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Search results for: quantile regressions
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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...
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Quality and Quantity
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Application of Generalized Regression Neural Network and Gaussian Process Regression for Modelling Hybrid Micro-Electric Discharge Machining: A Comparative Study
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The Modifiable Areal Unit Problem – Analysis of Correlation and Regression
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Advancing Knowledge Quality and Quantity in Knowledge Markets
PublicationZaproponowano modelowe podejscie do oceny jakosci i ilosci wiedzy na powstajacych rynkach wiedzy. Przedstawiony model oparty jest na systemach wieloagentowych.
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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...
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Quantile-transformed multi-attention residual framework (QT-MARF) for medium-term PV and wind power prediction
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Monitoring and diagnosis of quality and quantity in drinking water systems.
PublicationArtykuł przedstawia nowe podejście do inteligentnego monitorowania oraz diagnostyki Systemu Wodnego (WS) jako problem statusu, warunków operacyjnych oraz rozpoznawania zdarzeń. Oprogramowanie komputerowego systemu monitorowania i diagnostyki zawiera narzędzia dla identyfikacji modeli dyskretnych statusów oraz określenia parametrów ilości i jakości wody dla każdego statusu. Dla każdego stanu decyzje podejmowane są przy użyciu metody...
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A proposal for a knowledge market based on quantity and quality of knowledge
PublicationThe paper proposes an autonomous market environment in which it is possible to trade knowledge based on its quantity and quality.
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Detecting Apples in the Wild: Potential for Harvest Quantity Estimation
PublicationKnowing the exact number of fruits and trees helps farmers to make better decisions in their orchard production management. The current practice of crop estimation practice often involves manual counting of fruits (before harvesting), which is an extremely time-consuming and costly process. Additionally, this is not practicable for large orchards. Thanks to the changes that have taken place in recent years in the field of image...
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Piotr Paradowski dr
PeopleDr Piotr Paradowski's areas of expertise in quantitative social science methods include truncated and censored models, quantile regressions, survival analysis, panel data models, discrete regressions and qualitative choice models, instrumental variable estimation, and hierarchical modeling. He is also an expert in statistical matching and statistical methods to handle missing data. In addition, he conducts research on income and...
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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...
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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...
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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...
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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.
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Iterative‐recursive estimation of parameters of regression models with resistance to outliers on practical examples
PublicationHere, identification of processes and systems in the sense of the least sum of absolute values is taken into consideration. The respective absolute value estimators are recognised as exceptionally insensitive to large measurement faults or other defects in the processed data, whereas the classical least squares procedure appears to be completely impractical for processing the data contaminated with such parasitic distortions. Since...
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Metrological analysis of surface quality aspects in minimum quantity cooling lubrication
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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...
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Prediction of Peptide Retention at Different HPLC Conditions from Multiple Linear Regression Models
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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...
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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...
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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...
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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...
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Bulk quantity and physical properties of boron nitride nanocapsules with a narrow size distribution
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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...
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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,...
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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...
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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...
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Hierarchical model predictive control of integrated quality and quantity in drinking water distribution systems
PublicationW artykule zaproponowane zostało zintegrowane podejście do sterowania ilością i jakością w systemach zaopatrzenia i dystrybucji wody. Sterowanie zintegrowane polega na optymalizowaniu kosztów operacyjnych zaspokajając zapotrzebowanie na wodę o wymaganej jakości i spełniając ograniczenia systemu. To zagadnienie sterowania optymalizującego jest zagadnieniem złożonym z powodu nieliniowości, dużego wymiaru, ograniczeń na wyjście, występowania...
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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,...
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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...
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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,...
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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...
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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...
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Hazel Grouse occurrence in fragmented forests: habitat quantity and configuration is more important than quality
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Influence of Carbon Sorbent Quantity on Breakthrough Time in Absorbent Filters for Antismog Half Mask Application
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Mixed mechanical-metrological approach to quantify the fractographic damage in mechanical components subjected to cyclic loading
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Impact of Pre-Sowing Red Light Treatment of Sweet Corn Seeds on the Quality and Quantity of Yield
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Structural and Microhardness Changes After Turning of the AISI 1045 Steel for Minimum Quantity Cooling Lubrication
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Two Time-Scale Hierarchical Control of Integrated Quantity and Quality in Drinking Water Distribution Systems
PublicationThe paper considers a feedback optimising control of drinking water distribution systems (DWDS). Although the optimised pump and valves scheduling and disinfectant injection control attracted considerable attention over last two decades most of the contributions were limited to an open-loop optimisation repetitively performed during the DWDS operation. Also, while a strong interaction between the water quantity and quality exists...
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Complete tumour regressions induced by vaccination with IL-12 gene-transduced tumour cells in combination with IL-15 in a melanoma model in mice
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Elevated ambulatory systolic-diastolic pressure regression index is genetically determined in hypertensive patients with coronary heart disease
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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...
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Prediction of near-bottom water salinity in the Baltic Sea using Ordinary Least Squares and Geographically Weighted Regression models
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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...
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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...
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A targeted mass spectrometry immunoassay to quantify osteopontin in fresh-frozen breast tumors and adjacent normal breast tissues
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Genetic solver of optimization task of mpc for optimizing control of integrated quantity and quality in drinking water distribution systems
PublicationPredykcyjne sterowanie zintegrowana jakością i ilością wody pitnej umożliwia uzyskanie lepszej jakości sterowania niż w przypadku innych metod. Niestety wymaga rozwiązania nieliniowego, niewypukłego problemu optymalizacji. Z tego względu potrzebne jest wykorzystanie specjalizowanego solwera w celu rozwiązania problemu optymalizacji predykcyjnej w wymaganych czasie. W tym artykule przedstawiony jest dedykowany algorytm genetyczny...
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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....
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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
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