Wyszukiwarka
Filtry
wszystkich: 584
Wyniki wyszukiwania dla: NEURAL NETWORKS
-
A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multi-Head Convolutional Attention
PublikacjaTogether with fast advancement of the Internet of Things (IoT), smart healthcare applications and systems are equipped with increasingly more wearable sensors and mobile devices. These sensors are used not only to collect data, but also, and more importantly, to assist in daily activity tracking and analyzing of their users. Various human activity recognition (HAR) approaches are used to enhance such tracking. Most of the existing...
-
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...
-
Creating a radiological database for automatic liver segmentation using artificial intelligence.
PublikacjaImaging in medicine is an irreplaceable stage in the diagnosis and treatment of cancer. The subsequent therapeutic effect depends on the quality of the imaging tests performed. In recent years we have been observing the evolution of 2D to 3D imaging for many medical fields, including oncological surgery. The aim of the study is to present a method of selection of radiological imaging tests for learning neural networks.
-
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...
-
Modelowanie przepływu pary przez okołodźwiękowe wieńce turbinowe z użyciem sztucznych sieci neuronoych
PublikacjaNiniejszy artykuł stanowi opis modelu przepływu pary przez okołodźwiękowe stopnie turbinowe, stworzonego w oparciu o sztuczne sieci neuronowe (SSN). Przedstawiony model neuronowy pozwala na wyznaczenie rozkładu wybranych parametrów w analizowanym przekroju kanału przepływowego turbiny dla rozpatrywanego zakresu wartości ciśnienia wlotowego.
-
Deep Learning Optimization for Edge Devices: Analysis of Training Quantization Parameters
PublikacjaThis paper focuses on convolution neural network quantization problem. The quantization has a distinct stage of data conversion from floating-point into integer-point numbers. In general, the process of quantization is associated with the reduction of the matrix dimension via limited precision of the numbers. However, the training and inference stages of deep learning neural network are limited by the space of the memory and a...
-
Classifying Emotions in Film Music - A Deep Learning Approach
PublikacjaThe paper presents an application for automatically classifying emotions in film music. A model of emotions is proposed, which is also associated with colors. The model created has nine emotional states, to which colors are assigned according to the color theory in film. Subjective tests are carried out to check the correctness of the assumptions behind the adopted emotion model. For that purpose, a statistical analysis of the...
-
Speech Analytics Based on Machine Learning
PublikacjaIn this chapter, the process of speech data preparation for machine learning is discussed in detail. Examples of speech analytics methods applied to phonemes and allophones are shown. Further, an approach to automatic phoneme recognition involving optimized parametrization and a classifier belonging to machine learning algorithms is discussed. Feature vectors are built on the basis of descriptors coming from the music information...
-
Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy
PublikacjaThe diabetic retinopathy is a disease caused by long-standing diabetes. Lack of effective treatment can lead to vision impairment and even irreversible blindness. The disease can be diagnosed by examining digital color fundus photographs of retina. In this paper we propose deep learning approach to automated diabetic retinopathy screening. Deep convolutional neural networks (CNN) - the most popular kind of deep learning algorithms...
-
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...
-
Document Agents with the Intelligent Negotiations Capability
PublikacjaThe paper focus is on augmenting proactive document-agents with built -in intelligence to enable them to recognize execution context provided by devices visited durning the business process, and to reach collaboration agreement despite of their conflicting requirements. We propose a solution based on neural networks to improve simple multi-issue negotiation between the document and the device, practically with no excessive cost...
-
Optymalizacja treningu i wnioskowania sieci neuronowych
PublikacjaSieci neuronowe są jedną z najpopularniejszych i najszybciej rozwijających się dziedzin sztucznej inteligencji. Ich praktyczne wykorzystanie umożliwiło szersze użycie komputerów w wielu obszarach komunikacji, przemysłu i transportu. Dowody tego są widoczne w elektronice użytkowej, medycynie, a nawet w zastosowaniach militarnych. Wykorzystanie sztucznej inteligencji w wielu przypadkach wymaga jednak znacznej mocy obliczeniowej,...
