As part of the progressive process of extending spatial plans to cover an increasing number of marine areas, with the aim of objectively balancing the interests of various users of the marine area, ...it has become necessary to establish the value of marine areas as a yardstick or determinant of the user group for which a given marine area is of greater value. This study seeks to fill a research gap by attempting to develop a method to calculate the value of marine areas for the commercial shipping industry. This is done to make it possible in the future to prepare the ground for policy regulating the spatial rent of the sea, whose most important users are shipowners and their ships. We use the homogeneous basin of the Polish Marine Areas (PMA) in the Baltic Sea. Based on a literature review, we conclude that such a method does not exist, posing a significant challenge in the process of marine/maritime spatial planning (MSP) and maritime policy formulation. Conducting an in-depth analysis of 2020 data on ship traffic in the basin noted above, combined with a financial analysis of shipowners’ operating costs and profitability indicators, we can determine the value of marine areas both in aggregate for all shipping in the studied basin and for each of the five segments of shipping – the bulk cargo, ro-ro cargo, container, tanker, and passenger segments. In addition, through a dynamic analysis of ship traffic, it is possible to determine the value of sea area in Polish seawaters per unit of area (1 km²) at the average level and for the five specified market segments. The obtained values show that the total profits of shipowners in the Polish Marine Areas, which are at the level of more than EUR 103 million per year, and the average value of profits per 1 km² of marine area used by a ship provide future decision-makers with an objective point of reference to shape future policies for the fiscalization of public space, including the sea.
The paper presents the results of functionality analysis of application made in Microsoft .NET Core technology on Raspberry Pi 2 platform. The research was done by implementing applications using ...libraries provided by Microsoft and developed by .NET Core community. The focus was placed on usage of this technology in the Internet of Things applications. The comparison of the possibility of using Raspberry Pi interfaces in .NET Core, officially supported Python language and popular Node.js technology was made. The performance of these technologies was also compared
Artykuł prezentuje wpływ doboru funkcji okna wykorzystywanej w procesie obliczania spektrogramu, na skuteczność identyfikacji stanu emocjonalnego mówcy posługującego się mową polską. W badaniach ...wykorzystano następujące funkcje okna: Hamminga, Gaussa, Dolpha–Czebyszewa, Blackmana, Nuttalla, Blackmana-Harrisa. Ponadto został przedstawiony sposób przetwarzania spektrogramu przez sztuczną sieć neuronową (SSN), odpowiedzialną za identyfikację stanu emocjonalnego mówcy. Otrzymane wyniki pozwoliły na ocenę skuteczności rozpoznawania stanu emocjonalnego za pomocą SSN. Średnia skuteczność wahała się od około 70% do ponad 87%.
The problem of finding anomalies (outliers) in databases is one of the most important issues in modern data analysis. One of the reasons is the occurrence of this issue in almost every type of ...database, including numerical, categorical, time, mixed, or graphic data. There are currently many methods often dedicated to specific data analysis. Finally, this topic is extremely interesting per se, as a research problem that intrigues researchers. One of the classic methods of data analysis dedicated to finding the anomalies in the data is Isolation Forest. However, this method, with a few exceptions, has not been modified from the time of its first publication, and, in particular, it has not yet appeared in combination with the typical fuzzy methods used for grouping such as Fuzzy C-Means (FCM) clustering. In this study, we thoroughly analyze this approach, as well as several related ones. We examine the possibilities of this technique and analyze it in detail for characteristics of data (database size, number of attributes, records, their type, etc.). It is worth noting that FCM allows to obtain membership grades of elements forming Isolation Forest nodes to clusters on the basis of which these nodes are built. Hence, at the stage of calculating the anomaly scores, this information is effectively used, in particular to express how much a given element may belong to a group of similar elements, which can be inferred from the characteristics of the cluster in which it lies. In this study, we propose a set of methods enhancing the Isolation Forest on a basis of Fuzzy C-Means. The results of numerical experiments carried using 27 various datasets and reported in this paper lead us to the conclusion that FCM can play a pivotal role in an enhancement of Isolation Forest approach and raises up the values of particular measures of effectiveness of the anomaly detection methods.
•Fuzzy enhancements of Isolation Forest are proposed.•Fuzzy C-Means method is applied to split the tree nodes.•Different variants of search tree building are considered.•Numerical experiments’ results demonstrating the efficiency are presented.•The experiments on real world logistic dataset are conducted.
Effective management of containerized applications is crucial to ensuring their performance and reliability. The aim of this work was to indicate which configuration settings of the Kubernetes ...orchestrator have the greatest impact on microservice application performance under conditions of increased load. For each of the established configuration variants, the throughput and response time of the test application based on the microservices paradigm were measured. Research findings indicate that excessive horizontal scaling degrades application performance and that memory usage settings may play a greater role in optimizing system performance than CPU usage.
