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Open access

Hongli Li, Xiaohuai Chen, Yinbao Cheng, Houde Liu, Hanbin Wang, Zhenying Cheng and Hongtao Wang

Abstract

Due to the variety of measurement tasks and the complexity of the errors of coordinate measuring machine (CMM), it is very difficult to reasonably evaluate the uncertainty of the measurement results of CMM. It has limited the application of CMM. Task oriented uncertainty evaluation has become a difficult problem to be solved. Taking dimension measurement as an example, this paper puts forward a practical method of uncertainty modeling and evaluation of CMM task oriented measurement (called SVCMM method). This method makes full use of the CMM acceptance or reinspection report and the Monte Carlo computer simulation method (MCM). The evaluation example is presented, and the results are evaluated by the traditional method given in GUM and the proposed method, respectively. The SVCMM method is verified to be feasible and practical. It can help CMM users to conveniently complete the measurement uncertainty evaluation through a single measurement cycle.

Open access

M. Przybyła, M. Kordasz, R. Madoński, P. Herman and P. Sauer

Abstract

This paper presents a practical verification of an Active Disturbance Rejection Control (ADRC) method in governing a multidimensional system. The experiments were conducted on a two degrees of a freedom planar manipulator with only partial knowledge about the mathematical model of the plant. This multi input multi output system was controlled with a set of two, independent, single input single output ADRC controllers, each regulating one of the manipulator degree of freedom. Modeling uncertainty (nonlinearities, cross-coupling effects, etc.) and external disturbances were assumed to be a part of the disturbance, to be estimated with an observer and cancelled on-line in the control loop. The ADRC robustness was experimentally compared with the results obtained from using two decentralized, classic PID controllers. Both control methods were tested under various conditions, e.g. changing the inertial parameters of the plant. Significantly better results, in terms of parametric robustness, have been reported for the ADRC approach.

Open access

Alexander Rubtsov

Approach to Stochastic Modeling of Power Systems

This paper presents an approach to modeling power system that contains sources of stochastic disturbance. It is based on frequency analysis of linearized model of power system. Power system dynamic properties are accounted by equivalent transfer functions of machines and their control equipment. This will allow more accurate calculations for different analysis tasks. Methodology of system linearization is proposed and results of linearized model test are delivered.

The research was made in frame of a project with funding participation of the European Commission.

Open access

Tamás Právetz, György Sipos and Zsuzsanna Ladányi

Abstract

The riverbed morphology of sand-bedded rivers is dynamically changing as a consequence of quasi continuous bedload transport. In the meantime, the dimension, size and dynamics of developing bedforms is highly depending on the regime of the river and sediment availability, both affected by natural and anthropogenic factors. Consequently, the assessment of morphological changes as well as the monitoring of riverbed balance is challenging in such a variable environment. In relation with a general research on the longer term sediment regime of River Maros, a fairly large alluvial river in the Carpathian Basin, the primary aim of the present investigation was to assess uncertainties related to morphological monitoring, i.e. testing the reproducibility of hydromorphological surveys and digital elevation model generation by performing repeated measurements among low water conditions on selected representative sites. Surveys were conducted with the combination of an ADCP sonar, GPS and total station. The most appropriate way of digital elevation modelling (DEM) was tested and 30-point Kriging was identified to be optimal for comparative analysis. Based on the results, several uncertainties may affect the reproducibility of measurements and the volumetric deviation of DEM pairs generated. The mean horizontal difference of survey tracks was 3-4 m in case of each site, however this could not explain all the DEM deviation. Significant riverbed change between measurements could also be excluded as the main factor. Finally, it was found that results might be affected greatly by systematic errors arising during motor boat ADCP measurements. Nevertheless, the observed, normalised and aggregated DEM uncertainty (600-360 m3/rkm) is significantly lower than the changes experienced between surveys with a month or longer time lag. Consequently, the developed measurement strategy is adequate to monitor long term morphological and sediment balance change on sand bedded large river.

