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Optimization of University Scientific Research Performance Evaluation Management Based on back-propagation artificial neural network

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SENSORS AND MATERIALS 2024年第4期36卷 1575-1590页

作者： Ma, Wanzhi Chu, Na Ningxia Normal Univ Sch Educ Sci Guyuan 756000 Ningxia Peoples R China Woosuk Univ Dept Educ & Culture Contents Dev Jeonju 55338 South Korea Ningxia Normal Univ Sch Foreign Languages Guyuan Ningxia Peoples R China

Public audits in universities have exposed fraudulent practices in scientific research expenditure, posing a significant obstacle to progress. To address this issue, it is imperative to conduct risk assessment and analysis, thereby improving fund control and advocating for standardized research cost management. Such measures are crucial in alleviating the burden on institutions and researchers, fostering a more effective and efficient scientific research environment. In this study, an analysis of the factors affecting scientific research performance yielded three key elements: external environment, individual researchers, and information platform. After applying the nonlinear mapping ability and adaptability of back -propagation artificial neural network (BP -ANN) reverse neural network and obtaining simulation results for the generalization function of discrete information, we established a model for research performance evaluation. Subsequently, research cases were selected to conduct training and fitting experiments, ultimately scoring research performance through a comprehensive evaluation process. The experiment showed that the prediction results obtained using the BPANN algorithm, following learning from preprocessed samples, exhibit high accuracy. Moreover, these results can be updated and adapted with new sample inputs, highlighting the strong feasibility of this method.

关键词： back-propagation artificial neural network colleges and universities scientific research performance evaluation management

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Eggshell crack detection based on computer vision and acoustic response by means of back-propagation artificial neural network

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EUROPEAN FOOD RESEARCH AND TECHNOLOGY 2011年第3期233卷 457-463页

作者： Pan, Lei-qing Zhan, Ge Tu, Kang Tu, Sicong Liu, Peng Nanjing Agr Univ Coll Food Sci & Technol Nanjing 210095 Jiangsu Peoples R China Univ New S Wales Sydney NSW 2052 Australia

An experimental system utilizing acoustic response system (ARS) and computer vision system (CVS) for eggshell crack detection was implemented. Firstly, the acoustic response signals were captured and analyzed, and six parameters (f (1), f (2), f (3), f (4): the dominant response frequency;CS: the mean value of coefficient of skewness;CE: the mean value of coefficient of excess) were analyzed. The ARS including a back-propagation artificial neural network model with a structure of 6 input nodes, 15 hidden nodes, and one output node was built to detect eggshell cracks. Secondly, the eggshell images were captured and processed by the computer vision system, and five geometrical characteristic parameters of crack and noise regions on the eggshell images were acquired. The CVS including a back-propagation artificial neural network model with a structure of 5 input nodes, 10 hidden nodes, and one output node was built to detect eggshell cracks. Finally, the quality of eggs, with or without cracks, was evaluated based on detection results from both CVS and ARS. This method allows the fusion of information obtained from CVS to ARS. The results showed that the detection accuracy of cracked eggs were 68 and 92%, respectively, by CVS and ARS. However, the accuracy equaled to 98% by the information infusion of two techniques. The result was superior to only one technique, and the method based on the information fusion of computer vision and acoustic response was applicable for detecting egg cracks. This research provides a new technology detection of cracked egg.

关键词： Eggshell Crack Detection CVS ARS back-propagation artificial neural network

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Application of Northern Goshawk back-propagation artificial neural network in the Prediction of Monohydroxycarbazepine Concentration in Patients with Epilepsy

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ADVANCES IN THERAPY 2024年第4期41卷 1450-1461页

作者： Xu, Yichao Shao, Rong Yang, Mingdong Chen, Meng Xu, Junjun Dai, Haibin Zhejiang Univ Sch Med Affiliated Hosp 2 Ctr Clin Pharmacol Hangzhou Zhejiang Peoples R China Zhejiang Univ Sch Med Affiliated Hosp 2 Dept Pharm 88 Jiefang Rd Hangzhou 310009 Zhejiang Peoples R China

IntroductionA northern goshawk back-propagation artificial neural network (NGO-BPANN) model was established to predict monohydroxycarbazepine (MHD) concentration in patients with *** data were collected from 108 Han Chinese patients with epilepsy on oxcarbazepine monotherapy. The results of 14 genotype variates were selected as the input layer in the first BPANN model, and the variables that had a more significant impact on the plasma concentration of MHD were retained. With demographic characteristics and clinical laboratory test results, the genotypes of SCN1A rs2298771 and SCN2A rs17183814 were used to construct the BPANN model. The BPANN model was comprehensively validated and used to predict the MHD plasma concentration of five patients with epilepsy in our *** model demonstrated favorable fitness metrics, including a mean squared error of 0.00662, a gradient magnitude of 0.00753, an absence of validation tests amounting to zero, and a correlation coefficient of 0.980. Sex, BMI, and the genotype SCN1A rs2298771 were ranked highest by the absolute mean impact value (MIV), which is primarily associated with the concentration of MHD. The test group exhibited a range of - 20.84% to 31.03% bias between the predicted and measured values, with a correlation coefficient of 0.941 between the two. With BPANN, the MHD nadir concentration could be predicted *** NGO-BPANN model exhibits exceptional predictive capability and can be a practical instrument for forecasting MHD concentration in patients with *** Trial ***-OOC-17012141.

