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Search results for: DATA-DRIVEN APPROACH
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Fundamentals of Data-Driven Surrogate Modeling
PublicationThe primary topic of the book is surrogate modeling and surrogate-based design of high-frequency structures. The purpose of the first two chapters is to provide the reader with an overview of the two most important classes of modeling methods, data-driven (or approx-imation), as well as physics-based ones. These are covered in Chap-ters 1 and 2, respectively. The remaining parts of the book give an exposition of the specific aspects...
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Dis/Trust and data-driven technologies
PublicationThis concept paper contextualises, defines, and systematises the concepts of trust and distrust (and their interrelations), providing a critical review of existing literature so as to identify gaps, disjuncture, and continuities in the use of these concepts across the social sciences and in the context of the consolidation of the digital society. Firstly, the development of the concept of trust is explored by looking at its use...
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The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry
Open Research DataThe developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic...
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Multilevel pharmacokinetics-driven modeling of metabolomics data
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Enhanced uniform data sampling for constrained data‐driven modeling of antenna input characteristics
PublicationData-driven surrogates are the most popular replacement models utilized in many fields of engineering and science, including design of microwave and antenna structures. The primary practical issue is a curse of dimensionality which limits the number of independent parameters that can be accounted for in the modelling process. Recently, a performance-driven modelling technique has been proposed where the constrained domain of the...
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Improved Uniform Sampling in Constrained Domains for Data-Driven Modelling of Antennas
PublicationData-driven surrogate modelling of antenna structures is an attractive way of accelerating the design process, in particular, parametric optimization. In practice, construction of surrogates is hindered by curse of dimensionality as well as wide ranges of geometry parameters that need to be covered in order to make the model useful. These difficulties can be alleviated by constrained performance-driven modelling with the surrogate...
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Visual GQM approach to quality driven development of electronic documents.
PublicationJednym z celów projektu europejskiego MEORIAL jest opracowanie nowej technologii wytwarzania webowych systemów informacyjnych wykorzystujących interaktywne dokumenty cyfrowe wytworzone z papierowych oryginałów z zastosowaniem zaawansowanych technik przetwarzania i rozpoznania obrazów. Wieloelementowy model cyklu życia dokumentu cyfrowego przedstawiony w artykule stanowi postawę opracowanej technologii.
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Low-Cost Data-Driven Surrogate Modeling of Antenna Structures by Constrained Sampling
PublicationFull-wave electromagnetic (EM) analysis has become one of the major design tools for contemporary antenna structures. Although reliable, it is computationally expensive which makes automated simulation-driven antenna design (e.g., parametric optimization) difficult. This difficulty can be alleviated by utilization of fast and accurate replacement models (surrogates). Unfortunately, conventional data-driven modeling of antennas...
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Design of microstrip antenna subarrays: a simulation-driven surrogate-based approach
PublicationA methodology for computationally efficient simulation-driven design of microstrip antenna subarrays is presented. Our approach takes into account the effect of the feed (here, a corporate network) on the subarray side-lobe level and allows adjustment of both radiation and reflection responses of the structure under design within a single automated process. This process is realized as surrogate-based optimization that produces...
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Data-driven, probabilistic model for attainable speed for ships approaching Gdańsk harbour
PublicationThe growing demand for maritime transportation leads to increased traffic in ports. From this arises the need to observe the consequences of the specific speed ships reach when approaching seaports. However, usually the analyzed cases refer only to the statistical evaluation of the studied phenomenon or to the empirical modelling, ignoring the mutual influence of variables such as ship type, length or weather conditions. In this...
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Application of mechanistic and data-driven models for nitrogen removal in wastewater treatment systems
PublicationIn this dissertation, the application of mechanistic and data-driven models in nitrogen removal systems including nitrification and deammonification processes was evaluated. In particular, the influential parameters on the activity of the Nitrospira activity were assessed using response surface methodology (RSM). Various long-term biomass washout experiments were operated in two parallel sequencing batch reactor (SBR) with a different...
