Search
Search results for: REGRESSION
-
Application of Generalized Regression Neural Network and Gaussian Process Regression for Modelling Hybrid Micro-Electric Discharge Machining: A Comparative Study
Publication -
The Modifiable Areal Unit Problem – Analysis of Correlation and Regression
Publication -
Determination of benzo(a)pyrene content in PM10 using regression methods
PublicationThe paper presents an attempt of application of multidimensional linear regression to estimation of an empirical model describing the factors influencing on B(a)P content in suspended dust PM10 in Olsztyn and Elbląg city regions between 2010 and 2013. During this period annual average concentration of B(a)P in PM10 exceeded the admissible level 1.5-3 times. Conducted investigations confirm that the reasons of B(a)P concentration...
-
Aerodynamic Shape Optimization for Delaying Dynamic Stall of Airfoils by Regression Kriging
PublicationThe phenomenon of dynamic stall produce adverse aerodynamic loading which can adversely affect the structural strength and life of aerodynamic systems. Aerodynamic shape optimization (ASO) provides an effective approach for delaying and mitigating dynamic stall characteristics without the addition of auxiliary system. ASO, however, requires multiple evaluations time-consuming computational fluid dynamics models. Metamodel-based...
-
Reducing Monitoring Costs in Industrially Contaminated Rivers: Cluster and Regression Analysis Approach
PublicationMonitoring contamination in river water is an expensive procedure, particularly for developing countries where pollution is a significant problem. This study was conducted to provide a pollution monitoring strategy that reduces the cost of laboratory analysis. The new monitoring strategy was designed as a result of cluster and regression analysis on field data collected from an industrially influenced river. Pollution sources in...
-
Application of Regression Line to Obtain Specified Number of Points in Reduced Large Datasets
PublicationModern measurement techniques like scanning technology or sonar measurements, provide large datasets, which are a reliable source of information about measured object, however such datasets are sometimes difficult to develop. Therefore, the algorithms for reducing the number of such sets are incorporated into their processing. In the reduction algorithms based on the...
-
Decisional DNA with embedded Relief-F linear regression for knowledge and experience management
PublicationOmowiono zastowania Decyzyjnego DNA, regresji liniowej oraz funkcji RELIEF-F w procesach formalnego modelowania i wspomagania zarzadzania wiedza oraz zarzadzania doswiadczeniem.
-
Regression points in non-intrusive polynomial chaos expansion method and D-optimal design
PublicationThe paper addresses selected issues of uncertainty quantification in the modelling of a system containing surgical mesh used in ventral hernia repair. Uncertainties in the models occur e.g. due to variability of abdominal wall properties among others. In order to include them, a non-intrusive regression-based polynomial chaos expansion method is employed. Its accuracy depends on the choice of regression points. In the study a relation...
-
Buried Object Characterization by Data-Driven Surrogates and Regression-Enabled Hyperbolic Signature Extraction
PublicationThis work addresses artificial-intelligence-based buried object characterization using FDTD-based electromagnetic simulation toolbox of a Ground Penetrating Radar (GPR) to generate B-scan data. In data collection, FDTD-based simulation tool, gprMax is used. The task is to estimate geophysical parameters of a cylindrical shape object of various radii, buried at different positions in the dry soil medium simultaneously and independently...
-
Tolerance-Aware Multi-Objective Optimization of Antennas by Means of Feature-Based Regression Surrogates
PublicationAssessing the immunity of antenna design to fabrication tolerances is an important consideration, especially when the manufacturing process has not been predetermined. At the same time, the antenna parameter tuning should be oriented toward improving the performance figures pertinent to both electrical (e.g., input matching) and field properties (e.g., axial ratio bandwidth) as much as possible. Identification of available trade-offs...
-
Improved Modeling of Microwave Structures Using Performance-Driven Fully-Connected Regression Surrogate
PublicationFast replacement models (or surrogates) have been widely applied in the recent years to accelerate simulation-driven design procedures in microwave engineering. The fundamental reason is a considerable—and often prohibitive—CPU cost of massive full-wave electromagnetic (EM) analyses related to solving common tasks such as parametric optimization or uncertainty quantification. The most popular class of surrogates are data-driven...
-
Prediction of Peptide Retention at Different HPLC Conditions from Multiple Linear Regression Models
Publication -
Determination Of Gas Mixture Components Using Fluctuation Enhanced Sensing And The LS-SVM Regression Algorithm
PublicationThis paper analyses the effectiveness of determining gas concentrations by using a prototype WO3 resistive gas sensor together with fluctuation enhanced sensing. We have earlier demonstrated that this method can determine the composition of a gas mixture by using only a single sensor. In the present study, we apply Least-Squares Support-Vector-Machine-based (LS-SVM-based) nonlinear regression to determine the gas concentration...
