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Interactive 3D Vase Design Based on Gradient Boosting Decision Trees

algorithmS 2024年第9期17卷 407页

作者： Wang, Dongming Xu, Xing Xia, Xuewen Jia, Heming Minnan Normal Univ Coll Phys & Informat Engn Zhangzhou 363000 Peoples R China Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China

Traditionally, ceramic design began with sketches on rough paper and later evolved into using CAD software for more complex designs and simulations. With technological advancements, optimization algorithms have gradually been introduced into ceramic design to enhance design efficiency and creative diversity. The use of Interactive Genetic algorithms (IGAs) for ceramic design is a new approach, but an IGA requires a significant amount of user evaluation, which can result in user fatigue. To overcome this problem, this paper introduces the LightGBM algorithm and the catboost algorithm to improve the IGA because they have excellent predictive capabilities that can assist users in evaluations. The algorithms are also applied to a vase design platform for validation. First, bicubic B & eacute;zier surfaces are used for modeling, and the genetic encoding of the vase is designed with appropriate evolutionary operators selected. Second, user data from the online platform are collected to train and optimize the LightGBM and catboost algorithms. Finally, LightGBM and catboost are combined with an IGA and applied to the vase design platform to verify their effectiveness. Comparing the improved algorithm to traditional IGAs, KD trees, Random Forest, and XGBoost, it is found that IGAs improve with LightGBM, and catboost performs better overall, requiring fewer evaluations and less time. Its R2 is higher than other proxy models, achieving 0.816 and 0.839, respectively. The improved method proposed in this paper can effectively alleviate user fatigue and enhance the user experience in product design participation.

关键词： Interactive Genetic algorithm bicubic B & eacute zier surface LightGBM algorithm catboost algorithm 3D vase

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Lipoproteins and metabolites in diagnosing and predicting Alzheimer's disease using machine learning

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LIPIDS IN HEALTH AND DISEASE 2024年第1期23卷 1-12页

作者： Wang, Fenglin Wang, Aimin Huang, Yiming Gao, Wenfeng Xu, Yaqi Zhang, Wenjing Guo, Guiya Song, Wangchen Kong, Yujia Wang, Qinghua Wang, Suzhen Shi, Fuyan Shandong Second Med Univ Sch Publ Hlth Dept Hlth Stat Weifang 261053 Shandong Peoples R China Shandong Second Med Univ Affiliated Hosp Dept Rheumatol & Immunol Weifang 261031 Shandong Peoples R China

Background Alzheimer's disease (AD) is a chronic neurodegenerative disorder that poses a substantial economic burden. The Random forest algorithm is effective in predicting AD;however, the key factors influencing AD onset remain unclear. This study aimed to analyze the key lipoprotein and metabolite factors influencing AD onset using machine-learning methods. It provides new insights for researchers and medical personnel to understand AD and provides a reference for the early diagnosis, treatment, and early prevention of *** A total of 603 participants, including controls and patients with AD with complete lipoprotein and metabolite data from the Alzheimer's disease Neuroimaging Initiative (ADNI) database between 2005 and 2016, were enrolled. Random forest, Lasso regression, and catboost algorithms were employed to rank and filter 213 lipoprotein and metabolite variables. Variables with consistently high importance rankings from any two methods were incorporated into the models. Finally, the variables selected from the three methods, with the participants' age, sex, and marital status, were used to construct a random forest predictive *** Fourteen lipoprotein and metabolite variables were screened using the three methods, and 17 variables were included in the AD prediction model based on age, sex, and marital status of the participants. The optimal random forest modeling was constructed with "mtry" set to 3 and "ntree" set to 300. The model exhibited an accuracy of 71.01%, a sensitivity of 79.59%, a specificity of 65.28%, and an AUC (95%CI) of 0.724 (0.645-0.804). When Mean Decrease Accuracy and Gini were used to rank the proteins, age, phospholipids to total lipids ratio in intermediate-density lipoproteins (IDL_PL_PCT), and creatinine were among the top five *** Age, IDL_PL_PCT, and creatinine levels play crucial roles in AD onset. Regular monitoring of lipoproteins and their metabolites in older individuals is significant for ea

关键词： Alzheimer's disease Random forest Lasso regression catboost algorithm

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Machine learning prediction of electric flux in concrete and mix proportion optimization design

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MATERIALS TODAY COMMUNICATIONS 2024年 38卷

作者： Dai, Jinpeng Yang, Xiaoyuan He, Jie Wang, Qicai Zhang, Zhijie Lanzhou Jiaotong Univ Natl & Prov Joint Engn Lab Rd & Bridge Disaster Pr Lanzhou 730070 Peoples R China Jiangsu Res Inst Bldg Sci CO LTD State Key Lab High Preformance Civil Engn Mat Nanjing 210008 Peoples R China Southeast Univ Sch Mat Sci & Engn Nanjing 211189 Peoples R China Lanzhou Jiaotong Univ Civil Engn Dept Lanzhou 730070 Peoples R China

