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Search results for: NEURON MODELS
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2D Mathematical Model of the Commutator Sliding Contact of an Electrical Machine
PublicationW 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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Determination of selected parameters in a 1D open channel flow model
PublicationOkreślenie parametrów jest ważnym etapem tworzenia modelu matematycznego. W przypadku jednowymiarowego modelu przepływu ze swobodną powierzchnią opisanego układem równań de Saint-Venanta, jedną z grup parametrów wymagających identyfikacji są parametry związane z tarciem. Mogą one być określone w różny sposób, ale w większości przypadków problem ten stanowi zagadnienie żle postawione. W takich przypadkach najczęściej stosowaną...
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Implementation of continuum damage in elasto-viscoplastic constitutive equations
PublicationW pracy opisano podstawowy wariant modelu Chaboche, oraz założenia i implementacje kontynualnego modelu zniszczenia do komercyjnego programu MES. W pracy zaprazentowano dwa podejścia zobrazowane na przykładach obliczeń numerycznych.
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Effects of different hydraulic models on predicting longitudinal profiles of reactive pollutants in activated sludge reactors
PublicationW pracy przedstawiono wpływ dyspersji na prognozowanie stężeń azotu amonowego w komorach osadu czynnego w oczyszczalni ścieków Gdańsk-Wschód. W tym celu wykorzystano jednowymiarowe równanie adwekcji-dyspersji z członem źródłowym (szybkością nitryfikacji). Współczynnik dyspersji został wyznaczony na podstawie pomiarów znacznika fluoroscencyjnego. Model został zweryfikowany w oparciu o pomiary stężeń azotu w 6 sekcjach komory tlenowej...
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Applying Hidden Markov Models to Visual Activity Analysis for Simple Digital Control Panel Operations
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Application of hidden Markov models to eye tracking data analysis of visual quality inspection operations
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Hydrophobic hydration and pairwise hydrophobic interaction of Lennard-Jones and Mie particles in different water models
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Comparison of Language Models Trained on Written Texts and Speech Transcripts in the Context of Automatic Speech Recognition
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Construction and Validation of Simulation Models of Samples Made from 316L Steel by Applying Additive Technique
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Serum trace elements in animal models and human depression: Part III. Magnesium. Relationship with copper
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Matrix Strengthening the Identification of Observations with Split Functional Models in the Squared Msplit(q) Estimation Process
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Conception of selecting reliability models for taking operating decisionsrelated to sea-going ship´s systems
PublicationPrzedstawiono możliwości podejmowania decyzji dotyczących sytuacji, gdy decydent dysponuje informacją aprioryczną o stanie technicznym urządzeń, na przykład uzyskaną z badań niezawodności urządzeń niezbędnych do wykonania zadania, wyrażoną w formie prawdopodobieństwa. Do opracowania modelu umożliwiającego podejmowanie takich decyzji eksploatacyjnych wykorzystano elementy bayesowskiej statystycznej teorii decyzji.
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Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design
PublicationThe design of concrete mixtures is crucial in concrete technology, aiming to produce concrete that meets specific quality and performance criteria. Modern standards require not only strength but also eco-friendliness and production efficiency. Based on the Three Equation Method, conventional mix design methods involve analytical and laboratory procedures but are insufficient for contemporary concrete technology, leading to overengineering...
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Innovative Urban Blue Space Design in a Changing Climate: Transition Models in the Baltic Sea Region
PublicationWaterfront areas in cities are subject to constant changes. The desire to integrate the transformed waterside areas with the urban fabric involves shaping high-quality public spaces related to water, which are often referred to as urban blue spaces (UBS). The aim of the research was to examine the transformation processes of urban waterfront areas in the Baltic Sea Region and identify emerging transition models and types of blue...
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Matrix Strengthening the Identification of Observations with Split Functional Models in the Squared Msplit(q) Estimation Process
PublicationThis article addresses the issue of raising the level of identification of observations with either single or more split functional models in the squared Msplit(q) estimation process. The theoretical part of the study presents the theoretical grounds for the classical method for estimating parameters in a split functional model and proposes a modification of the computational algorithm to increase the quality of the determinations...
