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Search results for: TIME SERIES CLASSIFICATIONLEARNING SYSTEMSCAPSULE NETWORKSDATA MININGMULTI-HEAD CONVOLUTIONAL NEURAL NETWORKSSIGNAL PROCESSING
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Improving Accuracy of Respiratory Rate Estimation by Restoring High Resolution Features With Transformers and Recursive Convolutional Models
PublicationNon-contact evaluation of vital signs has been becoming increasingly important, especially in light of the COVID- 19 pandemic, which is causing the whole world to examine people’s interactions in public places at a scale never seen before. However, evaluating one’s vital signs can be a relatively complex procedure, which requires both time and physical contact between examiner and examinee. These re- quirements limit the number...
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Multi-layered tissue head phantoms for noninvasive optical diagnostics
PublicationExtensive research in the area of optical sensing for medical diagnostics requires development of tissue phantoms with optical properties similar to those of living human tissues. Development and improvement of in vivo optical measurement systems requires the use of stable tissue phantoms with known characteristics, which are mainly used for calibration of such systems and testing their performance over time. Optical and mechanical...
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Applying artificial neural networks for modelling ship speed and fuel consumption
PublicationThis paper deals with modelling ship speed and fuel consumption using artificial neural network (ANN) techniques. These tools allowed us to develop ANN models that can be used for predicting both the fuel consumption and the travel time to the destination for commanded outputs (the ship driveline shaft speed and the propeller pitch) selected by the ship operator. In these cases, due to variable environmental conditions, making...
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Image Processing Techniques for Distributed Grid Applications
PublicationParallel approaches to 2D and 3D convolution processing of series of images have been presented. A distributed, practically oriented, 2D spatial convolution scheme has been elaborated and extended into the temporal domain. Complexity of the scheme has been determined and analysed with respect to coefficients in convolution kernels. Possibilities of parallelisation of the convolution operations have been analysed and the results...
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GIS for processing multidimensional marine data in SAAS model
PublicationGeographic Information Systems (GIS) have always been a useful tool for visualization and processing of geospatial data. However, their capabilities of analysis non-standard information such as hydroacoustic soundings has thus far been very limited. This paper proposes a general-purpose GIS which uses techniques such as OLAP, WCS and WCPS for processing of multidimensional spatio-temporal data. The versatility of the GIS is exemplified...
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Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform
PublicationTraffic monitoring from closed-circuit television (CCTV) cameras on embedded systems is the subject of the performed experiments. Solving this problem encounters difficulties related to the hardware limitations, and possible camera placement in various positions which affects the system performance. To satisfy the hardware requirements, vehicle detection is performed using a lightweight Convolutional Neural Network (CNN), named...
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Neural Networks Based on Ultrafast Time-Delayed Effects in Exciton Polaritons
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Characterizing the Scalability of Graph Convolutional Networks on Intel® PIUMA
PublicationLarge-scale Graph Convolutional Network (GCN) inference on traditional CPU/GPU systems is challenging due to a large memory footprint, sparse computational patterns, and irregular memory accesses with poor locality. Intel’s Programmable Integrated Unffied Memory Architecture (PIUMA) is designed to address these challenges for graph analytics. In this paper, a detailed characterization of GCNs is presented using the Open-Graph Benchmark...
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Deep neural networks for data analysis
e-Learning CoursesThe aim of the course is to familiarize students with the methods of deep learning for advanced data analysis. Typical areas of application of these types of methods include: image classification, speech recognition and natural language understanding. Celem przedmiotu jest zapoznanie studentów z metodami głębokiego uczenia maszynowego na potrzeby zaawansowanej analizy danych. Do typowych obszarów zastosowań tego typu metod należą:...
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Neural network training with limited precision and asymmetric exponent
PublicationAlong with an extremely increasing number of mobile devices, sensors and other smart utilities, an unprecedented growth of data can be observed in today’s world. In order to address multiple challenges facing the big data domain, machine learning techniques are often leveraged for data analysis, filtering and classification. Wide usage of artificial intelligence with large amounts of data creates growing demand not only for storage...
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A Simple Neural Network for Collision Detection of Collaborative Robots
PublicationDue to the epidemic threat, more and more companies decide to automate their production lines. Given the lack of adequate security or space, in most cases, such companies cannot use classic production robots. The solution to this problem is the use of collaborative robots (cobots). However, the required equipment (force sensors) or alternative methods of detecting a threat to humans are usually quite expensive. The article presents...
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Nanocrystalline diamond sheets as protective coatings for fiber-optic measurement head
PublicationFiber-optic sensors find numerous applications in science and industry, but their full potential is limited because of the risk of damaging the measurement head, in particular, due to the vulnerability of unprotected tips of the fiber to mechanical damage and aggressive chemical agents. In this paper, we report the first use of a new nanocrystalline diamond structure in a fiber-optic measurement head as a protective coating of...
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Testing heart rate asymmetry in long, nonstationary 24 hour RR-interval time series
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Neural network agents trained by declarative programming tutors
PublicationThis paper presents an experimental study on the development of a neural network-based agent, trained using data generated using declarative programming. The focus of the study is the application of various agents to solve the classic logic task – The Wumpus World. The paper evaluates the effectiveness of neural-based agents across different map configurations, offering a comparative analysis to underline the strengths and limitations...
