海丁微名片在CAR分析中ROI思想的深层解析

海丁微名片是一款移动职业社交应用,包含个人微名片,人脉发现,海丁悬赏,名片分享。充分利用社交人脉网络的传播力量,为用户梳理规整有价值的人脉平台。通过半年的努力,已经形成从Web到移动,微信到APP的完整生态链,积累用户达上百万,用户的生态链关系和用户行为日志记录达数亿记录。因此,对整体产品过程数据的分析和总结变得尤为重要。CAR分析的方法是企业在高成熟度过程改进和量化项目管理中常用的方法,基于ROI思想的分析预测投资回报并判断计划后续的活动则是其中非常重要的一环。

Soil Organic Matter Mapping by Decision Tree Modeling

Based on a case study of Longyou County, Zhejiang Province, the decision tree, a data mining method, was used to analyze the relationships between soil organic matter (SOM) and other environmental and satellite sensing spatial data. The decision tree associated SOM content with some extensive easily observable landscape attributes, such as landform, geology, land use, and remote sensing images, thus transforming the SOM-related information into a clear, quantitative, landscape factor-associated regular syst…

A new mathematical model for predicting flow stress of X70HD under hot deformation

To realize numerical simulation of rolling and obtain the hot forming process parameters for X70 HD steel, the flow stress behaviors of X70 HD steel were investigated under different temperatures（820-1100 ℃ and strain rates（0.01-10 s-1） on a Gleeble-3500 thermo-simulation machine. A new flow stress model was established. The linear and exponential relationship methods were applied to the parameters with respect to temperature and deformation rates. The rise of curve ends under certain conditions was analyzed. The flow stress of X70 HD steel predicted by the proposed model agrees well with the experimental results. So, it greatly improves the precision of the metal thermoplastic processing through finite element method and practical application of engineering.

Anomaly detection of earthquake precursor data using long short-term memory networks

Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predictive model for normal data.Furthermore,the prediction errors from the predictive models are used to indicate normal or abnormal behavior.An additional advantage of using the LSTM networks is that the earthquake precursor data can be directly fed into the network without any elaborate preprocessing as required by other approaches.Furthermore,no prior information on abnormal data is needed by these networks as they are trained only using normal data.Experiments using three groups of real data were conducted to compare the anomaly detection results of the proposed method with those of manual recognition.The comparison results indicated that the proposed LSTM network achieves promising results and is viable for detecting anomalies in earthquake precursor data.

Research on traffic flow forecasting model based on cusp catastrophe theory

This paper intends to describe the relationship between traffic parameters by using cusp catastrophe theory and to deduce highway capacity and corresponding speed forecasting value through suitable transformation of catastrophe model. The five properties of a catastrophe system are outlined briefly, and then the data collected on freeways of Zhujiang River Delta, Guangdong province, China are examined to ascertain whether they exhibit qualitative properties and attributes of the catastrophe model. The forecasting value of speed and capacity for freeway segments are given based on the catastrophe model. Furthermore, speed-flow curve on freeway is drawn by plotting out congested and uncongested traffic flow and the capacity value for the same freeway segment is also obtained from speed-flow curve to test the feasibility of the application of cusp catastrophe theory in traffic flow analysis. The calculating results of catastrophe model coincide with those of traditional traffic flow models regressed from field observed data, which indicates that the deficiency of traditional analysis of relationship between speed, flow and occupancy in two-dimension can be compensated by analysis of the relationship among speed, flow and occupancy based on catastrophe model in three-dimension. Finally, the prospects and problems of its application in traffic flow research in China are discussed.

Microstructure evolution,mechanical properties and tailoring of coefficient of thermal expansion for Cu/Mo/Cu clad sheets fabricated by hot rolling

The law of microstructure evolution and mechanical properties of hot roll bonded Cu/Mo/Cu clad sheets were systematically investigated and the theoretical prediction model of the coefficient of thermal expansion(CTE)of Cu/Mo/Cu clad sheets was established successfully.The results show that the deformation of Cu and Mo layers was gradually coherent with an increase in rolling reduction and temperature and excellent interface bonding was achieved under the condition of a large rolling reduction.The development of the microstructure and texture through the thickness of Cu and Mo layers was inhomogeneous.This phenomenon can be attributed to the friction between the roller and sheet surface and the uncoordinated deformation between Cu and Mo.The tensile strength of the clad sheets increased with increasing rolling reduction and the elongation was gradually decreased.The CTE of Cu/Mo/Cu clad sheets was related to the volume fraction of Mo.The finite element method can simulate the deformation and stress distribution during the thermal expansion process.The simulation result indicates that the terminal face of the clad sheets was sunken inward.