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Data set generation at novel test-rig for validation of numerical models for modeling granular flows
PublicationSignificant effort has been exerted on developing fast and reliable numerical models for modeling particulate flow; this is challenging owing to the complexity of such flows. To achieve this, reliable and high-quality experimental data are required for model development and validation. This study presents the design of a novel test-rig that allows the visualization and measurement of particle flow patterns during the collision...
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Path integrals formulations leading to propagator evaluation for coupled linear physics in large geometric models
PublicationReformulating linear physics using second kind Fredholm equations is very standard practice. One of the straightforward consequences is that the resulting integrals can be expanded (when the Neumann expansion converges) and probabilized, leading to path statistics and Monte Carlo estimations. An essential feature of these algorithms is that they also allow to estimate propagators for all types of sources, including initial conditions....
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Fast Multi-Objective Antenna Optimization Using Sequential Patching and Variable-Fidelity EM Models
PublicationIn this work, a technique for fast multi-objective design optimization of antenna structures is presented. In our approach, the initial approximation of the Pareto set representing the best possible trade-offs between conflicting design objectives is obtained by means of sequential patching of the design space. The latter is a stencil-based search that aims at creating a path that connects the extreme Pareto-optimal designs (obtained...
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CONSTRUCTION TECHNOLOGY OF TIMBER-FRAME HOUSES RESISTANT TO DYNAMIC LOADS – STUDY ON MODELS OF EXTERIOR WALLS
PublicationThe aim of this paper is to show the numeric representation of experimental studies concerning the behaviour of exterior wall models of a timber-frame house under harmonic loading. A single wall model according to traditional technology of timberframe house walls (filling with mineral wool) was tested. The analysis was conducted for the following frequencies: 0.5 Hz, 1.0 Hz, 2.0 Hz and 5.0 Hz for various values of the specified...
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Influence of separation gap on the response of colliding models of steel structures under seismic and paraseismic excitations
PublicationAs a result of high urbanization, the need to erect closely-spaced buildings forces designers to consider collisions between structures taking place during ground motions. Experimental and numerical studies have confirmed that such collisions (often referred to as structural pounding) may cause serious damage to the structural elements and even lead to total collapse of colliding structures. The main reason of earthquake-induced...
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Variable-fidelity CFD models and co-Kriging for expedited multi-objective aerodynamic design optimization
PublicationPurpose – Strategies for accelerated multi-objective optimization of aerodynamic surfaces are investigated, including the possibility of exploiting surrogate modeling techniques for computational fluid dynamic (CFD)-driven design speedup of such surfaces. The purpose of this paper is to reduce the overall optimization time. Design/methodology/approach – An algorithmic framework is described that is composed of: a search space reduction,...
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Monitoring of n-butanol vapors biofiltration process using an electronic nose combined with calibration models
PublicationMalodours, by definition, are generally unpleasant, nuisance smells that are a mixture of volatile chemical compounds which can be perceptible even at low concentrations. Due to the more frequent occurrence of odour nuisance associated with the odour sensations, and thus the need to remove them from the air, deodorization techniques are commonly used. Biofiltration is one of the methods of reducing odorants in the air stream. In...
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Application of the Msplitmethod for filtering airborne laser scanning data-sets to estimate digital terrain models
PublicationALS point cloud filtering involves the separation of observations representing the physical terrain surface from those representing terrain details. A digital terrain model (DTM) is created from a subset of points representing the ground surface. The accuracy of the generated DTM is influenced by several factors, including the survey method used, the accuracy of the source data, the applied DTM generation algorithm, and the survey...
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Aerodynamic shape optimization by variable-fidelity computational fluid dynamics models: a review of recent progress
PublicationA brief review of some recent variable-fidelity aerodynamic shape optimization methods is presented.We discuss three techniques that—by exploiting information embedded in low-fidelity computationalfluid dynamics (CFD) models—are able to yield a satisfactory design at a low computational cost, usu-ally corresponding to a few evaluations of the original, high-fidelity CFD model to be optimized. Thespecific techniques considered here...
