检索结果-内蒙古大学图书馆

Integrated finite element analysis and machine learning approach for exploring the flexural behavior of steel-spontaneous combustion coal gangue aggregate concrete composite beams with studs

引用

STRUCTURES 2025年 72卷

作者： Zhang, Tirui Wang, Qinghe Zhang, Xinlong Liu, Yuting Ding, Jinan Shenyang Jianzhu Univ Sch Civil Engn Shenyang 110168 Peoples R China China Northeast Architectural Design & Res Inst Co Shenyang 110000 Peoples R China Shenyang Jianzhu Univ Sch Management Shenyang 110168 Peoples R China

This study investigates how varying the replacement ratio of spontaneous-combustion coal gangue aggregate (SCGA) affects the steel-concrete composite beams' flexural behavior with studs. Using ABAQUS software, pushout models of studs and flexural models of composite beams were developed, with their reliability confirmed through comparisons with previously published experimental findings. A parameter analysis was conducted to quantify the impact of the SCGA replacement ratio on the studs' shear resistance and the composite beams' flexural behavior. Additionally, machine learning techniques were employed to set up models for predicting the studs' shear resistance and assessing both the composite beams' flexural capacity and bending stiffness, with their predictive performance evaluated accordingly. The impact of each parameter on the studs' shear resistance and the composite beams' flexural behavior was also quantified. The findings suggest that the studs' shear resistance and the composite beams' flexural behavior decline as the SCGA replacement ratio increases. Specifically, compared with natural concrete specimens, the shear resistance of studs exhibiting replacement ratios of 50 % and 100 % was found to drop by 4.04 %-5.68 % and 7.90 %-11.18 %, respectively. The composite beams' flexural capacity decreased by 1.05 %-2.62 % and 2.24 %-5.44 %, while the bending stiffness decreased by 3.62 %-4.65 % and 8.27 %-10.00 %, respectively. The XGBoost model, optimized with advanced algorithms, effectively predicts the studs' shear resistance and assesses both the composite beams' flexural capacity and bending stiffness, with prediction errors maintained within 15 % and a minimum comprehensive performance index of 0.20. SHapley Additive exPlanations value analysis identified the stud's diameter and the SCGA concrete's compressive strength as the main influence factors of the studs' shear resistance. In terms of composite beams' flexural capacity, the key factor is the SCGA co

关键词： Spontaneous-combustion coal gangue aggregate concrete Steel-concrete composite beam Shear stud Flexural behavior Finite element analysis machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

On the measurement of the aerodynamic features of composite structures under subsonic airflow using mathematical modeling and machine learning method

引用

MEASUREMENT 2024年 238卷

作者： Yuan, Ruijia Shi, Fan Fouly, Ahmed Albahkali, Thamer Anhui Univ Finance & Econ Sch Management Sci & Engn Bengbu 233030 Peoples R China King Saud Univ Coll Engn Dept Mech Engn POB 800 Riyadh 11421 Saudi Arabia

This study investigates the aerodynamic features of functionally graded triply periodic minimal surface (FGTPMS) structures under subsonic airflow using a combination of mathematical modeling and machine learning methods. The modified supersonic piston theory of Krumhaar is used to quantify the aerodynamic stiffness and damping matrices. Using characteristics and six distinct patterns of volume distribution of FG-TPMS materials in terms of their mechanical qualities, Hamilton's principle, and parabolic shear deformation theory are used to extract the governing equations of the presented composite structure. After that, the differential quadrature method at the discrete places and Lagrange interpolated polynomials and Kronecker product are used to solve the governing equations in the numerical domain. This study highlights the potential of integrating advanced computational techniques with machine learning to address complex engineering problems. The findings provide valuable insights into the aerodynamic optimization of sandwich structures, paving the way for their more efficient and effective design in aerospace engineering. As an important outcome for related industries, the displacement field along with the Z direction increases by increasing the angle of airflow by approximately 9 % while this value for the X, and Y directions are 7 %, and 6 %, respectively. The proposed methodology offers a powerful tool for engineers and researchers seeking to optimize the performance of advanced composite materials under subsonic airflow conditions.