-
Towards Knowledge Sharing Oriented Adaptive Control
PublikacjaIn this paper, we propose a knowledge sharing oriented approach to enable a robot to reuse other robots' knowledge by adapting itself to the inverse dynamics model of the knowledge-sharing robot. The purpose of this work is to remove the heavy fine-tuning procedure required before using a new robot for a task via reusing other robots' knowledge. We use the Neural Knowledge DNA (NK-DNA) to help robots gain empirical knowledge and...
-
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.
-
Using Long-Short term Memory networks with Genetic Algorithm to predict engine condition
PublikacjaPredictive maintenance (PdM) is a type of approach for maintenance processes, allowing maintenance actions to be managed depending on the machine's current condition. Maintenance is therefore carried out before failures occur. The approach doesn’t only help avoid abrupt failures but also helps lower maintenance cost and provides possibilities to manufacturers to manage maintenance budgets in a more efficient way. A new deep neural...
-
Spatiotemporal Assessment of Satellite Image Time Series for Land Cover Classification Using Deep Learning Techniques: A Case Study of Reunion Island, France
PublikacjaCurrent Earth observation systems generate massive amounts of satellite image time series to keep track of geographical areas over time to monitor and identify environmental and climate change. Efficiently analyzing such data remains an unresolved issue in remote sensing. In classifying land cover, utilizing SITS rather than one image might benefit differentiating across classes because of their varied temporal patterns. The aim...
-
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...
-
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...
-
A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels
PublikacjaBiodiesel has been emerging as a potential and promising biofuel for the strategy of reducing toxic emissions and improving engine performance. Computational methods aiming to offer numerical solutions were inevitable as a study methodology which was sometimes considered the only practical method. Artificial neural networks (ANN) were data-processing systems, which were used to tackle many issues in engineering and science, especially...
-
WYKORZYSTANIE SIECI NEURONOWYCH DO SYNTEZY MOWY WYRAŻAJĄCEJ EMOCJE
PublikacjaW niniejszym artykule przedstawiono analizę rozwiązań do rozpoznawania emocji opartych na mowie i możliwości ich wykorzystania w syntezie mowy z emocjami, wykorzystując do tego celu sieci neuronowe. Przedstawiono aktualne rozwiązania dotyczące rozpoznawania emocji w mowie i metod syntezy mowy za pomocą sieci neuronowych. Obecnie obserwuje się znaczny wzrost zainteresowania i wykorzystania uczenia głębokiego w aplikacjach związanych...
-
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....
-
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...
-
BIG DATA SIGNIFICANCE IN REMOTE MEDICAL DIAGNOSTICS BASED ON DEEP LEARNING TECHNIQUES
PublikacjaIn this paper we discuss the evaluation of neural networks in accordance with medical image classification and analysis. We also summarize the existing databases with images which could be used for training deep models that can be later utilized in remote home-based health care systems. In particular, we propose methods for remote video-based estimation of patient vital signs and other health-related parameters. Additionally, potential...
-
AUTOMATED NEGOTIATIONS OVER COLLABORATION PROTOCOL AGREEMENTS
PublikacjaThe dissertation focuses on the augmentation of proactive document - agents with built-in intelligence to recognize execution context provided by devices visited during a business process, and to reach collaboration agreement despite conflicting requirements. The proposed solution, based on intelligent bargaining using neural networks to improve simple multi-issue negotiation between the document and thedevice, requires practically...
-
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...
-
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...
-
Iterative Global Sensitivity Analysis Algorithm with Neural Network Surrogate Modeling
PublikacjaGlobal sensitivity analysis (GSA) is a method to quantify the effect of the input parameters on outputs of physics-based systems. Performing GSA can be challenging due to the combined effect of the high computational cost of each individual physics-based model, a large number of input parameters, and the need to perform repetitive model evaluations. To reduce this cost, neural networks (NNs) are used to replace the expensive physics-based...
-
Collaborative Data Acquisition and Learning Support
PublikacjaWith the constant development of neural networks, traditional algorithms relying on data structures lose their significance as more and more solutions are using AI rather than traditional algorithms. This in turn requires a lot of correctly annotated and informative data samples. In this paper, we propose a crowdsourcing based approach for data acquisition and tagging with support for Active Learning where the system acts as an...
-
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...
