Publikační činnost Katedry ekonomiky a systémů řízení / Publications of Department of Economics and Control Systems (545)

Permanent URI for this collectionhttp://hdl.handle.net/10084/64766

Kolekce obsahuje bibliografické záznamy publikační činnosti (článků) akademických pracovníků Katedry ekonomiky a systémů řízení (545) v časopisech registrovaných ve Web of Science od roku 2003 po současnost.
Do kolekce jsou zařazeny:
a) publikace, u nichž je v originálních dokumentech jako působiště autora (adresa) uvedena Vysoká škola báňská-Technická univerzita Ostrava (VŠB-TUO),
b) publikace, u nichž v originálních dokumentech není v adrese VŠB-TUO uvedena, ale autoři prokazatelně v době jejich zpracování a uveřejnění působili na VŠB-TUO.

Bibliografické záznamy byly původně vytvořeny v kolekci Publikační činnost akademických pracovníků VŠB-TUO, která sleduje publikování akademických pracovníků od roku 1990.

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Virtual reality as a tool for sustainable training and education of employees in industrial enterprises
(MDPI, 2023) Holuša, Věroslav; Vaněk, Michal; Beneš, Filip; Švub, Jiří; Staša, Pavel
The paper deals with the possibilities of using Virtual Reality in the training and safety of enterprises active in the raw materials industry. It examines the influence and impact on their employees. The main impetus for starting research in this area has been a need for more use of the full potential of Virtual Reality in the industrial sector. Virtual Reality (VR) has become a promising education and employee training tool. It provides an immersive and interactive learning environment, allowing users to engage with simulations, scenarios, and simulations in real time. VR can facilitate the acquisition of practical skills, help learners retain information better, and foster the development of soft skills, such as communication, teamwork, and leadership. The paper is divided into the following sections. The first two are devoted to the introduction to the issue and a review of the literature. The materials and methods section describes the possibilities of using photogrammetry to create virtual scenes and 3D models usable in Virtual Reality. This section also describes the research methods used to evaluate the approach for teaching and training employees. The last two sections evaluate and discuss the results achieved. Having regarded the research realized, it was found that our approach to researching the education of employees and the development of their skills brings excellent benefits and, compared to the traditional educational approach, is much more time-efficient so that employees can improve their work habits and behavior in a relatively short period. In employee training, VR can simulate real-life scenarios, providing workers with hands-on experience in a safe, controlled environment. This technology can also help companies save time and resources, eliminating the need for travel and reducing expenditure on expensive equipment. However, despite its many benefits, VR in education and training can be cost-demanding and requires specialized hardware and software, which may limit its widespread adoption.
Utilizing Random Forest with iForest-based outlier detection and SMOTE to detect movement and direction of RFID tags
(MDPI, 2023) Alfian, Ganjar; Syafrudin, Muhammad; Fitriyani, Norma Latif; Alam, Sahirul; Pratomo, Dinar Nugroho; Subekti, Lukman; Octava, Muhammad Qois Huzyan; Yulianingsih, Ninis Dyah; Atmaji, Fransiskus Tatas Dwi; Beneš, Filip
In recent years, radio frequency identification (RFID) technology has been utilized to monitor product movements within a supply chain in real time. By utilizing RFID technology, the products can be tracked automatically in real-time. However, the RFID cannot detect the movement and direction of the tag. This study investigates the performance of machine learning (ML) algorithms to detect the movement and direction of passive RFID tags. The dataset utilized in this study was created by considering a variety of conceivable tag motions and directions that may occur in actual warehouse settings, such as going inside and out of the gate, moving close to the gate, turning around, and static tags. The statistical features are derived from the received signal strength (RSS) and the timestamp of tags. Our proposed model combined Isolation Forest (iForest) outlier detection, Synthetic Minority Over Sampling Technique (SMOTE) and Random Forest (RF) has shown the highest accuracy up to 94.251% as compared to other ML models in detecting the movement and direction of RFID tags. In addition, we demonstrated the proposed classification model could be applied to a web-based monitoring system, so that tagged products that move in or out through a gate can be correctly identified. This study is expected to improve the RFID gate on detecting the status of products (being received or delivered) automatically.
