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A Novel Prediction Method for the Remaining Life of Corrosion Damage in Oil and Gas Pipeline Systems

IEEE ACCESS 2024年 12卷 176104-176123页

作者： Cui, Jian Wang, Jie Dong, Lei Peking Univ Dept Ind Engn & Management Beijing 100871 Peoples R China China Petr Engn & Construct Corp Beijing Co Beijing 100011 Peoples R China

During long-term operation, oil and gas pipelines are often subjected to corrosion caused by complex environmental factors, which can lead to serious safety incidents. Therefore, accurately assessing the extent of corrosion damage and predicting the remaining life of pipelines has become a critical issue in the safety management of oil and gas pipelines. This paper aims to propose a high-precision corrosion rate prediction model and, based on this model, provide an accurate evaluation of the pipeline's remaining life to enhance the operational safety and reliability of oil and gas pipelines. First, the MFO-LSSVM model is employed to precisely predict the corrosion rate of oil and gas pipelines. This model combines the Moth-Flame Optimization (MFO) algorithm and Least Squares Support Vector Machine (LSSVM), which significantly improves the prediction accuracy under complex environmental conditions. Building on this, a combination of Long Short-Term Memory (LSTM) and deep Neural Networks (DNN) is used to predict the remaining life of the pipelines, capturing the time-dependent and nonlinear characteristics of the corrosion rate. Experimental results demonstrate that the MFO-LSSVM model achieves outstanding accuracy in corrosion rate prediction, with a root mean square error (RMSE) of 0.0023 and a mean absolute error (MAE) of 0.17%. For the remaining life prediction, the LSTM-DNN model shows a mean square error (MSE) of 0.0004 and a coefficient of determination (R-2) of 0.9999, significantly outperforming other individual models and closely aligning with the actual values. This multi-stage prediction framework effectively enhances the model's robustness and accuracy in complex environments. The innovation of this study lies in the development of a multi-stage prediction framework combining the MFO-LSSVM and LSTM-DNN models. The MFO-LSSVM model provides highly accurate corrosion rate predictions, while the LSTM-DNN model captures the time-evolution and nonlinear effects

关键词： Oil and gas pipeline systems corrosion damage remaining life prediction remaining life prediction MFO-LSSVM model MFO-LSSVM model deep learning algorithms deep learning algorithms deep learning algorithms

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Leakage detection in water pipelines using supervised classification of acceleration signals

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AUTOMATION IN CONSTRUCTION 2020年 117卷 103256-103256页

作者： Shukla, Harshit Piratla, Kalyan Clemson Univ Glenn Dept Civil Engn 109 Lowry Hall Clemson SC 29634 USA

Loss of treated water through buried pipeline leakage is one of the pressing challenges faced by water utilities across the world. Many current pipeline inspection techniques are ad-hoc in nature and only provide a snapshot status of the system. Although a few embedded sensor-based systems offer promise for continuous monitoring, they are limited in their ability to detect leakages of all magnitudes. Lack of a comprehensive understanding of the pipeline system's structural dynamics and the correlation to leakage detection is a primary challenge. To overcome this challenge, a deep learning algorithm that uses scalogram images of vibration signals collected from accelerometers attached to the pipeline surface is developed and validated in this study. The advantage is that the complex structural features of the water pipeline systems need not be modeled if sufficient leak-based vibration signal data is available for training purposes. A convolutional neural network (CNN) model adapted from a pre-trained AlexNet network is leveraged to demonstrate the proposed approach using vibration data collected from an experimental pipeline test bed. The CNN model is found to be able to detect leakages on polyvinyl chloride (PVC) pipelines with up to 95% accuracy. A maximum accuracy of 98% is observed with acceleration signal data collected from carefully selected locations. The approach presented in this study can be adapted for effective leakage detection in real world scenarios with minimum human intervention.

