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regression algorithm-based prediction of burnup history in pebble-bed high-temperature reactors

NUCLEAR ENGINEERING AND DESIGN 2025年 433卷

作者： Zhang, Hongjian Zhu, Qing Zhang, Liguo Ma, Tao Tsinghua Univ Inst Nucl & New Energy Technol Key Lab Adv Reactor Engn & Safety Minist Educ Beijing 100084 Peoples R China

The pebble-bed high-temperature reactor operates with continuous refueling, featuring a vast number of fuel spheres within the core that are challenging to locate and track, leading to an unknown burnup history of the nuclear fuel. This poses difficulties for nuclear material accounting, source term analysis, and the establishment of experimental benchmarks in pebble-bed high-temperature reactors. Currently, the online burnup measurement system in HTR-PM can measure the activity of radioactive nuclides, which are highly correlated with the burnup history and can be utilized to construct the loss function in burnup history inversion. To reliably provide anticipated optimal solutions, this paper conducts an in-depth study on the construction of the above loss function through regression on a large dataset for burnup history prediction. The following three aspects of work are undertaken: 1) Simulating the core pebble flow using a discrete element model, simplifying the burnup history into an irradiation history sequence that includes neutron flux rates and irradiation durations, and demonstrating the rationality of this simplification;2) Utilizing the above irradiation history sequence as input into a nuclear inventory software to generate a substantial dataset of known burnup histories and nuclide activities. Constructing weighted regression and deep neural network models, with online measurable nuclides as input data and fuel sphere burnup history as output data, to quantify the data potential of solving the burnup history from online measurable nuclides and to evaluate the contribution of different nuclides to the solution;3) Analyzing the contribution to the solution of online measurable nuclides from the perspectives of burnup chains and nuclide characteristics, and establishing criteria for selecting input data for the burnup history prediction model. The research outcomes of this paper represent a crucial step in predicting the burnup history of pebble-bed high-

关键词： HTGR Burnup measurement regression algorithm Discrete element method Nuclear material accounting

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Real-time prediction of multivariate ROP (rate of penetration) based on machine learning regression algorithms: algorithm comparison, model evaluation and parameter analysis

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ENERGY EXPLORATION & EXPLOITATION 2023年第5期41卷 1779-1801页

作者： Liu, Wei Fu, Jiasheng Tang, Chunjing Huang, Xinyu Sun, Ting CNPC Engn Technol R&D Co Ltd Beijing Peoples R China Natl Engn Res Ctr Oil & Gas Drilling & Complet Tec Beijing Peoples R China China Univ Petr Coll Petr Engn Beijing Peoples R China China Univ Petr Beijing Peoples R China

ROP (Rate of Penetration) is a comprehensive indicator of the rock drilling process and how efficiently predicting drilling rates is important to optimize resource allocation, reduce drilling costs and manage drilling hazards. However, the traditional model is difficult to consider the multiple factors, which makes the prediction accuracy difficult to meet the real drilling requirements. In order to provide efficient, accurate and comprehensive information for drilling operation decision-making, this study evaluated the applicability of four typical regression algorithms based on machine learning for predicting pore pressure in Troll West field, namely SVR (Support Vector regression), Linear regression, regression Tree and Gradient Boosting regression. These methods allow more parameters input. By comparing the prediction results of these typical regression algorithms based on R-2(R-Square), explained variance, mean absolute error, mean squared error, median absolute error and other performance indicators, it was found that each method predicted different results, among which Gradient Boosting regression has the best results, their prediction accuracy is high and the error is very low. The prediction accuracy of these methods is positively correlated with the proportion of the training data set. With the increase of logging features, the prediction accuracy is gradually improved. In the prediction of adjacent wells, the ROP prediction methods can achieve a certain prediction effect, which shows that this method is suitable for ROP prediction in Troll West field.

关键词： ROP machine learning real-time prediction regression algorithm algorithm comparison parameters combination analysis

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Physics-driven regression algorithm on Solder Joint Fatigue Life Prediction for Mobile SiP Packages 73

Physics-driven Regression Algorithm on Solder Joint Fatigue ...

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IEEE 73rd Electronic Components and Technology Conference (ECTC)

作者： Che, Fa Xing Ong, Yeow Chon Ng, Hong Wan Pan, Ling Sinha, Koustav Glancey, Christopher Kumar, Gokul Micron Semicond Asia Operat Pte Ltd 990 Bendemeer Rd Singapore 339942 Singapore Micron Technol Inc 8000 South Fed Way Boise ID USA

ISBN: (纸本)9798350334982

Express reliability assessment method has been developed for board level temperature cycling solder joint reliability of System-in-Packages (SiP) for mobile applications through the validated regression equation by combining finite element analysis (FEA) and statistical analysis. The developed equation accuracy for calculating solder joint strain energy density was validated by FEA simulation results and experimental data. This proposed approach for new package reliability assessment considerably reduces cycle time from days to minutes since it does not require numerical FEA simulations for life prediction.

