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Search results for: DATA-DRIVEN MODELLING, MICROWAVE DESIGN, SURROGATE MODELLING, ARTIFICIAL NEURAL NETWORKS, DEEP LEARNING
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A design framework for rigorous constrained EM-driven optimization of miniaturized antennas with circular polarization
PublicationCompact radiators with circular polarization are important components of modern mobile communication systems. Their design is a challenging process which requires maintaining simultaneous control over several performance figures but also the structure size. In this work, a novel design framework for multi-stage constrained miniaturization of antennas with circular polarization is presented. The method involves sequential optimization...
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Asking Data in a Controlled Way with Ask Data Anything NQL
PublicationWhile to collect data, it is necessary to store it, to understand its structure it is necessary to do data-mining. Business Intelligence (BI) enables us to make intelligent, data-driven decisions by the mean of a set of tools that allows the creation of a potentially unlimited number of machine-generated, data-driven reports, which are calculated by a machine as a response to queries specified by humans. Natural Query Languages...
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ENVIRONMENTAL MODELLING & SOFTWARE
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MATHEMATICAL AND COMPUTER MODELLING
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Generating actionable evidence from free-text feedback to improve maternity and acute hospital experiences: A computational text analytics & predictive modelling approach
PublicationBackground Patient experience surveys are a key source of evidence for supporting decision-making and quality improvement in healthcare services. These surveys contain two main types of questions: closed and open-ended, asking about patients’ care experiences. Apart from the knowledge obtained from analysing closed-ended questions, invaluable insights can be gleaned from free-text data. Advanced analytics techniques are increasingly...
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Inverse and forward surrogate models for expedited design optimization of unequal-power-split patch couplers
PublicationIn the paper, a procedure for precise and expedited design optimization of unequal power split patchcouplers is proposed. Our methodology aims at identifying the coupler dimensions that correspond to thecircuit operating at the requested frequency and featuring a required power split. At the same time, thedesign process is supposed to be computationally efficient. The proposed methodology involves two typesof auxiliary models (surrogates):...
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Deep learning super-resolution for the reconstruction of full wavefield of Lamb waves
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OmicSelector: automatic feature selection and deep learning modeling for omic experiments
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Variable‐fidelity modeling of antenna input characteristics using domain confinement and two‐stage Gaussian process regression surrogates
PublicationThe major bottleneck of electromagnetic (EM)-driven antenna design is the high CPU cost of massive simulations required by parametric optimization, uncertainty quantification, or robust design procedures. Fast surrogate models may be employed to mitigate this issue to a certain extent. Unfortunately, the curse of dimensionality is a serious limiting factor, hindering the construction of conventional data-driven models valid over...
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Optimization-Based Robustness Enhancement of Compact Microwave Component Designs with Response Feature Regression Surrogates
PublicationThe ability to evaluate the effects of fabrication tolerances and other types of uncertainties is a critical part of microwave design process. Improving the immunity of the device to parameter deviations is equally important, especially when the performance specifications are stringent and can barely be met even assuming a perfect manufacturing process. In the case of modern miniaturized microwave components of complex topologies,...
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Application of Feed Forward Neural Networks for Modeling of Heat Transfer Coefficient During Flow Condensation for Low and High Values of Saturation Temperatur
PublicationMost of the literature models for condensation heat transfer prediction are based on specific experimental parameters and are not general in nature for applications to fluids and non-experimental thermodynamic conditions. Nearly all correlations are created to predict data in normal HVAC conditions below 40°C. High temperature heat pumps operate at much higher parameters. This paper aims to create a general model for the calculation...
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Optimization of Wireless Networks for Resilience to Adverse Weather Conditions
PublicationIn this chapter, we consider how adverse weather conditions such as rain or fog affect the performance of wireless networks, and how to optimize these networks so as to make them robust to these conditions. We first show how to analyze the weather conditions in order to make them useful for network optimization modelling. Using an example realistic network, we show how to optimize two types of wireless networks: free-space optical...
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Marek Kubale prof. dr hab. inż.
