Real-Time Tsunami Data Assimilation of S-Net Pressure Gauge Records during the 2016 Fukushima Earthquake

2021 ◽  
Author(s):  
Yuchen Wang ◽  
Kenji Satake
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Pressure Gauge ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Tsunami Detection ◽  
Seafloor Observation ◽  
Net Pressure

Abstract The 2016 Fukushima earthquake (M 7.4) generated a moderate tsunami, which was recorded by the offshore pressure gauges of the Seafloor Observation Network for Earthquakes and Tsunamis (S-net). We used 28 S-net pressure gauge records for tsunami data assimilation and forecasted the tsunami waveforms at four tide gauges on the Sanriku coast. The S-net raw records were processed using two different methods. In the first method, we removed the tidal components by polynomial fitting and applied a low-pass filter. In the second method, we used a real-time tsunami detection algorithm based on ensemble empirical mode decomposition to extract the tsunami signals, imitating real-time operations for tsunami early warning. The forecast accuracy scores of the two detection methods are 60% and 74%, respectively, for a time window of 35 min, but they improve to 89% and 94% if we neglect the stations with imperfect modeling or insufficient offshore observations. Hence, the tsunami data assimilation approach can be put into practice with the help of the real-time tsunami detection algorithm.

Based on the CO2 Gas Shielded Welding Molten Pool Image Edge Detection Algorithm

2013 ◽  
Vol 437 ◽  
pp. 840-844 ◽  
Author(s):  
Xiao Gang Liu ◽  
Bing Zhao
Keyword(s):  
Edge Detection ◽  
Molten Pool ◽  
High Speed ◽  
Vision System ◽  
Detection Algorithm ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Defect Recognition ◽  
Pool Image

This paper use the passive vision system through high-speed camera collects molten pool images; and then according to the frequency domain characteristics of the weld pool image Butterworth low-pass filter; gradient method for image enhancement obtained after pretreatment. Research Roberts, Sobel, Prewitt, Log, Zerocross, and Canny 6 both traditional differential operator edge detection processing results. Through comparison and analysis of choosing threshold for [0.1, 0. Canny operator can get the ideal molten pool edge character, for subsequent welding molten pool defect recognition provides favorable conditions.

scholarly journals Study of Harmonic Detection Methods under Non-Ideal Conditions in Ship Power Network

2015 ◽  
Vol 22 (3) ◽  
pp. 82-89
Author(s):  
Xiao-Yan Xu ◽  
Janusz Mindykowski ◽  
Tomasz Tarasiuk ◽  
Chen Cheng
Keyword(s):  
Phase Lock Loop ◽  
Mean Value ◽  
Detection Methods ◽  
Harmonic Detection ◽  
Phase Voltage ◽  
Rapid Variation ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Three Phase ◽  
In Series

Abstract An improved harmonic detection method based on average arithmetic is proposed. According to the research results, the designed solution uses an LPF (low-pass-filter) and a mean value module connected in series instead of the conventional mean value module, and simultaneously, a three-phase voltage phase-locked module instead of commonly used PLL (phase lock loop) module is applied in order to reduce the influence caused by three-phase distorted voltage and rapid variation of load. The experimental results show that the application of this solution leads to increase in the accuracy of harmonics detection for distorted three-phase voltage and rapid variation of load.

scholarly journals Depth-averaged instantaneous currents in a tidally dominated shelf sea from glider observations

2016 ◽  
Author(s):  
Lucas Merckelbach
Keyword(s):  
Real Time ◽  
North Sea ◽  
Current Data ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Real Time Mode ◽  
The North ◽  
Shelf Sea ◽  
The North Sea ◽  
Better Than

