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ORCIDVáclav Smídl
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2020 – today
- 2024
[j29]Vít Skvára
, Václav Smídl, Tomás Pevný:
Anomaly detection in multifactor data. Neural Comput. Appl. 36(34): 21561-21580 (2024)- [j28]Michaela Masková, Matej Zorek, Tomás Pevný, Václav Smídl:
Deep anomaly detection on set data: Survey and comparison. Pattern Recognit. 151: 110381 (2024) - [j27]Simon Mandlík, Tomás Pevný, Václav Smídl, Lukás Bajer:
Malicious Internet Entity Detection Using Local Graph Inference. IEEE Trans. Inf. Forensics Secur. 19: 3554-3566 (2024) - [c60]Milan Papez, Martin Rektoris, Václav Smídl, Tomás Pevný:
Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs. ICLR 2024 - [c59]Antonie Brozová, Václav Smídl:
Avoiding Undesirable Solutions of Deep Blind Image Deconvolution. VISIGRAPP (3): VISAPP 2024: 559-566 - [i16]Simon Mandlík, Tomás Pevný, Václav Smídl, Lukás Bajer:
Malicious Internet Entity Detection Using Local Graph Inference. CoRR abs/2408.03287 (2024) - [i15]Milan Papez, Martin Rektoris, Tomás Pevný, Václav Smídl:
Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs. CoRR abs/2408.07394 (2024) - [i14]Milan Papez, Martin Rektoris, Václav Smídl, Tomás Pevný:
GraphSPNs: Sum-Product Networks Benefit From Canonical Orderings. CoRR abs/2408.09451 (2024) - 2023
- [c58]Jan Laksar, Václav Smídl, Tomás Komrska, Lukás Adam:
Optimal Current Setpoints for Five-Phase Synchronous Drive. IECON 2023: 1-6 - [c57]Jakub Sevcik, Václav Smídl, Antonín Glac, Zdenek Peroutka:
Neural ODE for Estimation of Flux Linkage Models of Synchronous Machines. IECON 2023: 1-6 - [i13]Vít Skvára, Tomás Pevný, Václav Smídl:
Is AUC the best measure for practical comparison of anomaly detectors? CoRR abs/2305.04754 (2023) - 2022
- [j26]Lukás Adam, Václav Mácha, Václav Smídl, Tomás Pevný:
General framework for binary classification on top samples. Optim. Methods Softw. 37(5): 1636-1667 (2022) - [j25]Josef Justa, Václav Smídl, Ales Hamácek:
Deep Learning Methods for Speed Estimation of Bipedal Motion from Wearable IMU Sensors. Sensors 22(10): 3865 (2022) - [j24]Vít Skvára, Jan Francu, Matej Zorek, Tomás Pevný, Václav Smídl:
Comparison of Anomaly Detectors: Context Matters. IEEE Trans. Neural Networks Learn. Syst. 33(6): 2494-2507 (2022) - [c56]Marko Sahan, Václav Smídl, Radek Marik:
Batch Active Learning for Text Classification and Sentiment Analysis. CCRIS 2022: 111-116 - [c55]Mirza Ramicic, Václav Smídl, Andrea Bonarini:
Informed Sampling of Prioritized Experience Replay. ICDL 2022: 215-222 - [c54]Jakub Sevcik, Václav Smídl, Martin Votava:
Identification of Thermal Model Parameters Using Deep Learning Techniques. ISIE 2022: 978-981 - [i12]Petr Pecha, Miroslav Kárný, Emilie Pechová, Václav Smídl, Ondrej Tichý:
Potential Hazard of Accidental Radioactive Discharges into the Calm Atmosphere. ERCIM News 2022(129): 0 (2022) - 2021
- [j23]Stepán Janous, Jakub Talla, Václav Smídl, Zdenek Peroutka:
Constrained LQR Control of Dual Induction Motor Single Inverter Drive. IEEE Trans. Ind. Electron. 68(7): 5548-5558 (2021) - [c53]Jan Kotera, Filip Sroubek, Václav Smídl:
Improving Neural Blind Deconvolution. ICIP 2021: 1954-1958 - [c52]Marko Sahan, Václav Smídl, Radek Marík:
