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Wyniki wyszukiwania dla: FIBER-REINFORCED CONCRETE BEAM, CHAINED MACHINE LEARNING MODEL, DUCTILITY INDEX, BENDING LOAD CAPACITY, ARTIFICIAL NEURAL NETWORKS
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Laboratory fatigue assessment of large geocomposite-reinforced double-layered asphalt concrete beams
PublikacjaGeosynthetic reinforcement of asphalt layers has been used for several decades. Evaluation of the influence of these materials on pavement fatigue life is still ongoing, especially for new types of geocomposites. This paper presents the evaluation of fatigue performance of large asphalt concrete beams reinforced with a new type of composite in which square or hexagonal polypropylene stiff monolithic paving grid with integral junctions...
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Optical method supported by machine learning for urinary tract infection detection and urosepsis risk assessment
PublikacjaThe study presents an optical method supported by machine learning for discriminating urinary tract infections from an infection capable of causing urosepsis. The method comprises spectra of spectroscopy measurement of artificial urine samples with bacteria from solid cultures of clinical E. coli strains. To provide a reliable classification of results assistance of 27 algorithms was tested. We proved that is possible to obtain...
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An Automated Method for Biometric Handwritten Signature Authentication Employing Neural Networks
PublikacjaHandwriting biometrics applications in e-Security and e-Health are addressed in the course of the conducted research. An automated graphomotor analysis method for the dynamic electronic representation of the handwritten signature authentication was researched. The developed algorithms are based on dynamic analysis of electronically handwritten signatures employing neural networks. The signatures were acquired with the use of the...
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RMS-based damage detection in reinforced concrete beams: numerical simulations
PublikacjaImage-based damage detection methods using guided waves are well known and widely applied approaches in structural diagnostics. They are usually utilized in detection of surface damages or defects of plate-like structures. The article presents results of the study of applicability of imaging wave-based methods in detection in miniscule internal damage in the form of debonding. The investigations were carried out on numerical models...
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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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Process of Medical Dataset Construction for Machine Learning-Multifield Study and Guidelines
PublikacjaThe acquisition of high-quality data and annotations is essential for the training of efficient machine learning algorithms, while being an expensive and time-consuming process. Although the process of data processing and training and testing of machine learning models is well studied and considered in the literature, the actual procedures of obtaining data and their annotations in collaboration with physicians are in most cases...
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Application of artificial neural networks (ANN) as multiple degradation classifiers in thermal and flow diagnostics
PublikacjaPrzedyskutowano problem zwiększenia dokładności rozpoznawania wielokrotnych degradacji eksploatacyjnych urządzeń składowych dużych obiektów energetycznych. Zastosowani sieć neuronową (SSN) o skokowych funkcjach przejścia. Sprawdzono możliwości przyspieszenia treningu sieci neuronowych. Zastosowano modułową metodę budowy SSN, polegającą na dedykowaniu pojedynczej sieci do rozpoznawania tylko jednego typu degradacji.
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Accidental wow defect evaluation using sinusoidal analysis enhanced by artificial neural networks
PublikacjaArtykuł przedstawia metodę do wyznaczania charakterystyki pasożytniczych modulacji częstotliwości (kołysanie) obecnych w archiwalnych nagraniach dźwiękowych. Prezentowane podejście wykorzystuje śledzenie zmian sinusoidalnych komponentów dźwięku które odzwierciedlają przebieg kołysania. Analiza sinusoidalna wykorzystana jest do ekstrakcji składowych tonalnych ze zniekształconych nagrań dźwiękowych. Dodatkowo, w celu zwiększenia...
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OBTAINING FLUID FLOW PATTERN FOR TURBINE STAGE WITH NEURAL MODEL.
PublikacjaIn the paper possibility of applying neural model to obtaining patterns of proper operation for fluid flow in turbine stage for fluid-flow diagnostics is discussed. Main differences between Computational Fluid Dynamics (CFD) solvers and neural model is given, also limitations and advantages of both are considered. Time of calculations of both methods was given, also possibilities of shortening that time with preserving the accuracy...
