Temat: Convolutional Neural Network

Skocz do pozycji: 1.

Tytuł:: Noise quantization simulation analysis of optical convolutional networks
Autorzy:: Zhang, Ye
Zhang, Saining
Zhang, Danni
Su, Yanmei
Yi, Junkai
Wang, Pengfei
Wang, Ruiting
Luo, Guangzhen
Zhou, Xuliang
Pan, Jiaoqing
Tematy:: optical neural network
convolutional neural network
noise
quantization; Pokaż więcej
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Powiązania:: https://bibliotekanauki.pl/articles/27310111.pdf Link otwiera się w nowym oknie
Opis:: Optical neural network (ONN) has been regarded as one of the most prospective techniques in the future, due to its high-speed and low power cost. However, the realization of optical convolutional neural network (CNN) in non-ideal cases still remains a big challenge. In this paper, we propose an optical convolutional networks system for classification problems by applying general matrix multiply (GEMM) technology. The results show that under the influence of noise, this system still has good performance with low TOP-1 and TOP-5 error rates of 44.26% and 14.51% for ImageNet. We also propose a quantization model of CNN. The noise quantization model reaches a sufficient prediction accuracy of about 96% for MNIST handwritten dataset.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Grid Search of Convolutional Neural Network model in the case of load forecasting
Autorzy:: Tran, Thanh Ngoc
Tematy:: load forecasting
grid search
convolutional neural network; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/1841362.pdf Link otwiera się w nowym oknie
Opis:: The Convolutional Neural Network (CNN) model is one of the most effective models for load forecasting with hyperparameters which can be used not only to determine the CNN structure and but also to train the CNN model. This paper proposes a frame work for Grid Search hyperparameters of the CNN model. In a training process, the optimalmodels will specify conditions that satisfy requirement for minimum of accuracy scoresof Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). In the testing process, these optimal models will be used to evaluate the results along with all other ones. The results indicated that the optimal models have accuracy scores near the minimum values. Load demand data of Queensland (Australia) and Ho Chi Minh City (Vietnam) were utilized to verify the accuracy and reliability of the Grid Search framework.
Dostawca treści:: Biblioteka Nauki

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na półce

Skocz do pozycji: 3.

Tytuł:: Application of visual classification algorithms for identification of underwater audio signals
Autorzy:: Gnyś, Piotr
Szczęsna, Gabriela
Domínguez-Brito, Antonio C.
Cabrera-Gámez, Jorge
Tematy:: audio processing
audio classification
convolutional neural network; Pokaż więcej
Wydawca:: Politechnika Gdańska
Powiązania:: https://bibliotekanauki.pl/articles/23956852.pdf Link otwiera się w nowym oknie
Opis:: An audio processing and classification pipeline is presented in this work. The main focus is on the classification of sounds in a marine acoustic environment, however, the presented approach can be applied to other audio data. Audio samples from heterogeneous sources automatically spliced, normalized and transformed into spectrogram based visual representation are tagged on the pipeline input. The said representation is then used to train a convolutional neural network that can identify the presented categories in future recordings.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Deep Learning-based Beamforming Approach Incorporating Linear Antenna Arrays
Autorzy:: Bhalke, Daulappa
Paikrao, Pavan D.
Anguera, Jaume
Tematy:: adaptive beamforming
antenna arrays
convolutional neural network; Pokaż więcej
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Powiązania:: https://bibliotekanauki.pl/articles/58906713.pdf Link otwiera się w nowym oknie
Opis:: This research delves into exploring machine learning and deep learning techniques relied upon in antenna design processes. First, the general concepts of machine learning and deep learning are introduced. Then, the focus shifts to various antenna applications, such as those relying on millimeter waves. The feasibility of employing antennas in this band is examined and compared with conventional methods, emphasizing the acceleration of the antenna design process, reduction in the number of simulations, and improved computational efficiency. The proposed method is a low-complexity approach which avoids the need for eigenvalue decomposition, the procedure for computing the entire matrix inversion, as well as incorporating signal and interference correlation matrices in the weight optimization process. The experimental results clearly demonstrate that the proposed method outperforms the compared beamformers by achieving a better signal-to-interference ratio.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Skin Lesion Analysis Toward Melanoma Detection Using Deep Learning Techniques
Autorzy:: Sherif, Fatma
Mohamed, Wael A.
Mohra, A.S.
Tematy:: melanoma
skin cancer
convolutional neural network
deep learning; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/226719.pdf Link otwiera się w nowym oknie
Opis:: In the last few years, a great attention was paid to the deep learning Techniques used for image analysis because of their ability to use machine learning techniques to transform input data into high level presentation. For the sake of accurate diagnosis, the medical field has a steadily growing interest in such technology especially in the diagnosis of melanoma. These deep learning networks work through making coarse segmentation, conventional filters and pooling layers. However, this segmentation of the skin lesions results in image of lower resolution than the original skin image. In this paper, we present deep learning based approaches to solve the problems in skin lesion analysis using a dermoscopic image containing skin tumor. The proposed models are trained and evaluated on standard benchmark datasets from the International Skin Imaging Collaboration (ISIC) 2018 Challenge. The proposed method achieves an accuracy of 96.67% for the validation set. The experimental tests carried out on a clinical dataset show that the classification performance using deep learning-based features performs better than the state-of-the-art techniques.
Dostawca treści:: Biblioteka Nauki

