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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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LOS and NLOS identification in real indoor environment using deep learning approach
PublicationVisibility conditions between antennas, i.e. Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) can be crucial in the context of indoor localization, for which detecting the NLOS condition and further correcting constant position estimation errors or allocating resources can reduce the negative influence of multipath propagation on wireless communication and positioning. In this paper a deep learning (DL) model to classify LOS/NLOS...
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Systemy z Uczeniem Maszynowym / Systems with Machine Learning 2022/2023
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Machine-Learning-Powered EM-Based Framework for Efficient and Reliable Design of Low Scattering Metasurfaces
PublicationPopularity of metasurfaces has been continuously growing due to their attractive properties including the ability to effectively manipulate electromagnetic (EM) waves. Metasurfaces comprise optimized geometries of unit cells arranged as a periodic lattice to obtain a desired EM response. One of their emerging application areas is the stealth technology, in particular, realization of radar cross section (RCS) reduction. Despite...
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System Loss Model for Body Area Networks in Room Scenarios
PublicationThis paper presents an analysis of system loss in Body Area Networks for room scenarios, based on a wideband measurement campaign at 5.8 GHz. The measurements were performed with a fixed antenna transmitting vertically and horizontally polarised signals, while the user wears dualpolarised antennas. The average system losses in co- and crosspolarised channels are 41.4 and 42.6 dB for vertically polarised transmitted signals and...
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Analysis of the Capability of Deep Learning Algorithms for EEG-based Brain-Computer Interface Implementation
PublicationMachine learning models have received significant attention for their exceptional performance in classifying electroencephalography (EEG) data. They have proven to be highly effective in extracting intricate patterns and features from the raw signal data, thereby contributing to their success in EEG classification tasks. In this study, we explore the possibilities of utilizing contemporary machine learning algorithms in decoding...
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Creating a radiological database for automatic liver segmentation using artificial intelligence.
PublicationImaging in medicine is an irreplaceable stage in the diagnosis and treatment of cancer. The subsequent therapeutic effect depends on the quality of the imaging tests performed. In recent years we have been observing the evolution of 2D to 3D imaging for many medical fields, including oncological surgery. The aim of the study is to present a method of selection of radiological imaging tests for learning neural networks.
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Evolving gene regulatory networks controlling foraging strategies of prey and predators in an artificial ecosystem
PublicationCo-evolution of predators and prey is an example of an evolutionary arms race, leading in nature to selective pressures in positive feedback. We introduce here an artificial life ecosystem in which such positive feedback can emerge. This ecosystem consists of a 2-dimensional liquid environment and animats controlled by evolving artificial gene regulatory networks encoded in linear genomes. The genes in the genome encode chemical...
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Numerical investigations on early indicators of fracture in concrete at meso-scale.
PublicationFracture is a major reason of the global failure of concretes. The understanding of fracture is important to ensure the safety of structures and to optimize the material behaviour. In particular an early prediction possibility of fracture in concretes is of major importance. In this paper, concrete fracture under bending was numerically analysed using the Discrete Element Method (DEM). The real mesoscopic structure of a concrete...
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Reduced model of gyroscopic system
PublicationThe paper presents the method of model reduction for the system with gyroscopic interactions. Two methods were used to obtain the approximate discrete models of the continuous structure: the modal decomposition method and the rigid finite element method. The first approach is used for this part of a system for which it is easy to formulate orthogonality conditions, meanwhile the second one is used for other part. The method enables...
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Neural reliability model of diesel engines
PublicationW artykule przedstawiono wyniki weryfikacji hipotezy zakładającej celowość zastosowania modelu niezawodnościowego silnika tłokowego z zapłonem samoczynnym w postaci sztucznej sieci neuronowej. Weryfikację przeprowadzono w oparciu o wyniki badań eksploatacyjnych.
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Chemical and Mechanical Properties of 70-Year-Old Concrete
PublicationThe aim of this research is to determine the durability and strength of concrete continuous footing based on the chosen mechanical, physical, and chemical properties of the concrete. The presented investigations constitute some opinions from experts on the bearing capacity of concrete continuous footing and the possibilities of carrying additional loads and extended working life. The cylindrical specimens were taken from continuous...