-
Evaluating Performance and Accuracy Improvements for Attention-OCR
PublikacjaIn this paper we evaluated a set of potential improvements to the successful Attention-OCR architecture, designed to predict multiline text from unconstrained scenes in real-world images. We investigated the impact of several optimizations on model’s accuracy, including employing dynamic RNNs (Recurrent Neural Networks), scheduled sampling, BiLSTM (Bidirectional Long Short-Term Memory) and a modified attention model. BiLSTM was...
-
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...
-
Closed-loop stimulation of temporal cortex rescues functional networks and improves memory
PublikacjaMemory failures are frustrating and often the result of ineffective encoding. One approach to improving memory outcomes is through direct modulation of brain activity with electrical stimulation. Previous efforts, however, have reported inconsistent effects when using open-loop stimulation and often target the hippocampus and medial temporal lobes. Here we use a closed-loop system to monitor and decode neural activity from direct...
-
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...
-
SegSperm - a dataset of sperm images for blurry and small object segmentation
Dane BadawczeMany deep learning applications require figure-ground segmentation. The performance of segmentation models varies across modalities and acquisition settings.
-
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.
-
Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks
PublikacjaThe increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated...
-
Musical Instrument Identification Using Deep Learning Approach
PublikacjaThe work aims to propose a novel approach for automatically identifying all instruments present in an audio excerpt using sets of individual convolutional neural networks (CNNs) per tested instrument. The paper starts with a review of tasks related to musical instrument identification. It focuses on tasks performed, input type, algorithms employed, and metrics used. The paper starts with the background presentation, i.e., metadata...
-
Study of Statistical Text Representation Methods for Performance Improvement of a Hierarchical Attention Network
PublikacjaTo effectively process textual data, many approaches have been proposed to create text representations. The transformation of a text into a form of numbers that can be computed using computers is crucial for further applications in downstream tasks such as document classification, document summarization, and so forth. In our work, we study the quality of text representations using statistical methods and compare them to approaches...
-
Application of gas chromatographic data and 2D molecular descriptors for accurate global mobility potential prediction
PublikacjaMobility is a key feature affecting the environmental fate, which is of particular importance in the case of persistent organic pollutants (POPs) and emerging pollutants (EPs). In this study, the global mobility classification artificial neural networks-based models employing GC retention times (RT) and 2D molecular descriptors were constructed and validated. The high usability of RT was confirmed based on the feature selection...
-
Special techniques and future perspectives: Simultaneous macro- and micro-electrode recordings
PublikacjaThere are many approaches to studying the inner workings of the brain and its highly interconnected circuits. One can look at the global activity in different brain structures using non-invasive technologies like positron emission tomography (PET) or functional magnetic resonance imaging (fMRI), which measure physiological changes, e.g. in the glucose uptake or blood flow. These can be very effectively used to localize active patches...
-
Efficiency comparison of selected endoscopic video analysis algorithms
PublikacjaIn the paper, selected image analysis algorithms were examined and compared in the task of identifying informative frames, blurry frames, colorectal cancer and healthy tissue on endoscopic videos. In order to standardize the tests, the algorithms were modified by removing from them parts responsible for the classification, and replacing them with Support Vector Machines and Artificial Neural Networks. The tests were performed in...
-
Artificial intelligence support for disease detection in wireless capsule endoscopy images of human large bowel
PublikacjaIn the work the chosen algorithms of disease recognition in endoscopy images were described and compared for theirs efficiency. The algorithms were estimated with regard to utility for application in computer system's support for digestive system's diagnostics. Estimations were achieved in an advanced testing environment, which was built with use of the large collection of endoscopy movies received from Medical University in Gdańsk....
-
Collective citizens' behavior modelling with support of the Internet of Things and Big Data
PublikacjaIn this paper, collective human behaviors are modelled by a development of Big Data mining related to the Internet of Things. Some studies under MapReduce architectures have been carried out to improve an efficiency of Big Data mining. Intelligent agents in data mining have been analyzed for smart city systems, as well as data mining has been described by genetic programming. Furthermore, artificial neural networks have been discussed...
-
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...
-
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...
-
Using deep learning to increase accuracy of gaze controlled prosthetic arm
PublikacjaThis paper presents how neural networks can be utilized to improve the accuracy of reach and grab functionality of hybrid prosthetic arm with eye tracing interface. The LSTM based Autoencoder was introduced to overcome the problem of lack of accuracy of the gaze tracking modality in this hybrid interface. The gaze based interaction strongly depends on the eye tracking hardware. In this paper it was presented how the overall the...
