scholarly journals A Multi-Position Approach in a Smart Fiber-Optic Surveillance System for Pipeline Integrity Threat Detection

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 712
Author(s):  
Javier Tejedor ◽  
Javier Macias-Guarasa ◽  
Hugo F. Martins ◽  
Sonia Martin-Lopez ◽  
Miguel Gonzalez-Herraez
Keyword(s):  
Classification Accuracy ◽  
Surveillance System ◽  
Gaussian Mixture ◽  
High Energy ◽  
Time Domain Reflectometry ◽  
Real Field ◽  
Threat Detection ◽  
Related Data ◽  
Detection Mode ◽  
Model Training

We present a new pipeline integrity surveillance system for long gas pipeline threat detection and classification. The system is based on distributed acoustic sensing with phase-sensitive optical time domain reflectometry (ϕ-OTDR) and pattern recognition for event classification. The proposal incorporates a multi-position approach in a Gaussian Mixture Model (GMM)-based pattern classification system which operates in a real-field scenario with a thorough experimental procedure. The objective is exploiting the availability of vibration-related data at positions nearby the one actually producing the main disturbance to improve the robustness of the trained models. The system integrates two classification tasks: (1) machine + activity identification, which identifies the machine that is working over the pipeline along with the activity being carried out, and (2) threat detection, which aims to detect suspicious threats for the pipeline integrity (independently of the activity being carried out). For the machine + activity identification mode, the multi-position approach for model training obtains better performance than the previously presented single-position approach for activities that show consistent behavior and high energy (between 6% and 11% absolute) with an overall increase of 3% absolute in the classification accuracy. For the threat detection mode, the proposed approach gets an 8% absolute reduction in the false alarm rate with an overall increase of 4.5% absolute in the classification accuracy.

scholarly journals Real Field Deployment of a Smart Fiber-Optic Surveillance System for Pipeline Integrity Threat Detection: Architectural Issues and Blind Field Test Results

2018 ◽  
Vol 36 (4) ◽  
pp. 1052-1062 ◽  
Author(s):  
Javier Tejedor ◽  
Carl H. Ahlen ◽  
Miguel Gonzalez-Herraez ◽  
Javier Macias-Guarasa ◽  
Hugo F. Martins ◽  
...  
Keyword(s):  
Field Test ◽  
Surveillance System ◽  
Fiber Optic ◽  
Real Field ◽  
Threat Detection ◽  
Test Results ◽  
Field Deployment

Low-Cost Scalable Home Video Surveillance System

2014 ◽  
Vol 19 (2-3) ◽  
pp. 51-58
Author(s):  
Jaromir Przybylo ◽  
Joanna Grabska-Chrzastowska ◽  
Przemyslaw Korohoda
Keyword(s):  
Video Surveillance ◽  
Video Processing ◽  
Surveillance System ◽  
Low Cost ◽  
Video Recording ◽  
Video Surveillance System ◽  
Threat Detection ◽  
Computing Power ◽  
Home Video ◽  
Depth Analysis

Abstract Automated and intelligent video processing and analysis systems are becoming increasingly popular in video surveillance. Such systems must meet a number of requirements, such as threat detection and real-time video recording. Furthermore, they cannot be expensive and must not consume too much energy because they have to operate continuously. The work presented here focuses on building a home video surveillance system matching the household budget and possibly making use of hardware available in the house. Also, it must provide basic functionality (such as video recording and detecting threats) all the time, and allow for a more in-depth analysis when more computing power be available.

An Intelligent Video Surveillance System for Android Smart Phone

2013 ◽  
Vol 850-851 ◽  
pp. 884-888 ◽  
Author(s):  
Gang Yang ◽  
Xin Tan ◽  
Yong Rui Zhang
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Surveillance System ◽  
Smart Phone ◽  
Image Data ◽  
Gaussian Mixture ◽  
Moving Object ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Surveillance Technology

Video surveillance technology is playing an important role, and it is widely used in some fields. With the popularity of Android OS, it draws researchers attention to increase the development of video surveillance systems on the platform. This paper presents a smart real-time video surveillance system based on Android smart phone. This system detects moving object by using improved GMM (Gaussian Mixture Mode) algorithm, recognizes invading human with cascade classifier, processes image data with coder & decoder, transmits data over RTP (Real-time Transport Protocol). It also applies some methods to improve the accuracy of moving object detection and recognition, speed up recognition process. The experimental evidences show that it can realize real-time video surveillance and smart alarm.

scholarly journals Comprehensive Survey of Recent Drug Discovery Using Deep Learning

2021 ◽  
Vol 22 (18) ◽  
pp. 9983
Author(s):  
Jintae Kim ◽  
Sera Park ◽  
Dongbo Min ◽  
Wankyu Kim
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Drug Design ◽  
De Novo ◽  
Molecular Structures ◽  
De Novo Drug Design ◽  
Related Data ◽  
Benchmark Datasets ◽  
Comprehensive Survey ◽  
Model Training

Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug–target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.

