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Integrating Wavelet Analysis and BPANN to Simulate the Annual Runoff With Regional Climate Change: A Case Study of Yarkand River, Northwest China

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WATER RESOURCES MANAGEMENT 2014年第9期28卷 2523-2537页

作者： Xu, Jianhua Chen, Yaning Li, Weihong Nie, Qin Song, Chunan Wei, Chunmeng E China Normal Univ Res Ctr East West Cooperat China Dept Geog Shanghai 200241 Peoples R China Chinese Acad Sci Xinjiang Inst Ecol & Geog State Key Lab Desert & Oasis Ecol Urumqi 830011 Peoples R China

Selecting the Yarkand River as a typical representative of an inland river in northwest China, We identified the variation pattern of hydro-climatic process based on the hydrological and meteorological data during the period of 1957 similar to 2008, and constructed an integrated model to simulate the change of annual runoff (AR) with annual average temperature (AAT) and annual precipitation (AP) by combining wavelet analysis (WA) and artificial neural network (ANN) at different time scale. The results showed that the pattern of hydro-climatic process is scale-dependent in time. At 16-year and 32-year time scale, AR presents a monotonically increasing trend with the similar trend of AAT and AP. But at 2-year, 4-year, and 8-year time scale, AR exhibits a nonlinear variation with fluctuations of AAT and AP. The back propagation artificial neural network based on wavelet decomposition (BPANNBWD) well simulated the change of AR with AAT and AP at the all five time scales. Compared to the traditional statistics model, the simulation effect of BPANNBWD is better than that of multiple linear regression (MLR) at every time scale. The results also revealed the fact that the simulation effect at a larger time scale (e.g. 16-year or 32-year scale) is better than that at a smaller time scale (e.g. 2-year or 4-year scale).

关键词： Annual runoff Regional climate change Wavelet decomposition back-propagation artificial neural network Inland river

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artificial neural network-Based Three-dimensiona Continuous Response Relationship Construction of 3Cr20Ni10W2 Heat-Resisting Alloy and Its Application in Finite Element Simulation

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HIGH TEMPERATURE MATERIALS AND PROCESSES 2018年第5期37卷 411-424页

作者： Li, Le Wang, Li-yong Beijing Informat Sci & Technol Univ Minist Educ Key Lab Modern Measurement & Control Technol Beijing 100192 Peoples R China Beijing Informat Sci & Technol Univ Collaborat Innovat Ctr Elect Vehicles Beijing Beijing 100192 Peoples R China

The application of accurate constitutive relationship in finite element simulation would significantly contribute to accurate simulation results, which plays a critical role in process design and optimization. In this investigation, the true stress-strain data of 3Cr20Ni10W2 heat-resisting alloy were obtained from a series of isothermal compression tests conducted in a wide temperature range of 1203-1403K and strain rate range of 0.01-10s(-1) on a Gleeble 1500 testing machine. Then the constitutive relationship was modeled by an optimally constructed and well-trained back-propagation artificial neural network (BP-ANN). The evaluation of the BP-ANN model revealed that it has admirable performance in characterizing and predicting the flow behaviors of 3Cr20Ni10W2 heat-resisting alloy. Meanwhile, a comparison between improved Arrhenius-type constitutive equation and BP-ANN model shows that the latter has higher accuracy. Consequently, the developed BP-ANN model was used to predict abundant stress-strain data beyond the limited experimental conditions and construct the three-dimensional continuous response relationship for temperature, strain rate, strain, and stress. Finally, the three-dimensional continuous response relationship was applied to the numerical simulation of isothermal compression tests. The results show that such constitutive relationship can significantly promote the accuracy improvement of numerical simulation for hot forming processes.

关键词： constitutive relationship heat-resisting alloy back-propagation artificial neural network accuracy improvement numerical simulation

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artificial neural network modeling of pH dependent structural descriptor-mobility relationship for capillary zone electrophoresis of tripeptides

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JOURNAL OF LIQUID CHROMATOGRAPHY & RELATED TECHNOLOGIES 2008年第15期31卷 2348-2362页

作者： Olajos, M. Chovan, T. Mittermayr, S. Kenesei, T. Hajos, P. Molnar, I. Darvas, F. Guttman, A. Inst Analyt Chem Horvath Lab Bioseparat Sci Innsbruck Austria Univ Pannonia Inst Chem Engn Veszprem Hungary Univ Hlth Sci & Technol Inst Biomed Engn Hall In Tirol Austria Molnar Inst Appl Chromatog Berlin Germany

