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 Comparison of Geospatial Trajectory Clustering and Feature Trajectory Clustering for Public Transportation Trip Data(Springer Nature Switzerland AG, 2023) Cogollos Adrian, Hector; Baruque Zanon, Bruno; Porras Alfonso, Santiago; Doležel, PetrOne of the techniques for the analysis of travel patterns on a public transport network is the clustering of the users movements, in order to identify movement patterns. This paper analyses and compares two different methodologies for public transport trajectory clustering: feature clustering and geospatial trajectory clustering. The results of clustering trip features, such as origin, destination, or distance, are compared against the clustering of travelled trajectories by their geospatial characteristics. Algorithms based on density and hierarchical clustering are compared for both methodologies. In geospatial clustering, different metrics to measure distances between trajectories are included in the comparison. Results are evaluated by analysing their quality through the silhouette coefficient and graphical representations of the clusters on the map. The results show that geospatial trajectory clustering offers better quality than feature trajectory clustering. Also, in the case of long and complete trajectories, density clustering using edit distance with real penalty distance outperforms other combinations.Konferenční objektpeer-reviewedpostprint Omezený přístup Object detection for robotic grasping using a cascade of convolutional networks(IEEE (Institute of Electrical and Electronics Engineers), 2023) Rais, Vítek; Doležel, PetrRobot guidance in industry is a significant issue that needs to be dealt with in modern manufacturing facilities. One of the common tasks in this area is the pick and place problem. For proper implementation of an automatic pick and place application using a robotic arm for object grasping, it is necessary to detect the accurate pose of the objects of interest. In this contribution, a novel engineering approach to object positioning, based on image processing is proposed. In this approach, the operation is composed of a cascade of convolutional neural networks. This cascade consists of 2 different types of networks. The first one is the object detection network called YOLOv5. It is used to process the raw image data from the scene to provide precise localization and determine the position of the objects of interest. After that, crops of the detected objects are created and processed by the second neural network, namely EfficientNet. This classification network is used to determine the rotation angle of the detected objects. The proposed approach provides a precision rate of 0.997 and a recall rate of 0.999 for locating and determining the correct position. For angle classification, EfficientNet provides an accuracy of 0.951. All tests are performed on the testing set of the legitimate positioning problem.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 Omezený přístup Employing Quantile and Probability Plots for Comparing and Assessing Goodness of Fit for Stochastic Models of the DCT Coefficients of Lossy Compressed Images(IEEE (Institute of Electrical and Electronics Engineers), 2023) Kotov, Dmytro; Fedorov, Oleksii; Omelchenko, Anatolii; Pidanič, Jan; Doležel, PetrThe paper employs probability plots to performgoodness of fit tests for AC DCT coefficients of compressed images. Attention is paid to various probabilistic models of the DCT coefficients, conventional and rarely used. The Laplacian, generalized Gaussian and doubly Gamma distributions comprise the list. A variation of the method of moments, which involves the 2nd and 4th sample moments as well as Sheppard’s corrections to estimate the shape and scale parameters of the distributions is used. Two types of images are considered, namely, texturelike images and those possessing vast regions of monotonicity. Special effort has been put into adjusting the apparatus of the probability plots to make them suitable for dealing with discrete data, in our case with the quantized DCT coefficients of lossy compressed images. As the source of the DCT coefficients, JPEG images are used. This does not lead us to a significant loss of generality: conclusions drawn in this paper remain applicable to a broad variety of formats of lossy compressed images.Článekpeer-reviewedpostprint Omezený přístup Genetic Algorithm-Based Task Assignment for Fleet of Unmanned Surface Vehicles in Dynamically Changing Environment(Taylor & Francis Inc, 2023) Dvořák, Miroslav; Doležel, Petr; Štursa, Dominik; Chouai, MohamedUnmanned vehicles are gaining the attention of professional operators and the general public. The implementation of unmanned vehicles is evident in, among other fields, emergency management, agriculture, traffic monitoring, post-disaster operations, and delivery of goods. Naturally, a group of unmanned vehicles can cooperatively complete operations more proficiently than a single vehicle. However, several issues must be resolved before a stable and reliable group of unmanned vehicles can be generally deployed to solve tasks in civil infrastructures and in industrial facilities. Here, a framework for the guidance of a fleet of unmanned surface vehicles is proposed. The framework utilizes several levels of control, namely Global Planning Level, Local Planning Level, and Low-Level Control. While the individual vehicles are completely autonomous in their operational locomotion and obstacle avoidance (low-level control and local planning), the task assignment for each vehicle (or group of them) is provided by a global planning process, based on the genetic algorithm. The framework provides a concept to solve complex tasks for the fleet of unmanned surface vehicles (USVs). This includes, but is not necessarily limited to, a dynamically changing environment, different types of USVs with special abilities, multiple types of areal restrictions and obstacles, different restrictions for individual USVs, cooperation of multiple USVs to solve their subtasks, energy consumption optimization, etc. The framework can be advantageously applied to tasks such as warehouse logistics, surface maintenance, area exploration, etc. At the end of the study, the application of the framework is presented using a simulated example of cooperative problem solving using six vehicles.