scholarly journals Automatic Extraction of Gravity Waves from All-Sky Airglow Image Based on Machine Learning

2019 ◽  
Vol 11 (13) ◽  
pp. 1516 ◽  
Author(s):  
Chang Lai ◽  
Jiyao Xu ◽  
Jia Yue ◽  
Wei Yuan ◽  
Xiao Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Gravity Waves ◽  
Classification Model ◽  
Atmospheric Gravity Waves ◽  
Detection Model ◽  
Wave Patterns ◽  
Airglow Image ◽  
Atmospheric Gravity

With the development of ground-based all-sky airglow imager (ASAI) technology, a large amount of airglow image data needs to be processed for studying atmospheric gravity waves. We developed a program to automatically extract gravity wave patterns in the ASAI images. The auto-extraction program includes a classification model based on convolutional neural network (CNN) and an object detection model based on faster region-based convolutional neural network (Faster R-CNN). The classification model selects the images of clear nights from all ASAI raw images. The object detection model locates the region of wave patterns. Then, the wave parameters (horizontal wavelength, period, direction, etc.) can be calculated within the region of the wave patterns. Besides auto-extraction, we applied a wavelength check to remove the interference of wavelike mist near the imager. To validate the auto-extraction program, a case study was conducted on the images captured in 2014 at Linqu (36.2°N, 118.7°E), China. Compared to the result of the manual check, the auto-extraction recognized less (28.9% of manual result) wave-containing images due to the strict threshold, but the result shows the same seasonal variation as the references. The auto-extraction program applies a uniform criterion to avoid the accidental error in manual distinction of gravity waves and offers a reliable method to process large ASAI images for efficiently studying the climatology of atmospheric gravity waves.

IDENTIFICATION MODEL OF STEEL-TEEMING LADLES IN STEELMAKING

2021 ◽  
Vol 1 (100) ◽  
pp. 68-77
Author(s):  
YAROSLAV M. TROFIMENKO ◽  
EVGENIY V. ERSHOV
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Production Process ◽  
Steel Production ◽  
Detection Model ◽  
Simulation Results ◽  
Identification Model

A model for detecting objects in the image and a model for identifying steel-teeming ladles are discussed in the article. The object detection model is based on the use of a convolutional neural network. The identification model is based on the comparison of steel-teeming ladle features specific to the production process. The authors describe the adaptations of models to the conditions of the YOLOv3 architecture and the parameters of steel-teeming ladles in steel production. Simulation results are given at the end of the article.

scholarly journals Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lee Ming Jun Melvin ◽  
Rajesh Elara Mohan ◽  
Archana Semwal ◽  
Povendhan Palanisamy ◽  
Karthikeyan Elangovan ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Urban Environment ◽  
Field Trial ◽  
Detection Algorithm ◽  
Data Set ◽  
Detection Model ◽  
Inspection Robot ◽  
Online Test

AbstractDrain blockage is a crucial problem in the urban environment. It heavily affects the ecosystem and human health. Hence, routine drain inspection is essential for urban environment. Manual drain inspection is a tedious task and prone to accidents and water-borne diseases. This work presents a drain inspection framework using convolutional neural network (CNN) based object detection algorithm and in house developed reconfigurable teleoperated robot called ‘Raptor’. The CNN based object detection model was trained using a transfer learning scheme with our custom drain-blocking objects data-set. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trial. The experimental results indicate that our trained object detection algorithm has detect and classified the drain blocking objects with 91.42% accuracy for both offline and online test images and is able to process 18 frames per second (FPS). Further, the maneuverability of the robot was evaluated from various open and closed drain environment. The field trial results ensure that the robot maneuverability was stable, and its mapping and localization is also accurate in a complex drain environment.

Pavement Pothole Detection Based on Cascade and Fusion Convolutional Neural Network Using 2D Images under Complex Pavement Conditions

2020 ◽  
Vol 14 ◽  
Author(s):  
Guoqiang Chen ◽  
Bingxin Bai ◽  
Huailong Yi
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Feature Fusion ◽  
Detection Accuracy ◽  
Detection Model ◽  
Automatic Driving ◽  
Proposed Model ◽  
Multi Level ◽  
Pothole Detection