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The Algorithm of Modelling and Analysis of Latent Semantic Relations: Linear Algebra vs. Probabilistic Topic Models
PublicationThis paper presents the algorithm of modelling and analysis of Latent Semantic Relations inside the argumentative type of documents collection. The novelty of the algorithm consists in using a systematic approach: in the combination of the probabilistic Latent Dirichlet Allocation (LDA) and Linear Algebra based Latent Semantic Analysis (LSA) methods; in considering each document as a complex of topics, defined on the basis of separate...
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CAUSALITY IN MODELS OF THERMAL PROCESSES IN SHIP ENGINE ROOMS WITH THE USE OF BOND GRAPH (BG) METHOD
PublicationWith a single approach to modeling elements of different physical nature, the method of Bond Graph (BG) is particularly well suited for modeling energy systems consisting of mechanical, thermal, electrical and hydraulic elements that operate in the power system engine room. The paper refers to the earlier presented new concept of thermal process modeling using the BG method. The authors own suggestions for determining causality...
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On low-fidelity models for variable-fidelity simulation-driven design optimization of compact wideband antennas
PublicationThe paper addresses simulation-driven design optimization of compact antennas involving variable-fidelity electromagnetic (EM) simulation models. Comprehensive investigations are carried out concerning selection of the coarse model discretization density. The effects of the low-fidelity model setup on the reliability and computational complexity of the optimization process are determined using a benchmark set of three ultra-wideband...
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Methodology of research on the impact of ITS services on the safety and efficiency of road traffic using transport models
PublicationThe current assessment of the impact of Intelligent Transport System (ITS) services on the level of traffic safety and efficiency is based mainly on expert assessments, statistical surveys or several traffic safety models requiring development. There is no structured, uniform assessment method that would give the opportunity to compare the impact of ITS services and their different configurations. The paper presents the methodology...
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Amides as models to study the hydration of proteins and peptides — spectroscopic and theoretical approach on hydration in various temperatures
PublicationInteractions with water are one of the key factors which determine protein stability and activity in aqueous solutions. However, the protein hydration is still insufficiently understood. N-methylacetamide (NMA) is regarded as a minimal part of the peptide backbone and the relative simplicity of its structure makes it a good model for studies on protein–water interactions. In this paper, the influence of NMA and N,N-dimethylacetamide...
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Which Curve Fits Best: Fitting ROC Curve Models to Empirical Credit-Scoring Data
PublicationIn the practice of credit-risk management, the models for receiver operating characteristic (ROC) curves are helpful in describing the shape of an ROC curve, estimating the discriminatory power of a scorecard, and generating ROC curves without underlying data. The primary purpose of this study is to review the ROC curve models proposed in the literature, primarily in biostatistics, and to fit them to actual credit-scoring ROC data...
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Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models
PublicationThis work addresses artificial-intelligence-based buried object characterization using 3-D full-wave electromagnetic simulations of a ground penetrating radar (GPR). The task is to characterize cylindrical shape, perfectly electric conductor (PEC) object buried in various dispersive soil media, and in different positions. The main contributions of this work are (i) development of a fast and accurate data driven surrogate modeling...
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Methodology of research on the impact of ramp metering on the safety and efficiency of road traffic using transport models
PublicationThe methods currently used to assess the impact of Intelligent Transport Systems (ITS) services on traffic safety and efficiency are mainly based on expert assessments, statistical studies or traffic models that need further development. There is no structured, uniform evaluation method to compare the impact of different ITS services and their different configurations. The impact of ITS deployment on the road network adjacent to...
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Cost-Efficient EM-Driven Size Reduction of Antenna Structures by Multi-Fidelity Simulation Models
PublicationDesign of antenna systems for emerging application areas such as the Internet of Things (IoT), fifth generation wireless communications (5G), or remote sensing, is a challenging endeavor. In addition to meeting stringent performance specifications concerning electrical and field properties, the structure has to maintain small physical dimensions. The latter normally requires searching for trade-off solutions because miniaturization...
