The implementation (hybrid unit power supply) of a portable plant power by employ (solar cell and fuel cell) using deep learning technology for neural networks

2021 ◽  
Author(s):  
Raghad Hameed Ahmed ◽  
Ali Abdulwahhab Abdulrazzaq ◽  
Mohammed Taha Yunis
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Solar Cell ◽  
Fuel Cell ◽  
Power Supply ◽  
Learning Technology ◽  
Unit Power ◽  
Plant Power

scholarly journals Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1579
Author(s):  
Dongqi Wang ◽  
Qinghua Meng ◽  
Dongming Chen ◽  
Hupo Zhang ◽  
Lisheng Xu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Channel Model ◽  
Expert Knowledge ◽  
Automatic Detection ◽  
Data Representation ◽  
Learning Technology ◽  
Arrhythmia Detection ◽  
Automatic Feature Extraction

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

scholarly journals Using CycleGAN to Achieve the Sketch Recognition Process of Sketch-Based Modeling

2021 ◽  
pp. 26-34
Author(s):  
Yuqian Li ◽  
Weiguo Xu
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Learning Technology ◽  
Modeling Technology ◽  
Image Translation ◽  
Mapping Process ◽  
And Training ◽  
Difficult Part ◽  
2D Sketches

AbstractArchitects usually design ideation and conception by hand-sketching. Sketching is a direct expression of the architect’s creativity. But 2D sketches are often vague, intentional and even ambiguous. In the research of sketch-based modeling, it is the most difficult part to make the computer to recognize the sketches. Because of the development of artificial intelligence, especially deep learning technology, Convolutional Neural Networks (CNNs) have shown obvious advantages in the field of extracting features and matching, and Generative Adversarial Neural Networks (GANs) have made great breakthroughs in the field of architectural generation which make the image-to-image translation become more and more popular. As the building images are gradually developed from the original sketches, in this research, we try to develop a system from the sketches to the images of buildings using CycleGAN algorithm. The experiment demonstrates that this method could achieve the mapping process from the sketches to images, and the results show that the sketches’ features could be recognised in the process. By the learning and training process of the sketches’ reconstruction, the features of the images are also mapped to the sketches, which strengthen the architectural relationship in the sketch, so that the original sketch can gradually approach the building images, and then it is possible to achieve the sketch-based modeling technology.

Non–human primate epidural ECoG analysis using explainable deep learning technology

2021 ◽  
Author(s):  
Hoseok Choi ◽  
Seokbeen Lim ◽  
Kyeongran Min ◽  
Kyoung-ha Ahn ◽  
Kyoung-Min Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Hand Movement ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Learning Technology ◽  
3D Information

Abstract Objective: With the development in the field of neural networks, Explainable AI (XAI), is being studied to ensure that artificial intelligence models can be explained. There are some attempts to apply neural networks to neuroscientific studies to explain neurophysiological information with high machine learning performances. However, most of those studies have simply visualized features extracted from XAI and seem to lack an active neuroscientific interpretation of those features. In this study, we have tried to actively explain the high-dimensional learning features contained in the neurophysiological information extracted from XAI, compared with the previously reported neuroscientific results. Approach: We designed a deep neural network classifier using 3D information (3D DNN) and a 3D class activation map (3D CAM) to visualize high-dimensional classification features. We used those tools to classify monkey electrocorticogram (ECoG) data obtained from the unimanual and bimanual movement experiment. Main results: The 3D DNN showed better classification accuracy than other machine learning techniques, such as 2D DNN. Unexpectedly, the activation weight in the 3D CAM analysis was high in the ipsilateral motor and somatosensory cortex regions, whereas the gamma-band power was activated in the contralateral areas during unimanual movement, which suggests that the brain signal acquired from the motor cortex contains information about both contralateral movement and ipsilateral movement. Moreover, the hand-movement classification system used critical temporal information at movement onset and offset when classifying bimanual movements. Significance: As far as we know, this is the first study to use high-dimensional neurophysiological information (spatial, spectral, and temporal) with the deep learning method, reconstruct those features, and explain how the neural network works. We expect that our methods can be widely applied and used in neuroscience and electrophysiology research from the point of view of the explainability of XAI as well as its performance.

scholarly journals Digital Image Watermarking Processor Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1183
Author(s):  
Jae-Eun Lee ◽  
Ji-Won Kang ◽  
Woo-Suk Kim ◽  
Jin-Kyum Kim ◽  
Young-Ho Seo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Integrated Circuit ◽  
High Speed ◽  
Learning Algorithm ◽  
Image Watermarking ◽  
Number System ◽  
Learning Technology ◽  
Optimization Methodology ◽  
Dedicated Processor

Much research and development have been made to implement deep neural networks for various purposes with hardware. We implement the deep learning algorithm with a dedicated processor. Watermarking technology for ultra-high resolution digital images and videos needs to be implemented in hardware for real-time or high-speed operation. We propose an optimization methodology to implement a deep learning-based watermarking algorithm in hardware. The proposed optimization methodology includes algorithm and memory optimization. Next, we analyze a fixed-point number system suitable for implementing neural networks as hardware for watermarking. Using these, a hardware structure of a dedicated processor for watermarking based on deep learning technology is proposed and implemented as an application-specific integrated circuit (ASIC).

scholarly journals Image recognition algorithms based on deep learning

2021 ◽  
Vol 2137 (1) ◽  
pp. 012056
Author(s):  
Hongli Ma ◽  
Fang Xie ◽  
Tao Chen ◽  
Lei Liang ◽  
Jie Lu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Development Process ◽  
Research Direction ◽  
Learning Technology ◽  
Convolutional Network ◽  
Important Research Direction