Background: Background: The development of deep learning technology has promoted the industrial intelligence, and automatic driving vehicles have become a hot research direction. As to the problem that pavement potholes threaten the safety of automatic driving vehicles, the pothole detection under complex environment conditions is studied. Objective: The goal of the work is to propose a new model of pavement pothole detection based on convolutional neural network. The main contribution is that the Multi-level Feature Fusion Block and the Detector Cascading Block are designed and a series of detectors are cascaded together to improve the detection accuracy of the proposed model. Methods: A pothole detection model is designed based on the original object detection model. In the study, the Transfer Connection Block in the Object Detection Module is removed and the Multi-level Feature Fusion Block is redesigned. At the same time, a Detector Cascading Block with multi-step detection is designed. Detectors are connected directly to the feature map and cascaded. In addition, the structure skips the transformation step. Results: The proposed method can be used to detect potholes efficiently. The real-time and accuracy of the model are improved after adjusting the network parameters and redesigning the model structure. The maximum detection accuracy of the proposed model is 75.24%. Conclusion: The Multi-level Feature Fusion Block designed enhances the fusion of high and low layer feature information and is conducive to extracting a large amount of target information. The Detector Cascade Block is a detector with cascade structure, which can realize more accurate prediction of the object. In a word, the model designed has greatly improved the detection accuracy and speed, which lays a solid foundation for pavement pothole detection under complex environmental conditions.

scholarly journals Automated Detection of Gastric Cancer by Retrospective Endoscopic Image Dataset Using U-Net R-CNN

2021 ◽  
Vol 11 (23) ◽  
pp. 11275
Author(s):  
Atsushi Teramoto ◽  
Tomoyuki Shibata ◽  
Hyuga Yamada ◽  
Yoshiki Hirooka ◽  
Kuniaki Saito ◽  
...  
Keyword(s):  
Neural Network ◽  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Semantic Segmentation ◽  
False Positives ◽  
Detection Performance ◽  
Automated Detection ◽  
Detection Model

Upper gastrointestinal endoscopy is widely performed to detect early gastric cancers. As an automated detection method for early gastric cancer from endoscopic images, a method involving an object detection model, which is a deep learning technique, was proposed. However, there were challenges regarding the reduction in false positives in the detected results. In this study, we proposed a novel object detection model, U-Net R-CNN, based on a semantic segmentation technique that extracts target objects by performing a local analysis of the images. U-Net was introduced as a semantic segmentation method to detect early candidates for gastric cancer. These candidates were classified as gastric cancer cases or false positives based on box classification using a convolutional neural network. In the experiments, the detection performance was evaluated via the 5-fold cross-validation method using 1208 images of healthy subjects and 533 images of gastric cancer patients. When DenseNet169 was used as the convolutional neural network for box classification, the detection sensitivity and the number of false positives evaluated on a lesion basis were 98% and 0.01 per image, respectively, which improved the detection performance compared to the previous method. These results indicate that the proposed method will be useful for the automated detection of early gastric cancer from endoscopic images.

scholarly journals Observation evidences of atmospheric Gravity Waves induced by seismic activity from analysis of subionospheric LF signal spectra

2007 ◽  
Vol 7 (5) ◽  
pp. 625-628 ◽  
Author(s):  
A. Rozhnoi ◽  
M. Solovieva ◽  
O. Molchanov ◽  
P.-F. Biagi ◽  
M. Hayakawa
Keyword(s):  
Gravity Waves ◽  
Fresnel Zone ◽  
Signal Amplitude ◽  
Magnetic Storms ◽  
Frequency Range ◽  
Atmospheric Gravity Waves ◽  
Period Range ◽  
Before And After ◽  
Atmospheric Gravity ◽  
Large Earthquakes

Abstract. We analyze variations of the LF subionospheric signal amplitude and phase from JJY transmitter in Japan (F=40 kHz) received in Petropavlovsk-Kamchatsky station during seismically quiet and active periods including also periods of magnetic storms. After 20 s averaging, the frequency range of the analysis is 0.28–15 mHz that corresponds to the period range from 1 to 60 min. Changes in spectra of the LF signal perturbations are found several days before and after three large earthquakes, which happened in November 2004 (M=7.1), August 2005 (M=7.2) and November 2006 (M=8.2) inside the Fresnel zone of the Japan-Kamchatka wavepath. Comparing the perturbed and background spectra we have found the evident increase in spectral range 10–25 min that is in the compliance with theoretical estimations on lithosphere-ionosphere coupling by the Atmospheric Gravity Waves (T>6 min). Similar changes are not found for the periods of magnetic storms.

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