scholarly journals Fast and Fine Location of Total Lightning from Low Frequency Signals Based on Deep-Learning Encoding Features

2021 ◽  
Vol 13 (11) ◽  
pp. 2212
Author(s):  
Jingxuan Wang ◽  
Yang Zhang ◽  
Yadan Tan ◽  
Zefang Chen ◽  
Dong Zheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Lightning Discharge ◽  
Low Frequency ◽  
New Method ◽  
Discharge Process ◽  
Matching Efficiency ◽  
Triggered Lightning ◽  
Artificial Intelligence Technology ◽  
Total Lightning

Lightning location provides an important means for the study of lightning discharge process and thunderstorms activity. The fine positioning capability of total lightning based on low-frequency signals has been improved in many aspects, but most of them are based on post waveform processing, and the positioning speed is slow. In this study, artificial intelligence technology is introduced for the first time to lightning positioning, based on low-frequency electric-field detection array (LFEDA). A new method based on deep-learning encoding features matching is also proposed, which provides a means for fast and fine location of total lightning. Compared to other LFEDA positioning methods, the new method greatly improves the matching efficiency, up to more than 50%, thereby considerably improving the positioning speed. Moreover, the new algorithm has greater fine-positioning and anti-interference abilities, and maintains high-quality positioning under low signal-to-noise ratio conditions. The positioning efficiency for return strokes of triggered lightning was 99.17%, and the standard deviation of the positioning accuracy in the X and Y directions was approximately 70 m.

scholarly journals Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach

2021 ◽  
Vol 13 (10) ◽  
pp. 265
Author(s):  
Jie Chen ◽  
Bing Han ◽  
Xufeng Ma ◽  
Jian Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Low Frequency ◽  
Learning Approach ◽  
Decision Algorithm ◽  
Underwater Target

Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods.

scholarly journals Deep learning for low-frequency extrapolation from multioffset seismic data

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. R989-R1001 ◽  
Author(s):  
Oleg Ovcharenko ◽  
Vladimir Kazei ◽  
Mahesh Kalita ◽  
Daniel Peter ◽  
Tariq Alkhalifah
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Waveform Inversion ◽  
Low Frequency ◽  
Low Frequencies ◽  
Seismic Surveys ◽  
Full Waveform ◽  
Frequency Components ◽  
Marine Seismic

Low-frequency seismic data are crucial for convergence of full-waveform inversion (FWI) to reliable subsurface properties. However, it is challenging to acquire field data with an appropriate signal-to-noise ratio in the low-frequency part of the spectrum. We have extrapolated low-frequency data from the respective higher frequency components of the seismic wavefield by using deep learning. Through wavenumber analysis, we find that extrapolation per shot gather has broader applicability than per-trace extrapolation. We numerically simulate marine seismic surveys for random subsurface models and train a deep convolutional neural network to derive a mapping between high and low frequencies. The trained network is then tested on sections from the BP and SEAM Phase I benchmark models. Our results indicate that we are able to recover 0.25 Hz data from the 2 to 4.5 Hz frequencies. We also determine that the extrapolated data are accurate enough for FWI application.

scholarly journals Intelligent Force-Measurement System Use in Shock Tunnel

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6179
Author(s):  
Yunpeng Wang ◽  
Zonglin Jiang
Keyword(s):  
Deep Learning ◽  
Measurement System ◽  
Force Measurement ◽  
Aerodynamic Force ◽  
Low Frequency ◽  
Shock Tunnel ◽  
New Method ◽  
Test Results ◽  
Force Measurement System ◽  
The Impact

The inertial vibration of the force measurement system (FMS) has a large influence on the force measuring result of aircraft, especially on some tests carried out in high-enthalpy impulse facilities, such as in a shock tunnel. When force tests are conducted in a shock tunnel, the low-frequency vibrations of the FMS and its motion cannot be addressed through digital filtering because of the inertial forces, which are caused by the impact flow during the starting process of the shock tunnel. Therefore, this paper focuses on the dynamic characteristics of the performance of the FMS. A new method—i.e., deep-learning-based single-vector dynamic self-calibration (DL-based SV-DSC) of an impulse FMS, is proposed to increase the accuracy of aerodynamic force measurements in a shock tunnel. A deep-learning technique is used to train the dynamic model of the FMS in this study. Convolutional neural networks with a simple structure are applied to describe the dynamic modeling so that the low-frequency vibration signals are eliminated from the test results of the shock tunnel. By validation of the force test results measured in a shock tunnel, the current trained model can realize intelligent processing of the balance signals of the FMS. Based on this new method of dynamic calibration, the reliability and accuracy of force data processing are well verified.

scholarly journals Evolution Characteristics during Initial Stage of Triggered Lightning Based on Directly Measured Current

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 658
Author(s):  
Zefang Chen ◽  
Yang Zhang ◽  
Yanfeng Fan ◽  
Jingxuan Wang ◽  
Dong Zheng ◽  
...  
Keyword(s):  
Lightning Discharge ◽  
Ground Level ◽  
Current Data ◽  
Discharge Process ◽  
Measured Current ◽  
Critical Range ◽  
Initial Stage ◽  
Evolution Characteristics ◽  
Triggered Lightning ◽  
Initial Process