Active Learning for Text Classification and Fake News Detection. ISCSIC 2021: 87-94 - [i11]Milan Papez, Tomás Pevný, Václav Smídl:
Fitting large mixture models using stochastic component selection. CoRR abs/2110.04776 (2021) - 2020
- [j22]Marek Fehér, Ondrej Straka, Václav Smídl:
Model predictive control of electric drive system with L1-norm. Eur. J. Control 56: 242-253 (2020) - [j21]Josef Justa, Václav Smídl, Ales Hamácek:
Fast AHRS Filter for Accelerometer, Magnetometer, and Gyroscope Combination with Separated Sensor Corrections. Sensors 20(14): 3824 (2020) - [j20]Michal Kroneisl, Václav Smídl, Zdenek Peroutka, Martin Janda:
Predictive Control of IM Drive Acoustic Noise. IEEE Trans. Ind. Electron. 67(7): 5666-5676 (2020) - [c51]Antonín Glac, Václav Smídl, Zdenek Peroutka, Christoph M. Hackl:
Comparison of IPMSM Parameter Estimation Methods for Motor Efficiency. IECON 2020: 895-900 - [c50]Michal Kroneisl, Václav Smídl:
Bayesian optimization of FCS-MPC Parameters for Reduction of Induction Motor Electromagnetic Noise. ISIE 2020: 271-276 - [c49]Niklas Heim, Tomás Pevný, Václav Smídl:
Neural Power Units. NeurIPS 2020 - [c48]Tomás Pevný, Václav Smídl, Martin Trapp, Ondrej Polácek, Tomás Oberhuber:
Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations. PGM 2020: 341-352 - [i10]Lukás Adam, Václav Mácha, Václav Smídl, Tomás Pevný:
General Framework for Binary Classification on Top Samples. CoRR abs/2002.10923 (2020) - [i9]Václav Mácha, Lukás Adam, Václav Smídl:
Nonlinear classifiers for ranking problems based on kernelized SVM. CoRR abs/2002.11436 (2020) - [i8]Tomás Pevný, Václav Smídl, Martin Trapp, Ondrej Polácek, Tomás Oberhuber:
Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations. CoRR abs/2005.01297 (2020) - [i7]Niklas Heim, Tomás Pevný, Václav Smídl:
Neural Power Units. CoRR abs/2006.01681 (2020) - [i6]Lukás Adam, Václav Mácha, Václav Smídl:
DeepTopPush: Simple and Scalable Method for Accuracy at the Top. CoRR abs/2006.12293 (2020) - [i5]Jakub Sevcik, Lukás Adam, Jan Prikryl, Václav Smídl:
Solvability of the Power Flow Problem in DC Overhead Wire Circuit. CoRR abs/2010.03430 (2020) - [i4]Vít Skvára, Jan Francu, Matej Zorek, Tomás Pevný, Václav Smídl:
Comparison of Anomaly Detectors: Context Matters. CoRR abs/2012.06260 (2020)
2010 – 2019
- 2019
- [j19]Ondrej Tichý, Lenka Bodiova, Václav Smídl:
Bayesian Non-Negative Matrix Factorization With Adaptive Sparsity and Smoothness Prior. IEEE Signal Process. Lett. 26(3): 510-514 (2019) - [c47]Jakub Sevcik, Václav Smídl, Martin Votava:
Identification of Thermal Model of Power Module Using Expectation-Maximization Algorithm. IECON 2019: 119-125 - [c46]Jaroslav Dragoun, Václav Smídl:
Adaptive control of LCL filter with time-varying parameters using reinforcement learning. IECON 2019: 267-272 - [c45]Levon Gevorkov, Václav Smídl, Martin Sirový:
Model of Hybrid Speed and Throttle Control for Centrifugal Pump System Enhancement. ISIE 2019: 563-568 - [i3]Václav Smídl, Jan Bím, Tomás Pevný:
Anomaly scores for generative models. CoRR abs/1905.11890 (2019) - [i2]Niklas Heim, Václav Smídl, Tomás Pevný:
Rodent: Relevance determination in ODE. CoRR abs/1912.00656 (2019) - 2018
- [j18]Jakub Sevcik, Václav Smídl, Filip Sroubek:
An Adaptive Correlated Image Prior for Image Restoration Problems. IEEE Signal Process. Lett. 25(7): 1024-1028 (2018) - [j17]Václav Smídl, Stepán Janous, Lukás Adam, Zdenek Peroutka:
