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A Preliminary Study on the Use of the linear regression method for Multigroup Cross-Section Interpretation

NUCLEAR SCIENCE AND ENGINEERING 2021年第4期195卷 437-452页

作者： Lu, Cihang Wu, Zeyun Virginia Commonwealth Univ Dept Mech & Nucl Engn Richmond VA 23219 USA

Computational modeling and simulations are widely used for evaluation of the performance and safety features of innovative nuclear reactor designs. Multigroup-based deterministic neutronics codes are often employed in these reactor design calculations because they can provide fast predictions of the neutron flux distribution and other neutronics characteristic parameters. Nevertheless, providing accurate multigroup cross sections for deterministic codes is an onerous job, which makes establishing an exhaustive cross-section library computationally prohibitive. Partly because of these reasons, multigroup neutron cross sections are normally stored only at certainty state points in the data library of these deterministic codes, and linear interpolation methodology is commonly utilized to estimate the cross sections at unknown states. However, the applicability of linear interpolation is limited, and the precision of its results is moderate. In this paper, we discuss a preliminary feasibility study that we performed on providing more precise multigroup cross sections for deterministic neutronics codes by using the linear regression methodology. Compared to the traditional linear interpolation method, the linear regression approach principally showed improved computational efficiency considering the use of more data in the cross-section library, and constructed hypothesis functions for the responses of interest with a higher order of accuracy. In this study, a case study on Lightbridge Corporation's metallic fuel element was carried out to demonstrate the feasibility and advantages of linear regression in multigroup cross-section interpretation. A reference cross-section library was established through calculations conducted with the Monte Carlo neutronic code Serpent. Because of the preliminary nature of this feasibility study, only the macroscopic total cross section is considered. linear interpolation and linear regression were both used to estimate cross sections at

关键词： Reactor physics cross-section interpretation Monte Carlo linear regression method

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Force model of robot bone grinding based on finite element analysis

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MEASUREMENT 2025年 243卷

作者： Yang, Kai Jia, Qingxuan Feng, Chao Huang, Juxiang Chen, Gang Yang, Zheng Beijing Univ Posts & Telecommun Sch Automat Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Beijing Jishuitan Hosp Guizhou Hosp Natl Reg Med Ctr Guiyang 550014 Guizhou Peoples R China Capital Med Univ Beijing Jishuitan Hosp Natl Ctr Orthopaed Beijing 100035 Peoples R China

Bone grinding represents a formidable challenge during open epiphyseal surgery, as grinding force may result in potential damage to bone and surrounding tissues. To address this issue, this study establishes a correlation between grinding force and bone density, as well as feed rate and rotational speed. Given the limited availability of experimental data on bone grinding, a finite element analysis (FEA) model is developed. Subsequently, the impact of bone density, feed rate and rotational speed on grinding force is determined through linear regression method. The accuracy and sensitivity of the grinding force model are then assessed, revealing a maximum prediction deviation of 4.87%. Finally, based on the established model, safe operating parameters and boundary conditions for robot bone grinding are identified.

关键词： Grinding Bone Force Finite element analysis linear regression method

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Improving Long-Term Flood Forecasting Accuracy Using Ensemble Deep Learning Models and an Attention Mechanism

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JOURNAL OF HYDROLOGIC ENGINEERING 2024年第6期29卷 04024042-04024042页

作者： Kordani, Marjan Nikoo, Mohammad Reza Fooladi, Mahmood Ahmadianfar, Iman Nazari, Rouzbeh Gandomi, Amir H. Shahid Chamran Univ Ahvaz Dept Hydrol & Water Resources Ahvaz *** Iran Sultan Qaboos Univ Dept Civil & Architectural Engn Muscat 123 Oman Isfahan Univ Technol Dept Civil Engn Esfahan *** Iran Behbahan Khatam Alanbia Univ Technol Dept Civil Engn Behbahan *** Iran Univ Memphis Dept Civil Engn Herff Coll Engn Memphis TN 38152 USA Univ Technol Sydney Dept Engn & IT Ultimo NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary

Floods, as major natural disasters, cause massive property destruction and death. Understanding the occurrence time of this event by advance notice helps consider operational flood prevention systems and platforms. Precise flood forecasting provides a suitable time for policymakers and the public to consider helpful responses to this event. This study introduces an innovative methodology to enhance the precision of long-term flood predictions by employing a multistep forecasting approach. Our approach leverages historical time-series data on precipitation and streamflow to train an autoencoder algorithm. The primary objective is to develop advanced forecasting models to predict 12-step-weekly ahead flood occurrences during the critical April to July period from 2019 to 2021 within the DuPage River basin, Illinois, USA. In order to achieve this goal, we explore three deep learning techniques: bidirectional long short-term memory (BI-LSTM), ensemble long short-term memory (E-LSTM), and ensemble long short-term memory-gated recurrent unit (E-LSTM-GRU). Then, we integrate an attention mechanism (AM) that utilizes dynamic fusion techniques to emphasize the salient features of ensemble models. A dedicated fusion model is developed for each forecasting stage, effectively consolidating the predictions from various deep-learning models. Additionally, two traditional machine learning techniques, namely MLP and SVM models, are used to compare and justify the efficiency of applied deep learning models. The performance evaluation of our approach using statistical error metrics, including coefficient of determination (R2), normalized root mean square error, normalized mean absolute error, mean absolute percentage error, Nash-Sutcliffe efficiency, Kling-Gupta efficiency, and percent bias, for the 12th step prediction reveals impressive results, with average values of 0.976, 2.393, 1.892, 20.956, 0.967, 0.923, and 2.307, respectively. These findings underscore the capability of our

关键词： Long-term flood forecasting Deep learning techniques Long-short-term memory (LSTM) model Attention mechanism Ensemble models linear regression method

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Compact Representation of the Local Atomic Structure of Matter for Machine Learning in XANES-Spectroscopy Data Processing

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JOURNAL OF SURFACE INVESTIGATION 2024年第2期18卷 400-407页

作者： Viklenko, I. A. Srabionyan, V. V. Durymanov, V. A. Gladchenko-Dzhevelekis, Ya. N. Razdorov, V. N. Avakyan, L. A. Bugaev, L. A. Southern Fed Univ Rostov Na Donu 344090 Russia

The paper introduces a method for representing data on the local atomic structure as histograms of pair radial distribution functions categorized by atom types. This method is used to construct a structure descriptor essential for determining the material structure using machine learning and artificial intelligence methods. A distinctive feature of the approach is the simultaneous use of two sets of pair radial distribution functions: for pairs of all atom types and for pairs involving a selected absorbing atom. The developed approach is tested for determining the nearest environment of silver atoms in color centers in sodium-silicate glasses based on the spectra of X-ray absorption near the absorption edge of Ag. The informativeness of the proposed structure descriptor is demonstrated by its ability to recreate a three-dimensional model of the silver color center's structure from the corresponding pair distance histograms. Using multiple machine-learning methods, we demonstrate that the proposed descriptor enables the high-quality reproduction of X-ray absorption near edge structure (XANES) spectra for color centers in glass within the framework of the finite-difference method, which results in a four-order-of-magnitude cut in the calculation time for the XANES spectra. The constructed machine-learning model establishes a fundamental connection between the atomic structure of color centers in glasses and the silver XANES spectrum, which is essential for determining the structure of glasses.

关键词： X-ray absorption near-edge structure machine learning local atomic structure color centers gradient boosting random forest algorithms linear regression method

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Combining large broiler populations into a single genomic evaluation: dealing with genetic divergence

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JOURNAL OF ANIMAL SCIENCE 2024年第102期102卷 skae360页

作者： Tabet, Joe-Menwer Bussiman, Fernando Breen, Vivian Misztal, Ignacy Lourenco, Daniela Univ Georgia Dept Anim & Dairy Sci Athens GA 30602 USA Cobb Vantress Inc Siloam Springs AR 72761 USA

Combining breeding populations that have diverged at some point is a conventional practice, particularly in the poultry industry, where generation intervals are short and genetic evaluations should be frequently available. This study aimed to assess the feasibility of combining large, distantly genetically connected broiler populations into a single genomic evaluation within the single-step GBLUP framework. The pedigree data for broiler lines 1 and 2 consisted of 428,790 and 477,488 animals, being 156,088 and 186,387 genotyped, respectively. Phenotypic data for body weight (kg), carcass yield (%), mortality (1 to 2), and feet health (1 to 7) were collected for 397,974 animals in line 1 and 458,881 in line 2. A 4-trait model was employed for the analyses, and genetic differences between the populations were addressed through different approaches: introducing an additional fixed effect accounting for the line of origin (M2) or making each fixed effect origin-specific (M3). Those models were compared against a conventional model (M1) that did not account for animal origin in the evaluation. Unknown parent groups (UPG) and Metafounders (MF) were fit to account for the genetic differences in M1, M2, and M3;they were set based on the animal's line of origin and sex. Accuracy, bias, and dispersion were used to assess the performances of the models using the linear regression method. Validations were performed separately within individual lines and collectively after combining the 2 lines to better assess the advantages of combining the 2 populations. Overall, the accuracy increased when the 2 populations were combined compared to the accuracies obtained from evaluating each line individually. Notably, there were no apparent differences among the models regarding accuracy and dispersion. Regarding bias, using models M2 or M3 with UPG yields the least biased estimates in the combined evaluation. Thus, when combining different populations into a single genomic evaluation, acc

