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Wyniki wyszukiwania dla: BACKSTEPPING, NEURAL NETWORKS, RBF, DYNAMIC SHIP POSITIONING
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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...
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Deep Video Multi-task Learning Towards Generalized Visual Scene Enhancement and Understanding
PublikacjaThe goal of this thesis was to develop efficient video multi-task convolutional architectures for a range of diverse vision tasks, on RGB scenes, leveraging i) task relationships and ii) motion information to improve multi-task performance. The approach we take starts from the integration of diverse tasks within video multi-task learning networks. We present the first two datasets of their kind in the existing literature, featuring...
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The Analysis of Cross-Polarisation Discrimination for Body Area Networks in Cylindrical Metallic Environment
PublikacjaThe analysis of cross-polarisation discrimination for Body Area Networks in an untypical environment of cylindrical metallic room has been performed in the paper. This analysis was done based on the measurements carried out for dynamic narrowband off-body channels operating at the frequency of 2.45 GHz. The results have shown that there is a strong dependence of the depolarisation effect on the existence of direct component in...
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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...
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A Novel Method for the Deblurring of Photogrammetric Images Using Conditional Generative Adversarial Networks
PublikacjaThe visual data acquisition from small unmanned aerial vehicles (UAVs) may encounter a situation in which blur appears on the images. Image blurring caused by camera motion during exposure significantly impacts the images interpretation quality and consequently the quality of photogrammetric products. On blurred images, it is difficult to visually locate ground control points, and the number of identified feature points decreases...
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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...
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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....
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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...
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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...
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Determination of chlorine concentration using single temperature modulated semiconductor gas sensor
PublikacjaA periodic temperature modulation using sinusoidal heater voltage was applied to a commercial SnO2 semiconductor gas sensor. Resulting resistance response of the sensor was analyzed using a feature extraction method based on Fast Fourier Transformation (FFT). The amplitudes of the higher harmonics of the FFT from the dynamic nonlinear responses of measured gas were further utilized as an input for Artificial Neural...
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The Innovative Faculty for Innovative Technologies
PublikacjaA leaflet describing Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology. Multimedia Systems Department described laboratories and prototypes of: Auditory-visual attention stimulator, Automatic video event detection, Object re-identification application for multi-camera surveillance systems, Object Tracking and Automatic Master-Slave PTZ Camera Positioning System, Passive Acoustic Radar,...
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Evaluation of position estimation based on accelerometer data
PublikacjaThe paper concerns the problem of integrating data from accelerometers. A suitable model of a MEMS accelerometer is presented which is a part of inertial measurement units (IMU). Such units allow to measure orientation as well as to localize systems. They also appear to be applicable for systems positioning. The main purpose of the paper is to discuss conditions that must be satisfied to calculate the location of the sensor by...
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Modelling an accelerometer for robot position estimation
PublikacjaThe article describes a new model of a MEMS accelerometer for usage in inertial measurement units (IMU). Such units allow to measure orientation and location of the sensor/system and therefore can be applied for systems positioning. The main purpose of the paper is to model pertinent accelerometer functions substantial in determination of the location of the sensor by means of double integration of acceleration. The model takes...
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Monitoring the gas turbine start-up phase on the platform using a hierarchical model based on Multi-Layer Perceptron networks
PublikacjaVery often, the operation of diagnostic systems is related to the evaluation of process functionality, where the diagnostics is carried out using reference models prepared on the basis of the process description in the nominal state. The main goal of the work is to develop a hierarchical gas turbine reference model for the estimation of start-up parameters based on multi-layer perceptron neural networks. A functional decomposition...
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Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
PublikacjaMany studies have been performed to put quantifying uncertainties into the seismic risk assessment of reinforced concrete (RC) buildings. This paper provides a risk-assessment support tool for purpose of retrofitting and potential design strategies of RC buildings. Machine Learning (ML) algorithms were developed in Python software by innovative methods of hyperparameter optimization, such as halving search, grid search, random...
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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...
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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...
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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...