-
Assessment of wastewater quality indicators for wastewater treatment influent using an advanced logistic regression model
PublicationInfluent quality indicators play a significant role in wastewater treatment plant performance due to their correlation with reactor operations and effluent quality. However, selecting a specific/best parameter indicator for predicting influent wastewater quality is one of the challenges in wastewa- ter treatment. This study, therefore, focused on determining suitable variables as influent quality indicators. For this purpose, a...
-
Deep-Learning-Based Precise Characterization of Microwave Transistors Using Fully-Automated Regression Surrogates
PublicationAccurate models of scattering and noise parameters of transistors are instrumental in facilitating design procedures of microwave devices such as low-noise amplifiers. Yet, data-driven modeling of transistors is a challenging endeavor due to complex relationships between transistor characteristics and its designable parameters, biasing conditions, and frequency. Artificial neural network (ANN)-based methods, including deep learning...
-
Optimization-Based Robustness Enhancement of Compact Microwave Component Designs with Response Feature Regression Surrogates
PublicationThe ability to evaluate the effects of fabrication tolerances and other types of uncertainties is a critical part of microwave design process. Improving the immunity of the device to parameter deviations is equally important, especially when the performance specifications are stringent and can barely be met even assuming a perfect manufacturing process. In the case of modern miniaturized microwave components of complex topologies,...
-
Towards Designing an Innovative Industrial Fan: Developing Regression and Neural Models Based on Remote Mass Measurements
PublicationThis article presents the process of the construction and testing a remote, fully autonomous system for measuring the operational parameters of fans. The measurement results obtained made it possible to create and verify mathematical models using linear regression and neural networks. The process was implemented as part of the first stage of an innovative project. The article presents detailed steps of constructing a system to...
-
Sensitivity analysis based on non-intrusive regression-based polynomial chaos expansion for surgical mesh modelling
PublicationThe modelling of a system containing implants used in ventral hernia repair and human tissue suffers from many uncertainties. Thus, a probabilistic approach is needed. The goal of this study is to define an efficient numerical method to solve non-linear biomechanical models supporting the surgeon in decisions about ventral hernia repair. The model parameters are subject to substantial variability owing to, e.g., abdominal wall...
-
Rapid Design Tuning of Miniaturized Rat-Race Couplers Using Regression-Based Equivalent Network Surrogates
PublicationA simple technique for fast design tuning of compact rat-race couplers is presented. Our approach involves equivalent circuit representation, corrected by nonlinear functions of frequency with coefficients extracted through nonlinear regression. At the same time, the tuning process connects two levels of coupler representation: EM simulation of the entire circuit and re-optimization of the coupler building blocks (slow-wave cells...
-
Application of Multinomial Logistic Regression to Model the Impact of Rainfall Genesis on the Performance of Storm Overflows: Case Study
PublicationIn this study, a mathematical model was proposed to analyze the performance of storm overfows. The model included the infuence of rainfall genesis on the duration of storm overfow, its volume, and the maximum instantaneous fow. The multinomial logistic regression model, which has not been used so far to model objects located in a stormwater system, was proposed to simulate the duration of storm overfow. The Iman–Conover method,...
-
Generalized regression neural network and fitness dependent optimization: Application to energy harvesting of centralized TEG systems
PublicationThe thermoelectric generator (TEG) system has attracted extensive attention because of its applications in centralized solar heat utilization and recoverable heat energy. The operating efficiency of the TEG system is highly affected by operating conditions. In a series-parallel structure, due to diverse temperature differences, the TEG modules show non-linear performance. Due to the non-uniform temperature distribution (NUTD) condition,...
-
Elevated ambulatory systolic-diastolic pressure regression index is genetically determined in hypertensive patients with coronary heart disease
Publication -
Fracture mechanics model of cutting power versus widespread regression equations while wood sawing with circular saw blades
PublicationA comparison of the theoretical cutting power consumption results forecasted with the model (FM_CM model) which include work of separation (fracture toughness) in addition to plasticity and friction, and two widespread regression equations while wood sawing with circular saw blades has been described. in and cutting power consumption forecasted. In computations of the cutting power consumption during rip sawing of Scots pine wood...
-
Three-dimensional geographically weighted inverse regression (3GWR) model for satellite derived bathymetry using Sentinel-2 observations
PublicationCurrent trends of development of satellite derived bathymetry (SDB) models rely on applying calibration techniques including analytical approaches, neuro-fuzzy systems, regression optimization and others. In most of the cases, the SDB models are calibrated and verified for test sites, that provide favourable conditions for the remote derivation of bathymetry such as high water clarity, homogenous bottom type, low amount of sediment...
