Thermo-hydraulic performance of concentric tube heat exchangers with turbulent flow: Predictive correlations and iterative methods for pumping power and heat transfer by Samer Ali, Chadi Nohra, Jalal Faraj, Talib Dbouk, Mahmoud Khaled

“…These correlations showed an average error of less than 2.33% when compared with the CFD data. …”
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Direction-of-Arrival Estimation Based on Variational Bayesian Inference Under Model Errors by Can Wang, Kun Guo, Jiarong Zhang, Xiaoying Fu, Hai Liu

“…To address this limitation, this paper proposes an orientation estimation method based on variational Bayesian inference to combat non-uniform noise and gain/phase error. The gain and phase errors of the array are modeled separately for calibration purposes, with the objective of improving the accuracy of the fit during the iterative process. …”
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An iterative approach for deriving and solving an accurate regression equation by Manaye Getu Tsige, Andreas Malcherek, Ivo Baselt

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A New Type Iterative Ridge Estimator: Applications and Performance Evaluations by Aydın Karakoca

“…The usage of the ridge estimators is very common in presence of multicollinearity in multiple linear regression models. The ridge estimators are used as an alternative to ordinary least squares in case of multicollinearity as they have lower mean square error. …”
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Historical information based iterative soft Kalman time-varying channel estimation method by Lu CHENG, Lihua YANG, Zenghao WANG, Jie ZHANG, Yan LIANG

“…For high-speed mobile MIMO-OFDM systems,a historical information based iterative soft-Kalman filter time-varying channel estimation method was proposed.Considering that the channels experienced by different trains in the high-speed railway environment have strong correlation,the channel information of the historical train was firstly used to obtain the optimal basis function,which can be employed to model the channel.By the optimal basis function,the computational complexity was reduced and the channel estimation accuracy was improved for the proposed method.Secondly,the soft-Kalman filter and data detection were jointed to estimate the base coefficient in each iteration.To reduce the effect of data detection error propagation on the channel estimation,the soft data detection scheme was employed and the soft detection error was treated as noise in each iteration.In addition,the soft-Kalman filter used in the proposed method does not involve the AR model tracking factor,thereby avoiding the computational complexity introduced by the estimated tracking factor.The simulation results show that the proposed method has better estimation performance,and is more suitable for time-varying channel acquisition of actual high-speed mobile scenarios.…”
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Historical information based iterative soft Kalman time-varying channel estimation method by Lu CHENG, Lihua YANG, Zenghao WANG, Jie ZHANG, Yan LIANG

“…For high-speed mobile MIMO-OFDM systems,a historical information based iterative soft-Kalman filter time-varying channel estimation method was proposed.Considering that the channels experienced by different trains in the high-speed railway environment have strong correlation,the channel information of the historical train was firstly used to obtain the optimal basis function,which can be employed to model the channel.By the optimal basis function,the computational complexity was reduced and the channel estimation accuracy was improved for the proposed method.Secondly,the soft-Kalman filter and data detection were jointed to estimate the base coefficient in each iteration.To reduce the effect of data detection error propagation on the channel estimation,the soft data detection scheme was employed and the soft detection error was treated as noise in each iteration.In addition,the soft-Kalman filter used in the proposed method does not involve the AR model tracking factor,thereby avoiding the computational complexity introduced by the estimated tracking factor.The simulation results show that the proposed method has better estimation performance,and is more suitable for time-varying channel acquisition of actual high-speed mobile scenarios.…”
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Enhancing Real-Time Simulation of the Complexity Natural Gas Pipeline Network Through MLP-Newton Algorithm for High Precision and Reliability by Hankai Zhai, Xinming Song, Xiaoli Wang, Wentao Cui, Yeru Chen, Shuai Wang

“…First, a steady-state simulation model was established, and the nonlinear equations were converted to linear equations using Newton’s iterative method. …”
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SAR Observation Error Estimation Based on Maximum Relative Projection Matching by Y. Zhang, B. P. Wang, Y. Fang, Z. X. Song

“…First, the method estimates the precise position parameters of the reference position by the sparse reconstruction method of joint error parameters. Second, a relative error estimation model is constructed based on the maximum correlation of base-space projection. …”
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A Data Centric HitL Framework for Conducting aSsystematic Error Analysis of NLP Datasets using Explainable AI by Ahmed El-Sayed, Aly Nasr, Youssef Mohamed, Ahmed Alaaeldin, Mohab Ali, Omar Salah, Abdullatif Khalid, Shaimaa Lazem

“…Abstract The interest in data-centric AI has been recently growing. As opposed to model-centric AI, data-centric approaches aim at iteratively and systematically improving the data throughout the model life cycle rather than in a single pre-processing step. …”
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FATIGUE ANALYSIS AND OPTIMIZATION OF TRANSVERSE STAILIZER BAR LINK BASED ON MEASURED LOAD SPECTRUM by LIU Zhilei, DU Jian, HAN Jie