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Charakterystyka pęknięć w szynach typu head check
PublicationZmęczenie kontaktowe powierzchni tocznej szyny (RCF) jest jedną z waznych przyczyn jej uszkodzenia i ma obecnie duże znaczenie w utrzymaniu nawierzchni kolejowej w odpowiednim stanie niezawodności oraz wpływa na trwałość szyn. W wyniku dużych oddziaływań dynamicznych na powierzchni szyny powstają poziome małe pęknięcia, które w dalszej fazie rozwoju przechodzą pionowo przez krawędź główki szyny powodując powstawanie mikroszczelin....
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OBTAINING FLUID FLOW PATTERN FOR TURBINE STAGE WITH NEURAL MODEL.
PublicationIn the paper possibility of applying neural model to obtaining patterns of proper operation for fluid flow in turbine stage for fluid-flow diagnostics is discussed. Main differences between Computational Fluid Dynamics (CFD) solvers and neural model is given, also limitations and advantages of both are considered. Time of calculations of both methods was given, also possibilities of shortening that time with preserving the accuracy...
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Impact of Propeller Emergence on Hull, Propeller, Engine, and Fuel Consumption Performance in Regular Head Waves
PublicationIn this study, the impact of propeller emergence on the performance of a ship (speed), propeller (thrust, torque, and RPM), a diesel engine (torque and RPM) and fuel consumption are analysed under severe sea conditions. The goal is to describe the variation in the system variables and fuel consumption rather than analysing the motion of the ship or the phenomenon of propeller ventilation in itself. A mathematical model of the...
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Activation maps of convolutional neural networks as a tool for brain degeneration tracking in early diagnosis of dementia in Parkinson's disease based on magnetic resonance imaging
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Towards neural knowledge DNA
PublicationIn this paper, we propose the Neural Knowledge DNA, a framework that tailors the ideas underlying the success of neural networks to the scope of knowledge representation. Knowledge representation is a fundamental field that dedicates to representing information about the world in a form that computer systems can utilize to solve complex tasks. The proposed Neural Knowledge DNA is designed to support discovering, storing, reusing,...
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Musical Instrument Tagging Using Data Augmentation and Effective Noisy Data Processing
PublicationDeveloping signal processing methods to extract information automatically has potential in several applications, for example searching for multimedia based on its audio content, making context-aware mobile applications (e.g., tuning apps), or pre-processing for an automatic mixing system. However, the last-mentioned application needs a significant amount of research to reliably recognize real musical instruments in recordings....
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Diamond protection for reusable ZnO coated fiber-optic measurement head in optoelectrochemical investigation of bisphenol A
PublicationDue to the global problem with plastic contaminating the environment, with bisphenol A (BPA) being one of the highest demand, effective monitoring and purification of the pollutants are required. The electrochemical methods constitute a good solution but, due to polymerization of electrochemical oxidation bisphenol A products and their adsorption to the surfaces, measurement head elements are clogged by the formed film. In this...
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Selected Technical Issues of Deep Neural Networks for Image Classification Purposes
PublicationIn recent years, deep learning and especially Deep Neural Networks (DNN) have obtained amazing performance on a variety of problems, in particular in classification or pattern recognition. Among many kinds of DNNs, the Convolutional Neural Networks (CNN) are most commonly used. However, due to their complexity, there are many problems related but not limited to optimizing network parameters, avoiding overfitting and ensuring good...
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Measurements and Visualization of Sound Intensity Around the Human Head in Free Field Using Acoustic Vector Sensor
PublicationThis paper presents measurements and visualization of sound intensity around the human head simulator in a free field. A Cartesian robot, applied for precise positioning of the acoustic vector sensor, was used to measure sound intensity. Measurements were performed in a free field using a head and torso simulator and the setup consisting of four different loudspeaker configurations. The acoustic vector sensor was positioned around...
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Neural Architecture Search for Skin Lesion Classification
PublicationDeep 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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A FPTAS for minimizing total completion time in a single machine time-dependent scheduling problem
PublicationIn this paper a single machine time-dependent scheduling problem with total completion time criterion is considered. There are given n jobs J1,…,Jn and the processing time pi of the ith job is given by pi=a+bisi, where si is the starting time of the ith job (i=1,…,n),bi is its deterioration rate and a is the common base processing time. If all jobs have deterioration rates different and not smaller than a certain constant u>0,...
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SIGNAL PROCESSING
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An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks
PublicationIn this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent...
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Influence of pulse waves on the transmission of near-infrared radiation in outer-head tissue layers.
PublicationIn this study, we investigate the effect of pulse waves on the transmission of near-infrared radiation in the outer tissue layers of the human head. This effect is important in using optical radiation to monitor brain conditions based on measuring the transmission changes in the near-infrared radiation between the source and the detector, placed on the surface of the scalp. This is because the signal related to the changes in the...
