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Wyniki wyszukiwania dla: Multi-scale learning
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IEEE Transactions on Multi-Scale Computing Systems
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IEEE Transactions on Molecular, Biological, and Multi-Scale Communications
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Multi-agent large-scale parallel crowd simulation
PublikacjaThis paper presents design, implementation and performance results of a new modular, parallel, agent-based and large scale crowd simulation environment. A parallel application, implemented with C and MPI, was implemented and run in this parallel environment for simulation and visualization of an evacuation scenario at Gdansk University of Technology, Poland and further in the area of districts of Gdansk. The application uses a...
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Thresholding Strategies for Large Scale Multi-Label Text Classifier
PublikacjaThis article presents an overview of thresholding methods for labeling objects given a list of candidate classes’ scores. These methods are essential to multi-label classification tasks, especially when there are a lot of classes which are organized in a hierarchy. Presented techniques are evaluated using the state-of-the-art dedicated classifier on medium scale text corpora extracted from Wikipedia. Obtained results show that the...
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Vident-real: an intra-oral video dataset for multi-task learning
Dane BadawczeWe introduce Vident-real, a large dataset of 100 video sequences of intra-oral scenes from real conservative dental treatments performed at the Medical University of Gdańsk, Poland. The dataset can be used for multi-task learning methods including:
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Machine Learning in Multi-Agent Systems using Associative Arrays
PublikacjaIn this paper, a new machine learning algorithm for multi-agent systems is introduced. The algorithm is based on associative arrays, thus it becomes less complex and more efficient substitute of artificial neural networks and Bayesian networks, which is confirmed by performance measurements. Implementation of machine learning algorithm in multi-agent system for aided design of selected control systems allowed to improve the performance...
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A study of rigorous ODE integrators for multi-scale set-oriented computations
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Multi-scale modelling of concrete beams subjected to three-point bending
PublikacjaW artykule przedstawiono wyniki numeryczne MES dwuskalowego modelowania belek betonowych z nacięciem na poziomie skali makro i mezo. Obliczenia wykonano przy wykorzystaniu modelu degradacji sztywności z nielokalnym osłabieniem. Beton został opisany na poziomie skali mezo jako stochastyczny materiał 3-składnikowy złożony z kruszywa, zaczynu cementowego oraz stref kontaktu. Natomiast na poziomie skali makro został opisany jako materiał...
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Vehicle Tracking Using a Multi-scale Bayesian Algorithm for a Perspective Image of a Road
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POPULATION-BASED MULTI-AGENT APPROACH TO SOLVING MACHINE LEARNING PROBLEMS
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Multi-agent large-scale parallel crowd simulation with NVRAM-based distributed cache
PublikacjaThis paper presents the architecture, main components and performance results for a parallel and modu-lar agent-based environment aimed at crowd simulation. The environment allows to simulate thousandsor more agents on maps of square kilometers or more, features a modular design and incorporates non-volatile RAM (NVRAM) with a fail-safe mode that can be activated to allow to continue computationsfrom a recently analyzed state in...
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Dataset Characteristics and Their Impact on Offline Policy Learning of Contextual Multi-Armed Bandits
PublikacjaThe Contextual Multi-Armed Bandits (CMAB) framework is pivotal for learning to make decisions. However, due to challenges in deploying online algorithms, there is a shift towards offline policy learning, which relies on pre-existing datasets. This study examines the relationship between the quality of these datasets and the performance of offline policy learning algorithms, specifically, Neural Greedy and NeuraLCB. Our results...
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Supervised-learning-based development of multi-bit RCS-reduced coding metasurfaces
PublikacjaCoding metasurfaces have been introduced as efficient tools allowing meticulous control over the electromagnetic (EM) scattering. One of their relevant application areas is radar cross section (RCS) reduction, which principally relies on the diffusion of impinging EM waves. Despite its significance, careful control of the scattering properties poses a serious challenge at the level of practical realization. This article is concerned...
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Deep Video Multi-task Learning Towards Generalized Visual Scene Enhancement and Understanding
PublikacjaThe goal of this thesis was to develop efficient video multi-task convolutional architectures for a range of diverse vision tasks, on RGB scenes, leveraging i) task relationships and ii) motion information to improve multi-task performance. The approach we take starts from the integration of diverse tasks within video multi-task learning networks. We present the first two datasets of their kind in the existing literature, featuring...
