Wyszukiwarka
Filtry
wszystkich: 1026
wyświetlamy 1000 najlepszych wyników Pomoc
Wyniki wyszukiwania dla: cross-sensitivity, multiple linear regression, artificial neural networks
-
Open-Set Speaker Identification Using Closed-Set Pretrained Embeddings
PublikacjaThe paper proposes an approach for extending deep neural networks-based solutions to closed-set speaker identification toward the open-set problem. The idea is built on the characteristics of deep neural networks trained for the classification tasks, where there is a layer consisting of a set of deep features extracted from the analyzed inputs. By extracting this vector and performing anomaly detection against the set of known...
-
Methods of Artificial Intelligence for Prediction and Prevention Crisis Situations in Banking Systems
PublikacjaIn this paper, a support vector machine has been studied due to prediction of bank crisis. To prevent outcomes of crisis situations, artificial neural networks have been characterized as applied to stock market investments, as well as to test the credibility of the bank's customers. Finally, some numerical experiments have been presented.
-
Wykorzystanie sztucznych sieci neuronowych do szacowania wpływu drgań na budynki jednorodzinne
PublikacjaW artykule przedstawiono metodę prognozowania wpływu drgań na budynki mieszkalne z wykorzystaniem sztucznych sieci neuronowych. Drgania komunikacyjne mogą doprowadzić do uszkodzenia elementów konstrukcyjnych, a nawet do awarii budynku. Najczęstszym efektem są jednak rysy, pękanie tynku i wypraw. Metody oparte na sztucznej inteligencji są przybliżone, ale stanowią wystarczająco dokładną i ekonomiczną alternatywę dla tradycyjnych...
-
Machine Learning Algorithm-Based Tool and Digital Framework for Substituting Daylight Simulations In Early- Stage Architectural Design Evaluation
PublikacjaThe aim of this paper is to examine the new method of obtaining the simulation-based results using backpropagation of errors artificial neural networks. The primary motivation to conduct the research was to determine an alternative, more efficient and less timeconsuming method which would serve to achieve the results of daylight simulations. Three daylight metrics: Daylight Factor, Daylight Autonomy and Daylight Glare Probability have...
-
Metody sztucznej inteligencji do wspomagania bankowych systemów informatycznych
PublikacjaW pracy opisano zastosowania nowoczesnych metod sztucznej inteligencji do wspomagania bankowych systemów informatycznych. Wykorzystanie w systemach informatycznych algorytmów ewolucyjnych, harmonicznych, czy sztucznych sieci neuronowych w połączeniu z nowoczesną technologią mikroprocesorową umożliwiają zasadniczy wzrost konkurencyjności banku. Dlatego w pracy omówiono wybrane zastosowania bankowe ze szczególnym uwzględnieniem zbliżeniowych...
-
On a Method of Efficiency Increasing in Kaplan Turbine
PublikacjaThis paper presents a method of increasing efficiency in Kaplan-type turbine. The method is based on blade profile optimisation together with modelling the interaction between rotor and stator blades. Loss coefficient was chosen as the optimisation criterion, which is related directly to efficiency. Global optimum was found by means of Genetic Algorithms, and Artificial Neural Networks were utilised for approximations to reduce...
-
Advanced sensitivity analysis of the impact of the temporal distribution and intensity of rainfall on hydrograph parameters in urban catchments
PublikacjaKnowledge of the variability of the hydrograph of outflow from urban catchments is highly important for measurements and evaluation of the operation of sewer networks. Currently, hydrodynamic models are most frequently used for hydrograph modeling. Since a large number of their parameters have to be identified, there may be problems at the calibration stage. Hence, sensitivity analysis is used to limit the number of parameters....
-
Deep Learning: A Case Study for Image Recognition Using Transfer Learning
PublikacjaDeep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities,...
-
Deep Learning
PublikacjaDeep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities,...
-
Automatic music set organizatio based on mood of music / Automatyczna organizacja bazy muzycznej na podstawie nastroju muzyki
PublikacjaThis work is focused on an approach based on the emotional content of music and its automatic recognition. A vector of features describing emotional content of music was proposed. Additionally, a graphical model dedicated to the subjective evaluation of mood of music was created. A series of listening tests was carried out, and results were compared with automatic mood recognition employing SOM (Self Organizing Maps) and ANN (Artificial...