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The congruence of mental models in entrepreneurial teams – implications for performance and satisfaction in teams operating in an emerging economy
PublicationPurpose – The paper aims to explore the relationship between the congruence of mental models held by the members of entrepreneurial teams operating in an emerging economy (Poland) and entrepreneurial outcomes (performance and satisfaction). Design/methodology/approach – The data obtained from 18 nascent and 20 established entrepreneurial teams was analysed to answer hypotheses. The research was quantitative and was conducted using...
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Inverse and forward surrogate models for expedited design optimization of unequal-power-split patch couplers
PublicationIn the paper, a procedure for precise and expedited design optimization of unequal power split patchcouplers is proposed. Our methodology aims at identifying the coupler dimensions that correspond to thecircuit operating at the requested frequency and featuring a required power split. At the same time, thedesign process is supposed to be computationally efficient. The proposed methodology involves two typesof auxiliary models (surrogates):...
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The Effectiveness of Using a Pretrained Deep Learning Neural Networks for Object Classification in Underwater Video
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Modelling changes in the energy efficiency of buildings using neural networks on the example of Zielona Góra
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Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks
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Artificial Neural Network (ANN)-Based Voltage Stability Prediction of Test Microgrid Grid
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Sympathetic Neural Outflow and Chemoreflex Sensitivity Are Related to Spontaneous Breathing Rate in Normal Men
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Taking decisions in the diagnostic intelligent systems on the basis information from an artificial neural network
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Application of artificial neural networks (ANN) as multiple degradation classifiers in thermal and flow diagnostics
PublicationPrzedyskutowano 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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Neural network based control system architecture proposal for underwatership hull cleaning robot.
PublicationPrzedstawiono model matematyczny podwodnej głowicy roboczej, oraz określono metodę jej pozycjonowania i orientacji w lokalnym środowisku. Zaproponowano architekturę układu sterowania, opartego na bazie sieci neuronowych, za pomocą którego można sterować podwodnym robotem, przeznaczonym do czyszczenia burt statku.
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A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks
PublicationThis article proposes the use of a feedforward neural network (FNN) to select the starting point for the first iteration in well-known iterative location estimation algorithms, with the research objective of finding the minimum size of a neural network that allows iterative position estimation algorithms to converge in an example positioning network. The selected algorithms for iterative position estimation, the structure of the...
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An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks
PublicationIn this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent...
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An automated, low-latency environment for studying the neural basis of behavior in freely moving rats
PublicationBackground Behavior consists of the interaction between an organism and its environment, and is controlled by the brain. Brain activity varies at sub-second time scales, but behavioral measures are usually coarse (often consisting of only binary trial outcomes). Results To overcome this mismatch, we developed the Rat Interactive Foraging Facility (RIFF): a programmable interactive arena for freely moving rats with multiple feeding...
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Dataset Related Experimental Investigation of Chess Position Evaluation Using a Deep Neural Network
PublicationThe idea of training Articial Neural Networks to evaluate chess positions has been widely explored in the last ten years. In this paper we investigated dataset impact on chess position evaluation. We created two datasets with over 1.6 million unique chess positions each. In one of those we also included randomly generated positions resulting from consideration of potentially unpredictable chess moves. Each position was evaluated...
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Enhancing Renal Tumor Detection: Leveraging Artificial Neural Networks in Computed Tomography Analysis
PublicationRenal cell carcinoma is one of the most common cancers in Europe, with a total incidence rate of 18.4 cases per 100 000 population. There is currently significant overdiagnosis (11% to 30.9%) at times of planned surgery based on radiological studies. The purpose of this study was to create an artificial neural network (ANN) solution based on computed tomography (CT) images as an additional tool to improve the differentiation of...
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Predicting Ice Phenomena in a River Using the Artificial Neural Network and Extreme Gradient Boosting
PublicationForecasting 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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An automatic selection of optimal recurrent neural network architecture for processes dynamics modelling purposes
PublicationA problem related to the development of algorithms designed to find the structure of artificial neural network used for behavioural (black-box) modelling of selected dynamic processes has been addressed in this paper. The research has included four original proposals of algorithms dedicated to neural network architecture search. Algorithms have been based on well-known optimisation techniques such as evolutionary algorithms and...