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Predicting oil flow rate through orifice plate with robust machine learning algorithms

FLOW MEASUREMENT AND INSTRUMENTATION 2021年 81卷 102047-102047页

作者： Abad, Abouzar Rajabi Behesht Tehrani, Pezhman Soltani Naveshki, Mohammad Ghorbani, Hamzeh Mohamadian, Nima Davoodi, Shadfar Aghdam, Saeed Khezerloo-ye Moghadasi, Jamshid Saberi, Hossein Islamic Azad Univ Omidiyeh Branch Dept Petr Engn Omidiyeh Iran Univ Tehran Dept Petr Engn Kish Int Campus Kish Iran Sahand Univ Technol Petr Engn Dept Tabriz Iran Islamic Azad Univ Ahvaz Branch Young Res & Elite Club Ahvaz Iran Islamic Azad Univ Omidiyeh Branch Young Res & Elite Club Omidiyeh Iran Tomsk Polytech Univ Sch Earth Sci & Engn Lenin Ave Tomsk Russia Amirkabir Univ Technol Dept Petr Engn Tehran Iran Petr Univ Technol Petr Engn Dept Ahvaz Iran Univ Guilan Dept Elect Engn Rasht Iran

Measuring fluid flow rate passing through pipelines is a basic strategy for developing the infrastructure of fluiddependent industries. It is a challenging issue for trade, transportation, and reservoir management purposes. Predicting the flow rate of fluid is also regarded as one of the crucial steps for the development of oil fields. In this study, a novel deep machine learning model, convolutional neural network (CNN), was developed to predict oil flow rate through orifice plate (Qo) from seven input variables, including fluid temperature (Tf), upstream pressure (Pu), root differential pressure (VAP), percentage of base sediment and water (BS&W%), oil specific gravity (SG), kinematic viscosity (v), and beta ratio (13, the ratio of pipe diameter to orifice diameter). Due to the absence of accurate and credible methods for determining Qo, deep learning can be a useful alternative to traditional machine learning methods. Justifying the promising performance of the developed CNN model over conventional machine learning models, three different machine learning algorithms, including radial basis function (RBF), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM), were also developed and their prediction performance was compared with that of the CNN model. A sensitivity analysis was also performed on the influence degree of each input variable on the output variable (Qo). The study outcomes indicate that the CNN model provided the highest Qo prediction accuracy among all the four models developed by presenting a root mean squared error (RMSE) of 0.0341 m3/s and a coefficient of determination (R2) of 0.9999, when applied to the dataset of 3303 data records compiled from oil fields around Iran. The Spearman correlation coefficient analysis results display that VAP, Pu, and Tf were the most influential variables on the oil flow rate in respect of the large dataset evaluated.

关键词： Oil flow rate Measurement Orifice flow meter Ultrasonic flow meter Deep learning machine learning algorithm Convolutional neural network

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Retrieval of Total Phosphorus Concentration in the Surface Water of Miyun Reservoir Based on Remote Sensing Data and machine learning algorithms

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REMOTE SENSING 2021年第22期13卷 4662-4662页

作者： Qiao, Zhi Sun, Siyang Jiang, Qun'ou Xiao, Ling Wang, Yunqi Yan, Haiming Beijing Forestry Univ Sch Soil & Water Conservat Beijing 100083 Peoples R China Beijing Forestry Univ Key Lab Soil & Water Conservat & Desertificat Pre Beijing 100083 Peoples R China Beijing Forestry Univ Sch Soil & Water Conservat Jinyun Forest Ecosyst Res Stn Beijing 100083 Peoples R China Hebei GEO Univ Sch Land Resources & Urban Rural Planning Shijiazhuang 050031 Hebei Peoples R China

Some essential water conservation areas in China have continuously suffered from various serious problems such as water pollution and water quality deterioration in recent decades and thus called for real-time water pollution monitoring system underwater resources management. On the basis of the remote sensing data and ground monitoring data, this study firstly constructed a more accurate retrieval model for total phosphorus (TP) concentration by comparing 12 machine learning algorithms, including support vector machine (SVM), artificial neural network (ANN), Bayesian ridge regression (BRR), lasso regression (Lasso), elastic net (EN), linear regression (LR), decision tree regressor (DTR), K neighbor regressor (KNR), random forest regressor (RFR), extra trees regressor (ETR), AdaBoost regressor (ABR) and gradient boosting regressor (GBR). Then, this study applied the constructed retrieval model to explore the spatial-temporal evolution of the Miyun Reservoir and finally assessed the water quality. The results showed that the model of TP concentration built by the ETR algorithm had the best accuracy, with the coefficient R-2 reaching over 85% and the mean absolute error lower than 0.000433. The TP concentration in Miyun Reservoir was between 0.0380 and 0.1298 mg/L, and there was relatively significant spatial and temporal heterogeneity. It changed remarkably during the periods of the flood season, winter tillage, planting, and regreening, and it was lower in summer than in other seasons. Moreover, the TP in the southwest part of the reservoir was generally lower than in the northeast, as there was less human activities interference. According to the Environmental Quality Standard for the surface water environment, the water quality of Miyun Reservoir was overall safe, except only for an over-standard case occurrence in the spring and September. These conclusions can provide a significant scientific reference for water quality monitoring and management in Miyun Reservo

