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The structure of knowledge resources supporting a model of IT technologies architecture = Struktury zasobów wiedzy wspierających model architektury technologii IT
PublikacjaThis paper introduces an abstract model for tools used in the software engineering process. The main goal of the research is to develop a universal model which allows a description of tools used in the process. The model supports a decision making process and gives an option to choose the best tooling for the situation. The model considers many factors including, but not exclusive to, the company structure, project environment...
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Proposal of New Tracer Concentration Model in Lung PCT Study Comparison with Commonly Used Gamma-variate Model
PublikacjaPerfusion computed tomography (pCT) is one of the methods that enable non-invasive imaging of the hemodynamics of organs and tissues. On the basis of pCT measurements, perfusion parameters such as blood flow (BF), blood volume (BV), mean transit time (MTT) and permeability surface (PS) are calculated and then used for quantitative evaluation of the tissue condition. To calculate perfusion parameters it is necessary to approximate...
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Analiza możliwości zastosowania środowiska SCILAB do wspomagania projektowania podsystemów energetycznych statków = Analysis of capability of using SCILAB for adding design of power ship subsystems
PublikacjaW artykule przedstawiono koncepcję wykorzystania środowiska symulacyjnego Scilab, modeli matematycznych oraz badań symulacyjnych przy projektowaniu podsystemów energetycznych statku. Zredagowano przykładową strukturę zawierającą model wysokoprężnego silnika oraz model śruby okrętowej. Przedstawione procedury oraz modele matematyczne zostaną włączone do systemu ekspertowego wspomagającego projektowanie statków. This paper deals...
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Silo music: Experimental investigations and a mechanical model
PublikacjaW artykule opisano zagadnienie zjawisk akustycznych oraz drgań występujących w silosach podczas przepływu materiałów granulowanych.
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Właściwości nie-quasi-statycznego modelu tranzystora wewnętrznego MOS
PublikacjaPrzedstawiono założenia i właściwości oryginalnego nie-quasi-statycznego modelu małosygnałowego tranzystora wewnętrznego MOS oraz dokonano przeglądu i podziału znanych w literaturze przedmiotu małosygnałowych modeli tranzystora polowego.
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Model silnika spalinowego w formie grafów wiązań (GW).A model of the IC engine in the form of the bond graph (BG).
PublikacjaPrzedstawiono uzasadnienie użycia metody grafów wiązań do do modelowania silnika spalinowego jako źródła energii w systemach energetycznych składających się z elementów o różnej naturze fizycznej, na przykład w pojazdach hybrydowych. Przedstawiono propozycję formalizacji charakterystyki silników spalinowych wynikającą z przyjętej metody modelowania. Analityczną formę charakterystyki przedstawiono jako wielowymiarową funkcję wektorową....
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Performance improvement of NN based RTLS by customization of NN structure - heuristic approach
PublikacjaThe purpose of this research is to improve performance of the Hybrid Scene Analysis – Neural Network indoor localization algorithm applied in Real-time Locating System, RTLS. A properly customized structure of Neural Network and training algorithms for specific operating environment will enhance the system’s performance in terms of localization accuracy and precision. Due to nonlinearity and model complexity, a heuristic analysis...
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Measuring and Analyzing Audio Levels in Film, Commercials, and Movie Trailers Using Leq(A) Values and the LUFS Loudness Model . Analiza pomiarów dźwięku w filmie oraz w reklamach filmowych z wykorzystaniem modelu głośności
PublikacjaThe purpose of this paper is to describe the measurement of loudness levels in movies, movie trailers, and commercials displayed before feature films at movie theaters. In the initial section, the paper discusses the issues related to measurement of loudness levels, provides recommendations regarding permissible loudness levels during movie screenings, and mentions the applied units of measurement. The following section of the...
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Inteligentne systemy agentowe w systemach zdalnego nauczania
PublikacjaW pracy omówiono inteligentne systemy agentowe w systemach zdalnego nauczania. Po krótkim przedstawieniu ewolucji systemów zdalnego nauczania i ich wybranych zastosowań, scharakteryzowano inteligentne agenty edukacyjne. Omówiono wykorzystanie programowania genetycznego oraz algorytmów neuro-ewolucyjnych do implementacji oprogramowania tej klasy. Ponadto, nawiązano do modelu Map-Reduce, który efektywnie wspiera architekturę nowoczesnego...
