Filtry
wszystkich: 2625
wybranych: 2118
-
Katalog
- Publikacje 2118 wyników po odfiltrowaniu
- Czasopisma 136 wyników po odfiltrowaniu
- Konferencje 22 wyników po odfiltrowaniu
- Wydawnictwa 1 wyników po odfiltrowaniu
- Osoby 172 wyników po odfiltrowaniu
- Projekty 7 wyników po odfiltrowaniu
- Kursy Online 73 wyników po odfiltrowaniu
- Wydarzenia 7 wyników po odfiltrowaniu
- Oferty 1 wyników po odfiltrowaniu
- Dane Badawcze 88 wyników po odfiltrowaniu
Filtry wybranego katalogu
wyświetlamy 1000 najlepszych wyników Pomoc
Wyniki wyszukiwania dla: multiphase machine
-
Detection of Cystic Fibrosis Symptoms Based on X-Ray Images Using Machine Learning- Pilot Study
Publikacja -
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...
-
Active Learning on Ensemble Machine-Learning Model to Retrofit Buildings Under Seismic Mainshock-Aftershock Sequence
PublikacjaThis research presents an efficient computational method for retrofitting of buildings by employing an active learning-based ensemble machine learning (AL-Ensemble ML) approach developed in OpenSees, Python and MATLAB. The results of the study shows that the AL-Ensemble ML model provides the most accurate estimations of interstory drift (ID) and residual interstory drift (RID) for steel structures using a dataset of 2-, to 9-story...
-
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...
-
Influence of geometry of iron poles on the cogging torque of a field control axial flux permanent magnet machine
Publikacja -
Quantitative Soil Characterization for Biochar–Cd Adsorption: Machine Learning Prediction Models for Cd Transformation and Immobilization
Publikacja -
Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach
Publikacja -
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...
-
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...
-
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...
-
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...
-
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...
-
Rapid Surrogate-Aided Multi-Criterial Optimization of Compact Microwave Passives Employing Machine Learning and ANNs
PublikacjaThis article introduces an innovative method for achieving low-cost and reliable multi-objective optimization (MO) of microwave passive circuits. The technique capitalizes on the attributes of surrogate models, specifically artificial neural networks (ANNs), and multi-resolution electromagnetic (EM) analysis. We integrate the search process into a machine learning (ML) framework, where each iteration produces multiple infill points...
-
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...
-
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,...
-
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....
-
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...
-
Determination of tributyltin in marine sediment.
PublikacjaPrzedstawiono wyniki badań międzynarodowego porównawczego, międzylaboratoryjnego oznaczania zawartości tributylocyny w osadzie morskim. Badania były organizowane przez Comite Consultatif pour la Quantite de Matiere (CCQM) pilot study P-18 international intercomparison i była to ich pierwsza część - badania pilotażowe. W badaniach uczestniczyło 11 laboratoriów. Uzyskano zadowalająco zgodne wyniki oznaczeń tributylocyny.
-
On organic coatings in marine applications
PublikacjaZnaczna liczba konstrukcji eksploatowanych w warunkach morskich ulega zniszczeniu wskutek zmęczeniowego oddziaływania cyklicznych naprężeń mechanicznych. Również w przypadku powłok organicznych chroniących wspomniane konstrukcje obserwuje się przedwczesną degradację zmęczeniową. W poniższym artykule autorzy przedstawiają metodologię oceny wpływu cyklicznych naprężeń mechanicznych na trwałość ochronnych układów powłokowych. Badaniu...
-
Organotin compounds in marine sediments
PublikacjaZwiązki cynoorganiczne są szeroko wykorzystywane w różnych sektorach gospodarki. Farby zawierające czynniki przeciwporostowe (biocydy), były wykorzystywane w ochronie przeciwkorozyjnej kadłubów statków i okrętów, są głównym źródłem emisji trójbutylo- i trójfenylocyny do środowiska morskiego.Międzynarodowa Organizacja Morska (IMO) wprowadziła w roku 2001 powszechny zakaz stosowania tych związków jako biocydów w powłokach malarskich....
