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Search results for: prediction model
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Hybrid Numerical-Analytical Approach for Force Prediction in End Milling of 42CrMo4 Steel
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Towards trustworthy multi‐modal motion prediction: Holistic evaluation and interpretability of outputs
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The Influence of Input Data Standardization Method on Prediction Accuracy of Artificial Neural Networks
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Autocovariance based weighting strategy for time series prediction with weighted LS-SVM
PublicationPrzedstawiono metodę konstrukcji algorytmów z funkcją jądra, a także dwa algorytmy uzyskane poprzez użycie różnych funkcji straty. Zaproponowano kowariacyjną strategię ważenia algorytmów z kwadratową funkcją straty do problemu predykcji chaotycznych przebiegów czasowych.
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Prediction of Protein Structure by Template-Based Modeling Combined with the UNRES Force Field
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Recent improvements in prediction of protein structure by global optimization of a potential energy function
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The Round Robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential
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Study of Noise Propagation for Small Vessels
PublicationThe paper presents the results of the noise propagation analysis in ship structures tested in a number of AHTS (Anchor Handling Tug Supply) vessels. Statistical Energy Analysis (SEA) based on numerical model developed specially for the purpose of this numerical investigation were conducted. This numerical model enabled the analysis of both the structural elements and the acoustic spaces. For the detailed studies 47 points fixed...
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Road traffic can be predicted by machine learning equally effectively as by complex microscopic model
PublicationSince high-quality real data acquired from selected road sections are not always available, a traffic control solution can use data from software traffic simulators working offline. The results show that in contrast to microscopic traffic simulation, the algorithms employing neural networks can work in real-time, so they can be used, among others, to determine the speed displayed on variable message road signs. This paper describes...
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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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Fast and reliable knowledge-based design closure of antennas by means of iterative prediction-correction scheme
PublicationA novel framework for expedited antenna optimization with an iterative prediction-correction scheme is proposed. The methodology is comprehensively validated using three real-world antenna structures: narrow-band, dual-band and wideband, optimized under various design scenarios. The keystone of the proposed approach is to reuse designs pre-optimized for various sets of performance specifications and to encode them into metamodels...
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Remarks on steam condensation modeling related to steam turbine large output
PublicationIn the paper numerical simulations have been performed to predict the performance of the diferent steam models. All of the considered models of steam condensation have been validated on the base of benchmark experiment employing expansion in nozzle and next on the low pressure part of the steam turbine equipped with the so-called Baumann stage. For numerical analysis three models have been finally used - the ideal steam model without...
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The analysis of soil resistance during screw displacement pile installation
PublicationThe analysis of soil resistances during screw displacement pile installation based on model and field tests. The investigations were carried out as a part of research project, financed by the Polish Ministry of Science and Higher Education. A proposal of empirical method for prediction of rotation resistance (torque) during screw auger penetration in non-cohesive subsoil based on CPT result.
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Estimation and Prediction of Vertical Deformations of Random Surfaces, Applying the Total Least Squares Collocation Method
PublicationThis paper proposes a method for determining the vertical deformations treated as random fields. It is assumed that the monitored surfaces are subject not only to deterministic deformations, but also to random fluctuations. Furthermore, the existence of random noise coming from surface’s vibrations is also assumed. Such noise disturbs the deformation’s functional models. Surface monitoring with the use of the geodetic levelling...
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Progressive failure analysis of laminates in the framework of 6-field nonlinear shell theory
PublicationThe paper presents the model of progressive failure analysis of laminates incorporated into the 6-field non-linear shell theory with non-symmetrical strain measures of Cosserat type. Such a theory is specially recommended in the analysis of shells with intersections due to its specific kinematics including the so-called drilling rotation. As a consequence of asymmetry of strain measures, modified laminates failure criteria must...
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Prediction of energy consumption and evaluation of affecting factors in a full-scale WWTP using a machine learning approach
PublicationTreatment 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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Efficient uncertainty quantification using sequential sampling-based neural networks
PublicationUncertainty quantification (UQ) of an engineered system involves the identification of uncertainties, modeling of the uncertainties, and the forward propagation of the uncertainties through a system analysis model. In this work, a novel surrogate-based forward propagation algorithm for UQ is proposed. The proposed algorithm is a new and unique extension of the recent efficient global optimization using neural network (NN)-based...
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Wideband Modeling of DC-DC Buck Converter with GaN Transistors
PublicationThe general wideband modeling method of the power converter is presented on the example of DC-DC buck converter with GaN High Electron Mobility Transistors (HEMT). The models of all basic and parasitic components are briefly described. The two methods of Printed Circuit Board (PCB) layout parameter extraction are presented. The results of simulation in Saber@Sketch simulation software and measurements are compared. Next, the model...