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Global Surrogate Modeling by Neural Network-Based Model Uncertainty
PublikacjaThis work proposes a novel adaptive global surrogate modeling algorithm which uses two neural networks, one for prediction and the other for the model uncertainty. Specifically, the algorithm proceeds in cycles and adaptively enhances the neural network-based surrogate model by selecting the next sampling points guided by an auxiliary neural network approximation of the spatial error. The proposed algorithm is tested numerically...
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A novel approach exploiting properties of convolutional neural networks for vessel movement anomaly detection and classification
PublikacjaThe article concerns the automation of vessel movement anomaly detection for maritime and coastal traffic safety services. Deep Learning techniques, specifically Convolutional Neural Networks (CNNs), were used to solve this problem. Three variants of the datasets, containing samples of vessel traffic routes in relation to the prohibited area in the form of a grayscale image, were generated. 1458 convolutional neural networks with...
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Deep learning in the fog
PublikacjaIn the era of a ubiquitous Internet of Things and fast artificial intelligence advance, especially thanks to deep learning networks and hardware acceleration, we face rapid growth of highly decentralized and intelligent solutions that offer functionality of data processing closer to the end user. Internet of Things usually produces a huge amount of data that to be effectively analyzed, especially with neural networks, demands high...
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MACHINE LEARNING–BASED ANALYSIS OF ENGLISH LATERAL ALLOPHONES
PublikacjaAutomatic classification methods, such as artificial neural networks (ANNs), the k-nearest neighbor (kNN) and selforganizing maps (SOMs), are applied to allophone analysis based on recorded speech. A list of 650 words was created for that purpose, containing positionally and/or contextually conditioned allophones. For each word, a group of 16 native and non-native speakers were audio-video recorded, from which seven native speakers’...
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2D Mathematical Model of the Commutator Sliding Contact of an Electrical Machine
PublikacjaW artykule przedstawiono model matematyczny 2D komutatorowego zestyku ślizgowego z wieloma stopniami swobody. W modelu uwzględniono zmienne wymuszenia działające na szczotkę. Wymuszenia te są wynikiem falistości wirującego komutatora. Szczotka została zamodelowana jako system wielu mas, elementów sprężystych i tłumików rozłożonych w kierunku stycznym i promieniowym. Zamodelowano wszystkie oddziaływania lepkosprężyste pomiędzy komutatorem...
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Predicting Ice Phenomena in a River Using the Artificial Neural Network and Extreme Gradient Boosting
PublikacjaForecasting ice phenomena in river systems is of great importance because these phenomena are a fundamental part of the hydrological regime. Due to the stochasticity of ice phenomena, their prediction is a difficult process, especially when data sets are sparse or incomplete. In this study, two machine learning models—Multilayer Perceptron Neural Network (MLPNN) and Extreme Gradient Boosting (XGBoost)—were developed to predict...
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Size effect in concrete beams under bending – influence of the boundary layer and the numerical description of cracks
PublikacjaIn the paper the size effect phenomenon in concrete is analysed. The results of numerical simulations of using FEM on geometrically similar un-notched and notched concrete beams under bending are presented. Concrete beams of four different sizes and five different notch heights under three-point bending test were simulated. In total 18 beams were analysed. Two approaches were used to describe cracks in concrete. First, eXtended...
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Influence of local bush wear on water lubricated sliding bearing load carrying capacity
PublikacjaOne of main problems concerning water-lubricated bearings is their durability. There are known cases of bearings with life time measured in decades, and some, whose refurbishment was necessary just days after start-up. Obtaining stable fluid film friction plays key role in the durability of these bearings. Unfortunately, their load-carrying capacity is limited due to water's low-viscosity. The conducted experimental...
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Advancing Solar Energy: Machine Learning Approaches for Predicting Photovoltaic Power Output
PublikacjaThis research is primarily concentrated on predicting the output of photovoitaic power, an essential field in the study of renewable energy. The paper comprehensively reviews various forecasting methodologies, transitioning from conventional physical and statistical methods to advanced machine learning (ML) techniques. A significant shift has been observed from traditional point forecasting to machine learning-based forecasting...
