Conference papers (NTIS)
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Item Tensile Properties of 3D Printed Infill Structures with Different Densities(Technical University of Liberec, 2025) Heczko, Jan; Krystek, Jan; Laš, VladislavThe tensile properties of 3D printed rectilinear infill pattern are investigated. Stress-strain curves are obtained for specimens with different infill densities and the Young’s modulus and ultimate tensile strength are evaluated.
Item Time homogenization in modelling of rubber damage and ageing(CRC Press/Balkema, 2025) Heczko, JanThe contribution is focused on cumulation of the effects of damage and ageing of elastomers on their stiffness properties. An earlier model is enhanced by introducing permanent set to an approximation of hyperelastic parameters as functions of time and loading type. The nature of the model, however, does not offer any straightforward way of extrapolation in case of different loading modes or service conditions. Therefore, a complex material model that takes into account different mechanisms of ageing and damage is considered. In order to enable numerical simulations of high-cycle loading, the method of asymptotic series is applied resulting in a time-homogenized version of the model. In addition to savings in computational time, the model is formally similar to the original approximation.
Item Short cycle covers and the colouring defect of a cubic graph(Elsevier B.V., 2025) Karabáš, Ján; Máčajová, Edita; Nedela, Roman; Škoviera, MartinA longstanding conjecture of Alon and Tarsi, and indepentry Jaeger (1985), suggests that the edges of every bridgeless graph can be covered with cycles of total length at most 7/5 •m, where m is the number of edges. We study the relationship between cycle covers and structural properties of cubic graphs, focusing on their colouring defect. This invariant, introduced by Steffen in 2015, is defined as the minimum number of edges left uncovered by any set of three perfect matchings of a cubic graph. We show that every bridgeless cubic graph with colouring defect not exceeding 3 admits a cycle cover of length at most 4/3 •m + 1, just one step above the universal lower bound of 4/3 •m for all cubic graphs. We also prove that, regardless of defect, the same bound holds for bridgeless cubic graphs that have an edge whose end vertices removed yield a 3-edge-colourable graph and the edge lies on a 5-cycle. Motivated by our investigations, we introduce a new invariant for cubic graphs, their covering excess, to measure the deviation of the length of a shortest cycle cover from the mentioned lower bound. Finally, we show that every bridgeless cubic graph with covering excess at most 1 admits a cycle double cover.Item Colouring defect of strong snarks(Elsevier B.V., 2025) Karabáš, Ján; Máčajová, Edita; Nedela, Roman; Škoviera, MartinA strong snark is a 2-connected cubic graph which is not 3-edge-colourable and remains so after deleting any edge and suppressing the resulting 2-valent vertices. Strong snarks were introduced by Jaeger in 1985 as a class of cubic graphs that might include counterexamples to the cycle double cover conjecture, the 5-flow conjecture, or to other related longstanding conjectures. With these conjectures still widely open, strong snarks merit further investigation. In this paper we study colouring defect of strong snarks, an invariant introduced by Steffen in 2015 as the minimum number of edges of a cubic graph left uncovered by any set of three perfect matchings. This invariant provides one of measures of edge uncolourability of cubic graphs recently studied by several authors. Our main result shows that the colouring defect of a strong snark is at least 6, and that the bound is sharp.Item RailSafeNet: Visual Scene Understanding for Tram Safety(Springer Cham, 2026) Valach, Ondřej; Gruber, IvanTram-human interaction safety is an important challenge, given that trams frequently operate in densely populated areas, where collisions can range from minor injuries to fatal outcomes. This paper addresses the issue from the perspective of designing a solution leveraging digital image processing, deep learning, and artificial intelligence to improve the safety of pedestrians, drivers, cyclists, pets, and tram passengers. We present RailSafeNet, a real-time framework that fuses semantic segmentation, object detection and a rule-based Distance Assessor to highlight track intrusions. Using only monocular video, the system identifies rails, localises nearby objects and classifies their risk by comparing projected distances with the standard 1435 mm rail gauge. Experiments on the diverse RailSem19 dataset show that a class-filtered SegFormer B3 model achieves 65% intersection-over-union (IoU), while a fine-tuned YOLOv8 attains 75.6% mean average precision (mAP) calculated at an intersection over union (IoU) threshold of 0.50. RailSafeNet therefore delivers accurate, annotation-light scene understanding that can warn drivers before dangerous situations escalate. Code available at https://github.com/oValach/RailSafeNet.Item Saudi Sign Language Translation Using T5(Springer Cham, 