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Edgewise Compressive Behavior of Composite Structural Insulated Panels with Magnesium Oxide Board Facings
PublicationEdgewise compression response of a composite structural insulated panel (CSIP) with magnesium oxide board facings was investigated. The discussed CSIP is a novel multifunctional sandwich panel introduced to the housing industry as a part of the wall, floor, and roof assemblies. The study aims to propose a computational tool for reliable prediction of failure modes of CSIPs subjected to concentric and eccentric axial loads. An advanced...
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Genre-Based Music Language Modeling with Latent Hierarchical Pitman-Yor Process Allocation
PublicationIn this work we present a new Bayesian topic model: latent hierarchical Pitman-Yor process allocation (LHPYA), which uses hierarchical Pitman-Yor pr ocess priors for both word and topic distributions, and generalizes a few of the existing topic models, including the latent Dirichlet allocation (LDA), the bi- gram topic model and the hierarchical Pitman-Yor topic model. Using such priors allows for integration of -grams with a topic model,...
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Genre-Based Music Language Modeling with Latent Hierarchical Pitman-Yor Process Allocation
PublicationIn this work we present a new Bayesian topic model: latent hierarchical Pitman-Yor process allocation (LHPYA), which uses hierarchical Pitman-Yor pr ocess priors for both word and topic distributions, and generalizes a few of the existing topic models, including the latent Dirichlet allocation (LDA), the bi- gram topic model and the hierarchical Pitman-Yor topic model. Using such priors allows for integration of -grams with a topic...
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Is the Identification of SNP-miRNA Interactions Supporting the Prediction of Human Lymphocyte Transcriptional Radiation Responses?
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Prediction of the Mechanical Properties of P91 Steel by Means of Magneto-acoustic Emission and Acoustic Birefringence
PublicationThe paper describes an application of nondestructive volumetric magnetic and ultrasonic techniques for evaluation of the selected mechanical parameter variations of P91 steel having direct influence on its suitability for further use in critical components used in power plants. Two different types of deformation processes were carried out. First, a series of the P91 steel specimens was subjected to creep and second, one to plastic...
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Wind resource assessment and energy yield prediction for the small wind turbine on the Szubieniczne Hill
PublicationThe goal of this study is to preliminary assess the wind resources on the Szubieniczne Hill in order to predict the annual energy production for planned small wind turbine. The analyzed site is located close to the Gdańsk University of Technology campus, in complex urban environment additionally surrendered by forested hills. The assessment is based on Computational Fluid Dynamics simulations which allow to evaluate the wind energy potential...
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Prediction of cutting forces during micro end milling considering chip thickness accumulation
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Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines
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Prediction of Early Childhood Caries Based on Single Nucleotide Polymorphisms Using Neural Networks
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Prediction of the Segmental Pelvic Ring Fractures Under Impact Loadings During Car Crash
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Prediction of Peptide Retention at Different HPLC Conditions from Multiple Linear Regression Models
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An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12
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In silico epitope prediction of Borrelia burgdorferi sensu lato antigens for the detection of specific antibodies
PublicationDespite many years of research, serodiagnosis of Lyme disease still faces many obstacles. Difficulties arise mainly due to the low degree of amino acid sequence conservation of the most immunogenic antigens among B. burgdorferi s.l. genospecies, as well as differences in protein production depending on the environment in which the spirochete is located. Mapping B-cell epitopes located on antigens allows for a better understanding...
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Artificial Neural Network (ANN)-Based Voltage Stability Prediction of Test Microgrid Grid
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Prediction of Sorption Processes Using the Deep Learning Methods (Long Short-Term Memory)
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New, fast and cheap prediction tests for BRCA1 gene mutations identification in clinical samples.
PublicationDespite significant progress in cancer therapy, cancer is still the second cause of mortality in the world. The necessity to make quick therapeutic decisions forces the development of procedures allowing to obtain a reliable result in a quick and unambiguous manner. Currently, detecting predictive mutations, including BRCA1, is the basis for effectively treating advanced breast cancer. Here, we present new insight on gene mutation...
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Prediction of corrosion fatigue crack propagation life for welded joints under cathodic potentials
PublicationPrzy ochronie katodowej poniżej potencjału optymalnego obserwuje się przyspieszenie propagacji pęknięcia korozyjno-zmęczeniowego w porównaniu do konstrukcji nie chronionej. Zaproponowano własną formułę empiryczną na wpływ potencjału ochronnego na prędkość propagacji pęknięcia oraz własny wzór na [delta]K w złączu pachwinowym. Wyprowadzono wzór na krzywą ''S-Np'' (naprężenie - długość okresu propagacji pęknięcia). Zaprezentowana...
