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Search results for: FIBER-REINFORCED CONCRETE BEAM, CHAINED MACHINE LEARNING MODEL, DUCTILITY INDEX, BENDING LOAD CAPACITY, ARTIFICIAL NEURAL NETWORKS
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Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning
PublicationThe aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was...
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3-D finite-difference time-domain modelling of ground penetrating radar for identification of rebars in complex reinforced concrete structures
PublicationThis paper presents numerical and experimental investigations to identify reinforcing bars using the ground penetrating radar (GPR) method. A novel element of the paper is the inspection of different arrangements of reinforcement bars. Two particular problems, i.e. detection of few adjacent transverse bars and detection of a longitudinal bar located over or under transverse reinforcement, have been raised. An attention was also...
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Data augmentation for improving deep learning in image classification problem
PublicationThese 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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Corrosion behavior of concrete produced with diatomite and zeolite exposed to chlorides
PublicationChloride induced reinforcement corrosion is widely accepted to be the most frequent mechanism causing premature degradation of reinforced concrete structures. The electrochemical impedance of reinforcing steel in diatomite- and zeolite-containing concrete exposed to sodium chloride was assessed. Chemical, physical and mineralogical properties of three concrete samples (20% diatomite, 20% zeolite, and a reference containing neither)...
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Modeling lignin extraction with ionic liquids using machine learning approach
PublicationLignin, next to cellulose, is the second most common natural biopolymer on Earth, containing a third of the organic carbon in the biosphere. For many years, lignin was perceived as waste when obtaining cellulose and hemicellulose and used as a biofuel for the production of bioenergy. However, recently, lignin has been considered a renewable raw material for the production of chemicals and materials to replace petrochemical resources....
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A Bayesian regularization-backpropagation neural network model for peeling computations
PublicationA Bayesian regularization-backpropagation neural network (BRBPNN) model is employed to predict some aspects of the gecko spatula peeling, viz. the variation of the maximum normal and tangential pull-off forces and the resultant force angle at detachment with the peeling angle. K-fold cross validation is used to improve the effectiveness of the model. The input data is taken from finite element (FE) peeling results. The neural network...
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Fatigue Performance of Double-Layered Asphalt Concrete Beams Reinforced with New Type of Geocomposites
PublicationThe reinforcement of asphalt layers with geosynthetics has been used for several decades, but proper evaluation of the influence of these materials on pavement fatigue life is still a challenging task. The presented study investigates a novel approach to the reinforcement of asphalt layers using a new type of geogrid composite, in which square or hexagonal polypropylene stiff monolithic paving grid with integral junctions is bonded...
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Effect of Base-Connection Strength and Ductility on the Seismic Performance of Steel Moment-Resisting Frames
PublicationColumn-base connections in steel moment-resisting frames (SMFs) in seismic regions are commonly designed to develop the capacity of adjoining column with an intent to develop a plastic hinge in the column member, rather than in the connection (i.e., a strong-base design). Recent research has shown base connections to possess high ductility, indicating that this practice may be not only expensive but also unnecessary. This suggests...
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Computers and Concrete
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Simplified method of applying loads to flat slab floor structural model
PublicationThe article analyses the impact of the live load position on the surface of a reinforced concrete flat slab floor of 32.0 m × 28.8 m. Four variants of a live load position are investigated: located on the entire concrete slab, set in a chessboard pattern, applied by bands and imposed separately in each of the slab panels. Conclusions are drawn upon differences in bending moments, the time of calculation and the size of output files....
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Improving platelet‐RNA‐based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification
PublicationLiquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community...
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Biotrickling filtration of n-butanol vapors: process monitoring using electronic nose and artificial neural network
PublicationBiotrickling filtration is one of the techniques used to reduce odorants in the air. It is based on the aerobic degradation of pollutants by microorganisms located in the filter bed. The research presents the possibility of using the electronic nose prototype combined with artificial neural network for biofiltration process monitoring in terms of reduction in n-butanol concentration and odour intensity of treated air. The study...
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A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection
PublicationComputational cost is an important consideration for memory encoding prediction models that use data from dozens of implanted electrodes. We propose a method to reduce computational expense by selecting a subset of all the electrodes to build the prediction model. The electrodes were selected based on their likelihood of measuring brain activity useful for predicting memory encoding better than chance (in terms of AUC). A logistic...
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Simulation model for evaluation of QoS routing algorithm in large packet networks
PublicationThe variety of traffic transferred via current telecommunication networks includes also voice, which should meet quality requirements. One of mechanisms, which can support QoS in current packet networks, is routing. There exist many routing proposals which should introduce the QoS into the network but practically they don't. Following paper presents the realization of simulation model for evaluation of a new routing algorithm DUMBRA...