-
General concept of reduction process for big data obtained by interferometric methods
PublikacjaInterferometric sonar systems apply the phase content of the sonar signal to measure the angle of a wave front returned from the seafloor or from a target. It collect a big data – datasets that are so large or complex that traditional data processing application software is inadequate to deal with them. The recording a large number of data is associated with the difficulty of their efficient use. So data have to be reduced. The main...
-
Analysis of 2D Feature Spaces for Deep Learning-based Speech Recognition
Publikacjaconvolutional neural network (CNN) which is a class of deep, feed-forward artificial neural network. We decided to analyze audio signal feature maps, namely spectrograms, linear and Mel-scale cepstrograms, and chromagrams. The choice was made upon the fact that CNN performs well in 2D data-oriented processing contexts. Feature maps were employed in the Lithuanian word recognition task. The spectral analysis led to the highest word...
-
IEEE International Conference on Neural Networks
Konferencje -
Simulating Power Generation from Photovoltaics in the Polish Power System Based on Ground Meteorological Measurements—First Tests Based on Transmission System Operator Data
PublikacjaThe Polish power system is undergoing a slow process of transformation from coal to one that is renewables dominated. Although coal will remain a fundamental fuel in the coming years, the recent upsurge in installed capacity of photovoltaic (PV) systems should draw significant attention. Owning to the fact that the Polish Transmission System Operator recently published the PV hourly generation time series in this article, we aim...
-
Problems of modelling toxic compounds emitted by a marine internal combustion engine for the evaluation of its structure parameters
PublikacjaThe paper presents the possibility of using an analytical study of the engine exhaust ignition to evaluate the technical condition of the selected components. Software tools available for the analysis of experimental data commonly use multiple regression model that allows the study of the effects and iterations between model input quantities and one output variable. The use of multi-equation models gives a lot of freedom in the...
-
Monitoring of n-butanol vapors biofiltration process using an electronic nose combined with calibration models
PublikacjaMalodours, 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...
-
Control of the cultivation of cartilages for using in the biobearings.
PublikacjaBiotribologiczne charakterystyki biołożysk są zależne od procesu hodowli żywej tkanki chrząstki w bioreaktorze. Z kolei proces ten, jest wielowymiarowym procesem dynamicznym sterowanym za pomocą odpowiedniego układu automatycznej regulacji. Praca przedstawia prawo i algorytm sterowania takiego procesu. W tym celu zastosowano sztuczne sieci neuronowe (Artificial Neural Networks - ANN) i zaprezentowano wyniki obliczeń.
-
Anna Sobieraj-Żłobińska dr inż.
OsobyAnna Sobieraj-Żłobińska (ur. 1977 w Przasnyszu) ukończyła Liceum Ogólnokształcące im. Komisji Edukacji Narodowej w Przasnyszu. Od 1996 kontynuowała naukę na Wydziale Geodezji i Gospodarki Przestrzennej na Akademii Rolniczo-Technicznej im. Michała Oczapowskiego w Olsztynie. W 2001 zdobyła tytuł magistra inżyniera na Uniwersytecie Warmińsko-Mazurskim w Olsztynie (temat pracy dyplomowej „Określenie modelu regresji wielokrotnej do...
-
Adaptive CAD-Model Construction Schemes
PublikacjaTwo advanced surrogate model construction techniques are discussed in this paper. The models employ radial basis function (RBF)interpolation scheme or artificial neural networks (ANN) with a new training algorithm. Adaptive sampling technique is applied withrespect to all variables. Histograms showing the quality of the models are presented. While the quality of RBF models is satisfactory, theperformance of the ANN models obtained...
-
Sztuczne sieci neuronowe oraz metoda wektorów wspierających w bankowych systemach informatycznych
PublikacjaW artykule zaprezentowano wybrane metod sztucznej inteligencji do zwiększania efektywności bankowych systemów informatycznych. Wykorzystanie metody wektorów wspierających czy sztucznych sieci neuronowych w połączeniu z nowoczesną technologią mikroprocesorową umożliwia znaczący wzrost konkurencyjności banku poprzez dodanie nowych funkcjonalności. W rezultacie możliwe jest także złagodzenie skutków kryzysu finansowego.