Direct Speed Control of a PMSM Drive Using SDRE and Convex Constrained Optimization. IEEE Trans. Ind. Electron. 65(1): 532-542 (2018) - [j16]David Uzel, Zdenek Peroutka, Václav Smídl, Tomás Kosan, Karel Zeman:
Self-Sensing Control of Wound Rotor Synchronous Motor Drive for Mine Hoist. IEEE Trans. Ind. Electron. 65(3): 2009-2017 (2018) - [j15]Jakub Talla, Viet Quoc Leu, Václav Smídl, Zdenek Peroutka:
Adaptive Speed Control of Induction Motor Drive With Inaccurate Model. IEEE Trans. Ind. Electron. 65(11): 8532-8542 (2018) - [c44]Antonín Glac, Václav Smídl, Zdenek Peroutka:
Optimal Feedforward Torque Control of Synchronous Machines with Time-Varying Parameters. IECON 2018: 613-618 - [c43]Stepán Janous, Jakub Talla, Zdenek Peroutka, Václav Smídl:
Predictive Control of Parallel Induction Motors Fed by Single Inverter with Common Current Sensors. IECON 2018: 5843-5848 - [i1]Vít Skvára, Tomás Pevný, Václav Smídl:
Are generative deep models for novelty detection truly better? CoRR abs/1807.05027 (2018) - 2017
- [j14]Jan Kotera, Václav Smídl, Filip Sroubek:
Blind Deconvolution With Model Discrepancies. IEEE Trans. Image Process. 26(5): 2533-2544 (2017) - [c42]Stepán Janous, Jakub Talla, Zdenek Peroutka, Václav Smídl:
Model predictive control of multiple induction machines fed from single inverter. IECON 2017: 6330-6335 - [c41]Marek Feher, Ondrej Straka, Václav Smídl:
Efficient MPC for permanent magnet synchronous motor. MED 2017: 36-41 - 2016
- [j13]Ondrej Tichý, Václav Smídl, Martin Sámal:
Model-based extraction of input and organ functions in dynamic scintigraphic imaging. Comput. methods Biomech. Biomed. Eng. Imaging Vis. 4(3-4): 135-145 (2016) - [j12]Ondrej Tichý, Václav Smídl:
Non-parametric Bayesian models of response function in dynamic image sequences. Comput. Vis. Image Underst. 151: 90-100 (2016) - [c40]Humphrey Mokom Njawah Achiri, Lubos Streit, Václav Smídl, Zdenek Peroutka:
Experimental validation of IGBT thermal impedances from voltage-based and direct temperature measurements. IECON 2016: 3396-3401 - [c39]Jan Michalik, Václav Smídl, Zdenek Peroutka:
LQ lookahead in finite control set MPC of current-source rectifier. IECON 2016: 3464-3469 - 2015
- [j11]Ondrej Tichý, Václav Smídl:
Estimation of input function from dynamic PET brain data using Bayesian blind source separation. Comput. Sci. Inf. Syst. 12(4): 1273-1287 (2015) - [j10]Leopoldo Armesto, Vicent Girbés, Antonio Sala, Miroslav Zima, Václav Smídl:
Duality-Based Nonlinear Quadratic Control: Application to Mobile Robot Trajectory-Following. IEEE Trans. Control. Syst. Technol. 23(4): 1494-1504 (2015) - [j9]Václav Smídl, Stepán Janous, Zdenek Peroutka:
Improved Stability of DC Catenary Fed Traction Drives Using Two-Stage Predictive Control. IEEE Trans. Ind. Electron. 62(5): 3192-3201 (2015) - [j8]Ondrej Tichý, Václav Smídl:
Bayesian Blind Separation and Deconvolution of Dynamic Image Sequences Using Sparsity Priors. IEEE Trans. Medical Imaging 34(1): 258-266 (2015) - [c38]Ondrej Tichý, Václav Smídl:
Variational blind source separation toolbox and its application to hyperspectral image data. EUSIPCO 2015: 1326-1330 - [c37]Ondrej Tichý, Václav Smídl:
Bayesian Blind Source Separation with Unknown Prior Covariance. LVA/ICA 2015: 352-359 - [c36]Humphrey Mokom Njawah Achiri, Václav Smídl, Zdenek Peroutka:
