Wyniki wyszukiwania - BazTech

1

Hybrid wavelet transform - MLR and ANN models for river flow prediction: Case study of Brahmaputra river (Pancharatna station)

Khandekar Sachin Dadu, Aswar Dinesh Shrikrishna, Sabale Pandurang Digamber, Khandekar Varsha Sachin, Bajad Mohankumar Namdeorao, Khaple Shivakumar

Przegląd Naukowy Inżynieria i Kształtowanie Środowiska

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2024

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Vol. 33, No. 1

69--94

EN

In this research, discrete wavelet transform (DWT) is combined with MLR and ANN to develop WMLR and WANN hybrid models, respectively, for the Brahmaputra river (Pancharatna station) flow forecasting. Daily flow data for the period of 10 year were decomposed (up to fifth level) into detailed and approximation coefficients (using Daubechies wavelets db1, db2, db3, db8 and db10) which were fed as input to MLR and ANN to get the predicted discharge values two days, four days, seven days and 14 days ahead. For all lead times, the WMLR-db10 model was found to be superior as compared to WANN-db1, WANN-db2, WANN-db3, WANN-db8, WMLR-db1, WMLR-db2, WMLR-db3, WMLR-db8 and single MLR and ANN models. During testing period, the values of determination coefficient (R2) and RMSE for WMLR-db10 model for two-, four-, seven- and 14-day lead time were found to be, respectively, 0.996 (751.87 m3·s–1), 0.991 (1,174.80 m3·s–1), 0.984 (1,585.02 m3·s–1), and 0.968 (2,196.46 m3·s–1). Also, it was observed that for lower order wavelets (db1, db2, db3) WANN’s performance was better, and for higher order wavelets (db8, db10) WMLR’s performance was better. Correspondingly, it was observed that all hybrid models’ efficiency increased with increase in the decomposition level.

2

Wybrane zagadnienia analizy szeregów czasowych

Adamski Krzysztof

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki

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2023

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nr 29

7--104

PL

Artykuł obejmuje przegląd wybranych zagadnień związanych z analizą i predykcją szeregów czasowych zawierających dane z notowaniami giełdowymi. Przedstawiona została taksonomia szeregów czasowych oraz scharakteryzowane główne kierunki spotykane w analizie danych giełdowych. Szerzej opisane zostały wybrane narzędzia analizy technicznej. W kolejnych rozdziałach dokonany został przegląd wybranych metod uczenia maszynowego, zarówno metod algorytmicznych jak i metod wykorzystujących sieci neuronowe, przydatnych w analizie i predykcji szeregów czasowych.

EN

The article provides an overview of selected problems related to the analysis and prediction of time series containing stock market data. The taxonomy of time series is presented, and the main directions encountered in the analysis of financial data are characterized. Selected tools of technical analysis are described in more detail. Subsequent chapters provide a review of selected machine learning methods, divided into a section on algorithmic methods and a section on neural networks useful in the analysis and prediction of time series.

3

An application of machine learning methods to cutting tool path clustering and rul estimation in machining

Zegarra Fabio C., Vargas-Machuca Juan, Roman-Gonzalez Avid, Coronado Alberto M.

Journal of Machine Engineering

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2023

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Vol. 23, No. 4

5--17

EN

Machine learning has been widely used in manufacturing, leading to significant advances in diverse problems, including the prediction of wear and remaining useful life (RUL) of machine tools. However, the data used in many cases correspond to simple and stable processes that differ from practical applications. In this work, a novel dataset consisting of eight cutting tools with complex tool paths is used. The time series of the tool paths, corresponding to the three-dimensional position of the cutting tool, are grouped according to their shape. Three unsupervised clustering techniques are applied, resulting in the identification of DBA-k-means as the most appropriate technique for this case. The clustering process helps to identify training and testing data with similar tool paths, which is then applied to build a simple two-feature prediction model with the same level of precision for RUL prediction as a more complex four-feature prediction model. This work demonstrates that by properly selecting the methodology and number of clusters, tool paths can be effectively classified, which can later be used in prediction problems in more complex settings.

