Research on Lifetime Remaining of Bridge Based on Gray Theory

2014 ◽  
Vol 945-949 ◽  
pp. 1270-1273
Author(s):  
Ying Zhang ◽  
Hai Xin Huang ◽  
Shou Shan Cheng
Keyword(s):  
Dynamic Property ◽  
Traditional Model ◽  
Gray Model ◽  
Gray Theory ◽  
New Information ◽  
Information Updates ◽  
Improved Model ◽  
Bridge Assessment

The method of predication is the key for evaluating lifetime remaining of existing bridge in bridge assessment. Improved Gray Model,which can supplement and make full use of new information in time,on the basis of Gray Theory is tried to be used here,and compared with traditional model by analysizing an engineering case.The results show that the improved model can not only improve the precision of prediction,but also comply with the nature of dynamic property of bridge lifetime remaining,i.e.,as the latest inspection information updates the predication value.

Prediction of Composite Foundation Settlement Based on Multi-Variable Gray Model

2014 ◽  
Vol 580-583 ◽  
pp. 669-673
Author(s):  
Yue Wang ◽  
Guang Yang
Keyword(s):  
Prediction Model ◽  
Composite Foundation ◽  
Foundation Settlement ◽  
Gray Model ◽  
Settlement Prediction ◽  
Gray Theory ◽  
Rigid Pile ◽  
Rigid Pile Composite Foundation ◽  
Observation Results

In this paper, based on the principle of gray theory, MGM(1,n) multi-variable settlement prediction model was established. Combined with specific examples of projects, settlement of rigid pile composite foundation was predicted, and the prediction results are compared with both prediction results of GM(1,1) gray model which is currently comparatively mature and the settlement observation results. The study shows that it is effective to use MGM(1,n) multivariate gray model to predict composite foundation settlement.

scholarly journals A planning model for the chemical integrated system under uncertainty by grey programming approach

2013 ◽  
Vol 15 (2) ◽  
pp. 16-22 ◽  
Author(s):  
Jingzheng Ren ◽  
Shiyu Tan ◽  
Lichun Dong ◽  
Zhiming Zhou ◽  
Suzhao Gao ◽  
...  
Keyword(s):  
Production Planning ◽  
Programming Model ◽  
Production Capacity ◽  
Integrated System ◽  
Decision Makers ◽  
Programming Approach ◽  
Traditional Model ◽  
Market Demand ◽  
Actual Case ◽  
Improved Model

A model to optimize the planning of the chemical integrated system comprised by multi-devices and multi-products has been proposed in this paper. With the objective to make more profits, the traditional model for optimizing production planning has been proposed. The price of chemicals, the market demand, and the production capacity have been considered as mutative variables, then an improved model in which some parameters are not constant has been developed and a new method to solve the grey linear programming has been proposed. In the grey programming model, the value of credibility can be suggested by the decision-makers, and the results of the production planning calculated by the model can help them to achieve their desired target. An actual case has been studied by the proposed methodology, and the proposed methodology can be popularized to other cases.

Grey New Information Unbiased GRM(1,1) Model Based on Accumulated Generating Operation in Reciprocal Number and its Application

2012 ◽  
Vol 426 ◽  
pp. 81-84
Author(s):  
W.Y. Xiao ◽  
Y.Y. Luo ◽  
Xiao Yi Che
Keyword(s):  
Data Processing ◽  
Compatibility Condition ◽  
Sequence Data ◽  
Modeling Method ◽  
Parameters Optimization ◽  
Gray Model ◽  
Calculation Formula ◽  
Model Accuracy ◽  
New Information ◽  
Modeling Data

Monotonically decreasing sequence data for the traditional modeling method using the gray model accuracy is not high , and GM (1, 1) modeling method has inherent deviation , Model does not meet the compatibility condition, using Accumulated Generating Operation in reciprocal number ,make best use of last information and GM (1, 1) modeling , is deduced and the parameters optimization grey derivative calculation formula ,and then established GRM(1,1) based on accumulated generating operation in reciprocal number on the equidistance, Gray provides a new method of modeling . Data processing examples show that the model's practicality and reliability.