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Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review
PublicationFiber-reinforced concrete (FRC) is extensively used in diverse structural engineering applications, and its mechanical properties are crucial for designing and evaluating its performance. The compressive, flexural, splitting tensile, and shear strengths of FRCs are among the most important attributes, which have been discussed more extensively than other properties. The accurate prediction of these properties, which are required...
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Data-Driven Surrogate-Assisted Optimization of Metamaterial-Based Filtenna Using Deep Learning
PublicationIn this work, a computationally efficient method based on data driven surrogate models is pro-posed for the design optimization procedure of a Frequency Selective Surface (FSS)-based filtering antenna (Filtenna). A Filtenna acts as a as module that simultaneously pre-filters unwanted sig-nals, and enhances the desired signals at the operating frequency. However, due to a typically large number of design variables of FSS unit elements,...
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An Approach to Data Reduction and Integrated Machine Classification
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Towards High-Value Datasets Determination for Data-Driven Development: A Systematic Literature Review
PublicationOpen government data (OGD) is seen as a political and socio-economic phenomenon that promises to promote civic engagement and stimulate public sector innovations in various areas of public life. To bring the expected benefits, data must be reused and transformed into value-added products or services. This, in turn, sets another precondition for data that are expected to not only be available and comply with open data principles,...
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A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine
PublicationRNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these methods has been the accuracy and confidence of the predictions, and our in-house experiments show that they fail to accurately predict RPI involving short RNA sequences such as TERRA RNA....
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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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A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine
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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...
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Fast multi-objective optimization of antenna structures by means of data-driven surrogates and dimensionality reduction
PublicationDesign of contemporary antenna structures needs to account for several and often conflicting objectives. These are pertinent to both electrical and field properties of the antenna but also its geometry (e.g., footprint minimization). For practical reasons, especially to facilitate efficient optimization, single-objective formulations are most often employed, through either a priori preference articulation, objective aggregation,...
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A New Approach For High Speed Data Transmission Monitoring
PublicationW artykule przedstawiono nowatorski sposób monitorowania szybkiej transmisji danych. Technika została zaprezentowana dla przypadku transmisji różnicowej na płycie drukowanej. Cechą szczególną rozwiązania jest możliwość pomiaru jakości transmisji w linii bez konieczności montowania dedykowanych złącz pomiarowych, które mogłyby degradować transmisję.
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Data-driven models for fault detection using kernel pca:a water distribution system case study
PublicationKernel Principal Component Analysis (KPCA), an example of machine learning, can be considered a non-linear extension of the PCA method. While various applications of KPCA are known, this paper explores the possibility to use it for building a data-driven model of a non-linear system-the water distribution system of the Chojnice town (Poland). This model is utilised for fault detection with the emphasis on water leakage detection....
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Firing map for periodically and almost-periodically driven integrate-and-fire models: a dynamical systems approach
PublicationWe consider the Leaky Integrate-and-Fire and Perfect Integrator models of neuron’s dynamics with the input function being periodic and almost-periodic (in the sense of Stepanov). In particular we analyze properties and dynamics of the so-called firing map, which iterations give timings of consecutive spikes of a neuron. In case of a periodic input function we provide a detailed description of the sequence of interspike-intervals,...
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Open Data Capability Architecture - An Interpretive Structural Modeling Approach
PublicationDespite of increasing availability of open data as a vital organizational resource, large numbers of startups and organizations fail when it comes to utilizing open data effectively. This shortcoming is attributable to the poor understanding of what types of capabilities are required to successfully conduct data related activities. At the same time, research on open data capabilities and how they relate to one another remains sparse....
