KR-20260063544-A - METHOD AND APPARATUS FOR DESIGNING FILTER FOR SIGNAL PROCESSING

KR20260063544AKR 20260063544 AKR20260063544 AKR 20260063544AKR-20260063544-A

Abstract

The present disclosure provides a filter design device for designing a filter for signal processing, comprising: a preprocessing unit that decomposes a test signal into a plurality of decomposed signals using a preset signal decomposition algorithm; a filter configuration unit that takes the test signal as input and targets the plurality of decomposed signals to train a neural network to configure a neural network-based filter; and a filter adjustment unit that performs optimization of the neural network-based filter using reinforcement learning when decomposing an actual signal through the neural network-based filter.

Inventors

백수환
이중형
이범석
김성겸
김민진

Assignees

포스코홀딩스 주식회사

Dates

Publication Date: 20260507
Application Date: 20241030

Claims (12)

In a device for designing a filter for signal processing, A preprocessing unit that decomposes a test signal into multiple decomposed signals using a preset signal decomposition algorithm; A filter component that configures a neural network-based filter by training a neural network using the above test signal as input and targeting the above plurality of decomposition signals; and A filter design device comprising a filter adjustment unit that performs optimization of the neural network-based filter using reinforcement learning when decomposing an actual signal through the neural network-based filter.
In Article 1, The above signal decomposition algorithm includes an empirical mode decomposition (EMD) algorithm, and The above plurality of decomposition signals are filter design devices including intrinsic mode functions (IMFs).
In Article 1, The filter design device comprising the above filter component, which configures the neural network-based filter to enable the reconstruction of the test signal using the plurality of decomposition signals.
In Article 1, The above test signal is composed of a plurality of channels, and The above plurality of decomposition signals are each configured with the same number of channels as the test signal, forming a filter design device.
In Article 1, The filter adjustment unit is a filter design device that determines a compensation based on the result of adjusting the parameters of the neural network-based filter when the actual signal is decomposed through the neural network-based filter, and performs optimization of the neural network-based filter using the compensation.
In Article 5, The above compensation is a filter design device determined based on at least one of the accuracy of signal decomposition, the signal-to-noise ratio (SNR), or the quality of the decomposed mode.
In a method for designing filters for signal processing, A step of decomposing a test signal into multiple decomposed signals using a preset signal decomposition algorithm; A step of configuring a neural network-based filter by training a neural network using the above test signal as input and targeting the above plurality of decomposition signals; and A filter design method comprising the step of optimizing the neural network-based filter using reinforcement learning when decomposing an actual signal through the neural network-based filter.
In Article 7, The above signal decomposition algorithm includes an empirical mode decomposition (EMD) algorithm, and The above plurality of decomposition signals are a filter design method including intrinsic mode functions (IMFs).
In Article 7, A filter design method in which the step of configuring the filter above enables the reconstruction of the test signal using the plurality of decomposition signals.
In Article 7, The above test signal is composed of a plurality of channels, and A filter design method in which the above plurality of decomposition signals are each composed of the same number of channels as the above test signal.
In Article 7, A filter design method comprising the step of performing optimization of the above filter, wherein when the actual signal is decomposed through the neural network-based filter, a reward is determined based on the result of adjusting the parameters of the neural network-based filter, and optimization of the neural network-based filter is performed using the reward.
In Article 11, The above compensation is a filter design method determined based on at least one of the accuracy of signal decomposition, the signal-to-noise ratio (SNR), or the quality of the decomposed mode.

Description

Method and apparatus for designing a filter for signal processing The present disclosure relates to a technique for designing an artificial intelligence-based filter used to decompose a signal. In the field of signal processing, signals are decomposed using bandpass filters or predefined frequency bands. A bandpass filter is a type of frequency-selective filter that allows a frequency band between a defined lower frequency limit (f1) and an upper frequency limit (f2) to pass through. This method is effective when one wants to separate components of a specific frequency range from a signal. For example, it is used in applications such as separating only the human voice from a voice signal or extracting only data from a specific channel from a communication signal. However, to effectively utilize a bandpass filter, the frequency band of the signal to be separated must be accurately known in advance. This can be problematic when the characteristics of the signal are not well known or when dealing with complex signals containing various frequency components. Furthermore, because the signal is decomposed based on a fixed frequency band, it may be difficult to respond to dynamic changes in the signal or the appearance of unforeseen frequency components. In other words, if the signal characteristics change, it becomes necessary to select a new frequency band and design a new filter. Additionally, for signals containing various frequency components that change over time, it is difficult to extract sufficient information using a single frequency band filter. This can be particularly challenging when analyzing signals arising from natural phenomena or complex signals originating from various sources. For example, it is difficult to predefine clear frequency bands for signals generated in environments where various equipment coexists, such as vibration or sound signals within a factory. Consequently, existing signal decomposition methods may have limitations in such environments. Accordingly, there is a need for specific measures that can decompose signals more consistently and efficiently, even in environments where diverse equipment is mixed, such as factories. FIG. 1 is a diagram illustrating the configuration of a filter design device for signal processing according to one embodiment. FIG. 2 is a diagram illustrating the operation of a filter design device for signal processing according to one embodiment. FIGS. 3 to 5 are drawings for illustrating specific examples of a filter design device for signal processing according to one embodiment. FIG. 6 is a diagram illustrating the procedure of a filter design method for signal processing according to one embodiment. FIG. 7 is a diagram illustrating a specific procedure for a filter design method for signal processing according to one embodiment. Hereinafter, some embodiments of the present disclosure will be described in detail with reference to the exemplary drawings. In assigning reference numerals to the components of each drawing, the same components may have the same reference numeral as much as possible, even if they are shown in different drawings. Furthermore, in describing the embodiments, if it is determined that a detailed description of related known components or functions may obscure the essence of the technical concept, such detailed description may be omitted. Where terms such as "comprising," "having," or "consisting of" are used in this specification, other parts may be added unless "only" is used. Where a component is expressed in the singular, it may include a plural unless otherwise specified. Additionally, terms such as first, second, A, B, (a), (b), etc., may be used to describe the components of the present disclosure. These terms are used merely to distinguish the components from other components, and the nature, order, sequence, or number of the components are not limited by such terms. In describing the positional relationship of components, where it is stated that two or more components are "connected," "combined," or "joined," it should be understood that while the two or more components may be directly "connected," "combined," or "joined," they may also be "connected," "combined," or "joined" with other components "intervened." Here, the other components may be included in one or more of the two or more components that are "connected," "combined," or "joined" with one another. In describing the temporal flow relationship regarding components, methods of operation, or methods of production, for example, when the temporal or sequential relationship is described using "after," "following," "next," or "before," it may include cases where the relationship is not continuous unless "immediately" or "directly" is used. Meanwhile, where numerical values or corresponding information regarding a component (e.g., levels, etc.) are mentioned, even without separate explicit notation, the numerical values or corresponding information may be interpreted