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JOz Model AiR
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An Improved Convolutional Neural Network for Steganalysis in the Scenario of Reuse of the Stego-Key
PublicationThe topic of this paper is the use of deep learning techniques, more specifically convolutional neural networks, for steganalysis of digital images. The steganalysis scenario of the repeated use of the stego-key is considered. Firstly, a study of the influence of the depth and width of the convolution layers on the effectiveness of classification was conducted. Next, a study on the influence of depth and width of fully connected...
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Machinability investigation in electric discharge machining of carbon fiber reinforced composites for aerospace applications
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Artificial Intelligence Aided Architectural Design
PublicationTools and methods used by architects always had an impact on the way building were designed. With the change in design methods and new approaches towards creation process, they became more than ever before crucial elements of the creation process. The automation of architects work has started with computational functions that were introduced to traditional computer-aided design tools. Nowadays architects tend to use specified tools...
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Flexible Knowledge–Vision–Integration Platform for Personal Protective Equipment Detection and Classification Using Hierarchical Convolutional Neural Networks and Active Leaning
PublicationThis work is part of an effort to develop of a Knowledge-Vision Integration Platform for Hazard Control (KVIP-HC) in industrial workplaces, adaptable to a wide range of industrial environments. The paper focuses on hazards resulted from the non-use of personal protective equipment (PPE). The objective is to test the capability of the platform to adapt to different industrial environments by simulating the process of randomly selecting...
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Experimental and numerical investigations of size effects in reinforced concrete beams with steel or basalt bars.
PublicationW artykule przedstawiono wyniki obliczeń efektów skali w belkach betonowych zbrojonych prętami stalowymi i bazaltowymi podczas zginania. Zastosowano model sprężysto-plastyczny z nielokalnym osłabieniem. Rozkład wytrzymałości na rozciąganie był stochastyczny – przestrzennie skorelowany. Belki były geometrycznie podobne. Wyniki porównano z modelem efektu skali Bazanta
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The Neural Knowledge DNA Based Smart Internet of Things
PublicationABSTRACT The Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade. Smartness, however, remains a substantial challenge for IoT applications. Recent advances in networked sensor technologies, computing, and machine learning have made it possible for building new smart IoT applications. In this paper, we propose a novel approach: the Neural Knowledge DNA based Smart Internet...
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Towards neural knowledge DNA
PublicationIn this paper, we propose the Neural Knowledge DNA, a framework that tailors the ideas underlying the success of neural networks to the scope of knowledge representation. Knowledge representation is a fundamental field that dedicates to representing information about the world in a form that computer systems can utilize to solve complex tasks. The proposed Neural Knowledge DNA is designed to support discovering, storing, reusing,...
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Cleaner energy for sustainable future using hybrid photovoltaics-thermoelectric generators system under non-static conditions using machine learning based control technique
PublicationIn addition to the load demand, the temperature difference between the hot and cold sides of the thermoelectric generator (TEG) module determines the output power for thermoelectric generator systems. Maximum power point tracking (MPPT) control is needed to track the optimal global power point as operating conditions change. The growing use of electricity and the decline in the use of fossil fuels have sparked interest in photovoltaic-TEG...
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An influence of the ship's block coefficient implementation on the evaluation of it's hull girder bending
PublicationAn influence of the three different ways of implementation ship's block coefficient δ (three geometrical models) on the stresses due to wave bending moment have been investigated. Two models have been applied and compared: beam one (description of the shape usingparameters) and FEM shell model (direct representation of the shape). The outcomes have been compared to Polish Register of Shipping (PRS) rules. The results show that...
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BEHAVIOR OF REINFORCED CONCRETE BEAMS CONTAINING LIGHTWEIGHT AGGREGATE IN THE TENSILE ZONE
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Experimental investigations of size effect in reinforced concrete beams failing by shear
PublicationW artykule omówiono wyniki doświadczalne efektu skali w zbrojonych betonowych belkach niszczących się przez ścinanie. Doświadczenia wykonano dla belek o różnych wymiarach ze zbrojeniem stalowym i bazaltowym. Podczas doświadczeń pomierzono siłę oraz szerokości lokalizacji i rys. Wyniki porównano z modelem skali wg Bazanta.
