Application Analysis of Structure Deformation Monitoring Prediction Methods

2010 ◽  
Vol 163-167 ◽  
pp. 2674-2677
Author(s):  
Li Ting Zhang ◽  
Hao Zhang
Keyword(s):  
Prediction Models ◽  
Prediction Method ◽  
Information Model ◽  
Deformation Monitoring ◽  
Dynamic Monitoring ◽  
Prediction Methods ◽  
Structure Deformation ◽  
New Information ◽  
Application Analysis ◽  
Scientific Prediction

Nowadays structure construction and monitoring, hazard forecasting depends more and more on scientific prediction methods. This paper focus on prediction method, which based on GM(1,1) model of Gary Theory. With a set of dynamic monitoring data from one structure, we introduce the modeling method of old information model, new information model and equal dimension new information model. An analysis of these prediction models reliability is performed to compare these models. The result of these models can predict the structure settlement proved that a suitable prediction method could provide help to structure safety and reduce unnecessary lost.

A Traffic Prediction Method Based on the Hybrid Model of CLWT

2015 ◽  
Vol 713-715 ◽  
pp. 1564-1569
Author(s):  
Jin Long Fei ◽  
Wei Lin ◽  
Tao Han ◽  
Yue Fei Zhu
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Network Traffic ◽  
Prediction Models ◽  
Prediction Method ◽  
Support Vector ◽  
Prediction Methods ◽  
Recurrent Nerve ◽  
Nerve Network ◽  
Traffic Time Series

Current prediction models for network traffic cannot accurately depict the multi-properties of the Internet traffic. This paper proposes a wavelet-based hybrid model prediction method for network traffic called CLWT model and proposes a prediction method for traffic based on this model. The traffic time series can be rapidly decomposed respectively into approximate time series and detail time series with LF and HF response. The approximate time series predicts by making use of Least Squares Support Vector Machine and proceeds error calibration by using Generalized Recurrent Nerve Network. The detail time series predict it by making use of self-adaption chaotic prediction methods after the medium-soft threshold noise reduction. Finally the prediction value of time series is got by making use of promoting wavelet reconstitution. The effectiveness for the prediction methods mentioned in the paper has been validated by simulation experiment. High prediction accuracy is obtained compared with the existing methods.

Dynamic Prediction Method for Hydraulic Structure Concrete Strength

2015 ◽  
Vol 799-800 ◽  
pp. 778-783
Author(s):  
Jia Xiang Tao ◽  
Jun Rong Zhang ◽  
Cun Cai ◽  
Gang Cui ◽  
Jun Lu
Keyword(s):  
Prediction Models ◽  
Hydraulic Structure ◽  
Concrete Strength ◽  
Prediction Method ◽  
Time Varying ◽  
Dynamic Prediction ◽  
Safety Testing ◽  
New Information ◽  
Service Period

At present, we usually obtain the prediction models of hydraulic structure concrete strength through lots of tests or engineering experience. But when we use the models to predict the concrete strength at a certain time , there may be some discrepancies with the actual value, even some unreasonable situations. During the service period of hydraulic structure, we can get some new information about the concrete strength by the means of safety testing, monitoring or experimenting. With reference to these new information, the Bayes dynamic prediction method can update the time-varying model of concrete strength mentioned above. This method can more accurately predict the concrete strength value of the next moment.

scholarly journals Monitoring Dynamic Deformation of Building Using Unmanned Aerial Vehicle

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yongquan Ge ◽  
Xianzhi Yu ◽  
Mingzhi Chen ◽  
Chengxin Yu ◽  
Yingchun Liu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Dynamic Deformation ◽  
Motion Parallax ◽  
Steel Structure ◽  
Steel Structures ◽  
Deformation Monitoring ◽  
Dynamic Monitoring ◽  
Structure Deformation ◽  
Aerial Vehicle ◽  
Mp Method