-
Deep learning approach on surface EEG based Brain Computer Interface
PublikacjaIn this work we analysed the application of con-volutional neural networks in motor imagery classification for the Brain Computer Interface (BCI) purposes. To increase the accuracy of classification we proposed the solution that combines the Common Spatial Pattern (CSP) with convolutional network (ConvNet). The electroencephalography (EEG) is one of the modalities we try to use for controlling the prosthetic arm. Therefor in this...
-
Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review
PublikacjaIn this review, we present the applications of chemometric techniques for green and sustainable chemistry. The techniques, such as cluster analysis, principal component analysis, artificial neural networks, and multivariate ranking techniques, are applied for dealing with missing data, grouping or classification purposes, selection of green material, or processes. The areas of application are mainly finding sustainable solutions...
-
Shape Optimisation of Kaplan Turbine Blades Using Genetic Algorithms
PublikacjaThis monograph is a comprehensive guide to a method of blade profile optimisation for Kaplan-type turbines. This method is based on modelling the interaction between rotor and stator blades. Additionally, the shape of the draft tube is investigated. The influence of the periodic boundary condition vs. full geometry is also discussed. Evolutionary algorithms (EA) are used as an optimisation method together with artificial neural...
-
ANN for human pose estimation in low resolution depth images
PublikacjaThe paper presents an approach to localize human body joints in 3D coordinates based on a single low resolution depth image. First a framework to generate a database of 80k realistic depth images from a 3D body model is described. Then data preprocessing and normalization procedure, and DNN and MLP artificial neural networks architectures and training are presented. The robustness against camera distance and image noise is analysed....
-
Comparison of selected electroencephalographic signal classification methods
PublikacjaA variety of methods exists for electroencephalographic (EEG) signals classification. In this paper, we briefly review selected methods developed for such a purpose. First, a short description of the EEG signal characteristics is shown. Then, a comparison between the selected EEG signal classification methods, based on the overview of research studies on this topic, is presented. Examples of methods included in the study are: Artificial...
-
Surface EMG-based signal acquisition for decoding hand movements
Dane BadawczeBiosignal processing plays a crucial role in modern hand prosthetics. The challenge is to restore functionality of a lost limb based on the signals acquired from the surface of the stump. The number of sensors (emg channels) used for signal acquisition influence the quality of a prosthetic hand. Modern algorithms (including neural networks) can significantly...
-
Adaptacyjny algorytm filtracji sygnału fonokardiograficznego wykorzystujący sztuczną sieć neuronową
PublikacjaPodstawowym problemem podczas projektowania systemu autodiagnostyki chorób serca, bazującego na analizie sygnału fonokardiograficznego (PCG), jest konieczność zapewnienia, niezależnie od warunków zewnętrznych, sygnału o wysokiej jakości. W artykule, bazując na zdolności Sztucznej Sieci Neuronowej (SSN) do predykcji sygnałów periodycznych oraz quasi-periodycznych, został opracowany adaptacyjny algorytm filtracji dźwięków serca....
-
Mathematical modeling and prediction of pit to crack transition under cyclic thermal load using artificial neural network
PublikacjaThe formation of pitting is a major problem in most metals, which is caused by extremely localized corrosion that creates small holes in metal and subsequently, it changes into cracks under mechanical load, thermo-mechanical stress, and corrosion process factors. This research aims to study pit to crack transition phenomenon of steel boiler heat tubes under cyclic thermal load, and mathematical modeling...
-
How to Sort Them? A Network for LEGO Bricks Classification
PublikacjaLEGO bricks are highly popular due to the ability to build almost any type of creation. This is possible thanks to availability of multiple shapes and colors of the bricks. For the smooth build process the bricks need to properly sorted and arranged. In our work we aim at creating an automated LEGO bricks sorter. With over 3700 different LEGO parts bricks classification has to be done with deep neural networks. The question arises...
-
Examining Classifiers Applied to Static Hand Gesture Recognition in Novel Sound Mixing System
PublikacjaThe main objective of the chapter is to present the methodology and results of examining various classifiers (Nearest Neighbor-like algorithm with non-nested generalization (NNge), Naive Bayes, C4.5 (J48), Random Tree, Random Forests, Artificial Neural Networks (Multilayer Perceptron), Support Vector Machine (SVM) used for static gesture recognition. A problem of effective gesture recognition is outlined in the context of the system...