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An A-Team approach to learning classifiers from distributed data sources
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An A-Team Approach to Learning Classifiers from Distributed Data Sources
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Measuring the Efficiency of Polish Municipalities – Data Envelopment Analysis Approach
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Application of data driven methods in diagnostic of selected process faults of nuclear power plant steam turbine
PublicationArticle presents a comparison of process anomaly detection in nuclear power plant steam turbine using combination of data driven methods. Three types of faults are considered: water hammering, fouling and thermocouple fault. As a virtual plant a nonlinear, dynamic, mathe- matical steam turbine model is used. Two approaches for fault detection using one class and two class classiers are tested and compared.
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Identification of High-Value Dataset determinants: is there a silver bullet for efficient sustainability-oriented data-driven development?
PublicationOpen Government Data (OGD) are seen as one of the trends that has the potential to benefit the economy, improve the quality, efficiency, and transparency of public administration, and change the lives of citizens, and the society as a whole facilitating efficient sustainability-oriented data-driven services. However, the quick achievement of these benefits is closely related to the “value” of the OGD, i.e., how useful, and reusable...
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Reliable data-driven modeling of high-frequency structures by means of nested kriging with enhanced design of experiments
PublicationData-driven (or approximation) surrogate models have been gaining popularity in many areas of engineering and science, including high-frequency electronics. They are attractive as a way of alleviating the difficulties pertinent to high computational cost of evaluating full-wave electromagnetic (EM) simulation models of microwave, antenna, and integrated photonic components and devices. Carrying out design tasks that involve massive...
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Data-driven models for fault detection using kernel PCA: A water distribution system case study
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O-43 Data-driven selection of active iEEG channels during verbal memory task performance
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Influence of input data on airflow network accuracy in residential buildings with natural wind - and stack - driven ventilation.
PublicationW artykule omówiono wpływ danych wejściowych na dokładność modelu przepływu sieciowego powietrza w budynkach mieszkalnych z naturalną i kominową wentylacją. Zastosowano połączony model AFN-BES. Wyniki numeryczne omówiono dla 8 różnych przypadków z różnymi danymi ciśnienia wiatru. Wyniki pokazały, że ogromny wpływ danych wejściowych dotyczących ciśnienia wiatru na wyniki numeryczne.
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Low-cost data-driven modelling of microwave components using domain confinement and PCA-based dimensionality reduction
PublicationFast data-driven surrogate models can be employed as replacements of computationally demanding full-wave electromagnetic simulations to facilitate the microwave design procedures. Unfortunately, practical application of surrogate modelling is often hindered by the curse of dimensionality and/or considerable nonlinearity of the component characteristics. This paper proposes a simple yet reliable approach to cost-efficient modelling...
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Fast multi-objective design optimization of microwave and antenna structures using data-driven surrogates and domain segmentation
PublicationPurpose Strategies and algorithms for expedited design optimization of microwave and antenna structures in multi-objective setup are investigated. Design/methodology/approach Formulation of the multi-objective design problem oriented towards execution of the population-based metaheuristic algorithm within the segmented search space is investigated. Described algorithmic framework exploit variable fidelity modeling, physics- and...
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Modelling of Capillary Pressure-driven Water Flow in Unsaturated Concrete Using Coupled DEM/CFD Approach.
PublicationSformułowano nowatorskie, połączone podejście do modelowania dwufazowego przepływu wody napędzanego kapilarami w nienasyconym betonie. Dzięki połączeniu metody elementów dyskretnych (DEM) z obliczeniową dynamiką płynów (CFD) w warunkach izotermicznych proces został zbadany numerycznie w mezoskali w warunkach dwuwymiarowych. Niewielkie próbki betonu o uproszczonej mezostrukturze cząstek poddano w pełni sprzężonym hydromechanicznym...
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A coarse‐grained approach to NMR ‐data‐assisted modeling of protein structures
PublicationThe ESCASA algorithm for analytical estimation of proton positions from coarse-grained geometry developed in our recent work has been implemented in modeling protein structures with the highly coarse-grained UNRES model of polypeptide chains (two sites per residue) and nuclear magnetic resonance (NMR) data. A penalty function with the shape of intersecting gorges was applied to treat ambiguous distance restraints, which automatically...