Mitigation of electric drivetrain oscillation resulting from abrupt current derating at low coolant flow rate. IECON 2015: 3638-3642 - [c35]Jan Michalik, Zdenek Peroutka, Václav Smídl:
Finite control set MPC of active current-source rectifier with full state space model. IECON 2015: 4121-4126 - [c34]Jan Prikryl, Jakub Novotny, Václav Smídl:
On Distributed Traffic Signal Control. ITSC 2015: 894-899 - 2014
- [j7]Václav Smídl, Radek Hofman:
Efficient Sequential Monte Carlo Sampling for Continuous Monitoring of a Radiation Situation. Technometrics 56(4): 514-528 (2014) - [c33]Ondrej Tichý, Václav Smídl:
Kinetic modeling of the dynamic PET brain data using blind source separation methods. BMEI 2014: 329-334 - [c32]Filip Sroubek, Václav Smídl, Jan Kotera:
Understanding image priors in blind deconvolution. ICIP 2014: 4492-4496 - [c31]Václav Smídl, Stepán Janous, Zdenek Peroutka:
Extending horizon of finite control set MPC of PMSM drive with input LC filter using LQ lookahead. IECON 2014: 581-586 - [c30]David Uzel, Václav Smídl, Zdenek Peroutka:
Reduced-order Kalman filter in phase coordinates for IPMSM with higher flux harmonics. IECON 2014: 825-830 - [c29]Tomas Glasberger, Vendula Muzikova, Václav Smídl, Zdenek Peroutka:
Sensorless permanent magnet synchronous drive with DTC based on high frequency injections. IECON 2014: 850-856 - [c28]Radim Dudek, Václav Smídl, Zdenek Peroutka:
Start-stop system for a city bus based on model predictive control. IECON 2014: 3973-3978 - 2013
- [j6]Emre Özkan, Václav Smídl, Saikat Saha, Christian Lundquist, Fredrik Gustafsson:
Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters. Autom. 49(6): 1566-1575 (2013) - [c27]Miroslav Zima, Leopoldo Armesto, Vicent Girbés, Antonio Sala, Václav Smídl:
Extended Rauch-Tung-Striebel controller. CDC 2013: 2900-2905 - [c26]Václav Smídl, Radek Hofman:
Adaptive importance sampling in particle filtering. FUSION 2013: 9-16 - [c25]Václav Smídl, Matej Gasperin:
Rao-Blackwellized point mass filter for reliable state estimation. FUSION 2013: 312-318 - [c24]David Uzel, Václav Smídl, Zdenek Peroutka:
Resolver motivated sensorless rotor position estimation of wound rotor synchronous motors with Kalman filter. IECON 2013: 3084-3089 - [c23]Václav Smídl, Robert Nedved, Tomás Kosan, Zdenek Peroutka:
FPGA implementation of marginalized particle filter for sensorless control of PMSM drives. IECON 2013: 8227-8232 - [c22]Václav Smídl, David Vosmik, Zdenek Peroutka:
Kalman filters unifying model-based and HF injection-based sensorless control of PMSM drives. IECON 2013: 8233-8238 - [c21]Tomas Glasberger, Vendula Muzikova, Zdenek Peroutka, Václav Smídl:
Sensorless direct torque control of PMSM with reduced model Extended Kalman filter. IECON 2013: 8239-8244 - [c20]Stepán Janous, Václav Smídl, Zdenek Peroutka:
Feed-forward guided generalized predictive control of PMSM drive. ISIE 2013: 1-6 - [c19]David Vosmik, Václav Smídl, Zdenek Peroutka:
Sensorless PMSM control: Hybrid rotor position estimator using maximum likelihood model selection. ISIE 2013: 1-6 - [c18]Václav Smídl, Ondrej Tichý:
Sparsity in Bayesian Blind Source Separation and Deconvolution. ECML/PKDD (2) 2013: 548-563 - 2012
- [j5]Václav Smídl, Zdenek Peroutka:
