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Machine-Learning-Powered EM-Based Framework for Efficient and Reliable Design of Low Scattering Metasurfaces
PublikacjaPopularity of metasurfaces has been continuously growing due to their attractive properties including the ability to effectively manipulate electromagnetic (EM) waves. Metasurfaces comprise optimized geometries of unit cells arranged as a periodic lattice to obtain a desired EM response. One of their emerging application areas is the stealth technology, in particular, realization of radar cross section (RCS) reduction. Despite...
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Integrating Experience-Based Knowledge Representation and Machine Learning for Efficient Virtual Engineering Object Performance
PublikacjaMachine learning and Artificial Intelligence have grown significant attention from industry and academia during the past decade. The key reason behind interest is such technologies capabilities to revolutionize human life since they seamlessly integrate classical networks, networked objects and people to create more efficient environments. In this paper, the Knowledge Representation technique of Set of Experience...
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Decisional DNA (DDNA) Based Machine Monitoring and Total Productive Maintenance in Industry 4.0 Framework
PublikacjaThe entire manufacturing spectrum is transforming with the advent of Industry 4.0. The features of Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) were utilized for developing Virtual Engineering Objects (VEO), Virtual Engineering Process (VEP) and Virtual Engineering Factory (VEF), which in turn facilitate the creation of smart factories. In this study, DDNA based Machine Monitoring for Total Maintenance...
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Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance
PublikacjaIdentification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing event-related intracranial EEG (iEEG) responses from 115 patients performing a free recall verbal memory task. Our approach employed new interpretable...
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A novel hybrid adaptive framework for support vector machine-based reliability analysis: A comparative study
PublikacjaThis study presents an innovative hybrid Adaptive Support Vector Machine - Monte Carlo Simulation (ASVM-MCS) framework for reliability analysis in complex engineering structures. These structures often involve highly nonlinear implicit functions, making traditional gradient-based first or second order reliability algorithms and Monte Carlo Simulation (MCS) time-consuming. The application of surrogate models has proven effective...
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Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers
PublikacjaIn recent years, fiber-reinforced polymers (FRP) in reinforced concrete (RC) members have gained significant attention due to their exceptional properties, including lightweight construction, high specific strength, and stiffness. These attributes have found application in structures, infrastructures, wind power equipment, and various advanced civil products. However, the production process and the extensive testing required for...
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Edyta Gołąb-Andrzejak dr hab.
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Generative Process Planning with Reasoning based on Geometrical Product Specification
PublikacjaThe focus of this paper is on computer aided process planning for parts manufacture in systems of definite process capabilities, involving the use of multi-axis machining centers for parts shaping and grinding machines for finishing. It presents in particular a decision making scheme for setup determination as a part of generative process planning. The planning procedurę consists of two stages. The first stage is associated with...
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Ireneusz Czarnowski Prof.
OsobyIRENEUSZ CZARNOWSKI is a graduate of the Faculty of Electrical Engineering at Gdynia Maritime University. He gained a doctoral degree in the field of computer science at Poznan University of Technology and a postdoctoral degree in the field of computer science at Wroclaw University of Science and Technology. Since 1998 is associated with Gdynia Maritime University, currently is a professor of computer science in the Department...
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In silico studies and β-cyclodextrin mediated neutral synthesis of 4-oxo-4,5,6,7-tetrahydroindoles of potential biological interest
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Magnesiate-Utilized/Benzyne-Mediated Approach to Indenopyridones from 2-Pyridones: An Attempt To Synthesize the Indenopyridine Core of Haouamine
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Correction to “Emerging Anticancer Activity of Candidal Glucosamine-6-Phosphate Synthase Inhibitors upon Nanoparticle-Mediated Delivery”
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The Effects of Acute and Chronic Haloperidol Treatment on Dopamine Release Mediated by the Medial Forebrain Bundle in the Striatum and Nucleus Accumbens
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ClpP/ClpX-mediated degradation of the bacteriophage λ O protein and regulation of λ phage and λ plasmid replication
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Comparison of siRNA-mediated silencing of glycosaminoglycan synthesis genes and enzyme replacement therapy for mucopolysaccharidosis in cell culture studies.
