A Feedforward Neural Network for Modeling of Average Pressure Frequency Response

Pettersson, Klas; Karzhou, Andrei; Pettersson, Irina

doi:10.1007/s40857-021-00259-w

Cited by 2 publications

(2 citation statements)

References 20 publications

(23 reference statements)

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“…With a convolution neural network, information can travel both forward and backward in the network [12]. CNNs are trained in image recognition processing utilizing forward propagation and backward propagation techniques, respectively, until a satisfactory model is achieved.…”

Section: Communication Mode Of Networkmentioning

confidence: 99%

A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation

Xing,

Ku,

Zhao

2024

International Journal of Intelligent Systems

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To divide a digital image into individual parts that share similar characteristics is known as digital image segmentation, and it is a vital research subject in the field of computer vision. Object recognition, medical imaging, surveillance, and video processing are just a few of the many real-world contexts where this study could prove useful. While digital image segmentation research has come a long way, there are still certain obstacles to overcome. Segmentation algorithms frequently encounter challenges in achieving both accuracy and efficiency when confronted with intricate settings, noisy pictures, or fluctuating lighting conditions. The absence of established evaluation standards adds complexity to the process of performing equitable comparisons among different segmentation methodologies. Due to the subjective nature of photo segmentation, attaining consistent results among specialists can be challenging. The integration of machine learning and deep neural networks into segmentation algorithms has introduced new challenges, including the need for large amounts of annotated data and the interpretability of the outcomes. Given these challenges, the objective of this study is to enhance the segmentation model. To this end, this research suggests a model of convolutional neural networks that is optimal for digital picture segmentation. The model is based on a dense convolution neural network, and it incorporates a transfer learning technique to significantly boost the model’s robustness and the quality of picture segmentation. The model’s adaptability to new datasets is improved by the incorporation of a transfer learning method. As demonstrated by experimental results on two publicly available datasets, the suggested methodology considerably enhances the resilience of digital picture segmentation.

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Section: Communication Mode Of Networkmentioning

confidence: 99%

A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation

Xing,

Ku,

Zhao

2024

International Journal of Intelligent Systems

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“…The data used for machine learning in this paper is available at [17]. The rest of this paper is organized as follows.…”

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confidence: 99%

A feedforward neural network for modelling of average pressure frequency response

Pettersson¹,

Andrey²,

Pettersson³

2020

Preprint

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The Helmholtz equation has been used for modelling the sound pressure field under a harmonic load. Computing harmonic sound pressure fields by means of solving Helmholtz equation can quickly become unfeasible if one wants to study many different geometries for ranges of frequencies. We propose a machine learning approach, namely a feedforward dense neural network, for computing the average sound pressure over a frequency range. The data is generated with finite elements, by numerically computing the response of the average sound pressure, by an eigenmode decomposition of the pressure. We analyze the accuracy of the approximation and determine how much training data is needed in order to reach a certain accuracy in the predictions of the average pressure response.

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A Feedforward Neural Network for Modeling of Average Pressure Frequency Response

Cited by 2 publications

References 20 publications

A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation

A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation

A feedforward neural network for modelling of average pressure frequency response

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