关键词： Infrastructure monitoring Automated pipeline leakage detection deep learning algorithms

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An effective automatic traffic sign classification and recognition deep convolutional networks

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第13期81卷 18915-18934页

作者： Mishra, Jayant Goyal, Sachin UIT RGPV Bhopal MP India

Automatic traffic sign recognition is essential for autonomous driving, assisted driving, and driver safety. Convolutional neural networks (CNNs) are the most widely used deep learning algorithm for traffic signs recognition. This research presents an effective technique for automatically recognizing traffic signs images. This technique mainly uses four GTSRB, GTSDB, BTSC, and TSRD standard traffic signs images datasets of different traffic sign images and provides the best result using our CNN model architecture. It helps to assists the driver in driving the motor vehicle safely. Drivers devote too much attention and effort to recognizing traffic signs by manually analyzing and recognizing their aspects. This study presents an automatic traffic sign recognition system to minimize motor vehicle accidents using representation to identify signs-this task utilizing a deep convolutional neural network. Here, our work presents a novel CNN architecture (.001) with Adam optimizer, batch size 128, and multi-interconnect layers to improve the performance of traffic signs detection. A Convolutional Neural Network (CNN) achieved more accuracy, results based on a complex network. Our model learns from the GTSDB dataset, which contains 43 traffic classes, and uses this information to predict the proper class of an anonymous traffic sign with 99.81% accuracy and minimum losses. Contradictory, the result is improved than the earlier study, which examined 98.20% accuracy that the approach can still detect traffic signs with extreme weather conditions and blur image conditions.

关键词： Automatic traffic sign recognition Convolutional neural networks (CNN) deep learning algorithms GTSRB GTSDB BTSC and TSRD Standard traffic sign image datasets

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Representation learning by hierarchical ELM auto-encoder with double random hidden layers

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IET COMPUTER VISION 2019年第4期13卷 411-419页

作者： Li, Rui Wang, Xiaodan Lei, Lei Wu, Chongming Air Force Engn Univ Coll Air & Missile Def Xian 710051 Shanxi Peoples R China Xijing Univ Coll Business Xian 710123 Shanxi Peoples R China

Recent research developments of extreme learning machine (ELM) with multilayer network architecture lead to a promising high performance with extremely fast training speed for representation learning. In this work, the authors are dedicated to develop an efficient and expressive representation learning method with hierarchical ELM, and proposing a novel architectural unit named as double random hidden layers ELM auto-encoder (DELM-AE). The novel DELM-AE consists of one input layer, two random hidden mapping layers for encoding feature, and one output layer for decoding feature. When stacking DELM-AE in the hierarchical structure, they can construct an H-DELM model, where the input of the current AE is the feature representation learned by the previous one, but the output is identical to the original input information and is not the input. Hence, the H-DELM can reproduce the original input data as much as possible to learn more expressive and compact feature. They validate their method on various widely public datasets, and the results demonstrate that H-DELM can bring significant performance improvements in terms of classification accuracy and robustness compared with existing relevant multilayer ELM and other deep learning algorithms at a slight computational cost.

关键词： learning (artificial intelligence) pattern classification random hidden mapping layers output layer decoding feature stacking DELM-AE hierarchical structure H-DELM model feature representation original input information original input data expressive feature compact feature deep learning algorithms extreme learning machine multilayer network architecture lead extremely fast training speed expressive representation learning method hierarchical ELM H-ELM novel architectural unit double random hidden layers ELM auto-encoder novel DELM-AE research developments relevant multilayer ELM

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Highly Elastic, Fatigue-Resistant, and Antifreezing MXene Functionalized Organohydrogels as Flexible Pressure Sensors for Human Motion Monitoring

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ACS APPLIED MATERIALS & INTERFACES 2024年第46期16卷 64002-64011页

作者： Han, Yutong Cao, Yuzhong Zhuang, Haozhe Yao, Yu Cao, Huina Li, Zhanhong Wang, Zifeng Zhu, Zhigang Univ Shanghai Sci & Technol Sch Hlth Sci & Engn Shanghai 200093 Peoples R China

