scholarly journals Neural Network-Based Laser Interferometer Compensation for Seismic Signal Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Kyunghyun Lee ◽  
Hyungkwan Kwon ◽  
Kwanho You
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Seismic Data ◽  
Seismic Waves ◽  
Laser Interferometer ◽  
Body Wave ◽  
Measurement Data ◽  
The Body ◽  
Fusion Algorithm ◽  
Heterodyne Laser Interferometer

We propose a seismic wave detection method in the frequency domain using a heterodyne laser interferometer, which is used in ultraprecision fields as a displacement measurement device. In seismology, it is important to accurately measure seismic waves. To overcome the limited frequency range and low resolution of accelerometers and velocimeters and to enhance the precision of seismic data analysis, we use the heterodyne laser interferometer as a seismic detection apparatus. We apply the data fusion algorithm with the adaptive standard deviation ratio (ς) derived from the neural network to improve the laser interferometer’s measurement precision. Moreover, by using the interferometric characteristics, we analyze the seismic data in the frequency domain. To determine the location of the epicenter from the body wave propagation analysis, we apply the STA/LTA algorithm to the measurement data. The effectiveness of the proposed laser interferometric seismometer is shown through experiments to locate the precise epicenter.

scholarly journals Earthquake Magnitude Estimation Using a Total Noise Enhanced Optimization Model

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1454 ◽  
Author(s):  
Kyunghyun Lee ◽  
Jinhwan Oh ◽  
Hyukwoo Lee ◽  
Kwanho You
Keyword(s):  
Seismic Waves ◽  
Laser Interferometer ◽  
Optimal Solution ◽  
Ground Vibration ◽  
Earthquake Magnitude ◽  
P Wave ◽  
Total Noise ◽  
Acceleration Amplitude ◽  
Precise Prediction ◽  
Heterodyne Laser Interferometer

In this paper, a heterodyne laser interferometer, which is used as a sensor for high-precision displacement measurement, is introduced to measure ground vibration and seismic waves as a seismometer. The seismic wave is measured precisely through the displacement variation obtained by the heterodyne laser interferometer. The earthquake magnitude is estimated using only the P-wave magnitudes for the first 3 s through the total noise enhanced optimization (TNEO) model. We use data from southern California to investigate the relationship between peak acceleration amplitude ( P d ) and the earthquake magnitude ( M g ). For precise prediction of the earthquake magnitude using only the P d value, the TNEO model derives the relation equation between P d and the magnitude, considering the noise present in each measured seismic data. The optimal solution is obtained from the TNEO model based objective function. We proved the performance of the proposed method through simulation and experimental results.

scholarly journals Automatic Image-Based Event Detection for Large-N Seismic Arrays Using a Convolutional Neural Network

2021 ◽  
Vol 13 (3) ◽  
pp. 389
Author(s):  
Miłosz Mężyk ◽  
Michał Chamarczuk ◽  
Michał Malinowski
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Wave Energy ◽  
Ambient Noise ◽  
Body Wave ◽  
The Body ◽  
Detection Accuracy ◽  
Spatial And Temporal Patterns ◽  
Large N ◽  
Noise Data

Passive seismic experiments have been proposed as a cost-effective and non-invasive alternative to controlled-source seismology, allowing body–wave reflections based on seismic interferometry principles to be retrieved. However, from the huge volume of the recorded ambient noise, only selected time periods (noise panels) are contributing constructively to the retrieval of reflections. We address the issue of automatic scanning of ambient noise data recorded by a large-N array in search of body–wave energy (body–wave events) utilizing a convolutional neural network (CNN). It consists of computing first both amplitude and frequency attribute values at each receiver station for all divided portions of the recorded signal (noise panels). The created 2-D attribute maps are then converted to images and used to extract spatial and temporal patterns associated with the body–wave energy present in the data to build binary CNN-based classifiers. The ensemble of two multi-headed CNN models trained separately on the frequency and amplitude attribute maps demonstrates better generalization ability than each of its participating networks. We also compare the prediction performance of our deep learning (DL) framework with a conventional machine learning (ML) algorithm called XGBoost. The DL-based solution applied to 240 h of ambient seismic noise data recorded by the Kylylahti array in Finland demonstrates high detection accuracy and the superiority over the ML-based one. The ensemble of CNN-based models managed to find almost three times more verified body–wave events in the full unlabelled dataset than it was provided at the training stage. Moreover, the high-level abstraction features extracted at the deeper convolution layers can be used to perform unsupervised clustering of the classified panels with respect to their visual characteristics.

Removal of scattered surface waves using multicomponent seismic data

Geophysics ◽  
1996 ◽  
Vol 61 (5) ◽  
pp. 1483-1488 ◽  
Author(s):  
Bastian Blonk ◽  
Gérard C. Herman
Keyword(s):  
Surface Waves ◽  
Seismic Data ◽  
Body Wave ◽  
Simulated Data ◽  
Inverse Scattering Method ◽  
The Body ◽  
Scattering Method ◽  
Wave Reflections ◽  
Shallow Subsurface