Evaluation of the functionality of bankruptcy models in mining companies
(Technická univerzita Košice, Fakulta baníctva, ekológie, riadenia a geotechnológií, 2022) Kozel, Roman; Vilamová, Šárka; Prachařová, Lenka; Sedláková, Zuzana
Mining companies are an important part of the national industry of the Czech Republic. Since mining companies are important for the industry, it is necessary to predict their economic development. Moreover, forecasting the economic development of an enterprise in terms of the risk of bankruptcy is an important activity for the financial management of any enterprise. One of the ways to predict economic development and assess the risk of possible bankruptcy is to use bankruptcy models. The aim of this paper is to determine the most appropriate model for predicting the bankruptcy risk of a mining company. The subject of the article is to identify the most suitable bankruptcy models applicable for bankruptcy risk prediction in Czech conditions of mining enterprises and to verify their functionality on real data of mining enterprises. On the basis of a search of expert sources and comparative analysis, it was found that the most suitable models for predicting the development of the enterprise in terms of bankruptcy risk are modified versions of traditional bankruptcy models. The analysis showed that the bankruptcy models are the IN05 Index, Altman's analysis for Czech companies and the modified Taffler's index. The authors' team conducted a thorough analysis during which they verified the functionality of the selected bankruptcy models on real data of mining companies. After a thorough analysis to test the functionality of bankruptcy models on real data from mining companies, the most appropriate model for estimating the evolution of bankruptcy probability risk was identified.
Determinants of economic growth: Panel data analysis of OPEC
(Elsevier, 2022) Pekarčíková, Kateřina; Vaněk, Michal; Sousedíková, Radmila
The article deals with the study of the determinants of economic growth of the Organization of the Petroleum Exporting Countries (OPEC), which are all developing countries. The issue is examined because the organization affects the price of oil worldwide. Panel data analysis with the random effects model is used. The panel data analysis examines the relationship between GDP and the selected determinants from 1980 to 2019. The analysed data are non-standard. They are affected by volatility, missing values, and credibility. For example, countries such as Venezuela, Iran, or Iraq may provide data which can distort the analysis. The results show a correlation of a total of five variables. The correlation has both positive and negative effects on GDP. Daily oil production, oil exports and oil prices positively affect GDP, contributing to GDP growth. Unemployment and exchange rate have a negative effect. No correlation was found between inflation, population growth, oil demand, and GDP. The analysis can be used as a basis for future decision-making by OPEC Member economies.
Methodology for calculating the cost price of the bending process for the needs of manufacturing logistics
(4S go, s.r.o., 2022) Janovská, Kamila; Vilamová, Šárka; Labounek, Dalibor; Kozel, Roman; Pala, Tomáš
The research of this paper aims to characterize and describe the methodological sequence of operations necessary for the correct calculation of the cost price when performing a bid calculation in the bending process step on a bending machine. The research focuses on determining the methodology and its application exclusively to sheet metal parts in the engineering industry in the processing of steel and stainless-steel sheets. To research this issue, we used empirical and quantitative research in a real work environment. The methodology for calculating the cost price of bending sheet metal parts yields the relationships between component inputs, the result of which is a time parameter that is expressed by the actual production costs. The results can be used in the real working environment of manufacturing companies for comparison with already established practices and a verification of their outputs. At the same time, it is possible to use the determined methodological procedure as a basis for implementation in the Aurendi web application.
Water production as an option for utilizing closed underground mines
(The Southern African Institute of Mining and Metallurgy, 2022) Dvořáček, Jaroslav; Malíková, Petra; Sousedíková, Radmila; Heviánková, Silvie; Rys, P.; Osičková, Ivana
Synopsis Each mining project goes through the same life-cycle, from prospecting and exploration to closure and post-closure periods. This prompts the question whether the closure of a mine constitutes the end of its life-cycle or whether the decommissioned mine can be employed for some other purpose. Best-practice references indicate that there are many viable options. In our opinion, the production of service water is one such option. Laboratory research was carried out on the water from a flooded underground coal mine in Ostrava-Karvina coal district, Czech Republic, concerning the production of service water from pumped mine -water. The research proved the practical feasibility of service or process water production. Given the effect of global climate change with regard to water resources, good prospects for this additional resource can be assumed since the water has to be pumped in any case for safety reasons.