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Graphene-Coated PVDF Membranes: Effects of Multi-Scale Rough Structure on Membrane Distillation Performance
PublikacjaGraphene-coated membranes for membrane distillation have been fabricated by using a wet-filtration approach. Graphene nanoplatelets have been deposited onto PVDF membrane surfaces. Morphology and physicochemical properties have been explored to evaluate the changes in the surface topography and related effects on the membrane performance in water desalination. The membranes have been tested in membrane distillation plants by using...
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Prediction of energy consumption and evaluation of affecting factors in a full-scale WWTP using a machine learning approach
PublikacjaTreatment of municipal wastewater to meet the stringent effluent quality standards is an energy-intensive process and the main contributor to the costs of wastewater treatment plants (WWTPs). Analysis and prediction of energy consumption (EC) are essential in designing and operating sustainable energy-saving WWTPs. In this study, the effect of wastewater, hydraulic, and climate-based parameters on the daily consumption of EC by...
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Rapid Surrogate-Aided Multi-Criterial Optimization of Compact Microwave Passives Employing Machine Learning and ANNs
PublikacjaThis article introduces an innovative method for achieving low-cost and reliable multi-objective optimization (MO) of microwave passive circuits. The technique capitalizes on the attributes of surrogate models, specifically artificial neural networks (ANNs), and multi-resolution electromagnetic (EM) analysis. We integrate the search process into a machine learning (ML) framework, where each iteration produces multiple infill points...
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Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Network
PublikacjaDesigning microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on computationally expensive population-based methods, especially when exe-cuted with full-wave electromagnetic (EM) analysis to guarantee reliability. This paper introduces a novel and cost-effective...
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RAGN-R: A multi-subject ensemble machine-learning method for estimating mechanical properties of advanced structural materials
PublikacjaThe utilization of advanced structural materials, such as preplaced aggregate concrete (PAC), fiber-reinforced concrete (FRC), and FRC beams has revolutionized the field of civil engineering. These materials exhibit enhanced mechanical properties compared to traditional construction materials, offering engineers unprecedented opportunities to optimize the design, construction, and performance of structures and infrastructures....
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Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects
PublikacjaMachine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects Hammed A. Mojeed & Rafal Szlapczynski Conference paper First Online: 14 September 2023 161 Accesses Part of the Lecture Notes in Computer Science book series (LNAI,volume 14125) Abstract Software development project requires proper planning to mitigate risk and...
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Expedited Machine-Learning-Based Global Design Optimization of Antenna Systems Using Response Features and Multi-Fidelity EM Analysis
PublikacjaThe design of antenna systems poses a significant challenge due to stringent per-formance requirements dictated by contemporary applications and the high com-putational costs associated with models, particularly full-wave electromagnetic (EM) analysis. Presently, EM simulation plays a crucial role in all design phases, encompassing topology development, parametric studies, and the final adjustment of antenna dimensions. The latter...
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Inter-rater reliability of the Brief Psychiatric Rating Scale and the Groningen Social Disabilities Schedule in a European multi-site randomized controlled trial on the effectiveness of acute psychiatric day hospitals
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TF-IDF weighted bag-of-words preprocessed text documents from Simple English Wikipedia
Dane BadawczeThe SimpleWiki2K-scores dataset contains TF-IDF weighted bag-of-words preprocessed text documents (raw strings are not available) [feature matrix] and their multi-label assignments [label-matrix]. Label scores for each document are also provided for an enhanced multi-label KNN [1] and LEML [2] classifiers. The aim of the dataset is to establish a benchmark...
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Concurrent Video Denoising and Deblurring for Dynamic Scenes
PublikacjaDynamic scene video deblurring is a challenging task due to the spatially variant blur inflicted by independently moving objects and camera shakes. Recent deep learning works bypass the ill-posedness of explicitly deriving the blur kernel by learning pixel-to-pixel mappings, which is commonly enhanced by larger region awareness. This is a difficult yet simplified scenario because noise is neglected when it is omnipresent in a wide...
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Musical Instrument Tagging Using Data Augmentation and Effective Noisy Data Processing
PublikacjaDeveloping signal processing methods to extract information automatically has potential in several applications, for example searching for multimedia based on its audio content, making context-aware mobile applications (e.g., tuning apps), or pre-processing for an automatic mixing system. However, the last-mentioned application needs a significant amount of research to reliably recognize real musical instruments in recordings....