In many exploration areas, the shallow subsurface is strongly heterogeneous. The heterogeneities can give rise to scattering of surface waves. These scattered waves can depreciate the quality of land seismic data when they mask the body‐wave reflections from the deeper part of the subsurface. Surface waves scattered near a line of receivers (inline‐scattered waves) can be removed by well‐known filtering techniques (see e.g., Yilmaz, 1987, section 1.6.2). However, surface waves scattered far from the receiver line (crossline‐scattered waves) are left intact partially by filtering because these waves can resemble body‐wave reflections. In previous papers, we have discussed an inverse scattering method for removing scattered surface waves from simulated data (Blonk and Herman, 1994), as well as from field data (Blonk et al., 1995). So far, we have limited our attention to the vertical components of the particle velocity which implies that surface waves and body‐wave reflections can be distinguished on the basis of their respective differences in phase velocity.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

scholarly journals Automatic Modulation Classification Based on Deep Feature Fusion for High Noise Level and Large Dynamic Input

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2117
Author(s):  
Hui Han ◽  
Zhiyuan Ren ◽  
Lin Li ◽  
Zhigang Zhu
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Noise Level ◽  
Background Noise ◽  
Dynamic Range ◽  
Feature Fusion ◽  
Superior Performance ◽  
Modulation Classification ◽  
High Background ◽  
Automatic Modulation Classification

Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB.

scholarly journals Development of a Deep Neural Network Model for Estimating Joint Location of Occupant Indoor Activities for Providing Thermal Comfort

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 696
Author(s):  
Eun Ji Choi ◽  
Jin Woo Moon ◽  
Ji-hoon Han ◽  
Yongseok Yoo
Keyword(s):  
Neural Network ◽  
Thermal Comfort ◽  
Deep Neural Network ◽  
Mean Squared Error ◽  
Thermal Environment ◽  
The Body ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Body Parts ◽  
Joint Location

The type of occupant activities is a significantly important factor to determine indoor thermal comfort; thus, an accurate method to estimate occupant activity needs to be developed. The purpose of this study was to develop a deep neural network (DNN) model for estimating the joint location of diverse human activities, which will be used to provide a comfortable thermal environment. The DNN model was trained with images to estimate 14 joints of a person performing 10 common indoor activities. The DNN contained numerous shortcut connections for efficient training and had two stages of sequential and parallel layers for accurate joint localization. Estimation accuracy was quantified using the mean squared error (MSE) for the estimated joints and the percentage of correct parts (PCP) for the body parts. The results show that the joint MSEs for the head and neck were lowest, and the PCP was highest for the torso. The PCP for individual activities ranged from 0.71 to 0.92, while typing and standing in a relaxed manner were the activities with the highest PCP. Estimation accuracy was higher for relatively still activities and lower for activities involving wide-ranging arm or leg motion. This study thus highlights the potential for the accurate estimation of occupant indoor activities by proposing a novel DNN model. This approach holds significant promise for finding the actual type of occupant activities and for use in target indoor applications related to thermal comfort in buildings.

scholarly journals Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation

2021 ◽  
Vol 11 (3) ◽  
pp. 1084
Author(s):  
Peng Wu ◽  
Ailan Che
Keyword(s):  
Neural Network ◽  
Elastic Wave ◽  
Frequency Domain ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Monitoring And Evaluation ◽  
Strong Nonlinearity ◽  
Filling Process ◽  
Time Frequency ◽  
Feature Parameters

The sand-filling method has been widely used in immersed tube tunnel engineering. However, for the problem of monitoring during the sand-filling process, the traditional methods can be inadequate for evaluating the state of sand deposits in real-time. Based on the high efficiency of elastic wave monitoring, and the superiority of the backpropagation (BP) neural network on solving nonlinear problems, a spatiotemporal monitoring and evaluation method is proposed for the filling performance of foundation cushion. Elastic wave data were collected during the sand-filling process, and the waveform, frequency spectrum, and time–frequency features were analysed. The feature parameters of the elastic wave were characterized by the time domain, frequency domain, and time-frequency domain. By analysing the changes of feature parameters with the sand-filling process, the feature parameters exhibited dynamic and strong nonlinearity. The data of elastic wave feature parameters and the corresponding sand-filling state were trained to establish the evaluation model using the BP neural network. The accuracy of the trained network model reached 93%. The side holes and middle holes were classified and analysed, revealing the characteristics of the dynamic expansion of the sand deposit along the diffusion radius. The evaluation results are consistent with the pressure gauge monitoring data, indicating the effectiveness of the evaluation and monitoring model for the spatiotemporal performance of sand deposits. For the sand-filling and grouting engineering, the machine-learning method could offer a better solution for spatiotemporal monitoring and evaluation in a complex environment.

scholarly journals A Product Pose Tracking Paradigm Based on Deep Points Detection

Machines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 112
Author(s):  
Loukas Bampis ◽  
Spyridon G. Mouroutsos ◽  
Antonios Gasteratos
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Visible Spectrum ◽  
The Body ◽  
Autonomous Control ◽  
Pose Tracking ◽  
Computer Aided ◽  
Manufacturing Procedure ◽  
Infrared Cameras ◽  
Representative Points

The paper at hand presents a novel and versatile method for tracking the pose of varying products during their manufacturing procedure. By using modern Deep Neural Network techniques based on Attention models, the most representative points to track an object can be automatically identified using its drawing. Then, during manufacturing, the body of the product is processed with Aluminum Oxide on those points, which is unobtrusive in the visible spectrum, but easily distinguishable from infrared cameras. Our proposal allows for the inclusion of Artificial Intelligence in Computer-Aided Manufacturing to assist the autonomous control of robotic handlers.

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