A High-Precision Real-Time Temperature Acquisition Method Based on Magnetic Nanoparticles by Yuchang Zhu, Li Ke, Yijing Wei, Xiao Zheng

“…Based on a low-frequency alternating magnetic field excitation model, we construct two additional magnetic field excitation models—alternating current–direct current superposition and dual-frequency superposition—to extract harmonic amplitude components from the magnetization response. …”
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Determination method for horizontal load-bearing capacity of pile group for large LNG tank by Chengguo WEI, Hanwen WANG, Wentao LIU, Wei CUI, Wangyu ZHAN

“…Conclusion In addition to factors like the numbers of piles, pile spacings, pile diameters, and constraint modes at the pile tops, this method integrates real pile-soil interactions by adopting a reasonable soil constitutive model and soil layer parameters. …”
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Distributed photovoltaic cluster output monitoring method based on time series data acquisition by Hua Ye, Xuegang Lu, Wei Zhang, Fei Cheng, Ying Zhao

“…In order to obtain a reliable distributed photovoltaic cluster output monitoring method, this paper analyzes the output relationship of cluster power stations, combining time series data analysis methods for distributed cluster processing and monitoring data processing, a combined model of ceemdan and Bayesian neural network is proposed, the representative power plant prediction values obtained by establishing a combination model are weighted to obtain the cluster output prediction values. …”
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Measuring Residual Stresses with Crack Compliance Methods: An Ill-Posed Inverse Problem with a Closed-Form Kernel by Marco Beghini, Tommaso Grossi

“…If the cut can be considered a controlled crack-like defect, by leveraging Bueckner’s superposition principle, the relaxed strains can be modeled through a weighted integral of the residual stress relieved by the cut. …”
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Modeling of Transport and Logistics Systems and the Study of the Structural Stability by D. V. Yatskin, A. A. Kochkarov, R. A. Kochkarov

“…The methodological base of the research includes the theory of optimal control and multicriteria optimization; elements of graph theory are used as modeling tools, as well as numerical analysis to evaluate stability coefficients. …”
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Experimental Study on Glass Deformation Calculation Using the Holographic Interferometry Double-Exposure Method by Yucheng Li, Yang Zhang, Deyu Jia, Song Gao, Muqun Zhang

“…This approach (1) eliminates object wave amplitude interference via dual-exposure superposition, establishing submicron linear mapping between fringe displacement and deformation amplitude; (2) introduces a fringe gradient-based direction detection algorithm resolving deformation vector ambiguity; and (3) implements an error-compensated fusion framework integrating theoretical modeling, MATLAB 2015b simulations, and experimental validation. …”
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Application of the generalized point source method for solving boundary value problems of mathematical physics by Sergey Yu. Knyazev, Elena E. Shcherbakova

“…The developed numerical technique allows greatly extend the application domain of the traditional point source method in solving applied problems for modeling fields of different physical nature, including the eigenvalue problems. …”
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A Space-Frequency Data Compression Method for Spatially Dense Laser Doppler Vibrometer Measurements by José Roberto de França Arruda, Sérgio Augusto Vianna do Rio, Luiz Antonio Silva Bernardes Santos

“…From the reduced model, smoothed curves with any desired frequency and spatial resolution can he produced whenever necessary. …”
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High-Frequency Dynamic Analysis of Plates in Thermal Environments Based on Energy Finite Element Method by Di Wang, Miaoxia Xie, Yueming Li

“…The approach could be conducted by three steps: (1) thermal analysis, (2) thermal stress analysis, and (3) forming element matrixes, joint matrixes, and the whole EFEM formulation for the energy analysis. The same mesh model is used for all the three steps. The comparison between EFEM results and classical modal superposition method results of simply supported plates in various uniform thermal environments and coupled plates in nonuniform thermal environments demonstrated that the derived energy governing equation and the proposed approach described well the smooth time- and locally space-averaged energy density. …”
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Pyramid Quantum Neural Network Based Resource Allocation with IoT: A Deep Learning Method by Khushwant Singh, Mohit Yadav, Kirti, Sunil Kumar, Bobur Sobirov

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Modeling of Tribological Processes for Abrasive Wear of Soil-Cutting Part by V. I. Myalenko

“…(Conclusions) It has been established that the obtained model of the tribolithic process for abrasive wear of soil-cutting part ensures the superposition of simulated loading results with the values of real soil force equivalents, thus providing a 0.5-1 year reduction in the terms of creating new parts and maintaining the normative indices of technological process reliability and quality.…”
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Prediction of Landslide Displacement Based on EMD-TAR Combined Model by CHEN Xi, GAO Yaping, TU Rui

“…This study aims to more accurately predict the displacement changes of landslides with nonlinear volatility development.The empirical mode decomposition is first employed to process the time series of monitoring surface displacement of a landslide,and then the irregularly changing displacement series is converted into modal components with regular changes,which generates displacement components at different frequencies.Each component is predicted separately so that the mutual influence of errors can be avoided.The comprehensive prediction of the changing trend of displacement series is based on the prediction of the changing trends of all components.The improved threshold autoregressive model able to well describe non-stationary harmonics is used to predict the landslide displacement components.Finally,the modal superposition yields the final predicted displacement.In this way,a combined prediction model based on empirical mode decomposition and threshold autoregressive model is established,and its prediction accuracy is verified with Baishuihe landslide data.Compared with a BP neural network model and a long short-term memory network model,the proposed model has a high prediction accuracy,which provides a new method for the prediction of landslide displacement.…”
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RESEARCH ON FATIGUE CRACK GROWTH MODEL BASED ON HRR FIELD by LIU JianHui, XU JianPing