Abstract. Ocean gliders have become ubiquitous observation platforms in the ocean in recent years. They are also increasingly used in coastal environments. The coastal observatory system COSYNA has pioneered the use of gliders in the North Sea, a shallow tidally energetic shelf sea. For operational reasons, the gliders operated in the North Sea are programmed to resurface every 3–5 hours. The glider's deadreckoning algorithm yields depth averaged currents, averaged in time over each subsurface interval. Under operational conditions these averaged currents are a poor approximation of the instantaneous tidal current. In this work an algorithm is developed that estimates the instantaneous current (tidal and residual) from glider observations only. The algorithm uses a second-order Butterworth low-pass filter to estimate the residual current component, and a Kalman filter based on the linear shallow water equations for the tidal component. A comparison of data from a glider experiment with current data from an ADCP deployed nearby shows that the standard deviations for the east and north current components are better than 7 cm s−1 in near-real time mode, and improve to better than 5 cm s−1 in delayed mode, where the filters can be run forward and backward. In the near-real time mode the algorithm provides estimates of the currents that the glider is expected to encounter during its next few dives. Combined with a behavioural and dynamic model of the glider, this yields predicted trajectories, the information of which is incorporated in warning messages issued to ships by the (German) authorities. In delayed mode the algorithm produces useful estimates of the depth averaged currents, which can be used in (process-based) analyses in case no other source of measured current information is available.

scholarly journals CSI-HC: A WiFi-Based Indoor Complex Human Motion Recognition Method

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Zhanjun Hao ◽  
Yu Duan ◽  
Xiaochao Dang ◽  
Tong Zhang
Keyword(s):  
Real Time ◽  
Human Motion ◽  
Detection Accuracy ◽  
Behavior Recognition ◽  
Motion Recognition ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Motion Data ◽  
Indoor Scenes ◽  
Human Motion Recognition

WiFi indoor personnel behavior recognition has become the core technology of wireless network perception. However, the existing human behavior recognition methods have great challenges in terms of detection accuracy, intrusion, and complexity of operations. In this paper, we firstly analyze and summarize the existing human motion recognition schemes, and due to the existence of the problems in them, we propose a noninvasive, highly robust complex human motion recognition scheme based on Channel State Information (CSI), that is, CSI-HC, and the traditional Chinese martial art XingYiQuan is verified as a complex motion background. CSI-HC is divided into two phases: offline and online. In the offline phase, the human motion data are collected on the commercial Atheros NIC and a powerful denoising method is constructed by using the Butterworth low-pass filter and wavelet function to filter the outliers in the motion data. Then, through Restricted Boltzmann Machine (RBM) training and classification, we establish offline fingerprint information. In the online phase, SoftMax regression is used to correct the RBM classification to process the motion data collected in real time and the processed real-time data are matched with the offline fingerprint information. On this basis, the recognition of a complex human motion is realized. Finally, through repeated experiments in three classical indoor scenes, the parameter setting and user diversity affecting the accuracy of motion recognition are analyzed and the robustness of CSI-HC is detected. In addition, the performance of the proposed method is compared with that of the existing motion recognition methods. The experimental results show that the average motion recognition rate of CSI-HC in three classic indoor scenes reaches 85.4%, in terms of motion complexity and indoor recognition accuracy. Compared with other algorithms, it has higher stability and robustness.

scholarly journals Depth-averaged instantaneous currents in a tidally dominated shelf sea from glider observations

2016 ◽  
Vol 13 (24) ◽  
pp. 6637-6649 ◽  
Author(s):  
Lucas Merckelbach
Keyword(s):  
Real Time ◽  
North Sea ◽  
Current Data ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Real Time Mode ◽  
The North ◽  
Shelf Sea ◽  
The North Sea ◽  
Better Than