4

The Long-Term Prediction of Landslide Processes within the Precarpathian Depression of the Cernivtsi Region of Ukraine

Hablovskyi Bohdan, Hablovska Nadiia, Shtohryn Liudmyla, Kasiyanchuk Dmytro, Kononenko Maryna

Journal of Ecological Engineering

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2023

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Vol. 24, nr 7

254--262

EN

The purpose of this publication was the long-term forecasting of the landslide processes activation for the territory of the Precarpathian depression within the Chernivtsi region, taking into account the complex effect of natural factors. On the basis of statistical analysis and processing of long-term observations of landslide activation and natural time factors in particular solar activity, seismicity, groundwater levels, precipitation and air temperature, the relationship was analysed, the main periods of landslide activation were determined, the contribution of each time factor to the complex probability indicator of landslide development was estimated and long-term forecasting was carried out. An analysis of the influence of geomorphology on the landslide development was performed by using GIS MapІnfo. By means of cross-correlation, Fourier spectral analysis, the periodicities were analysed and the relationships between the parameters were established. It was found that the energy of earthquakes precedes the activation of landslides by 1 year, which indicates the “preparatory” effect of earthquakes as a factor that reduces the stability of rocks. The main periodicities of the forecast parameters of 9–11, 19–21, 28–31 years were highlighted, which are consistent with the rhythms of solar activity. The forecasting was carried out using artificial neural networks and the prediction function of the Mathematical package Mathcad, based on the received data, the activation of landslides is expected in 2023–2026, 2030–2035, 2040–2044 with some short periods of calm. The main periods of the dynamics of the time series of landslides and natural factors for the territory of the Precarpathian depression within the Chernivtsi region were determined, and a long-term forecast of landslides was made. Taking into account the large areas of the spread of landslide processes, forecasting the likely activation is an important issue for this region, the constructed predictive time models make it possible to assess the danger of the geological environment for the purpose of early warning and making management decisions aimed at reducing the consequences of a natural disaster.

5

Analiza statystyczna danych oraz prognozy rynkowych cen energii (RCE) w Polsce z wyprzedzeniem do 1 godziny

Piotrowski Paweł, Baranowski Marcin, Bączek Adrian, Niedźwiecki Radosław

Elektro Info

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2023

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nr 4

81--85

PL

W artykule przedstawiono analizę statystyczną danych oraz prognozy rynkowych cen energii (RCE) z wyprzedzeniem do 1 godziny. Sformułowano wnioski końcowe z wykonanych prognoz oraz analiz statystycznych.

EN

The article presents a statistical analysis of data and forecasts of energy prices (RCE) in Poland up to 1 hour ahead. The conclusions have been drawn based on forecasts outcome and statistical analysis.

6

Earthquake network construction models: from Abe‑Suzuki to a multiplex approach

Lotfi Nastaran

Acta Geophysica

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2023

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Vol. 71, no. 3

1111--1117

EN

From the early stage of seismological research, a complex network is one of the statistical methods to investigate the complexity of earthquake systems. The benefit of using this method is to inspect the systems with minimum information about their entities and corresponding interactions. Achieving a high interest in studying the seismic events using the complex network resulted in defining models to map the seismic data into networks. Application of these models to the seismic data sets in nonidentical geographical regions has yielded promising results independent of time and location. In this review, we bring in the recent famous models varying from monolayer to multiplex and compare their proficiency in capturing the complexity of the seismicity by using two data sets from Iran and California.

7

Monitoring dynamiki ruchów powierzchni osuwisk w warunkach zmiennego poziomu wód gruntowych z wykorzystaniem reflektorów radarowych

Perski Zbigniew, Wojciechowski Tomasz

Przegląd Geologiczny

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2022

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Vol. 70, nr 9

661--670

EN

The main objective of the presented analysis was to investigate to what extent even minor changes in the hydrological and hydrogeological environment affect the changes in the dynamics of landslide surface displacement. The research was carried out for selected monitored landslides, which, in addition to the in-depth monitoring devices, were equipped with corner reflectors suitable for satellite radar interferometry measurements. The high temporal resolution of the interferometric data allowed demonstrating the existence of a relationship between the speed of surface movements and slight changes in the depth to the groundwater table. The analyses were performed for five landslides, which also showed a high dependence of these compounds on the geological structure of the landslide and its substrate.

