scholarly journals Toward Real-time Microseismic Event Detection using the YOLOv3 Algorithm

2022 ◽  
Author(s):  
Xiaoyu Zhu ◽  
Jeffrey Shragge
Keyword(s):  
Real Time ◽  
Event Detection ◽  
High Speed ◽  
Detection System ◽  
Gaussian White Noise ◽  
Detection Methods ◽  
Support Vector ◽  
Signal To Noise ◽  
Microseismic Events ◽  
Microseismic Event

Real-time microseismic monitoring is essential for understanding fractures associated with underground fluid injection in unconventional reservoirs. However, microseismic events recorded on monitoring arrays are usually contaminated with strong noise. With a low signal-to-noise ratio (S/R), the detection of microseismic events is challenging using conventional detection methods such as the short-term average/long-term average (STA/LTA) technique. Common machine learning methods, e.g., feature extraction plus support vector machine (SVM) and convolutional neural networks (CNNs), can achieve higher accuracy with strong noise, but they are usually time-consuming and memory-intensive to run. We propose the use of YOLOv3, a state-of-art real-time object detection system in microseismic event detection. YOLOv3 is a one-stage deep CNN detector that predicts class confidence and bounding boxes for images at high speed and with great precision. With pre-trained weights from the ImageNet 1000-class competition dataset, physics-based training of the YOLOv3 algorithm is performed on a group of forward modeled synthetic microseismic data with varying S/R. We also add randomized forward-modeled surface seismic events and Gaussian white noise to generate ``semi-realistic'' training and testing datasets. YOLOv3 is able to detect weaker microseismic event signals with low signal-to-noise ratios (e.g., S/N=0.1) and achieves a mean average precision of 88.71\% in near real time. Further work is required to test YOLOv3 in field production settings.

scholarly journals Automatic high-resolution microseismic event detection via supervised machine learning

2020 ◽  
Vol 222 (3) ◽  
pp. 1881-1895 ◽  
Author(s):  
Shan Qu ◽  
Zhe Guan ◽  
Eric Verschuur ◽  
Yangkang Chen
Keyword(s):  
Feature Selection ◽  
High Resolution ◽  
Event Detection ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Gaussian Kernel ◽  
Selection Strategy ◽  
Support Vector ◽  
Microseismic Events ◽  
Microseismic Event

SUMMARY Microseismic methods are crucial for real-time monitoring of the hydraulic fracturing dynamic status during the development of unconventional reservoirs. However, unlike the active-source seismic events, the microseismic events usually have low signal-to-noise ratio (SNR), which makes its data processing challenging. To overcome the noise issue of the weak microseismic events, we propose a new workflow for high-resolution microseismic event detection. For the preprocessing, fix-sized segmentation with a length of 2*wavelength is used to divide the data into segments. Later on, 191 features have been extracted and used as the input data to train the support vector machine (SVM) model. These features include 63 1-D time/spectral-domain features, and 128 2-D texture features, which indicate the continuity, smoothness, and irregularity of the events/noise. The proposed feature extraction maximally exploits the limited information of each segment. Afterward, we use a combination of univariate feature selection and random-forest-based recursive feature elimination for feature selection to avoid overfitting. This feature selection strategy not only finds the best features, but also decides the optimal number of features that are needed for the best accuracy. Regarding the training process, SVM with a Gaussian kernel is used. In addition, a cross-validation (CV) process is implemented for automatic parameter setting. In the end, a group of synthetic and field microseismic data with different levels of complexity show that the proposed workflow is much more robust than the state-of-the-art short-term-average over long-term-average ratio (STA/LTA) method and also performs better than the convolutional-neural-networks (CNN), for this case where the amount of training data sets is limited. A demo for the synthetic example is available: https://github.com/shanqu91/ML_event_detection_microseismic.

scholarly journals A novel GPU based intrusion detection system using deep autoencoder with Fruitfly optimization

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
R. Sekhar ◽  
K. Sasirekha ◽  
P. S. Raja ◽  
K. Thangavel
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Missing Values ◽  
Detection System ◽  
Radial Basis Function Network ◽  
Detection Methods ◽  
Support Vector ◽  
Parameter Method ◽  
List Type ◽  
Fuzzy C Means

Abstract Intrusion Detection Systems (IDSs) have received more attention to safeguarding the vital information in a network system of an organization. Generally, the hackers are easily entering into a secured network through loopholes and smart attacks. In such situation, predicting attacks from normal packets is tedious, much challenging, time consuming and highly technical. As a result, different algorithms with varying learning and training capacity have been explored in the literature. However, the existing Intrusion Detection methods could not meet the desired performance requirements. Hence, this work proposes a new Intrusion Detection technique using Deep Autoencoder with Fruitfly Optimization. Initially, missing values in the dataset have been imputed with the Fuzzy C-Means Rough Parameter (FCMRP) algorithm which handles the imprecision in datasets with the exploit of fuzzy and rough sets while preserving crucial information. Then, robust features are extracted from Autoencoder with multiple hidden layers. Finally, the obtained features are fed to Back Propagation Neural Network (BPN) to classify the attacks. Furthermore, the neurons in the hidden layers of Deep Autoencoder are optimized with population based Fruitfly Optimization algorithm. Experiments have been conducted on NSL_KDD and UNSW-NB15 dataset. The computational results of the proposed intrusion detection system using deep autoencoder with BPN are compared with Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN), BPN, and Autoencoder with Softmax. Article Highlights A hybridized model using Deep Autoencoder with Fruitfly Optimization is introduced to classify the attacks. Missing values have been imputed with the Fuzzy C-Means Rough Parameter method. The discriminate features are extracted using Deep Autoencoder with more hidden layers.