-
Assessment of the factors influencing on the formation of energy-oriented modes of electric power consumption by water-drainage installations of the mines
PublikacjaPurpose. Performing the analysis to determine energy-efficient modes and assess the characteristics of the main indicators of electric power consumption by mine water-drainage installations based on the developed research mathematical model. Methods. To achieve the purpose set, a methodology is used to develop the multiple multifactor correlation-regression modeling with respect to the modes of electric power consumption by electrical...
-
THE ROLE OF INFERENCE IN MOBILE MEDICAL APPLICATION DESIGN
PublikacjaIn the early 21st century, artificial intelligence began to be used to process medical information. However, before this happened, predictive models used in healthcare could only consider a limited number of variables, and only in properly structured and organised medical data. Today, advanced tools based on machine learning techniques - which, using artificial neural networks, can explore extremely complex relationships - and...
-
Dynamic Bayesian Networks for Symbolic Polyphonic Pitch Modeling
PublikacjaSymbolic pitch modeling is a way of incorporating knowledge about relations between pitches into the process of an- alyzing musical information or signals. In this paper, we propose a family of probabilistic symbolic polyphonic pitch models, which account for both the “horizontal” and the “vertical” pitch struc- ture. These models are formulated as linear or log-linear interpo- lations of up to fi ve sub-models, each of which is...
-
Tacjana Niksa-Rynkiewicz dr inż.
OsobyTacjana Niksa-Rynkiewicz - doktor nauk ścisłych w zakresie nauk ścisłych w spesjalności informatyka (2011). Obecnie jest pracownikiem naukowym (adiunktem) Politechniki Gdańskiej. Rozwija swoje umiejętności i prowadzi badania nad zastosowaniem metod sztucznej inteligencji w przemysle. Jest autorką wielu prac naukowych i dydaktycznych. Rozprawa doktorska dotyczyła zagadnień związanych z rozwojem metod Sztucznej Inteligencji, a dokładnie...
-
Electrical and noise responses of carbon nanotube networks enhanced by UV light for detection of organic gases (ethanol, acetone)
Dane BadawczeCarbon nanotube networks of different optical transparencies were investigated via resistance and 1/f noise measurements for detection of ethanol and acetone. The sensor resistive and noise responses were collected for dark and UV-assisted conditions, revealing the improvement in sensor sensitivity and limit of detection after applying UV light (275...
-
Electrical and noise responses of carbon nanotube networks enhanced by UV light for nitrogen dioxide sensing
Dane BadawczeNetworks consisting of randomly oriented carbon nanotubes (CNN) were investigated toward nitrogen dioxide detection by means of electrical and low-frequency noise measurements. UV-activation of CNN layers improved gas sensitivity and reduced the limit of detection, especially by employing 275 nm-LED. This data set includes DC resistance measurements...
-
Diagnostyka łożysk silnika indukcyjnego na podstawie prądu zasilającego przy użyciu sztucznych sieci neuronowych
PublikacjaW artykule zawarto wyniki badań dotyczące diagnostyki łożysk silnika indukcyjnego na podstawie pomiarów prądu zasilającego z wykorzystaniem sztucznych sieci neuronowych. Zaprezentowano wyniki uczenia sieci oraz rezultaty testów przeprowadzonych na danych spoza zbioru uczącego. Badania wykonane zostały na obiektach z celowo wprowadzonymi uszkodzeniami łożysk. Przedstawiona nowa koncepcja zakłada użycie zestawu sieci neuronowych...
-
Knowledge representation of motor activity of patients with Parkinson’s disease
PublikacjaAn approach to the knowledge representation extraction from biomedical signals analysis concerning motor activity of Parkinson disease patients is proposed in this paper. This is done utilizing accelerometers attached to their body as well as exploiting video image of their hand movements. Experiments are carried out employing artificial neural networks and support vector machine to the recognition of characteristic motor activity...
-
A Method for Optimising the Blade Profile in Kaplan Turbine
PublikacjaThis paper introduces a method of blade profile optimisation for Kaplan-type turbines, based on modelling the interaction between rotor and stator blades. Rotor and stator blade geometry is described mathematically by means of a midline curve and thickness distribution. Genetic algorithms are then used to find a global optimum that minimises the loss coefficient. This allows for variety of possible blade shapes and configurations....