Conductive organohydrogels-based flexible pressure sensors have gained considerable attention in health monitoring, artificial skin, and human-computer interaction due to their excellent biocompatibility, wearability, and versatility. However, hydrogels' unsatisfactory mechanical and unstable electrical properties hinder their comprehensive application. Herein, an elastic, fatigue-resistant, and antifreezing poly(vinyl alcohol) (PVA)/lipoic acid (LA) organohydrogel with a double-network structure and reversible cross-linking interactions has been designed, and MXene as a conductive filler is functionalized into organohydrogel to further enhance the diverse sensing performance of flexible pressure sensors. The as-fabricated MXene-based PVA/LA organohydrogels (PLBM) exhibit stable fatigue resistance for over 450 cycles under 40% compressive strain, excellent elasticity, antifreezing properties (<-20 degrees C), and degradability. Furthermore, the pressure sensors based on the PLBM organohydrogels show a fast response time (62 ms), high sensitivity (S = 0.0402 kPa(-1)), and excellent stability (over 1000 cycles). The exceptional performance enables the sensors to monitor human movements, such as joint flexion and throat swallowing. Moreover, the sensors integrating with the one-dimensional convolutional neural networks and the long-short-term memory networks deep learning algorithms have been developed to recognize letters with a 93.75% accuracy, representing enormous potential in monitoring human motion and human-computer interaction.

关键词： organohydrogels MXene flexible pressure sensors deep learning algorithms human-computer interaction

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Lensless shadow microscopy-based shortcut analysis strategy for fast quantification of microplastic fibers released to water

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WATER RESEARCH 2024年 258卷 121758页

作者： Su, Yu Yang, Chenqi Peng, Yao Yang, Cheng Wang, Yanhua Wang, Yong Yan, Feng Xing, Baoshan Ji, Rong Southeast Univ Sch Energy & Environm Nanjing 211189 Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing 210023 Peoples R China Nanjing Univ Sch Environm State Key Lab Pollut Control & Resource Reuse Nanjing 210023 Peoples R China Univ Massachusetts Stockbridge Sch Agr Amherst MA 01003 USA Shaanxi Normal Univ Sch Geog & Tourism Xian 710119 Peoples R China

Fast quantification is the primary challenge in monitoring microplastic fiber (MPF) pollution in water. The process of quantifying the number of MPFs in water typically involves filtration, imaging on a filter membrane, and manual counting. However, this routine workflow has limitations in terms of speed and accuracy. Here, we present an alternative analysis strategy based on our high-resolution lensless shadow microscope (LSM) for rapid imaging of MPFs on a chip and modified deep learning algorithms for automatic counting. Our LSM system was equipped with wide field-of-view submicron-pixel imaging sensors (>1 cm(2);similar to 500 nm/pixel) and could simultaneously capture the projection image of >3-mu m microplastic spheres within 90 s. The algorithms enabled accurate classification and detection of the number and length of >10-mu m linear and branched MPFs derived from melamine cleaning sponges in each image (similar to 0.4 gigapixels) within 60 s. Importantly, neither MPF morphology (dispersed or aggregated) nor environmental matrix had a notable impact on the automatic recognition of the MPFs by the algorithms. This new strategy had a detection limit of 10 particles/mL and significantly reduced the time of MPF imaging and counting from several hours with membrane-based methods to just a few minutes per sample. The strategy could be employed to monitor water pollution caused by microplastics if an efficient sample separation and a comprehensive sample image database were available.

关键词： Water pollution monitoring Microplastic fibers On-chip imaging Lensless shadow microscopy deep learning algorithms

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IILS: Intelligent imaging layout system for automatic imaging report standardization and intra-interdisciplinary clinical workflow optimization

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EBIOMEDICINE 2019年 44卷 162-181页

