Wyniki wyszukiwania - BazTech

1

Searching for Determinism in Mobile Gravimetry Data

Przyborski Marek, Serafin Marcin, Załęska-Fornal Agata, Pyrchla Jerzy, Pyrchla Krzysztof, Szulwic Jakub, Rudnicki Jacek

Polish Maritime Research

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2025

|

nr 4

150-156

EN

The accuracy of gravimetric measurements is essential in various fields, from the safety of the navigation of unmanned autonomous vessels to searching for natural resources to the level of underground water to the accuracy of geodetic data. Usually, we have to deal with measurements contaminated by environmental noise, as well as noise generated by different mechanical devices, city transportation systems, and human beings; some of those sources have a periodical nature. In the presented article, we consider the problem of the influence of noise on registered data. A gravimeter is, in fact, a vibration analyzer, so most of the artificial noise caused by engines, machines, and other technical systems is included in the final recorded data. By testing a statistical hypothesis, we try to convince the reader that in recorded time series, there is other information that is deterministic in nature and may have an important impact on the analysis of gravimetric data.

2

FPGA implementation of normalized correlation function

Mroczek Krzysztof

International Journal of Electronics and Telecommunications

|

2025

|

Vol. 71, No. 3

20

EN

Correlation analysis is a frequently used tool in signal detection and classification tasks. This paper presents the design and FPGA implementations of a hardware module for calculating the Pearson correlation coefficient. This module is designed for use in signal template matching, where a measurement signal is correlated with a template. It has been described in Verilog and implemented on Intel Cyclone V FPGA. The module consists of two main parts, which are: a correlation filter and normalization modules. Correlation filters performing the calculation in the time domain and in the frequency domain are described. The project has been verified in simulation using ModelSim and checked on hardware. As a result of this work, hardware IP cores are developed enabling parametrization and programming in data word-lengths, filter size, calculation speed, FFT/IFFT size, length, and number of processing templates. Developed resources are intended to be used in FPGA-based hardware, e.g. DAQ systems, working with sampling frequencies from kHz to above 130 MHz.

3

Wpływ czynników zewnętrznych na predykcję zużycia energii elektrycznej w budynku biurowym z panelami fotowoltaicznymi

Kawa Kazimierz, Mularczyk Rafał, Bauer Waldemar, Kucharska Edyta

Przegląd Elektrotechniczny

|

2024

|

R. 100, nr 2

221--226

PL

Planowanie zużycia energii elektrycznej ze względu na pojawiające się niespodziewanie przerwy w dostawie staje istotnym aspektem zarządzania utrzymania budynków. Analiza szeregów czasowych pozwala na predykowanie zużycia energii elektrycznej w kolejnych latach na podstawie danych historycznych. Celem badania jest weryfikacja wpływu czynników zewnętrznych na predykcję ilości zużycia energii elektrycznej. W badaniach zostały wykorzystane metody analizy szeregów czasowych: model naiwny z sezonowością, regresji liniowej oraz Facebook Prophet. Wyniki pokazują, że zaproponowane modele w zadawalający sposób są w stanie prognozować zapotrzebowanie na energię.

EN

Planning for electricity consumption due to power outages occurring unexpectedly is becoming an important aspect of building maintenance management. Time series analysis makes it possible to predict electricity consumption in future years based on historical data. The purpose of the study is to verify the influence of external factors on the prediction of the amount of electricity consumption. The study used time series analysis methods: naive model with seasonality, linear regression and Facebook Prophet. The results show that the proposed models are able to predict energy demand satisfactorily.