The influence of economic factors on world copper production
(Polskie Towarzystwo Przeróbki Kopalin, 2022) Černý, Igor; Vaněk, Michal; Kubesa, Jiří
Copper is a very important mineral that has a wide application in industry, especially in the electrical industry and energy industry. With the increase in electromobility, its potential will grow in the future. Any shortage of copper on the world market could thus endanger modern industry. Therefore, the authors decided to deal with the influence of economic factors (price, population, GDP and cumulative inflation) on copper production and with creation of suitable econometric models that best expressing the relationship between production and economic factors for the period 2010-2019 in their article. The influence of economic factors on world copper production is examined using the Pearson correlation coefficient. It was found that copper production is inversely proportional to the price of copper, it is a strong dependence. In contrast, the correlation between copper production and other factors is very strong and positive. Using econometric modeling, it was discovered that exponential regression is the best expression for the relationship between copper production and its price and logarithmic regression most appropriate for the relationship between copper production and all other economic factors.
Predicting breast cancer from risk factors using SVM and extra-trees-based feature selection method
(MDPI, 2022) Alfian, Ganjar; Syafrudin, Muhammad; Fahrurrozi, Imam; Fitriyani, Norma Latif; Atmaji, Fransiskus Tatas Dwi; Widodo, Tri; Bahiyah, Nurul; Beneš, Filip; Rhee, Jongtae
Developing a prediction model from risk factors can provide an efficient method to recognize breast cancer. Machine learning (ML) algorithms have been applied to increase the efficiency of diagnosis at the early stage. This paper studies a support vector machine (SVM) combined with an extremely randomized trees classifier (extra-trees) to provide a diagnosis of breast cancer at the early stage based on risk factors. The extra-trees classifier was used to remove irrelevant features, while SVM was utilized to diagnose the breast cancer status. A breast cancer dataset consisting of 116 subjects was utilized by machine learning models to predict breast cancer, while the stratified 10-fold cross-validation was employed for the model evaluation. Our proposed combined SVM and extra-trees model reached the highest accuracy up to 80.23%, which was significantly better than the other ML model. The experimental results demonstrated that by applying extra-trees-based feature selection, the average ML prediction accuracy was improved by up to 7.29% as contrasted to ML without the feature selection method. Our proposed model is expected to increase the efficiency of breast cancer diagnosis based on risk factors. In addition, we presented the proposed prediction model that could be employed for web-based breast cancer prediction. The proposed model is expected to improve diagnostic decision-support systems by predicting breast cancer disease accurately.
Efficient use of critical raw materials for optimal resource management in EU countries
(MDPI, 2022) Domaracká, Lucia; Matušková, Simona; Taušová, Marcela; Seňová, Andrea; Kowal, Barbara
The European Commission has established a Critical Raw Materials List (CRM) for the European Union (EU), which is subject to regular review and updating. CRMs are needed in many key industries such as automotive, steel, aerospace, renewable energy, etc. To address this issue, we studied publicly available data from databases developed by the EU for monitoring the progress of individual countries in key areas for the development of society. The paper analyzes indicators of import reliance, net additions to stock, domestic material consumption (DMC), resource productivity, and circular material use rate. Prospective products and technologies, in electromobility, digitalization, Industry 4.0, and energy transformation, are changing and increasing the demand for raw materials. The aim of this article is to look at the ways forward in order to use critical raw materials as efficiently as possible while at the same time ensuring the optimal economy of the countries. From the sources and databases of data available for the EU, we analyzed a number of variables and suggested options for future developments in the efficient use of critical raw materials. We defined what we believed to be the optimal management means in relation to critical raw materials and worked backwards to find a path to efficient use of critical raw materials.