“…Through the kinematics compliance（KC） test, the accuracy of suspension dynamics model was verified, by using virtual iteration method, the accuracy of the vehicle dynamic model is verified by the measured load spectrum signal as excitation. …”
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Cable External Breakage Source Localization Method Based on Improved Generalized Cross-Correlation Phase Transform with Multi-Sensor Fusion by Xuwen Wang, Jiang Li

“…Finally, a dynamic weighted fusion model is constructed through DBSCAN spatial clustering to determine the final sound source position. …”
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Potentials of computer simulation of lung tumors in comparison with ^99mТс-MIBI SPECT/CT data by N. V. Denisova, M. A. Gurko, S. M. Minin, Zh. Zh. Anashbaev, A. A. Zheravin, E. A. Samoilova, S. E. Krasilnikov

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Research on Stability of Removal Function in Figuring Process of Mandrel of X-Ray-Focusing Mirror with Variable Curvature by Jiadai Xue, Yuhao Li, Mingyang Gao, Dongyun Gu, Yanlin Wu, Yanwen Liu, Yuxin Fan, Peng Zheng, Wentao Chen, Zhigao Chen, Zheng Qiao, Yuan Jin, Fei Ding, Yangong Wu, Bo Wang

“…By introducing time-varying removal functions for material removal, the model establishes a variable-curvature factor function, which correlates actual downward pressure with parameters such as contact radius and contact angle, thus linking the variable-curvature surface with a planar reference. …”
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A new method for determining factors Influencing productivity of deep coalbed methane vertical cluster wells by HUANG Li, XIONG Xianyue, WANG Feng, SUN Xiongwei, ZHANG Yixin, ZHAO Longmei, SHI Shi, ZHANG Wen, ZHAO Haoyang, JI Liang, DENG Lin

“…Predictions using the neural network method were more accurate, with a relative error of less than 10% compared to measured values. 2） Using Kendall's tau-b correlation analysis, the discrete dominant factor was identified as the microstructural position, primarily located in uplifted positive structural zones, with the secondary factor being fracture development, categorized mainly as “well-developed” or “developed.” 3） By combining lasso regression-random forest- decision tree algorithm to iteratively eliminate irrelevant factors, the continuous dominant factors influencing productivity were ranked in descending order as: ash content, average construction discharge rate, total sand volume pumped, flowback rate at gas breakthrough, net pay thickness, acoustic travel time, gamma ray log value, average construction pressure, percentage of 100-mesh sand, and average gas measurement value. …”
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Static pressure prediction method for CO2 flooding oil reservoirs based on time series partitioning Transformer model by LI Chunlei, YANG Heshan, ZHANG Hongxia, CAO Yumin, JIANG Xingxing, JIN Caixia

“…Model parameters were selected based on correlation analysis， and iterative interpolation was used to fill in samples to construct a static pressure prediction sample set. …”
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APT attack threat-hunting network model based on hypergraph Transformer by Yuancheng LI, Yukun LIN

“…To solve the problem that advanced persistent threat (APT) in the Internet of things (IoT) environment had the characteristics of strong concealment, long duration, and fast update iterations, it was difficult for traditional passive detection models to quickly search, a hypergraph Transformer threat-hunting network (HTTN) was proposed.The HTTN model had the function of quickly locating and discovering APT attack traces in IoT systems with long time spans and complicated information concealment.The input cyber threat intelligence (CTI) log graph and IoT system kernel audit log graph were encoded into hypergraphs by the model, and the global information and node features of the log graph were calculated through the hypergraph neural network (HGNN) layer, and then they were extracted for hyperedge position features by the Transformer encoder, and finally the similarity score was calculated by the hyperedge, thus the threat-hunting of APT was realized in the network environment of the Internet of things system.It is shown by the experimental results in the simulation environment of the Internet of things that the mean square error is reduced by about 20% compared to mainstream graph matching neural networks, the Spearman level correlation coefficient is improved by about 0.8%, and improved precision@10 is improved by about 1.2% by the proposed HTTN model.…”
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Calculation Model of Thermodynamic Properties of Saturated Liquid for HFC-32 Refrigerant by Tian Zhen, Gu Bo, Wang Ting, Hao Yuancheng

“…The results show that, the average relative error and maximum relative error for all calculation models are less than 0.776% and 4.464%, respectively.…”
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Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines by Hongbo Liu, Xiangzhao Meng

“…The model’s prediction performance is evaluated using mainstream metrics such as the Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R²), Root Mean Square Error (RMSE), robustness analysis, overfitting analysis, and grey relational analysis. …”
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Predictive Model of Humidity in Greenhouses through Fuzzy Inference Systems Applying Optimization Methods by Sebastian-Camilo Vanegas-Ayala, Julio Barón-Velandia, Daniel-David Leal-Lara

“…The three-phase methodology applied made use of descriptive statistics techniques, correlation analysis, and prototyping paradigm for the iterative and incremental development of the predictive model, validated through error measurement. …”
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Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models by Mohammed Hilal Mukhsaf, Weiqin Li, Ghassan Husham Jani

“…Using a dataset of 74,000 samples, with 80% for training and 20% for testing, we enhanced model robustness with 50 Monte Carlo iterations and tenfold cross-validation. …”
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