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Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests
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Designing the Composition of Cement Stabilized Rammed Earth Using Artificial Neural Networks
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The Influence of Input Data Standardization Method on Prediction Accuracy of Artificial Neural Networks
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Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening
PublikacjaBeta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural...
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Neuronal Nitric Oxide Synthase Induction in the Antitumorigenic and Neurotoxic Effects of 2-Methoxyestradiol
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Leveraging Training Strategies of Artificial Neural Network for Classification of Multiday Electromyography Signals
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Adsorption chiller in a combined heating and cooling system: simulation and optimization by neural networks
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Self-organizing Artificial Neural Networks into Hydrographic Big Data Reduction Process
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Application of spatial neural simulators of turbine blade rows to fluid flow diagnostics
PublikacjaThis chapter presents the results of neural modelling of fluid flow in steam turbine row. In modelling working conditions of the flow channel varied, thus the aim of the work was to reconstruct the reference state - distributions of velocity, pressure, and losses in flow channel - with high accuracy for fluid flow diagnostics.
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Aerodynamic excitations generated in turbine shroud clearance determined bymeans of neural networks
PublikacjaSiły aerodynamiczne generowane w uszczelnieniach turbinowych z reguły opisywane są modelem liniowym. Przy dużych drganiach wirnika sposób ten daje niezbyt dokładne wyniki. Zaproponowano wykorzystanie sieci neuronowych do określania sił ciśnieniowych powstających w uszczelnieniu. Wyniki porównano z badaniami eksperymentalnymi.
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Adaptive Hyperparameter Tuning within Neural Network-based Efficient Global Optimization
PublikacjaIn this paper, adaptive hyperparameter optimization (HPO) strategies within the efficient global optimization (EGO) with neural network (NN)-based prediction and uncertainty (EGONN) algorithm are proposed. These strategies utilize Bayesian optimization and multiarmed bandit optimization to tune HPs during the sequential sampling process either every iteration (HPO-1itr) or every five iterations (HPO-5itr). Through experiments using...
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Visual Features for Improving Endoscopic Bleeding Detection Using Convolutional Neural Networks
PublikacjaThe presented paper investigates the problem of endoscopic bleeding detection in endoscopic videos in the form of a binary image classification task. A set of definitions of high-level visual features of endoscopic bleeding is introduced, which incorporates domain knowledge from the field. The high-level features are coupled with respective feature descriptors, enabling automatic capture of the features using image processing methods....
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Ontology-based text convolution neural network (TextCNN) for prediction of construction accidents
PublikacjaThe construction industry suffers from workplace accidents, including injuries and fatalities, which represent a significant economic and social burden for employers, workers, and society as a whole.The existing research on construction accidents heavily relies on expert evaluations,which often suffer from issues such as low efficiency, insufficient intelligence, and subjectivity.However, expert opinions provided in construction...
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Physics-guided neural networks (PGNNs) to solve differential equations for spatial analysis
PublikacjaNumerous examples of physically unjustified neural networks, despite satisfactory performance, generate contradictions with logic and lead to many inaccuracies in the final applications. One of the methods to justify the typical black-box model already at the training stage and lead to many inaccuracies in the final applications. One of the methods to justify the typical black-box model already at the training stage involves extending...
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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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Supply current signal and artificial neural networks in the induction motor bearings diagnostics
PublikacjaThis paper contains research results of the diagnostics of induction motor bearings based on measurement of the supply current with usage of artificial neural networks. Bearing failure amount is greater than 40% of all engine failures, which makes their damage-free operation crucial. Tests were performed on motors with intentionally made bearings defects. Chapter 2 introduces the concept of artificial neural networks. It presents...
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The application of neural networks in forecasting the influence of traffic-induced vibrations on residential buildings
PublikacjaTraffic-induced vibrations may cause the cracking of plaster, damage to structural elements and, in extreme cases, may even lead to the structural collapse of residential buildings. The aim of this article is to analyse the effectiveness of a method of forecasting the impact of vibrations on residential buildings using the concept of artificial intelligence. The article presents several alternative forecasting systems for which...
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Predicting the impact of traffic–induced vibrations on buildings using artificial neural networks
PublikacjaTraffic–induced vibrations may constitute a considerable load to a building, cause cracking of plaster, cracks in load–bearing elements or even a global structural collapse of the whole structure [1-4]. Vibrations measurements of real structures are costly and laborious, not justified in all cases. The aim of the paper is to create an original algorithm, to predict the negative dynamic impact on the examined residential building...