关键词： FG-TPMS Aerodynamic features Composite structure Subsonic airflow machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Diagnosis and classification of kidney transplant rejection using machine learning-assisted surface-enhanced Raman spectroscopy using a single drop of serum

引用

BIOSENSORS & BIOELECTRONICS 2024年 261卷 116523页

作者： Lee, Sanghwa Kim, Jin-Myung Lee, Kwanhee Cho, Haeyon Shin, Sung Kim, Jun Ki Asan Med Ctr Asan Inst Life Sci Dept Convergence Med Pungna 2 dong Seoul 05505 South Korea Univ Ulsan Asan Med Ctr Dept Surg Div Kidney & Pancreas TransplantatColl Med Seoul 05505 South Korea Univ Ulsan Coll Med Dept Biomed Engn Brain Korea Project 21 Seoul 05505 South Korea Univ Ulsan Coll Med Asan Med Ctr Dept Pathol Seoul 05505 South Korea

The quest to reduce kidney transplant rejection has emphasized the urgent requirement for the development of non-invasive, precise diagnostic technologies. These technologies aim to detect antibody-mediated rejection (ABMR) and T-cell-mediated rejection (TCMR), which are asymptomatic and pose a risk of potential kidney damage. The protocols for managing rejection caused by ABMR and TCMR differ, and diagnosis has traditionally relied on invasive biopsy procedures. Therefore, a convergence system using a nano-sensing chip, Raman spectroscopy, and AI technology was introduced to facilitate diagnosis using serum samples obtained from patients with no major abnormality, ABMR, and TCMR after kidney transplantation. Tissue biopsy and Banff score analysis were performed across the groups for validation, and 5 mu L of serum obtained at the same time was added onto the Au-ZnO nanorod-based Surface-Enhanced Raman Scattering sensing chip to obtain Raman spectroscopy signals. The accuracy of machine learning algorithms for principal component-linear discriminant analysis and principal component-partial least squares discriminant analysis was 93.53% and 98.82%, respectively. The collagen (an indicative of kidney injury), creatinine, and amino acid-derived signals (markers of kidney function) contributed to this accuracy;however, the high accuracy was primarily due to the ability of the system to analyze a broad spectrum of various biomarkers.

关键词： Kidney transplant rejection T-cell-mediated rejection (TCMR) Antibody-mediated rejection (ABMR) Nano-sized biomarker Surface-enhanced Raman spectroscopy machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Urine output as one of the most important features in differentiating in-hospital death among patients receiving extracorporeal membrane oxygenation: a random forest approach

引用

EUROPEAN JOURNAL OF MEDICAL RESEARCH 2023年第1期28卷 347-347页

作者： Chang, Sheng-Nan Hu, Nian-Ze Wu, Jo-Hsuan Cheng, Hsun-Mao Caffrey, James L. Yu, Hsi-Yu Chen, Yih-Sharng Hsu, Jiun Lin, Jou-Wei Natl Taiwan Univ Hosp Cardiovasc Ctr Yunlin Branch Douliu City Yunlin Taiwan Natl Taiwan Univ Coll Med Dept Med Taipei Taiwan Natl Formosa Univ Dept Informat Management Huwei Yunlin Taiwan Univ Calif San Diego Shiley Eye Inst La Jolla CA USA Natl Taiwan Univ Hosp Yunlin Branch Off Med Informat Douliu City Yunlin Taiwan Univ North Texas Hlth Sci Ctr Physiol & Cardiovasc Res Inst Ft Worth TX USA Natl Taiwan Univ Hosp Dept Surg Taipei Taiwan Natl Taiwan Univ Coll Med Dept Surg Taipei Taiwan Natl Taiwan Univ Hosp Dept Surg Yunlin Branch Douliu City Yunlin Taiwan

