Articles (KEV)
Permanent URI for this collection
Browse
Recent Submissions
Item Investigation of a bidirectional DC/DC converter with zero-voltage switching operation for battery interfaces(John Wiley and Sons, 2021) Bhajana, V.V.S. Kumar; Drábek, Pavel; Jára, Martin; Popuri, Madhuchandra; Iqbal, Atif; Babu, Chitti B.Item Time-Optimal Current Control of Synchronous Motor Drives(MDPI, 2023) Šmídl, Václav; Glac, Antonín; Peroutka, ZdeněkItem Global Simulation Model Design of Input-Serial, Output-Parallel Solid-State Transformer for Smart Grid Applications(MDPI, 2023) Takács, Kristián; Frivaldský, Michal; Kindl, Vladimír; Bernat, PetrItem Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer(MDPI, 2023) Moztarzadeh, Omid; Jamshidi, Mohammad; Sargolzaei, Saleh; Jamshidi, Alireza; Baghalipour, Nasimeh; Moghani, Malekzadeh Mona; Hauer, LukášItem An octonion-based nonlinear echo state network for speech emotion recognition in Metaverse(Elsevier, 2023) Daneshfar, Fatemeh; Jamshidi, MohammadItem A Digital Twinning Approach for the Internet of Unmanned Electric Vehicles (IoUEVs) in the Metaverse(MDPI, 2023) Ebadpour, Mohsen; Jamshidi, Mohammad; Talla, Jakub; Hashemi Dezaki, Hamed; Peroutka, ZdeněkItem Dynamic Inductive Coupling Based Wireless Rotor Monitoring System(IEEE, 2023) Kindl, Vladimír; Elis, Luděk; Pušman, Lukáš; Turjanica, Pavel; Kavalír, Tomáš; Zavřel, Martin; Peroutka, ZdeněkItem Increase of the Automotive Power Transistor Modules Manufacture Reliability Using Ai Detecting System for Soldering Splashes(University of Žilina, 2023) Koniar, Dušan; Klčo, Peter; Hargaš, Libor; Chnápko, Marek; Pocisková Dimová, Katarína; Kindl, VladimírItem Metaverse and AI Digital Twinning of 42SiCr Steel Alloys(MDPI, 2023) Khalaj, Omid; Jamshidi, Mohammad; Hassas, Parsa; Hosseininezhad, Marziyeh; Mašek, Bohuslav; Štádler, Ctibor; Svoboda, JiříItem A Super-Efficient GSM Triplexer for 5G-Enabled IoT in Sustainable Smart Grid Edge Computing and the Metaverse(MDPI, 2023) Jamshidi, Mohammad; Yahya, Salah I.; Nouri, Leila; Hashemi Dezaki, Hamed; Rezaei, Abbas; Chaudhary, AkmalItem Development of the diagnostic tools for the COMPASS-U tokamak and plans for the first plasma(Elsevier, 2023) Weinzettl, Vladimír; Bílková, Petra; Ďuran, Ivan; Hron, Martin; Pánek, Radomír; Markovič, Tomáš; Varavin, Mykyta; Cavalier, Jordan; Kovařík, Karel; Torres, André; Matveeva, Ekaterina; Böhm, Petr; Ficker, Ondřej; Horáček, Jan; Čeřovský, Jaroslav; Zajac, Jaromír; Adámek, Jiří; Dimitrova, Miglena; Imríšek, Martin; Sos, Miroslav; Tomešová, Eva; Vondráček, Petr; Mikszuta-Michalik, Katarzyna; Svoboda, Jakub; Naydenkova, Diana; Bogár, Klára; Caloud, Jakub; Ivanov, Vladislav; Lukeš, Samuel; Podolník, Aleš; Bogár, Ondřej; Entler, Slavomír; Havránek, Aleš; Preinhaelter, Josef; Jaulmes, Fabien; Dejarnac, Renaud; Balner, Vojtěch; Veselovský, Viktor; Bělina, Pavel; Král, Miroslav; Gerardin, Jonathan; Vlček, Jiří; Tadros, Momtaz; Turjanica, Pavel; Kindl, Vladimír; Řeboun, Jan; Rowan, William; Houshmandyar, Saeid; Scholz, Marek; Bielecki, Jakub; Makowski, Dariusz; Chernyshova, Maryna; Cipciar, DarioItem Droop Control Algorithm Design for Power Balancing in Island Inverter Based Microgrid(Universitatea "Stefan cel Mare" din Suceava, 2022) Dragoun, Jaroslav; Vinš, Martin; Talla, Jakub; Blahník, VojtěchItem Multi-Pole Winding Behavior in Multiphase Motors under Current Harmonics Operation(IEEE, 2022) Kalaj, Patrik; Komrska, Tomáš; Kindl, Vladimír; Čermák, Radek; Frank, Zdeněk; Laksar, Jan; Peroutka, ZdeněkItem Comparison of main design concepts of auxiliary drives for DC catenary fed light traction vehicles: SiC JFET vs Si IGBT technology(Taylor and Francis, 2021) Bhajana, V.V.Subrahman. Kumar; Drábek, Pavel; Jára, Martin; Peroutka, ZdeněkItem Analytical Method for Designing Three-Phase Air-Gapped Compensation Choke(MDPI, 2022) Kindl, Vladimír; Sobotka, Lukáš; Frivaldský, Michal; Skalický, MartinItem