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Bibliometric analysis of artificial intelligence in wastewater treatment: Current status, research progress, and future prospects
PublicationWastewater treatment is an important topic for improving water quality and environmental protection, and artificial intelligence has become a powerful tool for wastewater treatment. This work provides research progress and a literature review of artificial intelligence applied to wastewater treatment based on the visualization of bibliometric tools. A total of 3460 publications from 2000 to 2023 were obtained from the Web of Science...
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Electrochemical investigations of conductive coatings applied as anodes in cathodic protection of reinforced concrete
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Approximation of Fractional Order Dynamic Systems Using Elman, GRU and LSTM Neural Networks
PublicationIn the paper, authors explore the possibility of using the recurrent neural networks (RNN) - Elman, GRU and LSTM - for an approximation of the solution of the fractional-orders differential equations. The RNN network parameters are estimated via optimisation with the second order L-BFGS algorithm. It is done based on data from four systems: simple first and second fractional order LTI systems, a system of fractional-order point...
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The use of machine learning for face regions detection in thermograms
PublicationThe aim of this study is to analyse the methods of detecting characteristic points of the face in thermographic images. As part of the implementation an extensive analysis of scientific publications covering similar issues both for the analysis of images made in visible light and thermographic images was carried out. On the basis of this analysis, 3 models were selected and then they were implemented and tested on the basis of...
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Deep neural networks for human pose estimation from a very low resolution depth image
PublicationThe work presented in the paper is dedicated to determining and evaluating the most efficient neural network architecture applied as a multiple regression network localizing human body joints in 3D space based on a single low resolution depth image. The main challenge was to deal with a noisy and coarse representation of the human body, as observed by a depth sensor from a large distance, and to achieve high localization precision....
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The chemistry, properties and performance of flame-retardant rubber composites: Collecting, analyzing, categorizing, machine learning modeling, and visualizing
PublicationRubbers combine the flexibility with mechanical strength, supporting myriad applications, but suffer from inherent flammability. Formulation and production of flame-retardant rubber composites (FRRCs) have intensively been practiced over years, but not comprehensively reviewed. This necessity has outlined collecting, analyzing, screening, classifying, and interpreting the literature with the aim of classifying the FRRCs. We quantified...
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Neural Networks and the Evolution of Environmental Change
PublicationZmiany środowiskowe na Ziemii są odwieczne i liczą około 4 miliardy lat. Homo sapiens wpłynął na każdy aspekt środowiska ziemskiego w wyniku rozwoju ludzkości na przestrzeni ostatnich milionów lat. Ale nic tak nie wpłynęło na wzrost i szybkość zmian na Ziemi jak ludzka aktywność w ciągu ostatnich dwóch stuleci. Po raz pierwszy zmiany ekosystemów były tak intensywne i zachodziły na tka wielką skalę i z taką szybkością jak nigdy...
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Results of implementation of Feed Forward Neural Networks for modeling of heat transfer coefficient during flow condensation for low and high values of saturation temperature
Open Research DataThis database present results of implementation of Feed Forward Neural Networks for modeling of heat transfer coefficient during flow condensation for low and high values of saturation temperature. Databse contain one table and 7 figures.
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Comparative study of learning methods for artificial network
PublicationW artykule przedstawiono wyniki badań porównawczych metod uczenia sieci neuronowych takich jak: metoda propagacji wstecznej błędów, rekurencyjna metoda najmniejszych kwadratów, metoda Zangwill'a, metoda algorytmów ewolucyjnych. Celem tych badań jest dobieranie najefektywniejszej metody uczenia do projektowania adaptacyjnego neuronowego regulatora napięcia generatora synchronicznego.metody uczenia, sieć neuronowa, neuronowy regulator...
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Seismic probabilistic assessment of steel and reinforced concrete structures including earthquake-induced pounding
PublicationRecent earthquakes demonstrate that prioritizing the retrofitting of buildings should be of the utmost importance for enhancing the seismic resilience and structural integrity of urban structures. To have a realistic results of the pounding effects in modeling process of retrofitting buildings, the present research provides seismic Probability Factors (PFs), which can be used for estimating collision effects without engaging in...
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Driver’s Condition Detection System Using Multimodal Imaging and Machine Learning Algorithms
PublicationTo this day, driver fatigue remains one of the most significant causes of road accidents. In this paper, a novel way of detecting and monitoring a driver’s physical state has been proposed. The goal of the system was to make use of multimodal imaging from RGB and thermal cameras working simultaneously to monitor the driver’s current condition. A custom dataset was created consisting of thermal and RGB video samples. Acquired data...
