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GEOENERGY SCIENCE AND ENGINEERING 2025年 244卷

作者： Yang, Yuxiang Cen, Xiao Ni, Haocheng Liu, Yibing Chen, Zhangxing John Yang, Jin Hong, Bingyuan China Univ Petr Coll Safety & Ocean Engn Beijing 102249 Peoples R China Univ Calgary Dept Chem & Petr Engn Calgary AB T2N 1N4 Canada Zhejiang Ocean Univ Natl & Local Joint Engn Res Ctr Harbor Oil & Gas S Sch Petrochem Engn & Environm Sch Naval Architecture & MaritimeZhejiang Key Lab Zhoushan 316022 Peoples R China CNOOC Res Inst Co Ltd Beijing 100028 Peoples R China Eastern Inst Technol Ningbo 315200 Peoples R China

The rate of penetration (ROP) is a key indicator of drilling efficiency. Many researchers have explored the application of machine learning in ROP prediction. However, few studies have focused on the robustness of the constructed models, and developing a ROP prediction model that can achieve both high accuracy and strong robustness remains a challenge. This paper introduces a novel machine learning approach to constructing a ROP prediction model through ensemble learning algorithms. The model is based on field data from oilfields, incorporating 16 input parameters that influence ROP. First, the feasibility of the collected dataset is verified using correlation analysis. Then, ROP prediction models are developed based on various machine learning algorithms, including Decision Tree Regression (DTR), Random Forest (RF), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). By comparing the performance of these models under noise levels of 0%, 1.7%, and 5.1%, RF, LGBM, XGB, and SVR are selected as base learners. These base learners are then combined to construct multiple ensemble models, and the performance of the optimal ensemble model is evaluated under varying noise levels. The results show that the prediction error of the optimal model remains within 10%, and R2 is greater than 0.96. Finally, the Shapley Additive Explanations (SHAP) method is applied to perform interpretability analysis on the optimal ROP prediction model, examining the impact of different input factors on the model's predictive performance. Compared to single models and other ensemble models, the proposed ensemble model not only achieves higher accuracy but also demonstrates strong robustness and generalization capability.

关键词： Rate of penetration Robustness Machine learning ensemble algorithm

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Blockchain-based BATMAN protocol using mobile ad hoc network (MANET) with an ensemble algorithm

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY 2024年第3期23卷 1667-1677页

作者： Singh, Upendra Sharma, Sumit Kumar Shukla, Mukul Jha, Preeti Shri Govindram Seksaria Inst Technol & Sci Indore Madhya Pradesh India Iffco Tokyo Gen Insurance Ltd Gurgaon Haryana India Koneru Lakshmaiah Educ Fdn Hyderabad 500043 Telangana India

A MANET is a decentralized type of wireless network of mobile devices, and it can also be defined as an autonomous system of nodes. All the nodes in the network are connected by wireless links and are mobile. They can come together and form a network without any support from any existing network infrastructure. MANET is a new field of study based on blockchain in a wireless ad hoc environment. However, the main challenge for blockchain applications in ad hoc networks is how to adapt to the extreme computational complexity of block validation while preserving the characteristics of the blockchain and including nodes in the validation process. This article proposes a blockchain-based mobile network (MANET) with an ensemble algorithm. The proposed scheme provides a distributed environment for MANETS routing using a blockchain based on the Byzantine fault tolerance (BFT) protocol. Taking advantage of the better approach of mobile ad hoc networking (BATMAN) to incorporate the concept of blockchain into the MANET as a representative protocol. The proposed method named Extended-BATMAN (E-BATMAN) incorporates the concept of blockchain into the BATMAN protocol using MANET. As a secure, distributed, and reliable platform, Blockchain solves most BFT security issues, with each node performing repeated security operations individually. The experimental analysis of the proposed ensemble algorithm is based on four parameters such as packet delivery rate, average end-to-end latency, network throughput, and energy. All of these parameters show better results with the proposed ensemble protocol than with existing state-of-the-art protocols.