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Numerical analysis of behaviour of reinforced concrete elements under eccentric compression
PublicationPrzedstawiono wyniki modelowania nośności elementów żelbetowych stosując metodę elementów skończonych. Do opisu betonu zastosowano sprężysto-plastyczne prawo materiałowe wg Druckera-Pragera. Do modelowania stali zbrojeniowej zastosowano sprężysto-plastyczne prawo materiałowe według von Misesa. Obliczenia wykonano dla ścian żelbetowych.
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Artificial Intelligence
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Artificial intelligence
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Piotr Rajchowski dr inż.
PeoplePiotr Rajchowski (Member, IEEE) was born in Poland, in 1989. He received the E.Eng., M.Sc., and Ph.D. degrees in radio communication from the Gdańsk University of Technology (Gdańsk Tech), Poland, in 2012, 2013, and 2017, respectively. Since 2013, he has been working at the Department of Radiocommunication Systems and Networks, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, as a IT...
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A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine
PublicationRNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these methods has been the accuracy and confidence of the predictions, and our in-house experiments show that they fail to accurately predict RPI involving short RNA sequences such as TERRA RNA....
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A general theory for anisotropic Kirchhoff–Love shells with in-plane bending of embedded fibers
PublicationThis work presents a generalized Kirchhoff–Love shell theory that can explicitly capture fiber-induced anisotropy not only in stretching and out-of-plane bending, but also in in-plane bending. This setup is particularly suitable for heterogeneous and fibrous materials such as textiles, biomaterials, composites and pantographic structures. The presented theory is a direct extension of classical Kirchhoff–Love shell theory to incorporate...
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PTD4 Peptide Increases Neural Viability in an In Vitro Model of Acute Ischemic Stroke
PublicationIschemic stroke is a disturbance in cerebral blood flow caused by brain tissue ischemia and hypoxia. We optimized a multifactorial in vitro model of acute ischemic stroke using rat primary neural cultures. This model was exploited to investigate the pro-viable activity of cell-penetrating peptides: arginine-rich Tat(49–57)-NH2 (R49KKRRQRRR57-amide) and its less basic analogue, PTD4 (Y47ARAAARQARA57-amide). Our model included glucose...
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Training of Deep Learning Models Using Synthetic Datasets
PublicationIn order to solve increasingly complex problems, the complexity of Deep Neural Networks also needs to be constantly increased, and therefore training such networks requires more and more data. Unfortunately, obtaining such massive real world training data to optimize neural networks parameters is a challenging and time-consuming task. To solve this problem, we propose an easy-touse and general approach to training deep learning...
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Collaborative Data Acquisition and Learning Support
PublicationWith the constant development of neural networks, traditional algorithms relying on data structures lose their significance as more and more solutions are using AI rather than traditional algorithms. This in turn requires a lot of correctly annotated and informative data samples. In this paper, we propose a crowdsourcing based approach for data acquisition and tagging with support for Active Learning where the system acts as an...
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Test-supported numerical analysis for evaluation of the load capacity of thin-walled corrugated profiles
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GPU Power Capping for Energy-Performance Trade-Offs in Training of Deep Convolutional Neural Networks for Image Recognition
PublicationIn the paper we present performance-energy trade-off investigation of training Deep Convolutional Neural Networks for image recognition. Several representative and widely adopted network models, such as Alexnet, VGG-19, Inception V3, Inception V4, Resnet50 and Resnet152 were tested using systems with Nvidia Quadro RTX 6000 as well as Nvidia V100 GPUs. Using GPU power capping we found other than default configurations minimizing...
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Machine learning-based prediction of residual drift and seismic risk assessment of steel moment-resisting frames considering soil-structure interaction
PublicationNowadays, due to improvements in seismic codes and computational devices, retrofitting buildings is an important topic, in which, permanent deformation of buildings, known as Residual Interstory Drift Ratio (RIDR), plays a crucial role. To provide an accurate yet reliable prediction model, 32 improved Machine Learning (ML) algorithms were considered using the Python software to investigate the best method for estimating Maximum...
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Advanced current regulated PWM inverter with simplified load model.
PublicationW artykule przedstawionio nowy regulator prądu stojana silnika asynchronicznego zasilanego z falownika napięcia. Regulator wykorzystuje obliczenia siły elektromotorycznej silnika. Rozważonio wpływ zmian parametrów silnika na działanie regulatora. Wykorzystano uproszczony model silnika bez uwzględnienienia rezystancji stojana. Zastosowano stałą częstotliwość próbkowania prądu. Układ zrealizowania w programie symulacyjnym oraz na...
