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Wyniki wyszukiwania dla: BAYESIAN NETWORKS
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Application of Bayesian Networks for Forecasting Future Model of Farm
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Dynamic Bayesian Networks for Symbolic Polyphonic Pitch Modeling
PublikacjaSymbolic pitch modeling is a way of incorporating knowledge about relations between pitches into the process of an- alyzing musical information or signals. In this paper, we propose a family of probabilistic symbolic polyphonic pitch models, which account for both the “horizontal” and the “vertical” pitch struc- ture. These models are formulated as linear or log-linear interpo- lations of up to fi ve sub-models, each of which is...
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Optimizing Construction Engineering Management Using Metaheuristic Methods and Bayesian Networks
PublikacjaThe construction of buildings invariably involves time and costs, and disruptions impact ongoing construction projects. Crisis situations in management strategies, structural confusion, and finan-cial miscalculations often arise due to misguided decision-making. This article proposes a method that combines the learning of Bayesian Networks and heuristic techniques to optimize deci-sion-making processes in construction scheduling....
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Application of Bayesian Networks in risk diagnostics arising from the degree of urban regeneration area degradation
PublikacjaUrban regeneration as a complex project, generates many extremely specific threats affecting the increase of investment risk. Its unique nature causes that probability parameter, normally applied in the process of risk quantification, is extremely difficult to estimate. Due to lack of historical data urban regeneration related activities are therefore associated with uncertainty. According to the authors, a useful tool for resolving...
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Risk Modelling with Bayesian Networks - Case Study: Construction of Tunnel under the Dead Vistula River in Gdansk
PublikacjaThe process of decision-making in public procurement of construction projects during the preparation and implementation phases ought to be supported by risk identification, assessment, and management. In risk assessment one has to take into account factors that lead to risk events (background info), as well as the information about the risk symptoms (monitoring info). Typically once the risks have been assessed a decision-maker...
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The Idea of Using Bayesian Networks in Forecasting Impact of Traffic-Induced Vibrations Transmitted through the Ground on Residential Buildings
PublikacjaTraffic–induced vibrations may constitute a considerable load to buildings. In this paper, vibrations transmitted through the ground caused by wheeled vehicles are considered. This phenomenon may cause cracking of plaster, cracks in load-bearing elements or even, in extreme cases, collapse of the whole structure. Measurements of vibrations of real structures are costly and laborious. Therefore, the aim of the present paper is to...
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A new fuzzy model of multi-criteria decision support based on Bayesian networks for the urban areas' decarbonization planning
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Application of Bayesian networks for inferring cause–effect relations from gene expression profiles of cancer versus normal cells
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Evidence-Based Risk Management for Civil Engineering Projects Using Bayesian Belief Networks (BBN)
PublikacjaThe authors are seeking new methods for improving the efficiency of the investments associated with the maintenance and operation of existing civil engineering structures. It is demonstrated how the knowledge about the elements of construction and operation phases and their relationships, combined with monitoring data can be used for more effective management of the risks associated with civil engineering projects. The methodology...
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Magdalena Apollo dr inż.
Osoby2017 - PhD in Civil Engineering, thesis: Risk management in construction investments related to urban regeneration projects, Gdansk University of Technology IX 2012–VI 2013 - Postgraduate Studies at Gdynia Maritime University: Research Project Management (IPMA D Certificate) 2010 – MSc in Management and Marketing, Gdansk University of Technology 2007 – MSc in Civil Engineering, Gdansk University of Technology 2007-2010 - structural...
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Prognostic and diagnostic capabilities of OOBN in assessing investment risk of complex construction projects
PublikacjaModelling decision problems using Bayesian networks is extremely valuable especially in case of issues related to uncertainty; it is also very helpful in constructing and understanding visual representation of the elements and their relations. This approach facilitates subsequent application of Bayesian networks, however there can be situations where using simple Bayesian networks is impractical or even ineffective. The aim of...
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Application possibilities of LBN for civil engineering issues
PublikacjaBayesian Networks (BN) are efficient to represent knowledge and for the reasoning in uncertainty. However the classic BN requires manual definition of the network structure by an expert, who also defines the values entered into the conditional probability tables. In practice, it can be time-consuming, hence the article proposes the use of Learning Bayesian Networks (LBN). The aim of the study is not only to present LBN, which can...
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Advanced Bayesian study on inland navigational risk of remotely controlled autonomous ship
PublikacjaThe arise of autonomous ships has necessitated the development of new risk assessment techniques and methods. This study proposes a new framework for navigational risk assessment of remotely controlled Maritime Autonomous Surface Ships (MASS). This framework establishes a set of risk influencing factors affecting safety of navigation of a remotely-controlled MASS. Next, model parameters are defined based on the risk factors, and...
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Expert systems in assessing the construction process safety taking account of the risk of disturbances
PublikacjaThe objective of the paper is to present the issue of safety manage-ment during the construction process. Threats in the form of disturb-ances may occur in the preparatory phase, during the execution of the construction project and also during its operational use. The arti-cle presents the concept of applying the methodology based, among others, on Learning Bayesian Networks, Artificial Neural Networks and Support Vector Machine,...