8

The SARIMA model-based monthly rainfall forecasting for the Turksvygbult Station at the Magoebaskloof Dam in South Africa

Tadesse Kassahun Birhanu, Dinka Megersa Olumana

Journal of Water and Land Development

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2022

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no. 53

100--107

EN

Rainfall forecast information is important for the planning and management of water resources and agricultural activities. Turksvygbult rainfall near the Magoebaskloof Dam (South Africa) has never been modelled and forecasted. Hence, the objective of this study was to forecast its monthly rainfall using the SARIMA model. GReTL and automatic XLSTAT software were used for forecasting. The trend of the long-term rainfall time series (TS) was tested by Mann-Kendall and its stationarity was proved by various unit root tests. The TS data from Oct 1976 to Sept 2015 were used for model training and the remaining data (Oct 2015 to Sept 2018) for validation. Then, all TS (Oct 1976 to Sept 2018) were used for out of sample forecasting. Several SARIMA models were identified using correlograms that were derived from seasonally differentiated TS. Model parameters were derived by the maximum likelihood method. Residual correlogram and Ljung–Box Q tests were used to check the forecast accuracy. Based on minimum Akaike information criteria (AI) value of 5642.69, SARIMA (2, 0, 3) (3, 1, 3)12 model was developed using GReTL as the best of all models. SARIMA (1, 0, 1) (3, 1, 3)12, with minimum AI value of 5647.79, was the second-best model among GReTl models. This second model was also the first best automatically selected model by XLSTAT. In conclusion, these two best models can be used by managers for rainfall forecasting and management of water resources and agriculture, and thereby it can contribute to economic growth in the study area. Hence, the developed SARIMA forecasting procedure can be used for forecasting of rainfall and other time series in different areas.

9

Trend Analyses of River Dragacina Runoff for Identification of the Water Availability and Accounting for Water Needs

Kusari Laura, Osmanaj Lavdim, Bungu Samir, Thaqi Premton, Hajdari Venera

Journal of Ecological Engineering

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2022

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Vol. 23, nr 7

246--256

EN

This study aimed to analyze the available amount of water in the Dragaçina River to meet the different water needs in the Municipality of Suhareka. The water problems in this city are more pronounced, especially in the vegetation period of July–September, where the area is significantly affected by drought. The Dragacina River carries about 10 hm3 of water per year, and affected neither by urbanism nor massive deforestation of the basin. However, there are no multi-year measurements of inflows for this river, whether they are average, maximum or minimum ones. Therefore, the study is based on several multi-annual monthly rainfall measurements and some characteristics of the Dragaçina River Basin. Knowing the average annual flow coefficient η = Peff / Pbruto it is possible to convert these precipitations to Peff [mm] flow and then to monthly flow. The inputs for other years from 1983/84 onwards are obtained by simulating time series. Then, for such inflows, the probability distribution functions of small waters are assigned and the usable volume balance is carried out. Assuming an average annual withdrawal from the reservoir QAmin mes. = 0.63 × Qmes. which should be constant throughout the years, then the length of the critical period will be 0.13 years or approximately 48 days, for PH = 95%. Starting from the initial acquired volume of 1 hm3 it is possible to achieve 95% < PH < 99%. Therefore, it follows from this analysis that this river can provide a significant amount of water for the needs of the Municipality of Suhareka.