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Diphosphination of CO2 and CS2 mediated by frustrated Lewis pairs - catalytic route to phosphanyl derivatives of formic and dithioformic acid
PublikacjaThe first example of CO2 diphosphination is described. Reactions of unsymmetrical diphosphanes with CE2 (E = O, S) catalyzed by BPh3 insert a CE2 molecule into the P-P bond with formation of the products of the general formula R2P-E-C(=E)-PR2. The obtained CO2 adducts arise from synergistic interaction of diphosphane and borane with CO2 molecule.
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A method of Functional Test interval selection with regards to Machinery and Economical aspects
PublikacjaThis paper discusses the problem of choosing the optimal frequency of functional test, including the reliability calculations and production efficiency, but also the effect of company risk management. The proof test as a part of the functional test interval is well described for the process industry. Unfortunately, this situation is not the case for the machinery safety functions with low demand mode. Afterwards, it is presented...
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Bogdan Wiszniewski prof. dr hab. inż.
OsobyBogdan Wiszniewski ukończył studia na Politechnice Gdańskiej w 1977 r. uzyskując tytuł zawodowy magistra inżyniera elektroniki, specjalności automatyka i informatyka. W 1984 r. uzyskał stopień naukowy doktora nauk technicznych, w 1998 r. doktora habilitowanego, a w 2006 r. tytuł profesora. Wykładał na uniwersytetach w Kanadzie, Stanach Zjednoczonych i Wielkiej Brytanii. Był głównym wykonawcą lub koordynatorem kilkunastu krajowych...
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Effect of the Relative Position of the Face Milling Tool towards the Workpiece on Machined Surface Roughness and Milling Dynamics
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Optimization of the femtosecond laser impulse for excitation and the spin-orbit-mediated dissociation in the NaRb molecule
Dane BadawczeHigh accuracy ab initio potential energy curves (1tSigma+, 2sSigma+, 1tPi), electronic transition dipole moment function (1tSigma+ - 1tPi), and spin-orbit coupling (2sSigma+ - 1tPi) have been calculated for the NaRb molecule. The time-dependent excitation and dissociation processes in the polar alkali diatomic NaRb molecule and the quantum properties...
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Non-enzymatic glutathione-mediated conjugation of unsymmetrical bisacridine C-2028 with anticancer activity
Dane BadawczeThe presented data complement the studies on the interplay between C-2028 (anticancer-active unsymmetrical bisacridine) and the glutathione S-transferase/glutathione (GST/GSH) system.
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Analysis of specific cutting resistance while cutting frozen pine blocks with narrow-kerf stellite tipped saws on frame sawing machines.
PublikacjaPrzedstawiono wyniki analizy oporów skrawania drewna zmrożonego podczas przecierania pryzm sosnowych na pilarce ramowej za pomocą cienkich pił. Badania prowadzono dla temperatur drewna -5 st. C, -20 st. C oraz dla porównania w temperaturze +18 st. C. Wilgotność drewna wynosiła 30%. Zaobserwowano znaczacy wzrost oporów skrawania wraz ze spadkiem jego temperatury.
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Hydrostatic drives as safe and energy saving machines. The drive investigation method compatible diagram of power increase opposite to the direction of power flow
PublikacjaProjektanci i producenci napędu hydrostatycznego nie dysponują możliwością dokładnego określania jego sprawności energetycznej zmieniającej się szeroko w polu pracy napędzanego urządzenia a więc w pełnym zakresie zmiany prędkości i obciążenia silnika hydraulicznego oraz lepkości zastosowanego czynnika roboczego. Dotyczy to zarówno określania strat i sprawności maszyn wyporowych (pompy i silnika hydraulicznego) zastosowanych w układzie...
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Journal of Machinery Manufacture and Reliability
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Machines, Computations and Universality (Universal Machines and Computations)
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Sylwester Kaczmarek dr hab. inż.
OsobySylwester Kaczmarek ukończył studia w 1972 roku jako mgr inż. Elektroniki, a doktorat i habilitację uzyskał z technik komutacyjnych i inżynierii ruchu telekomunikacyjnego w 1981 i 1994 roku na Politechnice Gdańskiej. Jego zainteresowania badawcze ukierunkowane są na: sieci IP QoS, sieci GMPLS, sieci SDN, komutację, ruting QoS, inżynierię ruchu telekomunikacyjnego, usługi multimedialne i jakość usług. Aktualnie jego badania skupiają...
