scholarly journals A CME Automatic Detection Method Based on Adaptive Background Learning Technology

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Zhenping Qiang ◽  
Xianyong Bai ◽  
Qinghui Zhang ◽  
Hong Lin
Keyword(s):  
Detection Method ◽  
Maximum Velocity ◽  
Position Angle ◽  
Image Sequences ◽  
Angular Width ◽  
Learning Technology ◽  
Dynamic Features ◽  
Automatic Methods ◽  
Difference Time ◽  
Minimum Velocity

In this paper, we describe a technique, which uses an adaptive background learning method to detect the CME (coronal mass ejections) automatically from SOHO/LASCO C2 image sequences. The method consists of several modules: adaptive background module, candidate CME area detection module, and CME detection module. The core of the method is based on adaptive background learning, where CMEs are assumed to be a foreground moving object outward as observed in running-difference time series. Using the static and dynamic features to model the corona observation scene can more accurately describe the complex background. Moreover, the method can detect the subtle changes in the corona sequences while filtering their noise effectively. We applied this method to a month of continuous corona images, compared the result with CDAW, CACTus, SEEDS, and CORIMP catalogs and found a good detection rate in the automatic methods. It detected about 73% of the CMEs listed in the CDAW CME catalog, which is identified by human visual inspection. Currently, the derived parameters are position angle, angular width, linear velocity, minimum velocity, and maximum velocity of CMES. Other parameters could also easily be added if needed.

scholarly journals Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 68
Author(s):  
Jiwei Fan ◽  
Xiaogang Yang ◽  
Ruitao Lu ◽  
Xueli Xie ◽  
Weipeng Li
Keyword(s):  
Deep Learning ◽  
Autonomous Navigation ◽  
Detection Method ◽  
Active Role ◽  
Absolute Error ◽  
Face Mask ◽  
Learning Technology ◽  
Flight System ◽  
Crowd Density ◽  
The Mean

Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.

Avoidance of obstacles with unknown trajectories: locally optimal paths and path complexity, Part II

Robotica ◽  
1993 ◽  
Vol 11 (5) ◽  
pp. 403-412 ◽  
Author(s):  
James Gil de Lamadrid ◽  
Jill Zimmermanf
Keyword(s):  
Path Planning ◽  
Maximum Velocity ◽  
Local Information ◽  
Planning Problem ◽  
Two Dimensional ◽  
Robot Trajectory ◽  
Optimal Paths ◽  
Sensor Information ◽  
Minimum Velocity ◽  
Path Planning Problem

SUMMARYContinuing the work presented in part I, ‡ we consider the problem of moving a point robot through a two dimensional workspace containing polygonal obstacles moving on unknown trajectories. We propose to use sensor information to predict the trajectories of the obstacles, and interleave path planning and execution. We define a locally minimum velocity path as an optimal robot trajectory, given only local information about obstacle trajectories. We show that the complexity of a path planning problem can be characterized by how frequently the robot must change directions to approximate the locally minimum velocity path. Our results apply to both robots with and without maximum velocity limits.

Road Edges Detection of a Novel Travel Aid for Visual Impaired People

2012 ◽  
Vol 591-593 ◽  
pp. 1810-1813
Author(s):  
Jie Ying ◽  
Shan Shan Xu
Keyword(s):  
Detection Method ◽  
Image Sequences ◽  
Extraction Rate ◽  
Stereo Image ◽  
The Road ◽  
Impaired People ◽  
Line Transfer

A road edges detection method of a novel electronic travel aid for the blind was proposed. Road edges extraction was realized by Canny filter and Hough line transfer. Multiple lines detected by Hough transfer were processed into one edge line. Road deviation judgment method was proposed to determine if the user has deviated from the road by judging the position and angle of the edge lines. Experiments showed that the correct extraction rate of road edges was 93%, and the road deviation judgment method was effective and fast. Experiments were done using the consecutive stereo image sequences captured on road.