This study explored nine machine learning (ML) methods, including linear, nonlinear and integrated learning models, to predict concrete electrical flux metrics reflecting the chloride ion permeability of concrete. Seven concrete mix parameters, including water (W), cement (C), fly ash (FA), ground granulated blast furnace slag (GGBS), fine aggregate (S), coarse aggregate (G) and water reducing admixture (WRA), were used as input parameters. A dataset consisting of 4029 sets of data was used to train and test the prediction model. K-fold crossvalidation confirms the model's accuracy and is performed by R2, MAE, MSE, and RMSE. The results showed that the nonlinear model outperformed the linear model;in particular, the model integrated with learning catboost exhibited higher prediction accuracy (R2 = 0.72, RMSE = 0.01, MSE = 0.01, MAE = 0.04). Based on this, a concrete chloride impermeability mix proportion optimization method targeting cost per square is proposed. The proposed model aims to achieve an ideal concrete mix proportion optimization based on ML algorithms and national specifications, which is of great significance to practical engineering.

关键词： Machine learning catboost algorithm Electrical flux Impermeability Mix proportion optimization

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Techno-economics of high ash coal gasification: A machine learning approach using catboost model

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JOURNAL OF CLEANER PRODUCTION 2024年 481卷

作者： Singh, Dharmendra Kumar Kumar, Sandeep Talaipalli Coal Min Project Lailunga Rd Raigarh 496111 CG India NTPC Sch Business Plot 5-14Sect 16 A Noida 201301 India

This work explores the techno-economics of high ash coal gasification using circulating fluidized bed (CFB) gasifier technology at the Talaipalli Coal Mining Project, India being developed by NTPC Limited. The research employs the categorical boosting machine learning algorithm to predict syngas composition, yield, and lower heating value offering an innovative approach to optimize the gasification process. The work compares CFB gasification with conventional coal combustion and establishes it as the most suitable gasification technology due to its feedstock flexibility, technological maturity, and cost-effectiveness. A comprehensive financial analysis reveals a favorable net present value (NPV) of Indian Rupee 20150 Million, an internal rate of return (IRR) of 9.54%, and a payback period of 8.3 years for a 65 MWth CFB gasifier. Sensitivity analysis highlights the influence of key factors such as syngas yield, capital costs, and selling price on financial viability. The environmental benefits, including reduced emissions and waste management, further underscore the value of coal gasification for sustainable energy production. This work aligns with government initiatives like the National Coal Gasification Mission, advocating future research in co-gasification and techno-economic assessments for similar projects.

关键词： catboost algorithm Techno-economic analysis High ash coal gasification Circulating fluidized bed technology Syngas yield and financial modelling

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Research on Wind Turbine Fault Detection Based on the Fusion of ASL-catboost and TtRSA

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SENSORS 2023年第15期23卷 6741-6741页

作者： Kong, Lingchao Liang, Hongtao Liu, Guozhu Liu, Shuo Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Peoples R China

The internal structure of wind turbines is intricate and precise, although the challenging working conditions often give rise to various operational faults. This study aims to address the limitations of traditional machine learning algorithms in wind turbine fault detection and the imbalance of positive and negative samples in the fault detection dataset. To achieve the real-time detection of wind turbine group faults and to capture wind turbine fault state information, an enhanced ASL-catboost algorithm is proposed. Additionally, a crawling animal search algorithm that incorporates the Tent chaotic mapping and t-distribution mutation strategy is introduced to assess the sensitivity of the ASL-catboost algorithm toward hyperparameters and the difficulty of manual hyperparameter setting. The effectiveness of the proposed hyperparameter optimization strategy, termed the TtRSA algorithm, is demonstrated through a comparison of traditional intelligent optimization algorithms using 11 benchmark test functions. When applied to the hyperparameter optimization of the ASL-catboost algorithm, the TtRSA-ASL-catboost algorithm exhibits notable enhancements in accuracy, recall, and other performance measures compared with the ASL-catboost algorithm and other ensemble learning algorithms. The experimental results affirm that the proposed algorithm model improvement strategy effectively enhances the wind turbine fault detection classification recognition rate.