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Analysis of load carrying capacity and destruction mechanism of an intermediate anchorage steel plate for pillar jib crane
PublicationPillar jib cranes are often mounted to existing floors with the use of an intermediate anchorage steel plate instead of using independent foundations, what is shown in the figure. Standards for steel design provide only basic rules for the design of such an intermediate plate. This study presents a simple design approach by using a numerical model in order to analyze different parameters that have significant influence in the anchorage...
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Systemy z Uczeniem Maszynowym / Systems with Machine Learning
e-Learning Courses -
Degradation of stiffness of building columns exposed to bending and impact load during ground motions
PublicationThe safety of civil engineering structures under external loads, including ground motions, is one of the issues related to the idea of sustainable society. The earthquake-induced soft-storey failure is a typical type of damage observed in buildings due to seismic excitation. The failure of an intermediate soft storey of a structure results in large vertical impact loading acting on the lower floors due to the fall of the upper...
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Three-point bending test of sandwich beams supporting the GFRP footbridge design process - validation
PublicationSome selected aspects concerning material and construction design issues for pedestrian footbridge made of GFRP composite materials are elaborated in this paper. The analysis is focused on validation tests, which are particularly important because of the advanced technology and materials that are used for this innovative bridge. The considered footbridge is a sandwich-type shell structure comprising of PET foam core and outer skins...
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Artificial Thermal Ageing of Polyester Reinforced and Polyvinyl Chloride Coated Technical Fabric
PublicationArchitectural fabric AF9032 has been subjected to artificial thermal ageing to determine changes of the material parameters of the fabric. The proposed method is based on the accelerated ageing approach proposed by Arrhenius. 300 mm x 50 mm samples were cut in the warp and fill directions and placed in a thermal chamber at 80 °C for up to 12 weeks or at 90 °C for up to 6 weeks. Then after one week of conditioning at ambient temperature,...
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Application of the neural networks for developing new parametrization of the Tersoff potential for carbon
PublicationPenta-graphene (PG) is a 2D carbon allotrope composed of a layer of pentagons having sp2- and sp3-bonded carbon atoms. A study carried out in 2018 has shown that the parameterization of the Tersoff potential proposed in 2005 by Ehrhart and Able (T05 potential) performs better than other potentials available for carbon, being able to reproduce structural and mechanical properties of the PG. In this work, we tried to improve the...
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Model szkolenia "Blended learning" z wykorzystaniem platformy Oracle I-learning.
PublicationW artykule zaproponowano modele organizacyjne szkoleń "blended learning", które pokazują możliwości współpracy firm prywatnych z instytucjami edukacyjnymi w dziedzinie e-learningu. W ramach wspólnego eksperymentu firm Oracle, Incenti S.A., WiedzaNet Sp. z o.o. oraz Centrum Edukacji Niestacjonarnej Politechniki Gdańskiej w semestrze letnim roku akademickiego 2003/2004 udostępniony będzie kurs dla studentów Wydziału Inzynierii Lądowej...
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3D DEM simulations of fracture in reinforced concrete beams
PublicationArtykuł dotyczy zachowania się belki żelbetowej bez zbrojenia pionowego przy trzypunktowym zginaniu. Belka uległa zniszczeniu wskutek ścinania z powodu obecności nadmiernego zbrojenia podłużnego. Eksperymenty przeprowadzono w skali laboratoryjnej z wykorzystaniem systemu mikro-CT, a następnie odtworzono je w analizach numerycznych stosując metodą elementów dyskretnych 3D (DEM). Zastosowano 4-fazowy model betonu z mezostrukturą,...
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Detection of People Swimming in Water Reservoirs with the Use of Multimodal Imaging and Machine Learning
PublicationEvery year in many countries, there are fatal unintentional drownings in different water reservoirs like swimming pools, lakes, seas, or oceans. The existing threats of this type require creating a method that could automatically supervise such places to increase the safety of bathers. This work aimed to create methods and prototype solutions for detecting people bathing in water reservoirs using a multimodal imaging system and...
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The use of a two-phase Monte Carlo material model to reflect the dispersion of asphalt concrete fracture parameters
PublicationThe work covers comprehensive laboratory tests of semi-circular bending (SCB) of asphalt concrete samples. The results of two test series, including four and 32 SCB specimens, indicate a substantial scatter of force–deflection (F-d) histories. The numerical analysis is aimed to reflect the maximum breaking load and fracture energy of the samples, pointing out their random character. The original simulation-based fictitious Monte...