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Neural networks based NARX models in nonlinear adaptive control
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Using neural networks to examine trending keywords in Inventory Control
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Application of neural networks for description of pressure distribution in slide bearing.
PublicationBadano rozkład ciśnienia hydrodynamicznego w łożysku ślizgowym dla wybranych wariantów łożyska. Wykazano, że zastosowanie sieci neuronowych umożliwia opis rozkładu ciśnienia hydrodynamicznego z uwzględnieniem zmian geometrycznych (bezwymiarowa długość - L) i mechanicznych (mimośrodowość względem H) łożyska.
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Identification of slide bearing main parameters using neural networks.
PublicationWykazano, że sieci neuronowe jak najbardziej nadają się do identyfikacji głównych parametrów geometrycznych i ruchowych hydrodynamicznych łożysk ślizgowych.
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Estimation the rhythmic salience of sound with association rules and neural networks
PublicationW referacie przedstawiono eksperymenty mające na celu automatyczne wyszukiwanie wartości rytmicznych we frazie muzycznej. W tym celu wykorzystano metody data mining i sztuczne sieci neuronowe.
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Accuracy improvement of the prestressed concrete structures precise geometry assessment by use of bubble micro-sampling algorithm
PublicationPrestressed concrete structures are well-known technology for a vast period, but nevertheless, this very technology is a leading solution, currently used in construction industry. Prestressed concrete structures have a huge advantage over conventional methods because it uses the properties of concrete in a very efficient way. The main idea behind this technology is to introduce into the cross-section of the structure, the internal...
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Determination of Thermophysical Parameters Involved in The Numerical Model to Predict the Temperature Field of Cast-In-Place Concrete Bridge Deck
PublicationThe paper dealswith a concept of a practical computationmethod to simulate the temperature distribution in an extradosed bridge deck. The main goal of the study is to develop a feasible model of hardening of concrete consistent with in-situ measurement capabilities. The presented investigations include laboratory tests of high performance concrete, measurements of temperature evolution in the bridge deck and above all, numerical...
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Monitoring the gas turbine start-up phase on the platform using a hierarchical model based on Multi-Layer Perceptron networks
PublicationVery often, the operation of diagnostic systems is related to the evaluation of process functionality, where the diagnostics is carried out using reference models prepared on the basis of the process description in the nominal state. The main goal of the work is to develop a hierarchical gas turbine reference model for the estimation of start-up parameters based on multi-layer perceptron neural networks. A functional decomposition...
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An Empirical System Loss Model for Body Area Networks in a Passenger Ferry Environment
PublicationThis paper presents a general empirical system loss model for estimating propagation loss in Body Area Networks in off-body communications at 2.45 GHz in a passenger ferry environment. The model is based on measurements, which were carried out in dynamic scenarios in the discotheque passenger ferry environment. The model consists of three components: mean system loss, attenuation resulting from the variable antenna position on...
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An Empirical System Loss Model for Body Area Networks in a Passenger Ferry Environment
PublicationThis paper presents a general empirical system loss model for estimating propagation loss in Body Area Networks in off-body communications at 2.45 GHz in a passenger ferry environment. The model is based on measurements, which were carried out in dynamic scenarios in the discotheque passenger ferry environment. The model consists of three components: mean system loss, attenuation resulting from the variable antenna position on...
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An Off-Body Channel Model for Body Area Networks in Indoor Environments
PublicationThis paper presents an off-body channel model for body area networks (BANs) in indoor environments. The proposed model, which is based on both simulations and measurements in a realistic environment, consists of three components: mean path loss, body shadowing, and multipath fading. Seven scenarios in a realistic indoor office environment containing typical scatterers have been measured: five were static (three standing and two...
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JOURNAL OF REINFORCED PLASTICS AND COMPOSITES
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Expedited Machine-Learning-Based Global Design Optimization of Antenna Systems Using Response Features and Multi-Fidelity EM Analysis
PublicationThe 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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Neural network simulator's application to reference performance determination of turbine blading in the heat-flow diagnostics.
PublicationIn the paper, the possibility of application of artificial neural networks to perform the fluid flow calculations through both damaged and undamaged turbine blading was investigated. Preliminary results are presented and show the potentiality of further development of the method for the purpose of heat-flow diagnostics.