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Doped Nanocrystalline Diamond Films as Reflective Layers for Fiber-Optic Sensors of Refractive Index of Liquids
PublikacjaThis paper reports the application of doped nanocrystalline diamond (NCD) films—nitrogen-doped NCD and boron-doped NCD—as reflective surfaces in an interferometric sensor of refractive index dedicated to the measurements of liquids. The sensor is constructed as a Fabry–Pérot interferometer, working in the reflective mode. The diamond films were deposited on silicon substrates by a microwave plasma enhanced chemical vapor deposition...
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Vibration of Steel–Concrete Composite Beams Using the Timoshenko Beam Model
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Comparative study on fracture evolution in steel fibre and bar reinforced concrete beams using acoustic emission and digital image correlation techniques
PublikacjaIn recent decades, the demand for sustainable construction practices has increased, but raw materials such as reinforcing steel remain scarce. Therefore, steel fibres have emerged as a popular and sustainable choice in the construction industry, offering a cost-effective alternative to traditional steel bar reinforcement for both flatwork and elevated structures. The purpose of this study is therefore to compare the performance...
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Improvement of the load capacity of the road overpass as a result of repairs after breakage caused by vehicle impacts
PublikacjaDamage of spans of the overpass caused by impact of underpassing vehicles are a frequent case. Objects that use prefabricated load-bearing elements that are not designed for such impacts are particularly exposed. After impact, such parts suffer extensive damage that need repair. Taking advantages of this recovery actions it is worth to perform strengthening that will protect object against possible future impacts. In this study...
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Verification of Selected Calculation Methods Regarding Shear Strength in Reinforced and Prestressed Concrete Beams
PublikacjaThe purpose of this article was an attempt to compare selected calculation methods regarding shear strength in reinforced and prestressed concrete beams. Several calculation methods were tested. This included codes: PN-EN 1992-1-1:2008 [1], ACI 318- 14 [2] and fib Model Code for Concrete Structures 2010 [3]. The analysis also consists of methods published in technical literature. Calculations of shear strengths were made based...
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Network lifetime maximization in wireless mesh networks for machine-to-machine communication
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Neural Network Subgraphs Correlation with Trained Model Accuracy
PublikacjaNeural Architecture Search (NAS) is a computationally demanding process of finding optimal neural network architecture for a given task. Conceptually, NAS comprises applying a search strategy on a predefined search space accompanied by a performance evaluation method. The design of search space alone is expected to substantially impact NAS efficiency. We consider neural networks as graphs and find a correlation between the presence...
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Restoration and preservation of the reinforced concrete poles of fence at the former Auschwitz concentration and extermination camp
PublikacjaThe objective of this study was to assess the present state of the reinforced concrete poles of fence at the former Auschwitz I and Auschwitz II-Birkenau concentration and extermination camp. The poles were subjected to renovation about 10 years ago. After this time some deficiencies of applied renovation method were noticed. Cracks appeared between fresh and original part of concrete cover. Analysis of the reasons of these failures...
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Restoration and preservation of the reinforced concrete poles of fence at the former Auschwitz concentration and extermination camp
PublikacjaThe objective of this study was to assess the present state of the reinforced concrete poles of fence at the former Auschwitz I and Auschwitz II-Birkenau concentration and extermination camp. The poles were subjected to renovation about 10 years ago. After this time some deficiencies of applied renovation method were noticed. Cracks appeared between fresh and original part of concrete cover. Analysis of the reasons of these failures...
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Determination of refractive index dispersion using fiber-optic low-coherence Fabry–Perot interferometer: implementation and validation
PublikacjaWe present the implementation and validation of low-coherence Fabry–Perot interferometer for refractive index dispersion measurements of liquids. A measurement system has been created with the use of four superluminescent diodes with different optical parameters, a fiber-optic coupler and an optical spectrum analyzer. The Fabry–Perot interferometer cavity has been formed by the fiber-optic end and mirror surfaces mounted on a micromechanical...
