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Search results for: data models
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Optimization of river network representation data models for web-based systems
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Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices
PublicationThere are growing opportunities to leverage new technologies and data sources to address global problems related to sustainability, climate change, and biodiversity loss. The emerging discipline of GeoAI resulting from the convergence of AI and Geospatial science (Geo-AI) is enabling the possibility to harness the increasingly available open Earth Observation data collected from different constellations of satellites and sensors...
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Comparison of Traffic Flow Models with Real Traffic Data Based on a Quantitative Assessment
PublicationThe fundamental relationship of traffic flow and bivariate relations between speed and flow, speed and density, and flow and density are of great importance in transportation engineering. Fundamental relationship models may be applied to assess and forecast traffic conditions at uninterrupted traffic flow facilities. The objective of the article was to analyze and compare existing models of the fundamental relationship. To that...
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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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SCRAMBLE’N’GAMBLE: a tool for fast and facile generation of random data for statistical evaluation of QSAR models
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Which Curve Fits Best: Fitting ROC Curve Models to Empirical Credit-Scoring Data
PublicationIn the practice of credit-risk management, the models for receiver operating characteristic (ROC) curves are helpful in describing the shape of an ROC curve, estimating the discriminatory power of a scorecard, and generating ROC curves without underlying data. The primary purpose of this study is to review the ROC curve models proposed in the literature, primarily in biostatistics, and to fit them to actual credit-scoring ROC data...
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Data set generation at novel test-rig for validation of numerical models for modeling granular flows
PublicationSignificant effort has been exerted on developing fast and reliable numerical models for modeling particulate flow; this is challenging owing to the complexity of such flows. To achieve this, reliable and high-quality experimental data are required for model development and validation. This study presents the design of a novel test-rig that allows the visualization and measurement of particle flow patterns during the collision...
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Application of hidden Markov models to eye tracking data analysis of visual quality inspection operations
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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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Application of the Msplitmethod for filtering airborne laser scanning data-sets to estimate digital terrain models
PublicationALS point cloud filtering involves the separation of observations representing the physical terrain surface from those representing terrain details. A digital terrain model (DTM) is created from a subset of points representing the ground surface. The accuracy of the generated DTM is influenced by several factors, including the survey method used, the accuracy of the source data, the applied DTM generation algorithm, and the survey...
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Minimizing Distribution and Data Loading Overheads in Parallel Training of DNN Acoustic Models with Frequent Parameter Averaging
PublicationIn the paper we investigate the performance of parallel deep neural network training with parameter averaging for acoustic modeling in Kaldi, a popular automatic speech recognition toolkit. We describe experiments based on training a recurrent neural network with 4 layers of 800 LSTM hidden states on a 100-hour corpora of annotated Polish speech data. We propose a MPI-based modification of the training program which minimizes the...
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Data-driven models for fault detection using kernel PCA: A water distribution system case study
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Application of computational intelligence models in IoMT big data for heart disease diagnosis in personalized health care
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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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Application of Msplit method for filtering airborne laser scanning data sets to estimate digital terrain models
PublicationALS point cloud filtering involves the separation of observations representing the physical terrain surface from those representing terrain details. A digital terrain model (DTM) is created from a subset of points representing the ground surface. The accuracy of the generated DTM is influenced by several factors, including the survey method used, the accuracy of the source data, the applied DTM generation algorithm, and the survey...
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Using Isolation Forest and Alternative Data Products to Overcome Ground Truth Data Scarcity for Improved Deep Learning-based Agricultural Land Use Classification Models
PublicationHigh-quality labelled datasets represent a cornerstone in the development of deep learning models for land use classification. The high cost of data collection, the inherent errors introduced during data mapping efforts, the lack of local knowledge, and the spatial variability of the data hinder the development of accurate and spatially-transferable deep learning models in the context of agriculture. In this paper, we investigate...