关键词： Plasma concentration Pharmacokinetic parameters Epilepsy Monohydroxycarbazepine back-propagation artificial neural network

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Creep Fracture Life Prediction of 9%Cr Heat-Resistant Steel Based on the back-propagation artificial neural network Considering Microstructure Characteristics

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STEEL RESEARCH INTERNATIONAL 2024年第0期

作者： Zhao, Liwei Shen, Junjie Li, Bo Xue, Ziyu Shao, Yuqing Ren, He Tianjin Univ Technol Sch Mech Engn Tianjin Key Lab Adv Mechatron Syst Design & Intell Tianjin 300384 Peoples R China Tianjin Univ Technol Natl Demonstrat Ctr Expt Mech & Elect Engn Educ Tianjin 300384 Peoples R China Northeastern Univ Sch Mat Sci & Engn MOE Key Lab Anisotropy & Texture Mat Shenyang 110819 Peoples R China

This study proposes a creep-life prediction model based on a back-propagation artificial neural network (BP-ANN). The model predicted the minimum creep rate by optimizing microstructure characteristics, including martensite lath width, sub-grain, precipitates (M23C6 and Laves phases), and dislocation density. The BP-ANN-based predicted creep life of 9%Cr heat-resistant steel is compared with the traditional time-temperature life prediction method and the machine learning support vector regression model. The results show that the BP-ANN model has better accuracy and performance. The % error between the predicted and the experimental creep life is 5% to 10%, primarily distributed in the two-factor band line (2% to 5%). The BP-ANN model gives the lowest mean absolute error (0.9926), root mean square error (29.411), average absolute relative error (4.7%), and multiple coefficient of determination (R2 0.96693). The results show that the BP-ANN model improved the creep fracture life prediction of 9%Cr heat-resistant steel.

关键词： 9%Cr heat-resistant steel back-propagation artificial neural network life prediction microstructure characteristic

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A creep life prediction model of P91 steel coupled with back-propagation artificial neural network (BP-ANN) and 0 projection method

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INTERNATIONAL JOURNAL OF PRESSURE VESSELS AND PIPING 2023年第1期206卷

作者： Wang, Lin Liu, Xinbao Fan, Ping Zhu, Lin Zhang, Kai Wang, Kai Song, Chaolu Ren, Siyu Northwest Univ Sch Chem Engn Xian 710069 Peoples R China Northwest Univ Sch Informat Sci & Technol Xian 710127 Peoples R China

In order to predict the long-term creep life of P91 steel, this work proposed a coupled model, combining the error-trained back-propagation artificial neural network (BP-ANN) and an improved 0 model. Both the shortterm rupture life obtained by creep experiments and the related creep data of National Institute for Materials Science (NIMS) database were used to validate the above model. In this model, creep parameters (temperature, stress) were used as the input and relative error of 5 0 model extrapolations as the output. Then, the prediction errors of BP-ANN method were introduced to correct the 5 0 model extrapolation results to achieve a more accurate prediction. Consequently, the long-term creep life of P91 steel was predicted. It demonstrated that with the proposed model, the prediction capability for long-term creep life of P91 steel with 200,000 h is much better than those of other 0 methods, which has a prediction uncertainty less than 5%.

关键词： P91 steel Creep life prediction back-propagation artificial neural network 0 projection model

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An optimal design of axial-flow fan blades by the machining method and an artificial neural network

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2002年第3期216卷 367-376页

作者： Lin, BJ Hung, CI Tang, EJ Natl Cheng Kung Univ Dept Mech Engn Tainan 701 Taiwan Natl Huwei Inst Technol Dept Automat Engn Huwei Yunlin Taiwan

The geometry design and machining of blades for axial-flow fans are important issues because the twisted profile and flowfield of blades are complicated. The rapid design of a blade that performs well and satisfies machining requirements is one of the goals in designing fluid machinery blades. In this study, an integrated approach combining computational fluid dynamics (CFD), an artificial neural network, an optimization method and a machining method is proposed to design a three-dimensional blade for an axial-flow fan. From the machining point of view, the three-dimensional surface geometry of a fan blade can be defined as the swept surface of the tool path created by using the generated machining method. By taking advantage of its powerful learning capability, a back-propagation artificial neural network is used to set up the flowfield models and to forecast the flow performance of the axial-flow fan. The desired optimal blade geometry is obtained by using a complex optimization method.