10

Short and long forecast to implement predictive maintenance in a pulp industry

Rodrigues João Antunes, Farinha José Torres, Mendes Mateus, Mateus Ricardo, Cardoso António Marques

Eksploatacja i Niezawodność

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2022

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Vol. 24, no. 1

33--41

EN

Predictive maintenance is very important for effective prevention of failures in an industry. The present paper describes a case study where a wood chip pump system was analyzed, and a predictive model was proposed. An Ishikawa diagram and FMECA are used to identify possible causes for system failure. The Chip Wood has several sensors installed to monitor the working conditions and system state. The authors propose a variation of exponential smoothing technique for short time forecasting and an artificial neural network for long time forecasting. The algorithms were integrated into a dashboard for online condition monitoring, where the users are alerted when a variable is determined or predicted to get out of the expected range. Experimental results show prediction errors in general less than 10 %. The proposed technique may be of help in monitoring and maintenance of the asset, aiming at greater availability.

11

Geometric approach to machine exploitation efficiency : modelling and assessment

Loska Andrzej, Paszkowski Waldemar

Eksploatacja i Niezawodność

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2022

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Vol. 24, no. 1

114--122

EN

This article presents a new approach to the exploitation assessment of machines and devices. A key aspect of this approach is the construction of the assessment model based on the geometric representation of measures associated with each other, which covers the full specifics of the exploitation process. This approach is successfully implemented by the Overall Equipment Effectiveness (OEE) model, which is fully susceptible to the geometric modelling process due to the three-way system of assessed exploitation aspects. The result of this approach is the vectored OEE model and its interpretation in terms of time series of changes in values of components. Methods of determining vector calculus measures were developed, including the second-order tensor and gradient. This is the subject of the variability of the reliability conditions of machines or production processes. It allows for the realisation of an exploitation assessment based on dynamic changes in the values of their components in the time domain. This is a significant difference to the classical static approach to such an assessment. The developed new geometric OEE model was confirmed by verification tests using the LabView software, based on two parallel data sets obtained with analytical and simulation methods using the FlexSim software.

12

udCATS : A Comprehensive Unsupervised Deep Learning Framework for Detecting Collective Anomalies in Time Series

Pham Truong Son, Nguyen Viet Hung, Le Anh Thang, Bui Van Duong

Annals of Computer Science and Information Systems

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2022

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Vol. 33

201--206

EN

Anomaly detection has recently gained enormous attention from the research community. It is widely applied in many industrial areas, such as information security, financing, banking, and insurance. The data in these fields can mainly be represented as time series data, the corollary being that time series anomaly detection plays an essential role in these applications. Therefore, many authors have tried to solve the problem of collective anomaly detection in time series. They have proposed several approaches, from classical methods such as Isolation Forests to modern deep learning networks such as Autoencoders. However, a comprehensive framework for handling this problem is still lacking. In this work, firstly, we propose using an Attention-based Bidirectional LSTM Autoencoder (Att-BiLSTM-AE) as an anomaly detection model. Furthermore, in the essential part of this paper, we developed a comprehensive unsupervised deep learning framework, udCATS, to solve the problem of detecting collective anomalies in time series. Our experiments show that the Att-BiLSTM-AE outperforms other detection models, and using it within the udCATS framework increases the detection accuracy.

13

Weather-based maize yield forecast in Saudi Arabia using statistical analysis and machine learning

Jayagopal Prabhu, Muthukumaran Venkatesan, Koti Manjula Sanjay, Kumar Sampath Satheesh, Rajendran Sukumar, Mathivanan Sandeep Kumar