scholarly journals Dopamine controls whether new declarative information updates reactivated memories through reconsolidation

2021 ◽  
Vol 118 (29) ◽  
pp. e2025275118
Author(s):  
María Carolina Gonzalez ◽  
Janine I. Rossato ◽  
Andressa Radiske ◽  
Lia R. M. Bevilaqua ◽  
Martín Cammarota
Keyword(s):  
Novelty Detection ◽  
Active Network ◽  
Novel Object ◽  
Psychotherapeutic Interventions ◽  
New Information ◽  
Object Recognition Memory ◽  
Information Updates ◽  
Reactivated Memories ◽  
With Memory

Consolidation and reconsolidation are independent memory processes. Consolidation stabilizes new memories, whereas reconsolidation restabilizes memories destabilized when reactivated during recall. However, the biological role of the destabilization/reconsolidation cycle is still unknown. It has been hypothesized that reconsolidation links new information with reactivated memories, but some reports suggest that new and old memories are associated through consolidation mechanisms instead. Object-recognition memory (ORM) serves to judge the familiarity of items and is essential for remembering previous events. We took advantage of the fact that ORM consolidation, destabilization, and reconsolidation can be pharmacologically dissociated to demonstrate that, depending on the activation state of hippocampal dopamine D1/D5 receptors, the memory of a novel object presented during recall of the memory of a familiar one can be formed via reconsolidation or consolidation, but only reconsolidation can link them. We also found that recognition memories formed through reconsolidation can be destabilized even if indirectly reactivated. Our results indicate that dopamine couples novelty detection with memory destabilization to determine whether a new recognition trace is associated with an active network and suggest that declarative reminders should be used with caution during reconsolidation-based psychotherapeutic interventions.

Update your understanding, with Bayesian statistics

2020 ◽  
pp. 233-236
Author(s):  
Susan D'Agostino
Keyword(s):  
Breast Cancer ◽  
Bayesian Statistics ◽  
Bayesian Approach ◽  
Mathematics Students ◽  
Risk Levels ◽  
New Information ◽  
Information Updates

“Update your understanding, with Bayesian statistics” provides an accessible introduction to Bayesian statistics, in which one begins with a belief, understanding, or prediction that is based on data and then, upon receiving new information, updates the prediction. Readers consider a Bayesian approach to hypothetical woman’s risk levels for breast cancer upon receiving notice of a positive mammogram. Mathematics students and enthusiasts are encouraged to consider a Bayesian approach whenever they are in a position to manage uncertainty in mathematics or life. At the chapter’s end, readers may check their understanding by working on a problem. A solution is provided.

Improved Model-Free Adaptive Control of Pneumatic Gravity Compensation System

2020 ◽  
Vol 24 (3) ◽  
pp. 357-365
Author(s):  
Yu Liu ◽  
Feng Peng ◽  
Zhen Hua ◽  
Changlong Liu ◽  
Guoxin Zhao ◽  
...  
Keyword(s):  
Adaptive Control ◽  
Tracking Error ◽  
Pressure Control ◽  
Adaptive Controller ◽  
Compensation System ◽  
Step Response ◽  
Traditional Model ◽  
Gravity Compensation ◽  
Model Free ◽  
Improved Model

A pneumatic gravity compensation system is typically nonlinear in behavior. It is difficult to establish an accurate mathematical model for it, and it is particularly difficult to realize high-precision pressure control. A pneumatic gravity compensation system driven by a frictionless cylinder is built. Considering that the traditional model-free adaptive control is slow for pseudo-gradient identification, an improved model-free adaptive control is proposed to predict the changes in the pseudo gradient and accelerate the process of pseudo gradient identification. The static and dynamic gravity compensation of the pneumatic gravity compensation system is realized. Finally, the experimental results show that the steady-error of step response of the improved model-free adaptive controller is less than 200 Pa, and the rise time is approximately 13 seconds. The sinusoidal tracking error (0.04 Hz) is approximately 1.94 KPa.