BackgroundIt is common to support cardiovascular function in critically ill patients with extracorporeal membrane oxygenation (ECMO). The purpose of this study was to identify patients receiving ECMO with a considerable risk of dying in hospital using machine learning *** total of 1342 adult patients on ECMO support were randomly assigned to the training and test groups. The discriminatory power (DP) for predicting in-hospital mortality was tested using both random forest (RF) and logistic regression (LR) *** output on the first day of ECMO implantation was found to be one of the most predictive features that were related to in-hospital death in both RF and LR models. For those with oliguria, the hazard ratio for 1 year mortality was 1.445 (p < 0.001, 95% CI 1.265-1.650).ConclusionsOliguria within the first 24 h was deemed especially significant in differentiating in-hospital death and 1 year mortality.

关键词： Extracorporeal membrane oxygenation machine learning algorithm Random forest Oliguria

来源：评论

学校读者我要写书评

暂无评论

Skillful Prediction of Indian Monsoon Intraseasonal Precipitation Using Central Indian Ocean Mode and machine learning

引用

GEOPHYSICAL RESEARCH LETTERS 2024年第24期51卷

作者： Zhou, Lei Yu, Yanwei Yan, Bingqi Zhao, Xingyu Qin, Jianhuang Tan, Wei Tang, Youmin Li, Xiaofeng Li, Xiaojing Dong, Junyu Chen, Dake Murtugudde, Raghu Shanghai Jiao Tong Univ Sch Oceanog Shanghai Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhuhai Zhuhai Peoples R China Ocean Univ China Coll Comp Sci & Technol Qingdao Peoples R China Hohai Univ Coll Oceanog Nanjing Peoples R China Shandong Univ Sci & Technol Coll Ocean Sci & Engn Qingdao Peoples R China Univ Northern British Columbia Environm Sci & Engn Prince George BC Canada Chinese Acad Sci Big Data Ctr Inst Oceanol CAS Key Lab Ocean Circulat & Waves Qingdao Peoples R China Chinese Acad Sci Ctr Ocean Mega Sci Qingdao Peoples R China Minist Nat Resources Inst Oceanog 2 State Key Lab Satellite Ocean Environm Dynam Hangzhou Peoples R China Univ Maryland Dept Atmospher & Ocean Sci College Pk MD USA Indian Inst Technol Mumbai India

Monsoonal precipitation is dominated by intraseasonal variabilities, whose skillful prediction lead time is currently less than 5 days and remains a grand challenge. Here we show that an intrinsic variability in the Indian Ocean, the Central Indian Ocean (CIO) mode, when combined with a machine learning (ML) algorithm, can produce skillful predictions of intraseasonal precipitation over the monsoon region with a lead time of over 15 days, which is close to the theoretical predictability limit. This remarkable skill improvement stems from the fact that the CIO mode is dynamically related to the intraseasonal monsoon rainfall, while the data-driven ML algorithm suppresses unwanted high-frequency noise. Using the CIO mode and the ML algorithm, the forecast system hybridizes physical fundamentals and versatility of data-driven algorithms. The identification of CIO mode and the verification of its significant contribution to intraseasonal predictions advance our understanding of the coupled monsoon system and also underscores the great potential of ML techniques in weather forecasts and climate predictions.

关键词： intraseasonal rainfall forecast Indian summer monsoon Central Indian Ocean mode machine learning algorithm monsoon intraseasonal oscillation

来源：评论

学校读者我要写书评

暂无评论

Conceptual machine learning Framework for Performance-Based Resource Allocation to the Institutions: A Hybrid Approach 3rd

Conceptual Machine Learning Framework for Performance-Based ...