Investigation of UHF signal strength propagation at warehouse management applications based on drones and RFID technology utilization
(MDPI, 2022) Beneš, Filip; Staša, Pavel; Švub, Jiří; Alfian, Ganjar; Kang, Yong-Shin; Rhee, Jong-Tae
As a part of the supply chain, inventory management includes, among other things, maintaining the storage of stock, controlling the amount of product for sale and order fulfilment. In business terms, inventory management means the right stock, at the right levels, in the right place, at the right time. In the case of large outdoor warehouses, common identification methods are lengthy and inappropriate. One way to determine inventory easily and quickly is to deploy UAV’s (unmanned aerial vehicle) for product identification purposes. In this case, however, there is a problem in determining where the goods are located. A drone moves at higher altitudes, which can lead to a situation where we will not be able to determine the exact location of the goods. This article deals with a method of determining the correct flight level suitable to distinguish the identified items located at least 2 m apart. The evaluation is performed based on an RSSI (received signal strength indicator) value. The experiment proved that even at maximum reading distance of selected passive UHF RFID tags the two objects can be distinguished.
How economic indicators impact the EU internal demand for critical raw materials
(Elsevier, 2021) Černý, Igor; Vaněk, Michal; Maruszewska, Ewa Wanda; Beneš, Filip
To be able to better predict future demand for critical raw materials, to better negotiate trade agreements or to better stimulate the extraction of such raw materials, we investigate how economic indicators (raw material average annual price, EU GDP at purchasing power parity, cumulative EU inflation, and EU population) impact the EU internal demand for 11 raw materials in the period from 1994 to 2012. The results show that none of the critical raw materials has an identical trend in the development of internal EU demand with any economic indicator. There is a strong correlation between some of the factors studied and the internal EU demand for magnesite, tungsten, silicon (metal), chromium, and cobalt. The results also show that for most critical raw materials the correlation between the price of a critical raw material and its internal EU demand is lower than between other economic indicators and internal EU demand for certain critical raw material. This finding is consistent with economic theory indicating that the price is not a determinant of demand, but influences the quantity demanded. The demand curve shifts due to other factors and it can be observed in our findings on silicon (metal) element. The correlation between silicon (metal) demand and economic indicators such as cumulative EU inflation, the EU population and EU GDP are very high suggesting that this element is the most appropriate critical raw material to predict its internal EU demand. Using Spearman's rank correlation coefficient, the research results show that correlation between EU internal demand for critical raw materials and its economic indicators is positively affected by the volatility of this demand but negatively affected by its average value during reference period. The results may also be important with regard to recycling activities and identifying investment needs to alleviate Europe's reliance on imports of raw materials.
Mine closure and resuming production options
(Elsevier, 2021) Dvořáček, Jaroslav; Bauer, Viliam; Sousedíková, Radmila; Matušková, Simona; Csikósová, Adriana
There is a long history of mining in territories that are currently part of the Czech Republic. The period after 1989 was marked by the reduction of mining and mine closures. In 2017, the Government of the Czech Republic took action concerning strategic raw materials in domestic conditions, implying options for resuming exploitation of deposits mined in the past. The paper gives evidence that there is a historic precedent for disruption and resumption of mining. The paper also highlights the importance of the method of mine closure and technical liquidation for possible resumption of mining.
EU documents of major importance relevant to issues of mineral resource utilisation
(Polskie Towarzystwo Przeróbki Kopalin, 2020) Dvořáček, Jaroslav; Sousedíková, Radmila; Moravec, Ladislav
RAW MATERIALS INITIATIVE and REPORT ON CRITICAL RAW MATERIALS FOR THE EU are two documents of major importance as regards the issues of mineral resources of the European Union. The former document calls upon the EU Member States to maximize utilisation of domestic mineral resources, especially as regards those labelled as critical, the latter concerns occurrence of some such critical minerals in the Czech Republic. In actual fact, compliance with the implications of these documents means renewal of exploitation of residual mineral resources. Nonetheless, such activity anticipates positive economic results, and these are conditioned by investment means available for resumption of production. Both investment and operating expenses can be cut down if existing mining capacities are utilized. This paper investigates possibilities of mining resumption in the Czech Republic from the point of view of the methods employed for decommissioning and closure of mines. The so-called "wet" preservation of mines is recommended both for a future easy option of accessing decommissioned underground works and the possibilities of using pit water itself or employing its energy.
