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- Publikacje 1926 wyników po odfiltrowaniu
- Czasopisma 57 wyników po odfiltrowaniu
- Konferencje 40 wyników po odfiltrowaniu
- Wydawnictwa 1 wyników po odfiltrowaniu
- Osoby 67 wyników po odfiltrowaniu
- Projekty 5 wyników po odfiltrowaniu
- Kursy Online 11 wyników po odfiltrowaniu
- Wydarzenia 3 wyników po odfiltrowaniu
- Dane Badawcze 256 wyników po odfiltrowaniu
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Wyniki wyszukiwania dla: MULTILAYER NEURAL NETWORK
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Video of LEGO bricks on conveyor belt - Wide Brics
Dane BadawczeThe set contains videos of LEGO bricks (wide bricks, with each side having more than 1 stud) moving on a white conveyor belt. The videos were prepared for training neural network for recognition of LEGO bricks. The bricks were separated as much as possible and in most cases they should not overlap. The images were taken from different sides by stationary...
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Video of LEGO bricks on conveyor belt - minifigures, animals, plants and accessories
Dane BadawczeThe set contains videos of LEGO bricks (minifigures, animals, plants and accessories) moving on a white conveyor belt. The images were prepared for training neural network for recognition of LEGO bricks. The bricks were separated as much as possible and in most cases they should not overlap. The images were taken from different sides by stationary camera...
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Video of LEGO bricks on conveyor belt - Narrow Brics
Dane BadawczeThe set contains videos of LEGO bricks (narrow bricks, with on side no wider than 1 stud) moving on a white conveyor belt. The videos were prepared for training neural network for recognition of LEGO bricks. The bricks were separated as much as possible and in most cases they should not overlap. The images were taken from different sides by stationary...
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Comparison of image pre-processing methods in liver segmentation task
PublikacjaAutomatic liver segmentation of Computed Tomography (CT) images is becoming increasingly important. Although there are many publications in this field there is little explanation why certain pre-processing methods were utilised. This paper presents a comparison of the commonly used approach of Hounsfield Units (HU) windowing, histogram equalisation, and a combination of these methods to try to ascertain what are the differences...
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Andrzej Stateczny prof. dr hab. inż.
OsobyProf. dr hab. inż. Andrzej Stateczny jest profesorem Politechniki Gdańskiej i prezesem firmy Marine Technology Ltd. Jego zainteresowania naukowe koncentrują się głównie wokół nawigacji, hydrografii i geoinformatyki. Obecnie prowadzone badania obejmują nawigację radarową, nawigację porównawczą, hydrografię, metody sztucznej inteligencji w zakresie przetwarzania obrazów i fuzji danych wielosensorycznych. Był kierownikiem lub głównym...
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Deep Learning Basics 2023/24
Kursy OnlineA course about the basics of deep learning intended for students of Computer Science. It includes an introduction to supervised machine learning, the architecture of basic artificial neural networks and their training algorithms, as well as more advanced architectures (convolutional networks, recurrent networks, transformers) and regularization and optimization techniques.
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Wstępne wyniki badań dostępności sieci ASG-Eupos podczas inwentaryzacji trasy kolejowej Kościerzyna - Kartuzy = Preliminary research results of ASG-Eupos network availability during surveying of Kościerzyna-Kartuzyrailway route
PublikacjaW artykule przedstawiono wyniki pomiarów dostępności określonej wartości błędu współrzędnych, wyznaczonych przy wykorzystaniu sieci ASG-EUPOS, uzyskane podczas pomiarów inwentaryzacyjnych testowego odcinka linii kolejowej. Pomiary z wykorzystaniem4 odbiorników GNSS przeprowadzono w miesiącu lutym bieżącego roku na odcinku trasy kolejowej Kościerzyna - Kartuzy. Program badań zakładał montaż, na pokładzie platformy kolejowej napędzanej...