关键词： machine learning algorithm retrieval model remote sensing data total phosphorus concentration Miyun Reservoir

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Explainable machine learning Models and Architectures 1

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2023年

作者： Suman Lata Tripathi Mufti Mahmud

ISBN: (数字)9781394186570

ISBN: (纸本)9781394185849

EXPLAINABLE machine learning MODELS AND ARCHITECTURES This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, and the efficient hardware of machine learning applications. machine learning and deep learning modules are now an integral part of many smart and automated systems where signal processing is performed at different levels. Signal processing in the form of text, images, or video needs large data computational operations at the desired data rate and accuracy. Large data requires more use of integrated circuit (IC) area with embedded bulk memories that further lead to more IC area. Trade-offs between power consumption, delay and IC area are always a concern of designers and researchers. New hardware architectures and accelerators are needed to explore and experiment with efficient machine-learning models. Many real-time applications like the processing of biomedical data in healthcare, smart transportation, satellite image analysis, and IoT-enabled systems have a lot of scope for improvements in terms of accuracy, speed, computational powers, and overall power consumption. This book deals with the efficient machine and deep learning models that support high-speed processors with reconfigurable architectures like graphic processing units (GPUs) and field programmable gate arrays (FPGAs), or any hybrid system. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.

关键词： VLSI Design machine learning algorithm Neural Network VLSI Architecture Hardware Trojan Trojan Detection and Prevention FPGA SOC Hardware Accelerator Edge Accelerator In memory Computing On Chip Computing SRAM in ML Novel Material Memristor Neuromorphic

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Exploration of Various machine learning Techniques for Identifying and Mitigating DDoS Attacks 20

Exploration of Various Machine Learning Techniques for Ident...

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20th Annual International Conference on Privacy, Security and Trust (PST)

作者： Falowo, Olufunsho I. Okpala, Izunna Kojo, Emmanuel Azumah, Sylvia Li, Chengcheng Univ Cincinnati Sch Informat Technol Cincinnati OH 45221 USA

ISBN: (纸本)9798350313871

This study is conducted as a result of some of the key observations documented in a recent study published by IEEE, titled "Threat Actors' Tenacity to Disrupt: Examination of Major Cybersecurity incidents" where some of the key findings therein that study describes how DDoS attack together with other attack techniques were used by threat actors to disrupt public and private enterprises on multiple occasions. In this study, there is a focus on DDoS attacks and exploration of how various machine learning techniques may be leveraged to identify and mitigate them. Real world examples of major DDoS attacks that happened and reported or announced to the public were examined and it was found that in the last seven years, there has been an increase in the use of DDoS attacks to cause major disruptions to organizations. In a total of over 700 major cybersecurity incidents that we analyzed from 2015 to 2022, evidence of DDoS attacks were found every year. Hence, in that subset of dataset we examined, there was an increase from 8 to 31 major DDoS attacks from 2021 to 2022 which is an estimate of 288 percent increase. Due to the volume of network traffic analysis involved in the detection of DDoS events which more often may be very difficult for humans to manually or efficiently analyze, the validation of the importance of utilizing AI technologies such machine learning algorithms as effective capabilities for the detection of DDoS attacks and DDoS mitigation are assessed in this study. Identification of specific machine learning algorithms that may be used for the detection and mitigation of DDoS attacks were looked into. Given that there is no one-size-fits-all solution to the mitigation of DDoS attacks, this study echoes the leveraging of the CIS Benchmarks and also encourages organizations to adopt referencing one of the NIST frameworks that was released to the public on January 26, 2023 titled "AI Risk Management Framework" as guidelines in ensuring multiple layers of cont

关键词： Accuracy Score Artificial Intelligence CIS Benchmark DDoS Attacks AI Risk Management Framework learning Rate machine learning algorithm Precision Score Threat Actors Cybersecurity

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Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms - A case study in the Miyun Reservoir, China

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ECOLOGICAL INDICATORS 2021年 124卷 107356-107356页

