Fakulta elektrotechniky a informatiky / Faculty of Electrical Engineering and Informatics
Stálý URI pro tuto komunituhttps://hdl.handle.net/10195/3847
Práce obhájené před rokem 2008 jsou uloženy pouze v kolekci Vysokoškolské kvalifikační práce
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Konferenční objektpeer-reviewedpostprint Omezený přístup ECG Hearbeat Classification Based on Multi-scale Convolutional Neural Networks(Springer Nature Switzerland AG, 2023) Rozinek, Ondřej; Doležel, PetrClinical applications require automating ECG signal processing and classification. This paper investigates the impact of multiscale input filtering techniques and feature map blocks on the performance of CNN models for ECG classification. We conducted an ablation study using the AbnormalHeartbeat dataset, with 606 instances of ECG time series divided into five classes. We compared five multiscale input filtering techniques and four multiscale feature map blocks against a base model and non-multiscale input. Results showed that the combination of mean filter for multiscale input and residual connections for multiscale block achieved the highest accuracy of 64.47%. Residual connections were consistently effective across different filtering techniques, highlighting their potential to enhance CNN model performance for ECG classification. These findings can guide the design of future CNN models for ECG classification tasks, with further experimentation needed for optimal combinations in specific applications.Konferenční objektpeer-reviewedpostprint Omezený přístup Automated Dataset Enhancement Using GAN for Assessment of Degree of Degradation around Scribe(IEEE (Institute of Electrical and Electronics Engineers), 2023) Doležel, Petr; Pakosta, Marek; Rozsívalová, Veronika; Štursa, DominikCoil coating is a method of applying an organic coating material to a rolled metal strip substrate in a continuous automated process. It is used to provide a high quality, durable finish to a variety of surfaces. The degradation resistance of coil-coated materials is assessed according to European Standard EN 13523-8 by exposing a coil-coated test specimen to a salt fog at a defined temperature for a defined period of time. After this process, a sample is tested according to the International Organisation for Standardisation ISO 4628 standard to determine the degree of degradation. In this study, a GAN-based technique for automated training set enhancement is proposed to assess the degree of degradation around a scribe. The presented technique is capable of enhancing a manually generated dataset of images with synthetic samples to help refine the performance of the area degradation detector.Konferenční objektpeer-reviewedpostprint Omezený přístup Multi-Scale Neural Model for Tool-Narayanaswamy-Moynihan Model Parameter Extraction(Springer Nature Switzerland AG, 2023) Pakosta, Marek; Doležel, Petr; Svoboda, Roman; Baruque Zanon, BrunoGlass transitions are an important phenomenon in amor phous materials with potential for various applications. The Tool-Narayanaswamy-Moynihan (TNM) model is a widely used empirical model that describes the enthalpy relaxation behavior of these materials. However, determining the appropriate values for its parameters can be challeng ing. To address this issue, a multi-scale convolutional neural model is pro posed that can accurately predict the TNM parameters directly from the set of differential scanning calorimetry curves, experimentally measured using the sample of the considered amorphous material. The resulting Mean Absolute Error of the model over the test set is found to be 0.0252, indicating a high level of accuracy. Overall, the proposed neural model has the potential to become a valuable tool for practical application of the TNM model in the glass industry and related fields.Konferenční objektpeer-reviewedpostprint (accepted version) Omezený přístup Classification of Polymers Based on the Degree of Their Transparency in SWIR Spectrum(Springer Nature Switzerland AG, 2022) Štursa, Dominik; Kopecký, Dušan; Roleček, Jiří; Doležel, Petr; Baruque Zanon, BrunoDetection, classification and sorting of polymeric particles is a common task required in recycling industry. In the proposed work, an innovative method for detection of polymeric particles and their classification is introduced. The method is based on evaluation of images of polymeric particles, obtained from short-wavelength infrared (SWIR) camera, by convolutional neural network (CNN). Compared to conventionally used spectroscopes or hyper-spectral imaging, this method utilizes single wavelength (1 050 nm) and a degree of polymer transparency serves as the main descriptor. Five different polymers (ABS, ABS-T, Nylon, PETG, PLA) in form of regular blocks (size 15 × 15 × 0.3 mm) were used in the experiment. In total 203 images (size 288 × 288 px) were prepared for CNN training and 67 for testing. Scalable ASP U-Net was tested in 6 combinations and their outputs were compared. According to used intersection over union metrics over all outputs, the topology with 64 filters and depth of 3 exhibited the best results.