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Item Active fault diagnosis for stochastic large scale systems under non-separable costs(2024) Straka, Ondřej; Punčochář, IvoThe paper focuses on active fault diagnosis of stochastic large-scale systems decomposed into several coupled subsystems, where the subsystem fault-free and faulty behavior is described in the multiple-model framework. In the active approach, the detector generates optimal excitation input to improve the diagnosis. This paper proposes a solution to the problems with cost functions in a generally non-separable form. Unlike the separable form, the generally non-separable form facilitates penalizing missed detections, false alerts, and incorrect, false identifications involving several subsystems simultaneously. Three approaches are proposed to treat such cost functions in the offline stage of the active fault diagnosis algorithm. Their performance is illustrated using a simple example and an elaborate example involving a power network system.
Item Grid-Based Bayesian Filters With Functional Decomposition of Transient Density(2023) Tichavský, Petr; Straka, Ondřej; Duník, JindřichThe paper deals with the state estimation of nonlinear stochastic dynamic systems with special attention to grid-based Bayesian filters such as the point-mass filter (PMF) and the marginal particle filter (mPF). In the paper, a novel functional decomposition of the transient density describing the system dynamics is proposed. The decomposition approximates the transient density in a closed region. It is based on a non-negative matrix/tensor factorization and separates the density into functions of the future and current states. Such decomposition facilitates a thrifty calculation of the convolution involving the density, which is a performance bottleneck of the standard PMF/mPF implementations. The estimate quality and computational costs can be efficiently controlled by choosing an appropriate decomposition rank. The performance of the PMF with the transient density decomposition is illustrated in a terrain-aided navigation scenario and a problem involving a univariate non-stationary growth model.Item Augmented physics-based machine learning for navigation and tracking(2024) Imbiriba, Tales; Straka, Ondřej; Duník, Jindřich; Closas, PauThis article presents a survey of the use of AI/ML techniques in navigation and tracking applications, with a focus on the dynamical models typically involved in corresponding state estimation problems. When physics-based models are either not available or not able to capture the complexity of the actual dynamics, recent works explored the use of deep learning models. This article tradeoffs both models and presents promising solutions in between, whereby physics-based models are augmented by data-driven components. The article uses two target tracking examples, both with syntethic and real data, to illustrate the various choices of the models and their parameters, highlighting their benefits and challenges. Finally, the paper provides some conclusions and an outlook for future research in this relevant area.Item Identification of GNSS Measurement Error: From Time to Elevation Dependency(2023) Kost, Oliver; Duník, Jindřich; Straka, Ondřej; Daniel, OndřejThis paper deals with the identification of the GNSS measurement error properties. For this purpose, an extended version of the measurement difference method, which does not rely on knowledge of the state dynamics, is developed. The method provides elevation-dependent estimates of the pseudorange and pseudorange-rate measurement error properties in the form of both; the autocovariance function and the parametric model including the correlation time constants. The identification method is thoroughly discussed and verified using simulated and real Galileo E5b data and the identified models are utilised in the positioning solution. The identified measurement model improves the positioning accuracy by more than twenty percent.Item Design of Efficient Point-Mass Filter for Linear and Nonlinear Dynamic Models(2023) Duník, Jindřich; Matoušek, Jakub; Straka, Ondřejhis letter deals with the state estimation of nonlinear stochastic dynamic systems in the Bayesian framework. The emphasis is laid on the numerical solution to the Chapman-Kolmogorov equation by the widely-used point-mass method. It is shown, that the standard prediction step of the point-mass filter can be decomposed into two parts; advection and diffusion solution. This decomposition allows application of the fast Fourier transform, which speeds up the prediction step by several orders of magnitude making the point-mass filter attractive even for higher dimensional models. The proposed efficient point-mass filter is illustrated in a numerical simulation with available source codes and is compared with the particle filter.Item Using LSTM neural networks for cross-lingual phonetic speech segmentation with an iterative correction procedure(2024) Hanzlíček, Zdeněk; Matoušek, Jindřich; Vít, JakubThis article describes experiments on speech segmentation using long short-term memory recurrent neural networks. The main part of the paper deals with multi-lingual and cross-lingual segmentation, that is, it is performed on a language different from the one on which the model was trained. The experimental data involves large Czech, English, German, and Russian speech corpora designated for speech synthesis. For optimal multi-lingual modeling, a compact phonetic alphabet was proposed by sharing and clustering phones of particular languages. Many experiments were performed exploring various experimental conditions and data combinations. We proposed a simple procedure that iteratively adapts the inaccurate default model to the new voice/language. The segmentation accuracy was evaluated by comparison with reference segmentation created by a well-tuned hidden Markov model-based framework with additional manual corrections. The resulting segmentation was also employed in a unit selection text-to-speech system. The generated speech quality was compared with the reference segmentation by a preference listening test.