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Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality
PublikacjaHigh-quality echocardiogram images are the cornerstone of accurate and reliable measurements of the heart. Therefore, this study aimed to develop, validate and compare machine learning and deep learning algorithms for accurate and automated assessment of transthoracic echocardiogram image quality. In total, 4090 single-frame two-dimensional transthoracic echocardiogram...
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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...
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Intelligent turbogenerator controller based on artifical neural network
PublikacjaThe paper presents a desing of an intelligent controller based on neural network (ICNN). The ICNN ensures at the same time two fundamental functions : the maintaining of generator voltage at the desired value and the damping of the electromechanical oscillations. Its performance is evaluted on a single machine infinite bus power system through computer simulations. The dynamic and transient operation of the proposed controller...
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Detection of the First Component of the Received LTE Signal in the OTDoA Method
PublikacjaIn a modern world there is a growing demand for localization services of various kinds. Position estimation can be realized via cellular networks, especially in the currently widely deployed LTE (Long Term Evolution) networks. However, it is not an easy task in harsh propagation conditions which often occur in dense urban environments. Recently, time-methods of terminal localization within the network have been the focus of attention,...
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Dynamic GPU power capping with online performance tracing for energy efficient GPU computing using DEPO tool
PublikacjaGPU accelerators have become essential to the recent advance in computational power of high- performance computing (HPC) systems. Current HPC systems’ reaching an approximately 20–30 mega-watt power demand has resulted in increasing CO2 emissions, energy costs and necessitate increasingly complex cooling systems. This is a very real challenge. To address this, new mechanisms of software power control could be employed. In this...
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Hybrid Expert System for Computer-Aided Design of Ship Thruster Subsystems
PublikacjaThe article presents an expert system supporting the design of ship's power subsystems, in particular the thruster subsystem. The proposed hybrid expert system uses the results of simulation tests as the additional source of knowledge. The results of system operation are collated in a report which can be used as part of ship design description. The work oriented on developing the expert system is the continuation of the research...
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The experimental identification of the dynamic coefficients of two hydrodynamic journal bearings operating at constant rotational speed and under nonlinear conditions.
PublikacjaHydrodynamic bearings are commonly used in ship propulsion systems. Typically, they are calculated using numerical or experimental methods. This paper presents an experimental study through which it has been possible to estimate 24 dynamic coefficients of two hydrodynamic slide bearings operating under nonlinear conditions. During the investigation, bearing mass coefficients are identified by means of a newly developed algorithm....
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Neural Architecture Search for Skin Lesion Classification
PublikacjaDeep neural networks have achieved great success in many domains. However, successful deployment of such systems is determined by proper manual selection of the neural architecture. This is a tedious and time-consuming process that requires expert knowledge. Different tasks need very different architectures to obtain satisfactory results. The group of methods called the neural architecture search (NAS) helps to find effective architecture...
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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...
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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....
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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...
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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...
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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...
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Proposal of a System Loss Model for Body Area Network in Passenger Ferry Environment
PublikacjaIn the paper, proposal of an empirical off-body system loss model for Body Area Networks working in a passenger ferry environment at 2.45 GHz has been presented. The measurements were carried out for dynamic scenarios in the discotheque passenger ferry environment. The general model formula consists of three components: mean system loss, attenuation resulting from the variable antenna position on the human body, and attenuation...
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Development of a tropical disease diagnosis system using artificial neural network and GIS
PublikacjaExpert systems for diagnosis of tropical diseases have been developed and implemented for over a decade with varying degrees of success. While the recent introduction of artificial neural networks has helped to improve the diagnosis accuracy of such systems, this aspect is still negatively affected by the number of supported diseases. A large number of supported diseases usually corresponds to a high number of overlapping symptoms,...
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Evaluation of a Small Inland Ferry’s Energy Requirements from the Acceleration Stage of Towing Tank Model Tests
PublikacjaComputing the power required to meet a ship’s operational needs is one of the most important tasks in naval design. The power required to propel a vessel is directly related to the resistance the hull experiences as it moves through the water. The conventional method of determining a ship’s resistance involves towing tank tests of ship models at a fixed speed; however, for short-range vessels, where constant speed is not the primary...