-
Variable‐fidelity modeling of antenna input characteristics using domain confinement and two‐stage Gaussian process regression surrogates
PublicationThe major bottleneck of electromagnetic (EM)-driven antenna design is the high CPU cost of massive simulations required by parametric optimization, uncertainty quantification, or robust design procedures. Fast surrogate models may be employed to mitigate this issue to a certain extent. Unfortunately, the curse of dimensionality is a serious limiting factor, hindering the construction of conventional data-driven models valid over...
-
Prediction of near-bottom water salinity in the Baltic Sea using Ordinary Least Squares and Geographically Weighted Regression models
Publication -
Gender wage gap convergence and skills heterogeneity in Poland (2005-2014) - quantile regression analysis based on microdata from EUSILC.
PublicationIn this article we quantify the magnitude and evolution of gender wage differentials in Poland over the years 2005 – 2014 using microlevel data from EU-SILC database (Statistics on Income and Living Conditions). In the study gender wage gap is examined through quantile regression analysis. It is shown that the gender wage gap varies along the wage distribution with workers’ skills heterogeneity playing a role. Additionally, the...
-
Application of Multivariate Adaptive Regression Splines (MARSplines) Methodology for Screening of Dicarboxylic Acids Cocrystal Using 1D and 2D Molecular Descriptors
PublicationDicarboxylic acids (DiAs) are probably one of the most popular cocrystals formers. Due to the high hydrophilicity and non-toxicity, they are promising solubilizes of active pharmaceutical ingredients (APIs). Although DiAs appear to be highly capable of forming multicomponent crystals with various compounds, some systems reported in the literature are physical mixtures the solid state without forming stable intermolecular complex....
-
Prediction of skin color, tanning and freckling from DNA in Polish population: linear regression, random forest and neural network approaches
Publication -
Teicoplanin-Modified HPLC Column as a Source of Experimental Parameters for Prediction of the Anticonvulsant Activity of 1,2,4-Triazole-3-Thiones by the Regression Models
Publication -
Metabolic activity of tree saps of different origin towards cultured human cells in the light of grade correspondence analysis and multiple regression modeling
PublicationTree saps are nourishing biological media commonly used for beverage and syrup production. Although the nutritional aspect of tree saps is widely acknowledged, the exact relationship between the sap composition, origin, and effect on the metabolic rate of human cells is still elusive. Thus, we collected saps from seven different tree species and conducted composition-activity analysis. Saps from trees of Betulaceae, but not from...
-
Accurate Modeling of Frequency Selective Surfaces Using Fully-Connected Regression Model with Automated Architecture Determination and Parameter Selection Based on Bayesian Optimization
PublicationSurrogate modeling has become an important tool in the design of high-frequency structures. Although full-wave electromagnetic (EM) simulation tools provide an accurate account for the circuit characteristics and performance, they entail considerable computational expenditures. Replacing EM analysis by fast surrogates provides a way to accelerate the design procedures. Unfortunately, modeling of microwave passives is a challenging...
-
Fast and Efficient Four-Class Motor Imagery EEG Signals Analysis Using CSP-Ridge Regression Algorithm for the Purpose of Brain-Computer Interface."
Publication -
Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String
PublicationA new method of Hansen solubility parameters (HSPs) prediction was developed by combining the multivariate adaptive regression splines (MARSplines) methodology with a simple multivariable regression involving 1D and 2D PaDEL molecular descriptors. In order to adopt the MARSplines approach to QSPR/QSAR problems, several optimization procedures were proposed and tested. The effectiveness of the obtained models was checked via standard...
-
Novel liquid chromatography method based on linear weighted regression for the fast determination of isoprostane isomers in plasma samples using sensitive tandem mass spectrometry detection
PublicationA simple, fast, sensitive and accurate methodology based on a LLE followed by liquid chromatography–tandem mass spectrometry for simultaneous determination of four regioisomers (8-iso prostaglandin F2α, 8-iso-15(R)-prostaglandin F2α, 11β-prostaglandin F2α, 15(R)-prostaglandin F2α) in routine analysis of human plasma samples was developed. Isoprostanes are stable products of arachidonic acid peroxidation and are regarded as the...
-
SZACOWANIE ZAWARTOŚCI BENZO(a)PIRENU W PYLE ZAWIESZONYM PM10 W AGLOMERACJI TRÓJMIEJSKIEJ ZA POMOCĄ WIELOWYMIAROWEJ REGRESJI LINIOWEJ=ESTIMATION OF BENZO(A)PYRENE CONTENT IN SUSPENDED DUST PM10 IN TRI-CITY AGGLOMERATION USING MULTIDIMENSIONAL LINEAR REGRESSION
PublicationW pracy przedstawiono próbę oszacowania przy pomocy wielowymiarowej regresji liniowej modelu empirycznego opisującego czynniki wpływające na zawartość B(a)P w pyle zawieszonym PM10 w Aglomeracji Trójmiejskiej w latach 2008-2011. Na przestrzeni tych lat średnioroczne stężenie B(a)P w PM10 wzrosło ponad dwukrotnie i ponad trzykrotnie przewyższa poziom docelowy. Z przeprowadzonych analiz wynika, że główną przyczyną wzrostu stężenia...