Acta Geophysica

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2022

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Vol. 70, no 6

2901--2916

EN

Crop yield is completely vulnerable to extreme weather events. Growing research investigation to establish climate change, implications in the sectors are influencing the connection. Forecasting maize output with some lead time can help producers to prepare for requirement and, in many cases, limited human resources, as well as support in strategic business decisions. The major purpose is to illustrate the relationship between various climatic characteristics and maize production, as well as to predict forecasts using ARIMA and machine learning approaches. When compared to ARIMA, the proposed method performs better in forecasting maize yields. Consequently, the neural network provides the majority of the prospective talents for forecasting maize production. Seasonal growth is susceptible of forecasting crop yields with tolerable competencies, and efforts are essential to quantify the proposed methodology that forecasts overall crop yield in diverse neighbourhoods in Saudi Arabia’s regions. The proposed combined ARIMA-LSTM model requires less training, with parameter adjustment having less effect on data prediction without bias. To monitor progress, the model may be trained repeatedly using roll back. The correlations between estimated yield and measured yield at irrigation and rain-fed sites were analysed to further validate the robustness of the optimal ARIMA-LSTM method, and the results demonstrated that the proposed model can serve as an effective approach for different types of sampling sites and has better adaptability to inter-annual fluctuations in climate with findings indicating a dependable and viable method for enhancing yield estimates.

14

Sztuczna inteligencja – klucz do rozwoju sieci 5G

Koper Zbigniew

Wiadomości Elektrotechniczne

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2021

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R. 89, nr 11

13--16

PL

Rozwój sieci 5G jest niemożliwy bez zastosowania sztucznej inteligencji (AI). Zastosowania AI obejmuje planowanie sieci, diagnostykę sieci oraz jej optymalizację i kontrolę. W artykule omówiono wybrane zagadnienia związane z wykorzystaniem ML, koncentrując się na szeregach czasowych i ich wykorzystaniu do przewidywania stanu sieci.

15

Głębokie sieci rekurencyjne i konwolucyjne w detekcji wad spawalniczych dla systemów z robotem przemysłowym

Adamczak Arkadiusz

Pomiary Automatyka Robotyka

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2021

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R. 25, nr 2

17--22

PL

Podczas procesów spawania metodą MIG/MAG w produkcji wielkoseryjnej na stanowiskach zrobotyzowanych, często wymagana jest automatyczna kontrola jakości wykonanego spawu. Określanie defektów spawalniczych jest trudne, a powód ich wystąpienia nie zawsze jest znany. Jednym z warunków poprawnie wykonanej spoiny jest stabilność podczas procesu spawania, co przekłada się na ciągłość i zwiększenie ogólnej wydajności produkcji. W artykule przedstawiono wyniki badań nad systemem detekcji defektów spoiny łączącego analizę i klasyfikację szeregów czasowych parametrów spawania dla metody MIG/MAG wraz z równoczesną analizą i klasyfikacją danych obrazowych spoiny dla systemów zrobotyzowanych. Wykorzystane zostały konstrukcje głębokich sieci neuronowych rekurencyjnych i konwolucyjnych. Przedstawiono również konstrukcję sieci neuronowej zawierającej dwa wejścia systemowe, umożliwiającej w jednym czasie klasyfikację zdjęcia spoiny wraz z szeregiem czasowym dla zastosowania w stanowisku zrobotyzowanym. Przedstawione wyniki prac badawczych otrzymano podczas realizacji projektu „Opracowanie metody bazującej na zastosowaniu głębokich sieci neuronowych do inspekcji wizyjnej połączeń spawanych w toku prac B+R” finansowanego z Wielkopolskiego Regionalnego Programu Operacyjnego na lata 2014–2020 i realizowanego w zakładzie ZAP-Robotyka Sp. z o.o. w Ostrowie Wielkopolskim.

EN

During MIG/MAG welding processes in large-scale production on robotic stations, automatic quality control of the weld is often required. Determining welding defects is difficult and the reason for their occurrence is not always known. One of the conditions for a correctly made weld is stability during the welding process, which translates into continuity and increase in overall production efficiency. The article presents the results of research on the creation of a weld defect detection system combining the analysis and classification of time series of welding parameters for the MIG/MAG method along with the simultaneous analysis and classification of weld image data for robotic systems. For this purpose, the structures of deep recursive and convolutional neural networks were used. The design of a neural network with two system inputs allowing for the classification of the weld photo together with the time series for use in a robotic station is also presented. The research results presented in this article were obtained during the implementation of the project entitled „Development of a method based on the use of deep neural networks for visual inspection of welded joints in the course of R&D works” implemented at the company ZAP-Robotyka Sp. z o.o. in Ostrów Wielkopolski.