Modeling hot strip rolling process under framework of generalized additive model

This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models(GAM)to generate a practical model with generalization and precision.Specifically,the proposed modeling method includes the following steps.Firstly,the influence factors are screened using mechanism knowledge and data-mining methods.Secondly,the unary GAM without interactions including cleaning the data,building the sub-models,and verifying the sub-models.Subsequently,the interactions between the various factors are explored,and the binary GAM with interactions is constructed.The relationships among the sub-models are analyzed,and the integrated model is built.Finally,based on the proposed modeling method,two prediction models of mechanical property and deformation resistance for hot-rolled strips are established.Industrial actual data verification demonstrates that the new models have good prediction precision,and the mean absolute percentage errors of tensile strength,yield strength and deformation resistance are 2.54%,3.34%and 6.53%,respectively.And experimental results suggest that the proposed method offers a new approach to industrial process modeling.

Dynamic Model for Evaluation of Risk Factors During Work in Hot Environment

Mathematical model is developed for prediction of physiological changes in man during work in hot environment taking into consideration intensity of work, clothing and environment. To evaluate human functional state the heat stress index was calculated. Modeling researches made the conclusion that the main risk factor during work in hot environment is water losses that happens through thermoregulatory sweat evaporation. Modeling showed that in humid environment man wearing protective clothing has short time to work as water losses became more than 2% of human weight that means body dehydration. Preliminary model prediction can be used as preventive method to avoid hazard of human health.

Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics

Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing the required specimens is impossible.By this time,several models have been established to evaluate UCS and E from rock substantial properties.Artificial neural networks are powerful tools which are employed to establish predictive models and results have shown the priority of this technique compared to classic statistical techniques.In this paper,ANN and multivariate statistical models considering rock textural characteristics have been established to estimate UCS of rock and to validate the responses of the established models,they were compared with laboratory results.For this purpose a data set for 44 samples of sandstone was prepared and for each sample some textural characteristics such as void,mineral content and grain size as well as UCS were determined.To select the best predictors as inputs of the UCS models,this data set was subjected to statistical analyses comprising basic descriptive statistics,bivariate correlation,curve fitting and principal component analyses.Results of such analyses have shown that void,ferroan calcitic cement,argillaceous cement and mica percentage have the most effect on USC.Two predictive models for UCS were developed using these variables by ANN and linear multivariate regression.Results have shown that by using simple textural characteristics such as mineral content,cement type and void,strength of studied sandstone can be estimated with acceptable accuracy.ANN and multivariate statistical UCS models,revealed responses with 0.87 and 0.76 regressions,respectively which proves higher potential of ANN model for predicting UCS compared to classic statistical models.

Indoor Nitrogen Oxides Occurrence, Modeling and Prediction

Outdoor air quality, building materials, HVAC （Heating, Ventilation, Air Conditioning） systems and people activity are important factors in human exposition of polluted indoor air. The degree of signification varies in dependence on pollution character and its sources. Buildings eliminate significantly people exposition of outdoor pollutants, but on the other hand, buildings are significant source of indoor pollution. The contamination of indoor air is largely from the use of gas for heating and cooking appliances. A comprehensive analysis of indoor air pollution by nitrogen oxides shows that the extent of indoor air pollution and consequent exposure varies as a result of many factors mainly the differing dislribution of appliances and their level of use. This study aims to formulate a mathematical model for the production of nitrogen oxides indoors. The physical processes that determine the concentrations of indoor nitrogen oxides as a function of outdoor concentrations, indoor emission rates and building characteristics have been mathematically described. The mathematical model developed has been parameterized for typical Slovak residences. The modeling of the occurrence of indoor nitrogen oxides and verification of the model is presented in this paper.