关键词： Mobile ad hoc network (MANET) Byzantine fault tolerance ensemble algorithm Blockchain

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An efficient ensemble algorithm for numerical approximation of stochastic Stokes-Darcy equations

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2019年 343卷 249-275页

作者： Jiang, Nan Qiu, Changxin Missouri Univ Sci & Technol Dept Math & Stat Rolla MO 65409 USA

We propose and analyze an efficient ensemble algorithm for fast computation of multiple realizations of the stochastic Stokes-Darcy model with a random hydraulic conductivity tensor. The algorithm results in a common coefficient matrix for all realizations at each time step making solving the linear systems much less expensive while maintaining comparable accuracy to traditional methods that compute each realization separately. Moreover, it decouples the Stokes-Darcy system into two smaller sub-physics problems, which reduces the size of the linear systems and allows parallel computation of the two sub-physics problems. We prove the ensemble method is long time stable and first-order in time convergent under a time-step condition and two parameter conditions. Numerical examples are presented to support the theoretical results and illustrate the application of the algorithm. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Stokes-Darcy equations Uncertainty quantification ensemble algorithm Finite element method Partitioned method

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Financial Distress Prediction with a Novel Diversity-Considered GA-MLP ensemble algorithm

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NEURAL PROCESSING LETTERS 2022年第2期54卷 1175-1194页

作者： Zhang, Rui Zhang, Zuoquan Wang, Di Du, Marui Beijing Jiaotong Univ Sch Sci Beijing Peoples R China

An effective financial distress prediction model is essential, as financial distress of listed companies will cause big problems for listed company owners, shareholders, banks, and regulators. Due to the special circumstances of China, Chinese listed companies that received the 'special treatment' label are considered to be in financial distress in this paper. We construct a relatively new and complete financial distress data set of Chinese listed companies from 2014 to 2019. Their 14 financial indicators of year t - 2 are used to predict whether they will receive the ST label in year t. When building the prediction model, we first propose GA-MLP algorithm in which the Multi-Layer Perception (MLP) is optimized using Genetic algorithm (GA) which could automatically determine the parameter values and the number of neurons in each hidden layer at the same time, and then a novel diversity-considered GA-MLP ensemble algorithm (DGAMLPE) is proposed to improve the performance. We objectively test the proposed algorithms on the constructed financial distress data set and five other datasets, and compare them with other 7 algorithms in terms of accuracy, F1-score, and AUC. Experimental results show that DGAMLPE performs very well on the financial distress data set, and it outperform other tested algorithms for the data set tested.

关键词： Financial distress Chinese listed companies Multi-layer perception Genetic algorithm ensemble algorithm

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An efficient, partitioned ensemble algorithm for simulating ensembles of evolutionary MHD flows at low magnetic Reynolds number

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NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS 2018年第6期34卷 2129-2152页

作者： Jiang, Nan Schneier, Michael Missouri Univ Sci & Technol Dept Math & Stat Rolla MO 65409 USA Florida State Univ Dept Sci Comp Tallahassee FL 32306 USA

Studying the propagation of uncertainties in a nonlinear dynamical system usually involves generating a set of samples in the stochastic parameter space and then repeated simulations with different sampled parameters. The main difficulty faced in the process is the excessive computational cost. In this paper, we present an efficient, partitioned ensemble algorithm to determine multiple realizations of a reduced Magnetohydrodynamics (MHD) system, which models MHD flows at low magnetic Reynolds number. The algorithm decouples the fully coupled problem into two smaller subphysics problems, which reduces the size of the linear systems that to be solved and allows the use of optimized codes for each subphysics problem. Moreover, the resulting coefficient matrices are the same for all realizations at each time step, which allows faster computation of all realizations and significant savings in computational cost. We prove this algorithm is first order accurate and long time stable under a time step condition. Numerical examples are provided to verify the theoretical results and demonstrate the efficiency of the algorithm.