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na półce

Skocz do pozycji: 6.

Tytuł:: A grinding surface roughness class recognition combining red and green information
Autorzy:: Huang, Jiefeng
Yi, Huaian
Fang, Runji
Song, Kun
Tematy:: roughness measurement
convolutional neural network
red and green information; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czasopisma i Monografie PAN
Powiązania:: https://bibliotekanauki.pl/articles/58973654.pdf Link otwiera się w nowym oknie
Opis:: The current machine vision-based surface roughness measurement mainly relies on the design of feature indicators associated with roughness to measure the surface roughness. However, the process is tedious and complicated. Moreover, most existing deep learning methods for workpiece surface roughness measurement use a monochromatic light source to acquire images. In the case of surface roughness in a grinding process with low roughness and random texture characteristics, the feature information obtained by monochromatic light source acquisition is relatively small. It is difficult to extract the workpiece surface roughness features, which can easily cause problems for subsequent measurement. Based on the problems above, this paper proposes a grinding surface roughness measurement method combining red-green information and a convolutional neural network. The technique uses a particular red-green block to highlight the grinding surface texture features. Finally, it classifies the grinding surface roughness measurement with a classification detection technique of the convolutional neural network. Experimental results show that the accuracy of the grinding surface roughness measurement method combining red-green information and the convolutional neural network is significantly improved compared with that of the grinding surface roughness measurement method without using the red-green data.
Dostawca treści:: Biblioteka Nauki

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na półce

Skocz do pozycji: 7.

Tytuł:: Fringe pattern inpainting based on dual-exposure fused fringe guiding CNN denoiser prior
Autorzy:: Peng, Guangze
Chen, Wenjing
Tematy:: fringe projection profilometry
phase calculation
convolutional neural network
denoiser prior; Pokaż więcej
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Powiązania:: https://bibliotekanauki.pl/articles/2086757.pdf Link otwiera się w nowym oknie
Opis:: The intensity of some pixels of the captured fringe will be saturated when fringe projection profilometry is used to measure objects with high reflectivity, which will significantly affect the reconstruction of the measured object. In this paper, we propose a fringe pattern inpainting method based on the convolutional neural network (CNN) denoiser prior guided by additional information from a fringe captured in short exposure time. First, a binary mask obtained by Otsu algorithm from the modulation information of the short exposure fringe is used to detect the high-saturation region in the normal exposure fringe. Then, the corrected gray-scales of the region of the short exposure fringe selected by the mask are inserted in the saturated region of the normal fringe to form an initial fringe for iteration. At last, fringe pattern inpainting is achieved by using a CNN denoiser prior. The correct phase can be reconstructed from the inpainted fringes. The computer simulation and experiments verify the effectiveness of the proposed method.
Dostawca treści:: Biblioteka Nauki

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na półce

Skocz do pozycji: 8.

Tytuł:: Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis
Autorzy:: Karabacak, Yunus Emre
Tematy:: wear stage estimation
milling
convolutional neural network
time-frequency analysis; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Powiązania:: https://bibliotekanauki.pl/articles/27312777.pdf Link otwiera się w nowym oknie
Opis:: CNC milling machines are frequently used in the manufacturing of mechanical parts in the industry. One of the most important components of milling machines is the cutting tool. Monitoring the cutting tool wear is important for the reliability, continuity, and quality of production. Monitoring the tool and detecting the stage of wear are difficult processes. In this work, the convolutional neural network (CNN), which is a deep learning method in which the features are extracted by an inner process, was performed to detect the wear stages of the milling tool. These stages that define the total lifespan of the tool are known as initial wear (IW), steady-state wear (SSW), and accelerated wear (AW). Short Time Fourier Transform (STFT) was applied to signals, and signal spectrograms were used to train CNN models with different complex architectures. Vibration signals, acoustic emission signals, and motor current signals from The Nasa Ames Milling Dataset were used to obtain the spectrograms. Pre-trained CNNs (GoogleNet, AlexNet, ResNet-50, and EfficientNet-B0) detected the tool wear stage with varying accuracies. It has been seen that the time duration of model training increases as the size of the dataset grows and the network architecture becomes more complex. The recommended method has also been tested on the 2010 PHM Data Challenge Dataset. CNN shows promise for condition monitoring of milling operations and detecting tool wear stage.
Dostawca treści:: Biblioteka Nauki