关键词： axial-flow fan optimal blade design generated machining method back-propagation artificial neural network complex optimization method

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Modeling chlorophyll-a concentrations using an artificial neural network for precisely eco-restoring lake basin

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ECOLOGICAL ENGINEERING 2016年第0期95卷 422-429页

作者： Lu, Fang Chen, Zhi Liu, Wenquan Shao, Hongbo Concordia Univ Dept Bldg Civil & Environm Engn Montreal PQ H3G 1M8 Canada SOA Inst Oceanog 1 Key Lab Marine Sedimentol & Environm Geol Qingdao 266061 Peoples R China Jiangsu Acad Agr Sci Inst Agrobiotechnol Nanjing 210014 Jiangsu Peoples R China

A back-propagation artificial neural network (BPANN) model was developed in this study for the prediction of chlorophyll-a concentration in Lake Champlain. 21 years of monitoring data (1992-2012) of water quality parameters was used to train, validate and test the BPANN models. The optimal input parameters of the model were selected on the basis of the performance of models built with different combinations of input variables. To verify the model performances, the trained models were applied to field monitoring data from Lake Champlain. Prediction accuracy was measured by using the coefficient of determination (R-2) and RMSE-observations standard deviation ratio (RSR). The R-2 values of the best-performed model in the training set, validation set, testing set, and all-year data were 0.82, 0.93, 0.81, and 0.87, respectively. The corresponding RSR values of the three data sets and all-year were 0.62, 0.38, 0.53, and 0.48, respectively. Results indicated that the developed BPANN model can predict chlorophyll-a concentrations in Lake Champlain with high accuracy and provide a quick assessment of chlorophyll-a variation for lake management and eco-restoration. (C) 2016 Elsevier B.V. All rights reserved.

关键词： back-propagation artificial neural network Chlorophyll-a Prediction Lake water quality Eco-restoring

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Hydroxyl and acid number prediction in polyester resins by near infrared spectroscopy and artificial neural networks

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ANALYTICA CHIMICA ACTA 2004年第2期511卷 313-322页

作者： Marengo, E Bobba, M Robotti, E Lenti, M Univ Piemonte Orientale Amedeo Avogadro Dipartimento Sci Ambiente & Vita I-15100 Alessandria Italy PPG Ind Italia Quattordio Plant I-15048 Quattordio AL Italy

back-propagation artificial neural networks (BP-ANN) are applied for modeling hydroxyl number and acid value of a set of 62 samples of polyester resins from their near infrared (NIR) spectra. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR) and partial least squares (PLS). The set of available samples is split into: (i) a training set, for models calculation;(ii) a test set. for setting the correct number of latent variables in PCR and PLS and for selecting the end point of the training phase of BP-ANN;(iii) a "production set" of samples, which are predicted to evaluate the models predictive ability. This approach guarantees that the predictive ability of the models is evaluated by genuine predictions. BP-ANN resulted always better than the classical PCR and PLS, from the point of view of the predictive ability. The study of the breakdown number of experiments to include in the training set showed instead that this factor does influence PCR and PLS at a lesser degree than what happens for BP-ANN. The latter approach requires a larger number of experiments for obtaining good results. The choice of optimal training sets is efficiently performed by Kohonen self-organizing maps (SOMs). It can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for monitoring the polyesterification of dicarboxylic acids with diols by predicting the acid and hydroxyl numbers directly along the process line. (C) 2004 Elsevier B.V. All fights reserved.

关键词： FT-NIR back-propagation artificial neural network Kohonen self-organising maps acid value hydroxyl number chemometrics

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Comprehensive prediction method for die-roll height of fine-blanking components

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第9-12期98卷 2819-2829页

作者： Zhuang, Xincun Zhang, Wen Wu, Yanlin Zhao, Zhen Shanghai Jiao Tong Univ Inst Forming Technol & Equipment Sch Mat Sci & Engn Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Natl Engn Res Ctr Die & Mold CAD Shanghai 200030 Peoples R China

This study proposes an economic and effective approach for predicting the die-roll height of fine-blanking components by fully considering the effect of three types of parameters: geometric features of components, process parameters of fine-blanking, and material parameters of fine-blanked sheets. Two prediction submodels that involved geometric features and process parameters individually were established based on a back-propagation artificial neural network (BP ANN), merged into one model, and finally generalised to different materials by correction according to experimental results. During modelling, simulations rather than experiments were used to obtain data for supporting BP ANN considering the demand for a single controlling variable and for saving economic and time costs. Analyses showed that geometric features had a more distinct influence on die-roll height;furthermore, the effect of process parameters is complicated and can be described more suitably by BP ANN. The prediction results of the combined model were favourable compared with the experimental results. Furthermore, the experimental curve used for correction during generalisation of the model showed that a material with higher yield strength generally had smaller die-roll.

关键词： Die-roll Fine-blanking Geometric feature back-propagation artificial neural network Correction curve

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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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