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Search results for: machine voting
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Acceptor doping of barium cerate
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Optimal routing in a transportation network
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Resilient Routing in Communication Networks
PublicationThis important text/reference addresses the latest issues in end-to-end resilient routing in communication networks. The work highlights the main causes of failures of network nodes and links, and presents an overview of resilient routing mechanisms, covering issues related to the Future Internet (FI), wireless mesh networks (WMNs), and vehicular ad-hoc networks (VANETs). For each of these network architectures, a selection of...
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Taxonomy of Schemes for Resilient Routing
PublicationThis chapter provides a taxonomy of schemes for resilient routing followed by a discussion of their application to contemporary architectures of communication networks. In particular, a general classification of schemes for resilient routing is first presented followed by a description of the reference schemes for IP networks. The chapter in its later part focuses on the representative techniques of resilient routing for a multi-domain...
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Path Coloring and Routing in Graphs.
PublicationW rozdziale omówione zostały problemy kolorowania ścieżek i routingu w grafach. Podano podstawowe definicje związane z tymi problemami, znane wyniki wraz z dyskusją złożoności obliczeniowej dla grafów ogólnych i dla kilku podstawowych klas grafów oraz zastosowania.
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Machine learning techniques combined with dose profiles indicate radiation response biomarkers
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Machine Learning and data mining tools applied for databases of low number of records
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From the Dynamic Lattice Liquid Algorithm to the Dedicated Parallel Computer – mDLL Machine
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Evaluation of Machine Learning Methods for the Experimental Classification and Clustering of Higher Education Institutions
PublicationHigher education institutions have a big impact on the future of skills supplied on the labour market. It means that depending on the changes in labour market, higher education institutions are making changes to fields of study or adding new ones to fulfil the demand on labour market. The significant changes on labour market caused by digital transformation, resulted in new jobs and new skills. Because of the necessity of computer...
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Improving operating efficiency of a gas turboset via cooperation with an absorption refrigerating machine
PublicationThe analysis of increase of ambient air temperature entering the compressor on reduction in power output from the turbine and increase fuel use was conduced. For medium size gas turbine operates in winter and summer conditions elementary power and economical values was calculated. Conditions of the determination of turbine inlet air cooling solution (using thermal storage for reduce equipment size) are presented
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Overcoming “Big Data” Barriers in Machine Learning Techniques for the Real-Life Applications
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<title>Management system of ELHEP cluster machine for FEL photonics design</title>
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Detection of People Swimming in Water Reservoirs with the Use of Multimodal Imaging and Machine Learning
PublicationEvery year in many countries, there are fatal unintentional drownings in different water reservoirs like swimming pools, lakes, seas, or oceans. The existing threats of this type require creating a method that could automatically supervise such places to increase the safety of bathers. This work aimed to create methods and prototype solutions for detecting people bathing in water reservoirs using a multimodal imaging system and...
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Driver’s Condition Detection System Using Multimodal Imaging and Machine Learning Algorithms
PublicationTo this day, driver fatigue remains one of the most significant causes of road accidents. In this paper, a novel way of detecting and monitoring a driver’s physical state has been proposed. The goal of the system was to make use of multimodal imaging from RGB and thermal cameras working simultaneously to monitor the driver’s current condition. A custom dataset was created consisting of thermal and RGB video samples. Acquired data...
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Predicting emotion from color present in images and video excerpts by machine learning
PublicationThis work aims at predicting emotion based on the colors present in images and video excerpts using a machine-learning approach. The purpose of this paper is threefold: (a) to develop a machine-learning algorithm that classifies emotions based on the color present in an image, (b) to select the best-performing algorithm from the first phase and apply it to film excerpt emotion analysis based on colors, (c) to design an online survey...
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Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches
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Sensorless control of five-phase induction machine supplied by the VSI with output filter
PublicationIn this paper, a novel sensorless control structure based on multi-scalar variables is proposed. The tatic feedback control law is obtained by using the multi-scalar variables transformation, where the multi-scalar variables approach allows a full linearization of the nonlinear system. The control system could be described as “optimized” because of the minimized number of controllers. Furthermore, control system is divided into...
