Browsing by Subject "estimation"

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  • Kahiluoto, Joonas; Hirvonen, Jukka; Näykki, Teemu (Springer, 2019)
    Environmental Monitoring and Assessment 191, 259 (2019)
    Continuous sensor measurements are becoming an important tool in environmental monitoring. However, the reliability of field measurements is still too often unknown, evaluated only through comparisons with laboratory methods or based on sometimes unrealistic information from the measuring device manufacturers. A water turbidity measurement system with automatic reference sample measurement and measurement uncertainty estimation was constructed and operated in laboratory conditions to test an approach that utilizes validation and quality control data for automatic measurement uncertainty estimation. Using validation and quality control data for measurement uncertainty estimation is a common practice in laboratories and, if applied to field measurements, could be a way to enhance the usability of field sensor measurements. The measurement system investigated performed replicate measurements of turbidity in river water and measured synthetic turbidity reference solutions at given intervals during the testing period. Measurement uncertainties were calculated for the results using AutoMUkit software and uncertainties were attached to appropriate results. The measurement results correlated well (R2 = 0.99) with laboratory results and the calculated measurement uncertainties were 0.8–2.1 formazin nephelometric units (FNU) (k = 2) for 1.2–5 FNU range and 11–27% (k = 2) for 5–40 FNU range. The measurement uncertainty estimation settings (such as measurement range selected and a number of replicates) provided by the user have a significant effect on the calculated measurement uncertainties. More research is needed especially on finding suitable measurement uncertainty estimation intervals for different field conditions. The approach presented is also applicable for other online measurements besides turbidity within limits set by available measurement devices and stable reference solutions. Potentially interesting areas of application could be the measurement of conductivity, pH, chemical oxygen demand (COD)/total organic carbon (TOC), or metals.
  • Laakom, Firas; Raitoharju, Jenni; Nikkanen, Jarno; Iosifidis, Alexandros; Gabbouj, Moncef (IEEE, 2021)
    IEEE Access 9, 39560-39567
    In this paper, we describe a new large dataset for illumination estimation. This dataset, called INTEL-TAU, contains 7022 images in total, which makes it the largest available high-resolution dataset for illumination estimation research. The variety of scenes captured using three different camera models, namely Canon 5DSR, Nikon D810, and Sony IMX135, makes the dataset appropriate for evaluating the camera and scene invariance of the different illumination estimation techniques. Privacy masking is done for sensitive information, e.g., faces. Thus, the dataset is coherent with the new General Data Protection Regulation (GDPR). Furthermore, the effect of color shading for mobile images can be evaluated with INTEL-TAU dataset, as both corrected and uncorrected versions of the raw data are provided. Furthermore, this paper benchmarks several color constancy approaches on the proposed dataset.
  • Bernardo, Alexandre (Helsingin yliopisto, 2020)
    In insurance and reinsurance, heavy-tail analysis is used to model insurance claim sizes and frequencies in order to quantify the risk to the insurance company and to set appropriate premium rates. One of the reasons for this application comes from the fact that excess claims covered by reinsurance companies are very large, and so a natural field for heavy-tail analysis. In finance, the multivariate returns process often exhibits heavy-tail marginal distributions with little or no correlation between the components of the random vector (even though it is a highly correlated process when taking the square or the absolute values of the returns). The fact that vectors which are considered independent by conventional standards may still exhibit dependence of large realizations leads to the use of techniques from classical extreme-value theory, that contains heavy-tail analysis, in estimating an extreme quantile of the profit-and-loss density called value-at-risk (VaR). The need of the industry to understand the dependence between random vectors for very large values, as exemplified above, makes the concept of multivariate regular variation a current topic of great interest. This thesis discusses multivariate regular variation, showing that, by having multiple equivalent characterizations and and by being quite easy to handle, it is an excellent tool to address the real-world issues raised previously. The thesis is structured as follows. At first, some mathematical background is covered: the notions of regular variation of a tail distribution in one dimension is introduced, as well as different concepts of convergence of probability measures, namely vague convergence and $\mathbb{M}^*$-convergence. The preference in using the latter over the former is briefly discussed. The thesis then proceeds to the main definition of this work, that of multivariate regular variation, which involves a limit measure and a scaling function. It is shown that multivariate regular variation can be expressed in polar coordinates, by replacing the limit measure with a product of a one-dimensional measure with a tail index and a spectral measure. Looking for a second source of regular variation leads to the concept of hidden regular variation, to which a new hidden limit measure is associated. Estimation of the tail index, the spectral measure and the support of the limit measure are next considered. Some examples of risk vectors are next analyzed, such as risk vectors with independent components and risk vectors with repeated components. The support estimator presented earlier is then computed in some examples with simulated data to display its efficiency. However, when the estimator is computed with real-life data (the value of stocks for different companies), it does not seem to suit the sample in an adequate way. The conclusion is drawn that, although the mathematical background for the theory is quite solid, more research needs to be done when applying it to real-life data, namely having a reliable way to check whether the data stems from a multivariate regular distribution, as well as identifying the support of the limit measure.