scholarly journals DIGITAL IMAGE ANALYSIS OF BURNING DROPLETS IN THE PRESENCE OF BACKLIGHT DIFFRACTION AND SOOT

2019 ◽  
Vol 38 (1) ◽  
pp. 53 ◽  
Author(s):  
Ramya Bhaskar ◽  
Benjamin Shaw
Keyword(s):  
Image Analysis ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Detection Method ◽  
Soot Formation ◽  
Space Station ◽  
Image Sequences ◽  
Synthetic Image ◽  
International Space ◽  
Fuel Droplets

Approaches for analyzing digital images of moving and burning fuel droplets, with the goal of accurately measuring droplet edge coordinates, are discussed. Strategies for locating droplet edges in the presence of obscuration from soot and also backlight diffraction at the droplet edge are described. An outlier detection method is employed to identify outliers in droplet edge coordinates, and the resulting data can have significantly smaller standard deviations in droplet diameters if outliers are rejected, especially for droplets that exhibit significant soot formation. The approaches described herein are applied to images from droplet combustion experiments performed on the International Space Station as well as to synthetic image sequences that were generated to enable the accuracy of the algorithms to be assessed.

Dynamic Features Based Rumor Detection Method

2020 ◽  
Author(s):  
Zhongyi Meng ◽  
Shikang Yu ◽  
Ruqi Li ◽  
Guoping Jiang ◽  
Yurong Song
Keyword(s):  
Detection Method ◽  
Dynamic Features ◽  
Rumor Detection

scholarly journals Danger-Pose Detection System Using Commodity Wi-Fi for Bathroom Monitoring

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 884 ◽  
Author(s):  
Zizheng Zhang ◽  
Shigemi Ishida ◽  
Shigeaki Tagashira ◽  
Akira Fukuda
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Method ◽  
Detection System ◽  
Wearable Sensors ◽  
Low Frequency ◽  
Training Data ◽  
Support Vector ◽  
Dynamic Features ◽  
Pose Detection

A bathroom has higher probability of accidents than other rooms due to a slippery floor and temperature change. Because of high privacy and humidity, we face difficulties in monitoring inside a bathroom using traditional healthcare methods based on cameras and wearable sensors. In this paper, we present a danger-pose detection system using commodity Wi-Fi devices, which can be applied to bathroom monitoring, preserving privacy. A machine learning-based detection method usually requires data collected in target situations, which is difficult in detection-of-danger situations. We therefore employ a machine learning-based anomaly-detection method that requires a small amount of data in anomaly conditions, minimizing the required training data collected in dangerous conditions. We first derive the amplitude and phase shift from Wi-Fi channel state information (CSI) to extract low-frequency components that are related to human activities. We then separately extract static and dynamic features from the CSI changes in time. Finally, the static and dynamic features are fed into a one-class support vector machine (SVM), which is used as an anomaly-detection method, to classify whether a user is not in bathtub, bathing safely, or in dangerous conditions. We conducted experimental evaluations and demonstrated that our danger-pose detection system achieved a high detection performance in a non-line-of-sight (NLOS) scenario.

scholarly journals Bridge Crack Semantic Segmentation Based on Improved Deeplabv3+

2021 ◽  
Vol 9 (6) ◽  
pp. 671
Author(s):  
Huixuan Fu ◽  
Dan Meng ◽  
Wenhui Li ◽  
Yuchao Wang
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Semantic Segmentation ◽  
Learning Technology ◽  
Data Set ◽  
Original Algorithm ◽  
Bridge Maintenance ◽  
Segmentation Accuracy ◽  
Spatial Pyramid Pooling ◽  
Traditional Image