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Sensorless Multiscalar Control of Five-Phase Induction Machine with Inverter Output Filter
PublicationThe paper presents a complete solution for speed sensorless control system for five-phase induction motor with voltage inverter, LC filter and nonlinear control of combined fundamental and third harmonic flux distribution. The control principle, also known as multiscalar control, nonlinear control or natural variables control, is based on a use of properly selected scalar variables in control feedback to linearize controlled system....
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Application of Support Vector Machine for Determination of Impact of Traffic-Induced Vibrations on Buildings
PublicationThe aim of the article is to present an algorithm of Support Vector Machine created to forecast the impact of traffic-induced vibrations on residential buildings. The method is designed to classify the object into one of two classes. The classification into the first class means that there is no impact of vibrations on the building, while classification to the second class indicates the possible influence and suggests the execution...
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The application of a photopolymer material for the manufacture of machine elements using rapid prototyping techniques
PublicationThe paper discusses the application of polymer resin for 3D printing. The first section focuses on rapid prototyping technique and properties of the photopolymer, used as input material in the manufacture of machine components. Second part of the article was devoted to exemplary 3-D-printed elements for incorporation in machines. The article also contains detailed description of problems encountered in implementation of the selected...
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Application of sliding switching functions in backstepping based speed observer of induction machine
PublicationThe paper presents an analysis of the speed observer which is based on the backstepping and sliding mode approach. The speed observer structure is based on the extended mathematical model of an induction machine. The observer structure is based on the measured phase stator currents and transformed to ( αβ ) coordinate system. The stator voltage vector components are treated as known values. Additionally, such an observer structure...
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The methodology for determining of the value of cutting power for cross cutting on optimizing sawing machine
PublicationIn the article the methodology of forecasting the energy effects of the cross-cutting process using the classical method, which takes into account the specific cutting resistance, is presented. The values of cutting power for the cross-cutting process of two types of wood (softwood and hardwood) were forecasted for the optimizing sawing machine with using presented methodology. The cross-cutting process with high values of feed...
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DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors
PublicationWe present a physics-based machine learning approach to predict in vitro transcription factor binding affinities from structural and mechanical DNA properties directly derived from atomistic molecular dynamics simulations. The method is able to predict affinities obtained with techniques as different as uPBM, gcPBM and HT-SELEX with an excellent performance, much better than existing algorithms. Due to its nature, the method can...
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Machine-aided detection of SARS-CoV-2 from complete blood count
PublicationThe current gold standard for SARS-CoV-2 detection methods lacks the functionality to perform population screening. Complete blood count (CBC) tests are a cost-effective way to reach a wide range of people – e.g. according to the data of the Central Statistical Office of Poland from 2016, there are 3,000 blood diagnostic laboratories in Poland, and 46% of Polish people have at least one CBC test per year. In our work, we show...
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Slowly-closing valve behaviour during steam machine accelerated start-up
PublicationThe paper discusses the state of stress in a slowly-closing valve during accelerated start-up of a steam turbine. The valve is one of the first components affected by high temperature gradients and is a key element on which the power, efficiency and safety of the steam system depend. The authors calibrated the valve model based on experimental data and then performed extended Thermal-FSI analyses relative to experiment. The issue...
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Machine learning approach to packaging compatibility testing in the new product development process
PublicationThe paper compares the effectiveness of selected machine learning methods as modelling tools supporting the selection of a packaging type in new product development process. The main goal of the developed model is to reduce the risk of failure in compatibility tests which are preformed to ensure safety, durability, and efficacy of the finished product for the entire period of its shelf life and consumer use. This kind of testing...
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Machine learning-based seismic response and performance assessment of reinforced concrete buildings
PublicationComplexity and unpredictability nature of earthquakes makes them unique external loads that there is no unique formula used for the prediction of seismic responses. Hence, this research aims to implement the most well-known Machine Learning (ML) methods in Python software to propose a prediction model for seismic response and performance assessment of Reinforced Concrete Moment-Resisting Frames (RC MRFs). To prepare 92,400 data...