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na półce

Skocz do pozycji: 9.

Tytuł:: Selecting Update Blocks of Convolutional Neural Networks using Genetic Algorithm in Transfer Learning
Autorzy:: Hasan, Mehedi
Ibrahim, Muhammad
Ali, Sawkat
Tematy:: computer vision
transfer learning
convolutional neural network
EfficientNet
genetic algorithm; Pokaż więcej
Wydawca:: Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej
Powiązania:: https://bibliotekanauki.pl/articles/59150199.pdf Link otwiera się w nowym oknie
Opis:: The performance of convolutional neural networks (CNN) for computer vision problems depends heavily on their architectures. Transfer learning performance of a CNN strongly relies on selection of its trainable layers. Selecting the most effective update layers for a certain target dataset often requires expert knowledge on CNN architecture which many practitioners do not possess. General users prefer to use an available architecture (e.g. GoogleNet, ResNet, EfficientNet etc.) that is developed by domain experts. With the ever-growing number of layers, it is increasingly becoming difficult and cumbersome to handpick the update layers. Therefore, in this paper we explore the application of a genetic algorithm to mitigate this problem. The convolutional layers of popular pre-trained networks are often grouped into modules that constitute their building blocks. We devise a genetic algorithm to select blocks of layers for updating the parameters. By experimenting with EfficientNetB0 pre-trained on ImageNet and using three popular image datasets - namely Food-101, CIFAR-100 and MangoLeafBD - as target datasets, we show that our algorithm yields similar or better results than the baseline in terms of accuracy, and requires lower training and evaluation time due to learning a smaller number of parameters. We also devise a measure called block importance to measure each block’s efficacy as an update block and analyze the importance of the blocks selected by our algorithm.
Dostawca treści:: Biblioteka Nauki

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na półce

Skocz do pozycji: 10.

Tytuł:: A novel method for automatic detection of arrhythmias using the unsupervised convolutional neural network
Autorzy:: Zhang, Junming
Yao, Ruxian
Gao, Jinfeng
Li, Gangqiang
Wu, Haitao
Tematy:: convolutional neural network
arrhythmia detection
unsupervised learning
ECG classification; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Powiązania:: https://bibliotekanauki.pl/articles/23944827.pdf Link otwiera się w nowym oknie
Opis:: In recent years, various models based on convolutional neural networks (CNN) have been proposed to solve the cardiac arrhythmia detection problem and achieved saturated accuracy. However, these models are often viewed as “blackbox” and lack of interpretability, which hinders the understanding of cardiologists, and ultimately hinders the clinical use of intelligent terminals. At the same time, most of these approaches are supervised learning and require label data. It is a time-consuming and expensive process to obtain label data. Furthermore, in human visual cortex, the importance of lateral connection is same as feed-forward connection. Until now, CNN based on lateral connection have not been studied thus far. Consequently, in this paper, we combines CNNs, lateral connection and autoencoder (AE) to propose the building blocks of lateral connection convolutional autoencoder neural networks (LCAN) for cardiac arrhythmia detection, which learn representations in an unsupervised manner. Concretely, the LCAN contains a convolution layer, a lateral connection layer, an AE layer, and a pooling layer. The LCAN detects salient wave features through the lateral connection layer. The AE layer and competitive learning is used to update the filters of the convolution network—an unsupervised process that ensures similar weight distribution for all adjacent filters in each convolution layer and realizes the neurons’ semantic arrangement in the LCAN. To evaluate the performances of the proposed model, we have implemented the experiments on the well-known MIT–BIH Arrhythmia Database. The proposed model yields total accuracies and kappa coefficients of 98% and 0.95, respectively. The experiment results show that the LCAN is not only effective, but also a useful tool for arrhythmia detection.
Dostawca treści:: Biblioteka Nauki

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