“…Aiming at the safety and economic requirements in practical engineering,a type I fatigue crack propagation rate prediction model was proposed based on the Hutchinson-Rice-Rosengren(HRR)stress-strain field.The stress-strain distribution state of the crack tip region was described by the HRR stress-strain field,and a crack tip damage area was defined under the premise of considering the cyclic load and strain energy density theory.At the same time,in order to eliminate the problem of stress singularity value of the crack tip,a passivation radius calculation method considering the passivation effect of crack tip was established.Combining RICE′s plastic superposition method with plastic strain energy failure criterion,a type I fatigue crack propagation rate model was developed.The crack propagation rates of nine metallic materials were calculated using the proposed LIUX(HRR)prediction model,and the calculation results of the two crack propagation prediction models of Pandey(HRR)and CHEN-CAI Rice-Kujawski-Ellyin(RKE)were compared.At the same time,based on the different requirements of R2 fitting effect and fatigue crack propagation in the three stages,the scoring results of three prediction models were given,and corresponding scores were given from three aspects,safety,accuracy and economy.The final results show that the LIUX(HRR)prediction model in this paper has a wider scope of application,better fitting effect,and can better meet the needs of economy and safety in practical engineering.…”
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Prediction Method of Electromagnetic Wave Propagation on High Sea State Based on P-M Wave Spectrum by Lihua Li, Menglei Xiu, Shimin Feng

“…In order to realize the near sea surface communication under high sea states, a link-peak diffraction model is proposed. A P-M sea wave model is established based on the bilinear superposition method. …”
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ResNest-SVM-based method for identifying single-phase ground faults in active distribution networks by Qingwen Lian, Xiang Luo, Dong Lin, Caihua Lin, Bingxi Chen, Ziyi Guo

“…Secondly, the composition of the zero-mode transient current of the fault line is analyzed. A judgment method based on the superposition diagram of transient zero-sequence voltage and current is proposed. …”
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DYNAMIC ANALYSIS OF FPB ISOLATED BRIDGES BASED ON SIMPLIFIED MECHANICAL MODEL by LI Bing, WANG ShaoHua, DENG Bin

“…Simplified mechanical model of FPB isolated bridges was established,the response amplitude was calculated used mode superposition method.The accuracy of results was verified by finite element simulation based on SAP2000.It was considered that the response amplitude agreed well with that of finite element simulation.Under the action of El Centro seismic wave,the results can meet engineering precision requirements basically.The response of girder and pier were mainly influenced by the isolated mode and second mode.…”
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THE FEATURES OF LASER EMISSION ENERGY DISTRIBUTION AT MATHEMATIC MODELING OF WORKING PROCESS by A. M. Avsiyevich, O. G. Devoino, A. D. Kocherov, A. P. Pilipchuk

“…For more accurate describing of multimode laser emission energy distribution intensity the experimental and theoretic model, which based on experimental laser emission distribution shift presentation with given accuracy rating in superposition basic function form, is proposed. …”
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Calculation of Constrained Stress in Expansive Mortar with a Composite Creep Model by Hyeonggil Choi, Bongsuk Cho

“…The creep of mortar and concrete mixed with the expansive additive was evaluated using a composite model of the paste and aggregate. Under the assumption that the modeled creep deformation is proportional to the stress and the gel volume of the hydration products, which allows the law of superposition to be applied, the distribution stress was predicted by applying the step-by-step method at each time increment. …”
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‌Rockburst intensity grading prediction based on the LOF-ENN-KNN model‌ by Haoran Ge, Jiyong Zhang, Congbo Ma, Kai Hui, Yicong Li, Ziniu Wu

“…Through the comparison and exploration of different sampling methods and different combinations of LOF algorithm, single resampling technology (SMOTE, ADASYN) or simple technology superposition (LOF-SMOTE, LOF-ADASYN) is easy to introduce over-fitting or negative coupling effect, while LOF-ENN-KNN significantly improves the robustness and generalization ability of the model through modular design. …”
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Landslide Displacement Prediction Model Based on Time Series and CNN-GRU by FU Zhentao, LI Limin, WANG Lianxia, REN Ruibin, CUI Chengtao, FENG Qingqing

“…Landslide displacement prediction is an important basis for early landslide warning.This paper proposes a prediction model of landslide moving states based on time series and convolutional gated recurrent unit (CNN-GRU) to deal with the shortcomings of previous prediction models.Firstly,after employing wavelet analysis to determine the displacement of the trend term,the exponential smoothing method is adopted to decompose the cumulative displacement to obtain two displacement types of the trend term and the periodic term,and the trend term is fitted by a five-order polynomial.Then,the autocorrelation function is utilized to test the periodic displacement characteristics,and the gray correlation method is applied to determine the correlation degree between each factor and the periodic term.Meanwhile,the periodic term and the influencing factor are input into the CNN-GRU model for prediction,and finally the predicted cumulative displacement value is obtained by superposition.By taking the Baishui River landslide in the Three Gorges Reservoir area as an example,this paper selects the data from January 2004 to December 2012 for study,and the average absolute error percentage of the final prediction results is only 0.525%,with RMSE of 9.614 and R² of 0.993.Experimental results show that CNN-GRU has higher prediction accuracy.…”
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