scholarly journals Rapid Foreign Object Detection System on Seaweed Using VNIR Hyperspectral Imaging

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5279
Author(s):  
Dong-Hoon Kwak ◽  
Guk-Jin Son ◽  
Mi-Kyung Park ◽  
Young-Duk Kim
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Hyperspectral Imaging ◽  
High Speed ◽  
Detection System ◽  
Imaging Techniques ◽  
Foreign Object ◽  
Subtraction Method ◽  
Foreign Objects ◽  
Time Operation

The consumption of seaweed is increasing year by year worldwide. Therefore, the foreign object inspection of seaweed is becoming increasingly important. Seaweed is mixed with various materials such as laver and sargassum fusiforme. So it has various colors even in the same seaweed. In addition, the surface is uneven and greasy, causing diffuse reflections frequently. For these reasons, it is difficult to detect foreign objects in seaweed, so the accuracy of conventional foreign object detectors used in real manufacturing sites is less than 80%. Supporting real-time inspection should also be considered when inspecting foreign objects. Since seaweed requires mass production, rapid inspection is essential. However, hyperspectral imaging techniques are generally not suitable for high-speed inspection. In this study, we overcome this limitation by using dimensionality reduction and using simplified operations. For accuracy improvement, the proposed algorithm is carried out in 2 stages. Firstly, the subtraction method is used to clearly distinguish seaweed and conveyor belts, and also detect some relatively easy to detect foreign objects. Secondly, a standardization inspection is performed based on the result of the subtraction method. During this process, the proposed scheme adopts simplified and burdenless calculations such as subtraction, division, and one-by-one matching, which achieves both accuracy and low latency performance. In the experiment to evaluate the performance, 60 normal seaweeds and 60 seaweeds containing foreign objects were used, and the accuracy of the proposed algorithm is 95%. Finally, by implementing the proposed algorithm as a foreign object detection platform, it was confirmed that real-time operation in rapid inspection was possible, and the possibility of deployment in real manufacturing sites was confirmed.

scholarly journals Pure FPGA Implementation of an HOG Based Real-Time Pedestrian Detection System

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1174 ◽  
Author(s):  
Jian Luo ◽  
Chang Lin
Keyword(s):  
Real Time ◽  
Detection System ◽  
Pedestrian Detection ◽  
Image Sensor ◽  
Frame Rate ◽  
External Memory ◽  
Support Vector ◽  
Svm Classification ◽  
Prior Art ◽  
Memory Modules

In this study, we propose a real-time pedestrian detection system using a FPGA with a digital image sensor. Comparing with some prior works, the proposed implementation realizes both the histogram of oriented gradients (HOG) and the trained support vector machine (SVM) classification on a FPGA. Moreover, the implementation does not use any external memory or processors to assist the implementation. Although the implementation implements both the HOG algorithm and the SVM classification in hardware without using any external memory modules and processors, the proposed implementation’s resource utilization of the FPGA is lower than most of the prior art. The main reasons resulting in the lower resource usage are: (1) simplification in the Getting Bin sub-module; (2) distributed writing and two shift registers in the Cell Histogram Generation sub-module; (3) reuse of each sum of the cell histogram in the Block Histogram Normalization sub-module; and (4) regarding a window of the SVM classification as 105 blocks of the SVM classification. Moreover, compared to Dalal and Triggs’s pure software HOG implementation, the proposed implementation‘s average detection rate is just about 4.05% less, but can achieve a much higher frame rate.

scholarly journals Real Time In-situ Detection System Research for Methane in Offshore Shallow Gas based on Thin Film Interface

2018 ◽  
Vol 232 ◽  
pp. 04053
Author(s):  
Cheng-xing Miao ◽  
Qing Li ◽  
Sheng-yao Jia
Keyword(s):  
Real Time ◽  
Detection Efficiency ◽  
Detection System ◽  
Detection Methods ◽  
Shallow Gas ◽  
Micro Gas Sensor ◽  
Detection Probe ◽  
On Line ◽  
In Situ Detection

In order to get ridded of the non real-time detection methods of artificial site sampled and laboratory instrument analyzed in the field of methane detection in the offshore shallow gas, real-time in-situ detection system for methane in offshore shallow gas was designed by the film interface.The methane in the offshore shallow gas through the gas-liquid separation membrane of polymer permeation into the system internal detection probe, analog infrared micro gas sensor sensed the methane concentration and the corresponded output value, data acquisition and communication node fitted into standard gas concentration.Based on the experimental data compared with the traditional detection method, and further analyzed the causes of error produced by the case experiment. The application results show that the system can achieve a single borehole layout, long-term on-line in-situ on-line detection, and improve the detection efficiency and the timeliness of the detection data.