关键词： genetic and non-genetic differences linear regression method meta-founders single-step genomic best linear unbiased prediction unknown parent groups

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Optimization of Injection Production Matching Strategy in Reservoir Development Process Using Support Vector Machine Algorithm 24

Optimization of Injection Production Matching Strategy in Re...

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International Conference on Machine Intelligence and Digital Applications (MIDA)

作者： Jiang, Chunhua Daqing Oilfield Ltd Co Inst Geol Oil Prod Plant 6 Daqing 163000 Heilongjiang Peoples R China

ISBN: (纸本)9798400718144

This article explores how to use Support Vector Machine (SVM) algorithm to optimize the injection production matching strategy in the process of reservoir development. By establishing an SVM-based prediction model, this article aims to improve the injection and production efficiency of oil fields and address the limitations of traditional methods in processing nonlinear and complex data. The article first reviews the main challenges in current reservoir management and existing injection production matching methods, followed by a detailed introduction to the principle of SVM algorithm and its application in reservoir management. Through comparative experiments and case analysis, the effectiveness of the SVM model in improving oilfield recovery and economic benefits is verified. After adopting the SVM strategy, the injection production efficiency improves by about 15% and the oil-water ratio improves by 20%. The SVM model also tends to perform stably in oil fields of different geological types. During the one-year experimental period, the SVM algorithm maintains a performance improvement of about 5% in each month. The experimental data results show the effectiveness and reliability of SVM algorithm in oilfield injection production matching optimization, demonstrating the potential of SVM algorithm in improving oilfield development efficiency and economic benefits.

关键词： Reservoir management Injection production matching strategy SVM algorithm linear regression method

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Adaptive encoding based lossless data hiding method for VQ compressed images using tabu search

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INFORMATION SCIENCES 2022年 602卷 128-142页

作者： Weng, Shaowei Zhang, Tiancong Wu, Zhijie Lin, Juan Hong, Wien Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350108 Peoples R China Fujian Univ Technol Sch Elect Engn & Phys Fuzhou 350108 Peoples R China Guangdong Univ Technol Sch Informat Engn Guangzhou Peoples R China Fujian Polytech Normal Univ Fujian Prov Univ Engn Res Ctr ICH Digitalizat & Multisource Informa Fuzhou 350300 Peoples R China Taichung Univ Sci & Technol Dept Comp Sci & Informat Engn Taichung Taiwan

A novel joint image coding and reversible data hiding method for vector quantization (VQ) compressed images is proposed in this paper. Since the original VQ indices often exhibit uncorrelated, a rearrangement of them by considering their correlations may benefit the prediction performance so as to reduce the required bitrate. In this study, the tabu search algorithm is employed to rearrange codewords by fully exploiting their neighboring correlations, yielding a moepressible rearranged indices. By combining more highly-correlated indices of a to-be-predicted index into prediction, the improved linear regression method is then applied to achieve a sharper prediction-error histogram and less required additional information. After prediction, an adaptive run-length encoding method is presented to encode prediction errors, thereby eliminating unnecessary indicators. Experimental results demonstrate that the proposed method effectively reduces the bitrate of the compressed image while providing a comparable hiding capacity. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Vector quantization Reversible data hiding Index rearrangement Tabu search algorithm linear regression method Vector quantization Reversible data hiding Index rearrangement Tabu search algorithm linear regression method

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THE QUALITY OF COKE AND ITS CONSUMPTION BLAST FURNACE PROCESS

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METALURGIJA 2021年第3-4期60卷 181-184页

作者： Kardas, E. Prusak, R. Czestochowa Tech Univ Fac Prod Engn & Mat Technol Czestochowa Poland

The assessment of quality of coke and its consumption in blast furnace process is presented in the paper. The statistical analysis of three basic parameters of coke was made. As a main quality parameters: moisture content, ash content in the dry state, volatile matter content in dry state and calorific value were selected. Then influence of two selected parameters of coke on fuel consumption was calculated. The study was carried out in cooperation with a Blast-Furnace Department of a Polish steelworks and was based on the results coming from this Department. The analysis covers the period of three calendar years.