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Machine Graphics and Vision
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Influence of geometry of iron poles on the cogging torque of a field control axial flux permanent magnet machine
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Detection of Cystic Fibrosis Symptoms Based on X-Ray Images Using Machine Learning- Pilot Study
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Application of fiber optic sensors using Machine Learning algorithms for temperature measurement of lithium-ion batteries
PublikacjaOptical fiber sensorsusing low-coherence interferometry require processing ofthe output spectrum or interferogramto quickly and accurately determine the instantaneous value of the measured quantity, such as temperature.Methods based on machine learning are a good candidate for this application. The application of four such methods in an optical fiber temperature sensoris demonstrated.Using aZnO-coated...
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Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods
PublikacjaNowadays, Buckling-Restrained Brace Frames (BRBFs) have been used as lateral force-resisting systems for low-, to mid-rise buildings. Residual Interstory Drift (RID) of BRBFs plays a key role in deciding to retrofit buildings after seismic excitation; however, existing formulas have limitations and cannot effectively help civil engineers, e.g., FEMA P-58, which is a conservative estimation method. Therefore, there is a need to...
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Machine-Learning-Based Global Optimization of Microwave Passives with Variable-Fidelity EM Models and Response Features
PublikacjaMaximizing microwave passive component performance demands precise parameter tuning, particularly as modern circuits grow increasingly intricate. Yet, achieving this often requires a comprehensive approach due to their complex geometries and miniaturized structures. However, the computational burden of optimizing these components via full-wave electromagnetic (EM) simulations is substantial. EM analysis remains crucial for circuit...
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Efficient Calibration of Cost-Efficient Particulate Matter Sensors Using Machine Learning and Time-Series Alignment
PublikacjaAtmospheric particulate matter (PM) poses a significant threat to human health, infiltrating the lungs and brain and leading to severe issues such as heart and lung diseases, cancer, and premature death. The main sources of PM pollution are vehicular and industrial emissions, construction and agricultural activities, and natural phenomena such as wildfires. Research underscores the absence of a safe threshold for particulate exposure,...
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Development of an emulation platform for synchronous machine power generation system using a nonlinear functional level model
PublikacjaThe article presents the Power Hardware in the Loop (PHIL) approach for an autonomous power system analysis based on the synchronous generator model incorporating magnetic saturation effects. The model was prepared in the MATLAB/Simulink environment and then compiled into the C language for the PHIL platform implementation. The 150 kVA bidirectional DC/AC commercial-grade converter was used to emulate the synchronous generator....
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Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams
PublikacjaOne of the main issues associated with steel fiber–reinforced concrete (SFRC) beams is the ability to anticipate their flexural response. With a comprehensive grid search, several stacked models (i.e., chained, parallel) consisting of various machine learning (ML) algorithms and artificial neural networks (ANNs) were developed to predict the flexural response of SFRC beams. The flexural performance of SFRC beams under bending was...
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Cost-Efficient Measurement Platform and Machine-Learning-Based Sensor Calibration for Precise NO2 Pollution Monitoring
PublikacjaAir quality significantly impacts human health, the environment, and the economy. Precise real-time monitoring of air pollution is crucial for managing associated risks and developing appropriate short- and long-term measures. Nitrogen dioxide (NO2) stands as a common pollutant, with elevated levels posing risks to the human respiratory tract, exacerbating respiratory infections and asthma, and potentially leading to chronic lung...
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Ecology In Tribology: Selected Problems of Eliminating Natural Oil-Based Lubricants from Machine Friction Couples
PublikacjaThe elimination of mineral oil-based lubricants from machines has multiple beneficial effects on the natural environment. Firstly – these lubricants are a direct threat to the environment in the event of leaks; secondly – their elimination reduces the demand for crude oil from which they are obtained. In addition, in many cases, e.g. when replacing traditional lubricants with water, friction losses in the bearings can also be reduced...