Článekpeer-reviewedpublished Otevřený přístup Sequence of U-Shaped Convolutional Networks for Assessment of Degree of Delamination Around Scribe(Atlantis Press, 2022) Rozsívalová, Veronika; Doležel, Petr; Štursa, Dominik; Rozsíval, PavelThe application of protective layers is the primary method of keeping metallic structures resistant to degradation. The measurement of the layer resistance to delamination is one of the important indicators of the protection quality. Therefore, ISO 4628 standard has been issued to handle and quantify the main coating defects. Here, an innovative assessment of degree of delamination around a scribe according to ISO 4628 standard has been practically realized. It utilizes an computer-driven deep learning-based method. The assessment method is composed of two shallow U-shaped convolutional networks in a row; the first for preliminary and the second for refined detection of delamination area around a scribe. The experiments performed on 586 samples showed that the proposed sequence of U-shaped convolutional networks meets the edge computing standards, provides good generalization capability, and provides precise delamination area detection for a large variability of surfaces.Konferenční objektpeer-reviewedpostprint (accepted version) Otevřený přístup Spectral Classification of Microplastics using Neural Networks: Pilot Feasibility Study(SciTePress - Science and Technology Publications, 2022) Doležel, Petr; Roleček, Jiří; Honc, Daniel; Štursa, Dominik; Baruque Zanon, BrunoMicroplastics, i.e. synthetic polymers that have particle size smaller than 5 mm, are emerging pollutants that are widespread in the environment. In order to monitor environmental pollution by microplastics, it is necessary to have available rapid screening techniques, which provide the accurate information about the quality (type of polymer) and quantity (amount). Spectroscopy is an indispensable method, if precise classification of individual polymers in microplastics is required. In order to contribute to the topic of autonomous spectra matching when using spectroscopy, we decided to demonstrate the quality and efficiency of neural networks. We adopted three neural network architectures, and we tested them for application to spectra matching. In order to keep our study transparent, we use publicly available dataset of FTIR spectra. Furthermore, we performed a deep statistical analysis of all the architectures performance and efficiency to show the suitability of neural networks for spectra matching. The results presented at the end of this article indicated the overall suitability of the selected neural network architectures for spectra matching in microplastics classification.Článekpeer-reviewedpublished version Otevřený přístup Memory Efficient Grasping Point Detection of Nontrivial Objects(IEEE (Institute of Electrical and Electronics Engineers), 2021) Doležel, Petr; Štursa, Dominik; Kopecký, Dušan; Jecha, JiříRobotic manipulation with a nontrivial object providing various types of grasping points is of an industrial interest. Here, an efficient method of simultaneous detection of the grasping points is proposed. Specifically, two different 3 degree-of-freedom end effectors are considered for simultaneous grasping. The method utilizes an RGB data-driven perception system based on a specifically designed fully convolutional neural network called attention squeeze parallel U-Net (ASP U-Net). ASP U-Net detects grasping points based on a single RGB image. This image is transformed into a schematic grayscale frame, where the positions and poses of the grasping points are coded into gradient geometric shapes. In order to approve the ASP U-Net architecture, its performance was compared with nine competitive architectures using metrics based on generalized intersection over union and mean absolute error. The results indicate its outstanding accuracy and response time. ASP U-Net is also computationally efficient enough. With a more than acceptable memory size (77 MB), the architecture can be implemented using custom single-board computers. Here, its capabilities were tested and evaluated on the NVIDIA Jetson NANO platform.Konferenční objektpeer-reviewedpostprint Omezený přístup One Step Deep Learning Approach to Grasp Detection in Robotics(Springer Science and Business Media, 2021) Doležel, Petr; Štursa, Dominik; Honc, DanielGrasp point detection is a necessary ability to handle for industrial robots. In recent years, various deep learning-based techniques for robotic grasping have been introduced. To follow this trend, we introduce a convolutional neural network-based approach for model-free one step method for grasp point detection. This method provides all feasible grasp points suitable for parallel grippers, based on a single RGB image of the scene. A case study, which shows the outstanding accuracy of the presented approach as well as its acceptable response time, is presented at the end of this contribution.