-
Music Mood Visualization Using Self-Organizing Maps
PublikacjaDue to an increasing amount of music being made available in digital form in the Internet, an automatic organization of music is sought. The paper presents an approach to graphical representation of mood of songs based on Self-Organizing Maps. Parameters describing mood of music are proposed and calculated and then analyzed employing correlation with mood dimensions based on the Multidimensional Scaling. A map is created in which...
-
Zastosowanie sieci neuronowych do detekcji impulsów o znanym kształcie w obecności silnego szumu i trendu
PublikacjaDetekcja impulsów w odebranym sygnale radiowym, zwłaszcza w obecności silnego szumu oraz trendu, jest trudnym zadaniem. Artykuł przedstawia propozycje rozwiązań wykorzystujących sieci neuronowe do detekcji impulsów o znanym kształcie w obecności silnego szumu i trendu. Na potrzeby realizacji tego zadania zaproponowano dwie architektury. W pracy przedstawiono wyniki badań wpływu kształtu impulsu, mocy zakłóceń szumowych oraz trendu...
-
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...
-
Position Estimation in Mixed Indoor-Outdoor Environment Using Signals of Opportunity and Deep Learning Approach
PublikacjaTo improve the user's localization estimation in indoor and outdoor environment a novel radiolocalization system using deep learning dedicated to work both in indoor and outdoor environment is proposed. It is based on the radio signatures using radio signals of opportunity from LTE an WiFi networks. The measurements of channel state estimators from LTE network and from WiFi network are taken by using the developed application....
-
Improvement of speech intelligibility in the presence of noise interference using the Lombard effect and an automatic noise interference profiling based on deep learning
PublikacjaThe Lombard effect is a phenomenon that results in speech intelligibility improvement when applied to noise. There are many distinctive features of Lombard speech that were recalled in this dissertation. This work proposes the creation of a system capable of improving speech quality and intelligibility in real-time measured by objective metrics and subjective tests. This system consists of three main components: speech type detection,...
-
AUTOMATYCZNA KLASYFIKACJA MOWY PATOLOGICZNEJ
PublikacjaAplikacja przedstawiona w niniejszym rozdziale służy do automatycznego wykrywania mowy patologicznej na podstawie bazy nagrań. W pierwszej kolejności przedstawiono założenia leżące u podstaw przeprowadzonych badan wraz z wyborem bazy mowy patologicznej. Zaprezentowano również zastosowane algorytmy oraz cechy sygnału mowy, które pozwalają odróżnić mowę niezaburzoną od mowy patologicznej. Wytrenowane sieci neuronowe zostały następnie...
-
Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
PublikacjaWe present a novel deep learning model for the detection and reconstruction of dysarthric speech. We train the model with a multi-task learning technique to jointly solve dysarthria detection and speech reconstruction tasks. The model key feature is a low-dimensional latent space that is meant to encode the properties of dysarthric speech. It is commonly believed that neural networks are black boxes that solve problems but do not...
-
Residual MobileNets
PublikacjaAs modern convolutional neural networks become increasingly deeper, they also become slower and require high computational resources beyond the capabilities of many mobile and embedded platforms. To address this challenge, much of the recent research has focused on reducing the model size and computational complexity. In this paper, we propose a novel residual depth-separable convolution block, which is an improvement of the basic...
-
Fragmentation of Hydrographic Big Data Into Subsets During Reduction Process
PublikacjaThe article presented problems of fragmentation of hydrographic big data into smaller subsets during reduction process. Data reduction is a processing of reduce the value of the data set, in order to make them easier and more effective for the goals of the analysis. The main aim of authors is to create new reduction method. The article presented the first stage of this method – fragmentation of bathymetric data into subsets. It...
-
Urban scene semantic segmentation using the U-Net model
PublikacjaVision-based semantic segmentation of complex urban street scenes is a very important function during autonomous driving (AD), which will become an important technology in industrialized countries in the near future. Today, advanced driver assistance systems (ADAS) improve traffic safety thanks to the application of solutions that enable detecting objects, recognising road signs, segmenting the road, etc. The basis for these functionalities...