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Deep-Learning-Based Precise Characterization of Microwave Transistors Using Fully-Automated Regression Surrogates
PublikacjaAccurate models of scattering and noise parameters of transistors are instrumental in facilitating design procedures of microwave devices such as low-noise amplifiers. Yet, data-driven modeling of transistors is a challenging endeavor due to complex relationships between transistor characteristics and its designable parameters, biasing conditions, and frequency. Artificial neural network (ANN)-based methods, including deep learning...
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The trajectories of the financial crisis of companies at risk of bankruptcy
PublikacjaThis article concerns the assessment of the trajectory of the collapse of enterprises in Central Europe. The author has developed a model of a Kohonen artificial neural network. This model was used to determine 6 different classes of risk and was allowed to graphically determine the 5- to 10-year trajectory of going bankrupt. The study used data on 140 companies listed on the Warsaw Stock Exchange. This population was divided into...
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LSTM-based method for LOS/NLOS identification in an indoor environment
PublikacjaDue to the multipath propagation, harsh indoor environment significantly impacts transmitted signals which may adversely affect the quality of the radiocommunication services, with focus on the real-time ones. This negative effect may be significantly reduced (e.g. resources management and allocation) or compensated (e.g. correction of position estimation in radiolocalisation) by the LOS/NLOS identification algorithm. This paper...
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Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks
PublikacjaIn this article, the usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) is presented. The developed approach, using channel impulse response, allows higher efficiency in identifying the direct visibility conditions between nodes in off-body communication with comparison to the methods described in the literature. The effectiveness of the proposed deep feedforward neural network was checked on...
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Vehicle classification based on soft computing algorithms
PublikacjaExperiments and results regarding vehicle type classification are presented. Three classes of vehicles are recognized: sedans, vans and trucks. The system uses a non-calibrated traffic camera, therefore no direct vehicle dimensions are used. Various vehicle descriptors are tested, including those based on vehicle mask only and those based on vehicle images. The latter ones employ Speeded Up Robust Features (SURF) and gradient images...
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Data augmentation for improving deep learning in image classification problem
PublikacjaThese days deep learning is the fastest-growing field in the field of Machine Learning (ML) and Deep Neural Networks (DNN). Among many of DNN structures, the Convolutional Neural Networks (CNN) are currently the main tool used for the image analysis and classification purposes. Although great achievements and perspectives, deep neural networks and accompanying learning algorithms have some relevant challenges to tackle. In this...
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Optimized Computational Intelligence Model for Estimating the Flexural Behavior of Composite Shear Walls
PublikacjaThis article presents a novel approach to estimate the flexural capacity of reinforced concrete-filled composite plate shear walls using an optimized computational intelligence model. The proposed model was developed and validated based on 47 laboratory data points and the Transit Search (TS) optimization algorithm. Using 80% of the experimental dataset, the optimized model was selected by determining the unknown coefficients of...
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Efkleidis Katsaros
OsobyEfklidis Katsaros received the B.Sc. degree in mathematics from the Aristotle University of Thessaloniki, Greece, in 2016, and the M.Sc. degree (cum laude) in data science: statistical science from Leiden University, The Netherlands, in 2019. He is currently pursuing the Ph.D. degree in deep video multi-task learning with the Department of Biomedical Engineering, Gdańsk University of Technology, Poland. Since 2020, he has been...
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Comparative study on total nitrogen prediction in wastewater treatment 1 plant and effect of various feature selection methods on machine learning algorithms performance
PublikacjaWastewater characteristics prediction in wastewater treatment plants (WWTPs) is valuable and can reduce the number of sampling, energy, and cost. Feature Selection (FS) methods are used in the pre-processing section for enhancing the model performance. This study aims to evaluate the effect of seven different FS methods (filter, wrapper, and embedded methods) on enhancing the prediction accuracy for total nitrogen (TN) in the WWTP...
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An automated learning model for twitter sentiment analysis using Ranger AdaBelief optimizer based Bidirectional Long Short Term Memory
PublikacjaSentiment analysis is an automated approach which is utilized in process of analysing textual data to describe public opinion. The sentiment analysis has major role in creating impact in the day-to-day life of individuals. However, a precise interpretation of text still relies as a major concern in classifying sentiment. So, this research introduced Bidirectional Long Short Term Memory with Ranger AdaBelief Optimizer (Bi-LSTM RAO)...