作者： Wang, Yang Yan, Fangrong Lu, Xiaofan Zheng, Guanming Zhang, Xin Wang, Chen Zhou, Kefeng Zhang, Yingwei Li, Hui Zhao, Qi Zhu, Hu Chen, Fei Gao, Cailiang Qing, Zhao Ye, Jing Li, Aijing Xin, Xiaoyan Li, Danyan Wang, Han Yu, Hongming Cao, Lu Zhao, Chaowei Deng, Rui Tan, Libo Chen, Yong Yuan, Lihua Zhou, Zhuping Yang, Wen Shao, Mingran Dou, Xin Zhou, Nan Zhou, Fei Zhu, Yue Lu, Guangming Zhang, Bing Nanjing Univ Med Sch Affiliated Nanjing Drum Tower Hosp Dept Radiol Nanjing 210008 Jiangsu Peoples R China China Pharmaceut Univ Res Ctr Biostat & Computat Pharm Nanjing Jiangsu Peoples R China Univ Michigan Dept Stat Ann Arbor MI 48105 USA NanJing GaoChun Peoples Hosp Dept Radiol 9 Chunzhong Rd Nanjing Jiangsu Peoples R China Nanjing Univ Med Sch Affiliated Nanjing Drum Tower Hosp Dept Resp Nanjing 210008 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn 66 Xin Mofan Rd Nanjing Jiangsu Peoples R China Southeast Univ Affiliated Yancheng Hosp Dept Radiol Sch Med Yancheng Jiangsu Peoples R China Chongqing Three Gorges Cent Hosp Dept Radiol Chongqing 404000 Peoples R China Northern Jiangsu Peoples Hosp Dept Radiol 98 Nantong West Rd Yangzhou 225001 Jiangsu Peoples R China Ningbo 2 Hosp Dept Radiol 41 Xibei St Haishu Dist 315010 Zhejiang Peoples R China FL 8 Ocean Int Ctr EChaoyang Rd Side Rd Beijing 100000 Peoples R China Nanjing Univ Med Sch Affiliated Nanjing Drum Tower Hosp Dept Med Adm Nanjing 210008 Jiangsu Peoples R China Nanjing Univ Sch Med Jinling Hosp Dept Med Imaging Nanjing 210002 Jiangsu Peoples R China

Background: To achieve imaging report standardization and improve the quality and efficiency of the intra-interdisciplinaiy clinical workflow, we proposed an intelligent imaging layout system (IILS) for a clinical decision support system-based ubiquitous healthcare service, which is a lung nodule management system using medical images. Methods: We created a lung IILS based on deep learning for imaging report standardization and workflow optimization for the identification of nodules. Our IILS utilized a deep learning plus adaptive auto layout tool, which trained and tested a neural network with imaging data from all the main CT manufacturers from 11,205 patients. Model performance was evaluated by the receiver operating characteristic curve (ROC) and calculating the corresponding area under the curve (AUC). The clinical application value for our IILS was assessed by a comprehensive comparison of multiple aspects. Findings: Our ILLS is clinically applicable due to the consistency with nodules detected by ILLS, with its highest consistency of 0.94 and an AUC of 90.6% for malignant pulmonary nodules versus benign nodules with a sensitivity of 76. 5% and specificity of 89.1%. Applying this IILS to a dataset of chest CT images, we demonstrate performance comparable to that of human experts in providing a better layout and aiding in diagnosis in 100% valid images and nodule display. The IILS was superior to the traditional manual system in performance, such as reducing the number of clicks from 14.45 +/- 0.38 to 2, time consumed from 16.87 +/- 0.38 s to 6.92 +/- 0.10 s, number of invalid images from 7.06 +/- 0.24 to 0, and missing lung nodules from 46.8% to 0%. Interpretation: This IILS might achieve imaging report standardization, and improve the clinical workflow therefore opening a new window for clinical application of artificial intelligence. Fund: The National Natural Science Foundation of China. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Lung nodule Artificial intelligence deep learning algorithms Intelligent image layout system Standardized e-film and visualized structured report Clinical workflow

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Artificial intelligence-assisted automated heart failure detection and classification from electronic health records

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ESC HEART FAILURE 2024年第5期11卷 2481-+页

作者： Oo, Mon Myat Gao, Chuang Cole, Christian Hummel, Yoran Guignard-Duff, Magalie Jefferson, Emily Hare, James Voors, Adriaan A. de Boer, Rudolf A. Lam, Carolyn S. P. Mordi, Ify R. Tromp, Jasper Lang, Chim C. Univ Dundee Sch Med Div Mol & Clin Med Dundee DD1 9SY Scotland Univ Dundee Hlth Informat Ctr Sch Med Dundee Scotland Univ Dundee Sch Med Div Populat Hlth & Genom Dundee Scotland Us2 Ai Singapore Singapore Univ Med Ctr Groningen Groningen Netherlands Erasmus Univ Med Ctr Dept Cardiol Rotterdam Netherlands Natl Heart Ctr Singapore Singapore Singapore Duke NUS Med Sch Singapore Singapore Natl Univ Singapore Saw Swee Hock Sch Publ Hlth Singapore Singapore Natl Univ Hlth Syst Singapore Singapore

