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Forecasting the waste production hierarchical time series with correlation structure
(Springer, 2024-07-02) Eryganov, Ivan; Rosecký, Martin; Šomplák, Radovan; Smejkalová, Veronika
Continuous increase in society's prosperity causes overwhelming growth of the produced municipal solid waste. Circular economy initiatives help to solve this problem by creating closed production cycles, where the produced waste is recycled, or its energy is recovered. An embedment of such principles requires implementation of new waste management strategies. However, these novel strategies must be based on the accurate forecasts of future waste flows. Municipal solid waste production data demonstrate behavior of hierarchical time series. Among all possible approaches to hierarchical times series forecasting, this article is focused on the reconciliation of the base waste generation forecasts. The novel method, that is based on the game-theoretically optimal reconciliation of hierarchical time series, is presented. The modified approach enables to incorporate interdependencies between time series using correlation matrix and to obtain the forecasts corresponding to the unique solution of the optimization problem. The potential of the proposed abstract approach is demonstrated on the waste production data of paper, plastics (both primarily sorted by households), and mixed municipal solid waste from the Czech Republic.
A Novel Technique for the Extraction of Dynamic Events in Extreme Ultraviolet Solar Images
(The American Astronomical Society, 2024-11-07) Kalenská, Petra; Rajmic, Pavel; Gebrtová, Karolína; Druckmüller, Miloslav
High-spatial-resolution images of the solar corona acquired in the extreme ultraviolet (EUV), most notably with the Atmospheric Imaging Assembly (AIA) instrument on the Solar Dynamics Observatory (SDO) reveal the abundance of dynamic events which range from flaring bright points and jets to erupting prominences and coronal mass ejections (CMEs). In this work we present novel techniques to extract such dynamic events from the more steady background corona using 17.1 nm SDO-AIA images. The techniques presented here treat any time series of coronal images as a matrix that can be decomposed into two matrices representing the background and the dynamic component, respectively. The latter has the properties of a so-called sparse matrix, and the proposed methods are classified as methods based on sparse representations. The proposed methods are the median-filter method, the principal component pursuit, and the dynamic-mode decomposition, all of which include data pre-processing using the noise-adaptive fuzzy equalization method. The study reveals that the median-filter method and the dynamic-mode decomposition enhance all motions in the time series and produce similar results. On the other hand, the principal component pursuit enables the clear differentiation of CMEs from the background corona, thus providing a valuable tool for the characterization of their acceleration profiles in the low corona as seen in the EUV.
Probing the Density Fine Structuring of the Solar Corona with Comet Lovejoy
(IOP Publishing, 2022-10-01) Nistic, Giuseppe; Zimbardo, Gaetano; Perri, Silvia; Nakariakov, Valery M.; Duckenfield, Timothy j.; Druckmüller, Miloslav
The passage of sungrazing comets in the solar corona can be a powerful tool to probe the local plasma properties. Here, we carry out a study of the striae pattern appearing in the tail of sungrazing Comet Lovejoy, as observed by the Atmospheric Imaging Assembly (AIA) aboard the Solar Dynamics Observatory (SDO) during the inbound and outbound phases of the comet's orbit. We consider the images in EUV in the 171 angstrom bandpass, where emission from oxygen ions O4+ and O5+ is found. The striae are described as due to a beam of ions injected along the local magnetic field, with the initial beam velocity decaying because of collisions. Also, ion collisional diffusion contributes to ion propagation. Both the collision time for velocity decay and the diffusion coefficient for spatial spreading depend on the ambient plasma density. A probabilistic description of the ion beam density along the magnetic field is developed, where the beam position is given by the velocity decay and the spreading of diffusing ions is described by a Gaussian probability distribution. Profiles of emission intensity along the magnetic field are computed and compared with the profiles along the striae observed by AIA, showing a good agreement for most considered striae. The inferred coronal densities are then compared with a hydrostatic model of the solar corona. The results confirm that the coronal density is strongly spatially structured.
Machine Learning Method for Changepoint Detection in Short Time Series Data
(MDPI, 2023-10-05) Smejkalová, Veronika; Šomplák, Radovan; Rosecký, Martin; Šramková, Kristína
Analysis of data is crucial in waste management to improve effective planning from both short- and long-term perspectives. Real-world data often presents anomalies, but in the waste management sector, anomaly detection is seldom performed. The main goal and contribution of this paper is a proposal of a complex machine learning framework for changepoint detection in a large number of short time series from waste management. In such a case, it is not possible to use only an expert-based approach due to the time-consuming nature of this process and subjectivity. The proposed framework consists of two steps: (1) outlier detection via outlier test for trend-adjusted data, and (2) changepoints are identified via comparison of linear model parameters. In order to use the proposed method, it is necessary to have a sufficient number of experts’ assessments of the presence of anomalies in time series. The proposed framework is demonstrated on waste management data from the Czech Republic. It is observed that certain waste categories in specific regions frequently exhibit changepoints. On the micro-regional level, approximately 31.1% of time series contain at least one outlier and 16.4% exhibit changepoints. Certain groups of waste are more prone to the occurrence of anomalies. The results indicate that even in the case of aggregated data, anomalies are not rare, and their presence should always be checked.
Registration of Partially Focused Images for 2D and 3D Reconstruction of Oversized Samples
(Hindawi, 2017-12-18) Martišek, Dalibor; Druckmüllerová, Hana
Methods of fracture surface 3D reconstruction from a series of partially focused images acquired in a small field of view (e.g., by confocal microscope or CCD camera) are well known. In this case, projection rays can be considered parallel and recorded images do not differ in any geometrical transformation from each other. In the case of larger samples (oversized for microscope or CCD camera), it is necessary to use a wider viewing field (e.g., standard cameras); taken images primarily differ in scaling but may also differ in shifting and rotation.These images cannot be used for reconstruction directly; they must be registered; that is, we must determine all transformations in which the images differ and eliminate their effects.There are several ways to do this. This paper deals with the registration based on phase correlation.

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