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Optymalizacja zasobów chmury obliczeniowej z wykorzystaniem inteligentnych agentów w zdalnym nauczaniu
PublikacjaRozprawa dotyczy optymalizacji zasobów chmury obliczeniowej, w której zastosowano inteligentne agenty w zdalnym nauczaniu. Zagadnienie jest istotne w edukacji, gdzie wykorzystuje się nowoczesne technologie, takie jak Internet Rzeczy, rozszerzoną i wirtualną rzeczywistość oraz deep learning w środowisku chmury obliczeniowej. Zagadnienie jest istotne również w sytuacji, gdy pandemia wymusza stosowanie zdalnego nauczania na dużą skalę...
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Tomasz Zubowicz dr inż.
OsobyTomasz Zubowicz has received his M.Sc. Eng. degree in Control Engineering from the Faculty of Electrical and Control Engineering at the Gda{\'n}sk University of Technology (GUT) in $2008$. He received his Ph.D. Eng. (Hons.) in the field of Control Engineering from the same faculty in $2019$. In $2012$ he became a permanent staff member at the Department of Intelligent Control and Decision Support Systems at GUT and a member 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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Paweł Rościszewski dr inż.
OsobyPaweł Rościszewski received his PhD in Computer Science at Gdańsk University of Technology in 2018 based on PhD thesis entitled: "Optimization of hybrid parallel application execution in heterogeneous high performance computing systems considering execution time and power consumption". Currently, he is an Assistant Professor at the Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Poland....
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Hossein Nejatbakhsh Esfahani Dr.
OsobyMy research interests lie primarily in the area of Learning-based Safety-Critical Control Systems, for which I leverage the following concepts and tools:-Robust/Optimal Control-Reinforcement Learning-Model Predictive Control-Data-Driven Control-Control Barrier Function-Risk-Averse Controland with applications to:-Aerial and Marine robotics (fixed-wing UAVs, autonomous ships and underwater vehicles)-Multi-Robot and Networked Control...
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Revitalisation - Architectural and urban project ORUNIA UPDATE Grupa K. Piątkowska, J. Martyniuk Pęczek 2021/22
Kursy OnlineORUNIA - UPDATE - architectural and urban revitalization of Orunia areas in the area of Sandomierska, Równa, Głuche and Przy Torze Streets. Creation of a new, multi-functional city space. Development of a master plan for a district indicated by the city that requires the development of a revitalization strategy, designing a new architectural and urban structure with a complex function, development of a selected fragment of...
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PPAM 2022
WydarzeniaThe PPAM 2022 conference, will cover topics in parallel and distributed computing, including theory and applications, as well as applied mathematics.
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Muhammad Usman PhD
OsobyMuhammad Usman is a researcher at the Gdansk University of Technology, currently working on the BE-Light project focused on face skin analysis using multimodal imaging and machine learning methods. He previously worked as a Hardware Test Engineer at Apple Inc., specializing in the rigorous testing and validation of electronic systems, ensuring reliability and performance. He holds a Master of Science in Automation and Control from...
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CAD. Integrated Architectural Design, MSc Arch (2023/24)
Kursy OnlineDetailed understanding of optimizing the design process using parametric BIM (Building Information Modeling) in the Autodesk Revit Architecture program. Practical design exercises included familiarize students with methods of integrating parametric design and exchanging data with other CAD/BIM programs, modifying parametric objects and generating automatic 2D/3D architectural documentation. The lesson plan introduces students to...
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How personality traits, sports anxiety, and general imagery could influence the physiological response measured by SCL to imagined situations in sports?
Dane BadawczeThe data were collected to understand how individual differences in personality (e.g. neuroticism), general imagery, and situational sports anxiety are linked to arousal measuring with skin conductance level (SCL) in situational imagery (as scripted for sport-related scenes). Thirty persons participated in the study, aged between 14 and 42 years, with...
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Elective Project I _ Shelter_learning by doing
Kursy OnlineElective Project I _ Shelter - learning by doing “Your creativity and skills play an important role in making an impact in responding to humanitarian challenges and global crises” The world seems to be reeling from one crisis to another. Recently we experienced climate crises, global pandemic (Covid-19), economic uncertainty, wars, floods, wildfire, and earthquakes. Proceeding from the challenges facing humanity at the global...