PeopleDetails concerning: Qualifications, Experiences, Editorial boards, Ph.D. theses supervised, Books, and Recent articles can be found at http://eti.pg.edu.pl/katedra-algorytmow-i-modelowania-systemow/Marek_KubaleGoogle ScholarSylwetka prof. Marka Kubalego Prof. Marek Kubale pracuje na Wydziale ETI Politechniki Gdańskiej nieprzerwanie od roku 1969. W tym czasie napisał ponad 150 prac naukowych, w tym ponad 40 z listy JCR. Ponadto...
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Performance-Driven Inverse/Forward Modeling of Antennas in Variable-Thickness Domains
PublicationDesign of contemporary antenna systems is a challenging endeavor. The difficulties are partially rooted in stringent specifications imposed on both electrical and field characteristics, demands concerning various functionalities, but also constraints imposed upon the physical size of the radiators. Furthermore, conducting the design process at the level of full-wave electromagnetic (EM) simulations, otherwise dictated by reliability,...
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Evolving neural network as a decision support system — Controller for a game of “2048” case study
PublicationThe paper proposes an approach to designing the neuro-genetic self-learning decision support system. The system is based on neural networks being adaptively learned by evolutionary mechanism, forming an evolved neural network. Presented learning algorithm enables for a selection of the neural network structure by establishing or removing of connections between the neurons, and then for a finding the beast suited values of the network...
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Pantographic metamaterials: an example of mathematically driven design and of its technological challenges
PublicationIn this paper, we account for the research efforts that have been started, for some among us, already since 2003, and aimed to the design of a class of exotic architectured, optimized (meta) materials. At the first stage of these efforts, as it often happens, the research was based on the results of mathematical investigations. The problem to be solved was stated as follows: determine the material (micro)structure governed by those...
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Rapid dimension scaling of compact microwave couplers with power split correction
PublicationIn this paper, a technique for rapid re-design ofcompact microwave couplers with respect to operating frequency is discussed. Our methodology involves an inverse surrogate model setup using several reference designs optimized (at the level of equivalent circuit representation of the coupler) for a set of operating frequencies within a range of interest. The surrogate establishes the relationship between the operating frequency...
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Organizational Wisdom: The Impact of Organizational Learning on the Absorptive Capacity of an Enterprise
PublicationPurpose: In this article, we analyze the concept of organizational wisdom, indicating its key elements and verifieng the relationships between them. Design/Methodology/Approach: The study was conducted at Vive Textile Recycling Sp. z o.o in Poland. Empirical data was collected from 138 managers using the PAPI technique. Structural equation modelling (SEM) was performed to test the research hypotheses. Additionally, the significance...
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Information-driven network resilience: Research challenges and perspectives
PublicationInternet designed over 40 years ago was originally focused on host-to-host message delivery in a best-effort manner. However, introduction of new applications over the years have brought about new requirements related with throughput, scalability, mobility, security, connectivity, and availability among others. Additionally, convergence of telecommunications, media, and information technology was responsible for transformation...
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Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
PublicationMany studies have been performed to put quantifying uncertainties into the seismic risk assessment of reinforced concrete (RC) buildings. This paper provides a risk-assessment support tool for purpose of retrofitting and potential design strategies of RC buildings. Machine Learning (ML) algorithms were developed in Python software by innovative methods of hyperparameter optimization, such as halving search, grid search, random...
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TensorHive: Management of Exclusive GPU Access for Distributed Machine Learning Workloads
PublicationTensorHive 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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Tackling Air Pollution in Cities with Modelling and Simulation: Remote Group Model Building as an Educational Tool Supporting System Dynamics Modelling
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Analysis of circular polarization antenna design trade‐offs using low‐cost EM‐driven multiobjective optimization
PublicationCircular polarization (CP) antennas are vital components of modern communication systems. Their design involves handling several requirements such as low reflection and axial ratio (AR) within the frequency range of interest. Small size is an important criterion for antenna mobility which is normally achieved as a by‐product of performance‐oriented modifications of the structure topology. In this work, multiobjective optimization...
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Design of Cost-Efficient Optical Fronthaul for 5G/6G Networks: An Optimization Perspective
PublicationCurrently, 5G and the forthcoming 6G mobile communication systems are the most promising cellular generations expected to beat the growing hunger for bandwidth and enable the fully connected world presented by the Internet of Everything (IoE). The cloud radio access network (CRAN) has been proposed as a promising architecture for meeting the needs and goals of 5G/6G (5G and beyond) networks. Nevertheless, the provisioning of cost-efficient...