Blood glucose prediction model for type 1 diabetes based on artificial neural network with time-domain features
(Elsevier, 2020) Alfian, Ganjar; Syafrudin, Muhammad; Anshari, Muhammad; Beneš, Filip; Atmaji, Fransiskus Tatas Dwi; Fahrurrozi, Imam; Hidayatullah, Ahmad Fathan; Rhee, Jongtae
Predicting future blood glucose (BG) levels for diabetic patients will help them avoid potentially critical health issues. We demonstrate the use of machine learning models to predict future blood glucose levels given a history of blood glucose values as the single input parameter. We propose an Artificial Neural Network (ANN) model with time-domain attributes to predict blood glucose levels 15, 30, 45 and 60 min in the future. Initially, the model's features are selected based on the previous 30 min of BG measurements before a trained model is generated for each patient. These features are combined with time-domain attributes to give additional inputs to the proposed ANN. The prediction model was tested on 12 patients with Type 1 diabetes (T1D) and the results were compared with other data-driven models including the Support Vector Regression (SVR), K-Nearest Neighbor (KNN), C4.5 Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost) and eXtreme Gradient Boosting (XGBoost) models. Our results show that the proposed BG prediction model that is based on an ANN outperformed all other models with an average Root Mean Square Error (RMSE) of 2.82, 6.31, 10.65 and 15.33 mg/dL for Prediction Horizons (PHs) of 15, 30, 45 and 60 min, respectively. Our testing showed that combining time-domain attributes into the input data resulted in enhanced performance of majority of prediction models. The implementation of proposed prediction model allows patients to obtain future blood glucose levels, so that the preventive alerts can be generated before critical hypoglycemic/ hyperglycemic events occur.
Environment and risks of iron production
(Wydawnictwo Środkowo-Pomorskiego Towarzystwa Naukowego Ochrony Środowiska, 2020) Besta, Petr; Kozel, Roman; Janovská, Kamila; Vilamová, Šárka; Foltan, Drahomír; Piecha, Marian
Iron production is one of the production processes that create a large number of negative externalities towards their surroundings. Iron production is based on the use of a wide range of production operations, which include not only the blast furnace process but also the treatment and processing of ores, sintering, pelletizing and processing of metallurgical waste and its possible storage. All parts of the blast furnace process can have a negative impact on the environment. Within the individual parts of the blast furnace plant, a number of pollutants are produced which negatively affect the environment. They can have both solid and gaseous states. In the case of solid emissions, it is airborne dust, and the gaseous form represents pollutants in the form of sulphur, nitrogen or carbon oxides. From the point of view of the blast furnace plant structure itself, blast furnace, agglomeration processes, palletization processes or the processing of waste from production can be classified as emission points. The article deals with the classification of basic impacts of blast furnace production on the environment. It analyses in detail the negative externalities in ore sintering. It also deals with the analysis of research, which was focused on the degree of reduction of iron oxides ore. The efficiency of the reduction process is crucial in terms of resource use, but also the overall amount of negative externalities. The research was carried out in the environment of a selected iron producer in the Czech Republic.
Development of SW interface between healthcare standards - DASTA and HL7
(MDPI, 2020) Plischke, Simona; Machutová, Jana; Staša, Pavel; Unucka, Jakub
The prescription and administration of drugs are the most common process that takes place in hospitals. Although a relatively simple process, it is considered the riskiest process in hospitals because mistakes during drug administration are among the most common ones. The aim is to introduce technological and process changes that will contribute to maximally increase the safety of the medication process and the efficiency of drug management. To support the automation of the medication process, it is desirable to use the international standard Health Level 7 (HL7). However, the Czech healthcare system currently supports the local healthcare standard-DASTA. For that reason, the paper introduces some of the options how to transfer data from DASTA to HL7 and deals with the development of a software (SW) interface that converts data necessary for robotic preparation of patient medication from the Czech DASTA data standard to the HL7 international standard used by selected robotics. Based on the performed analyses, a combination of robotics for the preparation of single-dose packages of drugs with one of the automated warehouses is recommended.
