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Vibration monitoring for the West-East Gas Pipeline Project of China by phase optical time domain reflectometry (phase-OTDR)

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INSTRUMENTATION SCIENCE & TECHNOLOGY 2021年第1期49卷 65-80页

作者： Li, Gang Zeng, Kehong Zhou, Bin Yang, Wenming Lin, Xiaohui Wang, Fei Chen, Yun Ji, Xutao Zheng, Dahai Mao, Barerem-Melgueba China Petr Pipeline Telecom & Elect Engn Co Ltd Langfang Peoples R China South China Normal Univ South China Acad Adv Optoelect Guangdong Prov Key Lab Opt Informat Mat & Technol Guangzhou 510631 Peoples R China Univ Lome Ctr Informat & Calcul Lome Togo

Optical fiber phase optical time domain reflectometry (phase-OTDR) has been applied on the West-East Gas Pipeline Project (WEPP) of China for vibration monitoring. In order to improve the accuracy of the threat identification, a machine learning algorithm from the voice recognition is introduced. The feature extraction and pre-emphasis processes were modified to characterize the vibrations in the soil. The threat identification accuracy increased from 83% in 2017 to 90.6% in 2018 by continuously training the algorithm. Massive vibration measurements were recorded during the past two years and interesting results have been obtained from the data analysis. These results are expected to assist engineers to have a better understanding of how the threatening incidents occur near pipelines.

关键词： Distributed vibration sensor machine learning algorithm phase optical time domain reflectometer (OTDR) West-East Gas Pipeline Project (WEPP)

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3D-CNN-Based Sky Image Feature Extraction for Short-Term Global Horizontal Irradiance Forecasting

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WATER 2021年第13期13卷

作者： Yang, Hao Wang, Long Huang, Chao Luo, Xiong Univ Sci & Technol Beijing Shunde Grad Sch Foshan 528399 Peoples R China Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Beijing Key Lab Knowledge Engn Mat Sci Beijing 100083 Peoples R China

The instability and variability of solar irradiance induces great challenges for the management of photovoltaic water pumping systems. Accurate global horizontal irradiance (GHI) forecasting is a promising technique to solve this problem. To improve short-term GHI forecasting accuracy, ground-based sky image is valuable due to its correlation with solar generation. In previous studies, great efforts have been made to extract numerical features from sky image for data-driven solar irradiance forecasting methods, e.g., based on pixel-value color information, and based on the cloud motion detection method. In this work, we propose a novel feature extracting method for GHI forecasting that a three-dimensional (3D) convolutional neural network (CNN) is developed to extract features from sky images with efficient training strategies. Popular machine learning algorithms are introduced as GHI forecasting models and corresponding forecasting accuracy is fully explored with different input features on a large dataset. The numerical experiment illustrates that the minimum average root mean square error (RMSE) of 62 W/m2 is achieved by the proposed method with 15.2% improvement in Skill score against baseline forecasting method.

关键词： 3D CNN feature engineering global horizontal irradiance machine learning algorithm sky image

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Soil salinity inversion based on novel spectral index

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ENVIRONMENTAL EARTH SCIENCES 2021年第16期80卷 1-13页

作者： Zhou, Xiaohong Zhang, Fei Liu, Changjiang Kung, Hsiang-te Johnson, Verner Carl Xinjiang Univ Coll Resources & Environm Sci Key Lab Smart City & Environm Modeling Higher Edu Urumqi 830046 Peoples R China Xinjiang Univ Key Lab Oasis Ecol Urumqi 830046 Peoples R China Natl Adm Surveying Mapping & Geoinformat Engn Res Ctr Cent Asia Geoinformat Dev & Utilizat Urumqi 830002 Peoples R China Univ Memphis Dept Earth Sci Memphis TN 38152 USA Colorado Mesa Univ Dept Phys & Environm Sci Grand Junction CO 81501 USA