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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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Testing of low temperature behaviour of asphalt mixtures in bending creep test
PublikacjaThe paper presents a method of bending beam test and its importance for evaluation of asphalt mixtures behaviour at low temperatures. Two types of asphalt mixtures: asphalt concrete AC with normal paving grade bitumen and stone mastic asphalt SMA with SBS-modified bitumen were tested. Long-term oven ageing (LTOA) test was also used in the laboratory according to SHRP procedure. The Burgers model was applied and rheological parameters...
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Experimental tuning of AuAg nanoalloy plasmon resonances assisted by machine learning method
PublikacjaPlasmonic nanostructures based on AuAg nanoalloys were fabricated by thermal annealing of metallic films in an argon atmosphere. The nanoalloys were chosen because they can extend the wavelength range in which plasmon resonance occurs and thus allow the design of plasmonic platforms with the desired parameters. The influence of initial fabrication parameters and experimental conditions on the formation of nanostructures was investigated....
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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
PublikacjaFollowing the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides...
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Load carrying capacity of the eccentric joint in the truss made of open cross-sections
PublikacjaThe influence of eccentricity at intersections of truss members on the load carrying capacity of the truss joint is presented in the paper. The research truss elements were designed as cold-formed open cross section. Analytical calculations, numerical analysis and experimental research were conducted to reveal how the eccentricity affects the effort of material in the joint area. The results of analysis and investigations are compared...
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Perception of Pathologists in Poland of Artificial Intelligence and Machine Learning in Medical Diagnosis—A Cross-Sectional Study
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Numerical modeling of GPR field in damage detection of a reinforced concrete footbridge
PublikacjaThe paper presents a study on the use of the ground penetrating radar (GPR) method in diagnostics of a footbridge. It contains experimental investigations and numerical analyses of the electromagnetic field propagation using the finite difference time domain method (FDTD). The object of research was a reinforced concrete footbridge over a railway line. The calculations of the GPR field propagation were performed on a selected cross-section...
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When Neural Networks Meet Decisional DNA: A Promising New Perspective for Knowledge Representation and Sharing
PublikacjaABSTRACT In this article, we introduce a novel concept combining neural network technology and Decisional DNA for knowledge representation and sharing. Instead of using traditional machine learning and knowledge discovery methods, this approach explores the way of knowledge extraction through deep learning processes based on a domain’s past decisional events captured by Decisional DNA. We compare our approach with kNN (k-nearest...
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Multimedia industrial and medical applications supported by machine learning
PublikacjaThis article outlines a keynote paper presented at the Intelligent DecisionTechnologies conference providing a part of the KES Multi-theme Conference “Smart Digital Futures” organized in Rome on June 14–16, 2023. It briefly discusses projects related to traffic control using developed intelligent traffic signs and diagnosing the health of wind turbine mechanisms and multimodal biometric authentication for banking branches to provide...
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Numerical Modelling for Prediction of Compression Index from Soil Index Properties in Jimma town, Ethiopia
PublikacjaIn this study, correlations are developed to predict compression index (Cc) from index parameters so that one can be able to model Jimma soils with compression index using simple laboratory tests. Undisturbed and disturbed soil samples from twelve different locations in Jimma town were collected. Laboratory tests like specific gravity, grain size analysis, Atterberg limit, and one-dimensional consolidation test for a total of twenty-four...
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Machine learning applied to acoustic-based road traffic monitoring
PublikacjaThe motivation behind this study lies in adapting acoustic noise monitoring systems for road traffic monitoring for driver’s safety. Such a system should recognize a vehicle type and weather-related pavement conditions based on the audio level measurement. The study presents the effectiveness of the selected machine learning algorithms in acoustic-based road traffic monitoring. Bases of the operation of the acoustic road traffic...