-
Sensitivity analysis in design process of sandwich U-shaped composite footbridge
PublikacjaThe structure of the sandwich composite footbridge of a 14 metre span length and U-shaped cross-section was analysed. Sensitivity analysis was performed to support the design process of this innovative object. Linear discrete sensitivity analysis was performed by means of finite element method. The influence of vari-ation of several design variables i.e. thicknesses of inner and outer laminates on the mid-span deflection, as-sumed...
-
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...
-
Marek Biziuk prof. dr hab. inż.
OsobyUr. 25.06.1947 w Sokółce, Województwo Podlaskie. W latach 1964-1969 studiował na Wydziale Chemicznym PG. Stopień doktora nauk technicznych uzyskał w 1977 r., a stopień doktora habilitowanego nauk chemicznych w zakresie chemia uzyskał na Wydziale Chemicznym PG 24.05.1995 r. Tytuł naukowy profesora nauk chemicznych uzyskał na Wydz. Chemicznym PG 6.04.2001 r. Członek Komitetu Chemii Analitycznej PAN od 2008, członek Zespołu ds....
-
Evolution of Animats Following a Moving Target in an Artificial Ecosystem
PublikacjaMany biological animals, even microscopically small, are able to track moving sources of food. In this paper, we investigate the emergence of such behavior in artificial animals (animats) in a 2-dimensional simulated liquid environment. These "predators" are controlled by evolving artificial gene regulatory networks encoded in linear genomes. The fate of the predators is determined only by their ability to gather food and reproduce—no...
-
Rapid Surrogate-Aided Multi-Criterial Optimization of Compact Microwave Passives Employing Machine Learning and ANNs
PublikacjaThis article introduces an innovative method for achieving low-cost and reliable multi-objective optimization (MO) of microwave passive circuits. The technique capitalizes on the attributes of surrogate models, specifically artificial neural networks (ANNs), and multi-resolution electromagnetic (EM) analysis. We integrate the search process into a machine learning (ML) framework, where each iteration produces multiple infill points...
-
A comparative analysis of the effectiveness of corporate bankruptcy prediction models based on financial ratios: Evidence from Colombia, 2008 to 2015
PublikacjaLogit and discriminant analyses have been used for corporate bankruptcy prediction in several studies since the last century. In recent years there have been dozens of studies comparing the several models available, including the ones mentioned above and also probit, artificial neural networks, support vector machines, among others. For the first time for Colombia, this paper presents a comparative analysis of the effectiveness...
-
Data augmentation for improving deep learning in image classification problem
PublikacjaThese days deep learning is the fastest-growing field in the field of Machine Learning (ML) and Deep Neural Networks (DNN). Among many of DNN structures, the Convolutional Neural Networks (CNN) are currently the main tool used for the image analysis and classification purposes. Although great achievements and perspectives, deep neural networks and accompanying learning algorithms have some relevant challenges to tackle. In this...
-
CNN Architectures for Human Pose Estimation from a Very Low Resolution Depth Image
PublikacjaThe paper is dedicated to proposing and evaluating a number of convolutional neural network architectures for calculating a multiple regression on 3D coordinates of human body joints tracked in a single low resolution depth image. The main challenge was to obtain a high precision in case of a noisy and coarse scan of the body, as observed by a depth sensor from a large distance. The regression network was expected to reason about...
-
IEEE International Joint Conference on Neural Networks
Konferencje -
International Conference on Engineering Applications of Neural Networks
Konferencje -
Expert systems in assessing the construction process safety taking account of the risk of disturbances
PublikacjaThe objective of the paper is to present the issue of safety manage-ment during the construction process. Threats in the form of disturb-ances may occur in the preparatory phase, during the execution of the construction project and also during its operational use. The arti-cle presents the concept of applying the methodology based, among others, on Learning Bayesian Networks, Artificial Neural Networks and Support Vector Machine,...
-
Machine Learning in Multi-Agent Systems using Associative Arrays
PublikacjaIn this paper, a new machine learning algorithm for multi-agent systems is introduced. The algorithm is based on associative arrays, thus it becomes less complex and more efficient substitute of artificial neural networks and Bayesian networks, which is confirmed by performance measurements. Implementation of machine learning algorithm in multi-agent system for aided design of selected control systems allowed to improve the performance...