The aim of this work was to study the structural descriptor-mobility relationship of representative tripeptides in capillary zone electrophoresis (CZE) with the change of such separation parameters as pH, applied voltage and separation length in respect to their influence on electrophoretic migration properties. At the present stage of the work, the ionic charge was considered as structural descriptor. A multivariable linear regression (MLR) model and a back-propagation artificial neural network (BP-ANN) were applied to predict the electrophoretic mobilities of the model tripeptides with non-polar, polar, positively charged, negatively charged and aromatic R group characteristics. Here we present a comprehensive analysis on electrophoretic mobilities measured at pHs 2.5, 4.5, 7.5 and 9.5 at two different capillary lengths of 10cm and 30cm, as well as four applied electric field strengths ranging from 100 to 400 V/cm to teach and evaluate our mobility predicting models. The anticipated mobilities predicted by MLR and BP-ANN were compared to each other and to the experimental data, respectively. The BP-ANN model resulted in considerable higher precision in predictability that of the MLR method.

关键词： back-propagation artificial neural network capillary zone electrophoresis multivariable linear regression peptide mobility modeling

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Butene concentration prediction in ethylene/propylene/1-butene terpolymers by FT-IR spectroscopy through multivariate statistical analysis and artificial neural networks

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TALANTA 2009年第3期77卷 1111-1119页

作者： Marengo, Emilio Longo, Valentina Bobba, Marco Robotti, Elisa Zerbinati, Orfeo Di Martino, Silvana Univ Piemonte Orientale Dept Environm & Life Sci I-15100 Alessandria Italy Polimeri Europa Ferrara Plant I-44100 Ferrara Italy

This paper reports the development of calibration models for quality control in the production of ethylene/propylene/1-butene terpolymers by the use of multivariate tools and FT-IR spectroscopy. 1-Butene concentration prediction is achieved in terpolymers by coupling FT-IR spectroscopy to multivariate regression tools. A dataset of 26 terpolymers (14 coming from a constrained experimental design for mixtures, plus 12 terpolymers used for external validation) was analysed by FT-IR spectroscopy. An internal method of "Polimeri Europa" plant, based on C-13 NMR spectroscopy is used to determine the percentage of 1-butene in the samples. Then, different multivariate tools are used for 1-butene concentration prediction based on the FT-IR spectra recorded. Different multivariate calibration methods were explored: principal component regression (PCR), partial least squares (PLS), stepwise OLS regression (SWR) and artificial neural networks (ANNs). The model obtained by back-propagation neural networks turned out to be the best one. The performances of the BP-ANN model were further improved by variable selection procedures based on the calculation of the first derivative of the network. The proposed approach allows the monitoring in real time of the polymer synthesis and the estimation of the characteristics of the product attainable from the concentration of 1-butene. (C) 2008 Elsevier B.V. All rights reserved.

关键词： FT-IR back-propagation artificial neural network Principal component regression Partial least squares Ethylene/propylene/1-butene terpolymers Chemometrics

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Hyperspectral Imaging and Chemometrics for Nondestructive Quantification of Total Volatile Basic Nitrogen in Pacific Oysters (Crassostrea gigas)

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FOOD ANALYTICAL METHODS 2019年第3期12卷 799-810页

作者： Chen, Lipin Li, Zhaojie Yu, Fanqianhui Zhang, Xu Xue, Yong Xue, Changhu Ocean Univ China Coll Food Sci & Engn Qingdao 266003 Peoples R China

Total volatile basic nitrogen (TVB-N) content is used to evaluate Pacific oyster (Crassostrea gigas) freshness. In this work, hyperspectral imaging (HSI;400-1000nm) was used to measure the TVB-N content in Pacific oysters. Accordingly, Pacific oyster samples stored in 15 degrees C water were assessed at intervals after 1, 3, 5, 7, or 9 days. Minimum noise separation processing of the hyperspectral images was performed before determining the region of interest for data dimension reduction. The effects of multiplicative scatter correction (MSC) on the obtained data were then investigated. To simplify the calibration model, 12 characteristic wavelengths were selected from the Pacific oyster hypercube using the correlation coefficient method. Finally, multiple linear regression (MLR) and back-propagation artificial neural network (BP-ANN) models were built from the selected wavelengths. The experimental results showed that the correlation coefficients between the corrected, predicted, and cross-validated datasets were lower in the MLR model than in the BP-ANN. However, the MLR model outperformed the BP-ANN in terms of the root-mean-square errors of correction, prediction, and interaction verification. Overall, both the MLR and BP-ANN models demonstrated that the combination of HSI with chemometric methods can be used to detect and accurately predict Pacific Oyster freshness during storage.