Soil salinization is one of the most important causes for land degradation and desertification and is an important threat to land management, farming activities, water quality, and sustainable development, with the management of saline soil crucial in arid and semi-arid areas. The salt-affected soil is predominant in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in the Northwestern China. It influences the development of agricultural economy. Rapid and accurate measurement of the soil salt content (SSC) is significant for the soil salinization control. However, the traditional method of obtaining soil salt is time-consuming and laborious. Nowadays, it is an unprecedented perspective to monitor soil salinity through Sentinel-2A multispectral remote sensing image to construct three-dimensional spectral index. In this study, through soil salt data of 97 ground-truth measurements, Sentinel-2A data-derived spectral indices, based on particle swarm optimization support vector machine (PSO-SVM), gray wolf optimization support vector machine (GWO-SVM) and differential evolution support vector machine (DE-SVM) algorithm to construct a best soil salt inversion models. The results show that three-band (3D) spectral index has better correlation with soil salinity than single band and two-band (2D) spectral index, among TBI5 and TBI7 has a high correlation with the salinity of the soil, and the points are concentrated on the 1:1 line. Therefore, this approach could be applied to estimate the soil salinity in the Ebinur Lake region. The established models were validated using the machine learning algorithm. The DE-SVM model performed the best by the three Model of accuracy with R-2, Bias, and SEPC2 of 0.56, - 2.03, and 8.62, respectively. Therefore through the soil salinity value predicted by the modeling constructs a linear relationship with the indexes TBI5 and TBI7, and draw the soil salt inversion map, soil salinity around the lake is relatively high, and decreases

关键词： machine learning algorithm Sentinel-2A Spectral indices

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Wireless Self-Powered High-Performance Integrated Nanostructured-Gas-Sensor Network for Future Smart Homes

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ACS NANO 2021年第4期15卷 7659-7667页

作者： Song, Zhilong Ye, Wenhao Chen, Zhuo Chen, Zhesi Li, Mutian Tang, Wenying Wang, Chen Wan, Zhu'an Poddar, Swapnadeep Wen, Xiaolin Pan, Xiaofang Lin, Yuanjing Zhou, Qingfeng Fan, Zhiyong Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Kowloon Hong Kong Peoples R China Hong Kong Univ Sci & Technol Guangdong Hong Kong Macao Joint Lab Intelligent M Kowloon Hong Kong Peoples R China Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Southern Univ Sci & Technol Sch Microelect Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Engn Res Ctr Integrated Circuits Next Generat Com Minist Educ Shenzhen 518055 Peoples R China Dongguan Univ Technol Sch Elect Engn & Intelligentizat Dongguan 523808 Peoples R China

The accelerated evolution of communication platforms including Internet of Things (IoT) and the fifth generation (5G) wireless communication network makes it possible to build intelligent gas sensor networks for real-time monitoring chemical safety and personal health. However, this application scenario requires a challenging combination of characteristics of gas sensors including small formfactor, low cost, ultralow power consumption, superior sensitivity, and high intelligence. Herein, self-powered integrated nanostructured-gas-sensor (SINGOR) systems and a wirelessly connected SINGOR network are demonstrated here. The room-temperature operated SINGOR system can be self-driven by indoor light with a Si solar cell, and it features ultrahigh sensitivity to H-2, formaldehyde, toluene, and acetone with the record low limits of detection (LOD) of 10, 2, 1, and 1 ppb, respectively. Each SINGOR consisting of an array of nanostructured sensors has the capability of gas pattern recognition and classification. Furthermore, multiple SINGOR systems are wirelessly connected as a sensor network, which has successfully demonstrated flammable gas leakage detection and alarm function. They can also achieve gas leakage localization with satisfactory precision when deployed in one single room. These successes promote the development of using nanostructured-gas-sensor network for wide range applications including smart home/building and future smart city.

关键词： 3D nanostructured tin oxide gas sensor network self-powered device machine learning algorithm smart home

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Weighted voting ensemble method for predicting workpiece imaging dimensional deviation based on monocular vision systems

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OPTICS AND LASER TECHNOLOGY 2023年 159卷

作者： Zhang, Tie Zheng, Jingfu Zou, Yanbiao South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Peoples R China

Due to the different heights and positions of all kinds of workpieces, the imaging dimension of workpieces in monocular vision systems exists in deviation. To reduce the deviation and improve the accuracy of dimensional measurement, a BRR-SVR-BPNN weighted voting ensemble learning deviation prediction algorithm is proposed. The proposed method combines the probability inference feature of Bayesian Ridge regression (BRR), the maximum interval penalty feature of support vector regression (SVR), and the nonlinear expression feature of the backpropagation neural network (BPNN). And an automatic image processing method based on Halcon is pro-posed to establish the datasets for the deviation prediction algorithm. The experimental results show that the proposed algorithm has well prediction effect, high robustness, and accuracy on deviation prediction. It can be easily applied to predict the imaging dimension deviation of various workpieces or other similar tasks to improve the accuracy of workpiece dimension measurement in the industrial field.