关键词： blast furnace coke quality fuels consumption linear regression method

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Storing characteristics and main controlling factors of connate water in lower Paleozoic shales in southeast Chongqing, China

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第PartB期215卷

作者： Xu, Liangwei Wei, Hao Chen, Lei Liu, Luofu Jiang, Zhenxue Yang, Keji Li, Xiao Peking Univ Sch Earth & Space Sci MOE Key Lab Orogen Belt & Crustal Evolut Beijing 100781 Peoples R China Hebei GEO Univ Hebei Key Lab Strateg Crit Mineral Resources Shijiazhuang 050031 Peoples R China Univ Sci & Technol Dept Resource Engn Beijing 100083 Peoples R China China Univ Petr East China Shandong Prov Key Lab Deep Oil & Gas Qingdao 266580 Peoples R China China Univ Petr State Key Lab Petr Resources & Prospecting Beijing 102249 Peoples R China

Recent studies show that the shale gas content and storage space are influenced by connate water, while it is unclear how connate water occurs in different nanoscale pores in high and overmature shales, and research concerning the occurrence characteristics and factors controlling connate water in overmature shale gas formations is scarce. In this research, overmature lower Paleozoic shale samples from southeastern Chongqing were selected for connate water distribution and factor analysis. The results show that connate water in the lower Paleozoic shale occurs in organic matter and clay minerals, mainly occurs in clay minerals and tends to form clay-bound water in clay minerals. The effect of the connate water content (C-IW) on the inorganic nonmicropore (mesopore + macropore) surface area (SA) is greater than that on the organic nonmicropore SA. The CIW mainly affects the micropore structure parameters of clay minerals, and the total organic carbon (TOC) content is the main factor controlling the shale micropore structure. Connate water occurs in shale micropores and nonmicropores, and adsorption and filling states simultaneously exist and are distributed in < 10 nm mesopores and 0.4-0.6 nm micropores;this distribution reduces the pore structure parameters of inorganic matter in shale, especially the nonmicropore SA. The effect of the CIW on nonmicropore SA and micropore pore volume (PV) in inorganic minerals is significantly greater than that of organic matter. Connate water mainly occurs in inorganic mineral pores and mainly occupies the mineral nonmicropore SA. Connate water significantly reduces the inorganic pore structure parameters, while its effect on organic pores is limited.

关键词： Connate water content Equilibrium water saturation linear regression method Pore structure parameters Organic pores

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Investigation on linearisation of data-driven transport research: two representative case studies

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第7期14卷 675-683页

作者： Zou, Yun Kuang, Yan Zhi, Yue Qu, Xiaobo Sch Civil & Environm Engn Univ Technol Sydney Sydney NSW 4007 Australia Griffith Univ Griffith Sch Engn Gold Coast Qld 4222 Australia Binzhou Med Univ Sch Rehabil Med Yantai 264003 Peoples R China Chalmers Univ Technol Dept Architecture & Civil Engn S-41296 Gothenburg Sweden

Transportation engineering, as a practical engineering discipline, relies heavily on the accurate calibration of importation parameters from field data. In the real world, most transport relations possess inherent non-linearity. Two prevailing methods for handling non-linear regression are the non-linear least-squares method (LSM) with an iterative solution, and linearisation for the non-linear regression function. The second method applies a linear regression method to solve the non-linear regression problem but requires a data transformation of the observations from variant coordinates, and the objective function is suspected to be changed accordingly. This work describes the authors' investigation into the problem of non-linear regression through two illustrative examples, the calibration of three non-linear (either exponential or logarithmic) single-regime models for fundamental diagram and the regression of non-linear (power) bunker-consumption model, by applying the weighted LSM (WLSM) and the ordinary LSM to calibrate. It is found that linearising the regression model leads to deviations, and the data transformation can create even more concern with the WLSM because the weights can be redistributed after the data transformation. A further investigation into the linear regression and the non-linear regression gives more suggestions on the choice of regression method.

关键词： transportation least squares approximations mechanical engineering computing regression analysis data-driven transport research transportation engineering nonlinearity nonlinear regression function linear regression method nonlinear regression problem data transformation nonlinear bunker-consumption model regression model

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