The initiation of a leader is an important lightning discharge process, but how an upward positive leader (UPL) initiates is still not fully understood. The evolution characteristics of a UPL during its initial stage was systematically studied based on directly measured current data of 14 triggered lightning events in 2019. It was found that the initial stage of triggered lightning can be divided into two types: a single initial process form and a multiple initial process form, with percentages of 64.29% and 35.71%, respectively. Compared with the former, the latter usually lasts longer, and the corresponding lightning is often triggered under a lower ground-level quasi-static electric field. In each initial process, precursor current pulses (PCPs), PCP clusters and initial precursor current pulse (IPCPs) are typical current waveforms, and the pulse durations and transferred charges of PCPs increase linearly with time. However, in the multiple initial process form, the pulse durations and transferred charges of PCPs will reduce significantly after each previous initial process and then continue to increase in the following initial process. In each initial process, when the pulse duration and transferred charge of a PCP increase to a certain extent, PCP clusters and IPCPs begin to appear. For the emergence of PCP clusters, the average values of the threshold are 3.48 μs and 19.53 μC, respectively. For the occurrence of IPCPs, the corresponding values are 4.69 μs and 27.23 μC, respectively. The average values of pulse durations and transferred charges of IPCPs are larger than those of PCP clusters. Compared with adjacent PCP clusters, IPCPs contain more pulses, with a critical range of 6–7. IPCPs also last longer, and have a critical range of 138–198 μs.

scholarly journals A New Method of Denoising Crop Image Based on Improved SVD in Wavelet Domain

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rui Wang ◽  
Wanxiong Cai ◽  
Zaitang Wang
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Wavelet Coefficient ◽  
Real Life ◽  
Low Frequency ◽  
New Method ◽  
Wavelet Domain ◽  
Noise Image ◽  
Noise Ratio

In real life, images are inevitably interfered by various noises during acquisition and transmission, resulting in a significant reduction in image quality. The process of solving this kind of problem is called image denoising. Image denoising is a basic problem in the field of computer vision and image processing, which is essential for subsequent image processing and applications. It can ensure that people can obtain more effective information of images more accurately. This paper mainly studies a new method of crop image denoising with improved SVD in wavelet domain. The algorithm used in this study firstly carried out a 3-layer wavelet transform on the crop noise image, leaving the low-frequency subimage unchanged; then, for the high-frequency subimages distributed in the horizontal, vertical, and diagonal directions, the improved adaptive SVD algorithm was used to filter the noise; finally perform wavelet coefficient reconstruction. To effectively test the performance of the algorithm, field crop images were taken as test images, and the denoising performance of the algorithm, SVD algorithm, and the improved SVD algorithm used in this study were compared, and the peak signal-to--to-noise ratio (PSNR) was introduced. Quantitative evaluation of the denoising results of several types of algorithms. The experimental data in this paper show that when the noise standard deviation is greater than 20, the enhanced experimental results clearly achieve higher PSNR and SSIM values than WNNM. The average peak signal-to-noise ratio (PSNR) is about 0.1 dB higher, and the average SSIM is larger about 0.01. The results show that the algorithm used in this study is superior to the other two algorithms, which provides a more effective method for crop noise image processing.

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

Frame-Level Signal-to-Noise Ratio Estimation Using Deep Learning

2020 ◽  
Author(s):  
Hao Li ◽  
DeLiang Wang ◽  
Xueliang Zhang ◽  
Guanglai Gao
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Ratio Estimation ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Computing 3D Phase-Type Holograms Based on Deep Learning Method

Photonics ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 280
Author(s):  
Huadong Zheng ◽  
Jianbin Hu ◽  
Chaojun Zhou ◽  
Xiaoxi Wang
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Contrast Ratio ◽  
Angular Spectrum ◽  
Structural Similarity ◽  
Computational Time ◽  
Experimental Investigations ◽  
Training Strategy ◽  
Holographic Display ◽  
Phase Type

Computer holography is a technology that use a mathematical model of optical holography to generate digital holograms. It has wide and promising applications in various areas, especially holographic display. However, traditional computational algorithms for generation of phase-type holograms based on iterative optimization have a built-in tradeoff between the calculating speed and accuracy, which severely limits the performance of computational holograms in advanced applications. Recently, several deep learning based computational methods for generating holograms have gained more and more attention. In this paper, a convolutional neural network for generation of multi-plane holograms and its training strategy is proposed using a multi-plane iterative angular spectrum algorithm (ASM). The well-trained network indicates an excellent ability to generate phase-only holograms for multi-plane input images and to reconstruct correct images in the corresponding depth plane. Numerical simulations and optical reconstructions show that the accuracy of this method is almost the same with traditional iterative methods but the computational time decreases dramatically. The result images show a high quality through analysis of the image performance indicators, e.g., peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and contrast ratio. Finally, the effectiveness of the proposed method is verified through experimental investigations.

scholarly journals EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model

2019 ◽  
Vol 9 (11) ◽  
pp. 326 ◽  
Author(s):  
Hong Zeng ◽  
Zhenhua Wu ◽  
Jiaming Zhang ◽  
Chen Yang ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Speaker Recognition ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Single Subject ◽  
Emotion Classification ◽  
Eeg Signals ◽  
Electroencephalogram Eeg ◽  
Deep Learning Model

Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.

scholarly journals Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 667
Author(s):  
Wei Chen ◽  
Qiang Sun ◽  
Xiaomin Chen ◽  
Gangcai Xie ◽  
Huiqun Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Deep Learning ◽  
Heart Sounds ◽  
Learning Approaches ◽  
Automated Classification ◽  
Learning Methods ◽  
Artificial Intelligence Technology ◽  
Medical Big Data ◽  
Effective Models

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

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