2026) Alhejab, Ali; Železný, Tomáš; Alkanhal, Lamya; Gruber, Ivan; Alharbi, Yazeed; Straka, Jakub; Javorek, Václav; Hrúz, Marek; Alkalifah, Badriah; Ali, AhmedThis paper explores the application of T5 models for Saudi Sign Language (SSL) translation using a novel dataset. The SSL dataset includes three challenging testing protocols, enabling comprehensive evaluation across different scenarios. Additionally, it captures unique SSL characteristics, such as face coverings, which pose challenges for sign recognition and translation. In our experiments, we investigate the impact of pre-training on American Sign Language (ASL) data by comparing T5 models pre-trained on the YouTubeASL dataset with models trained directly on the SSL dataset. Experimental results demonstrate that pre-training on YouTubeASL significantly improves models' performance (roughly in BLEU-4), indicating cross-linguistic transferability in sign language models. Our findings highlight the benefits of leveraging large-scale ASL data to improve SSL translation and provide insights into the development of more effective sign language translation systems. Our code is publicly available at our GitHub repository.Item Lightweight Target-Speaker-Based Overlap Transcription for Practical Streaming ASR(Springer, 2026) Pražák, Aleš; Kunešová, Marie; Psutka, JosefOverlapping speech remains a major challenge for automatic speech recognition (ASR) in real-world applications, particularly in broadcast media with dynamic, multi-speaker interactions. We propose a light-weight, target-speaker-based extension to an existing streaming ASR system to enable practical transcription of overlapping speech with minimal computational overhead. Our approach combines a speaker-independent (SI) model for standard operation with a speaker-conditioned (SC) model selectively applied in overlapping scenarios. Overlap detection is achieved using a compact binary classifier trained on frozen SI model output, offering accurate segmentation at negligible cost. The SC model employs Feature-wise Linear Modulation (FiLM) to incorporate speaker embeddings and is trained on synthetically mixed data to transcribe only the target speaker. Our method supports dynamic speaker tracking and reuses existing modules with minimal modifications. Evaluated on a challenging set of Czech television debates with 16% overlap, the system reduced WER on overlapping segments from 68.0% (baseline) to 35.78% while increasing total computational load by 44%. The proposed system offers an effective and scalable solution for overlap transcription in continuous ASR services.Item An Exploration of ECAPA-TDNN and x-vector Speaker Representations in Zero-Shot Multi-speaker TTS(Springer, 2026) Kunešová, Marie; Hanzlíček, Zdeněk; Matoušek, JindřichZero-shot multi-speaker text-to-speech (TTS) systems rely on speaker embeddings to synthesize speech in the voice of an unseen speaker, using only a short reference utterance. While many speaker embeddings have been developed for speaker recognition, their relative effectiveness in zero-shot TTS remains underexplored. In this work, we employ a YourTTS-based TTS system to compare three different speaker encoders – YourTTS’s original H/ASP encoder, x-vector embeddings, and ECAPA-TDNN embeddings – within an otherwise fixed zero-shot TTS framework. All models were trained on the same dataset of Czech read speech and evaluated on 24 out-of-domain target speakers using both subjective and objective methods. The subjective evaluation was conducted via a listening test focused on speaker similarity, while the objective evaluation measured cosine distances between speaker embeddings extracted from synthesized and real utterances. Across both evaluations, the original H/ASP encoder consistently outperformed the alternatives, with ECAPA-TDNN showing better results than x-vectors. These findings suggest that, despite the popularity of ECAPA-TDNN in speaker recognition, it does not necessarily offer improvements for speaker similarity in zero-shot TTS in this configuration. Our study highlights the importance of empirical evaluation when reusing speaker recognition embeddings in TTS and provides a framework for additional future comparisons.Item Ruminal Probes with Reliable Wireless Data Transmission(IEEE, 2025) Čečil, Roman; Kumprechtová, Dana; Koukolová, VeronikaThe paper presents design of in-vivo ruminal pH probes and compares them with current state-of-the-art alternatives. The main advantage of the proposed solution is a novel, patented approach for transmitting measured data from bovine rumen to a server or cloud via a re-transmitter placed in a collar device at the animal’s neck. Additionally, in comparison to the commonly measured values such as ruminal temperature and pH, the proposed solution enhances the data set with oxido-reduction potential (ORP) values measured by the ruminal probe and activity data acquired by a MEMS accelerometer located in the collar device.Item Coordinated operation between distribution system operator and demand-side management(IEEE, 2025) Hering, Pavel; Střelec, MartinThis paper presents a coordination scheme that aims to minimize the required data exchange between the distribution