关键词： wind turbine catboost algorithm fault detection category imbalance intelligent optimization algorithm

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Space-Time Sea Surface pCO₂ Estimation in the North Atlantic Based on catboost

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REMOTE SENSING 2021年第14期13卷 2805-2805页

作者： Sun, Hongwei He, Junyu Chen, Yihui Zhao, Boyu Zhejiang Univ Ocean Coll Zhoushan 316021 Peoples R China Zhejiang Univ Ocean Acad Zhoushan 316021 Peoples R China Zhejiang Univ Econ Coll Hangzhou 310058 Peoples R China

Sea surface partial pressure of CO2 (pCO(2)) is a critical parameter in the quantification of air-sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we used chlorophyll-a concentration (Chla), sea surface temperature (SST), dissolved and particulate detrital matter absorption coefficient (Adg), the diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO(2) in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (catboost) algorithm. The results showed that the root mean square error (RMSE) is 8.25 mu atm, the mean bias error (MAE) is 4.92 mu atm and the coefficient of determination (R-2) can reach 0.946 in the validation set. Subsequently, the proposed algorithm was applied to the sea surface pCO(2) in the North Atlantic Ocean during 2003-2020. It can be found that the North Atlantic sea surface pCO(2) has a clear trend with latitude variations and have strong seasonal changes. Furthermore, through variance analysis and EOF (empirical orthogonal function) analysis, the sea surface pCO(2) in this area is mainly affected by sea temperature and salinity, while it can also be influenced by biological activities in some sub-regions.

关键词： sea surface pCO(2) ocean color remote sensing catboost algorithm temporal and spatial distribution

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Sensitivity analysis of low salinity waterflood alternating immiscible CO2 injection (Immiscible CO2-LSWAG) performance using machine learning application in sandstone reservoir

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2024年第11期14卷 3055-3077页

作者： Efras, Muhammad Ridho Dzulkarnain, Iskandar Ridha, Syahrir Syahputra, Loris Alif Rasool, Muhammad Hammad Merdeka, Mohammad Galang Pramana, Agus Astra Univ Teknol PETRONAS Dept Petr Engn Bandar Seri Iskandar 32610 Perak Malaysia Univ Teknol PETRONAS Inst Sustainable Energy & Resources Bandar Seri Iskandar 32610 Perak Malaysia Univ Teknol PETRONAS Dept Petr Engn Bandar Seri Iskandar 32610 Perak Malaysia Inst Teknol Bandung Dept Petr Engn Bandung 40132 Indonesia Univ Pertamina Dept Petr Engn Jakarta 12220 Indonesia

Low salinity water alternating immiscible gas CO2 (Immiscible CO2-LSWAG) injection is a popular technique for enhanced oil recovery (EOR) that combines the benefits of low salinity and immiscible CO2 flooding to increase and accelerate oil production. This approach modifies the displacement properties of the reservoir, resulting in higher sweep efficiency and greater oil production. The current study employs a combination of numerical and machine learning techniques to comprehensively investigate the performance of immiscible CO2-LSWAG injection in a sandstone reservoir. Furthermore, a detailed sensitivity analysis of various injection and reservoir parameters is conducted to gain deeper insights into their impact on the process. In order to predict the oil recovery factor (RF), the study employs 1000 experimental designs on initial oil-wet. The numerical simulation results indicate that immiscible CO2-LSWAG injection outperforms conventional immiscible CO2 and low salinity waterflood injection, resulting in a higher oil RF. The machine learning models of catboost and LightGBM used in this study produced R-2 scores higher than 0.95 with lower errors between the predicted and actual results. This indicates that machine learning models can provide a faster and more accurate alternative to numerical simulation. The sensitivity analysis results from the machine learning model reveal that the major contributing factors to oil RF are the chemical composition of the injected water and the injection rate. In summary, this study leverages machine learning for sensitivity analysis in immiscible CO2-LSWAG performance in oil-wet sandstone reservoirs. Key findings include the identification of top influencing parameters and high predictive accuracy of catboost and LightGBM algorithms. The results facilitate quick decision-making for field trials by focusing on major contributing factors, with future research suggested for broader applications.

关键词： Enhanced oil recovery Sensitivity analysis Low salinity waterflood alternate immiscible CO2 injection catboost algorithm LightGBM algorithm Machine Learning

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Prediction of Floor Failure Depth Based on Dividing Deep and Shallow Mining for Risk Assessment of Mine Water Inrush

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WATER 2024年第19期16卷 2786页

作者： Liu, Weitao Han, Mengke Zhao, Jiyuan Shandong Univ Sci & Technol Coll Safety & Environm Engn Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Energy & Min Engn Qingdao 266590 Peoples R China