Item Is it Possible to Re-educate RoBERTa? Expert-driven Machine Learning for Punctuation Correction.(2023) Machura, Jakub; Hana, Žižková; Frémund, Adam; Švec, JanAlthough Czech rule-based tools for automatic punctuation insertion rely on extensive grammar and achieve respectable precision, the pre-trained Transformers outperform rule-based systems in precision and recall (Machura et al. 2022). The Czech pre-trained RoBERTa model achieves excellent results, yet a certain level of phenomena is ignored, and the model partially makes errors. This paper aims to investigate whether it ispossible to retrain the RoBERTa language model to increase the number of sentence commas the model correctly detects. We have chosen a very specific and narrow type of sentence comma, namely the sentence comma delimiting vocative phrases, which is clearly defined in the grammar and is very often omitted by writers. The chosen approaches were further tested and evaluated on different types of texts.Item Guest Editorial for the TAES Special Section on Machine Learning Methods for Aerial and Space Positioning and Navigation(2024) Yu, Kegen; Duník, Jindřich; Braasch, Michael S.; Closas, Pau; Dovis, FabioPositioning and navigation plays a significant role in a wide range of fields, such as aerospace, defense, and transportation, especially due to the continuous performance enhancement of the four Global Navigation Satellite Systems (GNSS) [1], [2] and the advent of complementary local positioning systems [3], [4]. Nowadays, requirements on positioning and navigation are becoming stricter in areas such as reliability, accuracy, continuity, complexity, integrability, and safety to enable better location-based services. In many complex and harsh environments, it is still a demanding task (such as for aerial and space vehicles) to generate real-time valid location information and perform the desired navigation, which enables to fulfill the assigned duties [5].Item Measurement Difference Method: A Universal Tool for Noise Identification(2022) Kost, Oliver; Duník, Jindřich; Straka, OndřejThis paper deals with noise identification of a system described by the linear time-varying state-space model using correlation methods. In particular, the stress is laid on the measurement difference method (MDM) as a universal tool allowing estimation of moments and parameters of the state and measurement noises. The recent results are summarised in a common framework and the full (and weighted) MDM implementation is developed. This implementation provides unbiased and weakly consistent estimate of an arbitrary raw or central moment of the state and measurement noises. The performance of the method is illustrated in a numerical study.Item Comparison of wav2vec 2.0 models on three speech processing tasks(2024) Kunešová, Marie; Zajíc, Zbyněk; Šmídl, Luboš; Karafiát, MartinThe current state-of-the-art for various speech processing problems is a sequence-to-sequence model based on a self-attention mechanism known as transformer. The widely used wav2vec 2.0 is a self-supervised transformer model pre-trained on large amounts of unlabeled speech and then fine-tuned for a specific task. The data used for training and fine-tuning, along with the size of the transformer model, play a crucial role in both of these training steps. The most commonly used wav2vec 2.0 models are trained on relatively “clean” data from sources such as the LibriSpeech dataset, but we can expect there to be a benefit in using more realistic data gathered from a variety of acoustic conditions. However, it is not entirely clear how big the difference would be. Investigating this is the main goal of our article. To this end, we utilize wav2vec 2.0 models in three fundamental speech processing tasks: speaker change detection, voice activity detection, and overlapped speech detection, and test them on four real conversation datasets. We compare four wav2vec 2.0 models with different sizes and different data used for pre-training, and we fine-tune them either on in-domain data from the same dataset or on artificial training data created from the LibriSpeech corpus. Our results suggest that richer data that are more similar to the task domain bring better performance than a larger model.Item Enhancing reproducibility in single cell research with biocytometry: An inter-laboratory study(2024) Fikar, Pavel; Alvarez, Laura; Berne, Laura; Cienciala, Martin; Kan, Christopher; Kasl, Hynek; Luo, Mona; Novackova, Zuzana; Ordonez, Sheyla; Sramkova, Zuzana; Holubova, Monika; Lysak, Daniel; Avery, Lyndsay; Caro, Andres A.; Crowder, Roslyn N.; Diaz-Martinez, Laura A.; Donley, David W.; Giorno, Rebecca R.; Guttilla Reed, Irene K.; Hensley, Lori L.; Johnson, Kristen C.; Kim, Audrey Y.; Kim, Paul; LaGier, Adriana J.; Newman, Jamie J.; Padilla-Crespo, Elizabeth; Reyna, Nathan S.; Tsotakos, Nikolaos; Al-Saadi, Noha N.; Appleton, Tayler; Arosemena-Pickett, Ana; Bell, Braden A.; Bing, Grace; Bishop, Bre; Forde, Christa; Foster, Michael