-
New Two-center Ellipsoidal Basis Function Neural Network for Fault Diagnosis of Analog Electronic Circuits
PublikacjaIn the paper a new fault diagnosis-oriented neural network and a diagnostic method for localization of parametric faults in Analog Electronic Circuits (AECs) with tolerances is presented. The method belongs to the class of dictionary Simulation Before Test (SBT) methods. It utilizes dictionary fault signatures as a family of identification curves dispersed around nominal positions by component tolerances of the Circuit Under Test...
-
Neural network model of ship magnetic signature for different measurement depths
PublikacjaThis paper presents the development of a model of a corvette-type ship’s magnetic signature using an artificial neural network (ANN). The capabilities of ANNs to learn complex relationships between the vessel’s characteristics and the magnetic field at different depths are proposed as an alternative to a multi-dipole model. A training dataset, consisting of signatures prepared in finite element method (FEM) environment Simulia...
-
Signal Processing in the Investigation of Two-phase Liquid-gas Flow by Gamma-ray Absorption
Publikacjan this paper, the use of the gamma-absorption method applied in the investigation of the two-phase liquid-gas flow in the pipeline is described. An example of its application to the air transported by water in a horizontal pipeline is evaluated. In the measurements, Am-241 radioactive sources and probes with Nal (Tl) scintillation crystals have been used. The signals from the radiometric set were used to determine the velocity...
-
Data governance: Organizing data for trustworthy Artificial Intelligence
PublikacjaThe rise of Big, Open and Linked Data (BOLD) enables Big Data Algorithmic Systems (BDAS) which are often based on machine learning, neural networks and other forms of Artificial Intelligence (AI). As such systems are increasingly requested to make decisions that are consequential to individuals, communities and society at large, their failures cannot be tolerated, and they are subject to stringent regulatory and ethical requirements....
-
Inverse Nonlinear Eigenvalue Problem Framework for the Synthesis of Coupled-Resonator Filters With Nonresonant Nodes and Arbitrary Frequency-Variant Reactive Couplings
PublikacjaA novel, general circuit-level description of coupledresonator microwave filters is introduced in this article. Unlike well-established coupling-matrix models based on frequency-invariant couplings or linear frequency-variant couplings (LFVCs), a model with arbitrary reactive frequencyvariant coupling (AFVC) networks is proposed. The engineered formulation is more general than prior-art ones—with the only restriction that the coupling...
-
Jan Cudzik dr inż. arch.
OsobyJan Cudzik (dr inż. arch.) jest adiunktem w Katedrze Architektury Miejskiej i Przestrzeni Nadwodnych na Wydziale Architektury Politechniki Gdańskiej oraz kierownikiem Laboratorium Cyfrowych Technologii i Materiałów Przyszłości. Prowadzi badania nad architekturą kinetyczną, technikami cyfrowymi w projektowaniu architektonicznym, fabrykacją cyfrową oraz formami sztucznej inteligencji w architekturze i sztuce. Jego badania nad automatyzacją...
-
Evaluation of Facial Pulse Signals Using Deep Neural Net Models
PublikacjaThe reliable measurement of the pulse rate using remote photoplethysmography (PPG) is very important for many medical applications. In this paper we present how deep neural networks (DNNs) models can be used in the problem of PPG signal classification and pulse rate estimation. In particular, we show that the DNN-based classification results correspond to parameters describing the PPG signals (e.g. peak energy in the frequency...
-
Controlled grafting of vinylic monomers on polyolefins: a robust mathematical modeling approach
PublikacjaExperimental and mathematical modeling analyses were used for controlling melt free-radical grafting of vinylic monomers on polyolefins and, thereby, reducing the disturbance of undesired cross-linking of polyolefins. Response surface, desirability function, and artificial intelligence methodologies were blended to modeling/optimization of grafting reaction in terms of vinylic monomer content, peroxide initiator concentration,...
-
Bibliometric analysis of artificial intelligence in wastewater treatment: Current status, research progress, and future prospects
PublikacjaWastewater treatment is an important topic for improving water quality and environmental protection, and artificial intelligence has become a powerful tool for wastewater treatment. This work provides research progress and a literature review of artificial intelligence applied to wastewater treatment based on the visualization of bibliometric tools. A total of 3460 publications from 2000 to 2023 were obtained from the Web of Science...