-
Integrated Processing: Quality Assurance Procedure of the Surface Layer of Machine Parts during the Manufacturing Step "Diamond Smoothing"
Publikacja -
A pilot study to assess manufacturing processes using selected point measures of vibroacoustic signals generated on a multitasking machine
PublikacjaThe article presents the method for the evaluation of selected manufacturing processes using the analysis of vibration and sound signals. This method is based on the use of sensors installed outside the machining zone, allowing to be used quickly and reliably in real production conditions. The article contains a developed measurement methodology based on the specific location of microphones and vibration transducers mounted on...
-
The chemistry, properties and performance of flame-retardant rubber composites: Collecting, analyzing, categorizing, machine learning modeling, and visualizing
PublikacjaRubbers combine the flexibility with mechanical strength, supporting myriad applications, but suffer from inherent flammability. Formulation and production of flame-retardant rubber composites (FRRCs) have intensively been practiced over years, but not comprehensively reviewed. This necessity has outlined collecting, analyzing, screening, classifying, and interpreting the literature with the aim of classifying the FRRCs. We quantified...
-
Improving platelet‐RNA‐based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification
PublikacjaLiquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community...
-
Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls
PublikacjaEfficient seismic risk assessment aids decision-makers in formulating citywide risk mitigation plans, providing insights into building performance and retrofitting costs. The complexity of modeling, analysis, and post-processing of the results makes it hard to fast-track the seismic probabilities, and there is a need to optimize the computational time. This research addresses seismic probability and risk assessment of reinforced...
-
Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer
PublikacjaIn this study, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting (LightGBM) algorithms were used to model-predict the drying characteristics of banana slices with an indirect solar drier. The relationships between independent variables (temperature, moisture, product type, water flow rate, and mass of product) and dependent variables (energy consumption and size reduction) were established. For energy consumption,...
-
The Influence of Permanent Magnet Length and Magnet Type on Flux-control of Axial Flux Hybrid Excited Electrical Machine
Publikacja -
Molecular Simulations Using Boltzmann’s Thermally Activated Diffusion - Implementation on ARUZ – Massively-parallel FPGA-based Machine
Publikacja -
Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning
Publikacja -
Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
Publikacja -
A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection
PublikacjaComputational cost is an important consideration for memory encoding prediction models that use data from dozens of implanted electrodes. We propose a method to reduce computational expense by selecting a subset of all the electrodes to build the prediction model. The electrodes were selected based on their likelihood of measuring brain activity useful for predicting memory encoding better than chance (in terms of AUC). A logistic...
-
THE EFFECT OF WOOD DRYING METHOD ON THE GRANULARITY OF SAWDUST OBTAINED DURING THE SAWING PROCESS USING THE FRAME SAWING MACHINE
PublikacjaThe experimental results of the study focused on the effect of drying processes of warm air drying at the temperature of 6580°C and warm air-steam mixture drying at the temperature of 105°C of pine and beech wood to the size of sawdust grains created by cutting using RPW 15M frame saw is presented in the paper. Particle size analysis of dry sawdust was performed using two methods - screening method and optical method based on...
-
Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches
PublikacjaExamining the game-changing possibilities of explainable machine learning techniques, this study explores the fast-growing area of biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization of training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified and enhanced the forecasting of biochar output and composition from various biomass...
-
Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Network
PublikacjaDesigning microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on computationally expensive population-based methods, especially when exe-cuted with full-wave electromagnetic (EM) analysis to guarantee reliability. This paper introduces a novel and cost-effective...
-
Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
PublikacjaWetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral...
-
Hybrid Process Equipment: Improving the Efficiency of the Integrated Metalworking Machines Initial Designing
Publikacja -
An overview of torque meters and new devices development for condition monitoring of mining machines
Publikacja -
Dynamic Analysis of an Enhanced Multi-Frequency Inertial Exciter for Industrial Vibrating Machines
Publikacja -
1 kapitola: Status report on frame sawing machines for narrow-kerf sawing
PublikacjaW opracowaniu przedstawiono genezę pilarek ramowych oraz przecinania drewna cienkimi piłami. Omówiono ekonomiczne i ekologiczne zalety tego typu obróbki. Zaprezentowano kinematykę współczesnych pilarek dostępnych na rynkach światowych: Orbit, Mamuth oraz DSG Sonic. Szczegółowo opisano układ napędowy pilarek DTPX-30 i PRW15 z hybrydowym napędem głównym i całkowitym wyrównoważeniem dynamicznym układu.