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Video of LEGO bricks on conveyor belt - wheels, tires and caterpillars
Dane BadawczeThe set contains videos of LEGO bricks (wheels, tires and caterpillars) moving on a white conveyor belt. The images were prepared for training neural network for recognition of LEGO bricks. The bricks were separated as much as possible and in most cases they should not overlap. The images were taken from different sides by stationary camera located...
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Uszkodzenia wielowarstwowych posadzek betonowych
PublikacjaPosadzki wielowarstwowe to często projektowane elementy wewnętrznego wykończenia budowli. Mają bardzo duży wpływ na kształtowanie architektoniczne wnętrz oraz zapewnienie właściwych warunków użytkowania. W artykule przedstawiono przyczyny wystąpienia uszkodzeń posadzki w postaci rys i spękań. Górna trudno ścieralna warstwa posadzki grubości ok. 20 mm została wykonana metodą „mokre na suche”.
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Józef Pacyna prof. dr hab. inż.
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Style Transfer for Detecting Vehicles with Thermal Camera
PublikacjaIn this work we focus on nighttime vehicle detection for intelligent traffic monitoring from the thermal camera. To train a Convolutional Neural Network (CNN) detector we create a stylized version of COCO (Common Objects in Context) dataset using Style Transfer technique that imitates images obtained from thermal cameras. This new dataset is further used for fine-tuning of the model and as a result detection accuracy on images...
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Pancreatic neuroendocrine neoplasms — update of the diagnostic and therapeutic guidelines (recommended by the Polish Network of Neuroendocrine Tumours) [Nowotwory neuroendokrynne trzustki — uaktualnione zasady diagnostyki i leczenia (rekomendowane przez Polską Sieć Guzów Neuroendokrynych)]
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Workshop on Network-Related Data Management
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International Conference on Cryptology and Network Security
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IEEE Network Operations and Management Symposium
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International Conference on network and System Security
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Serum levels of IL-6, IL-8 and CRP as prognostic factors in epithelial ovarian cancer
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Usefulness of selected laboratory markers in ulcerative colitis
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Composition Patterns of Contemporary Polish Residential Building Facades
PublikacjaThis study aims to define the types of composition patterns of contemporary Polish multi-family building facades. The authors define the compositional patterns to determine their frequency. Analyses carried out on the basis of photos and visualizations of 113 examples of architecture from Poland identified groups of common features of facades. Statistical analyses of these features resulted in six types of compositions. Clear differences...
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Network and Operating System Support for Digital Audio and Video (Network and OS Support for Digital A/V)
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MP3vec: A Reusable Machine-Constructed Feature Representation for Protein Sequences
Publikacja—Machine Learning (ML) methods have been used with varying degrees of success on protein prediction tasks, with two inherent limitations. First, prediction performance often depends upon the features extracted from the proteins. Second, experimental data may be insufficient to construct reliable ML models. Here we introduce MP3vec, a transferable representation for protein sequences that is designed to be used specifically for sequence-to-sequence...
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Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks
PublikacjaThe increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated...
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IEEE International Joint Conference on Neural Networks
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Conference on Artificial Neural Networks and Expert systems
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International Conference on Engineering Applications of Neural Networks
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International Conference on Neural, Parallel and Scientific Computations
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Deep learning approach on surface EEG based Brain Computer Interface
PublikacjaIn this work we analysed the application of con-volutional neural networks in motor imagery classification for the Brain Computer Interface (BCI) purposes. To increase the accuracy of classification we proposed the solution that combines the Common Spatial Pattern (CSP) with convolutional network (ConvNet). The electroencephalography (EEG) is one of the modalities we try to use for controlling the prosthetic arm. Therefor in this...
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Spatiotemporal Assessment of Satellite Image Time Series for Land Cover Classification Using Deep Learning Techniques: A Case Study of Reunion Island, France
PublikacjaCurrent Earth observation systems generate massive amounts of satellite image time series to keep track of geographical areas over time to monitor and identify environmental and climate change. Efficiently analyzing such data remains an unresolved issue in remote sensing. In classifying land cover, utilizing SITS rather than one image might benefit differentiating across classes because of their varied temporal patterns. The aim...