Design and Implementation of a High-Speed Real-Time Position Detection System in Electromagnetic Coil Launcher

2020 ◽  
Vol 48 (9) ◽  
pp. 3203-3210
Author(s):  
Guan Xiao Cun ◽  
Shuai Wang ◽  
Denghua Guo ◽  
Shaohua Guan ◽  
Baolong Liu ◽  
...  
Keyword(s):  
Real Time ◽  
High Speed ◽  
Detection System ◽  
Electromagnetic Coil ◽  
Design And Implementation ◽  
Position Detection ◽  
Time Position

scholarly journals Nighttime Fire/Smoke Detection System Based on a Support Vector Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Chao-Ching Ho
Keyword(s):  
Support Vector Machine ◽  
Detection System ◽  
Time Algorithm ◽  
Detection Methods ◽  
Support Vector ◽  
Smoke Detection ◽  
Fire Smoke ◽  
Large Fires ◽  
Monitoring Zone

Currently, video surveillance-based early fire smoke detection is crucial to the prevention of large fires and the protection of life and goods. To overcome the nighttime limitations of video smoke detection methods, a laser light can be projected into the monitored field of view, and the returning projected light section image can be analyzed to detect fire and/or smoke. If smoke appears within the monitoring zone created from the diffusion or scattering of light in the projected path, the camera sensor receives a corresponding signal. The successive processing steps of the proposed real-time algorithm use the spectral, diffusing, and scattering characteristics of the smoke-filled regions in the image sequences to register the position of possible smoke in a video. Characterization of smoke is carried out by a nonlinear classification method using a support vector machine, and this is applied to identify the potential fire/smoke location. Experimental results in a variety of nighttime conditions demonstrate that the proposed fire/smoke detection method can successfully and reliably detect fires by identifying the location of smoke.

High-Speed Real-Time Ion Detection System for Time-of-Flight Mass Spectrometry Application

2012 ◽  
Vol 45 (6) ◽  
pp. 464-469 ◽  
Author(s):  
Nan Li ◽  
Hui Xu ◽  
Qingjiang Li ◽  
Yinan Wang ◽  
Jinling Xing ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Real Time ◽  
High Speed ◽  
Detection System ◽  
Time Of Flight ◽  
Ion Detection ◽  
Flight Mass Spectrometry ◽  
Mass Spectrometry Application

Remote and Intelligent Diagnostic Methods on Leakage Fault for Crude Oil Pipelines

2006 ◽  
Author(s):  
Lai-Bin Zhang ◽  
Zhao-Hui Wang ◽  
Wei Liang
Keyword(s):  
Real Time ◽  
Remote Monitoring ◽  
Data Exchange ◽  
Detection System ◽  
Leak Detection ◽  
Diagnostic Methods ◽  
Oil Fields ◽  
Detection Methods ◽  
Development Environment ◽  
Oil Pipelines

Oil and gas transportation pipelines are the key equipment in petroleum and chemical industry. At present, with the increase of transportation task in oil fields, real-time leak detection system becomes a demand that petroleum companies need to safeguard routines. At the heart of the leakage monitoring and detection procedures are the report of leakage event timely and of leakage point precisely. This paper presents a more realistic approach for using rarefaction-pressure wave technique in oil pipelines, which aims to two targets, one is the improvement of remote and intelligent degree, and the other is the improvement of the leakage location ability. This paper introduces a new scheme to meet the requirements of real time and high data transferring necessary for remote monitoring and leak detection methods for pipelines. The scheme is based on SCADA framework for remote pipeline leakage diagnosis, in which the Dynamic Data Exchange technology is utilized to construct the data-acquiring component to acquire the real-time information that could perform remote test and analysis. It also introduces a basic concept and structure of the remote leak detection system. Primarily, an embedded leak-detection package is designed to exchange the diagnostic information with the RTU data package of Modbus protocol, and then via fiber network, the SCADA-based remote monitoring and leak detection system is realized. Existing data acquisition apparatus applied in oil fields and city underground water pipeline is used, without changing the structure of pipeline supervisory system. This paper introduces the method of constructing DDE-based hot links between servers and client terminals, using Borland C++ Builder 6.0 development environment, and also explains the universality and friendliness of the method. It can easily access similar Windows’ applications simply by modifying Service names, Topic options and data Items. System feasibility was tested using negative-pressure data from oil-fields. Additionally, the applied results show that the whole running status of pipeline can be monitored effectively, and a higher automation grade and an excellent leak location precision of the system can be obtained.

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