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Sbírka příkladů – Vybrané účetní případy podnikatelských subjektů
(Vysoká škola báňská – Technická univerzita Ostrava, 2026) Hakalová, Jana; Palochová, Marcela; Slezák, Jiří
Předložená sbírka příkladů s názvem Sbírka příkladů – Vybrané účetní případy podnikatelských subjektů, představuje základní studijní podklad určený zejména pro výuku v předmětech Účetnictví a daně, Účetnictví A, Účetnictví B a stává se tak součástí řady vysokoškolských skript a publikací vydávaných pravidelně Katedrou účetnictví a daní na Ekonomické fakultě VŠB – Technická univerzita v Ostravě. Procvičením základních účetních případů zaměřených na účtování ve všech účtových třídách by studenti měli pochopit ekonomickou podstatu vybraných účetních transakcí aktiv, pasiv, nákladů a výnosů u podnikatelských subjektů. Autoři jsou přesvědčeni, že sbírka příkladů by mohla přispět k pochopení základních principů účetnictví a daňové problematiky, což představuje nezbytný základ a dobrý start pro další rozšiřování znalostí nejen z této podnikové problematiky. Svým zaměřením je tato sbírka základních vybraných účetních případů podnikatelských subjektů určena především posluchačům vysokých škol ekonomického zaměření se specializací zaměřenou na oblast účetnictví a daní menších obchodních korporací. Rovněž je sbírka určena podnikatelským subjektům, manažerům včetně široké veřejnosti, kteří se se základy účetnictví a daňové problematiky teprve seznamují.
Mutual Influence AI: Trust-Based Cooperation Mechanisms for LLM Multi-Agent Systems
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Oujazský, Václav; Novák, Pavel
This paper introduces Mutual Influence AI, a novel concept for adaptive cooperation in multi-agent systems. Unlike classical independent reasoning or cen- tralized orchestration, our approach introduces an ex- plicit mutual influence factor μ that captures trust- adjusted peer feedback and directly modulates large lan- guage model (LLM) generation. We present (i) a math- ematical formalization of mutual influence, (ii) a pro- totype implementation integrated with Microsoft Auto- Gen for LLM-based agents, and (iii) qualitative evi- dence that the framework improves adaptability, trans- parency, and coordination in multi-agent dialogues. Results show that Mutual Influence AI stabilizes group interactions efficiently while providing interpretable control over how agents influence each other. This positions Mutual Influence AI as a new paradigm for LLM-driven multi-agent systems with potential appli- cations ranging from collaborative problem solving to cybersecurity. Quantitatively, across 167 simulation runs, cross–role agreement increased from 0.19 (base- line) to 0.50 under influence (approx. +160%), with median revision depth (approx. 1.0). Under adversarial feedback, agreement still improved (0.18 to 0.47).
ABO-BTI: An Open-Source ABO Blood Typing Image Dataset for Medical AI Applications
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Sara, Daas; Hatem, Zehir; Asma, Chebli; Toufik, Hafs; Chaima, Hadef
Accurate blood type classification is cru- cial for safe transfusions and clinical decision-making, yet existing research is limited by the lack of stan- dardized, publicly available datasets for training and evaluating machine learning models. To address this gap, we introduce ABO-BTI (ABO Blood Typing Im- age), the first open-source dataset dedicated to blood type classification using high-resolution agglutination images. The dataset comprises 144 cases, with 432 images standardized to a resolution of 1280×590 pix- els after processing. This study evaluates the effective- ness of deep learning for blood type identification us- ing the ABO-BTI database. Three models, ResNet50, MobileNetV2, and a proposed deep learning architec- ture, were trained and tested on the dataset to as- sess its suitability for machine learning applications. The proposed model achieved an accuracy of 96.51%, significantly outperforming MobileNetV2 (12.64%) and ResNet50 (72.41%). Comparative analysis with tradi- tional machine learning methods further demonstrated that deep learning provides competitive performance while reducing reliance on handcrafted feature extrac- tion. These results highlight ABO-BTI as a valuable benchmark for advancing AI-driven blood type classifi- cation. The findings also suggest the potential integra- tion of deep learning-based classification into embed- ded systems for real-time blood typing in point of care and emergency settings. By providing a standardized dataset and demonstrating the viability of deep learn- ing models, this study lays the foundation for future re- search in automated blood classification, with implica- tions for both clinical applications and AI-driven med- ical diagnostics.
Unmanned Aerial Vehicles (UAV) in Cellular Network: Approach, Architecture, and Challenges
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Ibrahim, N. S. Amani; Asraf Saparudin, Faiz
Unmanned Aerial Vehicles (UAVs) have emerged as critical components of modern cellular net- works, offering potential for significantly enhancing cov- erage, capacity, and connectivity. This paper investi- gates the novel integration of UAVs into existing cellular infrastructure, presenting a comprehensive framework for their deployment and management. The study delves into the architectural complexities of UAV-cellular net- works, emphasizing design considerations and techno- logical requirements for seamless integration. Potential benefits, including rapid network deployment in remote areas, disaster recovery, and dynamic network optimiza- tion, are explored. However, the integration of UAVs also introduces challenges related to resource allocation, channel modeling, optimal placement and trajectory, and interference management. This paper provides a thorough analysis of these challenges, offering poten- tial mitigation strategies and innovative solutions. As a contribution to future research, a novel approach is suggested for optimizing UAV placement and trajectory to enhance network coverage and efficiency in serving ground Internet of Things (IoT) devices. Through a holistic examination of UAVs in cellular networks, this study offers a comprehensive overview of approaches, architectures, challenges, and optimization techniques for effective UAV integration.
Application of CSA Algorithm for the PMSM Speed Estimator of The FOC Control Method Using Extended Kalman Filter
(Vysoká škola báňská - Technická univerzita Ostrava, 2025) Tran, Thinh Cong; Brandštetter, Pavel; De, Huynh Tan; Vo, Hau Huu
Nowadays, Permanent Magnet Syn- chronous Motors (PMSM) are used more and more widely due to their advantages over other types of motors, such as high efficiency, constant torque, higher power density, and wide speed range. Many studies on this motor have been carried out in the industry. This paper proposes an application for the PMSM motor to estimate the speed of the motor rotor using an extended Kalman filter (EKF). This also means that the motor is controlled without using a speed sensor, so the system has the advantages of reducing the cost of manufacturing encoders, less damage, increased reliability, and reduced size due to the absence of moving mechanical parts of the sensor. The estimated performance depends heavily on the parameters of the covariance matrices in the filter. In the paper, the filter parameters are optimized using the Cuckoo Search Algorithm (CSA). The simulation results of the proposed algorithm on the PMSM motor show its advantages over traditional methods.