关键词： ensemble algorithm finite element method low magnetic Reynolds number magnetohydrodynamics partitioned method uncertainty quantification

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Novel ensemble algorithm for Multiple Activity Recognition in Elderly People Exploiting Ubiquitous Sensing Devices

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IEEE SENSORS JOURNAL 2021年第16期21卷 18214-18221页

作者： Liaqat, Sidrah Dashtipour, Kia Shah, Syed Aziz Rizwan, Ali Alotaibi, Abdullah Alhumaidi Althobaiti, Turke Arshad, Kamran Assaleh, Khaled Ramzan, Naeem Univ West Scotland Sch Engn & Comp Paisley PA1 2BE Renfrew Scotland Edinburgh Napier Univ Sch Comp Merchiston Campus Edinburgh EH10 5DT Midlothian Scotland Coventry Univ Res Ctr Intelligent Healthcare Coventry CV1 5FB W Midlands England Qatar Mobil Innovat Ctr Qatar Sci & Technol Pk Doha Qatar Taif Univ Coll Ranyah Dept Sci & Technol At Taif 21944 Saudi Arabia Northern Border Univ Fac Sci Ar Ar 91431 Saudi Arabia Ajman Univ Coll Engn & IT Ajman U Arab Emirates

Ambient assisted living is good way to look after ageing population that enables us to detect human's activities of daily living (ADLs) and postures, as number of older adults are increasing at rapid pace. Posture detection is used to provide the assessment for monitoring the activity of elderly people. Most of the existing approaches exploit dedicated sensing devices as cameras, thermal sensors, accelerometer, gyroscope, magnetometer and so on. Traditional methods such as recording data using these sensors, training and testing machine learning classifiers to identify various human postures. This paper exploits data recorded using ubiquitous devices such as smart phones we use on daily basis and classify different human activities such as standing, sitting, laying, walking, walking downstairs and walking upstairs. Moreover, we have used machine learning and deep learning classifiers including random forest, KNN, logistic regression, multilayer perceptron, decision tree, QDA and SVM, convolutional neural network and long short-term memory as ground truth and proposed a novel ensemble classification algorithm to classify each human activity. The proposed algorithm demonstrate classification accuracy of 98% that outperforms other algorithms.

关键词： Sensors Legged locomotion Feature extraction Machine learning algorithms Machine learning Support vector machines Senior citizens Posture detection ensemble algorithm deep learning machine learning ubiquitous devices

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Censorship data-driven DNS resolution anomaly detection: An ensemble algorithm model with multivariate feature fusion

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COMPUTER NETWORKS 2024年 252卷

作者： Li, Chao Cheng, Yanan Zhang, Zhaoxin Zhang, Zundong Harbin Inst Technol Fac Comp Harbin 150001 Peoples R China

The Domain Name System (DNS), as a critical component of Internet infrastructure, is crucial for maintaining network stability and security. However, vulnerabilities in the DNS resolution process make it susceptible to various network attacks. Current DNS resolution anomaly detection methods are challenged by the scarcity of diverse anomalous sample data and the difficulty in accurately capturing such anomalies. To address those challenges, we firstly introduce a proactive anomaly detection method based on bidirectional national censorship behavior, abbreviated as CB-BiDAM. This method can collect diverse anomalous resolution data from multiple network spaces, covering common types of resolution anomalies and effectively solving the problem of sample scarcity. Further, we extract multidimensional features from four key aspects: response content, DNS attributes, resolution paths, and timing, to gain a deeper understanding of the relationship between multifaceted information in the DNS resolution process and anomalous events. Finally, based on these multidimensional features, we construct a DNS resolution anomaly detection model named DRADC, using a stacked ensemble approach. Through a series of comparative experiments, the DRADC model significantly outperforms existing machine learning algorithms in key metrics such as accuracy (97.48%), recall (96.87%), and F1 score (97.09%). Feature ablation experiments further demonstrates that incorporating multidimensional features significantly improves model performance, with a 3% increase in accuracy and a 3.5% increase in precision compared to models relying solely on response content features. By providing an accurate detection model for DNS resolution anomalies, this study aids network administrators in more effectively identifying and countering network attacks. Additionally, our research also contributes to the detection and response to domain censorship mechanisms, which is crucial for maintaining the openness and free

关键词： DNS Anomaly detection ensemble algorithm Censorship Cybersecurity

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An ensemble algorithm Based Component for Geomagnetic Data Assimilation