关键词： Deviation predicting machine learning algorithm Weighted voting ensemble algorithm Image processing Monocular vision system

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Exploring sodium glucose cotransporter (SGLT2) inhibitors with machine learning approach: A novel hope in anti-diabetes drug discovery

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JOURNAL OF MOLECULAR GRAPHICS & MODELLING 2022年 111卷 108106页

作者： Moinul, Md Amin, Sk Abdul Kumar, Prabhat Patil, Umesh Kumar Gajbhiye, Asmita Jha, Tarun Gayen, Shovanlal Dr Hari Singh Gour Vishwavidyalaya Dept Pharmaceut Sci Sagar 470003 India Jadavpur Univ Dept Pharmaceut Technol Div Med & Pharmaceut Chem Nat Sci Lab Kolkata 700032 India Banaras Hindu Univ Inst Sci Dept Comp Sci Varanasi 221005 Uttar Pradesh India Jadavpur Univ Dept Pharmaceut Technol Lab Drug Design & Discovery Kolkata 700032 India

Conventional anti-diabetes agents exhibit some undesirable side effects. Recently, lactic acidosis and/or bladder cancer were also reported with the use of these agents. Hence, there is an urgent need for alternative anti diabetes in order to reduce/avoid the unwanted effects. In this scenario sodium glucose cotransporter 2 (SGLT2) inhibitors has already been established as an important class of anti-diabetic drug. The search for new generation SGLT2 inhibitors with high affinity is still an ongoing process. Here, we aim to develop computational models to predict the SGLT2 inhibitory activity of small molecules based on chemical structures. This work provides in-silico analysis to propose possible fragment/fingerprint identification recommended for SGLT2 inhibitors. Up-to-our knowledge, this study is an initiative to propose fingerprints responsible for SGLT2 ***, we used nine different algorithms to build machine learning (ML) models that could be used to prioritize compounds as SGLT2 inhibitors from large libraries. The best performing ML models were applied to virtually screen a large collection of FDA approved drugs. The best predicted compounds have been recommended to be biologically investigated in future in order to identify next generation SGLT2 inhibitors with different chemical structure.

关键词： Diabetes Transport SGLT2 Bayesian model Recursive portioning Fragments machine learning algorithm Virtual screening

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Toxicity prediction based on PAHs in fruits and vegetables − A machine learning approach

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Journal of the Saudi Society of Agricultural Sciences 2024年

作者： Nataraj, Sathees Kumar Renganathan, Alagammai Vasantha, Kavitha Texina, Staphney Department of Math and Science University of Technology Bahrain Salmabad Bahrain Department of Mechatronics Engineering University of Technology Bahrain Salmabad Bahrain

This study analyses the toxicity levels of fruits and vegetables by identifying the presence of polycyclic aromatic hydrocarbons (PAHs), particularly in areas affected by industrial and vehicle pollution. The presence of PAHs on plant surfaces, caused mostly by air pollution particulate matter, raises serious concerns about the nutritional value of these foods. Traditional techniques for measuring PAH levels include Gas Chromatography/Mass Spectrometry (GC/MS) and High-Performance Liquid Chromatography (HPLC). Although successful, these procedures are expensive and time-consuming. Toxicity detection is often based on expert knowledge or experimental analysis relative to the European Food Safety Authority's (EFSA) limits. In response to these challenges, this work uses artificial intelligence approaches to determine toxicity levels using 16 PAHs. Data on PAH concentrations in fruits and vegetables were collected from a variety of sources, classified as safe or harmful, and verified using statistical analysis. The validated dataset was subsequently classified using several machine learning techniques. The level of toxicity is scaled and compared to the expected outputs using the results obtained from the neural network. An experimental investigation validates the promising findings of level of toxicity classification using artificial intelligence approaches, which are then evaluated statistically. The study shows that machine learning algorithms obtained classification accuracies of over 90 %, including the evaluation of harmfulness. The accuracy for the two-class (Safe and Unsafe) category was 98.27 %, while the accuracy for the four-class (No harm, Low harm, Moderate Harm, and Severe harm) category was 98.68 %. These findings highlight the potential of artificial intelligence in improving food safety and public health by presenting an innovative multidisciplinary method to assessing the impacts of environmental pollutants on the edibility of fruits and vegetables in

关键词： Environmental contaminants Fruits & vegetables machine learning algorithm Polycyclic aromatic hydrocarbons (PAHs) Statistical measures

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Bi-linear mapping integrated machine learning based authentication routing protocol for improving quality of service in vehicular Ad-Hoc network

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2023年 4卷

作者： Satyanarayana, Raju K Selvakumar, K. Research Scholar Department of Information Technology Faculty of Engineering and Technology Annamalai University Chidambaram India Department of Information Technology Faculty of Engineering and Technology Annamalai University Chidambaram India