Deep neural network for predicting diabetic retinopathy from risk factors
(MDPI, 2020) Alfian, Ganjar; Syafrudin, Muhammad; Fitriyani, Norma Latif; Anshari, Muhammad; Staša, Pavel; Švub, Jiří; Rhee, Jongtae
Extracting information from individual risk factors provides an effective way to identify diabetes risk and associated complications, such as retinopathy, at an early stage. Deep learning and machine learning algorithms are being utilized to extract information from individual risk factors to improve early-stage diagnosis. This study proposes a deep neural network (DNN) combined with recursive feature elimination (RFE) to provide early prediction of diabetic retinopathy (DR) based on individual risk factors. The proposed model uses RFE to remove irrelevant features and DNN to classify the diseases. A publicly available dataset was utilized to predict DR during initial stages, for the proposed and several current best-practice models. The proposed model achieved 82.033% prediction accuracy, which was a significantly better performance than the current models. Thus, important risk factors for retinopathy can be successfully extracted using RFE. In addition, to evaluate the proposed prediction model robustness and generalization, we compared it with other machine learning models and datasets (nephropathy and hypertension-diabetes). The proposed prediction model will help improve early-stage retinopathy diagnosis based on individual risk factors.
Indoor positioning system based on fuzzy logic and WLAN infrastructure
(MDPI, 2020) Hrad, Jaromír; Vojtěch, Lukáš; Cihlář, Martin; Staša, Pavel; Neruda, Marek; Beneš, Filip; Švub, Jiří
This paper deals with the issue of mobile device localization in the environment of buildings, which is suitable for use in healthcare or crisis management. The developed localization system is based on wireless Local Area Network (LAN) infrastructure (commonly referred to as Wi-Fi), evaluating signal strength from different access points, using the fingerprinting method for localization. The most serious problems consist in multipath signal propagation and the different sensitivities (calibration) of Wi-Fi adapters installed in different mobile devices. To solve these issues, an algorithm based on fuzzy logic is proposed to optimize the localization performance. The localization system consists of five elements, which are mobile applications for Android OS, a fuzzy derivation model, and a web surveillance environment for displaying the localization results. All of these elements use a database and shared storage on a virtualized server running Ubuntu. The developed system is implemented in Java for Android-based mobile devices and successfully tested. The average accuracy is satisfactory for determining the position of a client device on the level of rooms.
Coal handling operational risk management: Stripped overburden transport in brown coal open pit mines
(Technická univerzita Košice, Fakulta baníctva, ekológie, riadenia a geotechnológií, 2020) Vaněk, Michal; Valverde, Gregorio Fidalgo; Černý, Igor; Hudeček, Vlastimil
This paper deals with the management of coal handling operational risks related to the transport of stripped overburden in giant brown coal pit quarries. It aims to identify and analyze the operational risks of currently applied continuous conveyance and to consider alternative transport, i.e., discontinuous transport. The Ishikawa diagram was used to identify the degree of operational risks affecting the net present value in both transport technologies. The operational risks examined were: human factor, suppliers, legislation, technology, environment, and market. Failure Mode and Effects Analysis was then used to evaluate the operational risks of continuous and discontinuous overburden transport technologies. The data for the analyses were obtained by means of a survey among experts in the field. The analyses show that the most significant operational risks of continuous transport are: lower demand for coal, an increase in the investment costs, conveyance breakdowns, the quality of the transported material, and work attitude. In the discontinuous technology, the identified operational risks were: increases in the cost of fuels, road maintenance and costs of tires, low-qualified labor; and work attitude. The comparison of the two examined technologies shows that discontinuous transport technology involves more operational risks than the continuous one.
Critical raw materials - what's the crux of the matter?
(Polskie Towarzystwo Przeróbki Kopalin, 2019) Dvořáček, Jaroslav; Sousedíková, Radmila; Jureková, Zdenka; Matyášová, Zuzana
The paper takes into account mineral commodities that have been listed as critical by the EU Commission. It concentrates attention on the issue of global demand/supply balances, and summarizes causes for critical listing of these commodities.

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