关键词： Total volatile basic nitrogen Pacific oysters (Crassostrea gigas) Hyperspectral imaging Multiplicative scatter correction Multiple linear regression back-propagation artificial neural network

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Construction of Processing Maps based on Expanded Data by BP-ANN and Identification of Optimal Deforming Parameters for Ti-6Al-4V Alloy

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2016年第2期17卷 171-180页

作者： Quan, Guo-zheng Wen, Hai-rong Jia-Pan Zou, Zhen-yu Chongqing Univ Sch Mat Sci & Engn State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

The intrinsic relationships between deforming parameters and microstructural mechanisms for Ti-6Al-4V alloy were analyzed by processing maps. A series of thermal compression tests were carried out in the temperatures range of 1023 similar to 1323 K (across beta-transus) and strain rates range of 0.01 similar to 10 s(-1) on a Gleeble-3500 thermo-mechanical simulator. Based on the stress-strain data collected from compression tests, a back-propagation artificial neural network (BP-ANN) model was developed, which presents reliable performance in tracking and predicting strain-stress data. By utilizing this model, the volume of stress-strain data was expanded. According to the intensive stress-strain data, the apparent activation energy was calculated to be 564.05 kJ mol(-1) and 300.20 kJ mol(-1) for alpha+beta-phase field and single beta-phase field, respectively. Moreover, the processing maps were constructed at finer intervals of temperature, from which, the stable regions with higher power dissipation efficiency (eta > 0.3) and unstable regions with negative instability parameter (xi < 0) were clarified clearly. By combining processing map with microstructure observations, two main stable softening mechanisms, i.e., globularization and dynamic recovery (DRV) were identified, and globularization-predominant (0.3 < eta < 0.55) parameter domain ((epsilon) over dot < 0.1 s(-1)) in alpha+beta-phase field and DRV-predominant (0.25 < (epsilon) over dot < 0.41) parameter domain (0.032 s(-1) < < 1 s(-1)) in beta-phase field were recommended.

关键词： back-propagation artificial neural network Kinetic analysis Processing maps Ti-6Al-4V alloy

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Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy, multivariate statistical tools, and artificial neural networks

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JOURNAL OF APPLIED POLYMER SCIENCE 2008年第6期109卷 3975-3982页

作者： Marengo, Emilio Longo, Valentina Robotti, Elisa Bobba, Marco Gosetti, Fabio Zerbinati, Orfeo Di Martino, Silvana Univ Piemonte Orientale Dept Environm & Life Sci I-15100 Alessandria Italy Polimeri Europa Ferrara Plant I-44100 Ferrara Italy

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back-propagation artificial neural network (BP-ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene-propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation;(b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP-ANN;(c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene-propylene copolymers, by predicting the concentration of propylene directly along the process line. (C) 2008 Wiley Periodicals, Inc.

关键词： FTIR spectroscopy back-propagation artificial neural network principal component regression partial least squares ethylene-propylene copolymers chemometrics

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Data mining assisted prediction of liquidus temperature for primary crystallization of different electrolyte systems

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2020年第0期196卷 103885-000页

作者： Lu, Hui Hu, Xiaojun Cao, Bin Ma, Liang Chai, Wanqiu Yang, Yunchuan Univ Sci & Technol Beijing State Key Lab Adv Met Beijing 10083 Peoples R China Guiyang Aluminium Magnesium Design & Res Inst Co Guiyang 550081 Peoples R China

Liquidus temperature for primary crystallization is an important physical and chemical property for electrolyte system. It plays a crucial role on the stability of the electric cell in electrolysis production process. So how to accurately predict the liquidus temperature for primary crystallization of electrolyte based on the composition of electrolyte is a meaningful research subject. In this work, data mining assisted prediction of liquidus temperature for primary crystallization of electrolyte systems was proposed. The essential differences between the complex industrial electrolyte system and electrolyte system prepared in laboratory were revealed by means of comparing the micro-morphology, phase composition and thermal analysis. To some extent, it was verified that the empirical formula has no versatility in the two different electrolyte systems. The prediction model of liquidus temperature for primary crystallization of different electrolyte systems was constructed by using SVM(support vector machine), BPANN(back-propagation artifical neural networks), RFR(random forest regression) and GBR(gradient boosting regression) algorithm, respectively. The electroyte system inculdes Na3AlF6(CR)-Al2O3-AlF3-CaF2, Na3AlF6(CR)-Al2O3-MgF2-CaF2-LiF, Na3AlF6(CR)Al2O3-MgF2-CaF2-KF-LiF, and Na3AlF6(CR)-Al2O3-AlF3-CaF2-MgF2-LiF-KF-NaF. For different electrolyte systems, ANN, SVM, RFR and other models all have good performances, they can effectively predict the liquidus temperature for primary crystallization of each electrolyte systems. For some electrolyte systems, ANN, SVM, RFR models are obviously superior to the prediction level of empirical formula described in the literature. It can be seen that data mining has a good application prospect in the prediction of the liquidus temperature for primary crystallization of electrolyte systems. We provide a new method for predicting the liquidus temperature for primary crystallization of different electrolyte systems based on the el