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THE CONCEPT OF MODELING OF SNOW IMPACT ON THE STRUCTURE OF THE SUSPENDED TAURON ARENA ROOF IN CRACOW
PublikacjaThe article presents studies and numerical simulations on modeling snow influence on TAURON ARENA suspended roof structure in Cracow. The scope of work includes experimental tests, functions solutions taking into account various cases of snow impact according to PN and EC, as well as numerical simulations for the sport and entertainment arena in the Czyzyny district. The FEM roof structure model developed in the SOFISITK software...
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Diagnosing wind turbine condition employing a neural network to the analysis of vibroacoustic signals
PublikacjaIt is important from the economic point of view to detect damage early in the wind turbines before failures occur. For this purpose, a monitoring device was built that analyzes both acoustic signals acquired from the built-in non-contact acoustic intensity probe, as well as from the accelerometers, mounted on the internal devices in the nacelle. The signals collected in this way are used for long-term training of the autoencoder...
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Highlighting interlanguage phoneme differences based on similarity matrices and convolutional neural network
PublikacjaThe goal of this research is to find a way of highlighting the acoustic differences between consonant phonemes of the Polish and Lithuanian languages. For this purpose, similarity matrices are employed based on speech acoustic parameters combined with a convolutional neural network (CNN). In the first experiment, we compare the effectiveness of the similarity matrices applied to discerning acoustic differences between consonant...
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System for monitoring road slippery based on CCTV cameras and convolutional neural networks
PublikacjaThe slipperiness of the surface is essential for road safety. The growing number of CCTV cameras opens the possibility of using them to automatically detect the slippery surface and inform road users about it. This paper presents a system of developed intelligent road signs, including a detector based on convolutional neural networks (CNNs) and the transferlearning method employed to the processing of images acquired with video...
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Distinct hippocampal-prefrontal neural assemblies coordinate memory encoding, maintenance, and recall
PublikacjaShort-term memory enables incorporation of recent experience into subsequent decision-making. This processing recruits both the prefrontal cortex and hippocampus, where neurons encode task cues, rules, and outcomes. However, precisely which information is carried when, and by which neurons, remains unclear. Using population decoding of activity in rat medial prefrontal cortex (mPFC) and dorsal hippocampal CA1, we confirm that mPFC...
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Benchmarking Deep Neural Network Training Using Multi- and Many-Core Processors
PublikacjaIn the paper we provide thorough benchmarking of deep neural network (DNN) training on modern multi- and many-core Intel processors in order to assess performance differences for various deep learning as well as parallel computing parameters. We present performance of DNN training for Alexnet, Googlenet, Googlenet_v2 as well as Resnet_50 for various engines used by the deep learning framework, for various batch sizes. Furthermore,...
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Neural Oscillation During Mental Imagery in Sport: An Olympic Sailor Case Study
PublikacjaThe purpose of the current study was to examine the cortical correlates of imagery depending on instructional modality (guided vs. self-produced) using various sports-related scripts. According to the expert-performance approach, we took an idiosyncratic perspective analyzing the mental imagery of an experienced two-time Olympic athlete to verify whether different instructional modalities of imagery (i.e., guided vs. self-produced)...
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Development of a tropical disease diagnosis system using artificial neural network and GIS
PublikacjaExpert systems for diagnosis of tropical diseases have been developed and implemented for over a decade with varying degrees of success. While the recent introduction of artificial neural networks has helped to improve the diagnosis accuracy of such systems, this aspect is still negatively affected by the number of supported diseases. A large number of supported diseases usually corresponds to a high number of overlapping symptoms,...
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Neural modelling of dynamic systems with time delays based on an adjusted NEAT algorithm
PublikacjaA problem related to the development of an algorithm designed to find an architecture of artificial neural network used for black-box modelling of dynamic systems with time delays has been addressed in this paper. The proposed algorithm is based on a well-known NeuroEvolution of Augmenting Topologies (NEAT) algorithm. The NEAT algorithm has been adjusted by allowing additional connections within an artificial neural network and...
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Heavy duty vehicle fuel consumption modelling using artificial neural networks
PublikacjaIn this paper an artificial neural network (ANN) approach to modelling fuel consumption of heavy duty vehicles is presented. The proposed method uses easy accessible data collected via CAN bus of the truck. As a benchmark a conventional method, which is based on polynomial regression model, is used. The fuel consumption is measured in two different tests, performed by using a unique test bench to apply the load to the engine. Firstly,...