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Experimental tuning of AuAg nanoalloy plasmon resonances assisted by machine learning method
PublicationPlasmonic nanostructures based on AuAg nanoalloys were fabricated by thermal annealing of metallic films in an argon atmosphere. The nanoalloys were chosen because they can extend the wavelength range in which plasmon resonance occurs and thus allow the design of plasmonic platforms with the desired parameters. The influence of initial fabrication parameters and experimental conditions on the formation of nanostructures was investigated....
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Preeclampsia Risk Prediction Using Machine Learning Methods Trained on Synthetic Data
PublicationThis paper describes a research study that investigates the use of machine learning algorithms on synthetic data to classify the risk of developing preeclampsia by pregnant women. Synthetic datasets were generated based on parameter distributions from three real patient studies. Four models were compared: XGBoost, Support Vector Machine (SVM), Random Forest, and Explainable Boosting Machines (EBM). The study found that the XGBoost...
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Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy
PublicationBiochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine-learning-based forecasts,...
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RSS-Based DoA Estimation for ESPAR Antennas Using Support Vector Machine
PublicationIn this letter, it is shown how direction-of-arrival (DoA) estimation for electronically steerable parasitic array radiator (ESPAR) antennas, which are designed to be integrated within wireless sensor network nodes, can be improved by applying support vector classification approach to received signal strength (RSS) values recorded at an antenna's output port. The proposed method relies on ESPAR antenna's radiation patterns measured...
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Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning
PublicationThe 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...
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A Proposed Machine Learning Model for Forecasting Impact of Traffic-Induced Vibrations on Buildings
PublicationTraffic-induced vibrations may cause various damages to buildings located near the road, including cracking of plaster, cracks in load-bearing elements or even collapse of the whole structure. Measurements of vibrations of real buildings are costly and laborious. Therefore the aim of the research is to propose the original numerical algorithm which allows us to predict, with high probability, the nega-tive dynamic impact of traffic-induced...
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Genetic Algorithm Approach for Gains Selection of Induction Machine Extended Speed Observer
PublicationThe subject of this paper is gains selection of an extended induction machine speed observer. A high number of gains makes manual gains selection difficult and due to nonlinear equations of the observer, well-known methods of gains selection for linear systems cannot be applied. A method based on genetic algorithms has been proposed instead. Such an approach requires multiple fitness function calls; therefore, using a quality index...
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Nonadaptive estimation of the rotor speed in an adaptive full order observer of induction machine
PublicationThe article proposes a new method of reproducing the angular speed of the rotor of a cage induction machine designed for speed observers based on the adaptive method. In the proposed solution, the value of the angular speed of the rotor is not determined by the classical law of adaptation using the integrator only by an algebraic relationship. Theoretical considerations were confirmed by simulation and experimental tests.
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Dynamic variables limitation for backstepping control of induction machine and voltage source converter
PublicationDynamic variables limitation for backstepping control of induction machine and voltage source converter The paper presents the method of control of an induction squirrel-cage machine supplied by a voltage source converter. The presented idea is based on an innovative method of the voltage source converter control, consisting in direct joining of the motor control system with the voltage source rectifier control system. The combined...
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Influence of frame sawing machine´s kinematics on saw blade tooth wear.
PublicationW pracy przedstawiono wpływ kinematyki pilarki ramowej na zużycie ostrzy piłtrakowych.
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Analytical model of torsional vibrations of typical sawing machine main drive system
PublicationPrzedstawiono model fizyczny i matematyczny drgań skrętnych napędu głównego typowej pilarki tarczowej. Model tworzą elementy sztywne SES połączone między sobą za pośrednictwem elementów sprężysto - tłumiących EST w układzie szeregowym. W modelu matematycznym uwzględniono: właściwości dynamiczne silnika napędowego, wymiary piły tarczowej, cechy materiału obrabianego (wymiary, rodzaj drewna, wilgotność drewna) oraz właściwości dynamiczne...