-
A graph coloring approach to scheduling of multiprocessor tasks on dedicated machines with availability constraints
PublikacjaWe address a generalization of the classical 1- and 2-processor unit execution time scheduling problem on dedicated machines. In our chromatic model of scheduling machines have non-simultaneous availability times and tasks have arbitrary release times and due dates. Also, the versatility of our approach makes it possible to generalize all known classical criteria of optimality. Under these stipulations we show that the problem...
-
Makespan minimization of multi-slot just-in-time scheduling on single and parallel machines
PublikacjaArtykuł podejmuje problem szeregowania zadań przy założeniu podziału czasu na sloty jednakowej długości, gdzie każde z zadań ma ustaloną długość oraz czas jego zakończenia, który jest relatywny do końca slotu. Problem znalezienia uszeregowania polega na dokonaniu przydziału zadań do poszczególnych slotów, przy czym w ogólności długość zadania może wymuszać sytuację, w której zadańie jest realizowane nie tylko w slocie, w którym...
-
Cogging torque analysis based on energy approach in surface-mounted PM machines
Publikacja -
Scheduling of unit-length jobs with bipartite incompatibility graphs on four uniform machines
PublikacjaThe problem of scheduling n identical jobs on 4 uniform machines with speeds s1>=s2>=s3>=s4 is considered.The aim is to find a schedule with minimum possible length. We assume that jobs are subject to mutual exclusion constraints modeled by a bipartite incompatibility graph of degree delta. We show that the general problem is NP-hard even if s1=s2=s3. If, however, delta<5 and s1>12s2 s2=s3=s4, then the problem can be solved to...
-
Scheduling of identical jobs with bipartite incompatibility graphs on uniform machines. Computational experiments
PublikacjaWe consider the problem of scheduling unit-length jobs on three or four uniform parallel machines to minimize the schedule length or total completion time. We assume that the jobs are subject to some types of mutual exclusion constraints, modeled by a bipartite graph of a bounded degree. The edges of the graph correspond to the pairs of jobs that cannot be processed on the same machine. Although the problem is generally NP-hard,...
-
Scheduling of unit-length jobs with cubic incompatibility graphs on three uniform machines
PublikacjaWe consider the problem of scheduling n identical jobs on 3 uniform machines with speeds s1, s2, and s3 to minimize the schedule length. We assume that jobs are subject to some kind of mutual exclusion constraints, modeled by a cubic incompatibility graph. We how that if the graph is 2-chromatic then the problem can be solved in O(n^2) time. If the graph is 3-chromatic, the problem becomes NP-hard even if s1>s2=s3.
-
Maritime Communication and Sea Safety of the Future - Machnine-type 5G Communication Concept
PublikacjaThe article presents the concept of a system based on 5G network and M2M communication increasing maritime safety. Generally, the focus was on presenting a proposal for a hierarchical, hybrid, cooperative system with M2M communication coordinated with BAN networks. The possi-ble applications of M2M communication at sea were also presented.
-
Energy Loss Coefficients ki in a Displacement Pump and Hydraulic Motor used in Hydrostatic Drives
PublikacjaThe article aims at defining and analysing the energy loss coefficients in design solutions of rotating displacement machines, with a piston machine as an example. The energy losses observed in these machines include mechanical loss, volumetric loss, and pressure loss. The scale and relations between these losses in different machines depend on machine design and manufacturing quality, and on operating parameters. The operating...
-
Parametric optimization of hybrid metalworking machinery quality
Publikacja -
On a matching distance between rooted phylogenetic trees
PublikacjaThe Robinson–Foulds (RF) distance is the most popular method of evaluating the dissimilarity between phylogenetic trees. In this paper, we define and explore in detail properties of the Matching Cluster (MC) distance, which can be regarded as a refinement of the RF metric for rooted trees. Similarly to RF, MC operates on clusters of compared trees, but the distance evaluation is more complex. Using the graph theoretic approach...
-
Analysing media coverage of India's vaccine diplomacy
Publikacja