The height irregularity and complexity of steel structures bring difficulties to dynamic deformation monitoring. PDMS (photogrammetric dynamic monitoring system) can obtain the dynamic deformation of the steel structure, but the flexibility of monitoring is limited because the camera station can only be placed on the ground. In this study, UAV (unmanned aerial vehicle) -PDMS is innovatively proposed to be used in monitoring dynamic deformation of steel structures, and it is verified in the steel frame test and Jinan Olympic Sports Center Tennis Stadium test. To solve the problem that the attitude of UAV cannot be strictly maintained in the hovering process, the improved Z-MP (zero-centered motion parallax) method is used, and the monitoring results are compared with the original Z-MP method. The feasibility of UAV-PDMS applied to steel structure deformation monitoring and the feasibility of improving the Z-MP method to reduce UAV hovering error are verified. The monitoring results showed that the steel structures of the Jinan Olympic Sports Center Tennis Stadium were robust, and the deformations were elastic and within the permissible value.

scholarly journals A New Approach for Cylindrical Steel Structure Deformation Monitoring by Dense Point Clouds

2021 ◽  
Vol 13 (12) ◽  
pp. 2263
Author(s):  
Dongfeng Jia ◽  
Weiping Zhang ◽  
Yuhao Wang ◽  
Yanping Liu
Keyword(s):  
Steel Structure ◽  
Steel Structures ◽  
Point Clouds ◽  
Deformation Monitoring ◽  
Deformation Analysis ◽  
Relative Deformation ◽  
Structure Deformation ◽  
Single Time ◽  
Cylindrical Steel ◽  
Cylinder Fitting

As fundamental load-bearing parts, the cylindrical steel structures of transmission towers relate to the stability of the main structures in terms of topological relation and performance. Therefore, the periodic monitoring of a cylindrical steel structure is necessary to maintain the safety and stability of existing structures in energy transmission. Most studies on deformation analysis are still focused on the process of identifying discrepancies in the state of a structure by observing it at different times, yet relative deformation analysis based on the data acquired in single time has not been investigated effectively. In this study, the piecewise cylinder fitting method is presented to fit the point clouds collected at a single time to compute the relative inclination of a cylindrical steel structure. The standard deviation is adopted as a measure to evaluate the degree of structure deformation. Meanwhile, the inclination rate of each section is compared with the conventional method on the basis of the piecewise cylinder fitting parameters. The validity and accuracy of the algorithm are verified by real transmission tower point cloud data. Experimental results show that the piecewise cylinder fitting algorithm proposed in this research can meet the accuracy requirements of cylindrical steel structure deformation analysis and has high application value in the field of structure deformation monitoring.

scholarly journals Measurement and Prediction of Short-Range Path Loss between 27 and 40 GHz in University Campus Scenarios

2020 ◽  
Author(s):  
Glaucio Ramos ◽  
Carlos Vargas ◽  
Luiz Mello ◽  
Paulo Pereira ◽  
Sandro Gonçalves ◽  
...  
Keyword(s):  
Short Range ◽  
Prediction Models ◽  
Path Loss ◽  
Prediction Method ◽  
University Campus ◽  
Millimetre Wave ◽  
Empirical Prediction ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Path Loss Measurements

Abstract In this paper, we present the results of short-range path loss measurements in the microwave and millimetre wave bands, at frequencies between 27 and 40 GHz, obtained in a campaign inside a university campus in Rio de Janeiro, Brazil. Existing empirical path loss prediction models, including the alpha-beta-gamma (ABG) model and the close-in free space reference distance with frequency dependent path loss exponent (CIF) model are tested against the measured data, and an improved prediction method that includes the path loss dependence on the height di erence between transmitter and receiver is proposed. A fuzzy technique is also applied to predict the path loss and the results are compared with those obtained with the empirical prediction models.

scholarly journals A Protocol of Scoping Review of Peak Point Prediction Methods for Epidemic Diseases: Applicable to Coronavirus 2019 Prediction

2021 ◽  
Vol 10 (1) ◽  
pp. 71
Author(s):  
Elham Nazari ◽  
Zahra Ebnehoseini ◽  
Hamed Tabesh
Keyword(s):  
Scoping Review ◽  
Quantitative Methods ◽  
Prediction Models ◽  
Data Extraction ◽  
Prediction Methods ◽  
Peak Time ◽  
Epidemic Disease ◽  
Qualitative And Quantitative Methods ◽  
Model Studies ◽  
Protocol Study