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Raw data of AuAg nanoalloy plasmon resonances used for machine learning method
Open Research DataRaw data used for machine learning process. UV-vis measurements of AuAg alloyed nanostructures created from thin films. Plasmonic band position dependence on fabrication parameters. Small presentation reviewing achieved structures and their properties.
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SELECTED PROBLEMS OF MACHINE DYNAMICS (2024)
e-Learning CoursesThe course is devoted towards lectures assocuated with the novel issues of machine and structures dynamics. The following lectures will be given during the SPMD course: - introduction to selected problems of machine dynamics, - definition of the machine and structure working environment, - internal and external loads on machines and structures, - dynamics of machines and structures, - strength of machines and structures, - special...
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Microgrinding with single-disk lapping kinematics.
PublicationGrinding operations are carried out with a variety of tool-workpiece configurations. The selection of a grinding process for a particular application depends on part shape, part size, ease of fixture, requirements concerning the acceptable shape errors. It is evident that lapping is very effective in eliminating the waviness while surface grinding is not. Dual disk machines for the double face grinding with planetary kinematics...
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Bogdan Wiszniewski prof. dr hab. inż.
PeopleBogdan Wiszniewski graduated from Gdansk University of Technology in 1977 and was awarded an MSc in computer science and engineering with honours. In 1984 and 1998, respectively, has got his PhD and DSc. In 2006 was awarded a Professor title by the President of Poland. Lectured at the universities in Canada, US and UK. Principal investigator or coordinator in many national and international R&D projects with the significant...
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Determining the operational loads of the hybrid metalworking machines drive
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Determining efficient values of continuous technological machines parameters
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On the Dynamics of an Enhanced Coaxial Inertial Exciter for Vibratory Machines
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A review on analytical models of brushless permanent magnet machines
PublicationThis study provides an in-depth investigation of the use of analytical and numerical methods in analyzing electrical machines. Although numerical models such as the finite-element method (FEM) can handle complex geometries and saturation effects, they have significant computational burdens, are time-consuming, and are inflexible when it comes to changing machine geometries or input values. Analytical models based on magnetic equivalent...
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Feedback Control of Multiphase Induction Machines with Backstepping Technique
PublicationThe paper presents the control possibility of five phase induction machines. In the proposed solution the machine model vector form is not transformed to the (dq)-coordinate system, that is connected to rotor flux vector, but utilizes the stationary system ( αβ ). Moreover, the nonlinear model linearization is based on demonstrated nonlinear variables transformation for i-orthogonal ( αβ )(n) planes. By introducing the backstepping...
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Multiscalar Model Based Control Systems for AC Machines
PublicationContents of the Chapter: Nonlinear transformations and feedback linearization. Models of the squirrel cage induction machine: Vector model of the squirrel cage induction machine. Multiscalar models of the squirrel cage induction machine.Feedback linearization of multiscalar models of the induction motor.Models of the double fed induction machine: Vector model of the double fed induction machine. Multiscalar model of the...
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Scheduling with Complete Multipartite Incompatibility Graph on Parallel Machines
PublicationIn this paper we consider a problem of job scheduling on parallel machines with a presence of incompatibilities between jobs. The incompatibility relation can be modeled as a complete multipartite graph in which each edge denotes a pair of jobs that cannot be scheduled on the same machine. Our research stems from the works of Bodlaender, Jansen, and Woeginger (1994) and Bodlaender and Jansen (1993). In particular, we pursue the...
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Scheduling on Uniform and Unrelated Machines with Bipartite Incompatibility Graphs
PublicationThe problem of scheduling jobs on parallel machines under an incompatibility relation is considered in this paper. In this model, a binary relation between jobs is given and no two jobs that are in the relation can be scheduled on the same machine. We consider job scheduling under the incompatibility relation modeled by a bipartite graph, under the makespan optimality criterion, on uniform and unrelated machines. Unrelated machines...