关键词： express reliability assessment finite element modeling regression algorithm solder fatigue life solder joint reliability temperature cycling

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Research on tunnel engineering monitoring technology based on BPNN neural network and MARS machine learning regression algorithm

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NEURAL COMPUTING & APPLICATIONS 2021年第1期33卷 239-255页

作者： Fei, Jianbo Wu, Zezhou Sun, Xiaohui Su, Dong Bao, Xiaohua Shenzhen Univ Underground Polis Acad Shenzhen 518060 Peoples R China Shenzhen Univ Coll Civil & Transportat Engn Shenzhen 518060 Peoples R China

Tunnel engineering is affected by a variety of factors, which results in large detection errors in tunnel engineering. In order to improve the monitoring effect of tunnel engineering, based on BPNN and MARS machine learning regression algorithm, this research constructs a tunnel engineering monitoring and prediction model. Moreover, the gray residual BP neural network designed in this study uses a series combination, and the residuals obtained from the gray model are used as the input data of the BP neural network, and the output of the combined model is used as the prediction result. By applying the monitoring data of the convergence of the upper surrounding of the tunnel surface section and deformation of the arch subsidence, it is verified that the proposed method based on the combined model of BPNN and MASR can predict and analyze the tunnel deformation monitoring data very well.

关键词： BPNN MARS regression algorithm Tunnel engineering Intelligent monitoring

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Head pose estimation by regression algorithm

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PATTERN RECOGNITION LETTERS 2020年 140卷 179-185页

作者： Abate, Andrea F. Barra, Paola Pero, Chiara Tucci, Maurizio Univ Salerno Dept Comp Sci Salerno Italy

Head pose estimation is a very in-depth topic in the context of biometric recognition, especially in video surveillance, because the rotation of the head can affect the recognition of some features of the face. Being able to recognize in advance the pose of the head in pitch, yaw and roll enable frontalization or the extraction of a frame in which a face is frontal in order to allow a more accurate recognition. In this work the Web-Shaped Model algorithm is used for a coding of the pose of the face and then we apply regression algorithms to predict the pose of the face. The proposed approach stimulates the sensitivity of the regression methods to identify the head pose estimation. The goals is to predict the value of the dependent variable for the three angular values, for which some information relating to the explanatory variables is available, in order to estimate the effect on the dependent variable. The presented method is tested on some of the most well-known datasets for the head pose estimation as Biwi, AFLW2000 and Pointing'04 and compared with the various state of the art methods that use these datasets. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Head pose estimation regression algorithm Image analysis

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Application of the regression algorithm to the Problem of Studying Horizontal Inhomogeneity of the Cloud Liquid Water Path by Ground-Based Microwave Measurements in the Angular Scanning Mode

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ATMOSPHERIC AND OCEANIC OPTICS 2020年第6期33卷 602-609页

作者： Biryukov, E. Yu. Kostsov, V. S. St Petersburg State Univ St Petersburg 198504 Russia

The results of the cloud liquid water path (LWP) "land-sea" gradient retrieval from ground-based measurements of the downwelling microwave radiation near the Gulf of Finland coastline in the suburbs of Saint-Petersburg are presented. The measurements were carried out at the Department of Physics, St. Petersburg State University, by an RPG-HATPRO radiometer operating in the angular scanning mode. The inverse problem is solved by linear regression with the use of different statistical models of cloudiness for training the algorithm. Seven-year average values of the gradient of LWP for summer and winter have been obtained. The results demonstrate the presence of a positive "land-sea" gradient of LWP (larger values over the land and smaller values over the sea) in both periods, which qualitatively agrees with available satellite data.

关键词： cloud liquid water water path troposphere horizontal inhomogeneity of atmospheric parameters remote sensing microwave radiometer inverse problems regression algorithm

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The Simulation Study of Marine Propeller Load Characteristics Based on regression algorithm

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JOURNAL OF COASTAL RESEARCH 2015年第Spe73期000卷 705-710页

作者： Xu, Aide Sun, Jianbo Dalian Maritime Univ Dalian 350000 Peoples R China

In this paper, in order to avoid the complex calculation with propeller chart, a new model method for marine propeller was put forward. According to the new model method, propeller thrust and torque are calculated based on regression algorithm and it can realize four quadrants simulation. After the propeller model was completed, basic ship operation such as start, stop and emergency astern are simulated with the help of mathematical model of the ship-propeller. Through analyzing the change curves of the revolution speed of the propeller, the navigation speed of the ship and the torque of the ship under all kinds of working conditions, some operable suggestions and theory evidences are provided for the practical operation of the ship.