-
Complete tumour regressions induced by vaccination with IL-12 gene-transduced tumour cells in combination with IL-15 in a melanoma model in mice
Publication -
Anna Sobieraj-Żłobińska dr inż.
PeopleAnna Sobieraj-Żłobińska (born in 1977 in Przasnysz) graduated from the National Education Commission High School in Przasnysz. From 1996 she continued her studies at the Faculty of Geodesy and Spatial Management at the University of Agriculture and Technology Michał Oczapowski in Olsztyn. In 2001, she obtained a master's degree in engineering at the University of Warmia and Mazury in Olsztyn (thesis topic "Determining a multiple...
-
Liniowe i nieliniowe modele wielowymiarowej kalibracji do predykcji stężenia substancji z pomiarów woltamperometrycznych
PublicationPomiary woltamperometryczne znajdują zastosowanie w wielu dziedzinach nauki i techniki, np. w przemyśle farmaceutycznym. Dane uzyskane w wyniku takich pomiarów zawierają informację odnośnie rodzaju i stężenia badanej substancji, jednakże są one często kłopotliwe w bezpośredniej interpretacji. Z tego powodu, istnieje konieczność wykorzystania odpowiednich metod matematycznych, które umożliwiają uzyskanie bezpośredniej i precyzyjnej...
-
Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models
PublicationThis work addresses artificial-intelligence-based buried object characterization using 3-D full-wave electromagnetic simulations of a ground penetrating radar (GPR). The task is to characterize cylindrical shape, perfectly electric conductor (PEC) object buried in various dispersive soil media, and in different positions. The main contributions of this work are (i) development of a fast and accurate data driven surrogate modeling...
-
Monitoring of n-butanol vapors biofiltration process using an electronic nose combined with calibration models
PublicationMalodours, by definition, are generally unpleasant, nuisance smells that are a mixture of volatile chemical compounds which can be perceptible even at low concentrations. Due to the more frequent occurrence of odour nuisance associated with the odour sensations, and thus the need to remove them from the air, deodorization techniques are commonly used. Biofiltration is one of the methods of reducing odorants in the air stream. In...
-
Adenocarcinoma, NOS - Male, 65 - Tissue image [11220630008697361]
Open Research DataThis is the histopathological image of STOMACH tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
-
Adenocarcinoma, NOS - Male, 65 - Tissue image [11220630008768561]
Open Research DataThis is the histopathological image of STOMACH tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
-
Adenocarcinoma, NOS - Male, 65 - Tissue image [11220630008761431]
Open Research DataThis is the histopathological image of STOMACH tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
-
Adenocarcinoma, NOS - Male, 65 - Tissue image [11220630008764781]
Open Research DataThis is the histopathological image of STOMACH tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
-
Adenocarcinoma, NOS - Male, 65 - Tissue image [11220630008762621]
Open Research DataThis is the histopathological image of STOMACH tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
-
Adenocarcinoma, NOS - Male, 65 - Tissue image [11220630008766861]
Open Research DataThis is the histopathological image of STOMACH tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
-
Computing methods for fast and precise body surface area estimation of selected body parts
PublicationCurrently used body surface area (BSA) formulas give satisfactory results only for individuals with typical physique, while for elderly, obese or anorectic people accurate results cannot be expected. Particularly noteworthy are the results for individuals with severe obesity (body-mass index greater than 35 kg/m2), for which BSA estimation errors reached 80%. The main goal of our study is the development of precise BSA models for...
-
Towards hand grip force assessment by using EMG estimators
PublicationThe purpose of this study was to propose a method to assess individual regression (calibration) curves to establish a relationship between an isometric grip force and surface electromyography (EMG) estimator. In this study 18 healthy volunteers (12 male (23.0 ± 2.0 years) and 6 female (23.2 ± 0.7 years)) had been examined. Ten EMG estimators (mean absolute value, root mean square, entropy, energy, turns per second, mean of zero...
-
Fuzzy regresion approach to road safety analysis at regional level
PublicationRoad safety modelling on regional level of NUTS 2 in the EU is the complex issue and authors of this article indicate this in previous publications. NUTS 2 are basic regions for the application of regional policies (0.8-3 m inhabitants). During multivariate models development they discovered that it is difficult to make regression model well described all regions, even if they are from one country. In the first step Poisson model...