system operator (DSO) and stakeholders actively operating on the demand side (e.g. prosumers, EV charging point operators). The proposed methodology reduces the complexity of coordination among various stakeholders with the objective of maximizing the utilization of the available grid hosting capacity. The proposed decoupled optimization approach consists of two distinct optimization problems: i) a region-based optimization method that determines secure operational limits for power injections at consumption points from the grid perspective, and ii) a demand-side optimization concerning the operation of technology asset groups implementing local operational policies, such as cost-optimal operation, operation in accordance with the maximum secure active power injections and provision of system services to DSO. The decoupled optimization problems are formally defined and the overall methodological approach is demonstrated using three use cases related to different demand-side operational policies.Item Modelování a identifikace portálových jeřábů(Západočeská univerzita v Plzni, 2025) Sukovatý, DanielPříspěvek se zabývá modelováním a identifikací dynamiky portálového jeřábu za účelem návrhu řízení s automatickým tlumením nežádoucích kmitů zavěšené zátěže. Jeřáb je aproximován jako trojité kyvadlo zavěšené na posuvném vozíku, přičemž každé rameno je popsáno polohou těžiště, délkou, hmotností, momentem setrvačnosti a koeficientem tlumení. Pohybové rovnice jsou odvozeny pomocí Lagrangeovy metody a linearizovány v dolní rovnovážné poloze, čímž vzniká stavový model vhodný pro syntézu regulátoru. Pro identifikaci neznámých parametrů zátěže je navržena metoda využívající první dvě rezonanční frekvence systému. Z porovnání charakteristického polynomu matice dynamiky a její Jordanovy formy je sestrojena účelová funkce, jejíž minimum odpovídá fyzikálním parametrům; ta je hledána negradientní optimalizační metodou Pattern Search. Rezonanční frekvence jsou odhadovány experimentálně pomocí reléové zpětné vazby na základě harmonické linearizace. V simulacích dosahují postupy dostatečné přesnosti, u reálného systému se však objevují významné odchylky zejména u druhé rezonance a výpočty parametrů jsou časově náročné. Diskutována je možnost využití online identifikace, například na bázi Kalmanova filtru.Item Multi-label Classification and Named Entity Recognition for Historical Documents(Springer, 2025) Gruber, Ivan; Hlaváč, Miroslav; Neduchal, Petr; Hrúz, MarekIn this paper, we present improvements to our processing pipeline for historical document digitization. The original pipeline is extended with two new functionalities - page labeling, and named entity recognition. We handle page labeling as a multi-label classification task, for which we choose the Query2Label approach. Query2Label is tested on our internal NKVD dataset and reaches a mean average precision equal to 80.03% on the test set. For the named entity recognition task we utilize pre-trained transformer-based models DeepPavlov and benchmark them on two entities - person name, and location. The best model reaches promising results despite not being trained on our data at all.Item Using Pre-trained Models for Phoneme Representation in Czech Speech Synthesis(Západočeská univerzita v Plzni, 2025) Vladař, LukášText-to-speech (TTS) systems, i.e., systems producing artificial speech, represent an importanttopic in the field of artificial intelligence. Modern approaches based on neural networksreach very good results, almost comparable to real human speech.Nguyen et al. (2023) argue that including a large-scale pre-trained model for phonemerepresentation in a neural TTS system can further improve the final synthetic speech. We usedtheir pre-trained model called XPhoneBERT to investigate whether it can also enhance the qualityof speech synthesis in the Czech language.Item Improving Sign Language Translation through Multimodal Language Alignment(Západočeská univerzita v Plzni, 2025) Majer, FilipOver 5% of the world’s population experiences disabling hearing loss and many of themrely on sign languages as their primary means of communication. Despite the widespread useof sign languages, most modern communication technologies are designed primarily for spokenlanguage, leaving deaf signers at a significant disadvantage. Recent advances in artificialintelligence offer new opportunities to address these communication barriers.The main objective of this work was to design a new system for sign language translation.At its core is a novel video feature extraction model that combines both spatial and temporalinformation. In addition, the entire system supports pretraining through language alignment.Item Monitorovací systém pro včelaře(Západočeská univerzita v Plzni, 2025) Březina, PavelVčely jsou klíčovými opylovači a hrají zásadní roli v ekosystémech po celém světě. Péče o silná a zdravá včelstva vyžaduje pravidelný dohled, avšak tradiční kontroly úlů jsou časově i fyzicky náročné. Jejich omezená frekvence zvyšuje riziko, že dojde k přehlédnutí raných příznaků nemocí, rojení či ztráty matky. Moderní systémy pro real-time monitorování umožňují tyto události včas detekovat nebo dokonce