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Customer Tiered Purchase Forecast by Mobile Edge Computing Based on Pareto/NBD and svr

China Communications 2021年第11期18卷 1-10页

作者： Yan Li Ying Zhang Fei Luo Wei Zou Yu Zhang Kaijun Zhou School of Business and Management Shanghai International Studies University550 Dalian Rd.(W)Hongkou DistrictShanghai200083China Institute of Mineral Resources Chinese Academy of Geological SciencesNo.26Baiwanzhuang StreetXicheng DistrictBeijing100037China

Mobile edge computing is trending nowadays for its computation efficiency and *** rapid development of e-commerce show great interest in mobile edge computing due to numerous rise of small and middle-sized enterprises(SMEs)in the *** paper predicts the overall sales volume of the enterprise through the classic ARIMA model,and notes that the behavior and arrival differences between the new and old customer groups will affect the accuracy of our forecasts,so we then use Pareto/NBD to explore the repeated purchases of customers at the individual level of the old customer and the svr model to predict the arrival of new customers,thus helping the enterprise to make layered and accurate marketing of new and old customers through machine *** general,machine learning relies on powerful computation and storage resources,while mobile edge computing typically provides limited computation resources ***,it is essential to combine machine learning with mobile edge computing to further promote the proliferation of data analysis among SMEs.

关键词： e-commerce customer behavior Pareto/NBD model svr model ARIMA model mobile edge computing

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Estimation of chlorophyll content in maize canopy using wavelet denoising and svr method

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International Journal of Agricultural and Biological Engineering 2018年第6期11卷 132-137页

作者： Haojie Liu Minzan Li Junyi Zhang Dehua Gao Hong Sun Liwei Yang Key Laboratory of Modern Precision Agriculture System Integration Research Ministry of EducationChina Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural information acquisition technology Ministry of AgricultureChina Agricultural UniversityBeijing 100083China

In order to estimate the chlorophyll content of maize plant non-destructively and rapidly,the research was conducted on maize at the heading stage using spectroscopy *** spectral reflectance of maize canopy was measured and processed following wavelet denoising and multivariate scatter correction(MSC)to reduce the noise ***,the signal to noise ratio(SNR)and curve smoothness(CS)were used to evaluate the denoising effect of different wavelet functions and decomposition *** a result,the Sym6 wavelet basis function and the 5th level decomposition were determined to denoise the original *** MSC method was used to eliminate the scattering effect after *** three spectral ranges were extracted by interval partial least squares(IPLS)including the 525-549 nm,675-749 nm and 850-874 ***,the chlorophyll content estimation model was developed by using support vector regression(svr)*** calibration Rc2 of the svr model was 0.831,the RMSEC was 1.3852 mg/L;the validation Rv2 was 0.809,the RMSEP was 0.8664 mg/*** results show that the SNR and CS indicators can be used to select the parameters for wavelet denoising and model can be used to estimate the chlorophyll content of maize canopy in the field.

关键词： maize canopy spectral reflectance wavelet denoising svr model chlorophyll content

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Investigation of Pile Construction and Productivity Loss: An Analysis of Macro Impact Factor

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World Journal of Engineering and Technology 2023年第4期11卷 932-964页

作者： Minhaz Ahmed Wang Xu School of Civil Engineering Lanzhou Jiaotong University Lanzhou China

Pile foundations are challenging to build due to subsurface obstacles, contractor ignorance, and difficulties with site planning. Given the unpredictable environment of the construction site, productivity losses during pile work are to be thought possible. Prior to finishing a site pre-investigation, a foundation’s area is usually sampled for statistical reasons. There are studies on pile construction outside of Bangladesh that are supported by relevant empirical data in the literature. Since Bangladesh, which is regarded as a third-world country, is ignored in this regard, the literature currently available about pile building and the associated productivity loss is unable to provide adequate information or appropriate empirical data. Due to this pile-building sector in Bangladesh has been experiencing a decline in production for quite some time now. Before attempting to increase productivity in pile construction, it is essential to investigate the potential losses and the variables that might have an influence. This study aims to accomplish the following objectives: 1) identify the primary factors that have an impact on pile construction;2) develop an svr model that accurately predicts productivity loss;and 3) figure out the projected loss by basing it on the historical scenario that is the most comparable to the current one. A Support Vector Regression (svr) model was developed after a study of the relevant literature. This model enabled the collection of 110 pile building projects from five significant locations in Bangladesh. The model was constructed using a list of eight inputs in addition to a list of five macro elements (labor, management, environment, material, and equipment) (soil condition, pile type, pile material, project size, project location, pile depth, pile quantity, and equipment quantity). Using 10-way cross validation, the svr achieves an accuracy of 87.2% in its predictions. On the basis of what has occurred in the past, we are able to estimat

关键词： Productivity Loss Macro-Effect Factor Pile Construction Regression model svr model

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Thruster fault feature extraction method for underwater vehicle

Thruster fault feature extraction method for underwater vehi...

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第32届中国控制与决策会议

作者： Dacheng Yu Ke Deng Wei Gong Mingjun Zhang Zhenzhong Chu College of Mechanical and Electrical Engineering Harbin Engineering University College of Information Engineering Shanghai Maritime University

A novel thruster fault feature extraction method for autonomous underwater vehicle（AUV） is presented in this *** using fractal dimension method to extract the thrust fault feature from state quantity,the noise feature value may be greater than the fault feature value,which lead to failure of fault feature *** solve this problem,this paper proposes an improved method based on empirical mode decomposition,fractal dimension and the short-time higher frequency component positioning algorithm to extract fault features from state *** this paper,the empirical mode decomposition（EMD） is used to replace the filtering method of the fractal *** rolling time windows are introduced into the high frequency part of EMD,and the fractal dimension mutation at the time of fault occurrence is captured by calculating the fractal dimension of small samples in each time *** feature extraction is completed by extracting the maximum value of the fractal dimension *** using the fractal dimension method to extract the thruster fault feature from the control quantity,the calculation time is too *** reduce the calculation time,this paper proposes a fault feature extraction and identification method based on fractal dimension and the support vector regression algorithm（svr）.In this paper,the ideal svr model between control quantity and fractal dimension for different thruster fault is established,and the fault feature is extracted from the original data by svr *** effectiveness of the improved method is verified by the pool experimental data of an underwater vehicle.

关键词： svr model fractal dimension fault feature extraction fault identification

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