-
Fault detection in measuring systems of power plants
PublikacjaThis paper describes possibility of forming diagnostic relations based on application of the artifical neural networks (ANNs), intended for the identifying of degradation of measuring instruments used in developed power systems. As an example a steam turbine high-power plant was used. And, simulative calculations were applied to forming diagnostic neural relations. Both degradation of the measuring instruments and simultaneously...
-
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...
-
Explainable machine learning for diffraction patterns
PublikacjaSerial crystallography experiments at X-ray free-electron laser facilities produce massive amounts of data but only a fraction of these data are useful for downstream analysis. Thus, it is essential to differentiate between acceptable and unacceptable data, generally known as ‘hit’ and ‘miss’, respectively. Image classification methods from artificial intelligence, or more specifically convolutional neural networks (CNNs), classify...
-
Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning
PublikacjaThe aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was...
-
Rotor Blade Geometry Optimisation in Kaplan Turbine
PublikacjaThe paper presents the description of method and results of rotor blade shape optimisation. The rotor blading constitutes a part ofturbine flow path. Optimisation consists in selection of the shape that minimises ratio of polytrophic loss. Shape of the blade isdefined by the mean camber line and thickness of the airfoil. Thickness is distributed around the camber line based on the ratio ofdistribution. Global optimisation was done...
-
Obtaining a Well-Trained Artificial Intelligence Algorithm from Cross-Validation in Endoscopy
PublikacjaThe article shortly discusses endoscopic video analysis problems and artificial intelligence algorithms supporting it. The most common method of efficiency testing of these algorithms is to perform intensive cross-validation. This allows for accurately evaluate their performance of generalization. One of the main problems of this procedure is that there is no simple and universal way of obtaining a specific instance of a well-trained...
-
Impact of Visual Image Quality on Lymphocyte Detection Using YOLOv5 and RetinaNet Algorithms
PublikacjaLymphocytes, a type of leukocytes, play a vital role in the immune system. The precise quantification, spatial arrangement and phenotypic characterization of lymphocytes within haematological or histopathological images can serve as a diagnostic indicator of a particular lesion. Artificial neural networks, employed for the detection of lymphocytes, not only can provide support to the work of histopathologists but also enable better...
-
Analysis of the Capability of Deep Learning Algorithms for EEG-based Brain-Computer Interface Implementation
PublikacjaMachine learning models have received significant attention for their exceptional performance in classifying electroencephalography (EEG) data. They have proven to be highly effective in extracting intricate patterns and features from the raw signal data, thereby contributing to their success in EEG classification tasks. In this study, we explore the possibilities of utilizing contemporary machine learning algorithms in decoding...
-
Multimodal Approach For Polysensory Stimulation And Diagnosis Of Subjects With Severe Communication Disorders
Publikacjais evaluated on 9 patients, data analysis methods are described, and experiments of correlating Glasgow Coma Scale with extracted features describing subjects performance in therapeutic exercises exploiting EEG and eyetracker are presented. Performance metrics are proposed, and k-means clusters used to define concepts for mental states related to EEG and eyetracking activity. Finally, it is shown that the strongest correlations...
-
Comparing the Effectiveness of ANNs and SVMs in Forecasting the Impact of Traffic-Induced Vibrations on Building
PublikacjaTraffic - induced vibrations may cause damage to structural elements and may even lead to structural collapse. The aim of the article is to compare the effectiveness of algorithms in forecasting the impact of vibrations on buildings using the Machine Learning (ML) methods. The paper presents two alternative approaches by using Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). Factors that may affect traffic-induced...
-
Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices
PublikacjaThere are growing opportunities to leverage new technologies and data sources to address global problems related to sustainability, climate change, and biodiversity loss. The emerging discipline of GeoAI resulting from the convergence of AI and Geospatial science (Geo-AI) is enabling the possibility to harness the increasingly available open Earth Observation data collected from different constellations of satellites and sensors...
-
COVID-19 severity forecast based on machine learning and complete blood count data
PublikacjaProper triage of COVID-19 patients is a key factor in eective case management, especially with limited and insucient resources. In this paper, we propose a machine-aided diagnostic system to predict how badly a patient with COVID-19 will develop disease. The prognosis of this type is based on the parameters of commonly used complete blood count tests, which makes it possible to obtain data from a wide range of patients.We chose...