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Towards More Realistic Probabilistic Models for Data Structures: The External Path Length in Tries under the Markov Model
PublicationTries are among the most versatile and widely used data structures on words. They are pertinent to the (internal) structure of (stored) words and several splitting procedures used in diverse contexts ranging from document taxonomy to IP addresses lookup, from data compression (i.e., Lempel- Ziv'77 scheme) to dynamic hashing, from partial-match queries to speech recognition, from leader election algorithms to distributed hashing...
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Correlation between in vitro and in vivo data on food digestion. What can we predict with static in vitro digestion models?
PublicationDuring the last decade, there has been a growing interest in understanding food's digestive fate in order to strengthen the possible effects of food on human health. Ideally, food digestion should be studied in vivo on humans but this is not always ethically and financially possible. Therefore, simple in vitro digestion models mimicking the gastrointestinal tract have been proposed as alternatives to in vivo experiments. Thus,...
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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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Predicting the Purchase of Electricity Prices for Renewable Energy Sources Based on Polish Power Grids Data Using Deep Learning Models for Controlling Small Hybrid PV Microinstallations
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Approaches to Static Digestion Models
PublicationIt is not possible to look in detail at the wide range of static digestion methods that have been used to date. However, this section looks at some of the general approaches that have been used to look at the digestion of various nutrients and bioactives. I have focussed on the two main nutrients that undergo digestion in the upper GI tract, namely protein and lipid. In the case of protein, the research has largely been driven...
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4D Models in World Wide Web
PublicationThe paper presents some results of research curried out within the framework of the European project named "Cultural Heritage Through Time" (CHT2). One of the main project aims were to develop a methodology for sharing multi-temporal information via the Internet (webGIS) for remote analysis of structures and landscapes over time. Reported in this paper results are focused on testing two technologies (Hexagon and Esri) for online...
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Diagnostic Models and Estimators for LDI in Transmission Pipelines
PublicationThis article considers and compares four analytical models of the pipeline flow process for leak detection and location tasks. The synthesis of these models is briefly outlined. Next, the methodology for generating data and diagnosing pipes is described, as well as experimental settings, assumptions and implemented scenarios. Finally, the quality of model-based diagnostic estimators has been evaluated for their bias, standard deviations...
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Identification of models and signals robust to occasional outliers
PublicationIn this paper estimation algorithms derived in the sense of the least sum of absolute errors are considered for the purpose of identification of models and signals. In particular, off-line and approximate on-line estimation schemes discussed in the work are aimed at both assessing the coefficients of discrete-time stationary models and tracking the evolution of time-variant characteristics of monitored signals. What is interesting,...
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Identification of models and signals robust to occasional outliers
PublicationIn this paper estimation algorithms derived in the sense of the least sum of absolute errors are considered for the purpose of identification of models and signals. In particular, off-line and approximate on-line estimation schemes discussed in the work are aimed at both assessing the coefficients of discrete-time stationary models and tracking the evolution of time-variant characteristics of monitored signals. What is interesting,...
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Training of Deep Learning Models Using Synthetic Datasets
PublicationIn order to solve increasingly complex problems, the complexity of Deep Neural Networks also needs to be constantly increased, and therefore training such networks requires more and more data. Unfortunately, obtaining such massive real world training data to optimize neural networks parameters is a challenging and time-consuming task. To solve this problem, we propose an easy-touse and general approach to training deep learning...
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Enhancing rheological muscle models with stochastic processes
PublicationPurpose: Biological musculoskeletal systems operate under variable conditions. Muscle stiffness, activation signals, and loads change during each movement. The presence of noise and different harmonic components in force production significantly influences the behaviour of the muscular system. Therefore, it is essential to consider these factors in numerical simulations. Methods: This study aims to develop a rheological mathematical...
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Simple SIR models with Markovian control
PublicationWe consider a random dynamical system, where the deterministic dynamics are driven by a finite-state space Markov chain. We provide a comprehensive introduction to the required mathematical apparatus and then turn to a special focus on the susceptible-infected-recovered epidemiological model with random steering. Through simulations we visualize the behaviour of the system and the effect of the high-frequency limit of the driving...