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TERRESTRIAL ATMOSPHERIC AND OCEANIC SCIENCES 2015年第1期26卷 53-61页

作者： Sun, Zhibin Kuang, Weijia Colorado State Univ USDA UV B Monitoring & Res Program Ft Collins CO 80523 USA NASA Goddard Space Flight Ctr Planetary Geodynam Lab Greenbelt MD 20771 USA

Geomagnetic data assimilation is one of the most recent developments in geomagnetic studies. It combines geodynamo model outputs and surface geomagnetic observations to provide more accurate estimates of the core dynamic state and provide accurate geomagnetic secular variation forecasting. To facilitate geomagnetic data assimilation studies, we develop a stand-alone data assimilation component for the geomagnetic community This component is used to calculate the forecast error covariance matrices and the gain matrix from a given geodynamo solution, which can then be used for sequential geomagnetic data assimilation. This component is very flexible and can be executed independently. It can also be easily integrated with arbitrary dynamo models.

关键词： Geomagnetic data assimilation ensemble algorithm Geodynamo

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A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets

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GISCIENCE & REMOTE SENSING 2022年第1期59卷 234-249页

作者： Zhang, Yuzhen Ma, Jun Liang, Shunlin Li, Xisheng Liu, Jindong Univ Sci & Technol Beijing Beijing Engn Res Ctr Ind Spectrum Imaging Sch Automat & Elect Engn Beijing Peoples R China Univ Maryland Dept Geog Sci College Pk MD 20742 USA

Accurately quantifying the aboveground biomass (AGB) of forests is crucial for understanding global change-related issues such as the carbon cycle and climate change. Many studies have estimated AGB from multiple remotely sensed datasets using various algorithms, but substantial uncertainties remain in AGB predictions. In this study, we aim to explore whether diverse algorithms stacked together are able to improve the accuracy of AGB estimates. To build the stacking framework, five base learners were first selected from a series of algorithms, including multivariate adaptive regression splines (MARS), support vector regression (SVR), multilayer perceptron (MLP) model, random forests (RF), extremely randomized trees (ERT), stochastic gradient boosting (SGB), gradient-boosted regression tree (GBRT) algorithm, and categorical boosting (CatBoost), based on diversity and accuracy metrics. Ridge and RF were utilized as the meta learner to combine the outputs of base learners. In addition, six important features were selected according to the feature importance values provided by the CatBoost, ERT, GBRT, SGB, MARS and RF algorithms as inputs of the meta learner in the stacking process. We then used stacking models with 3-5 selected base learners and ridge or RF to estimate AGB. The AGB data compiled from plot-level forest AGB, high-resolution AGB data derived from field and lidar data and the corresponding predictor variables extracted from the satellite-derived leaf area index, net primary production, forest canopy height, tree cover data, and Global Multiresolution Terrain Elevation Data 2010, as well as climate data, were randomly split into groups of 80% for training the model and 20% for model evaluation. The evaluation results showed that stacking generally outweighed the optimal base learner and provided improved AGB estimations, mainly by decreasing the bias. All stacking models had relative improvement (RI) values in bias of at least 22.12%, even reaching more tha

关键词： Stacking forest biomass ensemble algorithm remote sensing

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An ensemble algorithm Used in Video Recommendation System 17

An Ensemble Algorithm Used in Video Recommendation System

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International Conference on Big Data Research (ICBDR)

作者： Ma, Li Wang, Xingjun Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781450353564

At present, there are many recommendation algorithms, but no one is superior to another under any background or any data. In order to improve the performance of recommendation algorithm as much as possible, we propose an ensemble algorithm by combining two algorithms, one is a recommendation algorithm based on item clustering and matrix factorization, the other is collaborative filtering algorithm based on data smoothing. The former performs well in the domain of digital television, but it has not solved the data sparsity problem. The latter can decrease data sparsity, but it is poor in dealing with cold-start problem. 2We combine the two algorithms together by computing their weighted sums according to the predicting scores. Finally, compared with the two algorithms, the new ensemble algorithm performs better in video recommendation system.

关键词： Recommendation algorithm ensemble algorithm clustering collaborative filtering

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