4

Monitoring Agricultural Drought in Savanna Ecosystems Using the Vegetation Health Index – Implications of Climate Change

Susila Ketut Dharma, Trigunasih Ni Made, Saifulloh Moh

Ecological Engineering & Environmental Technology

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2024

|

Vol. 25, iss. 9

54--67

EN

This study aims to monitor the implications of climate change on savanna ecosystem drought using time series data from the Landsat 8 sensor, spanning from 2013 to 2022. We employed a remote sensing computational approach with the semi-automatic classification plugin (SCP) in the open-source QGIS software. Specifically, we utilized channels from the operational land imager (OLI), including Band 4 Red (0.636–0.673 µm) and Band 5 Near-Infrared (0.851–0.879 µm), as well as Thermal Infrared Sensor (TIRS) channels Band 10 TIRS-1 (10.60–11.19 µm) and Band 11 TIRS-2 (11.50–12.51 µm). These channels were used to calculate the vegetation health index (VHI) using the raster calculator, followed by data reclassification with specific thresholds to compare drought-affected areas. Our findings reveal a significant impact of climate change on savanna ecosystem drought over the decade, with the most extreme conditions observed in 2015 and 2019, where drought coverage reached 42.74% and 26.58%, respectively. Other years exhibited relatively low drought dynamics, affecting less than 3% of the area. This period aligns with the el niño-southern oscillation (ENSO) phenomenon, particularly the transition from El Niño to La Niña, known to cause global weather variations, and significantly influenced by the positive phase of the Indian Ocean dipole (IOD). The novelty of this research lies in two main aspects: firstly, the use of Landsat satellite sensors for this specific region has not been extensively studied before; secondly, the discovered impacts of drought in relation to global climate change phenomena are particularly noteworthy. A limitation of this study is the relatively short investigation period of just one decade, which does not fully capture the long-term impacts of climate change. Future research is recommended to utilize imagery with higher temporal resolution over extended periods to better represent extreme climate events and derive drought patterns over durations exceeding one decade.

5

Using deep neural networks to improve the precision of fast-sampled particle timing detectors

Kocot Mateusz, Misan Krzysztof, Avati Valentina, Bossini Edoardo, Grzanka Leszek, Minafra Nicola

Computer Science

|

2024

|

T. 25 (1)

43--61

EN

Measurements from particle timing detectors are often affected by the time walk effect caused by statistical fluctuations in the charge deposited by passing particles. The constant fraction discriminator (CFD) algorithm is frequently used to mitigate this effect both in test setups and in running experiments, such as the CMS-PPS system at the CERN’s LHC. The CFD is simple and effective but does not leverage all voltage samples in a time series. Its performance could be enhanced with deep neural networks, which are commonly used for time series analysis, including computing the particle arrival time. We evaluated various neural network architectures using data acquired at the test beam facility in the DESY-II synchrotron, where a precise MCP (MicroChannel Plate) detector was installed in addition to PPS diamond timing detectors. MCP measurements were used as a reference to train the networks and compare the results with the standard CFD method. Ultimately, we improved the timing precision by 8% to 23%, depending on the detector’s readout channel.The best results were obtained using a UNet-based model, which out performed classical convolutional networks and the multilayer perceptron.

6

Variations of temperature, salinity and oxygen of the Baltic Sea for the period 1950 to 2020

Stockmayer Vera, Lehmann Andreas

Oceanologia

|

2023

|

No. 65 (3)

466--483

EN

Variations of temperature, salinity and oxygen of the Baltic Sea on interannual to decadal timescales were studied for the period from 1950 to 2020. Both observational data and the output of a numerical circulation model of the Baltic Sea were analyzed. In addition, we investigated the influence of atmospheric parameters and river runoff on the observed hydrographic variations. Variability of sea surface temperature (SST) closely follows that of air temperature in the Baltic on all timescales examined. Interannual variations of SST are significantly correlated with the North Atlantic Oscillation in most parts of the sea in winter. The entire water column of the Baltic Sea has warmed over the period 1950 to 2020. The trend is strongest in the surface layer, which has warmed by 0.3–0.4°C decade−1, noticeably stronger since the mid-1980s. In the remaining water column, characterized by permanent salinity stratification in the Baltic Sea, warming trends are slightly weaker. A decadal variability is striking in surface salinity, which is highly correlated with river runoff into the Baltic Sea. Long-term trends over the period 1950–2020 show a noticeable freshening of the upper layer in the whole Baltic Sea and a significant salinity increase below the halocline in some regions. A decadal variability was also identified in the deep layer of the Baltic Sea. This can be associated with variations in saltwater import from the North Sea, which in turn are influenced by river runoff: fewer strong saltwater inflows were observed in periods of enhanced river runoff. Furthermore, our results suggest that changes in wind speed have an impact on water exchange with the North Sea. Interannual variations of surface oxygen are strongly anti-correlated with those of SST. Likewise, the positive SST trends are accompanied by a decrease in surface oxygen. In greater depths of the Baltic Sea, oxygen decrease is stronger, which is partly related to the observed increase of the vertical salinity gradient.