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Robust-adaptive dynamic programming-based time-delay control of autonomous ships under stochastic disturbances using an actor-critic learning algorithm
PublikacjaThis paper proposes a hybrid robust-adaptive learning-based control scheme based on Approximate Dynamic Programming (ADP) for the tracking control of autonomous ship maneuvering. We adopt a Time-Delay Control (TDC) approach, which is known as a simple, practical, model free and roughly robust strategy, combined with an Actor-Critic Approximate Dynamic Programming (ACADP) algorithm as an adaptive part in the proposed hybrid control...
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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....
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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...
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An ANN-Based Method for On-Load Tap Changer Control in LV Networks with a Large Share of Photovoltaics—Comparative Analysis
PublikacjaThe paper proposes a new local method of controlling the on-load tap changer (OLTC) of a transformer to mitigate negative voltage phenomena in low-voltage (LV) networks with a high penetration of photovoltaic (PV) installations. The essence of the method is the use of the load compensation (LC) function with settings determined via artificial neural network (ANN) algorithms. The proposed method was compared with other selected...
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An Analysis of the Performance of Lightweight CNNs in the Context of Object Detection on Mobile Phones
PublikacjaConvolutional Neural Networks (CNNs) are widely used in computer vision, which is now increasingly used in mobile phones. The problem is that smartphones do not have much processing power. Initially, CNNs focused solely on increasing accuracy. High-end computing devices are most often used in this type of research. The most popular application of lightweight CNN object detection is real-time image processing, which can be found...
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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...
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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...
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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...
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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...
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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...
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Cloud-based system for monitoring loads generated on the quay wall by ship propeller jets
PublikacjaThe paper presents a cloud-based system for monitoring loads generated on the quay wall by ship propeller jets during berthing and unberthing maneuvers. The system provides online measurements of the dynamic pressure generated loads by propellers and warns of exceeding the permissible jet velocities over the seabed protection along the berth. The system has a modular structure consisting of a network of pressure sensors, information...
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An Empirical System Loss Model for Body Area Networks in a Passenger Ferry Environment
PublikacjaThis paper presents a general empirical system loss model for estimating propagation loss in Body Area Networks in off-body communications at 2.45 GHz in a passenger ferry environment. The model is based on measurements, which were carried out in dynamic scenarios in the discotheque passenger ferry environment. The model consists of three components: mean system loss, attenuation resulting from the variable antenna position on...
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An Empirical System Loss Model for Body Area Networks in a Passenger Ferry Environment
PublikacjaThis paper presents a general empirical system loss model for estimating propagation loss in Body Area Networks in off-body communications at 2.45 GHz in a passenger ferry environment. The model is based on measurements, which were carried out in dynamic scenarios in the discotheque passenger ferry environment. The model consists of three components: mean system loss, attenuation resulting from the variable antenna position on...
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Characteristics of the Polarised Off-Body Channel in Indoor Environments
PublikacjaThis paper addresses the depolarisation effect in off-body Body Area Networks channels, based on measurements performed at 2.45 GHz in an indoor environment. Seven different scenarios, involving both static and dynamic users, were considered, taking a statistical perspective. The analysis of the cross-polarisation discrimination is performed, as well as the analysis of path loss in co- and cross-polarised channels. Results show...
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Characteristics of the polarised off-body channel in indoor environments
PublikacjaThis paper addresses the depolarisation effect in off-body body area networks channels, based on measurements performed at 2.45 GHz in an indoor environment. Seven different scenarios, involving both static and dynamic users, were considered, taking a statistical perspective. The analysis of the cross-polarisation discrimination is performed, as well as the analysis of path loss in co- and cross-polarised channels. Results show...
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Inteligentne systemy agentowe w systemach zdalnego nauczania
PublikacjaW pracy omówiono inteligentne systemy agentowe w systemach zdalnego nauczania. Po krótkim przedstawieniu ewolucji systemów zdalnego nauczania i ich wybranych zastosowań, scharakteryzowano inteligentne agenty edukacyjne. Omówiono wykorzystanie programowania genetycznego oraz algorytmów neuro-ewolucyjnych do implementacji oprogramowania tej klasy. Ponadto, nawiązano do modelu Map-Reduce, który efektywnie wspiera architekturę nowoczesnego...