检索结果-内蒙古大学图书馆

Data-driven performance analysis of an active chilled beam air conditioning system: A machine learning approach for energy efficiency and predictive maintenance

引用

RESULTS IN ENGINEERING 2024年 23卷

作者： Amin, Nima Hajimirza Etemad, Alireza Abdalisousan, Ashkan Islamic Azad Univ Dept Energy Engn Sci & Res Branch Tehran Iran Univ Coll Dublin UCD Energy Inst Sch Mech & Mat Engn Dublin Ireland Islamic Azad Univ Dept Technol & Engn Astara Branch Astara Iran

This study aims to enhance energy performance estimation and maintenance planning for office buildings in Tehran. Machine learning and data mining techniques were employed, utilizing Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Narrow Neural Networks, and optimizable SVMs. A comprehensive dataset of 2500 experimental samples was utilized with five input parameters (building size, occupancy patterns, environmental conditions, and ACB system parameters) and one output parameter (electricity consumption). GPR models performed better in estimating electricity consumption, achieving a Root Mean Square Error (RMSE) of 297.01 and an impressive R-squared value of 0.99. To optimize energy performance and design more energy- efficient buildings, this model accurately predicts energy consumption. Additionally, this study addressed maintenance planning for ACB systems, simulating maintenance issues with 2500 synthetic samples. The dataset included building age, size, number of floors, usage intensity, previous maintenance history, location, and environmental factors. Maintenance time was estimated using these features, and xgboost was employed to predict ACB system maintenance time. Low MSE, RMSE, and R-squared values in the xgboost model allow for accurate predictions. Using this model, facility managers can proactively plan maintenance activities, allocate resources, and minimize downtime. Through proactive measures, ACB performance and maintenance costs are optimized. The study accurately predicts ACB system energy consumption and maintenance time using the rational quadratic GPR model, applicable in Tehran and beyond. Additionally, MATLAB version R2022b was used for prediction, while DesignBuilder and EnergyPlus were utilized for technical analysis.

关键词： HVAC systems Active chilled beam system Data-driven modeling xgboost algorithm Machine learning Predictive maintenance analysis GPR Gaussian Process Regression

来源：评论

学校读者我要写书评

暂无评论

Ensemble learning-based hierarchical retrieval of similar cases for site planning

引用

JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2021年第6期8卷 1548-1561页

作者： Wang, Pengyue Guo, Maozu Han, Yunsong Zhao, Lingling Zhou, Xiaoping Zhang, Dayu Beijing Univ Civil Engn & Architecture Sch Architecture & Urban Planning Beijing 100044 Peoples R China Beijing Key Lab Intelligent Proc Bldg Big Data Beijing 100044 Peoples R China Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Beijing 100044 Peoples R China Harbin Inst Technol Sch Architecture Harbin 150001 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China

Site planning not only involves the arrangement of a large number of elements but also considers the complexity of urban systems;hence, the design process involves large workloads and is time consuming. Retrieving similar existing cases helps architects optimize or accelerate the design process. This paper proposes a computational approach that provides valuable references by retrieving similar cases. Three types of attributes are extracted to represent a given parcel: land-use attributes, geometric attributes (area, orientation, etc.), and environmental attributes (greening rate, surrounding facilities, etc.). The complete hierarchical retrieval process is divided into three phases. The first phase selects cases whose land-use attributes are consistent with the target parcel. Then, the similarity distances between the given target parcel and the selected cases are calculated using geometric attributes. The eXtreme Gradient Boosting (xgboost) classifier is adopted to determine which case is similar to the target parcel. Finally, similarity scores of the retrieved cases are calculated based on the environmental attributes to provide more options during the actual design. In total, 1189 cases with different land-use types in Beijing were collected for the case base. The comparative experimental results confirmed that the proposed ensemble learning-based hierarchical retrieval of similar cases approach improves the accuracy of retrieval results. Furthermore, we use a real-world target parcel to demonstrate the superiority and flexibility of the retrieval process.