LightGBM-Based Fault Diagnosis of Rotating Machinery Under Changing Working Conditions Using Modified Recursive Feature Elimination(IEEE, 2022) Nemat Saberi, Alireza; Belahcen, Anouar; Šobra, Jan; Vaimann, Toomashis article presents an intelligent and accurate framework for fault diagnosis of induction motors using light gradient boosting machine (LightGBM). The proposed framework offers promising generalization ability when the testing data contains new unseen operating conditions unavailable during the training process. After the acquisition of vibration signals and feature extraction in multiple domains, we perform an iterative feature selection (FS) approach by utilizing a modified version of recursive feature elimination (RFE) and the features' importance scores obtained by LightGBM. To prevent overfitting and subsequent selection bias, an outer resampling loop encompasses the whole process of our RFE-LightGBM algorithm. Moreover, instead of the conventional resampling methods based on K-fold cross-validation (CV) or leave-one-out CV (LOOCV), we use a new scheme called leave-one-loading-out CV (LOLO-CV). Leveraging LOLO-CV, the proposed FS method identifies the optimal feature subset, making the fault diagnosis robust under changing operating conditions. Then, the final classification is performed with optimal feature subset by training a new LightGBM model with adjusted hyperparameters employing Bayesian optimization. Experimental results from two real case studies show that our proposed fault diagnosis framework achieves accuracies between 98.55% and 100% for various testing scenarios. For example, for the worst-case testing scenario in the bearing dataset of Case Western Reserve University where the no-load data (0hp) is absent during the training process and is only used for testing, the testing accuracy of LightGBM classifier before and after applying the proposed RFE-LightGBM-FS method is 88.04% to 97.23%, respectively. Using the Bayesian hyperparameter optimization further improves the accuracy to 98.55%Item Uncertainty Quantification of Input Parameters in a 2-D Finite-Element Model for Broken Rotor Bar in an Induction Machine(IEEE, 2022) Billah, Md Masum; Martin, Floran; Belahcen, Anouar; Balasubramanian, Aswin; Vaimann, Toomas; Šobra, JanIn this article, a forward uncertainty propagation method is presented for a 2-D finite-element (FE) model in an induction machine. This method is applied to quantify the uncertainty of input parameters, for example, dimensions and material properties, and demonstrate their variability effect on harmonics related to the broken rotor bar (BRB) faults. To show the most influential input parameters in the case of BRB harmonics, a global sensitivity analysis is performed from the polynomial chaos expansion (PCE) approximation of the FE model. The results of this study indicate that BRB harmonics are highly sensitive to stator inner diameter, rotor outer diameter, rotor bar conductivity, and core materials. Moreover, the combined variability of these sensitive input parameters can attenuate the amplitude of the BRB harmonics 30%–90% compared to the simulation results at nominal values of input parameters and closely match with measurement results.Item Analytical Method for Compensation Choke Geometry Optimization to Minimize Losses(IEEE, 2022) Kindl, Vladimír; Skala, Bohumil; Frivaldský, MichalItem In-motion charged vehicle simulation considering traffic and power grid interactions(Elsevier, 2022) Ševčík, Jakub; Přikryl, Jan; Peroutka, ZdeněkItem Hard color-singlet exchange in dijet events in proton-proton collisions at √s=13 TeV(American Physical Society, 2021) Sirunyan, A. N.; Georgiev, Vjačeslav; Hammerbauer, Jiří; Linhart, Richard; Peroutka, Zdeněk; Urban, Ondřej; Vavroch, Ondřej; Zich, Jan; CMS, Collaboration; TOTEM, Collaboration
- «
- 1 (current)
- 2
- 3
- »