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An algorithm for selecting a machine learning method for predicting nitrous oxide emissions in municipal wastewater treatment plants
PublikacjaThis study presents an advanced algorithm for selecting machine learning (ML) models for nitrous oxide (N2O) emission prediction in wastewater treatment plants (WWTPs) employing the activated sludge process. The examined ML models comprised multivariate adaptive regression spline (MARS), support vector machines (SVM), and extreme gradient boosting (XGboost). The study explores the concept that involves new criteria to select the...
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Control of induction machine supplied by a current source inverter using the multi-scalar transformation and backstepping approach
PublikacjaThe paper describes the voltage control technique of squire-cage induction machines supplied by a current source inverter. The control system is based on new transformation of the electric drive system (machine and inverter) state variables to the multi-scalar variables form. The backstepping approach is used to obtain the feedback control law. The control system contains the structure of the observer...
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Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods
PublikacjaOne of the important factors determining the biochemical processes in bioreactors is the quality of the wastewater inflow to the wastewater treatment plant (WWTP). Information on the quality of wastewater, sufficiently in advance, makes it possible to properly select bioreactor settings to obtain optimal process conditions. This paper presents the use of classification models to predict the variability of wastewater quality at...
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Chip suction system in circular sawing machine: empirical research and computational fluid dynamics numerical simulations
PublikacjaThe experimental analysis of the wood chip removing system during its redesigning in the existing sliding table circular saw and computational fluid dynamic (CFD) numerical simulations of the air flow process is presented in the paper. The attention was focused on the extraction hood and the bottom shelter of the actual existing system. The main aim was to perform experimental research on the pressure distribution inside the...
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Prediction of energy consumption and evaluation of affecting factors in a full-scale WWTP using a machine learning approach
PublikacjaTreatment of municipal wastewater to meet the stringent effluent quality standards is an energy-intensive process and the main contributor to the costs of wastewater treatment plants (WWTPs). Analysis and prediction of energy consumption (EC) are essential in designing and operating sustainable energy-saving WWTPs. In this study, the effect of wastewater, hydraulic, and climate-based parameters on the daily consumption of EC by...
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Control Strategy of a Five-Phase Induction Machine Supplied by the Current Source Inverter With the Third Harmonic Injection
PublikacjaIn the five-phase induction machine (IM), it is possible to better use the electromagnetic circuit than in the three-phase IM. This requires the use of an adequate converter system which will be supplied by an induction machine. The electric drive system described, in this article, includes the five-phase induction machine supplied by the current source inverter (CSI). The proposed novelty—not presented previously—is the control...
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Prediction of fracture toughness in fibre-reinforced concrete, mortar, and rocks using various Machine learning techniques
PublikacjaMachine Learning (ML) method is widely used in engineering applications such as fracture mechanics. In this study, twenty different ML algorithms were employed and compared for the prediction of the fracture toughness and fracture load in modes I, II, and mixed-mode (I-II) of various materials, including fibre-reinforced concrete, cement mortar, sandstone, white travertine, marble, and granite. A set of 401 specimens of “Brazilian...
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Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach
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Quantitative Soil Characterization for Biochar–Cd Adsorption: Machine Learning Prediction Models for Cd Transformation and Immobilization
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Michał Mazur dr inż.
OsobyAktualne zainteresowania inżynieria mechaniczna, robotyka, drgania mechaniczne, analiza modalna, sterowanie, systemy czasu rzeczywistego Wybrane publikacje Kaliński K., Galewski M., Mazur M., Chodnicki M, 2017, Modelling and Simulation Of A New Variable Stiffness Holder for Milling Of Flexible Details, Polish Maritime Research, vol 24, ss. 115-124 Kaliński K. J., Mazur M.: Optimal control at energy performance index of the mobile...
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Detection of specific UL49 sequences of Marek's disease virus CVI988/Rispens strain using loop-mediated isothermal amplification
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Stromal-Epithelial Cross-Talk in the Intestinal Stem Cell Niche is Mediated by a Regulatory Network that Involves TGF-Beta and CTGF
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Microemulsion-Mediated Synthesis and Properties of Uniform Ln:CaWO4(Ln = Eu, Dy) Nanophosphors with Multicolor Luminescence for Optical and CT Imaging
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