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Machine learning applied to acoustic-based road traffic monitoring
PublikacjaThe motivation behind this study lies in adapting acoustic noise monitoring systems for road traffic monitoring for driver’s safety. Such a system should recognize a vehicle type and weather-related pavement conditions based on the audio level measurement. The study presents the effectiveness of the selected machine learning algorithms in acoustic-based road traffic monitoring. Bases of the operation of the acoustic road traffic...
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Prediction of maximum tensile stress in plain-weave composite laminates with interacting holes via stacked machine learning algorithms: A comparative study
PublikacjaPlain weave composite is a long-lasting type of fabric composite that is stable enough when being handled. Open-hole composites have been widely used in industry, though they have weak structural performance and complex design processes. An extensive number of material/geometry parameters have been utilized for designing these composites, thereby an efficient computational tool is essential for that purpose. Different Machine Learning...
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Recognition of Emotions in Speech Using Convolutional Neural Networks on Different Datasets
PublikacjaArtificial Neural Network (ANN) models, specifically Convolutional Neural Networks (CNN), were applied to extract emotions based on spectrograms and mel-spectrograms. This study uses spectrograms and mel-spectrograms to investigate which feature extraction method better represents emotions and how big the differences in efficiency are in this context. The conducted studies demonstrated that mel-spectrograms are a better-suited...
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Multi-Camera Vehicle Tracking Using Local Image Features and Neural Networks
PublikacjaA method for tracking moving objects crossing fields of view of multiple cameras is presented. The algorithm utilizes Artificial Neural Networks (ANNs). Each ANN is trained to recognize images of one moving object acquired by a single camera. Local image features calculated in the vicinity of automatically detected interest points are used as object image parameters. Next, ANNs are employed to identify the same objects captured...
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Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks
PublikacjaIn this article, the usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) is presented. The developed approach, using channel impulse response, allows higher efficiency in identifying the direct visibility conditions between nodes in off-body communication with comparison to the methods described in the literature. The effectiveness of the proposed deep feedforward neural network was checked on...
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The Effectiveness of Using a Pretrained Deep Learning Neural Networks for Object Classification in Underwater Video
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Machine learning approach to packaging compatibility testing in the new product development process
PublikacjaThe paper compares the effectiveness of selected machine learning methods as modelling tools supporting the selection of a packaging type in new product development process. The main goal of the developed model is to reduce the risk of failure in compatibility tests which are preformed to ensure safety, durability, and efficacy of the finished product for the entire period of its shelf life and consumer use. This kind of testing...
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Clothes Detection and Classification Using Convolutional Neural Networks
PublikacjaIn this paper we describe development of a computer vision system for accurate detection and classification of clothes for e-commerce images. We present a set of experiments on well established architectures of convolutional neural networks, including Residual networks, SqueezeNet and Single Shot MultiBox Detector (SSD). The clothes detection network was trained and tested on DeepFashion dataset, which contains box annotations...
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Coda wave interferometry in monitoring the fracture process of concrete beams under bending test
PublikacjaEarly detection of damage is necessary for the safe and reliable use of civil engineering structures made of concrete. Recently, the identification of micro-cracks in concrete has become an area of growing interest, especially using wave-based techniques. In this paper, a non-destructive testing approach for the characterization of the fracture process was presented. Experimental tests were made on concrete beams subjected to mechanical...
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Outlier detection method by using deep neural networks
PublikacjaDetecting outliers in the data set is quite important for building effective predictive models. Consistent prediction can not be made through models created with data sets containing outliers, or robust models can not be created. In such cases, it may be possible to exclude observations that are determined to be outlier from the data set, or to assign less weight to these points of observation than to other points of observation....
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Innovative Cold-formed GEB Section under Bending
PublikacjaThis paper is concerned with the numerical bending capacity study of the innovative cold-formed GEB sections. Both linear buckling analysis and non-linear static analysis incorporating geometric and material nonlinearity were carried out employing a shell structural model. The magnitudes of buckling load and limit load with respect to GEB section depth and thickness were obtained. The opened cold-formed section was tested assuming...