引用

3rd International Conference on Smart Systems - Innovations in Computing (SSIC)

作者： Solanki, Hiteshkumar Virparia, Paresh Informat & Lib Network Ctr Infoc Gandhinagar 382007 India Sardar Patel Univ PG Dept Comp Sci & Technol Vallabh Vidyanagar 388120 Gujarat India

ISBN: (纸本)9789811628771;9789811628764

The paper elaborates the conceptual framework for resource allocation model based on performance of Higher Educational Institutions (HEIs). It provides logical workflow to develop input-oriented and output-oriented prediction model to assign academic, financial, and research enhancing resources to the institutions. The input- and output-oriented parameters such as faculty student ratio, number of publications, number of citations, number of patents, capital and operational expenditure, student admission ratio, number of PhD scholars enrolled, and number of PhD scholars graduated would be considered for the conceptual framework. All parameters would be identified by applying supervised machine learning algorithm like correlation and regression analysis. The data envelopment analysis (DEA) would be used to measure relative efficiency of each institution. The unsupervised machine learning algorithm would be implemented to find out clusters of institutions based on their relative efficiency. Finally, the input-oriented and output-oriented predication model would be developed to allocate resources to the institutions. The conceptual framework is being developed as a hybrid model with combination of optimization techniques of operation research and machine learning algorithms. The prediction model plays a vital role to allocate resources to the HEIs based on their performance. It would become benchmarking and decision-making model in perspective with higher education sector in India for administrators, funding agencies, and policymakers.

关键词： Data envelopment analysis Higher education machine learning algorithm Performance assessment Resource allocation system

来源：评论

学校读者我要写书评

暂无评论

A machine learning Approach for the Classification of Wet and Dry Periods Using Commercial Microwave Link Data

引用

SN Computer Science 2022年第3期3卷 1-23页

作者： Kamtchoum, Evrad Venceslas Nzeukou Takougang, Armand Cyrille Tayou Djamegni, Clémentin Department of Mathematics and Computer Science University of Dschang Dschang 237 Cameroon Fotso Victor Institute of Technology University of Dschang Bandjoun 237 Cameroon

Rainfall estimation is essential for many reasons. The lack of rainfall monitoring and estimation infrastructure in some parts of the world, including sub-Saharan Africa, is a serious problem with severe consequences. For this reason, the possibility to use mobile network infrastructure, which are in continuous expansion all over the world, to monitor and estimate rainfall has been widely investigated during the last 2 decades. One of the critical tasks in the process of rainfall estimation using microwave links is the detection of rainy periods. Many methods have been proposed to realize this task in the literature, with satisfaction and underlying uncertainties resulting from mobile networks’ infrastructures and modeling assumptions. machine learning has been proven to be very powerful for classification tasks and patterns recognition in high-dimensional data, as evidenced by many applications in various fields of science and engineering. Deep learning in particular has been recently used to improve the wet–dry periods classification task in rainfall estimation using commercial microwave links. In this paper, we propose to use other machine learning techniques for the classification of dry and wet periods using commercial microwave link data. We evaluate and compare the ability of single machine learning classifiers, ensemble machine learning classifiers, and clustering algorithms to successfully realize this task. We consider a sample of four microwave links located in the Netherlands (Western Europe), for which minimum and maximum received signal levels are recorded for each 15 min period from 30/05/2012 to 01/09/2012. In general, the considered algorithms are performant for this classification task, particularly for ensemble machine learning classifiers, among which an ensemble voting classifier emerges as a good first choice with an overall accuracy, and precision of wet periods detection very close to 100%. Its precision of dry periods’ detection is also clos

关键词： Clustering machine learning algorithm Microwave links’ attenuation Rainfall observations Supervised classification

来源：评论

学校读者我要写书评

暂无评论

Advanced machine learning algorithm Application for Rotating machine Health Monitoring

Advanced Machine learning Algorithm Application for Rotating...

引用

International Symposium on Future I&C for Nuclear Power Plants（ISOFIC2014）

作者： Shigeru KANEMOTO Masaya WATANABE Noritaka YUSA Graduate Department of Computer and Information System The University of Aizu Department of Quantum Science and Energy Engineering Tohoku University

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health *** techniques include support vector machine,deep leaning neural network,*** inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of *** we cannot find remarkable difference of anomaly discrimination performance,some methods give us the very interesting eigen patterns corresponding to normal and abnormal *** results will be useful for future more sensitive and robust anomaly monitoring technology.