Článekpeer-reviewedpostprint Omezený přístup How the Presence of Crystalline Phase Affects Structural Relaxation in Molecular Liquids: The Case of Amorphous Indomethacin(MDPI AG (Multidisciplinary Digital Publishing Institute), 2023) Svoboda, Roman; Pakosta, Marek; Doležel, PetrThe influence of partial crystallinity on the structural relaxation behavior of low-molecular organic glasses is, contrary to, e.g., polymeric materials, a largely unexplored territory. In the present study, differential scanning calorimetry was used to prepare a series of amorphous indomethacin powders crystallized to various extents. The preparations stemmed from the two distinct particle size fractions: 50-125 mu m and 300-500 mu m. The structural relaxation data from the cyclic calorimetric measurements were described in terms of the phenomenological Tool-Narayanaswamy-Moynihan model. For the 300-500 mu m powder, the crystalline phase forming dominantly on the surface led to a monotonous decrease in the glass transition by similar to 6 degrees C in the 0-70% crystallinity range. The activation energy of the relaxation motions and the degree of heterogeneity within the relaxing matrix were not influenced by the increasing crystallinity, while the interconnectivity slightly increased. This behavior was attributed to the release of the quenched-in stresses and to the consequent slight increase in the structural interconnectivity. For the 50-125 mu m powder, distinctly different relaxation dynamics were observed. This leads to a conclusion that the crystalline phase grows throughout the bulk glassy matrix along the internal micro-cracks. At higher crystallinity, a sharp increase in T-g, an increase in interconnectivity, and an increase in the variability of structural units engaged in the relaxation motions were observed.Článekpeer-reviewedpostprint Omezený přístup CSU-Net: Contour Semantic Segmentation Self-Enhancement for Human Head Detection(IEEE (Institute of Electrical and Electronics Engineers), 2023) Chouai, Mohamed; Doležel, PetrThe computer vision community has made tremendous progress in solving a variety of semantic image understanding tasks, such as classification and segmentation. With the advancement of imaging technology and hardware, image semantic segmentation, through the use of deep learning, is among the most common topics which have been worked on in the last decade. However, image semantic segmentation suffers from several drawbacks such as insufficient detection of object boundaries. In this study, we present a new convolutional neural network architecture called CSU-Net that aims to self-enhance the results of semantic segmentation. The proposed model consists of two strongly concatenated encoder-decoder blocks. With this design, we reduced requirements on computing power and memory size to decrease costs and increase the training/prediction speed. This study also demonstrates the advantage of the proposed system for small training data sets. The proposed approach has been implemented on our private dataset, as well as on a publicly available dataset. A comparative analysis was carried out with four popular segmentation models and three other recently introduced architectures to show the efficiency of the proposed system. CSU-Net outperformed the other competing neural networks that we considered for the comparative study. As an example, it succeeded in improving the traditional U-Net result by approximately 50% in mean Intersection over Union (mIoU) for both tested datasets. Based on our experience, the CSU-Net can improve results of semantic segmentation in many applications.Konferenční objektpeer-reviewedpostprint (accepted version) Otevřený přístup Design and Implementation of Probabilistic Methods for Spectrum Sensing in Cognitive Radios(IEEE (Institute of Electrical and Electronics Engineers), 2022) Ponomarov, Andrii; Ivanenko, Stanislav; Fedorov, Oleksii; Bezruk, Valeriy; Pidanič, Jan; Doležel, PetrThe paper deals with new unconventional methods of detecting unoccupied frequency channels in cognitive radios. The main feature of these methods consists in their ability of detecting unknown signals in the presence of noise under the condition of a priori uncertainty. It makes it possible to increase the efficiency of detecting unoccupied frequency channels in cognitive radios due to the fact that these methods track changes in the probabilistic properties of observations. During the course of spectrum sensing of the frequency range, the detected signals are divided into known (classified training samples of which are available in the system) and unknown ones. Application of methods for recognizing specified signals in the presence of unknown signals makes it possible to simultaneously avoid the erroneous occupation of a frequency channel by a secondary user, in the case when previously unregistered signal occurs, and also refresh the cognitive radio database. To detect unknown signals, only information about probabilistic characteristics of the channel noise is used.