作者： Qun'ou, Jiang Lidan, Xu Siyang, Sun Meilin, Wang Huijie, Xiao Beijing Forestry Univ Sch Soil & Water Conservat Beijing 100083 Peoples R China Beijing Forestry Univ Key Lab Soil & Water Conservat & Desertificat Pre Beijing 100083 Peoples R China Beijing Forestry Univ Sch Soil & Water Conservat Jinyun Forest Ecosystem Res Stn Beijing 100083 Peoples R China

Monitoring the water pollution level in real time is the most critical issue for protecting the water quality of water reservoirs. Due to the restrictions on flight areas of Unmanned Arial Vehicles (UAV), four sensitive regions with the area of 1-2 km2 were first selected in this study based on the spatial distribution of total nitrogen (TN) concentration changes estimated by the Landsat remote sensing data. And then twelve machine learning algorithms including Support Vector machine (SVM), Artificial Neural Networks (ANN), Bayesian Ridge Regression (BRR), Lasso Regression (Lasso), Elastic Net (EN), Linear Regression (LR), Decision Tree Regression (DTR), K Neighbors Regression (KNR), Random Forest Regression (RFR), Extra Trees Regression (ETR), AdaBoost Regression (ABR) and Gradient Boosting Regression (GBR) were compared to construct a more accurate retrieval model by using the UAV hyper spectral remote sensing and ground monitoring data. And then the TN concentration was estimated after the process of dimensionality reduction and compressed sensing denoing. Finally, spatial heterogeneity of the TN concentration was analyzed in four sensitive areas of the Miyun reservoir. The results demonstrated that among the tested algorithms the Extra Trees Regression was best suitable for the construction of a TN concentration retrieval model on the basis of UAV hyper spectral data, and its absolute squared error was 0.000065. The spatial distribution of the TN showed that the concentration was highest within the water area of the Bulaotun village and the Houbajiazhuang village, while it was relatively low for the Chao river dam and Bai river dam. Additionally, no significant differences regarding the concentrations were shown in the single UAV flight area except the Houbajia village, which indicated that the water quality in Miyun reservoir was relatively stable and changed in a small interval. These conclusions can provide scientific references for water quality monitoring a

关键词： UAV hyper spectral remote sensing data machine learning algorithm Retrieval model Water quality parameter Total nitrogen concentration

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Crack initiation pressure prediction for SC-CO2 fracturing by integrated meta-heuristics and machine learning algorithms

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ENGINEERING FRACTURE MECHANICS 2021年 249卷 107750-107750页

作者： Yan, Hao Zhang, Jixiong Zhou, Nan Li, Baiyi Wang, Yuyao China Univ Min & Technol Sch Mines State Key Lab Coal Resources & Safe Min Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol State Key Lab Geomech & Deep Underground Engn Xuzhou 221116 Jiangsu Peoples R China

The replacement of water by supercritical carbon dioxide in coal and rock mass fracturing has shown excellent development prospects. However, large-scale industrial implementation of supercritical carbon dioxide fracturing (SCDF) requires an accurate estimation of crack initiation pressure, while laboratory test methods are time-consuming and involve too complicated testing and sample preparation procedures. To better predict the SCDF crack initiation pressure, this study applied three hybrid artificial intelligence models, which alternatively combined the particle swarm optimization algorithm (PSO) with the BP neural network (BPNN), extreme learning machine (ELM), and support vector machine (SVM). Their prediction results were compared with each other and with those of the conventional linear multivariate regression analyses (L-MRA). Data samples for the prediction models' training were experimentally obtained via the SCDF indoor tests with various influencing factors, including vertical principal stress, horizontal maximum principal stress, horizontal minimum principal stress, fracturing fluid injection rate, fracturing fluid temperature, coal and rock mass tensile strength, coal and rock mass elastic modulus, and coal and rock mass Poisson's ratio. The models' output variable was the SCDF crack initiation pressure. The correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate and compare the performance of the predictive models. Their prediction accuracy can be ranked in the decreasing order as PSO-SVM, PSO-ELM, PSO-BPNN, and L-MRA, with corresponding R values of 0.9339, 0.9135, 0.6971, and 0.6057, respectively. The PSO-SVM hybrid intelligent model was found to provide the most efficient SCDF crack initiation pressure prediction. The results obtained are quite instrumental in the promotion and application of SCDF technology.