-
COVID-19 severity forecast based on machine learning and complete blood count data
PublikacjaProper triage of COVID-19 patients is a key factor in eective case management, especially with limited and insucient resources. In this paper, we propose a machine-aided diagnostic system to predict how badly a patient with COVID-19 will develop disease. The prognosis of this type is based on the parameters of commonly used complete blood count tests, which makes it possible to obtain data from a wide range of patients.We chose...
-
Early warning models against bankruptcy risk for Central European and Latin American enterprises
PublikacjaThis article is devoted to the issue of forecasting the bankruptcy risk of enterprises in Latin America and Central Europe. The author has used statistical and soft computing methods to program the prediction models. It compares the effectiveness of twelve different early warningmodels for forecasting the bankruptcy risk of companies. In the research conducted, the author used data on 185 companies listed on the Warsaw Stock Exchange...
-
Investigating Feature Spaces for Isolated Word Recognition
PublikacjaThe study addresses the issues related to the appropriateness of a two-dimensional representation of speech signal for speech recognition tasks based on deep learning techniques. The approach combines Convolutional Neural Networks (CNNs) and time-frequency signal representation converted to the investigated feature spaces. In particular, waveforms and fractal dimension features of the signal were chosen for the time domain, and...
-
Automatic Rhythm Retrieval from Musical Files
PublikacjaThis paper presents a comparison of the effectiveness of two computational intelligence approaches applied to the task of retrieving rhythmic structure from musical files. The method proposed by the authors of this paper generates rhythmic levels first, and then uses these levels to compose rhythmic hypotheses. Three phases: creating periods, creating simplified hypotheses and creating full hypotheses are examined within this study....
-
Deep Features Class Activation Map for Thermal Face Detection and Tracking
PublikacjaRecently, capabilities of many computer vision tasks have significantly improved due to advances in Convolutional Neural Networks. In our research, we demonstrate that it can be also used for face detection from low resolution thermal images, acquired with a portable camera. The physical size of the camera used in our research allows for embedding it in a wearable device or indoor remote monitoring solution for elderly and disabled...
-
Zdzisław Kowalczuk prof. dr hab. inż.
OsobyW 1978 ukończył studia w zakresie automatyki i informatyki na Wydziale Elektroniki Politechniki Gdańskiej, następnie rozpoczął pracę na macierzystej uczelni. W 1986 obronił pracę doktorską, w 1993 habilitował się na Politechnice Śląskiej na podstawie pracy Dyskretne modele w projektowaniu układów sterowania. W 1996 mianowany profesorem nadzwyczajnym, w 2003 otrzymał tytuł profesora nauk technicznych. W 2006 założył i od tego czasu...
-
Optimized Deep Learning Model for Flood Detection Using Satellite Images
PublikacjaThe increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection...
-
Underground Water Level Prediction in Remote Sensing Images Using Improved Hydro Index Value with Ensemble Classifier
PublikacjaThe economic sustainability of aquifers across the world relies on accurate and rapid estimates of groundwater storage changes, but this becomes difficult due to the absence of insitu groundwater surveys in most areas. By closing the water balance, hydrologic remote sensing measures offer a possible method for quantifying changes in groundwater storage. However, it is uncertain to what extent remote sensing data can provide an...
-
Autonomous pick-and-place system based on multiple 3Dsensors and deep learning
PublikacjaGrasping objects and manipulating them is the main way the robot interacts with its environment. However, for robots to operate in a dynamic environment, a system for determining the gripping position for objects in the scene is also required. For this purpose, neural networks segmenting the point cloud are usually applied. However, training such networks is very complex and their results are unsatisfactory. Therefore, we propose...
-
Autonomous Perception and Grasp Generation Based on Multiple 3D Sensors and Deep Learning
PublikacjaGrasping objects and manipulating them is the main way the robot interacts with its environment. However, for robots to operate in a dynamic environment, a system for determining the gripping position for objects in the scene is also required. For this purpose, neural networks segmenting the point cloud are usually applied. However, training such networks is very complex and their results are unsatisfactory. Therefore, we propose...