7

Surgical phase classification and operative skill assessment through spatial context aware CNNs and time-invariant feature extracting autoencoders

Pradeep Chakka Sai, Sinha Neelam

Biocybernetics and Biomedical Engineering

|

2023

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Vol. 43, no. 4

700--724

EN

Automated surgical video analysis promises improved healthcare. We propose novel spatial context aware combined loss function for end-to-end Encoder-Decoder training for Surgical Phase Classification (SPC) on laparoscopic cholecystectomy (LC) videos. Proposed loss function leverages on fine-grained class activation maps obtained from fused multi-layer Layer-CAM for supervised learning of SPC, obtaining improved Layer-CAM explanations. Post classification, we introduce graph theory to incorporate known hierarchies of surgical phases. We report peak SPC accuracy of 96.16%, precision of 94.08% and recall of 90.02% on public dataset Cholec80, with 7 phases. Our proposed method utilizes just 73.5% of parameters as against existing state-of-the-art methodology, achieving improvement of 0.5% in accuracy, 1.76% in precision with comparable recall, with an order less standard deviation. We also propose DNN based surgical skill assessment methodology. This approach utilizes surgical phase prediction scores from the final fully-connected layer of spatial-context aware classifier to form multi-channel temporal signal of surgical phases. Time-invariant representation is obtained from this temporal signal through time- and frequency-domain analyses. Autoencoder based time-invariant features are utilized for reconstruction and identification of prominent peaks in dissimilarity curves. We devise a surgical skill measure (SSM) based on spatial-context aware temporal-prominence-of-peaks curve. SSM values are expected to be high when executed skillfully, aligning with expert assessed GOALS metric. We illustrate this trend on Cholec80 and m2cai16-tool datasets, in comparison with GOALS metric. Concurrence in the trend of SSM with respect to GOALS metric is obtained on these test videos, making it a promising step towards automated surgical skill assessment.

8

Continual learning of a time series model using a mixture of HMMs with application to the IoT fuel sensor verification

Głomb Przemysław, Cholewa Michał, Foszner Paweł, Bularz Jakub

Annals of Computer Science and Information Systems

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2023

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Vol. 35

259--264

EN

This paper presents an application of a mixture of Hidden Markov Models (HMMs) as a tool for verification of IoT fuel sensors. The IoT fuel sensors report the level of fuel in tanks of a petrol station, and are a key component for monitoring system reliability (billing), safety (fuel/oil leak detection) and security (theft prevention). We propose an algorithm for learning a mixture of HMMs based on a continual learning principle, i.e. it adapts the model while monitoring a sensor over time, signalling unexpected or anomalous sensor reports. We have tested the proposed approach on a real-life data of 15 fuel tanks being monitored with the FuelPrime system, where it has shown a very good performance (average area under ROC curve of 0.94) of detecting anomalies in the sensor data. Additionally we show that the proposed method can be used for trend monitoring and present qualitative analysis of the short and long term learning performance. The proposed method has promising performance score, the resulting model has a high degree of explainability, limited memory and computation requirements and can be easily generalized to other domains of sensor verification.