Abstract. Ocean gliders have become ubiquitous observation platforms in the ocean in recent years. They are also increasingly used in coastal environments. The coastal observatory system COSYNA has pioneered the use of gliders in the North Sea, a shallow tidally energetic shelf sea. For operational reasons, the gliders operated in the North Sea are programmed to resurface every 3–5 h. The glider's dead-reckoning algorithm yields depth-averaged currents, averaged in time over each subsurface interval. Under operational conditions these averaged currents are a poor approximation of the instantaneous tidal current. In this work an algorithm is developed that estimates the instantaneous current (tidal and residual) from glider observations only. The algorithm uses a first-order Butterworth low pass filter to estimate the residual current component, and a Kalman filter based on the linear shallow water equations for the tidal component. A comparison of data from a glider experiment with current data from an acoustic Doppler current profilers deployed nearby shows that the standard deviations for the east and north current components are better than 7 cm s−1 in near-real-time mode and improve to better than 6 cm s−1 in delayed mode, where the filters can be run forward and backward. In the near-real-time mode the algorithm provides estimates of the currents that the glider is expected to encounter during its next few dives. Combined with a behavioural and dynamic model of the glider, this yields predicted trajectories, the information of which is incorporated in warning messages issued to ships by the (German) authorities. In delayed mode the algorithm produces useful estimates of the depth-averaged currents, which can be used in (process-based) analyses in case no other source of measured current information is available.

scholarly journals Collision detection algorithm for collaborative robots considering joint friction

2018 ◽  
Vol 15 (4) ◽  
pp. 172988141878899 ◽  
Author(s):  
Juliang Xiao ◽  
Qiulong Zhang ◽  
Ying Hong ◽  
Guodong Wang ◽  
Fan Zeng
Keyword(s):  
Collision Detection ◽  
Angular Acceleration ◽  
Detection Algorithm ◽  
Friction Model ◽  
Frequency Noise ◽  
Robot Interaction ◽  
Pass Filter ◽  
Low Pass Filter ◽  
First Order ◽  
Different Response

This article proposes a collision detection algorithm without external sensors that can detect potential collisions in man–robot interaction. The algorithm is based on a modified first-order momentum deviation observer that also takes friction into account. The collision detection algorithm uses joint angles, angular velocities, and torques during the detection process, without any need to consider angular acceleration. The algorithm also uses an accurate friction model that is based on a Stribeck model with second-order Fourier series compensation. The friction model is applied in advance so that compensation can be made in real time during collision detection. Identification data are filtered through a first-order low-pass filter to reduce high-frequency noise. In order to verify the algorithm, a simulation and experiment were carried out using a collaborative robot experimental platform. The results confirmed that collisions can be detected by setting appropriate threshold values. Different possible responses can be implemented according to different response strategies, with the ultimate arbiter being that collision forces are kept strictly within ordinary human tolerances. This makes sure that safety can be preserved in man–robot interaction processes.

scholarly journals Detection of Computer Graphics Using Attention-Based Dual-Branch Convolutional Neural Network from Fused Color Components

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4743
Author(s):  
Peisong He ◽  
Haoliang Li ◽  
Hongxia Wang ◽  
Ruimei Zhang
Keyword(s):  
Neural Network ◽  
Computer Graphics ◽  
Convolutional Neural Network ◽  
Video Game ◽  
Detection Methods ◽  
Post Processing ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Generalized Patterns ◽  
Color Components

With the development of 3D rendering techniques, people can create photorealistic computer graphics (CG) easily with the advanced software, which is of great benefit to the video game and film industries. On the other hand, the abuse of CGs has threatened the integrity and authenticity of digital images. In the last decade, several detection methods of CGs have been proposed successfully. However, existing methods cannot provide reliable detection results for CGs with the small patch size and post-processing operations. To overcome the above-mentioned limitation, we proposed an attention-based dual-branch convolutional neural network (AD-CNN) to extract robust representations from fused color components. In pre-processing, raw RGB components and their blurred version with Gaussian low-pass filter are stacked together in channel-wise as the input for the AD-CNN, which aims to help the network learn more generalized patterns. The proposed AD-CNN starts with a dual-branch structure where two branches work in parallel and have the identical shallow CNN architecture, except that the first convolutional layer in each branch has various kernel sizes to exploit low-level forensics traces in multi-scale. The output features from each branch are jointly optimized by the attention-based fusion module which can assign the asymmetric weights to different branches automatically. Finally, the fused feature is fed into the following fully-connected layers to obtain final detection results. Comparative and self-analysis experiments have demonstrated the better detection capability and robustness of the proposed detection compared with other state-of-the-art methods under various experimental settings, especially for image patch with the small size and post-processing operations.

