Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process

Hyperspectral X-ray CT (HXCT) technology provides not only structural imaging but also the information of material components therein. The main purpose of this study is to investigate the effect of various reconstruction algorithms on reconstructed X-ray absorption spectra (XAS) of components shown in the CT image by means of HXCT. In this paper, taking 3D printing polymer as an example, seven kinds of commonly used polymers such as thermoplastic elastomer (TPE), carbon fiber reinforced polyamide (PA-CF), acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), ultraviolet photosensitive resin (UV9400), polyethylene terephthalate glycol (PETG), and polyvinyl alcohol (PVA) were selected as samples for hyperspectral CT reconstruction experiments. Seven kinds of 3D printing polymer and two interfering samples were divided into a training set and test sets. First, structural images of specimens were reconstructed by Filtered Back-Projection (FBP), Algebra Reconstruction Technique (ART) and Maximum-Likelihood Expectation-Maximization (ML-EM). Secondly, reconstructed XAS were extracted from the pixels of region of interest (ROI) compartmentalized in the images. Thirdly, the results of principal component analysis (PCA) demonstrated that the first four principal components contain the main features of reconstructed XAS, so we adopted Artificial Neural Network (ANN) trained by the reconstructed XAS expressed by the first four principal components in the training set to identify that the XAS of corresponding polymers exist in both of test sets from the training set. The result of ANN displays that FBP has the best performance of classification, whose ten-fold cross-validation accuracy reached 99%. It suggests that hyperspectral CT reconstruction is a promising way of getting image features and material features at the same time, which can be used in medical imaging and nondestructive testing.

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“…The activation function of hidden layer we chose to use in this paper is Rectified Linear Unit (ReLU) [40] as shown in the following formula:…”

Section: Classification Of Reconstructed Xasmentioning

confidence: 99%

Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

Zheng

Wang

et al. 2020

Materials

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“…Common models are the alpha-beta-gamma (ABG) [1] and the close-in free space reference distance with frequency dependent path loss exponent (CIF) model [1]. More recently, non-traditional artificial intelligence techniques such as fuzzy clustering prediction [2,3], artificial neural networks [3][4][5], deep learning [6] and machine learning [5,7] have also been used to estimate the path loss in different environments.…”

Section: Introductionmentioning

confidence: 99%

Measurement and Prediction of Short-Range Path Loss between 27 and 40 GHz in University Campus Scenarios

Ramos

Vargas

Mello

et al. 2021

Preprint

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In this paper, we present the results of short-range path loss measurement in the microwave and millimetre wave bands, at frequencies between 27 and 40 GHz, obtained in a campaign inside a university campus in Rio de Janeiro, Brazil. Existing empirical path loss prediction models, including the alpha-beta-gamma (ABG) model and the close-in free space reference distance with frequency-dependent path loss exponent (CIF) model, are tested against the measured data, and an improved prediction method that includes the path loss dependence on the height diﬀerence between transmitter and receiver is proposed. The main contribution of this paper is the use of the Fuzzy technique to perform path loss predictions for short links in the millimetre wave range, from 27 to 40 GHz, providing lower errors when compared to the traditional ABG and CIF models. However, it should be noted that the Fuzzy technique uses a set of equations to perform the prediction and the attenuation coeﬃcient is not explicit as in the classical models. Also, a non-negligible correlation between the diﬀerence in height between transmitter and receiver positions and the path loss in such short links (i.e., the path inclination) has been observed and requires further investigation. If conﬁrmed, it could provide an additional parameter to improve the accuracy of the traditional ABG model.

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“…Compared with the ANN-based model established for LOS, the model for NLOS combines the diffraction loss calculated by the traditional PL model with ANN, and the comparative analysis verifies that the hybrid PL model combining ANN and the traditional PL model is more accurate. Reference [29] proposes a machine learning framework for modeling PL using a combination of three key techniques: ANN, Gaussian process, and principle component analysis (PCA) [30]. Compared with traditional PL models, the proposed model is more accurate and flexible, which will be beneficial for the site-specific design of wireless sensor network with high reliability.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Based Path Loss Prediction for Wireless Communication Network

et al. 2020

IEEE Access

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Accurate path loss (PL) prediction is essential for predicting transmitter coverage and optimizing wireless network performance. Traditional PL models are difficult to cope with the development trend of diversity, time-varying and mass wireless channels. In this work, the most widely used multilayer perceptron (MLP) neural network in artificial neural network (ANN) is employed to accurately predict PL. Three types of environmental features are defined and extracted, which describe the propagation environment only by considering limited environmental types instead of complex 3D environment modeling. Principal component analysis (PCA) is used to generate the low-dimensional environmental features, and eliminate redundant information among similar environmental types. Moreover, the information of base station (BS) and the receiver (Rx), including 3D locations, frequency, the transmitted power of BS, the antenna information, the feeder loss, and the received power of all the locations are obtained from the measurements. Different environmental features are combined with the information of BS and Rx to construct seven datasets for PL prediction models based on MLP neural networks. The impacts of the number of neurons in the hidden layer, the number of hidden layers, the number of training samples, and environmental features on PL prediction models are explored by considering the absolute value of mean error (AME), the mean absolute error (MAE), the standard deviation (STD) of error, the correlation coefficient, and the time ratio, respectively. This work aims to understand the propagation characteristics of radio waves, which can provide a theoretical basis for wireless network optimization and communication system design.

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Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process

Cited by 116 publications

References 27 publications

Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

Measurement and Prediction of Short-Range Path Loss between 27 and 40 GHz in University Campus Scenarios

Artificial Neural Network Based Path Loss Prediction for Wireless Communication Network

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