关键词： Marine propeller regression algorithm thrust and torque dynamic simulation

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regression algorithms-driven mechanical properties prediction of angle bracket connection on cross-laminated timber structures

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JOURNAL OF WOOD SCIENCE 2024年第1期70卷 3页

作者： Wu, Zhe Chen, Lin Xiong, Haibei Tongji Univ Dept Disaster Mitigat Struct Shanghai Peoples R China

The construction of structures using cross-laminated timber (CLT) has grown in popularity as a result of its environmentally friendly and high-strength characteristics. The primary function of angle bracket connections is to resist the force of CLT structures under horizontal forces, which is essential to ensure the seismic resilience and ductility of CLT structures. A regression algorithms-driven method for predicting the mechanical performance of angle bracket connections is introduced in this study. As input parameters, the geometric dimensions of the angle bracket connector, the connection method of the connector with the wall and floor slabs, and the properties of the CLT panel are utilized to predict the yield load, the maximal load, the initial stiffness, and the ductility ratio of the angle bracket connection. Prediction models were developed using the collected data from 110 angle bracket experiments, and each prediction model's performance was discussed in depth. Lastly, the permutation importance and SHapley Additive exPlanations (SHAP) value analysis were used to interpret the prediction models. The results showed that the extreme gradient boosting (XGB) algorithm could accurately predict the maximum and yielding load of the angle bracket connection, with R2 reaching 0.968 and 0.939. Furthermore, in predicting the initial stiffness of the angle bracket, the XGB algorithm performed the best with an average ratio of predicted to actual values of 0.985. The results indicated that this study proposed an accurate and efficient method for angle bracket connection to predicting its mechanical properties and confirmed the trustworthiness and feasibility of the prediction models.

关键词： Machine learning regression algorithm Cross-laminated timber structure Angle bracket connection Mechanical property

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regression algorithms Evaluation for Analysis of Crosstalk in High-Speed Digital System

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2024年第6期18卷 1449-1461页

作者： Kim, Minhyuk Soonchunhyang Univ Dept Elect Engn Asan 31538 South Korea

As technology advances, processor speeds are increasing at a rapid pace and digital systems require a significant amount of data bandwidth. As a result, careful consideration of signal integrity is required to ensure reliable and high-speed data processing. Crosstalk has become a vital area of research in signal integrity for electronic packages, mainly because of the high level of integration. Analytic formulas were analyzed in this study to identify the features that can predict crosstalk in multi -conductor transmission lines. Through the analysis, five variables were found and obtained a dataset consisting of 302,500, data points. The study evaluated the performance of various regression models for optimization via automatic machine learning by comparing the machine learning predictions with the analytic solution. Extra tree regression consistently outperformed other algorithms, with coefficients of determination exceeding 0.9 and root mean square logarithmic errors below 0.35. The study also notes that different algorithms produced varied predictions for the two metrics.

关键词： Signal integrity Crosstalk Machine learning regression algorithm Optimization

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RR-Raft: A Raft Consensus algorithm Based on Reputation regression Model and RSA Signature 2

RR-Raft: A Raft Consensus Algorithm Based on Reputation Regr...

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2nd International Conference on Mobile Internet, Cloud Computing and Information Security (MICCIS)

作者： Li, Shuang Deng, Xiaohong Zou, Yijie Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou Peoples R China Gannan Univ Sci & Technol Sch Informat Engn Ganzhou Peoples R China

ISBN: (纸本)9798350389913;9798350389906

To address the issue that existing Raft consensus algorithms in blockchain cannot achieve low latency, high security, and high efficiency simultaneously, a Raft consensus algorithm based on reputation regression and RSA signatures, named RR-Raft, is designed. The algorithm first introduces a reputation measurement function and a reputation consumption function into the Logistic regression algorithm, constructing a reputation model based on R-Logistic regression. This model solves the centralized reputation problem in traditional regression algorithms, thereby enhancing the enthusiasm of nodes for consensus. Secondly, to address the issue of vote splitting during the leader election phase of the consensus mechanism, a quick leader node election strategy based on the reputation model is proposed to improve consensus efficiency. Lastly, to prevent the Leader node from tampering with the logs as a malicious actor, a fast RSA aggregate signature based on Barrett reduction is proposed to enhance the security of consensus.

关键词： Blockchain Consensus Mechanism Raft algorithm RSA Signature regression algorithm

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