Cracks are the main goal of bridge maintenance and accurate detection of cracks will help ensure their safe use. Aiming at the problem that traditional image processing methods are difficult to accurately detect cracks, deep learning technology was introduced and a crack detection method based on an improved DeepLabv3+ semantic segmentation algorithm was proposed. In the network structure, the densely connected atrous spatial pyramid pooling module was introduced into the DeepLabv3+ network, which enabled the network to obtain denser pixel sampling, thus enhancing the ability of the network to extract detail features. While obtaining a larger receptive field, the number of network parameters was consistent with the original algorithm. The images of bridge cracks under different environmental conditions were collected, and then a concrete bridge crack segmentation data set was established, and the segmentation model was obtained through end-to-end training of the network. The experimental results showed that the improved DeepLabv3+ algorithm had higher crack segmentation accuracy than the original DeepLabv3+ algorithm, with an average intersection ratio reaching 82.37%, and the segmentation of crack details was more accurate, which proved the effectiveness of the proposed algorithm.

scholarly journals DAYA DUKUNG SEDIMEN DASAR LAUT DI PERAIRAN PELABUHAN CIREBON DAN SEKITARNYA

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Asep Permana ◽  
Purnomo Raharjo
Keyword(s):  
Bearing Capacity ◽  
Neap Tide ◽  
Maximum Velocity ◽  
Standard Penetration Test ◽  
Development Planning ◽  
Penetration Test ◽  
Sea Floor ◽  
The North ◽  
Minimum Velocity ◽  
Foundation Structure

Daya dukung sedimen dasar laut dan aspek keteknikan pada perencanaan pengembangan pelabuhan Cirebon lebih ditekankan pada faktor geoteknik, geofisika dan oseanografi. Pada saat pasang arah arus cenderung ke arah selatan dan baratdaya, sedangkan pada saat surut cenderung ke arah utara dan timurlaut dengan kecepatan rata-rata maksimum 0.11 m/detik dan minimum 0.08 m/detik. Morfologi dasar laut di perairan pelabuhan Cirebon sangat landai bervariasi antara - 6,5 m (LWS) dan -8.00 m, sedangkan kolamnya sendiri antara 0.00 -2.00 m, Daya dukung tanah pada kedalaman 18.00 - 27.00 m dari LWS di bagian atas diselingi oleh pasir lepas hingga lempung pasiran merupakan tanah bersifat lunak (soft) dengan N SPT = 22 hingga 32 tumbukan (blows). Data sondir di sekitar lokasi dermaga menunjukan nilai harga Qc = 2-4 kg/cm2 pada kedalaman 2.00-11.50 m dan nilai Qc > 150 kg/cm2 dijumpai pada kedalaman 14.00-15.50 m. Sedangkan lapisan bawah di daerah Astanajapura pada kedalaman lebih dari 20.00 meter tertumpu pada pasir, padat, keras, nilai SPT antara 35 hingga lebih dari 50 tumbukan. Analisis mineral lempung yang ada di daerah selidikan memperlihatkan bahwa lempung monmorilonite sangat dominan dan diketahui bahwa tanah yang mengandung monmorilonite sangat mudah mengembang oleh tambahan kadar air sehingga tekanan pengembangannya dapat merusak struktur bangunan pondasi. Bottom sediments bearing capacity on Cirebon harbor development planning are focused on Geotechnique, geophysical and oceanographically aspects. During tidal spring, current tend to the south and southwest wards and during the neap tide tend to the north and northeast with mean maximum velocity was 0.11 m/sec and minimum velocity was 0.08 m/sec. The sea floor morphology in the Cirebon harbor waters is slightly gentle and the water depth varies from -6.5 to 8.5 m (LWS), while the depth of the pond itself are between 0.00 to -2.00 meters. The bearing capacity from SPT (Standard Penetration Test) at depth between 18.00 - 27.00 m are composed of loose sand to sandy clay, soft, with (N) SPT values about 22 to 32 blows. Sondir data obtained at depth 2.00 - 11.50 and Qc value about 2 - 4 kg/cm2 while at depth 14.00 m to 15.50 m Qc value data about more > 150 kg/cm2 was found at depth more than 20.00 meters. The lower part layers in Astanajapura are composed of sand, dense, hard, with SPT value data obtained are 35 to more than 50 blows. Clay mineral analysis showed montmorilonite is dominant in this survey area. So that very easy to swell and will influenced the foundation structure construction.

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