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Approximate and analytic flow models for leak detection and identification
PublicationThe article presents a comprehensive quantitative comparison of four analytical models that, in different ways, describe the flow process in transmission pipelines necessary in the task of detecting and isolating leaks. First, the analyzed models are briefly presented. Then, a novel model comparison framework was introduced along with a methodology for generating data and assessing diagnostic effectiveness. The study presents basic...
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Multi-level models of transport systems for traffic management
PublicationThe region of Pomorskie uses a variety of tools for forecasting and analysing transport. They can be operated, calibrated and updated with data that will be collected and stored in the TRISTAR system. An initiative of the Department of Highway Engineering of the Gdansk University of Technology is designed to develop and implement an integrated and hierarchical system for forecasting and analysing transport called MST (Multilevel...
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Comparative testing of numerical models of river ice jams
PublicationIce processes in general, and ice jams in particular, play a dominant role in the hydrologic regime of Canadian rivers, often causing extreme floods and affecting the life cycle of many aquatic, terrestrial, and avian species. Various numerical models have been developed to help simulate the formation and consequences of these very dynamic and often destructive jam events. To test and compare the performance of existing models,...
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Numerical simulation of asphalt mixtures fracture using continuum models
PublicationThe paper considers numerical models of fracture processes of semi-circular asphalt mixture specimens subjected to three-point bending. Parameter calibration of the asphalt mixture constitutive models requires advanced, complex experimental test procedures. The highly non-homogeneous material is numerically modelled by a quasicontinuum model. The computational parameters are averaged data of the components, i.e. asphalt, aggregate...
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Online brand communities’ contribution to digital business models
PublicationAbstract Purpose – There is limited research examining social drivers and mediators of online brand community identification in the context of business models development. This study aims to identify them behind the social mechanisms and present essential factors which should be applied in business models to foster value co-creation. Design/methodology/approach – Data were collected from a convenience sample of 712 cases gathered among...
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Electron-impact ionization of fluoromethanes – Review of experiments and binary-encounter models
PublicationExperiments and recommended data on electron-impact ionization of methane and fluoromethanes (CH3F, CH2F2, CHF3, CF4) are reviewed and compared with binary-encounter models (Gryzinski’s, ´ Deutsch and Märk’s, and Kim and Rudd’s). A good agreement between recent experiments and the two latter classical-like models is shown. Kim and Rudd’s model (calculated presently in the restricted HartreeFock 6-31**G orbital basis) predicts well...
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Variable length sliding models for banking clients face biometry
PublicationAn experiment was organized in 100 bank branches to acquire biometric samples from nearly 5000 clients including face images. A procedure for creating face verification models based on continuously expanding database of biometric samples is proposed, implemented, and tested. The presented model applies to circumstances where it is possible to collect and to take into account new biometric samples after each positive verification...
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The use of mathematical models for diagnosis of activated sludge systems in WWTP
PublicationIn this study diagnosis of activated sludge systems in wastewater treatment plant (WWTP) was investigated. Diagnosis of technical objects can be realized in many ways. One of the divisions of the diagnostic methods include modelling with or without a model of the object. The first of these is the analysis of the symptoms for which, based on the parameter values, the abnormality in the diagnosed objects are sought. Another way is...
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Bees Detection on Images: Study of Different Color Models for Neural Networks
PublicationThis paper presents an approach to bee detection in video streams using a neural network classifier. We describe the motivation for our research and the methodology of data acquisition. The main contribution to this work is a comparison of different color models used as an input format for a feedforward convolutional architecture applied to bee detection. The detection process has is based on a neural binary classifier that classifies...
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Numerical Issues and Approximated Models for the Diagnosis of Transmission Pipelines
PublicationThe chapter concerns numerical issues encountered when the pipeline flow process is modeled as a discrete-time state-space model. In particular, issues related to computational complexity and computability are discussed, i.e., simulation feasibility which is connected to the notions of singularity and stability of the model. These properties are critical if a diagnostic system is based on a discrete mathematical model of the flow...
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Models of using the Internet by young Poles and their social capital.
PublicationHighlights • Study examining Polish youth on internet usage styles. • Online communication is the most common form of spending time on the Internet. •...