scholarly journals Real-Time Vehicle Detection Algorithm Based on Vision and Lidar Point Cloud Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hai Wang ◽  
Xinyu Lou ◽  
Yingfeng Cai ◽  
Yicheng Li ◽  
Long Chen
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Depth Information ◽  
Detection Accuracy ◽  
Classification Rate

Vehicle detection is one of the most important environment perception tasks for autonomous vehicles. The traditional vision-based vehicle detection methods are not accurate enough especially for small and occluded targets, while the light detection and ranging- (lidar-) based methods are good in detecting obstacles but they are time-consuming and have a low classification rate for different target types. Focusing on these shortcomings to make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. Firstly, the obstacles are detected by the grid projection method using the lidar point cloud information. Then, the obstacles are mapped to the image to get several separated regions of interest (ROIs). After that, the ROIs are expanded based on the dynamic threshold and merged to generate the final ROI. Finally, a deep learning method named You Only Look Once (YOLO) is applied on the ROI to detect vehicles. The experimental results on the KITTI dataset demonstrate that the proposed algorithm has high detection accuracy and good real-time performance. Compared with the detection method based only on the YOLO deep learning, the mean average precision (mAP) is increased by 17%.

scholarly journals Real-Time Filter Method Based on the Structural Frequency of FAST Cabin-Cable System

2014 ◽  
Vol 6 ◽  
pp. 129302
Author(s):  
Wenhua Xu ◽  
Hong Bao ◽  
Jianwei Mi ◽  
Guigeng Yang
Keyword(s):  
Real Time ◽  
Fundamental Frequency ◽  
Variable Structure ◽  
Measured Data ◽  
Filter Method ◽  
Great Flexibility ◽  
Cable System ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

Due to great flexibility, low damping, and variable structure in the cabin-cable system of five hundred meter Aperture Spherical Radio Telescope (FAST), a real-time digital low-pass filter based on the analysis of frequency is presented in this paper. Firstly, by the Lomb-Scargle theorem, it can obtain the fundamental frequency of cabin-cable system. Then, using the obtained frequency, a digital low-pass filter is designed to filter the measured data. After being filtered, the measured data are used for coarse control. Finally, the results of the experiments on the FAST 5 m model show that calculating the fundamental frequency is accurate and the filter is effective.

scholarly journals Impact of the Incremental Analysis Updates on a Real-Time System of the North Atlantic Ocean

2008 ◽  
Vol 25 (11) ◽  
pp. 2055-2073 ◽  
Author(s):  
M. Benkiran ◽  
E. Greiner
Keyword(s):  
Real Time ◽  
North Atlantic ◽  
Weather Forecasting ◽  
Incremental Analysis ◽  
Real Time System ◽  
Pass Filter ◽  
Low Pass Filter ◽  
The North ◽  
The North Atlantic ◽  
The Impact

Abstract Incremental analysis updates (IAUs) are a procedure by which analysis increments can be incorporated into a model hindcast and forecast in a smooth manner. It is similar to nudging but has a better response, particularly in regions of missing data. The IAU procedure was popular in the late 1990s in weather forecasting centers, because it acts as a low-pass filter. The impact of the IAU is examined in the context of a real-time, eddy-permitting ocean forecasting system in the North Atlantic from Mercator Océan. Forecast scores and ocean physics are compared for the following three companion runs: a forced mode, a sequential analysis, and IAU. These comparisons confirm that the IAU is beneficial because it removes spinup effects such as spurious waves and tropical convective cells. Forecast scores are also slightly improved. In addition, contrary to the weather forecasting case where the model and data are fairly unbiased, the IAU has the advantage of correcting the systematic biases in the ocean data assimilation system.

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