9

Research on Earth rotation and geodynamics in Poland in 2019–2022

Bogusz Janusz, Brzezinski Aleksander, Godah Walyeldeen, Nastula Jolanta

Advances in Geodesy and Geoinformation

|

2023

|

Vol. 72, no. 2

art. no. e41, 2023

EN

This paper summarizes the activity of the chosen Polish geodetic research teams in 2019–2022 in the fields of the Earth rotation and geodynamics. This publication has been prepared for the needs of the presentation of Polish scientists’ activities on the 28th International Union of Geodesy and Geodynamics General Assembly, Berlin, Germany. The part concerning Earth rotation is mostly focused on the estimation of the geophysical excitation of polar motion using data from Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) missions, and on the improvement of the determination of Earth rotation parameters based on the Satellite Laser Ranging (SLR), Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS), and Global Navigation Satellite System (GNSS) satellite techniques. The part concerning geodynamics is focused on geodetic time series analysis for geodynamical purposes and monitoring of the vertical ground movements induced by mass transport within the Earth’s system, monitoring of the crustal movements using GNSS and newly applied Interferometric Synthetic Aperture Radar (InSAR), discussing the changes of the landslides and its monitoring using geodetic methods as well as investigations of seismic events and sea-level changes with geodetic methods. Finally, the recent research activities carried out by Polish scientists in the international projects is presented.

10

Microseismic signal denoising using simple bandpass filtering based on normal time–frequency transform

Yao Yanji, Wang Guocheng, Liu Lintao

Acta Geophysica

|

2023

|

Vol. 71, no. 5

2217--2232

EN

In active underground mining environments, monitoring mine vibrations has important implications for both safety and productivity. Microseismic data processing is crucial for subsurface real-time monitoring during mineral mining processes. Microseismic events are difficult to detect due to their small magnitudes and low signal-to-noise ratios (SNRs). Useful microseismic signals are usually obscured by long-period microseisms, random noise and artificial strong noise. We propose a useful microseismic denoising algorithm based on the normal time–frequency transform (NTFT) to determine the instantaneous frequency, amplitude and phase information from useful microseismic signals. The energy difference in the time–frequency domain between useful microseismic signals and strong noise is small. Therefore, based on the different phase characteristics of microseismic signals and noise in the NTFT phase spectrum, noise can be filtered out by reconstructing the microseismic signals in useful real-time frequency bands. The proposed simple bandpass filtering (SBPF) method is advantageous because the denoising result does not produce phase shifts, energy leakage or artefacts. The only parameter of the proposed method that needs to be defined is the instantaneous cutoff frequency; thus, the denoising operation is simple. We use both synthetic and real data to demonstrate the feasibility of the method for denoising complicated microseismic datasets.

11

Investigation of signals of the range 10-3 - 10-4 Hz registered by water-tube tiltmeters in the underground geodynamic laboratory in Książ (SW Poland)

Kaczorowski Marek, Kasza Damian, Zdunek Ryszard, Wronowski Roman

Artificial Satellites : Journal of Planetary Geodesy

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2022

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Vol. 57, No. 4

210--236

EN

The Geodynamic Laboratory in Książ includes investigations of various kinds of geodynamic signals. Among others, we registered harmonic signals of the range 10-3 - 10-4 Hz. These signals had been found in the measurement series of the long water-tube (WT) tiltmeters. The discovered signals consist of two classes of harmonics associated with various kinds of phenomena. The first class of these signals belongs to viscoelastic vibrations of the Earth’s solid body, while the second class is produced possibly by the extremely long atmospheric infrasound waves. The signals of the vibrations of the Earth had been well recognized by the characteristic frequencies of the Earth’s free vibrations’ resonance, which occur mainly after strong earthquakes. The atmospheric pressure microvibrations affected the water level in the hydrodynamic systems of the WTs as a result of an inverse barometric effect. We observed that signals from both classes blend in the harmonics of similar frequencies and jointly affect the hydrodynamic systems of the WTs. We found that the amplitude of the secondclass signals strongly depends on the location of water-tube gauges inside the underground, while the amplitudes of the first-class signals are similar for all the gauges. These observations clearly indicate the atmospheric origin of the second class of registered signals.