This paper presents the synthesis and physicochemical characterization of a new hybrid composite. Its main goals are evaluating the structure and studying the thermal and mechanical properties of the ...crosslinked polymeric materials based on varying chemical properties of the compounds. As an organic crosslinking monomer, bisphenol A glycerolate diacrylate (BPA.GDA) was used. Trimethoxyvinylsilane (TMVS) and N-vinyl-2-pyrrolidone (NVP) were used as comonomers and active diluents. The inorganic fraction was the silica in the form of nanoparticles (NANOSiO2). The hybrid composites were obtained by the bulk polymerization method using the UV initiator Irqacure 651 with a constant weight ratio of the tetrafunctional monomer BPA.GDA to TMVS or NVP (7:3 wt.%) and different wt.% of silica nanoparticles (0, 1, 3%). The proper course of polymerization was confirmed by the ATR/FTIR spectroscopy and SEM EDAX analysis. In the composites spectra the signals correspond to the C=O groups from NVP at 1672–1675 cm−1, and the vibrations of Si–O–C and Si–O–Si groups at 1053–1100 cm−1 from TMVS and NANOSiO2 are visible. Thermal stabilities of the obtained composites were studied by a differential scanning calorimetry DSC. Compared to NVP the samples with TMVS degraded in one stage (422.6–425.3 °C). The NVP-derived materials decomposed in three stages (three endothermic effects on the DSC curves). The addition of NANOSiO2 increases the temperature of composites maximum degradation insignificantly. Additionally, the Shore D hardness test was carried out with original metrological measurements of changes in diameter after indentation in relation to the type of material. The accuracy analysis of the obtained test results was based on a comparative analysis of graphical curves obtained from experimental tests. The values of the changes course of similarity in the examined factors, represented by those of characteristic coefficients were determined based on the Fréchet’s theory.
In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h ...of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R
was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R
to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.
Prediction of considerable wind power is a significant factor in modern power systems’ robust and resilient operation. As a result, many studies addressed up-to-day-ahead wind power forecasting. ...Taking into account the abilities of Machine Learning (ML) Models and their combinations, in this paper, the Authors would like to present the framework for 60-hour wind power forecasting in Poland. Possession of longer than one-day ahead of accurate wind power forecasts gives an ability to the Transmission System Operator (TSO) and Distribution System Operator (DSO) the to improve the control of grid traffic and assimilate more efficiently the Renewable Energy Sources (RES) generated power. The presented method uses the geographical coordinates of wind farms in the area of interest with their wind-power curves. It combines it with weather parameters such as wind speed and direction, wind gust, air temperature, and pressure for improved accuracy. The novelty of the proposed method is that model can adapt autonomously according to achieved past accuracy. A back-score accuracy has been tracked based on past predictions and measured wind power generation. Furthermore, the model combines different ML models that can be adapted or retrained if prediction performance drops below an acceptable level. In this paper, we would like to present a method and performance analysis of the adaptive ensemble-model method, whose accuracy has been calculated compared with actual data measured in the National Power System of Poland. Furthermore, the paper’s novelty is that the proposed method/framework can use only an approximate model of large wind generation at the beginning, and the model can be fine-tuned in the proposed method’s operation as a function of back-accuracy measurement triggering ensemble-model adaptation.
The COVID-19 pandemic demanded changes in healthcare systems worldwide. The lockdown brought about difficulties in healthcare access. However, trauma still required further attention considering its ...modifications. The presented study aims to investigate the variances in epidemiological patterns of trauma during the lockdown and the previous year, with a view to better understand the modifications in healthcare provision. The authors analyzed data from the first lockdown in 2020 (12 March–30 May) and the same period in 2019 from 35 hospitals in Lublin Province. A total of 10,806 patients in 2019 and 5212 patients in 2020 were included in the research. The uncovered changes adhered to the total admissions and mortality rate, the frequency of injuries in particular body regions, and injury mechanisms. The lockdown period resulted in a reduction in trauma, requiring an altered approach to healthcare provision. Our research indicates that the altered approach facilitated during such periods is essential for delivering tailored help to trauma patients.
The article presents simulation studies of multipath video transmission implemented as a live monitoring system based on adaptive streaming mechanisms. The transmission network consists of the Wi-Fi ...infrastructure and a single LTE cell. Both wireless networks are used during the operation of the video monitoring system. The analysis of the obtained results allows us to compare the features of two currently used multipath transmission protocols, MultiPath Quick UDP Internet Connections (MPQUIC) and MultiPath TCP (MPTCP) in the context of their use in adaptive video streaming systems. Also, the article contains a summary of the perceived advantages and disadvantages of using several transmission networks simultaneously for the implementation of video surveillance and monitoring systems.
PDF