关键词： site planning similarity retrieval parcel representation xgboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Predicting Green Supply Chain Impact With SNN-Stacking Model in Digital Transformation Context

引用

JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING 2023年第1期35卷 1页

作者： Li, Te Donta, Praveen Kumar Dongbei Univ Finance & Econ Sch Business Adm Dalian Peoples R China TU Wien Distributed Syst Grp Vienna Austria

Green supply chain management is crucial for sustainable enterprises. Achieving it hinges on creating a greener supply chain through AI-driven data analysis. This enables precise market alignment, optimized management, and sustainable development. This study explores the link between digital transformation and green supply chain management. It leverages AI, specifically the xgboost algorithm, to gauge sample contributions to market demand. It extracts multi-dimensional features in green supply chain management using NSCNN and CSCNN, combining them with the Stacking ensemble learning algorithm to form a new predictive model. This model, SNN-Stacking ensemble learning, outperforms traditional models, aiding resource planning, enhancing supply chain transparency, and promoting sustainable development by reducing environmental risks and resource waste. This research underscores the potential of digital technology in green supply chain management.

关键词： Demand forecast Digital transformation Green supply chain management SNN-Stacking ensemble learning xgboost algorithm

来源：评论

学校读者我要写书评

暂无评论

An xgboost Machine Learning Based Model for Predicting Ki-67 Value ≥ 15% in T2NXM0 Stage Primary Breast Cancer Receiving Neoadjuvant Chemotherapy Using Clinical Data and Delta-Radiomic Features on Ultrasound Images and Overall Survival Analysis: A 5-Year Postoperative Follow-Up Study

引用

TECHNOLOGY IN CANCER RESEARCH & TREATMENT 2024年 23卷 15330338241265989-15330338241265989页

作者： Lu, Yang Yang, Fei Tao, Yichao An, Pang Cent Hosp Xiaogan Dept Ultrasound Xiaogan 432000 Hubei Peoples R China Hubei Univ Med Xiangyang Peoples Hosp 1 Dept Radiol Xiangyang Peoples R China

Objective: To establish a model based on clinical and delta-radiomic features within ultrasound images using xgboost machine learning to predict proliferation-associated nuclear antigen Ki-67 value >= 15% in T2NXM0 stage primary breast cancer (BC). Method: Data were collected from 228 randomly selected BC patients who received ultrasound screening and postoperative pathologic assessment from April 2015 to September 2018. The patients were classified into the study group (n = 80) and control group (n = 148), and the data were apportioned into the training set and test set at a 7:3 ratio based on time intervals. In the training set, crucial factors were identified from clinical features and grayscale and delta-radiomic features within ultrasound images, by using the chi-square test, t-test, and rank-sum test. The clinical model, imaging model, and combined model were built using multivariate logistic regression, respectively. The model's predictive performance and clinical net benefit were assessed using DeLong's method and decision curve analysis. Meanwhile, an xgboost algorithm is used to establish a prediction model to verify the above results. Results: The crucial factors affecting Ki-67 value >= 15% included BMI, lymph node metastases, BC volume, CA153, pathology type, tumor boundaries, tumor morphology, elastography score, and delta-radscore. The predictive performance of the combined model [AUC 0.857, OR 0.0290, 95% CI 0.793-0.908] was considerably improved on the training set than the clinical model [AUC 0.724, OR 0.0422, 95% CI 0.648-0.792] and the imaging model [AUC 0.798, OR 0.0355, 95% CI 0.727-0.857]. The decision curve analysis also confirmed that the combined model delivered a higher clinical net benefit, and the verification on the test set yielded similar results. The nomogram and the calibration curve plotted based on the combined model achieved satisfactory clinical effects. The SHAP value of the xgboost algorithm also confirmed that lymph node meta

关键词： ultrasound examination delta-radiomics breast cancer tumor proliferating cell nuclear antigen 67 xgboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Predicting early gastric cancer risk using machine learning: A population-based retrospective study