Abstract 429: Assessment of Functional Mitochondrial Parameters in Single Cardiomyocytes Reveals Critical Events Not Identified in Cell Populations

2017 ◽  
Vol 121 (suppl_1) ◽  
Author(s):  
Giovanni Fajardo ◽  
Kristina Kooiker ◽  
Mingming Zhao ◽  
Michael Coronado ◽  
Gwanghyun Jung ◽  
...  
Keyword(s):  
Cell Death ◽  
Single Cell ◽  
Mitochondrial Function ◽  
Ischemia Reperfusion Injury ◽  
Ischemia Reperfusion ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
High Energy ◽  
Simultaneous Increase ◽  
Cell Populations

In cardiomyocytes (CMs), mitochondria play a dual role, maintaining the high energy supply required for rhythmic contraction, but also regulating critical cell death signaling. Impaired mitochondrial function can affect cellular homeostasis and contribute to sub-lethal injury; if mitochondrial impairment is more severe or persistent, pro-death pathways are activated. It is becoming increasingly clear, however, that within a population of cells there is considerable heterogeneity in mitochondrial function between individual CMs; and within a single cell there is also heterogeneity between individual mitochondria/mitochondrial regions. We have developed a high-throughput fluorescence imaging platform to quantitate single CM mitochondrial function in large numbers of cells, yielding information missed using standard assays that evaluate cell populations. When CMs are exposed to H 2 O 2 , they exhibit a dramatic hyperpolarization prior to the loss of mitochondrial membrane potential at the onset of cell contraction and death. There is marked heterogeneity in the timing of this response, with three distinct populations identified using Gaussian mixture models, and the duration of hyperpolarization longer for CMs that hypercontract earlier after H 2 O 2 exposure. This hyperpolarization is accompanied by a simultaneous increase in [Ca 2+ ] i and is preceded by an increase in ROS. Standard methods, which average populations of cells, miss these responses. Blockade of MPT opening with cyclosporine A delays hypercontracture and cell death but does not prevent hyperpolarization. Finally, we have used our platform to track individual mitochondria/mitochondrial regions within a single live CM, identifying mitochondrial heterogeneity within a single cell, which is increased after exposure to H 2 O 2 , isoproterenol or in CMs from mice after ischemia-reperfusion injury. By tracking individual CMs and individual mitochondria within a single CM, we have opened a window to the complex heterogeneities in mitochondrial stress response.

Two-Level Classification in Determining the Age and Gender Group of a Speaker

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Ergün Yücesoy
Keyword(s):  
Classification Accuracy ◽  
Age Groups ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Optimum Number ◽  
Mel Frequency Cepstral Coefficients ◽  
Age And Gender ◽  
Number Of Components ◽  
And Gender

In this study, the classification of the speakers according to age and gender was discussed. Age and gender classes were first examined separately, and then by combining these classes a classification with a total of 7 classes was made. Speech signals represented by Mel-Frequency Cepstral Coefficients (MFCC) and delta parameters were converted into Gaussian Mixture Model (GMM) mean supervectors and classified with a Support Vector Machine (SVM). While the GMM mean supervectors were formed according to the Maximum-a-posteriori (MAP) adaptive GMM-Universal Background Model (UBM) configuration, the number of components was changed from 16 to 512, and the optimum number of components was decided. Gender classification accuracy of the system developed using aGender dataset was measured as 99.02% for two classes and 92.58% for three classes and age group classification accuracy was measured as 67.03% for female and 63.79% for male. In the classification of age and gender classes together in one step, an accuracy of 61.46% was obtained. In the study, a two-level approach was proposed for classifying age and gender classes together. According to this approach, the speakers were first divided into three classes as child, male and female, then males and females were classified according to their age groups and thus a 7-class classification was realized. This two-level approach was increased the accuracy of the classification in all other cases except when 32-component GMMs were used. While the highest improvement of 2.45% was achieved with 64 component GMMs, an improvement of 0.79 was achieved with 256 component GMMs.

Improving the Performance of Language Identification System Using Different GMM-Training Approaches

2010 ◽  
Vol 121-122 ◽  
pp. 496-501
Author(s):  
Wei Li ◽  
Dong Ju Kim ◽  
Kwang Seok Hong
Keyword(s):  
System Performance ◽  
Linear Prediction ◽  
Gaussian Mixture ◽  
High Accuracy ◽  
Language Identification ◽  
Identification System ◽  
Training Method ◽  
Model Training ◽  
Optimal Approach ◽  
Feasible System

This paper proposed a feasible system for language identification (LID) and designed four different GMM-training approaches to improve the system performance by accuracy recognition rates. In our experiment, we used these model-training approaches to evaluation on the system performance, which utilizes Linear Prediction Cepstrum Coefficients (LPCC) and Gaussian Mixture Model (GMM), rely on a 10-language task. From all the results, we found an optimal approach for training GMM in LID system, which achieves high accuracy of 85.25%, and indicated that different GMM-training approaches have different performances for LID system, but an advisable training method that proposed in our paper can greatly improve the system performance.

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