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Learning from examples with data reduction and stacked generalization
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Stacking-Based Integrated Machine Learning with Data Reduction
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Micro-modelling of shear localization during quasi-static confined granular flow in silos using DEM
PublicationThe paper deals with the quasi-static confined flow of cohesionless sand in a plane strain model silo with parallel walls and a slowly movable bottom. Numerical modelling was carried out by the discrete element method (DEM) using spheres with contact moments to approximately capture a non-uniform shape of sand particles. Different initial void ratios of sand and silo wall roughness grades were employed. Regular triangular grooves...
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Machine Learning Applied to Aspirated and Non-Aspirated Allophone Classification—An Approach Based on Audio "Fingerprinting"
PublicationThe purpose of this study is to involve both Convolutional Neural Networks and a typical learning algorithm in the allophone classification process. A list of words including aspirated and non-aspirated allophones pronounced by native and non-native English speakers is recorded and then edited and analyzed. Allophones extracted from English speakers’ recordings are presented in the form of two-dimensional spectrogram images and...
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Modelling of Curvature of the Railway Track Geometrical Layout Using Particle Swarm Optimization
PublicationA method of railway track geometrical layout design, based on application of cubic C-Bezier curves for describing the layout curvature is presented in the article. The control points of a cubic C-Bezier curve are obtained in an optimization process carried out using Particle Swarm Optimization algorithm. The optimization criteria are based on the evaluation of the dynamic interactions and satisfaction of geometrical design requirements.
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AGENT-BASED APPROACH TO THE DESIGN OF RBF NETWORKS
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Arsalan Muhammad Soomar Doctoral Student
PeopleHi, I'm Arsalan Muhammad Soomar, an Electrical Engineer. I received my Master's and Bachelor's Degree in the field of Electrical Engineering from Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan. Currently enrolled as a Doctoral student at the Gdansk University of Technology, Gdansk, Poland. Also worked in Yellowlite. INC, Ohio as a Solar Design Engineer. HEADLINE Currently Enrolled as a Doctoral...
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Efkleidis Katsaros
PeopleEfklidis 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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Dataset Characteristics and Their Impact on Offline Policy Learning of Contextual Multi-Armed Bandits
PublicationThe 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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Fragmentation of Hydrographic Big Data Into Subsets During Reduction Process
PublicationThe article presented problems of fragmentation of hydrographic big data into smaller subsets during reduction process. Data reduction is a processing of reduce the value of the data set, in order to make them easier and more effective for the goals of the analysis. The main aim of authors is to create new reduction method. The article presented the first stage of this method – fragmentation of bathymetric data into subsets. It...
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Implementing artificial intelligence in forecasting the risk of personal bankruptcies in Poland and Taiwan
PublicationResearch background: The global financial crisis from 2007 to 2012, the COVID-19 pandemic, and the current war in Ukraine have dramatically increased the risk of consumer bankruptcies worldwide. All three crises negatively impact the financial situation of households due to increased interest rates, inflation rates, volatile exchange rates, and other significant macroeconomic factors. Financial difficulties may arise when the...
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CAD. Integrated Architectural Design BSc in Arch., sem 3 2022/2023
e-Learning CoursesDuring the course students will learn how to use BIM (Building Information Modelling) tools to model the project site and to develop preliminary design concept.Students will work in pairs or groups, using Autodesk Revit software for terrain, site and building modelling.
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Modeling and optimizing the removal of cadmium by Sinapis alba L. from contaminated soil via Response Surface Methodology and Artificial Neural Networks during assisted phytoremediation with sewage sludge
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Design, Realization and Measurements of Enhanced Performance 2.4 GHz ESPAR Antenna for Localization in Wireless Sensor Networks
PublicationThis paper presents the design, realization and measurements of an Electronically Steerable Parasitic Array Radiator (ESPAR) antenna with enhanced performance of estimating the incoming signal direction. Designed antenna is dedicated for 2.4 GHz ISM applications with emphasis on Wireless Sensor Networks (WSN). Proposed antenna provides different radiation patterns by proper configuration of the parasitic elements. Thus, several...