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The influence of climate change on the life insurance in the EU: A panel data approach
PublicationThe financial sector, as one of the most sensitive economic sectors, is alert to all trends and changes in the environment. The aim of the article is to study the impact of climate change on the life insurance market using panel data from 28 countries of the European Union (EU) for the last 9 years. This study is based on a panel model, where the amount of premiums under life insurance contracts is defined as a function of the...
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Machine-to-Machine communication and data processing approach in Future Internet applications
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An Approach to Imbalanced Data Classification Based on Instance Selection and Over-Sampling
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Health System Efficiency in European Countries: Network Data Envelopment Analysis Approach
PublicationPurpose: The article's main aim is to investigate the effectiveness of health systems in European countries based on EUROSTAT data. A comparative analysis of the health systems' effectiveness in different countries is based on their improvement (reform), using the best practices approach. Design/Methodology/Approach: The network DEA model and a slack-based model (NDEA – SBM) are used. A non-oriented model is used. The research...
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Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
PublicationThe field of cancer diagnostics has been revolutionized by liquid biopsies, which offer a bridge between laboratory research and clinical settings. These tests are less invasive than traditional biopsies and more convenient than routine imaging methods. Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and...
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Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
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Data-driven Models for Predicting Compressive Strength of 3D-printed Fiber-Reinforced Concrete using Interpretable Machine Learning Algorithms
Publication3D printing technology is growing swiftly in the construction sector due to its numerous benefits, such as intricate designs, quicker construction, waste reduction, environmental friendliness, cost savings, and enhanced safety. Nevertheless, optimizing the concrete mix for 3D printing is a challenging task due to the numerous factors involved, requiring extensive experimentation. Therefore, this study used three machine learning...
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TOWARDS EXPLAINABLE CLASSIFIERS USING THE COUNTERFACTUAL APPROACH - GLOBAL EXPLANATIONS FOR DISCOVERING BIAS IN DATA
PublicationThe paper proposes summarized attribution-based post-hoc explanations for the detection and identification of bias in data. A global explanation is proposed, and a step-by-step framework on how to detect and test bias is introduced. Since removing unwanted bias is often a complicated and tremendous task, it is automatically inserted, instead. Then, the bias is evaluated with the proposed counterfactual approach. The obtained results...
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Unsupervised Learning for Biomechanical Data Using Self-organising Maps, an Approach for Temporomandibular Joint Analysis
PublicationWe proposed to apply a specific machine learning technique called Self-Organising Maps (SOM) to identify similarities in the performance of muscles around human temporomandibular joint (TMJ). The performance was assessed by measuring muscle activation with the use of surface electromyography (sEMG). SOM algorithm used in the study was able to find clusters of data in sEMG test results. The SOM analysis was based on processed sEMG...
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MATCHED FILTER APPROACH FOR MICROSEISMIC SIGNAL PROCESSING OF REAL DATA FROM EAST POMERANIA SHALE GAS
PublicationThe microseismic monitoring is a method of monitoring of fracture propagation during hydraulic fracturing (HF)process. An array of several hundred geophones is placed on the surface to record little ground tremors induced by fracturing process. Filtration and summation of signals from geophones is essential to identify and locate fracturing events from underground. Authors propose a method of matched filtering, that is usually...
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Optimising approach to designing kernel PCA model for diagnosis purposes with and without a priori known data reflecting faulty states
PublicationFault detection plays an important role in advanced control of complex dynamic systems since precise information about system condition enables efficient control. Data driven methods of fault detection give the chance to monitor the plant state purely based on gathered measurements. However, they especially nonlinear, still suffer from a lack of efficient and effective learning methods. In this paper we propose the two stages learning...
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An Approach to Data Reduction for Learning from Big Datasets: Integrating Stacking, Rotation, and Agent Population Learning Techniques
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Weighted Ensemble with one-class Classification and Over-sampling and Instance selection (WECOI): An approach for learning from imbalanced data streams
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