Recently, most of the critical problems of real-time applications have been solved by one of the fast and most hastily growing technological tools, machine learning. The novel architectures of Vehicular Ad-hoc Networks reduce traffic congestion and accidents since the number of electric vehicles is increasing daily. There is a need for an efficient and secured routing protocol for better implementation of VANET. machine learning algorithms help in achieving such efficiency and security. This paper develops a Novel machine learning based Authentication Routing protocol by integrating the Long-Short-Term-Memory model with the Bi-Lear Mapping algorithm to analyze the routing data and authenticate the network users. The bi-Linear Mapping model assigns and compares the node's private and public keys, whereas LSTM analyses the routing table information. The proposed model is simulated in NS3 software, and the performance is verified. The results show that the proposed model obtained better throughput, PDR, energy efficiency, authentication which is better than the earlier methods. © 2023 The Author(s)

关键词： Authentication Blm machine learning algorithm Mlar Routing protocol Security analysis V-2-V Vanet

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A methodology for diagnosing FAC induced pipe thinning using accelerometers and deep learning models

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ANNALS OF NUCLEAR ENERGY 2020年 143卷

作者： Chae, Young Ho Kim, Seung Geun Kim, Hyeonmin Kim, Jung Taek Seong, Poong Hyun Korea Adv Inst Sci & Technol Dept Nucl & Quantum Engn 291 Daehak Ro Daejeon 34141 South Korea Korea Atom Energy Res Inst 34057 111Daedeok Daero 989Beon Gil Daejeon South Korea

The flow accelerated corrosion (FAC) phenomenon is critical phenomenon that undermine the safety of nuclear power plant. The problems of FAC are not only induces pipe's thinning but occurs widely in nuclear power plants. Therefore, precise diagnosis of FAC phenomenon is important but the FAC phenomenon has a complicated mechanism. As a result, accurately diagnosing the FAC phenomenon is difficult (Kain, 2014). To overcome these difficulties, we proposed a methodology utilizing vibration data and deep learning to diagnose FAC induced thinning. To minimize the effect of outliers, Cook's distance and the Hilbert transform were used. When there was a significant difference in the pipes' thickness, the support vector machine (SVM), convolutional neural network (CNN), and long-short term memory (LSTM) network all showed good results. However, when the difference in pipes' thickness was subtle and thinning was non-uniform, only LSTM successfully diagnosed the pipe's condition. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Flow accelerated corrosion Vibration signal machine learning algorithm Deep learning

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LightCUD: a program for diagnosing IBD based on human gut microbiome data

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BIODATA MINING 2021年第1期14卷 1-13页

作者： Xu, Congmin Zhou, Man Xie, Zhongjie Li, Mo Zhu, Xi Zhu, Huaiqiu Peking Univ Coll Engn Dept Biomed Engn State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Peking Univ Ctr Quantitat Biol Beijing 100871 Peoples R China Peking Univ Third Hosp Dept Crit Care Med Beijing 100191 Peoples R China

Background The diagnosis of inflammatory bowel disease (IBD) and discrimination between the types of IBD are clinically important. IBD is associated with marked changes in the intestinal microbiota. Advances in next-generation sequencing (NGS) technology and the improved hospital bioinformatics analysis ability motivated us to develop a diagnostic method based on the gut microbiome. Results Using a set of whole-genome sequencing (WGS) data from 349 human gut microbiota samples with two types of IBD and healthy controls, we assembled and aligned WGS short reads to obtain feature profiles of strains and genera. The genus and strain profiles were used for the 16S-based and WGS-based diagnostic modules construction respectively. We designed a novel feature selection procedure to select those case-specific features. With these features, we built discrimination models using different machine learning algorithms. The machine learning algorithm LightGBM outperformed other algorithms in this study and thus was chosen as the core algorithm. Specially, we identified two small sets of biomarkers (strains) separately for the WGS-based health vs IBD module and ulcerative colitis vs Crohn's disease module, which contributed to the optimization of model performance during pre-training. We released LightCUD as an IBD diagnostic program built with LightGBM. The high performance has been validated through five-fold cross-validation and using an independent test data set. LightCUD was implemented in Python and packaged free for installation with customized databases. With WGS data or 16S rRNA sequencing data of gut microbiome samples as the input, LightCUD can discriminate IBD from healthy controls with high accuracy and further identify the specific type of IBD. The executable program LightCUD was released in open source with instructions at the webpage . The identified strain biomarkers could be used to study the critical factors for disease development and recommend treatments regar

关键词： machine learning algorithm Human gut microbiome IBD Biomarker

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