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Modelling of sequencing batch reactor operating at various aeration modes
PublicationThe presented study involved designing a computer model of a sequencing batch reactor (SBR) at laboratory scale. The data pertaining to the technical aspects of the bioreactor and quality indicators of wastewater constituted the input for the employed simulation tool, i.e. GPS-X software package. The results of a simulation involving a 12-hour operation cycle are presented in this work; each cycle included 6 phases: filling, mixing,...
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Sensitivity analysis based on non-intrusive regression-based polynomial chaos expansion for surgical mesh modelling
PublicationThe modelling of a system containing implants used in ventral hernia repair and human tissue suffers from many uncertainties. Thus, a probabilistic approach is needed. The goal of this study is to define an efficient numerical method to solve non-linear biomechanical models supporting the surgeon in decisions about ventral hernia repair. The model parameters are subject to substantial variability owing to, e.g., abdominal wall...
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Evaluation and behaviour of slowly biodegradable substrate on mathematical modelling of oxygen uptake rate in activated sludge systems
PublicationThe effect of the readily biodegradable (Ss) fraction in activated sludge systems has been extensively investigated, but only minor information can be found about the effect of Xs on OUR in activated sludge systems. The aim of this study was to determine the evaluation and behaviour of Xs on mathematical modelling of OUR using ASM2d and its modification. The essential study was divided into two major parts: experimental investigation...
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Automatic singing voice recognition employing neural networks and rough sets
PublicationCelem prac opisanych w referacie jest automatyczne rozpoznawanie głosów śpiewaczych. Do tego celu utworzona została baza nagrań próbek śpiewu profesjonalnego i amatorskiego. Próbki poddane zostały parametryzacji parametrami zaproponowanymi przez autorów ściśle do tego celu. Sposób wyznaczenia parametrów i ich interpretacja fizyczna przedstawione są w referacie. Parametry wprowadzane są do systemów decyzyjnych, klasyfikatorów opartych...
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Extended Hopfield models of neural networks for combinatorial multiobjective optimization problems
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Neural Networks in the Diagnostics Process of Low-Power Solar Plant Devices
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Optimization of a three-bed adsorption chiller by genetic algorithms and neural networks
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Use of Neural Networks in Diagnostics of Rolling-Element Bearing of the Induction Motor
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Neural Networks Based on Ultrafast Time-Delayed Effects in Exciton Polaritons
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Musical phrase representation and recognition by means of neural networks and rough sets.
PublicationW artykule przedstawiono podstawowe definicje dotyczące frazy muzycznej. W eksperymentach posłużono się zapisem parametrycznym. W celu wzmocnienia procesu rozpoznawania wykorzystano kodowanie entropijne muzyki. W eksperymentach klasyfikacji oparto się o sztuczne sieci neuronowe i metodę zbiorów przybliżonych. Słowa kluczowe: fraza muzyczna, klasyfikacja, sztuczne sieci neuronowe, metoda zbiorów przybliżonych
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Comparison of effectiveness of musical sound separation algorithms employing neural networks.
PublicationNiniejszy referat przedstawia kilka algorytmów służących do separacji dźwięków instrumentów muzycznych. Zaproponowane podejście do dekompozycji miksów dźwiękowych opiera się na założeniu, że wysokość dźwięków w miksie jest znana, tzn. wejściem dla algorytmów jest przebieg zmian wysokości dźwięków składowych miksu. Proces estymacji fazy i amplitudy składowych harmonicznych wykorzystuje dopasowywanie zespolonych przebiegów harmonicznych...
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Estimation of musical sound separation algorithm effectiveness employing neural networks.
PublicationŚlepa separacja dźwięków sygnałów muzycznych zawartych w zmiksowanym materiale jest trudnym zadaniem. Jest to spowodowane tym, że dźwięki znajdujące się w relacjach harmonicznych mogą zawierać kolidujące składowe sinusoidalne (składowe harmoniczne). Ewaluacja wyników separacji jest również problematyczna, gdyż analiza błędu energetycznego często nie odzwierciedla subiektywnej jakości odseparowanych sygnałów. W tej publikacji zostały...