předpovídat pomocí senzorů a zvukové analýzy. Stávající komerčně dostupná řešení sice nabízejí širokou škálu monitorovacích funkcí, avšak jejich cena je často neúměrně vysoká a pro hobby včelaře finančně nedostupná. Navržený monitorovací systém přináší řešení za dostupnou cenu bez kompromisů na množství poskytovaných informací.Item Využití transformerů pro specifickou úlohu z praxe(Západočeská univerzita v Plzni, 2025) Tauš, DanielModerní podnikové systémy, například v logistice, často využívají transakční modely reprezentované XML. Manuální tvorba dokumentace k těmto transakcím a její následné vyhledávání jsou však časově náročné a vedou k nekonzistencím, což omezuje efektivitu. Tato práce proto zkoumá využití modelů architektury Transformer, představené v práci Vaswani et al. (2017), ke zlepšení těchto procesů. Konkrétně se zaměřuje na dvě hlavní oblasti: automatické generování textových popisů transakcí z jejich XML dat; vývoj systému pro efektivní sémantické a hybridní vyhledávání v těchto popisech.Item Spatial Analysis of Image Descriptions for Detection of Cognitive Disorders(Západočeská univerzita v Plzni, 2025) Lebeda, TomášNeurodegenerative diseases pose a significant challenge to modern medicine, not onlydue to their progressive and irreversible nature but also because they often remain undiagnoseduntil symptoms become severe. Early detection is essential to maximize the effectivenessof available treatments and to slow the progression of cognitive decline. However, currentdiagnostic approaches are often slow, costly, and inaccessible, typically requiring in-personclinical assessments or expensive procedures like MRI.Research by Bartoš et. al. (2024) has proposed an alternative approach that leveragescognitively demanding tasks - such as describing complex, detailed images in combinationwith other short-term memory tests as a means of early detection.This paper explores the potential of automating the analysis of such image descriptiontasks using machine learning techniques, with the goal of developing a fast, low-cost screeningtool that can be used at home, potentially with the assistance of a family member, therebyreducing the need for in-person clinical visits. Specifically, the focus is on one specific subcomponentof this approach: analyzing the sequence of objects as mentioned during imagedescription.Item Nonsense Word Repetition for Detection of Cognitive Disorders(Západočeská univerzita v Plzni, 2025) Tupý, JanCognitive impairments are neurological disorders that disrupt fundamental mental functionssuch as memory, attention, or language. Although they most commonly appear in olderadults, they can also result from brain injuries or psychiatric conditions. Due to the increasingprevalence of these disorders, there is a growing emphasis on early and reliable diagnosis.Traditional diagnostic procedures are time-consuming, which has led to a rising interest in automateddigital testing.One innovative approach involves testing with nonsense words that resemble regularvocabulary but lack meaning. This method enables researchers to focus on pure cognitiveprocesses without interference from semantic context. The aim of this study is to investigatewhether this type of task can provide objective and quantitative data on the state of cognitivefunctions and support the development of effective digital diagnostic tools.Item Towards Aesthetic Enrichment of Mirror Selfies via Automatic Image Analysis(Západočeská univerzita v Plzni, 2025) Vyskočil, Jiří; Zejkanová, KristinaMirror selfies have become a common form of self-expression on social media, often shared for their aesthetic value. This trend inspired a collaboration with the Ladislav Sutnar Faculty of Design and Art to explore how design elements — such as chairs or decorative objects — might enhance these images. We developed a system for automatic analysis of mirror selfies collected from social networks. The goal is to automatically extract visual metadata, including color palettes, human poses, face occlusion by phones, subject placement (e.g., golden ratio), and object directions relative to the person. The obtained metadata can support the creation of design elements that make selfies more visually appealing.Item Animal Identification with Independent Foreground and Background Modeling(Springer, 2025) Picek, Lukáš; Neumann, Lukáš; Matas, JiříWe propose a method that robustly exploits background andforeground in visual identification of individual animals. Experiments show that their automatic separation, made easy with methods like Segment Anything, together with independent foreground and backgroundrelated modeling, improves results. The two predictions are combined in a principled way, thanks to novel Per-Instance Temperature Scaling that helps the classifier to deal with appearance ambiguities in training and to produce calibrated outputs in the inference phase. For identity prediction from the background, we propose novel spatial and temporal models. On two problems, the relative error w.r.t. the baseline was reduced by 22.3% and 8.8%, respectively. For cases where objects appear in new locations, an example of background drift, accuracy doubles.