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Robust Parameter Estimation and Output Prediction for Reactive Carrier-Load Nonlinear Dynamic Networks
PublicationIn this paper an extension of on-line model simplification technique for a class of networked systems, namely reactive carrier-load nonlinear dynamic networked system (RCLNDNS), kept within point-parametric model (PPM) framework is addressed. The PPM is utilised to acquire a piece wise constant time-varying parameter linear structure for the RCLNDNS suitable for the on-line one step ahead prediction that may be applied to monitoring...
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Applying artificial intelligence for cellular networks optimization
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Efficient uncertainty quantification using sequential sampling-based neural networks
PublicationUncertainty quantification (UQ) of an engineered system involves the identification of uncertainties, modeling of the uncertainties, and the forward propagation of the uncertainties through a system analysis model. In this work, a novel surrogate-based forward propagation algorithm for UQ is proposed. The proposed algorithm is a new and unique extension of the recent efficient global optimization using neural network (NN)-based...
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Constrained aerodynamic shape optimization using neural networks and sequential sampling
PublicationAerodynamic shape optimization (ASO) involves computational fluid dynamics (CFD)-based search for an optimal aerodynamic shape such as airfoils and wings. Gradient-based optimization (GBO) with adjoints can be used efficiently to solve ASO problems with many design variables, but problems with many constraints can still be challenging. The recently created efficient global optimization algorithm with neural network (NN)-based prediction...
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Data governance: Organizing data for trustworthy Artificial Intelligence
PublicationThe rise of Big, Open and Linked Data (BOLD) enables Big Data Algorithmic Systems (BDAS) which are often based on machine learning, neural networks and other forms of Artificial Intelligence (AI). As such systems are increasingly requested to make decisions that are consequential to individuals, communities and society at large, their failures cannot be tolerated, and they are subject to stringent regulatory and ethical requirements....
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Designing of an effective structure of system for the maintenance of a technical object with the using information from an artificial neural network
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Integrated Control in High-Speed Networks Using Constrained Model Predictive Control
PublicationThis paper studies congestion control in high-speed communication networks using Model Predictive Control (MPC). Network traffic is assumed to consist of best-effort and priority traffic sources. An integrated controller consisting of two control parts is designed. The controller calculates the capacity for priority sources and the input rate of best-effort sources. MPC is desirable as it can take into account the constraints on...
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Artificial neural network based fatigue life assessment of friction stir welding AA2024-T351 aluminum alloy and multi-objective optimization of welding parameters
PublicationIn this paper, the fracture behavior and fatigue crack growth rate of the 2024-T351 aluminum alloy has been investigated. At first, the 2024-T351 aluminum alloys have been welded using friction stir welding procedure and the fracture toughness and fatigue crack growth rate of the CT specimens have been studied experimentally based on ASTM standards. After that, in order to predict fatigue crack growth rate and fracture toughness,...
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Damage Detection of Steel-Concrete Composite Beam
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Comparative study of methods for artificial neural network training.
PublicationPrzedstawiono wyniki badań porównawczych następujących metod uczenia sieci neuronowych: propagacji wstecznej błędów, rekursywnej metody najmniejszych kwadratów, metody Zangwill'a i algorytmów ewolucyjnych. Badania dotyczyły projektowania adaptacyjnego regulatora neuronowego napięcia generatora synchronicznego.
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Calculation of self and mutual inductances of the switched reluctance machine mathematical model.
PublicationA mathematical model of the switched reluctance machine (SRM) in a drive system obtained using Lagrange's energy method and a method of calculation of self and mutual inductances of the SRM are presented in the paper. The self and mutual inductances are elements of Lagrange's function in generalised coordinates and have been calculated using the finite element method (FEM). Selected calculation results for the particular machine...
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NETWORKS
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Jerzy Konorski dr hab. inż.
PeopleJerzy Konorski received his M. Sc. degree in telecommunications from Gdansk University of Technology, Poland, and his Ph. D. degree in computer science from the Polish Academy of Sciences, Warsaw, Poland. In 2007, he defended his D. Sc. thesis at the Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology. He has authored over 150 papers, led scientific projects funded by the European Union,...
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Bees Detection on Images: Study of Different Color Models for Neural Networks
PublicationThis paper presents an approach to bee detection in video streams using a neural network classifier. We describe the motivation for our research and the methodology of data acquisition. The main contribution to this work is a comparison of different color models used as an input format for a feedforward convolutional architecture applied to bee detection. The detection process has is based on a neural binary classifier that classifies...
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Computer Networks-laboratories - 2023
e-Learning CoursesAcquiring the skills to design, build and configure computer networks. Demonstration of skills to identify and analyze selected protocols and mechanisms of LAN and WAN networks.