Abstract Convolutional neural network is a very important research direction in deep learning technology. According to the current development of convolutional network, in this paper, convolutional neural networks are induced. Firstly, this paper induces the development process of convolutional neural network; then it introduces the structure of convolutional neural network and some typical convolutional neural networks. Finally, several examples of the application of deep learning is introduced.

scholarly journals DEEP LEARNING TECHNOLOGY IN PATTERN RECOGNITION

2021 ◽  
Author(s):  
O. Oksyuta ◽  
Le Xu ◽  
R. Lopatin
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Face Recognition ◽  
Learning Technology ◽  
Neural Net ◽  
Actual Recognition

The article discusses the methods of face recognition based on convolutional neural net-works, the problems of face recognition in the presence of interference or face masking, the main stages of training neural networks and the process of actual recognition.

scholarly journals Experimental Discussion on Fire Image Recognition Based on Deep Learning

2021 ◽  
Vol 2066 (1) ◽  
pp. 012071
Author(s):  
Yongyi Cui ◽  
Fang Qu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Rapid Development ◽  
Fire Detection ◽  
Learning Technology ◽  
Fire Recognition

Abstract Fire detection technology based on video images is an emerging technology that has its own unique advantages in many aspects. With the rapid development of deep learning technology, Convolutional Neural Networks based on deep learning theory show unique advantages in many image recognition fields. This paper uses Convolutional Neural Networks to try to identify fire in video surveillance images. This paper introduces the main processing flow of Convolutional Neural Networks when completing image recognition tasks, and elaborates the basic principles and ideas of each stage of image recognition in detail. The Pytorch deep learning framework is used to build a Convolutional Neural Network for training, verification and testing for fire recognition. In view of the lack of a standard and authoritative fire recognition training set, we have conducted experiments on fires with various interference sources under various environmental conditions using a variety of fuels in the laboratory, and recorded videos. Finally, the Convolutional Neural Network was trained, verified and tested by using experimental videos, fire videos on the Internet as well as other interference source videos that may be misjudged as fires.

scholarly journals Deep Learning and Medical Diagnosis: A Review of Literature

2018 ◽  
Vol 2 (3) ◽  
pp. 47 ◽  
Author(s):  
Mihalj Bakator ◽  
Dragica Radosav
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Medical Diagnosis ◽  
Medical Image ◽  
Deep Neural Networks ◽  
Medical Image Analysis ◽  
Research Articles ◽  
Learning Technology ◽  
Review Of Literature ◽  
Medical Field

In this review the application of deep learning for medical diagnosis is addressed. A thorough analysis of various scientific articles in the domain of deep neural networks application in the medical field has been conducted. More than 300 research articles were obtained, and after several selection steps, 46 articles were presented in more detail. The results indicate that convolutional neural networks (CNN) are the most widely represented when it comes to deep learning and medical image analysis. Furthermore, based on the findings of this article, it can be noted that the application of deep learning technology is widespread, but the majority of applications are focused on bioinformatics, medical diagnosis and other similar fields.

Precise Selection and Visualization of Maize Kernels Based on Electromagnetic Vibration and Deep Learning

2020 ◽  
Vol 63 (3) ◽  
pp. 629-643
Author(s):  
Chengshun Zhao ◽  
Longzhe Quan ◽  
Hailong Li ◽  
Ruiqi Liu ◽  
Jianyu Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Computer Hardware ◽  
Learning Technology ◽  
Average Precision ◽  
Maize Kernel ◽  
Kernel Selection ◽  
Maize Kernels ◽  
Selection Of

Abstract. With the development of precision agriculture, the selection of maize kernels has gained more importance in scientific research and practical significance in agricultural production. In this study, the deep learning technology of machine vision was used to select maize kernels, solving the problems of previous maize kernel selection for specific sorting problems, the cumbersome process of artificial feature modeling, the problem of a small number of features, and the challenge of limited data. First, the maximum size of a model based on convolutional neural networks (CNNs) that could run under finite hardware conditions was determined by experiments. Four different network models (Faster R-CNN, Model 1.0, Model 2.0, and Model 3.0) were then designed and trained using a data set of maize kernels. Finally, the accuracy of the models was verified by comparison test, and the detection results of the models were analyzed according to their precision, recall, FPR, F1, precision-recall curve, average precision (AP), mean average precision (mAP), and detection speed. The results show that for the validation set not used for training, Model 1.0 had the highest average recall rate of 98.42% among the four models. Without taking into account the identification of the removed kernels, only excellent maize kernels were identified, and the mAP of Model 1.0 was as high as 97.27%. Moreover, Model 1.0 requires less computer resources, and its computer hardware requirement is lower. The precision, recall, and F1 value of Model 2.0 were increased by 3.73%, 3.55%, and 3.79%, respectively, and the false positive rate of Model 2.0 was reduced by 1.31% on average compared with the Faster R-CNN model. By comparing Model 1.0, Model 2.0, and Model 3.0, it was found that the overall performance of Model 2.0 was best. The size of the network model has an effect on the accurate selection of maize kernels, and a moderate-size model is the best. This study laid a good foundation for the further application of deep learning technology in the real-time sorting of maize kernels and additional applications in the field of agriculture. Keywords: Convolutional neural networks, Deep learning, Maize kernel, Selection, Visualization.

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