A students' health model based on health influence factors

2014 ◽  
Author(s):  
Hongjun Teng
Keyword(s):  
Influence Factors ◽  
Model Based ◽  
Health Model

Research and Improvement of the Keep-Right-Except-to-Pass Rule

2014 ◽  
Vol 915-916 ◽  
pp. 459-463
Author(s):  
He Quan Zhang
Keyword(s):  
Fluid Mechanics ◽  
Traffic Flow ◽  
Influence Factors ◽  
Traffic Model ◽  
Light Traffic ◽  
The Road ◽  
Model Based ◽  
Light Passing ◽  
Improved Model ◽  
The Impact

In order to deal with the impact on traffic flow of the rule, we compare the influence factors of traffic flow (passing, etc.) into viscous resistance of fluid mechanics, and establish a traffic model based on fluid mechanics. First, in heavy and light traffic, we respectively use this model to simulate the actual segment of the road and find that when the traffic is heavy, the rule hinder the further increase in traffic. For this reason, we make further improvements to the model to obtain a fluid traffic model based on no passing and find that the improved model makes traffic flow increase significantly. Then, the improved model is applied to the light traffic, we find there are no significant changes in traffic flow .In this regard we propose a new rule: when the traffic is light, passing is allowed, but when the traffic is heavy, passing is not allowed.

scholarly journals A Short-Term Power Output Forecasting Model Based on Correlation Analysis and ELM-LSTM for Distributed PV System

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Deng Yongsheng ◽  
Jiao Fengshun ◽  
Zhang Jie ◽  
Li Zhikeng
Keyword(s):  
Correlation Analysis ◽  
Power Output ◽  
Short Term Memory ◽  
Rank Order ◽  
Influence Factors ◽  
Forecasting Model ◽  
Short Term ◽  
Pv System ◽  
Factors Affecting ◽  
Model Based

Accurate short-term power output forecasting results are conducive to reducing the scheduling difficulty of grid-connected operation of distributed photovoltaic (PV) systems, thus improving the safety and stability of power grid operation. In this paper, a one-day-ahead short-term power output forecasting model based on correlation analysis and combination algorithms for distributed PV system is proposed to solve the problems within the current methods. Firstly, the basic information of distributed PV system is introduced, and the main influence factors affecting the power output of distributed PV system are determined. Secondly, the influence factors with higher correlation with PV output are selected by Spearman rank-order correlation coefficient (SROCC) analysis in multiple timescales. Then, based on the multimodel univariate extreme learning machine (ELM) submodel and the single-model multivariate long short-term memory (LSTM) submodel, the ELM-LSTM model is established. The case study analysis based on the actual data indicates that the ELM-LSTM forecasting model proposed in this paper has higher forecasting accuracy than the traditional forecasting methods.

scholarly journals Positive Mental Health Model Based on Authentic Leadership and Elements of Socialization

Psychology ◽  
2018 ◽  
Vol 09 (04) ◽  
pp. 588-607 ◽  
Author(s):  
José Luis Calderón-Mafud ◽  
Manuel Pando Moreno ◽  
Cecilia Colunga-Rodríguez
Keyword(s):  
Mental Health ◽  
Authentic Leadership ◽  
Positive Mental Health ◽  
Model Based ◽  
Health Model

Research on the Embedded Software Reliability Evaluation Model Based on Influence Factors

2013 ◽  
Vol 397-400 ◽  
pp. 2355-2359
Author(s):  
Pei Tian ◽  
Li Na Gong ◽  
Wei Li ◽  
Wen Tao Tian
Keyword(s):  
Neural Network ◽  
Software Reliability ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Influence Factors ◽  
Embedded Software ◽  
Reliability Evaluation ◽  
Model Based ◽  
Preliminary Application

This paper firstly analyzes the reliability Influence factors of the embedded software, then puts forward an embedded software reliability evaluation model based on BP neural network, finally the preliminary application and verification of the model is given.

scholarly journals A Novel Clustering Model Based on Set Pair Analysis for the Energy Consumption Forecast in China

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Mingwu Wang ◽  
Dongfang Wei ◽  
Jian Li ◽  
Hui Jiang ◽  
Juliang Jin
Keyword(s):  
Energy Consumption ◽  
Influence Factors ◽  
Reliability And Validity ◽  
Set Pair Analysis ◽  
Model Based ◽  
Clustering Model ◽  
Group Centroid ◽  
Partition Method ◽  
Pair Analysis ◽  
Energy Consumption Forecast

The energy consumption forecast is important for the decision-making of national economic and energy policies. But it is a complex and uncertainty system problem affected by the outer environment and various uncertainty factors. Herein, a novel clustering model based on set pair analysis (SPA) was introduced to analyze and predict energy consumption. The annual dynamic relative indicator (DRI) of historical energy consumption was adopted to conduct a cluster analysis with Fisher’s optimal partition method. Combined with indicator weights, group centroids of DRIs for influence factors were transferred into aggregating connection numbers in order to interpret uncertainty by identity-discrepancy-contrary (IDC) analysis. Moreover, a forecasting model based on similarity to group centroid was discussed to forecast energy consumption of a certain year on the basis of measured values of influence factors. Finally, a case study predicting China’s future energy consumption as well as comparison with the grey method was conducted to confirm the reliability and validity of the model. The results indicate that the method presented here is more feasible and easier to use and can interpret certainty and uncertainty of development speed of energy consumption and influence factors as a whole.

scholarly journals Dynamic Pollution Prediction Model of Insulators Based on Atmospheric Environmental Parameters

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3066 ◽  
Author(s):  
Siyi Chen ◽  
Zhijin Zhang
Keyword(s):  
Prediction Model ◽  
Meteorological Data ◽  
Influence Factors ◽  
Environmental Parameters ◽  
Total Suspended Particulate ◽  
System Research ◽  
Model Based ◽  
Suspended Particulate ◽  
Relative Errors ◽  
Pollution Prediction

Pollution-induced flashover is one of the most serious power accidents, and the pollution degree of insulators depends on atmospheric environmental parameters. The pollution models used in the power system research are usually static, but the environmental parameters are dynamic. Therefore, the study on the dynamic pollution prediction model is of great importance. In this paper, the dynamic pollution prediction model of insulators based on atmospheric environmental parameters was built, and insulators’ structure coefficients were proposed based on the model. Firstly, the insulator dynamic pollution model based on meteorological data (PM2.5, PM10, TSP (total suspended particulate), and wind speed) was proposed, and natural pollution tests were also conducted as verification tests. Furthermore, insulator structure coefficients c1, c2 (c1: pollution ratio of U210BP/170 to XP-160; c2: calculated pollution ratio of U210BP/170T to XP-160) were then obtained, and their influence factors were discussed. At last, insulator structure coefficients were calculated, and it can be seen that the calculated error of insulator structure coefficients was acceptable, with the average re (relative errors) at 9.0% (c1) and 13.5% (c2), which verifies the feasibility of the model. Based on the results in this paper, the NSDD (non-soluble deposit density) of insulators with different structures can be obtained using the insulators’ structure coefficient and the reference XP-160 insulator’s NSDD.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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