引用

DIGITAL HEALTH 2024年 10卷

作者： Ke, Xing Cai, Xinyu Bian, Bingxian Shen, Yuanheng Zhou, Yunlan Liu, Wei Wang, Xu Shen, Lisong Yang, Junyao Shanghai Jiao Tong Univ Xinhua Hosp Dept Clin Lab Sch Med Shanghai Peoples R China Shanghai Jiao Tong Univ Coll Hlth Sci & Technol Fac Med Lab Sci Sch Med Shanghai Peoples R China Shanghai Acad Expt Med Inst Artificial Intelligence Med Shanghai Peoples R China Shanghai Jiao Tong Univ Ruijin Hosp Coll Basic Med Sci Dept PatholSch Med Shanghai Peoples R China Beijing Deepwise & League PHD Technol Co Ltd Dept Res Collaborat R&D Ctr Beijing Peoples R China Shanghai Jiao Tong Univ Key Lab Cell Differentiat & Apoptosis Chinese Minist Educ Sch Med Shanghai Peoples R China Shanghai Jiao Tong Univ Xinhua Hosp Dept Clin Lab Sch Med 1665 Kong Jiang Rd Shanghai 200092 Peoples R China Shanghai Jiao Tong Univ Ruijin Hosp Dept Pathol Sch Med 280 Chongqing South Ave Shanghai 200025 Peoples R China Shanghai Jiao Tong Univ Coll Basic Med Sci Sch Med 280 Chongqing South Ave Shanghai 200025 Peoples R China

Background Early detection and treatment are crucial for reducing gastrointestinal tumour-related mortality. The diagnostic efficiency of the most commonly used diagnostic markers for gastric cancer (GC) is not very high. A single laboratory test cannot meet the requirements of early screening, and machine learning methods are needed to aid the early diagnosis of GC by combining multiple *** Based on the xgboost algorithm, a new model was developed to distinguish between GC and precancerous lesions in newly admitted patients between 2018 and 2023 using multiple laboratory tests. We evaluated the ability of the prediction score derived from this model to predict early GC. In addition, we investigated the efficacy of the model in correctly screening for GC given negative protein tumour marker *** The XHGC20 model constructed using the xgboost algorithm could distinguish GC from precancerous disease well (area under the receiver operating characteristic curve [AUC] = 0.901), with a sensitivity, specificity and cut-off value of 0.830, 0.806 and 0.265, respectively. The prediction score was very effective in the diagnosis of early GC. When the cut-off value was 0.27, and the AUC was 0.888, the sensitivity and specificity were 0.797 and 0.807, respectively. The model was also effective at evaluating GC given negative conventional markers (AUC = 0.970), with the sensitivity and specificity of 0.941 and 0.906, respectively, which helped to reduce the rate of missed *** The XHGC20 model established by the xgboost algorithm integrates information from 20 clinical laboratory tests and can aid in the early screening of GC, providing a useful new method for auxiliary laboratory diagnosis.

关键词： Gastric cancer diagnostic efficacy machine learning xgboost algorithm early diagnosis

来源：评论

学校读者我要写书评

暂无评论

Application of unsupervised learning and deep learning for rock type prediction and petrophysical characterization using multi-scale data

GEOENERGY SCIENCE AND ENGINEERING

引用

GEOENERGY SCIENCE AND ENGINEERING 2023年 230卷

作者： Iraji, Shohreh Soltanmohammadi, Ramin Matheus, Gabriela Fernandes Basso, Mateus Vidal, Alexandre Campane State Univ Campinas UNICAMP Dept Mech Engn FEM BR-13083860 Campinas SP Brazil State Univ Campinas UNICAMP Ctr Energy & Petr Studies CEPETRO BR-13083896 Campinas SP Brazil State Univ Campinas UNICAMP Geosci Inst IG Dept Geol & Nat Resources BR-13083855 Campinas SP Brazil