Aims Electronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients. Methods and results We used EHR data between 1993 and 2021 from Tayside and Fife (similar to 20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing sig

关键词： deep learning algorithms Electronic health record data Epidemiology Heart failure Preserved ejection fraction Validation

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Convolutional Neural Network Based Approval Prediction of Enhancement Reports

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IEEE ACCESS 2021年 9卷 122412-122424页

作者： Cheng, Jun Sadiq, Mazhar Kalugina, Olga A. Nafees, Sadeem Ahmad Umer, Qasim Chaohu Univ Coll Informat Engn Chaohu 238000 Peoples R China COMSATS Univ Islamabad Dept Comp Sci Islamabad 45550 Pakistan Financial Univ Govt Russian Federat Dept English Language Profess Commun Moscow 125167 Russia Virtual Univ Pakistan Dept Comp Sci & Informat Technol Lahore 54000 Pakistan

For a given software enhancement report, identifying its possible approval status could help software developers by suggesting feature enhancements to compete in the software industry. An automatic solution for the approval prediction of enhancements could assist all the participants in resolving enhancements. The key challenges are the preprocessing of noisy textual information and the state-of-the-art feature models to combine the syntactical and semantic word information available in the given text. To this end, we propose a deep learning based approach for the approval prediction of enhancement reports that incorporates the users' sentiments involved in the text. First, we preprocess the textual information of all enhancement reports to avoid noise. Second, we compute the sentiment of each enhancement report using Senti4SD. Third, we combine the bag-of-words (BOW) representation and traditional word2vec based representation to learn the novel deep representation (a recurrent neural network (RNN) with attention based representation) of preprocessed text. Using an attention mechanism enables the model to remember the context over a long sequence of words in an enhancement report. Fourth, based on sentiment and deep representation, we train a deep learning based classifier for the approval prediction of enhancement reports. Finally, we reuse the 40, 000 enhancement reports from 10 real software applications to evaluate the proposed approach. The cross-application evaluation suggests that the proposed approach is accurate and outperforms the state-of-the-art. The results of the proposed approach improve the precision from 86.52% to 90.56%, recall from 66.45% to 80.10%, and f-measure from 78.12% to 85.01%.

关键词： Software Semantics deep learning Predictive models Feature extraction Training Convolutional neural networks deep learning algorithms classification enhancement reports approval prediction

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Artificial intelligence strengthens cervical cancer screening–present and future

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Cancer Biology & Medicine 2024年第10期21卷 864-879页

作者： Tong Wu Eric Lucas Fanghui Zhao Partha Basu Youlin Qiao School of Population Medicine and Public Health Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100730China Early Detection Prevention&Infections Branch International Agency for Research on Cancer(WHO)25 avenue Tony GarnierLyon 69007France Department of Cancer Epidemiology National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100021China

Cervical cancer is a severe threat to women’s *** majority of cervical cancer cases occur in developing *** WHO has proposed screening 70%of women with high-performance tests between 35 and 45 years of age by 2030 to accelerate the elimination of cervical *** to an inadequate health infrastructure and organized screening strategy,most low-and middle-income countries are still far from achieving this *** part of the efforts to increase performance of cervical cancer screening,it is necessary to investigate the most accurate,efficient,and effective methods and *** intelligence(AI)is rapidly expanding its application in cancer screening and diagnosis and deep learning algorithms have offered human-like interpretation capabilities on various medical *** will soon have a more significant role in improving the implementation of cervical cancer screening,management,and *** review aims to report the state of AI with respect to cervical cancer *** discuss the primary AI applications and development of AI technology for image recognition applied to detection of abnormal cytology and cervical neoplastic diseases,as well as the challenges that we anticipate in the future.

关键词： Cervical cancer screening artificial intelligence deep learning algorithms

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