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Application of fuzzy neural network for supporting measurements and control in a wastewater treatment plant
PublikacjaOczyszczanie ścieków jest jednym z ważniejszych aspektów ochrony środowiska. Nowoczesne systemy kontroli w oczyszczalniach ścieków pozwalają na poprawę jakości procesu oczyszczania redukując jednocześnie koszty. Systemy kontroli i optymalizacji jakie odkilku lat opracowuje się dla oczyszczalni ścieków, bazują zazwyczaj na skomplikowanych modelach matematycznych. Kluczowym problemem w zastosowaniu tych systemów jest duża liczba...
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Optimization of briquetting technology of fine-grained metallurgical materials based on statistical models of compressibility
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Numerical analysis of settlement of a high-rise building using two constitutive soil models
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Theoretical calculation of pKas of phosphoric (V) acidin the polarisable continuum and cluster-continuum models
PublikacjaW pracy oceniono dokładność modelu polaryzowalnego kontinuum (PCM) i mieszanego modelu klaster-kontinuum do przewidywania wartości trzech kolejnych stałych dysocjacji kwasu fosforowego (V). Obliczenia PCM na poziomie MP2/6-31+G(d,p) odtwarzają wartość pierwszego pKa z rozsądną niepewnością, jednak wartości kolejnych pKa obarczone są bardzo dużym błędem, nawet po podniesieniu poziomu teorii do metody zespolonej G3B3. Dopiero obliczenia...
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Organizational culture and coopetition: An exploratory study of the features, models and role in the Polish Aviation Industry
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Prediction of Peptide Retention at Different HPLC Conditions from Multiple Linear Regression Models
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SCRAMBLE’N’GAMBLE: a tool for fast and facile generation of random data for statistical evaluation of QSAR models
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Semi-Markow Processes as Models of Devices´ Operating Process of Ship Pro-pulsion System
PublikacjaReferat zawiera formalny opis procesu eksploatacji urządzeń.
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Adaptive Sampling for Non-intrusive Reduced Order Models Using Multi-Task Variance
PublikacjaNon-intrusive reduced order modeling methods (ROMs) have become increasingly popular for science and engineering applications such as predicting the field-based solutions for aerodynamic flows. A large sample size is, however, required to train the models for global accuracy. In this paper, a novel adaptive sampling strategy is introduced for these models that uses field-based uncertainty as a sampling metric. The strategy uses...
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Optimizing Medical Personnel Speech Recognition Models Using Speech Synthesis and Reinforcement Learning
PublikacjaText-to-Speech synthesis (TTS) can be used to generate training data for building Automatic Speech Recognition models (ASR). Access to medical speech data is because it is sensitive data that is difficult to obtain for privacy reasons; TTS can help expand the data set. Speech can be synthesized by mimicking different accents, dialects, and speaking styles that may occur in a medical language. Reinforcement Learning (RL), in the...
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Vessel Energy Requirement Prediction from Acceleration Stage Towing Tests on Scale Models
PublikacjaOne of the most crucial tasks for naval architects is computing the energy required to meet the ship’s operational needs. When predicting a ship’s energy requirements, a series of hull resistance tests on a scale model vessel is carried out in constant speed stages, while the acceleration stage measurements are ignored. Another important factor in seakeeping analysis is the ship’s hydrodynamic added mass. The second law of dynamics...
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On Nature-Inspired Design Optimization of Antenna Structures Using Variable-Resolution EM Models
PublikacjaNumerical optimization has been ubiquitous in antenna design for over a decade or so. It is indispensable in handling of multiple geometry/material parameters, performance goals, and constraints. It is also challenging as it incurs significant CPU expenses, especially when the underlying computational model involves full-wave electromagnetic (EM) analysis. In most practical cases, the latter is imperative to ensure evaluation reliability....
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Fast EM-driven optimization using variable-fidelity EM models and adjoint sensitivities
PublikacjaA robust and computationally efficient technique for microwave design optimization is presented. Our approach exploits variable-fidelity electromagnetic (EM) simulation models and adjoint sensitivities. The low-fidelity EM model correction is realized by means of space mapping (SM). In the optimization process, the SM parameters are optimized together with the design itself, which allows us to keep the number...