Open access

R. Ďuračiová

Abstract

This paper deals with uncertainty modeling in spatial object-relational databases by the use of Structured Query Language (SQL). The fundamental principles of uncertainty modeling by fuzzy sets are applied in the area of geographic information systems (GIS) and spatial databases. A spatial database system includes types of spatial data and implements the spatial extension of SQL. The implementation of the principles of fuzzy logic to spatial databases brings an opportunity for the efficient processing of uncertain data, which is important, especially when using various data sources (e.g., multi-criteria decision making (MCDM) on the basis of heterogeneous spatial data resources). The modeling and data processing of uncertainties are presented in relation to the applicable International Organization for Standardization (ISO) standards (standards of the series 19100 Geographic information) and the relevant specifications of the Open Geospatial Consortium (OGC). The fuzzy spatial query approach is applied and tested on a case study with a fundamental database for GIS in Slovakia.

Open access

Andrzej Brandyk, Adam Kiczko, Grzegorz Majewski, Małgorzata Kleniewska and Marcin Krukowski

Abstract

Knowledge on soil moisture is indispensable for a range of hydrological models, since it exerts a considerable influence on runoff conditions. Proper tools are nowadays applied in order to gain in-sight into soil moisture status, especially of uppermost soil layers, which are prone to weather changes and land use practices. In order to establish relationships between meteorological conditions and topsoil moisture, a simple model would be required, characterized by low computational effort, simple structure and low number of identified and calibrated parameters. We demonstrated, that existing model for shallow soils, considering mass exchange between two layers (the upper and the lower), as well as with the atmosphere and subsoil, worked well for sandy loam with deep ground water table in Warsaw conurbation. GLUE (Generalized Likelihood Uncertainty Estimation) linked with GSA (Global Sensitivity Analysis) provided for final determination of parameter values and model confidence ranges. Including the uncertainty in a model structure, caused that the median soil moisture solution of the GLUE was shifted from the one optimal in deterministic sense. From the point of view of practical model application, the main shortcoming were the underestimated water exchange rates between the lower soil layer (ranging from the depth of 0.1 to 0.2 m below ground level) and subsoil. General model quality was found to be satisfactory and promising for its utilization for establishing measures to regain retention in urbanized conditions.

Open access

Henryk Borowczyk

The Combinatorial Diagnostic Entropy and Symptoms' Informativity

This paper presents combinatorial measures of the system condition uncertainty (diagnostic entropy) and the diagnostic symptoms' informativity. The multi-valued diagnostic model has been assumed. Proposed measures can be used in the manner similar to Shannon entropy use for the diagnostic model analysis and the diagnostic algorithm planning process.

Open access

Bahman Amiri, K. Sudheer and Nicola Fohrer

Linkage Between In-Stream Total Phosphorus and Land Cover in Chugoku District, Japan: An Ann Approach

Development of any area often leads to more intensive land use and increase in the generation of pollutants. Modeling these changes is critical to evaluate emerging changes in land use and their effect on stream water quality. The objective of this study was to assess the impact of spatial patterns in land use and population density on the water quality of streams, in case of data scarcity, in the Chugoku district of Japan. The study employed artificial neural network (ANN) technique to assess the relationship between the total phosphorous (TP) in river water and the land use in 21 river basins in the district, and the model was able to reasonably estimate the TP in the stream water. Uncertainty analysis of ANN estimates was performed using the Monte Carlo framework, and the results indicated that the ANN model predictions are statistically similar to the characteristics of the measured TP values. It was observed that any reduction in forested area or increase in agricultural land in the watersheds may cause the increase of TP concentration in the stream. Therefore, appropriate watershed management practices should be followed before making any land use change in the Chugoku district.

Open access

Ismat Beg and Tabasam Rashid

Abstract

A notion for distance between hesitant fuzzy data is given. Using this new distance notion, we propose the technique for order preference by similarity to ideal solution for hesitant fuzzy sets and a new approach in modelling uncertainties. An illustrative example is constructed to show the feasibility and practicality of the new method.