Introduction: Given, widespread COVID-19 across the world a comprehensive literature review can be used to forecast COVID-19 peak in the countries. The present protocol study aimed to explore epidemic peak prediction models in communicable diseases.Material and Methods: This protocol study was conducted based on Arksey and O'Malley's. This framework encompasses purpose and hypothesis, modeling, model achievements aspects. A systematic search of English in PubMed was conducted to identify relevant studies. In the pilot step, two reviewers independently extracted the variables from 10 eligible studies to develop a primary list of variables and a data extraction form. In the second step, all eligible studies were assessed by researchers. In the third step, two data extraction forms were combined. The data were extracted and categories were created based on frequency. Qualitative and quantitative methods were used to synthesize the extracted data.Results: The current study were focused on forecasting the epidemic peak time that is a worlds’ concern issue. The results of current scoping review on prediction methods for epidemic disease can provide foundational knowledge, and have important value for the prediction model studies of COVID-19.Conclusion: Our findings will help researchers by a summary of evidence to present new ideas and further research especially for studies were focused on COVID-19. Our results can improve the understanding of prediction methods for COVID-19.

scholarly journals Tabel Prediksi Moyers dan Sitepu terhadap Lebar Mesiodistal Gigi Permanen Pengganti pada Etnis Arab Yaman di Surabaya

2020 ◽  
Vol 8 (1) ◽  
pp. 42
Author(s):  
Firyal Baktir ◽  
Dwi Prijatmoko ◽  
Masniari Novita
Keyword(s):  
Key Words ◽  
Prediction Method ◽  
Mixed Dentition ◽  
Prediction Methods ◽  
Tooth Size ◽  
Cross Sectional ◽  
Caucasian Race ◽  
Size Discrepancy ◽  
Tooth Size Discrepancy ◽  
Cross Sectional Design

There are several methods of analizing tooth size discrepancy in orthodontics include prediction methods for mixed dentition. Prediction method of Moyers and Sitepu most commonly used although both were obtained from 2 different races, Caucasian and Deutromelayu. Yemeni ethnic is one of the ethnic groups settled in Indonesia which descendants of the Caucasian race. The aim of the study was to observed the suitable prediction table for Yemeni ethnic. It was an observasional analitics study consist of 40 samples with cross sectional design. The results showed that slight difference for prediction of Moyers on the maxilla (1.02) and prediction of Sitepu on the mandibula (0.11). As conclusion, the most suitable predicition method for Yemeni ethnic is Moyers’s method for maxila and sitepu’s method for mandibula.   Key words: mesiodistal width permanen teeth, Moyers method, Sitepu method, Yemeni Etnic

scholarly journals A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform

2021 ◽  
Vol 13 (23) ◽  
pp. 4864
Author(s):  
Langfu Cui ◽  
Qingzhen Zhang ◽  
Liman Yang ◽  
Chenggang Bai
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Prediction Method ◽  
Sliding Window ◽  
Small Sample ◽  
Grey Theory ◽  
Sensing System ◽  
Performance Change ◽  
Sample Data ◽  
Platform System

An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.

MDAPlatform: a Component-based Platform for Constructing and Assessing miRNA-disease association Prediction Methods

2021 ◽  
Vol 16 ◽  
Author(s):  
Yayan Zhang ◽  
Guihua Duan ◽  
Cheng Yan ◽  
Haolun Yi ◽  
Fang-Xiang Wu ◽  
...  
Keyword(s):  
Computational Models ◽  
Critical Role ◽  
Prediction Method ◽  
Disease Association ◽  
Prediction Methods ◽  
Comparison Results ◽  
Disease Associations ◽  
Pros And Cons ◽  
Clinical Drugs ◽  
Validation Experiments

Background: Increasing evidence has indicated that miRNA-disease association prediction plays a critical role in the study of clinical drugs. Researchers have proposed many computational models for miRNA-disease prediction. However, there is no unified platform to compare and analyze the pros and cons or share the code and data of these models. Objective: In this study, we develop an easy-to-use platform (MDAPlatform) to construct and assess miRNA-disease association prediction method. Methods: MDAPlatform integrates the relevant data of miRNA, disease and miRNA-disease associations that are used in previous miRNA-disease association prediction studies. Based on the componentized model, it develops differet components of previous computational methods. Results: Users can conduct cross validation experiments and compare their methods with other methods, and the visualized comparison results are also provided. Conclusion: Based on the componentized model, MDAPlatform provides easy-to-operate interfaces to construct the miRNA-disease association method, which is beneficial to develop new miRNA-disease association prediction methods in the future.

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