16

Long term variation of sardine Sardina pilchardus spawning along the Atlantic coast of northwest Africa (21-26°N) : characterization and spatiotemporal variability in spawning habitat

Abdelouahab Hinde, Berraho Amina, Baibai Tarik, Larissi Jamila, Charib Said, Agouzouk Aziz, Makaoui Ahmed, Ettahiri Omar, Errhif Ahmed

Oceanologia

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2021

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No. 63 (1)

1--11

EN

Small pelagic fish such as sardine show strong recruitment variability often associated with environmental changes influencing the spawning process and ultimately, affecting population dynamics. Sardine (Sardina pilchardus, Walbaum 1792) is one of the most exploited pelagic species along the northwest African coast. The main spawning occurs during the cold season (autumn-winter). A time-series autumn-winter surveys extending from 1994 to 2015 sampled sardine eggs, along the southern area of the Moroccan Atlantic coast (26°N-21°N) were analyzed. The present work focuses on examining the inter-annual variability of the spawning habitat by analyzing the spatial-temporal variability of sardine egg distribution and density extracted from the data collected over the period 1994-2015. Generalized additive models (GAM) were used to detect the relationships between the sardine distribution, expressed as egg density and the presence or absence data and relevant hydrobiological environmental variables, such as salinity, temperature and zooplankton biomass. The generalized additive models showed significant relationships between the environment variables (SST, SSS and Zooplankton biomass) and sardine density, but not with sardine presence. Given that the study area is characterized by high mesoscale features and significant upwelling activities, the variability of upwelling processes could explain the changes of spawning ground position and thermal window.

17

Time series forecasting using the LSTM network

Pęczek Mateusz, Litawa Grzegorz

Journal of Engineering, Energy and Informatics

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2021

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No. 1

116--120

EN

Predicting time series is currently one of the problems that can be solved through the use of machine learning methods. A time series is a set of data points in which the sequence is measured at equal time intervals. Predicting the value of the time series can influence your decisions or help you achieve better results. Stock quotes are an example of a time series - the purpose of the created model is attempt to predict their value. One solution to the problem of predicting the results of the time series is the LSTM network. The network contains layered LSTM cells that have the ability to use previously observed relationships in the data set. The number of LSTM layers and cells in each layer is dependent on the designer and is selected based on expert knowledge. The results obtained from the model may seem correct and close to the real ones. Regardless of what values we get and how high the accuracy of the model will be, it should be remembered that stock prices are influenced by parameters and events that cannot be predicted. The predicted values obtained from the model should be treated as a guide or reference information. Stock quotes may change under the influence of geopolitical situations, company involvement, armed conflict or other random and unpredictable phenomenon, therefore, when making decisions, the results of the model should not be taken for granted.

PL

Przewidywanie szeregów czasowych jest obecnie jednym z problemów, które mogą zostać rozwiązane poprzez zastosowanie metod uczenia maszynowego. Szeregiem czasowym nazwiemy zbiór danych, w których pomiar odbywał się w jednakowych odstępach czasu. Przewidywanie wartości szeregu czasowego może wpłynąć na podejmowane decyzje lub pomóc w osiąganiu lepszych wyników. Przykładem szeregu czasowego są notowania giełdowe - celem utworzonego modelu jest próba przewidywania ich wartości. Jednym z rozwiązań problemu przewidywania wyników szeregów czasowych jest sieć LSTM. Sieć zawiera warstwowo ułożone komórki LSTM, które mają zdolność do wykorzystywania wcześniej zaobserwowanych zależności występujących w zbiorze danych. Liczba warstw i komórek LSTM w każdej warstwie jest zależna od projektanta i dobiera się ją w oparciu o wiedzę ekspercką. Wyniki otrzymane z modelu mogą wydawać się poprawne i zbliżone do rzeczywistych. Niezależnie od tego, jakie wartości otrzymamy i jak duża będzie dokładność modelu, należy pamiętać, że na notowania giełdowe wpływ mają parametry i zdarzenia, których nie da się przewidzieć. Wartości przewidywane, otrzymane z modelu, należy traktować jako pomoc lub informacje poglądowe. Notowania giełdowe mogą zmieniać się pod wpływem sytuacji geopolitycznej, upadku firmy, konfliktu zbrojnego lub innego losowego i niemożliwego do przewidzenia zjawiska, dlatego przy podejmowaniu decyzji nie należy traktować wyników modelu jako pewne.