This study integrates well log data, routine core analyses, microcomputed X-ray tomography (mu CT) images, and sedimentary petrography to accurately characterize and evaluate the carbonate reservoirs of the Barra Velha formation (Aptian) of the Santos Basin within Brazilian pre-salt region. In these carbonate reservoirs, the porous system is extremely diverse and variable, making it challenging to establish rock typing with comparable petrophysical properties. Based on this integrated study, the reservoir sequences were characterized and a precise definition of four reservoir rock types (RRTs) was performed by integrating the petrophysical values from the plugs and their corresponding well log data of two cored wells using K-means unsupervised classification algorithm. The classification results were combined with various conventional techniques to evaluate the quality and geological characteristics of the studied sequence. This evaluation encompassed different parameters such as flow and storage capacity, reservoir quality index, flow zone indicator, pore spaces interpretation, and average pore and throat radius. The study involved a detailed analysis of thin sections to identify various facies, including shrubstones, reworked, and spherulitestone, and to classify various forms of porosity such as interparticle, intraparticle, intercrystalline, vug, moldic, fracture, and growth framework porosity. Pore Network Modeling from mu CT analysis of plugs was used specifically for the characterization of pores and throats of plug samples from each RRT. These datasets were utilized as supporting evidence to offer a more accurate and inclusive knowledge of reservoir quality. The study aimed to develop predictive models by implementing deep learning and machine learning algorithms trained on well log data to estimate plug porosity and rock type. Two deep learning models, ResNet and 1D CNN, were trained and evaluated for plug porosity prediction, with the 1D CNN model showing

关键词： Micro -computed tomography ( mu CT) images K -means unsupervised classification algorithm Reservoir rock types (RRTs) Pore network modeling Deep learning algorithms ResNet 1D CNN xgboost algorithm

来源：评论

学校读者我要写书评

暂无评论

Landslide Hazard Assessment in Highway Areas of Guangxi Using Remote Sensing Data and a Pre-Trained xgboost Model

引用

REMOTE SENSING 2023年第13期15卷 3350-3350页

作者： Zhang, Yuze Deng, Lei Han, Ying Sun, Yunhua Zang, Yu Zhou, Minlu China Transport Telecommun & Informat Ctr Natl Engn Res Ctr Transportat Safety & Emergency I Beijing 100028 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China State Grid Siji Locat Serv Co Ltd Beijing 102200 Peoples R China GuangXi Commun Design Grp Co Ltd Nanning 530012 Peoples R China

This study presents a novel method for assessing landslide hazards along highways using remote sensing and machine learning. We extract geospatial features such as slope, aspect, and rainfall over Guangxi, China, and apply an extreme gradient boosting model pre-trained on contiguous United States datasets. The model produces susceptibility maps that indicate landslide probability at different scales. However, the lack of accurate data on historical landslides in Guangxi challenges the model evaluation and comparison between regions. To overcome this, we calibrate the model to fit the local conditions in Guangxi. The calibrated model agrees with the observed landslide locations, implying its capability to capture regional variations in landslide mechanisms. We apply the model at a 30 m resolution along the Heba Expressway and validate it against reports from July 2021 to March 2022. The model correctly predicts five of seven landslide events in this period with a reasonable alarm rate. This framework has the potential for large-scale landslide risk management by informing transportation planning and infrastructure maintenance decisions. More data on landslide timing and human disturbance events may improve the model's accuracy across diverse geographical areas and terrains.

关键词： landslide hazard assessment remote sensing data xgboost algorithm highway pre-trained model

来源：评论

学校读者我要写书评

暂无评论

Formability classifier for a TV back panel part with machine learning

引用

INTERNATIONAL JOURNAL OF MATERIAL FORMING 2023年第6期16卷 70-70页

作者： Fazily, Piemaan Cho, Donghyuk Choi, Hyunsung Cho, Joon Ho Lee, Jongshin Yoon, Jeong Whan Korea Adv Inst Sci & Technol Dept Mech Engn Daejeon 34141 South Korea Korea Inst Mat Sci 797 Changwon Daero Chang Won 051508 South Korea LG Elect Inc Met Forming Technol Team LG Ro Pyeongtaek Si Gyeonggi Do South Korea Deakin Univ Sch Engn 75 Pigdons Rd Burwood Vic 3216 Australia