12

The efficiency of single base and network RTK for Structural Health Monitoring

Topal Güldane Oku, Akpinar Burak

Advances in Geodesy and Geoinformation

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2022

|

Vol. 71, no. 2

art. no. e28, 2022

EN

With the developing technology and increasing construction, the importance of structural observations, which are of great significance in disaster management, has increased. Geodetic methods have been preferred in recent years due to their high accuracy and ease of use in Structural Health Monitoring (SHM) Surveys. In this study, harmonic oscillation tests have been carried out on a shake table to determine the usability of the Single Base and the Network Real-Time Kinematic (RTK) Global Navigation Satellite Systems (GNSS) method in SHM studies. It is aimed to determine the harmonic movements of different amplitudes and frequencies created by the shake table with 20 Hz multi-GNSS equipment. The amplitude and frequency values of the movements created using Fast Fourier Transform (FFT) and Time Series Analysis have been calculated. The precision of the analysis results has been determined by comparing the LVDT (Linear Variable Differential Transformer) data, which is the position sensor of the shake table, with the GNSS data. The advantages of the two RTK methods over each other have been determined using the calculated amplitude and frequency differences. As a result of all experiments, it has been determined that network and single base RTK GNSS methods effectively monitor structural behaviours and natural frequencies.

13

The study of seismicity of Alborz (Northern Iran) and Zagros (Southern Iran) regions by using time series analysis

Rahimi Mohammad, Zamani Ahmad, Ghotbi Ali Reza

Acta Geophysica

|

2022

|

Vol. 70, no 1

27--37

EN

Since many years ago Iran faced multiple and sometimes destructive earthquakes because of its positions in seismic Alpidebelt, the regions of Alborz (in north of Iran) and Zagros (in south of Iran) are seismically important due to their dense population and the presence of economic and strategic organization in them, these zones with their high tectonic movements and their positions in seismic Alpide-belt have high potentials for large earthquakes, and 80% of Iran’s earthquakes happen in these zones. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values, in this study a proper model has been proposed for the earthquakes that occurred in two regions of Alborz and Zagros during 1900−2021, in all these regions time series are analyzed individually, and proper models are proposed for the prediction of empirical recurrence rate of the earthquake with magnitudes larger than 6.0 mb. Then, a compression was made between the proposed models and the earthquakes that occurred to determine their accuracy.

14

Wielomodelowe prognozowanie liczby pasażerów przewożonych krajowym transportem lotniczym w Stanach Zjednoczonych w 2021 r.

Kozicki Bartosz, Sowa Bogdan

Gospodarka Materiałowa i Logistyka

|

2021

|

nr 7

23--32

PL

W artykule przedstawiono dane dotyczące liczby pasażerów przewiezionych pasażerskim transportem lotniczym krajowym i międzynarodowym w Stanach Zjednoczonych w ujęciu miesięcznym w latach 2003–2020 i ich prognozowania na 2021 r. Badania rozpoczęto od analizy i oceny dwóch szeregów czasowych dotyczących liczby pasażerów przewiezionych transportem lotniczym pasażerskim w Stanach Zjednoczonych w ujęciu krajowym i międzynarodowym. Zbudowano model Kleina, za pomocą którego wykonano prognozowanie szeregu czasowego liczby pasażerów przewożonych transportem lotniczym krajowym w ujęciu miesięcznym na rok 2021. Zbudowany model jest połączeniem prognozowania ilościowego i jakościowego .

EN

The article presents data on the number of passengers transported by domestic and international passenger air transport in the United States on a monthly basis in the years 2003–2020 and their forecasting for 2021. The research began with the analysis and evaluation of two time series concerning the number of passengers transported by passenger air transport in the United States in terms of national and international approach. The Klein model was built, which was used to forecast the time series of the number of passengers transported by domestic air transport on a monthly basis for the year 2021. The constructed model is a combination of quantitative and qualitative forecasting.