关键词： Crack initiation pressure Prediction model machine learning algorithm PSO

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Intelligent prediction of slope stability based on visual exploratory data analysis of 77 in situ cases

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International Journal of Mining Science and Technology 2023年第1期33卷 47-59页

作者： Guangjin Wang Bing Zhao Bisheng Wu Chao Zhang Wenlian Liu Faculty of Land Resources Engineering Kunming University of Science and TechnologyKunming 650093China Yunnan International Technology Transfer Center for Mineral Resources Development and Solid Waste Resource Utilization Kunming 650093China State Key Laboratory of Hydroscience and Engineering Department of Hydraulic EngineeringTsinghua UniversityBeijing 100084China State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhan 430071China China Nonferrous Metals Industry Kunming Survey and Design Research Institute Co.Ltd Kunming 650051China

Slope stability prediction research is a complex non-linear system *** carrying out slope stability prediction work,it often encounters low accuracy of prediction models and blind data *** on 77 field cases,5 quantitative indicators are selected to improve the accuracy of prediction models for slope *** indicators include slope angle,slope height,internal friction angle,cohesion and unit weight of rock and *** data aggregation in the prediction of slope stability is analyzed and visualized based on Six-dimension reduction methods,namely principal components analysis(PCA),Kernel PCA,factor analysis(FA),independent component analysis(ICA),non-negative matrix factorization(NMF)and t-SNE(stochastic neighbor embedding).Combined with classic machine learning methods,7 prediction models for slope stability are established and their reliabilities are examined by random cross ***,the significance of each indicator in the prediction of slope stability is discussed using the coefficient of variation *** research results show that dimension reduction is unnecessary for the data processing of prediction models established in this paper of slope *** forest(RF),support vector machine(SVM)and k-nearest neighbour(KNN)achieve the best prediction accuracy,which is higher than 90%.The decision tree(DT)has better accuracy which is 86%.The most important factor influencing slope stability is slope height,while unit weight of rock and soil is the least *** and SVM models have the best accuracy and superiority in slope stability *** results provide a new approach toward slope stability prediction in geotechnical engineering.

关键词： Slope stability prediction machine learning algorithm Dimensionality reduction visualization Random cross validation Coefficient of variation

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Nitrogen and organic matter managements improve rice yield and affect greenhouse gas emissions in China's rice-wheat system

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FIELD CROPS RESEARCH 2025年 326卷

作者： Zhang, Li Zhang, Feng Zhang, Kaiping Wang, Yue Agathokleous, Evgenios Fang, Chao Zhang, Zhike Wei, Haiyan Huo, Zhongyang Yangzhou Univ Agr Coll Jiangsu Key Lab Crop Genet & Physiol Jiangsu Key Lab Crop Cultivat & Physiol Yangzhou 225009 Peoples R China Yangzhou Univ Jiangsu Coinnovat Ctr Modern Prod Technol Grain Cr Yangzhou 225009 Peoples R China Yangzhou Univ Res Inst Rice Ind Engn Technol Daxue South Rd 88 Yangzhou 225009 Jiangsu Peoples R China Lanzhou Univ Coll Ecol State Key Lab Herbage Improvement & Grassland Agro Lanzhou 730000 Gansu Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Nanjing Univ Informat Sci & Technol Sch Ecol & Appl Meteorol Key Lab Ecosyst Carbon Source & Sink China Meteorol Adm ECSS CMA Nanjing 210044 Peoples R China Xiamen Univ Tan Kah Kee Coll Zhangzhou 363105 Peoples R China

Context: Mineral nitrogen (N) management and organic matter management in the paddy fields directly affect yield and soil greenhouse gas (GHG) emissions in the rice-wheat rotation system of China. However, comprehensive research on the combined impacts of these two practices remains insufficient, and there is a lack of quantitative analyses on a large regional scale as well as identification of the main drivers. Objective: This study aimed to elucidate the impact of mineral N management and organic matter management on rice yield and global warming potential (GWP) and their spatial distribution patterns, and to investigate influential factors. Methods: We combined machine learning algorithms based on meta-analysis to assess the effect of mineral N management (synthetic N fertilizer, slow-/controlled- release fertilizer) and organic matter management (organic fertilizer, biochar amendment, and straw return) on rice yield and GHG in the rice-wheat system by compiling 163 peer-reviewed journal articles and high-resolution multi-source databases in China. Results: Mineral N management significantly increased rice yield (412 %) and N2O (162.3 %), and reduced GHG emissions intensity (GHGI;20.1 %). Organic matter management increased CH4, GWP, and GHGI by 74.4 %, 60.8 %, and 55.1 %, respectively. machine learning (random forest (RF), support vector machine, multiple layer perceptron, and gradient boosting machine) suggested that RF was the optimal method for predicting rice yield and GHG (R2 = 0.43-0.90). The spatial distribution indicated that mineral N management boosted rice yield and N2O while reducing GHGI, especially in the Middle-lower Yangtze River (MLY) region, by 37.6 %, 277 %, and 25.2 %, respectively. Structural equation modeling and RF analysis revealed that field management practices and edaphic factors had major contributions to rice yield, while climatic factors were positively with CH4 and N2O emissions. Implications: Our findings provide insights into the

