Inverse Eigenvalue Problem Applied to Weight Optimisation in a Geodetic Network

Dalmolin, Quintino; Oliveira, Reginaldo de

doi:10.1179/003962611x12894696205028

Cited by 5 publications

(4 citation statements)

References 3 publications

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“…Values of interest could be the estimated parameters or the estimated observations. This correlates highly with the general detection of influential observations in linear regression [5,10,18]. For describing observations with high impact, several parameters exist [2].…”

Section: Analysis Of Network Configurationssupporting

confidence: 57%

Automatic optimization of height network configurations for detection of surface deformations

Holst

Eling

Kuhlmann

2013

Journal of Applied Geodesy

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Levellings are performed to observe height changes of different epochs at discrete surveying points. A reliable estimation of surface deformations by a bivariate polynomial needs a sufficient configuration of the underlying network. Because the spacial distribution of the surveying points is not homogeneous in the discussed regions, the network configuration has to be optimized. This study proposes an optimization procedure that estimates the optimal number and position of the surveying points considered for a reliable analysis. Furthermore, the already existing observations are accepted or rejected due to the network's geometry. Therefore, two different approaches are combined. First, the sampling theorem from time series analysis is used to estimate the number and position of the surveying points. Second, the partial redundancies from statistics take the reliability into account. Applying the optimization procedure to several test regions, the benefit of the optimized network configurations is discussed.

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Section: Analysis Of Network Configurationssupporting

confidence: 57%

Automatic optimization of height network configurations for detection of surface deformations

Holst

Eling

Kuhlmann

2013

Journal of Applied Geodesy

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“…It is important to note that optimization techniques are mainly used for decision making. For example, the optimization technique can be used to select the instruments to be used and the local points to be captured when estimating unknown parameters with defined quality [3].…”

Section: Optimization Of Geodetic Networkmentioning

confidence: 99%

“…dr Goran Marinković, dipl inž geod., Fakultet tehničkih nauka Novi Sad, e-mail: goranmarinkovic@uns.ac.rs 2 Tatjana Kuzmić, dipl.inž. geod Fakultet tehničkih nauka Novi Sad, e-mail: tatjana.kuzmic@uns.ac.rs3 Prof. dr Milan Trifković, dipl inž geod., Građevinski fakultet Subotica, e-mail: milantri@eunet.rs…”

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Optimizacija Geodetske Mreže Za Potrebe Obnove Premera U Katastarskoj Opštini Vojvoda Stepa

Marinković¹,

Kuzmić²,

Trifković³

2018

Zbornik GFS

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“…Schaffrin, 1981;Grafarend and Sanso, 1985;Cross, 1985;Schaffrin, 1985;Xu, 1989;Kuang, 1991Kuang, , 1996Blewitt, 2000;Amiri-Simkooei, 2004;Eshagh, 2005;Bagherbandi et al 2009;Dalmolin and Oliveira, 2013;Mehrabi and Voosoghi, 2014;Eshagh and AlizadehKhameneh, 2015); and…”

Section: Introductionmentioning

confidence: 99%

Design of Geodetic Networks Based on Outlier Identificationcriteria: An Example Applied to the Leveling Network

2018

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Abstract:We present a numerical simulation method for designing geodetic networks. The quality criterion considered is based on the power of the test of data snooping testing procedure. This criterion expresses the probability of the data snooping to identify correctly an outlier. In general, the power of the test is defined theoretically. However, with the advent of the fast computers and large data storage systems, it can be estimated using numerical simulation. Here, the number of experiments in which the data snooping procedure identifies the outlier correctly is counted using Monte Carlos simulations. If the network configuration does not meet the reliability criterion at some part, then it can be improved by adding required observation to the surveying plan. The method does not use real observations. Thus, it depends on the geometrical configuration of the network; the uncertainty of the observations; and the size of outlier. The proposed method is demonstrated by practical application of one simulated leveling network. Results showed the needs of five additional observations between adjacent stations. The addition of these new observations improved the internal reliability of approximately 18%. Therefore, the final designed network must be able to identify and resist against the undetectable outliers -according to the probability levels.Keywords: Reliability, Outlier, Numerical Simulation, Monte Carlo, Design of geodetic networks. Resumo:Nós apresentamos um método de simulação numérica para planejamento de redes geodésicas. O critério de qualidade considerado é baseado no poder do teste do procedimento estatístico data snooping. Esse critério expressa a probabilidade do procedimento data snooping de identificar corretamente um outlier. Em geral, o valor do poder do teste é sempre desconhecido na prática (definido na teoria). Porém, com o advento de computadores mais rápidos e sistemas de armazenamento de dados de maior capacidade, o poder do teste pode ser estimado por meio de simulações numéricas. O número de experimentos que o procedimento data snooping identifica corretamente um outlier é quantificado por meio do método Monte Carlo de simulação. Se a configuração inicial da rede não atender o critério de confiabilidade, então uma nova observação é adicionada no projeto da rede. O método não faz uso de observações reais coletadas em campo, dependendo apenas da configuração geométrica da rede, das incertezas das observações e da magnitude dos outliers. A eficiência do método é verificada por meio de um exemplo numérico de uma rede de nivelamento geométrico simulada. Os resultados mostraram a necessidade de cinco observações adicionais entre os pontos adjacentes. A adição destas novas observações melhorou a confiabilidade interna da rede de aproximadamente 18%. Portanto, a rede é projetada de modo que seja capaz de identificar outliers e ainda resistir a outilers não identificados nas observações -de acordo com os níveis de probabilidade.

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Inverse Eigenvalue Problem Applied to Weight Optimisation in a Geodetic Network

Cited by 5 publications

References 3 publications

Automatic optimization of height network configurations for detection of surface deformations

Automatic optimization of height network configurations for detection of surface deformations

Optimizacija Geodetske Mreže Za Potrebe Obnove Premera U Katastarskoj Opštini Vojvoda Stepa

Design of Geodetic Networks Based on Outlier Identificationcriteria: An Example Applied to the Leveling Network

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