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Experimental and Numerical Study on Pounding of Structures in Series
PublicationPounding between structures in series during earthquakes may cause serious damage in the structural elements. The aim of this paper is to show the results of an experimental and numerical study that is focused on pounding between more than two structures which may be described as “structures in series”. In this study, the shaking table experiments, as well as the numerical analyses, were performed using three tower models including...
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INFLUENCE OF DATA NORMALIZATION ON THE EFFECTIVENESS OF NEURAL NETWORKS APPLIED TO CLASSIFICATION OF PAVEMENT CONDITIONS – CASE STUDY
PublicationIn recent years automatic classification employing machine learning seems to be in high demand for tele-informatic-based solutions. An example of such solutions are intelligent transportation systems (ITS), in which various factors are taken into account. The subject of the study presented is the impact of data pre-processing and normalization on the accuracy and training effectiveness of artificial neural networks in the case...
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Analysing and processing of geotagged social media
PublicationThe use of location based data analysing tools is an important part of geomarketing strategies among entrepreneurs. One of the key elements of interest is social media data shared by the users. This data is analysed both for its content and its location information, the results help to identify trends represented in the researched regions. In order to verify the possibilities of analysing and processing of geotagged social media...
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Investigating the Ischaemic Phase of Skin NADH Fluorescence Dynamics in Recently Diagnosed Primary Hypertension: A Time Series Analysis
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Volterra series usefulness in modelling of the time-domain cross-talk phenomena in coupled microstrip lines with nonlinear termination
PublicationW pracy przedyskutowano możliwość wykorzystania szeregów Volterry do analizy zjawiska przesłuchu w sprzężonych liniach mikropaskowych z nieliniowym obciążeniem. Apracowano algorytm metody, zaś uzyskane wyniki numeryczne zweryfikowano poprzez porównania z wynikami badań eksperymentalnych linii obciążonych w torze transmisyjnym diodą Schottky'ego.
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Deep neural networks for human pose estimation from a very low resolution depth image
PublicationThe work presented in the paper is dedicated to determining and evaluating the most efficient neural network architecture applied as a multiple regression network localizing human body joints in 3D space based on a single low resolution depth image. The main challenge was to deal with a noisy and coarse representation of the human body, as observed by a depth sensor from a large distance, and to achieve high localization precision....
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Photoelectron spectroscopy of a series of acetate and propionate esters
PublicationThe electronic state and photoionization spectroscopy of a series of acetate esters: methyl acetate, isopropyl acetate, butyl acetate and pentyl acetate as well as two propionates: methyl propionate and ethyl propionate, have been determined using vacuum-ultraviolet photoelectron spectroscopy. These experimental investigations are complemented by ab initio calculations. The measured first adiabatic and vertical ionization energies...
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Digital Signal Processing
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Bees Detection on Images: Study of Different Color Models for Neural Networks
PublicationThis paper presents an approach to bee detection in video streams using a neural network classifier. We describe the motivation for our research and the methodology of data acquisition. The main contribution to this work is a comparison of different color models used as an input format for a feedforward convolutional architecture applied to bee detection. The detection process has is based on a neural binary classifier that classifies...
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Neural Modelling of Steam Turbine Control Stage
PublicationThe paper describes possibility of steam turbine control stage neural model creation. It is of great importance because wider application of green energy causes severe conditions for control of energy generation systems operation Results of chosen steam turbine of 200 MW power measurements are applied as an example showing way of neural model creation. They serve as training and testing data of such neural model. Relatively simple...
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Pervaporation in food processing
PublicationThis chapter is about pervaporation in food processing
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Elimination of Impulsive Disturbances From Archive Audio Signals Using Bidirectional Processing
PublicationIn this application-oriented paper we consider the problem of elimination of impulsive disturbances, such as clicks, pops and record scratches, from archive audio recordings. The proposed approach is based on bidirectional processing—noise pulses are localized by combining the results of forward-time and backward-time signal analysis. Based on the results of specially designed empirical tests (rather than on the results of theoretical analysis),...
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Windows in time
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TIME & SOCIETY
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Influence of genetic background and oxidative stress response on risk of mandibular osteoradionecrosis after radiotherapy of head and neck cancer
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CHEMICAL ENGINEERING AND PROCESSING
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Real-time Operating Systems - Seminar 2023/4
e-Learning CoursesPage to support seminar clases of Real-time Operating Systems
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TOXIC GASES IDENTIFICATION USING SINGLE ELECTROCATALYTIC SENSOR RESPONSES AND ARTIFICIAL NEURAL NETWORK
PublicationThe need for precise detection of toxic gases drives development of new gas sensors structures and methods of processing the output signals from the sensors. In literature, artificial neural networks are considered as one of the most effective tool for the analysis of gas sensors or sensors arrays responses. In this paper a method of toxic gas components identification using a electrocatalytic gas sensor as a detector and an artificial...
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Data Warehouses - Part-time studies - 2022/2023
e-Learning CoursesThe curse is led for part-time studies, on the first semester of postgraduate studies.
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Data Warehouses - Part-time studies - 2023/2024
e-Learning CoursesThe curse is led for part-time studies, on the first semester of postgraduate studies.
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Image Processing in Robotics
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Image Processing in Robotics
e-Learning Courses