This study proposes a machine learning-based methodology for evaluating the formability of sheet metals. An xgboost (eXtreme Gradient Boosting) machine learning classifier is developed to classify the formability of the TV back panel based on the forming limit curve (FLC). The input to the xgboost model is the blank thickness and cross-sectional dimensions of the screw holes, AC (Alternating Current), and AV (Audio Visual) terminals on the TV back panel. The training dataset is generated using finite element simulations and verified through experimental strain measurements. The trained classification model maps the panel geometry to one of three formability classes: safe, marginal, and cracked. Strain values below the FLC are classified as safe, those within 5% margin of the FLC are classified as marginal, and those above are classified as cracked. The statistical accuracy and performance of the classifier are quantified using the confusion matrix and multiclass Receiver Operating Characteristic (ROC) curve, respectively. Furthermore, in order to demonstrate the practical viability of the proposed methodology, the punch radius of the screw holes is optimized using Brent's method in a Java environment. Remarkably, the optimization process is completed swiftly, taking only 3.11 s. Hence, the results demonstrate that formability for a new design can be improved based on the predictions of the machine learning model.

关键词： Artificial Intelligence Forming Limit Diagram xgboost algorithm Brent's method Finite Element Analysis

来源：评论

学校读者我要写书评

暂无评论

An Interpretable Prediction Model for Stroke Based on xgboost and SHAP

引用

Journal of Clinical and Nursing Research 2023年第3期7卷 96-106页

作者： Tianshu Fang Jiacheng Deng Nanchang University Queen Mary School Nanchang UniversityNanchang 330031Jiangxi ProvinceChina Nanchang Key Laboratory of Medical and Technology Research Nanchang UniversityNanchang 330031Jiangxi ProvinceChina

Objective:To establish a stroke prediction and feature analysis model integrating xgboost and SHAP to aid the clinical diagnosis and prevention of ***:Based on the open data set on Kaggle,with the help of data preprocessing and grid parameter optimization,an interpretable stroke risk prediction model was established by integrating xgboost and SHAP and an explanatory analysis of risk factors was ***:The xgboost model’s accuracy,sensitivity,specificity,and area under the receiver operating characteristic(ROC)curve(AUC)were 96.71%,93.83%,99.59%,and 99.19%,*** explanatory analysis showed that age,type of residence,and history of hypertension were key factors affecting the incidence of ***:Based on the data set,our analysis showed that the established model can be used to identify stroke,and our explanatory analysis based on SHAP increases the transparency of the model and facilitates medical practitioners to analyze the reliability of the model.

关键词： Stroke risk prediction xgboost algorithm SHAP model Risk factor analysis

来源：评论

学校读者我要写书评

暂无评论

Photovoltaic Power Prediction Model Using Pre-train and Fine-tune Paradigm Based on LightGBM and xgboost

引用

Procedia Computer Science 2023年 224卷 407-412页

作者： Jiandong Ye Binqi Zhao Hua Deng Yangjiang Power Supply Bureau Guangdong Power Grid Co. Ltd Yangjiang China Institute of Tropical and Marine Meteorology Guangzhou CMA

Accurate photovoltaic power prediction is crucial for optimizing the performance of photovoltaic power plants and ensuring the stability of the power grid. However, existing forecasting methods are challenged when predicting the power output of multiple plants with different geographical locations and environmental conditions simultaneously, due to the complexity of the underlying factors that affect power generation. To address this issue, this paper proposes a novel LightGBM-xgboost photovoltaic power prediction model based on a pre-training and fine-tuning paradigm. The model leverages a large dataset to pre-train and learn rich feature representations within meteorological elements, which are then fine-tuned on specific downstream tasks to improve prediction accuracy and generalization ability. Experimental verification is conducted using data from three photovoltaic generators operating in Guangdong, China. Results demonstrate that the proposed model outperforms other methods when predicting the power output of multiple plants with different conditions simultaneously, highlighting the effectiveness of the proposed approach. Overall, this work contributes to advancing the state-of-the-art in photovoltaic power prediction and has practical implications for improving the control and scheduling of photovoltaic power plants, as well as ensuring the stability and security of the power grid.

关键词： Photovoltaic power prediction Pre-train and Fine-tune model LightGBM algorithm xgboost 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：