Grey Model Theory Used in Prediction of Subgrade Settlement

2011 ◽  
Vol 105-107 ◽  
pp. 1576-1579 ◽  
Author(s):  
Sheng Ji Jin ◽  
Da Sheng Zhang ◽  
Zhe Shu ◽  
Yun Zhao
Keyword(s):  
Model Theory ◽  
Prediction Accuracy ◽  
Measured Data ◽  
Grey Model ◽  
Rock Deformation ◽  
Gray Model ◽  
Gray Theory ◽  
Gray Prediction ◽  
Similar Information ◽  
Final Settlement

In order to study the rule of highway subgrade settlement, both on-site experiments and theoretical analysis methods were used to analysis high fill embankment settlement. Gray model theory can be effectively forecasted the embankment of the different stages and the final settlement. According to the measured data of deposition tests, the comparative analysis of predicted results was carried out and some conclusions were drawn: 1) It is feasible that gray model theory is used to predict the settlement of the subgrade; 2) gray theory predictions adapt to these situations that similar information is less known, uncertainties and some rock deformation issues; 3)gray prediction accuracy is higher. The prediction model of GM established (1, 1) is reliable.

Grey New Information GRM(1,1) Model Based on Accumulated Generating Operation of Reciprocal Number and its Application

2012 ◽  
Vol 426 ◽  
pp. 77-80 ◽  
Author(s):  
You Xin Luo
Keyword(s):  
Data Processing ◽  
Sequence Data ◽  
Processing Method ◽  
Parameters Optimization ◽  
New Method ◽  
Gray Model ◽  
Calculation Formula ◽  
Model Accuracy ◽  
New Information ◽  
Modeling Data

Monotonically decreasing sequence data for the traditional modeling method using the gray model accuracy is not high, using Accumulated Generating Operation in reciprocal number, utilize three gray derivative processing method is deduced and the parameters optimization grey derivative calculation formula, and then established GRM(1,1) based on accumulated generating operation in reciprocal number on the equidistance, Gray provides a new method of modeling. Data processing examples show that the model's practicality and reliability.

scholarly journals Research on Slope Deformation Prediction Based on Fractional-Order Calculus Gray Model

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Li Li ◽  
Yue Qiang ◽  
Shaohong Li ◽  
Zhongchao Yang
Keyword(s):  
Fractional Derivative ◽  
Fractional Order ◽  
Reference Method ◽  
Coupling Model ◽  
Slope Deformation ◽  
Gray Model ◽  
Deep Foundation ◽  
Deformation Prediction ◽  
Fractional Order Calculus ◽  
Gray Theory

Slope deformation prediction has important significance for slope prevention and control. Based on historical time series, the trend of displacement variation can be predicted in advance, and according to the development trend, risk warnings and treatment measures are proposed. The use of the mathematical model to predict slope deformation has been proved to be feasible by many studies; therefore, the choice of the predictive model and the practicability of the model are crucial issues in the prediction of slope deformation, and the mathematical prediction model used should be less complicated considering the practicality of the model. In view of slope deformation prediction, a fractional-order calculus gray model based on the coupling of gray theory and the fractional derivative method is proposed, which takes a deep foundation pit slope in Chongqing, Southwest China, as the study object. The fractional-order gray model is compared with the traditional gray models; therefore, the results show that the accuracy of slope deformation prediction based on the gray coupling model of cumulative displacement and fractional calculus is significantly higher than that of the conventional gray model, and its error is in the acceptable range compared with the actual monitoring data, which can meet the needs of engineering application. Compared with the traditional gray theory method, the gray coupling model of fractional-order calculus only increases the fractional derivative order, which is verified to be feasible, and can be used as a reference method for slope deformation prediction. It has a certain theoretical basis and a good application prospect in slope deformation prediction.

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