-
MobileNet family tailored for Raspberry Pi
PublikacjaWith the advances in systems-on-a-chip technologies, there is a growing demand to deploy intelligent vision systems on low-cost microcomputers. To address this challenge, much of the recent research has focused on reducing the model size and computational complexity of contemporary convolutional neural networks (CNNs). The state-of-the-art lightweight CNN is MobileNetV3. However, it was designed to achieve a good trade-off between...
-
Playback detection using machine learning with spectrogram features approach
PublikacjaThis paper presents 2D image processing approach to playback detection in automatic speaker verification (ASV) systems using spectrograms as speech signal representation. Three feature extraction and classification methods: histograms of oriented gradients (HOG) with support vector machines (SVM), HAAR wavelets with AdaBoost classifier and deep convolutional neural networks (CNN) were compared on different data partitions in respect...
-
Olgun Aydin Dr
OsobyOlgun Aydin finished his PhD by publishing a thesis about Deep Neural Networks. He works as a Senior Data Scientist in PwC Poland, gives lectures in Gdansk University of Technology in Poland and member of WhyR? Foundation. Olgun is a very big fan of R and author of the book called “R Web Scraping Quick Start Guide” , two video courses are called “Deep Dive into Statistical Modelling using R” and “Applied Machine Learning and Deep...
-
Paweł Burdziakowski dr inż.
Osobydr inż. Paweł Burdziakowski jest specjalista w zakresie fotogrametrii i teledetekcji lotniczej niskiego pułapu, nawigacji morskiej i lotniczej. Jest również licencjonowanym instruktorem lotniczym oraz programistą. Głównymi obszarami zainteresowania jest fotogrametria cyfrowa, nawigacja platform bezzałogowych oraz systemy bezzałogowe, w tym lotnicze, nawodne, podwodne. Prowadzi badania w zakresie algorytmów i metod poprawiających...
-
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...
-
BETWEEN IDEA AND INTERPRETATION - DESIGN PROCESS AUGMENTATION
PublikacjaThe following paper investigates the idea of reducing the human digital intervention to a minimum during the advanced design process. Augmenting the outcome attributes beyond the designer's capabilities by computational design methods, data collection, data computing and digital fabrication, altogether imitating the human design process. The primary technical goal of the research was verification of restrictions and abilities used...
-
LDRAW based positional renders of LEGO bricks
Dane Badawcze243 different LEGO bricks renders of size 250x250 in 5 colors in 120 viewing angles stored as JPEG images. The renders are used to train neural networks for bricks recognition. All images were generated using L3P (http://www.hassings.dk/l3/l3p.html) and POV-Ray (http://www.povray.org/) tools and were based on the 3D models from LDraw (https://www.ldraw.org/)...
-
Soft Sensor Application in Identification of the Activated Sludge Bulking Considering the Technological and Economical Aspects of Smart Systems Functioning
PublikacjaThe paper presented the methodology for the construction of a soft sensor used for activated sludge bulking identification. Devising such solutions fits within the current trends and development of a smart system and infrastructure within smart cities. In order to optimize the selection of the data-mining method depending on the data collected within a wastewater treatment plant (WWTP), a number of methods were considered, including:...
-
Predictions of cervical cancer identification by photonic method combined with machine learning
PublikacjaCervical cancer is one of the most commonly appearing cancers, which early diagnosis is of greatest importance. Unfortunately, many diagnoses are based on subjective opinions of doctors—to date, there is no general measurement method with a calibrated standard. The problem can be solved with the measurement system being a fusion of an optoelectronic sensor and machine learning algorithm to provide reliable assistance for doctors...
-
Automatic Emotion Recognition in Children with Autism: A Systematic Literature Review
PublikacjaThe automatic emotion recognition domain brings new methods and technologies that might be used to enhance therapy of children with autism. The paper aims at the exploration of methods and tools used to recognize emotions in children. It presents a literature review study that was performed using a systematic approach and PRISMA methodology for reporting quantitative and qualitative results. Diverse observation channels and modalities...
-
Performance Analysis of the OpenCL Environment on Mobile Platforms
PublikacjaToday’s smartphones have more and more features that so far were only assigned to personal computers. Every year these devices are composed of better and more efficient components. Everything indicates that modern smartphones are replacing ordinary computers in various activities. High computing power is required for tasks such as image processing, speech recognition and object detection. This paper analyses the performance of...