lack of reports on how to flexibly configure its probability distribution type. Therefore, further research is needed on both the methods and applications of flexibly configuring the probability distribution types of chaotic signals. Hence, this paper introduces a graphical language-based strategy enabling the flexible adjustment of probability distribution types for chaotic signals. It converts the task of setting these distribution types into solving and transforming the probability distribution of intrinsic chaotic signals, effectively resolving the challenge of flexible distribution setting for chaotic signals. In Section 2, we delve into an innovative approach that allows for flexible configuration of the probability distribution types of chaotic signals. Specifically, in Section 2.1, we demonstrate how the kernel density estimation algorithm is applied to overcome the challenge of obtaining smooth probability density functions of high-dimensional chaotic signals. Subsequently, in Section 2.2, we introduce a novel strategy based on graphical language that achieves highly flexible configuration of the probability distribution types of chaotic signals. Furthermore, Section 2.3 showcases, through an example, how our strategy can realize a uniform distribution of chaotic signals. Moving into Section 3, we explore the application of this strategy in suppressing common-mode conducted electromagnetic interference (EMI) in high-frequency power converters. The outstanding performance of this strategy not only validates its effectiveness but also highlights its broad prospects in practical applications. Strategy for Flexible Setting Probability Distribution of Chaotic Signals Based on Graphical Language  
Currently, the most common method for solving the probability density function of high-dimensional chaotic signals is the histogram method. However, the shape of the density function obtained using the histogram method is closely related to the number of bins, resulting in a stepped appearance lacking smoothness. This paper employs the kernel density estimation algorithm to address the challenge of obtaining smooth probability density functions for high-dimensional chaotic signals.