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Multiscale Methods Summer School 2023
Kursy OnlineSummer school on Multiscale Methods at Gdańsk University of Technology. 3. - 7. July: 10 hours online10. - 14. July: 10 hours online17. - 21. July: 10 hours online24. - 28. July: 30 hours online or in Gdańsk (you choose) Participation is for free! More info and registration: https://ftims.pg.edu.pl/en/science-app/summer-schools-2023/multi-scale-methods-summer-school
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Halucynacje chatbotów a prawda: główne nurty debaty i ich interpretacje
PublikacjaGeneratywne systemy sztucznej inteligencji (SI) są w stanie tworzyć treści medialne poprzez zastosowanie uczenia maszynowego do dużych ilości danych szkoleniowych. Te nowe dane mogą obejmować tekst (np. Bard firmy Google, LLaMa firmy Meta lub ChatGPT firmy OpenAI) oraz elementy wizualne (np. Stable Diffusion lub DALL-E OpenAI) i dźwięk (np. VALL-E firmy Micro- soft). Stopień zaawansowania tych treści może czynić je nieodróżnialnymi...
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Otwarte zasoby edukacyjne - przegląd inicjatyw w Polsce i na świecie
PublikacjaOtwarte zasoby edukacyjne (OZE) to materiały szkoleniowe oraz narzędzia wspierające zarówno uczenie, jak i nauczanie. Zjawisko to nierozerwalnie łączy się z szerszym pojęciem otwartej edukacji (OE), które postuluje zniesienie barier w nauczaniu tak, aby uczący się mogli zdobywać wiedzę zgodnie ze swoimi potrzebami edukacyjno-szkoleniowymi. Celem artykułu jest zapoznanie czytelników z zagadnieniem otwartych zasobów edukacyjnych,...
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Model-free and Model-based Reinforcement Learning, the Intersection of Learning and Planning
PublikacjaMy doctoral dissertation is intended as the compound of four publications considering: structure and randomness in planning and reinforcement learning, continuous control with ensemble deep deterministic policy gradients, toddler-inspired active representation learning, and large-scale deep reinforcement learning costs.
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Revisiting Supervision for Continual Representation Learning
Publikacja"In the field of continual learning, models are designed to learn tasks one after the other. While most research has centered on supervised continual learning, there is a growing interest in unsupervised continual learning, which makes use of the vast amounts of unlabeled data. Recent studies have highlighted the strengths of unsupervised methods, particularly self-supervised learning, in providing robust representations. The improved...
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E-learning courses
Kursy OnlineStrona zawiera zbiór kursów prowadzonych metodą e-learning. Kursy te są skierowane do studentów I stopnia kierunku informatyka na VII semestrze profilu Bazy danych, do studentów na kierunku informatyka na II semestrze studiów II stopnia na specjalności ZAD i ISI.
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Machine learning for PhD students
Kursy OnlineAn introductory course in machine learning for PhD students from Department of Geotechnical and Hydraulic Engineering
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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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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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e-Learning - user's guide for students
Kursy Onlinee-Learning - user's guide for students
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CZYNNIKI DECYDUJĄCE O PRZYDATNOŚCI KOMPUTEROWEGO MODELU PRZEPŁYWÓW W SIECI WODOCIĄGOWEJ
PublikacjaW pracy poddano analizie wielozadaniowy proces tworzenia komputerowego modelu przepływów. W efekcie zidentyfikowano szereg czynników ograniczających obszar stosowania modelu w praktyce inżynierskiej. W zakresie pozyskiwania danych strukturalnych i operacyjnych wskazano potencjalne źródła błędów, które przyczyniają się do zmniejszenia dokładności odwzorowania stanu rzeczywistego. Specjalną rangę nadano specyfikacji czynników związanych...
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Lifelong Learning Idea in Architectural Education
PublikacjaThe recent advances in IT and technology are forcing changes in the approach to educating society. In the 20th century, life-long learning was understood as educating adults in order to improve their occupational qualifications. Life-long learning allows the needs of the present-day world to be addressed through providing the individual with education at every stage of his/her life various forms. The search for a new model...
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Deep Learning
PublikacjaDeep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities,...
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Knowledge sharing and knowledge hiding in light of the mistakes acceptance component of learning culture- knowledge culture and human capital implications
PublikacjaPurpose: This study examines the micromechanisms of how knowledge culture fosters human capital development. Method: An empirical model was developed using the structural equation modeling method (SEM) based on a sample of 321 Polish knowledge workers employed in different industries. Findings: This study provides direct empirical evidence that tacit knowledge sharing supports human capital, whereas tacit knowledge hiding does...