  • Rankinen, Katri; Turtola, Eila; Lemola, Riitta; Futter, Martyn; Cano Bernal, José Enrique (Molecular Diversity Preservation International (MDPI), 2021)
    Water 2021, 13(4), 450
    Increased nutrient loading causes deterioration of receiving surface waters in areas of intensive agriculture. While nitrate and particulate phosphorus load can be efficiently controlled by reducing tillage frequency and increasing vegetation cover, many field studies have shown simultaneously increased loading of bioavailable phosphorus. In the latest phase of the Rural Programme of EU agri-environmental measures, the highest potential to reduce the nutrient loading to receiving waters were the maximum limits for fertilization of arable crops and retaining plant cover on fields with, e.g., no-till methods and uncultivated nature management fields. Due to the latter two measures, the area of vegetation cover has increased since 1995, suggesting clear effects on nutrient loading in the catchment scale as well. We modeled the effectiveness of agri-environmental measures to reduce phosphorus and nitrogen loads to waters and additionally tested the performance of the dynamic, process-based INCA-P (Integrated Nutrients in Catchments—Phosphorus) model to simulate P dynamics in an agricultural catchment. We concluded that INCA-P was able to simulate both fast (immediate) and slow (non-immediate) processes that influence P loading from catchments. Based on our model simulations, it was also evident that no-till methods had increased bioavailable P load to receiving waters, even though total P and total N loading were reduced.
  • Kilkki, Pekka (Suomen metsätieteellinen seura, 1983)
  • Heinonen, Reija; Mattila, Tuomas J. (American Society of Agronomy, 2021)
    Agronomy Journal. 2021; 001−5
    Smartphone-based visual assessment of vegetation cover is a promising, fast, and repeatable approach that allows land managers to compare measurements on their farm with other farms. This study determined the influence of the smartphone device on green cover measurements on several crops. The hypothesis was that different smartphones would provide similar green cover (reflectance) for management purposes (i.e., <10% difference). Forty fields in Finland were sampled between 10 and 28 July 2020 with Motorola Moto G7 and Samsung Galaxy A6 smartphones. The results were compared also with Sentinel-2 remote sensing of normalized difference vegetation index (NDVI) and biomass quadrants. The two smartphones had different green reflectance values that were correlated to each other. Both green reflectance measurements correlated with the NDVI that was measured with the Sentinel 2 satellite sensor and biomass. These findings suggest that smartphone-based monitoring can be used at least to classify vegetation to low, medium, and high density but that results from different cameras should not be compared.
  • Tukiainen, Janne (2006)
    This study performs tests developed by Haile et al. (2003) for the common values paradigm in first-price sealed-bid auctions, using data from bus transit auctions in the city of Helsinki. First the bidder's expected costs conditional on winning the auction are estimated using bids following Li et al. (2002). In common costs setting these costs are increasing in the number of bidders whereas with private costs they are invariant. Two tests for stochastic dominance between the cost distributions for different number of bidders are conducted. The first test compares quantile trimmed means and the second is a Kolmogorov-Smirnov type test based on subsampling. This study shows the need for additional robustness checks for some arbitrary choices in these tests. In the means test the choice of quantile does not matter asymptotically and is thus chosen arbitrarily. However the test is not robust to the choice of the quantile in this particular small data set. The second test is not robust to the choice of subsample size, number of subsamples taken or other repetitions of the test in some model specifications. Also pooling and controlling for observed bidder asymmetry are introduced. Results imply that the bus companies that have garages close to the contracted routes operate in an environment where the common costs components dominate the private ones and the bus companies that have garages far from these routes operate in an environment where private cost components dominate the common ones.