J.; Gray, Kassidy; Hasley, Bennett L.; Johnson, Kennedy; Jones, Destiny J.; LaShall, Allison C.; McGuire, Kennedy; McNaughton, Naomi; Morgan, Angelina M.; Norris, Lucas; Ossman, Landon A.; Rivera-Torres, Paollette A.; Robison, Madeline E.; Thibodaux, Kathryn; Valmond, Lescia; Georgiev, DanielBiomedicine today is experiencing a shift towards decentralized data collection, which promises enhanced reproducibility and collaboration across diverse laboratory environments. This inter-laboratory study evaluates the performance of biocytometry, a method utilizing engineered bioparticles for enumerating cells based on their surface antigen patterns. In centralized and aggregated inter-lab studies, biocytometry demonstrated significant statistical power in discriminating numbers of target cells at varying concentrations as low as 1 cell per 100,000 background cells. User skill levels varied from expert to beginner capturing a range of proficiencies. Measurement was performed in a decentralized environment without any instrument cross-calibration or advanced user training outside of a basic instruction manual. The results affirm biocytometry to be a viable solution for immunophenotyping applications demanding sensitivity as well as scalability and reproducibility and paves the way for decentralized analysis of rare cells in heterogeneous samples.Item 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 Covariance Intersection fusion with element-wise partial knowledge of correlation(Elsevier, 2022) Ajgl, Jiří; Straka, OndřejPrůnik Kovariancí je pravidlo lineární fúze pro slučování odhadů. Pokud není vzájemná korelační matice chyb dvou odhadů známa, je toto pravidlo optimální ve smyslu mezí. Článek rozpracovává případ, kdy jsou některé prvky této matice známy. Zavádí techniky pro konstruování třídy horních mezí sdružené matice střední kvadratické chyby. Všechny konfigurace fúzí až čtyř odhadů jsou uvažovány výslovně. Představené techniky jsou též použitelné pro fúze více než čtyř odhadů.Item Umělé neuronové sítě a počítačové vidění v medicíně a chirurgii(Česká chirurgická společnost ČLS JEP, 2022) Jiřík, Miroslav; Moulisová, Vladimíra; Hlaváč, Miroslav; Železný, Miloš; Liška, VáclavÚvod: Umělé neuronové sítě se stávají důležitou technologií při analýze dat a jejich vliv začíná prostupovat i do oblasti medicíny. Naše pracoviště se dlouhodobě věnuje experimentální chirurgii, na to navazuje náš zájem o pokrok v ostatních oblastech moderních technologií a tím i umělých neuronových sítí. V rámci aktuálního čísla chceme prozkoumat i tento aspekt technického pokroku. Hlavním cílem je kritické zhodnocení silných i slabých stránek technologie umělých neuronových sítí s ohledem na využití v klinické a experimentální chirurgii. Metody: V článku je věnována pozornost in-silico modelování a zejména pak možnostem neuronových sítí s ohledem na zpracování obrazových dat v medicíně. V textu je krátce shrnut historický vývoj hlubokého učení neuronových sítí a základní principy jejich fungování. Dále je představena taxonomie základních řešených úloh. Zmíněny jsou i možné problémy při učení i s možnostmi jejich řešení. Výsledky: Článek poukazuje na rozličné možnosti umělých neuronových sítí v biologických aplikacích. Na řadě biomedicínských aplikací umělých neuronových sítí popisuje rozdělení a princip základních úloh strojového učení a hlubokého učení - klasifikace, detekce a segmentace. Závěr: Aplikace metod umělých neuronových sítí mají v medicíně a chirurgii značný potenciál. Obcházejí potřebu zdlouhavého subjektivního nastavování parametrů znalostním inženýrem, neboť se učí přímo z dat. Při využití nevhodně vyváženého datasetu však může docházet k neočekávaným, avšak zpětně vysvětlitelným chybám. Řešení představuje vytvoření dostatečně bohatého datasetu pro učení a ověření funkceItem One Model is Not Enough: Ensembles for Isolated Sign Language Recognition(MDPI, 2022) Hrúz, Marek; Gruber, Ivan; Kanis, Jakub; Boháček, Matyáš; Hlaváč, Miroslav; Krňoul, ZdeněkItem An artificial intelligence model to identify snakes from across the world: Opportunities and challenges for global health and herpetology(PLOS, 2022) Bolon, Isabelle; Picek, Lukáš; Durso, Andrew; Alcoba, Gabriel; Chappuis, François; Castañeda, Rafael Ruiz deItem Anomaly detection-based condition monitoring(British Institute of Non-Destructive Testing, 2022) Káš, Martin; Wamba, Francis FomiItem Plant recognition by AI: Deep neural nets, transformers, and kNN in deep embeddings(Frontiers Media S.A., 2022) Picek, Lukáš; Šulc, Milan; Patel, Yash; Matas, JiříItem Automatic Fungi Recognition: Deep Learning Meets Mycology(MDPI, 2022) Picek, Lukáš; Šulc, Milan; Matas, Jiří; Heilmann-Clausen, Jacob; Jeppesen, Thomas S.; Lind, EmilItem Low-pressure turbine blade leading edge protection using robotic laser cladding technology(Springer, 2022) Vaníček, Ondřej; Chaluš, Michal; Liška, Jindřich; Glusa, Tomáš; Vlasák, Jakub; Vašíčková, Eva; Brom, Karel