关键词： Rice yield Global warming potential machine learning algorithm Random forest Rice-wheat system

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machine learning for VLSI Chip Design 1

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2023年

作者： Abhishek Kumar Suman Lata Tripathi K. Srinivasa Rao

ISBN: (数字)9781119910497

ISBN: (纸本)9781119910398

machine learning TECHNIQUES FOR VLSI CHIP DESIGN This cutting-edge new volume covers the hardware architecture implementation, the software implementation approach, the efficient hardware of machine learning applications with FPGA or CMOS circuits, and many other aspects and applications of machine learning techniques for VLSI chip design. Artificial intelligence (AI) and machine learning (ML) have, or will have, an impact on almost every aspect of our lives and every device that we own. AI has benefitted every industry in terms of computational speeds, accurate decision prediction, efficient machine learning (ML), and deep learning (DL) algorithms. The VLSI industry uses the electronic design automation tool (EDA), and the integration with ML helps in reducing design time and cost of production. Finding defects, bugs, and hardware Trojans in the design with ML or DL can save losses during production. Constraints to ML-DL arise when having to deal with a large set of training datasets. This book covers the learning algorithm for floor planning, routing, mask fabrication, and implementation of the computational architecture for ML-DL. The future aspect of the ML-DL algorithm is to be available in the format of an integrated circuit (IC). A user can upgrade to the new algorithm by replacing an IC. This new book mainly deals with the adaption of computation blocks like hardware accelerators and novel nano-material for them based upon their application and to create a smart solution. This exciting new volume is an invaluable reference for beginners as well as engineers, scientists, researchers, and other professionals working in the area of VLSI architecture development.

关键词： VLSI Design machine learning algorithm Neural Network VLSI Architecture Hardware Trojan Trojan Detection and Prevention FPGA SOC Hardware Accelerator Edge Accelerator

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Multi-hazard assessment for flood and Landslide risk in Kalimantan and Sumatra: Implications for Nusantara, Indonesia's new capital

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HELIYON 2024年第18期10卷 e37789页

作者： Heo, Sujung Sohn, Wonmin Park, Sangjin Lee, Dong Kun Seoul Natl Univ Interdisciplinary Program Landscape Architecture Seoul South Korea Michigan State Univ Sch Planning Design & Construct E Lansing MI USA Korea Inst Publ Adm Seoul South Korea Seoul Natl Univ Dept Landscape Architecture & Rural Syst Engn Seoul South Korea Seoul Natl Univ Res Inst Agr & Life Sci Seoul South Korea

Situated within the Ring of Fire and characterized by a tropical climate and high seismic activity, Indonesia is uniquely vulnerable to natural disasters such as floods and landslides. These events pose significant threats to both the population and infrastructure. This study predicts areas exposed to flood and landslide risk by considering various environmental factors related to climate, topography, and land use. The predictive performance of three machine learning models-na & iuml;ve Bayes, k-nearest neighbors, and random forest (RF)-was evaluated by comparing the AUC, RMSE, and R2 values of each model. Ultimately, the RF model, which demonstrated the highest accuracy, was used to prioritize disaster impact factors and generate hazard maps. The results identified the interaction of rainfall, land use, and slope aspect as the most critical determinants of hazard occurrence. The predicted hazard maps revealed that approximately 26.7 % of the study area was vulnerable to either floods or landslides, with 16.8 % of the area experiencing both. The new capital of Nusantara showed a relatively higher multi-hazard risk than did the overall study area and protected zones, with 22.1 % of the hazard area vulnerable to both flooding and landslides. In single hazard zones, areas classified as at risk for floods had a higher mean probability of experiencing both hazards (43 %), as compared to areas classified as at risk for landslides (22 %). As a result, urban planners and relevant stakeholders can now utilize the hazard maps developed in this study to prioritize infrastructure reinforcement and disaster risk areas, integrating land use planning with risk assessment to mitigate the impact of disasters. By employing these strategies, Indonesia and other countries facing similar challenges can now enhance their disaster preparedness and response capabilities in new capital regions and other areas, ultimately planning for more sustainable urban development.

关键词： Natural hazard machine learning algorithm Risk mitigation Protected area Nusantara Kalimantan Sumatra Indonesia

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