关键词： Aluminium electrolysis industry Electrolyte Liquidus temperature Support vector machine back-propagation artificial neural network Random forest regression Gradient boosting regression

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An empirical analysis on auto corporation training program planning by data mining techniques

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 5841-5850页

作者： Lin, W. T. Wang, S. J. Wu, Y. C. Ye, T. C. Chung Yuan Christian Univ Dept Ind & Syst Engn Chung Li City 32023 Taoyuan County Taiwan

Under limited resources in corporation education training, to enhance human resources quality, making education training program planning more efficient is a significant issue in training future talents. In accordance with Taiwan TrainQuali System (TTQS), the basic training structure is ton specify P (Plan) and D (Design). Ensuing results will be easier and successful. From TTQS database of Bureau of Employment and Vocational Training, corporations in Taoyuan. Hsinchu and Miaoli winning Gold Medals (Group B) have gaps outside control line in P and D. Enhancement is needed in the gap. The paper aims at a certain company winning Gold Medals in Taoyuan, Hsinchu and Miaoli to locate hidden or unobvious information with data mining, which will help future education training course planning and design. The researchers use two-stage clustering (SOM and K-means) under data mining theory to collect personnel training data of Automobile Corporation A in Taiwan and China with data mining and analysis. The results under the two algorithms will serve as reference for future education training courses. In the end, in combination of back-propagation neural network to develop education training prediction model, the research offers reference for writing knowledge management system to enhance effects of personnel participation in training at corporations. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： TTQS (Taiwan TrainQuali System) Human resources Data mining SOM (self-organization map) K-means back-propagation artificial neural network

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Assessing the impact of occlusal plane rotation on facial aesthetics in orthodontic treatment: a machine learning approach

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BMC ORAL HEALTH 2024年第1期24卷 1页

作者： Cai, Jingyi Min, Ziyang Deng, Yudi Jing, Dian Zhao, Zhihe Sichuan Univ West China Hosp Stomatol State Key Lab Oral Dis 14 3rd SectSouth Renmin Rd Chengdu 610041 Sichuan Peoples R China Sichuan Univ West China Hosp Stomatol Natl Clin Res Ctr Oral Dis 14 3rd SectSouth Renmin Rd Chengdu 610041 Sichuan Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples Hosp 9 Sch Med Dept Orthodont 639 Zhizaoju Rd Shanghai 200011 Peoples R China Shanghai Jiao Tong Univ Coll Stomatol 639 Zhizaoju Rd Shanghai 200011 Peoples R China

backgroundAdequate occlusal plane (OP) rotation through orthodontic therapy enables satisfying profile improvements for patients who are disturbed by their maxillomandibular imbalance but reluctant to surgery. The study aims to quantify profile improvements that OP rotation could produce in orthodontic treatment and whether the efficacy differs among skeletal types via machine *** and methodsCephalometric radiographs of 903 patients were marked and analyzed by trained orthodontists with assistance of Uceph, a commercial software which use artificial intelligence to perform the cephalometrics analysis. back-propagation artificial neural network (BP-ANN) models were then trained based on collected samples to fit the relationship among maxillomandibular structural indicators, SN-OP and P-A Face Height ratio (FHR), Facial Angle (FA). After corroborating the precision and reliability of the models by T-test and Bland-Altman analysis, simulation strategy and matrix computation were combined to predict the consequent changes of FHR, FA to OP rotation. Linear regression and statistical approaches were then applied for coefficient calculation and differences *** regression scores calculating the similarity between predicted and true values reached 0.916 and 0.908 in FHR, FA models respectively, and almost all pairs were in 95% CI of Bland-Altman analysis, confirming the effectiveness of our models. Matrix simulation was used to ascertain the efficacy of OP control in aesthetic improvements. Intriguingly, though FHR change rate appeared to be constant across groups, in FA models, hypodivergent group displayed more sensitive changes to SN-OP than normodivergent, hypodivergent group, and Class III group significantly showed larger changes than Class I and *** of OP could yield differently to facial aesthetic improvements as more efficient in hypodivergent groups vertically and Class III groups sagittally.

关键词： Orthodontics Occlusal plane Machine learning back-propagation artificial neural network Aesthetic improvement

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