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Prediction of fracture toughness in fibre-reinforced concrete, mortar, and rocks using various Machine learning techniques
PublicationMachine 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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Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches
PublicationExamining 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...
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ADAPTIVE PREDICTIONS OF THE EURO/ZŁOTY CURRENCY EXCHANGE RATE USING STATE SPACE WAVELET NETWORKS AND FORECAST COMBINATIONS
PublicationThe paper considers the forecasting of the euro/Polish złoty (EUR/PLN) spot exchange rate by applying state space wavelet network and econometric forecast combination models. Both prediction methods are applied to produce one-trading-day- ahead forecasts of the EUR/PLN exchange rate. The paper presents the general state space wavelet network and forecast combination models as well as their underlying principles. The state space...
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Sparse autoregressive modeling
PublicationIn the paper the comparison of the popular pitch determination (PD) algorithms for thepurpose of elimination of clicks from archive audio signals using sparse autoregressive (SAR)modeling is presented. The SAR signal representation has been widely used in code-excitedlinear prediction (CELP) systems. The appropriate construction of the SAR model is requiredto guarantee model stability. For this reason the signal representation...
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Predicting Ice Phenomena in a River Using the Artificial Neural Network and Extreme Gradient Boosting
PublicationForecasting ice phenomena in river systems is of great importance because these phenomena are a fundamental part of the hydrological regime. Due to the stochasticity of ice phenomena, their prediction is a difficult process, especially when data sets are sparse or incomplete. In this study, two machine learning models—Multilayer Perceptron Neural Network (MLPNN) and Extreme Gradient Boosting (XGBoost)—were developed to predict...
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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...
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Time window based features extraction from temperature modulated gas sensors for prediction of ammonia concentration
PublicationElectronic gas recognition systems, in literature commonly referred as electronic noses, enable the recognition of a type and a concentration of various volatile compounds. Typical electronic gas-analyzing device consists of four main elements, namely, gas delivery subsystem, an array of gas sensors, data acquisition and power supply circuits and data analysis software. The commercially available metal-oxide TGS sensors are widely...
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Fragmental Method KowWIN as the Powerful Tool for Prediction of Chromatographic Behavior of Novel Bioactive Urea Derivatives
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ADAPTATION OF ENGINEERING FEA-BASED ALGORITHMS TO LCF FAILURE AND MATERIAL DATA PREDICTION IN OFFSHORE DESIGN
PublicationThere is an ever growing industrial demand for quantitative assessment of fatigue endurance of critical structural details. Although FEA-based calculations have become a standard in engineering design, problems involving the Low-To-Medium cycle range (101-104) remain challenging. This paper presents an attempt to optimally choose material data, meshing density and other algorithm settings in the context of recent design of the...
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Prediction of NOx Emission Based on Data of LHD On-Board Monitoring System in a Deep Underground Mine
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Prediction of the structures of proteins with the UNRES force field, including dynamic formation and breaking of disulfide bonds
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UNRES server for physics-based coarse-grained simulations and prediction of protein structure, dynamics and thermodynamics
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From the pills to environment – Prediction and tracking of non-steroidal anti-inflammatory drug concentrations in wastewater
PublicationThe extend of environment pollution by pharmaceuticals is in a stage that required more automatic and integrated solutions. The non-steroidal anti-inflammatory drugs (NSAIDs) are one of the most popular pharmaceutical in the world and emerging pollutants of natural waters. The aim of the paper was to check the correlation of the sales data of selected NSAIDs (ibuprofen, naproxen, diclofenac) and their concentration in the WWTP...
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Advanced Scalar-valued Intensity Measures for Residual Drift Prediction of SMRFs with Fluid Viscous Dampers
PublicationMaximum Residual Inter-story Drift Ratio (RIDRmax) plays an important role to specify the state of a structure after severe earthquake and the possibility of repairing the structure. Therefore, it is necessary to predict the RIDRmax of Steel Moment-Resisting Frames (SMRFs) with high reliability by employing powerful Intensity Measures (IMs). This study investigates the efficiency and sufficiency of scalar-valued IMs for predicting...
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Rapid Microwave Design Optimization in Frequency Domain Using Adaptive Response Scaling
PublicationIn this paper, a novel methodology for cost-efficient microwave design optimization in the frequency domain is proposed. Our technique, referred to as adaptive response scaling (ARS), has been developed for constructing a fast replacement model (surrogate) of the high-fidelity electromagnetic-simulated model of the microwave structure under design using its equivalent circuit (low-fidelity model). The basic principle of ARS is...