15

A statistical study of COVID-19 pandemic in Egypt

Radwan Taha

Demonstratio Mathematica

|

2021

|

Vol. 54, nr 1

233--244

EN

The spread of the COVID-19 started in Wuhan on December 31, 2019, and a powerful outbreak of the disease occurred there. According to the latest data, more than 165 million cases of COVID-19 infection have been detected in the world (last update May 19, 2021). In this paper, we propose a statistical study of COVID-19 pandemic in Egypt. This study will help us to understand and study the evolution of this pandemic. Moreover, documenting of accurate data and taken policies in Egypt can help other countries to deal with this epidemic, and it will also be useful in the event that other similar viruses emerge in the future. We will apply a widely used model in order to predict the number of COVID-19 cases in the coming period, which is the autoregressive integrated moving average (ARIMA)model. This model depicts the present behaviour of variables through linear relationship with their past values. The expected results will enable us to provide appropriate advice to decision-makers in Egypt on how to deal with this epidemic.

16

Evaluation of the displacements of the permanent GPS stations of Beni Haroun Dam geodetic network (Algeria)

Khelifa Sofane, Dekkiche Hicham, Kahlouche Salem, Boudina Samia

Acta Geophysica

|

2021

|

Vol. 69, no. 6

2129--2143

EN

The purpose of this paper is the analysis of the daily coordinate time series of the five permanent GPS (Global Positioning System) stations of the geodetic monitoring network of the Beni-Haroun Dam (Algeria), in order to assess the spectral content of the dam displacements. The coordinate time series analysis was based on the singular spectrum analysis to assess their principal components (trend, seasonal components and noise in phase space), the spectral analysis to identify their noise spectrum (white or colored) and the wavelet thresholding method to determine their noise in frequency space. The results showed that the primary signal present in the analyzed time series is mainly composed of a trend and an annual component. The trend and the annual signal explain more than 95% of the total signal in the three coordinates (x, y, z) for all studied stations. The analyzed time series in the three coordinates (x, y, z) are characterized by a linear drift less than 1 mm/year, their annual amplitudes are in the range of 0.5–2 mm, and the amplitudes of their semiannual, four-monthly and quarterly signals are in the range of 0–0.5 mm. The noise spectrum in the analyzed time series is flicker noise, and the noise level is in the range of 0.2–0.7 mm, 0.3–0.5 mm and 0.5–1.2 mm in, respectively, x-, y- and z-coordinates. The low values of trend and noise level in the analyzed station coordinates indicate that the Beni-Haroun Dam is qualified as stable.

17

Effect of features extraction on improving LSTM network quality in ECG signal classification

Kłosowski Grzegorz

Przegląd Elektrotechniczny

|

2020

|

R. 96, nr 12

194--197

EN

This article focuses on the extraction of features extracted from ECG measurement signals to improve the quality of LSTM network operation. Two features were distinguished from each individual sequence of ECG signals: instantaneous frequency (IF) and spectral entropy (SE). Both of these features are extracted from ECG signals using short-time Fourier transform. The applied approach enables the conversion of original measurement sequences into spectral images, from which IF and SE coefficients are then generated. As a result of the research, it was found that feature extraction significantly improves ECG signal classification both in terms of forecasting accuracy and in terms of network learning speed.

PL

W niniejszym artykule skupiono się na ekstrakcji cech wyodrębnionych z sygnałów pomiarowych EKG w celu poprawy jakości działania sieci LSTM. Z każdej indywidualnej sekwencji sygnałów EKG wyróżniono dwie cechy: częstotliwość chwilową (IF) i entropię widmową (SE). Obie te cechy są wyodrębniane z sygnałów EKG przy użyciu krótkotrwałej transformaty Fouriera. Zastosowane podejście umożliwia konwersję oryginalnych sekwencji pomiarowych na obrazy widmowe, z których następnie generowane są współczynniki IF i SE. W wyniku badań stwierdzono, że ekstrakcja cech znacząco poprawia klasyfikację sygnału EKG zarówno pod względem dokładności prognozowania, jak i szybkości uczenia się.