18

Econometric Analysis of the Impact of Climate Change on the Sustainability of Agricultural Production in Ukraine

Skrypnyk Andriy, Zhemoyda Oleksandr, Klymenko Nataliia, Galaieva Liudmyla, Koval Tatiana

Journal of Ecological Engineering

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2021

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Vol. 22, nr 3

275--288

EN

The aim of this paper was to determine the mechanisms of climate change impact on the yield of the main exportoriented crops in the agro-climatic zones of Ukraine. The study of the problem of changing the acreage of the main export-oriented crops was conducted according to the data of the State Statistics Service of Ukraine on the time horizon 2000-2018 in the following order: first, the dynamics of the change of the acreage under corn, sunflower and wheat by the agro-climatic zones of Ukraine was analyzed; secondly, the trends of yield changes of these crops were investigated based on the increase in the difference in yields between the northern and southern zones; and, finally, the temporal and spatial expansion in the area of crop propagation were investigated by applying the panel regression method. The findings obtained indicate that the applied models confirm the assumption of the effects of climate change on crop yield changes and the zones expansion in the northern direction. If the country’s wheat area can be considered stable (variation is insignificant), then the corn and sunflower areas have grown steadily under the influence of increasing demand from national and world markets. At the same time, the growing acreage under corn and sunflower occurred in all climatic zones. Stable expansion of corn crops in the north direction in all three agroclimatic zones of Ukraine has been statistically confirmed. The article presents the findings of empirical analysis, which confirm that if the boundaries of soil and climatic zones change, the conditions of growing crops and their yield will consequently change as well. Thus, based on current global forecasts, the impact of weather on Ukraine’s agriculture will increase, and the most negative effects can be expected in the Steppe zone, where the likelihood of weather and climate risks increases, requiring the development of adaptation and mitigation measures as well as exploitation of new potential opportunities that are being opened. Studies have shown that there is an expansion in crops to the north and a change in their pattern, including a significant increase in the area under corn.

19

City Backbone Network Traffic Forecasting

Serikov Tansaule, Zhetpisbayeva Ainur, Аkhmediyarova Аinur, Mirzakulova Sharafat, Aigerim Kismanova, Tolegenova Aray, Wójcik Waldemar

International Journal of Electronics and Telecommunications

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2021

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Vol. 67, No. 3

319--324

EN

The work considers a one-dimensional time series protocol packet intensity, measured on the city backbone network. The intensity of the series is uneven. Scattering diagrams are constructed. The Dickie Fuller test and Kwiatkowski-Phillips Perron-Shin-Schmitt test were applied to determine the initial series to the class of stationary or non-stationary series. Both tests confirmed the involvement of the original series in the class of differential stationary. Based on the Dickie Fuller test and Private autocorrelation function graphs, the Integrated Moving Average Autoregression Model model is created. The results of forecasting network traffic showed the adequacy of the selected model.

20

Analiza statystyczna danych oraz prognozy generacji energii w farmie wiatrowej z wyprzedzeniem do 24 godzin (część 1.)

Piotrowski Paweł, Baczyński Dariusz, Kopyt Marcin, Aspras Rafał, Dryja Hubert, Makowski Paweł, Samul Kamil

Elektro Info

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2021

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nr 4

74--76

PL

W artykule przedstawiono analizę statystyczną danych z farmy wiatrowej oraz prognozy generacji energii z wyprzedzeniem do 24 godzin.

EN

The article presents a statistical analysis of wind farm data and energy generation forecasts up to 24 hours ahead.