On Prediction of Longitudinal Attitude of Planing Craft Based on Controllable Hydrofoils

The purpose of this research study was to examine the attitude response of a planing craft under the controllable hydrofoils.Firstly,a non-linear longitudinal attitude model was established.In the mathematical model,effects of wind loads were considered.Both the wetted length and windward area varied in different navigation conditions.Secondly,control strategies for hydrofoils were specified.Using the above strategies,the heave and trim of the planing craft was adjusted by controllable hydrofoils.Finally,a simulation program was developed to predict the longitudinal attitudes of the planing craft with wind loads.A series of simulations were performed and effects of control strategies on longitudinal attitudes were analyzed.The results show that under effects of wind loads,heave of fixed hydrofoils planing craft decreased by 6.3%,and pitch increased by 8.6% when the main engine power was constant.Heave decreased by less than 1% and trim angle decreased by 1.7% as a result of using variable attack angle hydrofoils;however,amplitude changes of heave and pitch were less than 1% under the control of changeable attack angle hydrofoils and longitudinal attitude.

Estimation of reservoir porosity using probabilistic neural network and seismic attributes

Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.

Research on Prediction of Ship Manoeuvrability

Based on the manoeuvring MMG （Mathematical Modeling Group） and the Runge-Kutta Method, a mathematical model for simulation of ship manoeuvrability is established. On this basis, a prediction program is compiled on platform Visual Basic 6.0. The turning motion, Zig-zag and crash stopping ability of a container ship are simulated by this program. Compared with the model test results, the error is less than 10%. In view of the admissible accuracy, the program is capable of predicting ship manoeuvrability.

Mathematical Model Prediction of Heat Losses from a Pilot Sirosmelt Furnace

A mathematical model was developed for simulating heat transfer through the sidewall, bottom and top of a pilot scale TSL （Top-Submerged-Lance） Sirosmelt furnace. With a feed rate of about 50 kg/h, the furnace has been used for investigating the technical feasibility of a variety of pyrometallurgical processes for smelting nonferrous and ferrous metals and for high temperature processing of solid wastes including electronic scraps, etc. The model was based on numerical solution of energy transport equations governing heat conduction in multi-layered linings in the sidewall, bottom and top lid of the furnace as well as convection and radiation of heat from the furnace outer surfaces to the ambient. Imperfect contacts between two neighboring solid lining layers due to air gap formation were considered. Temperature profiles were determined across the furnace bottom, top lid and three sections of the furnace sidewall, from which the heat loss rates through the corresponding parts of the furnace were calculated. The modelling results indicate that approximately 88% of heat is lost from the furnace sidewall, 7-8% from the bottom and 4-5% from the top lid. With increasing melt bath temperature, the proportion of total heat loss from the bottom decreases whereas that from the top lid increases and that from the sidewall is little changed. For a bath temperature of 1,300℃, total absolute heat loss rate from the furnace was found to be close to 12 kW.

TIME SERIES FORECASTING WITH MULTIPLE CANDIDATE MODELS:SELECTING OR COMBINING?

Various mathematical models have been commonly used in time series analysis and forecasting. In these processes, academic researchers and business practitioners often come up against two important problems. One is whether to select an appropriate modeling approach for prediction purposes or to combine these different individual approaches into a single forecast for the different/dissimilar modeling approaches. Another is whether to select the best candidate model for forecasting or to mix the various candidate models with different parameters into a new forecast for the same/similar modeling approaches. In this study, we propose a set of computational procedures to solve the above two issues via two judgmental criteria. Meanwhile, in view of the problems presented in the literature, a novel modeling technique is also proposed to overcome the drawbacks of existing combined forecasting methods. To verify the efficiency and reliability of the proposed procedure and modeling technique, the simulations and real data examples are conducted in this study.The results obtained reveal that the proposed procedure and modeling technique can be used as a feasible solution for time series forecasting with multiple candidate models.

Modeling on Heat and Mass Transfer in Stored Wheat during Forced Cooling Ventilation

A mathematical model based on the theory of heat and mass transfer in porous media was developed to simulate the evolution of grain temperature and moisture content in a wheat storage bin during ventilation with cooling air at the constant temperature and humidity.Unlike the previous works on this aspect,the present work was not focused on cooling the stored grain by ventilation with ambient air,but with the refrigerated air.Validation was performed by comparing between predicted and measured grain temperature and grain moisture content for two cases.Predicted data were in reasonable good agreement with measured ones.The model and the parameter values used in the model are applicable for predicting temperature and moisture of stored grains under ventilation conditions.