关键词： Health Monitoring Rotating machine Anomaly Diagnosis machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

On the Feasibility and Efficiency of Self-Powered Green Intelligent Highways

引用

ENERGIES 2022年第13期15卷 4693-4693页

作者： Minea, Marius Dumitrescu, Catalin Marian Univ Politehn Bucuresti Dept Telemat & Elect Transports Bucharest 060042 Romania

(1) Background: The present development of transport networks focusses on the better management of fuels and energy and the preservation of the environment. To fulfill these desiderates, some countries have already reconsidered the deployment plans of new highways. This research studies the feasibility of less polluting, quasi-self-powered, intelligent highway infrastructure functional blocks accommodating functions for the future introduction of smart wireless sensor grids and connected autonomous vehicles. Subject of investigation are the possibilities of energy harvesting, and the intelligent management of resources. (2) Methods: the research investigates the main technologies for energy harvesting and recommends an optimal solution. It also proposes a framework for the intelligent, AI-based management of energy and the use of an optimized backup solution relying on 5G beamforming for energy supply of the local wireless sensing network devices;(3) Results: recommendations are made for the best energy harvesting solution, an architecture of the energy management system, an algorithm for energy management and backup solution based on 5G beamforming;(4) Conclusions: the research emphasizes the advantages and drawbacks for different solutions regarding energy harvesting in an intelligent green highway scenario with a focus on the infrastructure developed to accommodate future connected and autonomous vehicles. The term "intelligent highway" must be understood in the automotive industry to describe a network of roads where cars communicate with the infrastructure and among themselves for the purpose of avoiding congestion and performing the seamless operation of services, and a space where cars and infrastructure cooperatively process information for obtaining better road safety, less pollution, and efficient energy management. With the recent recession of conventional fuel availability and the increase in prices, a solution to improving autonomy of both cars and infr

关键词： energy harvesting AI-based energy management beamforming wireless charging machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

An experimental study of objective pain measurement using pupillary response based on genetic algorithm and artificial neural network

引用

APPLIED INTELLIGENCE 2022年第2期52卷 1145-1156页

作者： Wang, Li Guo, Yikang Dalip, Biren Xiao, Yan Urman, Richard D. Lin, Yingzi Northeastern Univ Intelligent Human Machine Syst Lab Boston MA 02115 USA Univ Texas Arlington Coll Nursing & Hlth Innovat Arlington TX USA Harvard Med Sch Brigham & Womens Hosp Boston MA USA

Obtaining an objective measurement of the pain level of a patient has always been challenging for health care providers. The most common method of pain assessment in the hospital setting is asking the patients' verbal ratings, which is considered to be a subjective approach. In order to get an objective pain level of a patient, we propose measuring pain level objectively using the pupillary response and machine learning algorithms. Thirty-two healthy subjects were enrolled in this study at Northeastern University. A painful stimulus was applied to healthy subjects by asking them to place their hands inside a bucket filled with iced water. We extracted 11 features from the pupil diameter data. To get the optimal subset of the features, a genetic algorithm (GA) was used to select features for the artificial neural network (ANN) classifier. Before feature selection, the f1-score of ANN was 54.0 +/- 0.25% with all 11 features. After feature selection, ANN had the best performance with an accuracy of 81.0% using the selected feature subset, namely the Mean, the Root Mean Square (RMS), and the Pupillary Area Under Curve (PAUC). The experimental results suggested that pupillary response together with machine learning algorithms could be a promising method of objective pain level assessment. The outcomes of this study could improve patients' experience of pain measurement in telehealthcare, especially during a pandemic when most people had to stay at home.

关键词： Objective pain measurement Pupillary response Genetic algorithm Artificial neural network machine learning algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：