18

Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction

Acker Alexander, Wittkopp Thorsten, Nedelkoski Sasho, Bogatinovski Jasmin, Kao Odej

Annals of Computer Science and Information Systems

|

2020

|

Vol. 21

7--10

EN

The rapid growth and distribution of IT systems increases their complexity and aggravates operation and maintenance. To sustain control over large sets of hosts and the connecting networks, monitoring solutions are employed and constantly enhanced. They collect diverse key performance indicators (KPIs) (e.g. CPU utilization, allocated memory, etc.) and provide detailed information about the system state. Storing such metrics over a period of time naturally raises the motivation of predicting future KPI progress based on past observations. This allows different ahead of time optimizations like anomaly detection or predictive maintenance. Predicting the future progress of KPIs can be defined as a time series forecasting problem. Although, a variety of time series forecasting methods exist, forecasting the progress of IT system KPIs is very hard. First, KPI types like CPU utilization or allocated memory are very different and hard to be modelled by the same model. Second, system components are interconnected and constantly changing due to soft- or firmware updates and hardware modernization. Thus a frequent model retraining or fine-tuning must be expected. Therefore, we propose a lightweight solution for KPI series prediction based on historic observations. It consists of a weighted heterogeneous ensemble method composed of two models - a neural network and a mean predictor. As ensemble method a weighted summation is used, whereby a heuristic is employed to set the weights. The lightweight nature allows to train models individually on each KPI series and makes model retraining feasible when system changes occur. The modelling approach is evaluated on the available FedCSIS 2020 challenge dataset and achieves an overall R^2 score of 0.10 on the preliminary 10\% test data and 0.15 on the complete test data. We publish our code on the following github repository: https://github.com/citlab/fed\_challenge.

19

The application of the arima model in forecasting the passenger traffic on the example of border crossings between the subcarpathian province and Ukraine

Dejniak D.

Information Systems in Management

|

2018

|

Vol. 7, No. 3

155--170

EN

Accession of Poland to the European Union meant that its eastern border became the external frontier of the Community. The next step in the European integration was joining the Schengen Zone by Poland. Polish citizens may freely travel throughout the Schengen Zone and the state was obliged to tighten its eastern border. Under these circumstances conducting research on passenger traffic has become a vital issue, with particular focus on the eastern frontier. In the article an attempt is made at examining the possibility of forecasting passenger traffic on the example of border crossing points between the Subcarpathian Province and Ukraine using the ARIMA models. Confirmation of these possibilities seems to be crucial as the number of people crossing the border is characterized by high variability and sensitivity to the political situation. The study is based on the information provided by the Polish Border Guard. The conducted time series analysis is of a multi-purpose character. It may be used to support decision making processes of investment, organizational, as well as socio-political nature.

20

Real time search algorithm for observation outliers during monitoring engineering constructions

Latos D., Kolanowski B., Pachelski W., Sołoducha R.

Reports on Geodesy and Geoinformatics

|

2017

|

Vol. 104

103--114

EN

Real time monitoring of engineering structures in case of an emergency of disaster requires collection of a large amount of data to be processed by specific analytical techniques. A quick and accurate assessment of the state of the object is crucial for a probable rescue action. One of the more significant evaluation methods of large sets of data, either collected during a specified interval of time or permanently, is the time series analysis. In this paper presented is a search algorithm for those time series elements which deviate from their values expected during monitoring. Quick and proper detection of observations indicating anomalous behavior of the structure allows to take a variety of preventive actions. In the algorithm, the mathematical formulae used provide maximal sensitivity to detect even minimal changes in the object’s behavior. The sensitivity analyses were conducted for the algorithm of moving average as well as for the Douglas-Peucker algorithm used in generalization of linear objects in GIS. In addition to determining the size of deviations from the average it was used the so-called Hausdorff distance. The carried out simulation and verification of laboratory survey data showed that the approach provides sufficient sensitivity for automatic real time analysis of large amount of data obtained from different and various sensors (total stations, leveling, camera, radar).