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Optimizing Medical Personnel Speech Recognition Models Using Speech Synthesis and Reinforcement Learning
PublicationText-to-Speech synthesis (TTS) can be used to generate training data for building Automatic Speech Recognition models (ASR). Access to medical speech data is because it is sensitive data that is difficult to obtain for privacy reasons; TTS can help expand the data set. Speech can be synthesized by mimicking different accents, dialects, and speaking styles that may occur in a medical language. Reinforcement Learning (RL), in the...
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Integration and Visualization of the Results of Hydrodynamic Models in the Maritime Network-Centric GIS of Gulf of Gdansk
PublicationEnsuring of security in the coastal area makes on a seaside countries research in the field of infrastructure spatial information of environmental data. The paper presents the results of work on the construction of this infrastructure by integrating electronic navigational chart with ortophotomaps of coastal areas as well as numerical data from weather and hydrodynamic models. Paper focuses on a problems associated with creating...
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Methodology of research on the impact of ITS services on the safety and efficiency of road traffic using transport models
PublicationThe current assessment of the impact of Intelligent Transport System (ITS) services on the level of traffic safety and efficiency is based mainly on expert assessments, statistical surveys or several traffic safety models requiring development. There is no structured, uniform assessment method that would give the opportunity to compare the impact of ITS services and their different configurations. The paper presents the methodology...
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Exploring governance among social co-operatives: three models from Poland
PublicationThere has been overly interest regarding social enterprise and social entrepreneurship in theory and practice. In this paper the author introduces the workings of governance of small social enterprises i.e. social co-operatives, acting in most cases for the purpose of work and social integration of the marginalized, at the bottom of the pyramid of socio-economic system. The aim of this paper is to provide insights into under researched...
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Towards bees detection on images: study of different color models for neural networks
PublicationThis paper presents an approach to bee detection in videostreams using a neural network classifier. We describe the motivationfor our research and the methodology of data acquisition. The maincontribution to this work is a comparison of different color models usedas an input format for a feedforward convolutional architecture appliedto bee detection. The detection process has is based on a neural...
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Multiobjective Aerodynamic Optimization by Variable-Fidelity Models and Response Surface Surrogates
PublicationA computationally efficient procedure for multiobjective design optimization with variable-fidelity models and response surface surrogates is presented. The proposed approach uses the multiobjective evolutionary algorithm that works with a fast surrogate model, obtained with kriging interpolation of the low-fidelity model data enhanced by space-mapping correction exploiting a few high-fidelity training points. The initial Pareto...
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Modification of Selected Propagation Models in Terms of Path Loss Estimation in Container Terminal
PublicationIt is particularly important to look for any propagation model that could be useful for designing mobile radio networks in container terminal environment. Selected propagation models have been investigated. Firstly - basing on measurements results - they have been evaluated in this scope and the analysis has shown, that the adjustment is needed. This modification improved significantly the accuracy of path loss modelling. For the...
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Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review
PublicationThe aim of the presented review is to summarize the literature data on the accuracy and clinical applicability of artificial intelligence (AI) models as a valuable alternative to the current guidelines in predicting cardiac resynchronization therapy (CRT) response and phenotyping of patients eligible for CRT implantation. This systematic review was performed...
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A new method of presosns identification based on comparative analysis of 3D face models
PublicationThe article presents the use of modern close range photogrammetry for possessing highly accurate 3D models of the human face (including the ears). Modern methods used to obtain precise data describing the construction of a human face, and even the whole human body, should allow to get finished measurement material in a very short time. Those features belong to the optical scanning technology. Comparative analysis of models of the...
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Improving the Accuracy of Automatic Reconstruction of 3D Complex Buildings Models from Airborne Lidar Point Clouds
PublicationDue to high requirements of variety of 3D spatial data applications with respect to data amount and quality, automatized, effcient and reliable data acquisition and preprocessing methods are needed. The use of photogrammetry techniques—as well as the light detection and ranging (LiDAR) automatic scanners—are among attractive solutions. However, measurement data are in the form of unorganized point clouds, usually requiring transformation...
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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...