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Agnieszka Landowska dr hab. inż.
OsobyUkończyła studia na dwóch kierunkach: Finanse i bankowość na Uniwersytecie Gdańskim oraz Informatyka na WETI Politechniki Gdańskiej. Od 2000 roku jest związana z Politechniką Gdańską. W 2006 roku uzyskała stopień doktora w dziedzinie nauk technicznych, a w roku 2019 stopień doktora habilitowanego. Aktualnie jej praca naukowa dotyczy zagadnień interakcji człowiek-komputer oraz informatyki afektywnej (ang. affective computing), która...
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Colorectal neuroendocrine neoplasms — update of the diagnostic and therapeutic guidelines (recommended by the Polish Network of Neuroendocrine Tumours) [Nowotwory neuroendokrynne jelita grubego — uaktualnione zasady diagnostyki i leczenia (rekomendowane przez Polską Sieć Guzów Neuroendokrynych)]
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TensorHive: Management of Exclusive GPU Access for Distributed Machine Learning Workloads
PublikacjaTensorHive is a tool for organizing work of research and engineering teams that use servers with GPUs for machine learning workloads. In a comprehensive web interface, it supports reservation of GPUs for exclusive usage, hardware monitoring, as well as configuring, executing and queuing distributed computational jobs. Focusing on easy installation and simple configuration, the tool automatically detects the available computing...
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Intelligent Autonomous Robot Supporting Small Pets in Domestic Environment
PublikacjaIn this contribution, we present preliminary results of the student project aimed at the development of an intelligent autonomous robot supporting small pets in a domestic environment. The main task of this robot is to protect a freely moving small pets against accidental stepping on them by home residents. For this purpose, we have developed the mobile robot which follows a pet and makes an alarm signal when a human is approaching....
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Architektury klasyfikatorów obrazów
PublikacjaKlasyfikacja obrazów jest zagadnieniem z dziedziny widzenia komputerowego. Polega na całościowej analizie obrazu i przypisaniu go do jednej lub wielu kategorii (klas). Współczesne rozwiązania tego problemu są w znacznej części realizowane z wykorzystaniem konwolucyjnych głębokich sieci neuronowych (convolutional neural network, CNN). W tym rozdziale opisano przełomowe architektury CNN oraz ewolucję state-of-the-art w klasyfikacji...
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Adaptive Hounsfield Scale Windowing in Computed Tomography Liver Segmentation
PublikacjaIn computed tomography (CT) imaging, the Hounsfield Unit (HU) scale quantifies radiodensity, but its nonlinear nature across organs and lesions complicates machine learning analysis. This paper introduces an automated method for adaptive HU scale windowing in deep learning-based CT liver segmentation. We propose a new neural network layer that optimizes HU scale window parameters during training. Experiments on the Liver Tumor...
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Determination of the impact indicators of electromagnetic interferences on computer information systems
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Effects of chronic neuroleptic treatment on dopamine release: Insights from studies using 3-methoxytyramine
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Poor evidence for depolarization block but uncoupling of nigral from striatal dopamine metabolism after chronic haloperidol treatment in the rat
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Modeling human thinking about similarities by neuromatrices in the perspective of fuzzy logic
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Behavioral state classification in epileptic brain using intracranial electrophysiology
PublikacjaOBJECTIVE: Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. APPROACH: Data from seven patients (age [Formula: see text], 4 women) who underwent intracranial depth electrode...
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Resonant Frequencies in Microstrip Structure with Omega Medium Substrate
PublikacjaThe paper presents the research on a rectangular microstrip structure with multilayer substrate containing dielectric and omega medium layers. The effect of pseudochiral medium layer location in the substrate and its thickness on the resonant frequency of the rectangular microstrip structure is investigated. The numerical analysis of investigated structures is based on expansion of electric and magnetic fields. Utilizing the continuity...