Understanding and predicting floor failure depth is crucial for both mitigating mine water inrush hazards and safeguarding groundwater resources. Mining activities can significantly disturb the geological strata, leading to shifts and damage that may result in floor cracks. These disruptions can extend to confined aquifers, thereby increasing the risk of water inrushes. Such events not only pose a threat to the safety of mining operations but also jeopardize the sustainability of surrounding groundwater systems. Therefore, accurately predicting floor failure depth to take effective coal seam floor management measures is the key to reducing the impact of coal seam mining on water resources. Seventy-eight sets of data on coal seam floor failure depth in China were collected, and the main controlling factors were considered: mining depth (D1), working face inclination length (D2), coal seam inclination (D3), and mining thickness (D4). Firstly, the distance evaluation function based on Euclidean distance was constructed as the clustering effectiveness index, and the optimal cluster number K = 3 was determined. The collected data were clustered into three categories using the K-means clustering algorithm. It was found that the clustering results were positively correlated with the size of D1, indicating that D1 played a dominant role in the clustering. The D1 dividing points of the three types of samples were between 407.7 similar to 414.9 m and 750 similar to 900 m. On this basis, the grey correlation analysis method was used to analyze the order of the influence weights of the main controlling factors of coal seam floor failure depth. For the first group, the order was D2 > D1 > D3 > D4, while, in the other two, it was D1 > D2 > D3 > D4. D1 emerged as the most influential factor, surpassing D2. Therefore, D1 between 407.7 and 414.9 m could be used as the boundary, the first group could be classified as shallow mining, and the second and third groups could be classified

关键词： floor failure depth K-means clustering algorithm grey correlation analysis catboost algorithm groundwater

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A novel framework combining production evaluation and quantification of development parameters for shale gas wells

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2023年 226卷

作者： Niu, Wente Lu, Jialiang Sun, Yuping Mu, Ying Zhang, Jianzhong Guo, Wei Liu, Yuyang Univ Chinese Acad Sci Sch Engn Sci Beijing 101400 Peoples R China Chinese Acad Sci Inst Porous Flow & Fluid Mech Langfang 065000 Peoples R China Res Inst Petr Explorat & Dev Beijing 100089 Peoples R China

Accurate prediction of estimated ultimate recovery (EUR) and quantification of important engineering and geological parameters play a decisive role in production parameter optimization and investment decision of shale gas wells. This study proposes an integrated learning framework for EUR prediction and production parameter quantification in shale gas wells using catboost algorithm. catboost is designed to identify complex relationships efficiently and accurately between geological and engineering parameters and target values EUR. Furthermore, the EUR prediction model based on catboost was used to identify important features. Various machine learning interpretation methods are used to visualize the marginal effects of important and interesting features in an attempt to optimize production parameters by quantifying the features. Experiments are conducted to evaluate the performance of the proposed the integrated learning framework in the deep shale gas well data set of Luzhou block in Sichuan Basin, China. Results demonstrate that compared with other mainstream ensemble learning algorithms, the proposed EUR prediction model based on catboost algorithm performs well on small data sets, with a prediction error of only 12.31%. The post-interpretation tool of the model was used to find out four important characteristics affecting EUR in Luzhou block, namely, contents of brittle minerals, fracturing length, porosity and fracturing section. The optimal combination of geological and engineering parameters for the block to maximize EUR is identified by using the established efficient and accurate EUR prediction model as input and the visualization of the marginal effects of the features of interest as output. This work can effectively provide guidance for EUR prediction and production parameter optimization of shale gas wells.

关键词： Shale gas Production and EUR prediction catboost algorithm Model visualization Development parameter quantization

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A machine learning model of liquid bridge force and its application in discrete element method

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CONSTRUCTION AND BUILDING MATERIALS 2024年 411卷

作者： Cui, Hanwen Zhao, Honghua Ji, Shunying Zhang, Xingli Awadalseed, Waleed Tang, Hongxiang Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Dalian 116023 Peoples R China Dalian Univ Technol Dept Engn Mech Dalian 116023 Peoples R China Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116023 Peoples R China

Developing an accurate and efficient contact model for discrete element method (DEM) simulation that can describe the microscopic dynamics of low moisture content wet granular systems continues to be a major challenge. In this study, a improved energetic approach based on the traditional linear unit discretization method is introduced to solve liquid bridge problems. This improved approach significantly ameliorates the efficiency of constructing the databases while ensuring high data accuracy. Using this method, we generated databases consisting of 163,440,000 and 2043,000 data points for the sphere-sphere and sphere-wall systems, respectively. Machine learning capillary force models (ML-Capillary) are trained using the catboost algorithm. These models demonstrate excellent performance in predicting capillary force between particles as well as between particles and walls, considering different particle sizes, moisture levels, and contact angles, while neglecting the influence of gravity. The corresponding relative errors are below 1.5% and 2%, respectively, which are lower than existing simulation models. We incorporated the obtained models into the discrete element method and calculated capillary force between particle pairs and simulated wet granular column collapse tests. The simulation results show good agreement with experimental results in terms of parameters such as inter-particle capillary forces and particle column collapse run-out length. This study will provide an accurate and efficient numerical model for simulating low saturation wet granular systems using the discrete element method.

关键词： Discrete element method Liquid bridge Machine learning catboost algorithm Geometric approximation energetic approach

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