Advantages of Square-Root Extended Kalman Filter for Sensorless Control of AC Drives. IEEE Trans. Ind. Electron. 59(11): 4189-4196 (2012) - [c17]Václav Smídl, Radek Hofman:
Navigation of UAVs for tracking of atmospheric release of radiation. CDC 2012: 3098-3103 - [c16]Václav Smídl, Zdenek Peroutka:
Marginalized particle filter for sensorless control of PMSM drives. IECON 2012: 1877-1882 - [c15]Václav Smídl, Ondrej Tichý:
Automatic regions of interest in factor analysis for dynamic medical imaging. ISBI 2012: 158-161 - [c14]Václav Smídl, Fredrik Gustafsson:
Bayesian estimation of forgetting factor in adaptive filtering and change detection. SSP 2012: 197-200 - 2011
- [c13]Stepan Albrecht, Václav Smídl:
Model for memory-based music transcription and its Variational Bayes solution. EUSIPCO 2011: 1752-1755 - [c12]Emre Özkan, Saikat Saha, Fredrik Gustafsson, Václav Smídl:
Non-parametric bayesian measurement noise density estimation in non-linear filtering. ICASSP 2011: 5924-5927 - 2010
- [c11]Stepan Albrecht, Václav Smídl:
Improvements of continuous model for memory-based automatic music transcription. EUSIPCO 2010: 487-491 - [c10]Saikat Saha, Emre Özkan, Fredrik Gustafsson, Václav Smídl:
Marginalized particle filters for Bayesian estimation of Gaussian noise parameters. FUSION 2010: 1-8 - [c9]Václav Smídl:
Software analysis unifying particle filtering and marginalized particle filtering. FUSION 2010: 1-7 - [c8]Denis N. Sidorov, Daniil A. Panasetsky, Václav Smídl:
Non-stationary autoregressive model for on-line detection of inter-area oscillations in power systems. ISGT Europe 2010: 1-5
2000 – 2009
- 2008
- [j4]Václav Smídl, Anthony Quinn:
Variational Bayesian Filtering. IEEE Trans. Signal Process. 56(10-2): 5020-5030 (2008) - [c7]Václav Smídl, Josef Andrýsek:
Merging of multistep predictors for decentralized adaptive control. ACC 2008: 3414-3415 - [c6]Pavel Ettler, Josef Andrýsek, Václav Smídl, Miroslav Kárný:
Merging of Advices from Multiple Advisory Systems - With Evaluation on Rolling Mill Data. ICINCO-ICSO 2008: 66-71 - 2007
- [j3]Václav Smídl, Anthony Quinn:
On Bayesian principal component analysis. Comput. Stat. Data Anal. 51(9): 4101-4123 (2007) - [c5]Václav Smídl, Anthony Quinn:
Accelerated Particle Filtering using the Variational Bayes Approximation. ICASSP (3) 2007: 1173-1176 - 2006
- [c4]Václav Smídl, Anthony Quinn:
The Variational Bayes Approximation In Bayesian Filtering. ICASSP (3) 2006: 137-140 - 2005
- [j2]Václav Smídl, Anthony Quinn, Miroslav Kárný, Tatiana V. Guy:
Robust estimation of autoregressive processes using a mixture-based filter-bank. Syst. Control. Lett. 54(4): 315-323 (2005) - [j1]Václav Smídl, Anthony Quinn:
Mixture-based extension of the AR model and its recursive Bayesian identification. IEEE Trans. Signal Process. 53(9): 3530-3542 (2005) - [c3]Václav Smídl, Anthony Quinn:
The variational EM algorithm for on-line identification of extended AR models [speech processing example]. ICASSP (4) 2005: 117-120 - [c2]Václav Smídl, Jan Prikryl:
From Bayesian Decision-Makers to Bayesian Agents. SOAS 2005: 62-76 - 2001
- [c1]Václav Smídl, Miroslav Kárný, Martin Sámal, Werner Backfrieder, Zsolt Szabo:
Smoothness Prior Information in Principal Component Analysis of Dynamic Image Data. IPMI 2001: 225-231
Coauthor Index
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