Effects of modeling means on properties of monitoring models of spot welding quality

Analyzing and modeling the relation between monitoring information during welding and quality information of the joints is the foundation of monitoring resistance spot welding quality. According to the means of modeling, the known models can be divided into three large categories: single linear regression models, multiple linear regression models and multiple non linear models. By modeling the relations between dynamic resistance information and welding quality parameters with different means, this paper analyzes effects of modeling means on performances of monitoring models of resistance spot welding quality. From the test results, the following conclusions can be drawn: By comparison with two other kinds of models, artificial neural network (ANN) model can describe non linear and high coupling relationship between monitoring information and quality information more reasonably, improve performance of monitoring model remarkably, and make the estimated values of welding quality parameters more accurate and reliable.

Seepage safety monitoring model for an earth rock dam under influence of high-impact typhoons based on particle swarm optimization algorithm

Extreme hydrological events induced by typhoons in reservoir areas have presented severe challenges to the safe operation of hydraulic structures. Based on analysis of the seepage characteristics of an earth rock dam, a novel seepage safety monitoring model was constructed in this study. The nonlinear influence processes of the antecedent reservoir water level and rainfall were assumed to follow normal distributions. The particle swarm optimization （PSO） algorithm was used to optimize the model parameters so as to raise the fitting accuracy. In addition, a mutation factor was introduced to simulate the sudden increase in the piezometric level induced by short-duration heavy rainfall and the possible historical extreme reservoir water level during a typhoon. In order to verify the efficacy of this model, the earth rock dam of the Siminghu Reservoir was used as an example. The piezometric level at the SW1-2 measuring point during Typhoon Fitow in 2013 was fitted with the present model, and a corresponding theoretical expression was established. Comparison of fitting results of the piezometric level obtained from the present statistical model and traditional statistical model with monitored values during the typhoon shows that the present model has a higher fitting accuracy and can simulate the uprush feature of the seepage pressure during the typhoon perfectly.

Parsimonious Model for Blood Glucose Level Monitoring in Type 2 Diabetes patients

To establish the parsimonious model for blood glucose monitoring in patients with type 2 diabetes receiving oral hypoglycemic agent treatment. One hundred and fifty-nine adult Chinese type 2 diabetes patients were randomized to receive rapid-acting or sustained-release gliclazide therapy for 12 weeks.

Performance evaluation of quality monitor models in spot welding

Performance of quality monitor models in spot welding determines the monitor precision directly, so it’s crucial to evaluate it. Previously, mean square error (MSE) is often used to evaluate performances of models, but it can only show the total errors of finite specimens of models, and cannot show whether the quality information inferred from models are accurate and reliable enough or not. For this reason, by means of measure error theory, a new way to evaluate the performances of models according to the error distributions is developed as follows: Only if correct and precise enough the error distribution of model is, the quality information inferred from model is accurate and reliable.

Deformation prediction model of concrete face rockfill dams based on an improved random forest model

The unique structure and complex deformation characteristics of concrete face rockfill dams(CFRDs)create safety monitoring challenges.This study developed an improved random forest(IRF)model for dam health monitoring modeling by replacing the decision tree in the random forest(RF)model with a novel M5'model tree algorithm.The factors affecting dam deformation were preliminarily selected using the statistical model,and the grey relational degree theory was utilized to reduce the dimensions of model input variables.Finally,a deformation prediction model of CFRDs was established using the IRF model.The ten-fold cross-validation method was used to quantitatively analyze the parameters affecting the IRF algorithm.The performance of the established model was verified using data from three specific measurement points on the Jishixia dam and compared with other dam deformation prediction models.At point ES-10,the performance evaluation indices of the IRF model were superior to those of the M5'model tree and RF models and the classical support vector regression(SVR)and back propagation(BP)neural network models,indicating the satisfactory performance of the IRF model.The IRF model also outperformed the SVR and BP models in settlement prediction at points ES2-8 and ES4-10,demonstrating its strong anti-interference and generalization capabilities.This study has developed a novel method for forecasting and analyzing dam settlements with practical significance.Moreover,the established IRF model can also provide guidance for modeling health monitoring of other structures.

RESEARCH ON SEEPAGE MONITORING MODEL OF EARTH-ROCK DAM

With the characteristics of seepage flow in earth-rock dams, a seepagemonitoring model was established based on the finite element method for 3-D seepage flow togetherwith observed data and was used to analyze and monitor the seepage of dams. In order to find out andmonitor the seepage status of the whole dam, the separation of seepage a-mount for dam body, damfoundation and side banks was made theoretically by using the model. Practical example shows thatthe accuracy of computed results is satisfactory and the separation results are more objective.

Evaluation of health benefit using Ben MAP-CE with an integrated scheme of model and monitor data during Guangzhou Asian Games

Guangzhou is the capital and largest city（land area：7287 km2）of Guangdong province in South China.The air quality in Guangzhou typically worsens in November due to unfavorable meteorological conditions for pollutant dispersion.During the Guangzhou Asian Games in November 2010,the Guangzhou government carried out a number of emission control measures that significantly improved the air quality.In this paper,we estimated the acute health outcome changes related to the air quality improvement during the 2010 Guangzhou Asian Games using a next-generation,fully-integrated assessment system for air quality and health benefits.This advanced system generates air quality data by fusing model and monitoring data instead of using monitoring data alone,which provides more reliable results.The air quality estimates retain the spatial distribution of model results while calibrating the value with observations.The results show that the mean PM2.5concentration in November 2010 decreased by 3.5μg/m^3 compared to that in 2009 due to the emission control measures.From the analysis,we estimate that the air quality improvement avoided 106 premature deaths,1869 cases of hospital admission,and 20,026 cases of outpatient visits.The overall cost benefit of the improved air quality is estimated to be 165 million CNY,with the avoided premature death contributing 90%of this figure.The research demonstrates that Ben MAP-CE is capable of assessing the health and cost benefits of air pollution control for sound policy making.

An Emergency Scenario Reconstruction System Based on ESMM Event Situation Model

In order to keep decision-makers better informed with emergencies, it is useful to retrieve the user-oriented disaster relevant event information in an aggregated results list through meta-search engine. However, emergent event is dynamic which makes it difficult to use fixed search word or word combinations. This paper proposes an event situation monitoring model（ESMM） event detection model, which realizes heuristic query word vector dynamic expanding by adopting emergency fuzzy scenario reasoning ontology cluster. Disaster event facet information automatic searching is discussed as an example in this paper. The experimental results show that the proposed method can increase accuracy and extra clues not supplied by commercial search engines, which can be used as a supplement information source for government and individuals.