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Machine Learning in Multi-Agent Systems using Associative Arrays
PublikacjaIn this paper, a new machine learning algorithm for multi-agent systems is introduced. The algorithm is based on associative arrays, thus it becomes less complex and more efficient substitute of artificial neural networks and Bayesian networks, which is confirmed by performance measurements. Implementation of machine learning algorithm in multi-agent system for aided design of selected control systems allowed to improve the performance...
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Modelowanie ryzyka inwestycyjnego przy użyciu OOBN
PublikacjaCelem artykułu jest przedstawienie sieci Bayesa zorientowanych obiektowo (ang. Object Oriented Bayesian Networks – OOBN). Umożliwiają one dekompozycję złożonego modelu na pojedyncze obiekty, które reprezentują nie tylko różne grupy zagadnień, ale także pozwalają na modelowanie zależności czasowychmiędzy obiektami.Wykorzystanie obiektowych sieci Bayesa zaprezentowano na przykładzie projektu rewitalizacji. Przedstawiono zarówno wady,...
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A Model for Risk Assessment and Management of Construction Projects in Urban Conditions
PublikacjaThe authors are seeking new methods for improving the efficiency of the investments associated with the maintenance and operation of existing civil engineering structures. It is demonstrated how the knowledge about the elements of construction and operation phases and their relationships, combined with monitoring data can be used for more effective management of the risks associated with civil engineering projects. The methodology...
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Risk Diagnosis and Management with BBN for Civil Engineering Projects during Construction and Operation
PublikacjaThe authors demonstrate how expert knowledge about the construction and operation phases combined with monitoring data can be utilized for the diagnosis and management of risks typical to large civil engineering projects. The methodology chosen for estimating the probabilities of risk elements is known as Bayesian Belief Networks (BBN). Using a BBN model one can keep on updating the risk event probabilities as the new evidence...
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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
PublikacjaSurrogate 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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Ocena efektywności monitoringu obiektów inżynierskich za pomocą sieci Bayesa
PublikacjaW swojej pracy autorzy zaproponowali zastosowanie sieci Bayesa do projektowania monitoringu i podejmowania decyzji w działaniach eksploatacyjnych. Ponadto pokazano dwie metody oceny wartości informacji diagnostycznych. Pierwszą z nich jest wartość oczekiwana EVSI (ang. Expected Value of Sample Information), która stanowi podstawę do wyboru spośród alternatywnych obserwacji symptomów zmiennej diagnostycznej. Natomiast drugą metodą...
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Application of BN in Risk Diagnostics Arising from the Degree of Urban Regeneration Area Degradation
PublikacjaUrban regeneration as a complex project, generates many extremely specific threats affecting the increase of investment risk. Its unique nature causes that probability parameter, normally applied in the process of risk quantification, is extremely difficult to estimate. Due to lack of historical data urban regeneration related activities are therefore associated with uncertainty. According to the authors, a useful tool for resolving...
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Data-driven, probabilistic model for attainable speed for ships approaching Gdańsk harbour
PublikacjaThe 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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Improved Modeling of Microwave Structures Using Performance-Driven Fully-Connected Regression Surrogate
PublikacjaFast 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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Computer-assisted pronunciation training—Speech synthesis is almost all you need
PublikacjaThe research community has long studied computer-assisted pronunciation training (CAPT) methods in non-native speech. Researchers focused on studying various model architectures, such as Bayesian networks and deep learning methods, as well as on the analysis of different representations of the speech signal. Despite significant progress in recent years, existing CAPT methods are not able to detect pronunciation errors with high...
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Automatic recognition of males and females among web browser users based on behavioural patterns of peripherals usage
PublikacjaPurpose The purpose of this paper is to answer the question whether it is possible to recognise the gender of a web browser user on the basis of keystroke dynamics and mouse movements. Design/methodology/approach An experiment was organised in order to track mouse and keyboard usage using a special web browser plug-in. After collecting the data, a number of parameters describing the users’ keystrokes, mouse movements and clicks...
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Direct spectrum detection based on Bayesian approach
PublikacjaThe paper investigates the Bayesian framework's performance for a direct detection of spectrum parameters from the compressive measurements. The reconstruction signal stage is eliminated in by the Bayesian Compressive Sensing algorithm, which causes that the computational complexity and processing time are extremely reduced. The computational efficiency of the presented procedure is significantly...
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Observation Value Analysis – Integral Part of Bayesian Diagnostics
PublikacjaThe decision making process, in general, is understood as a process of selecting one of the available solutions to the problem. One of possible approaches supporting the process is Bayesian statistical decision theory providing a mathematical model to make decisions of a technical nature in conditions of uncertainty. Regarding above, a detailed subject of the research is to analyze the value of the observation, which is a part...
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Bayesian Optimization for solving high-frequency passive component design problems
PublikacjaIn this paper, the performance of the Bayesian Optimization (BO) technique applied to various problems of microwave engineering is studied. Bayesian optimization is a novel, non-deterministic, global optimization scheme that uses machine learning to solve complex optimization problems. However, each new optimization scheme needs to be evaluated to find its best application niche, as there is no universal technique that suits all...
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Soft-decision schemes for radar estimation of elevation at low grazing angles
PublikacjaIn modern radars, the problem of estimating elevation angle at low grazing angles is typically solved using superresolution techniques. These techniques often require one to provide an estimate of the number of waveforms impinging the array, which one can accomplish using model selection techniques. In this paper, we investigate the performance of an alternative approach, based on the Bayesian-like model averaging. The Bayesian...
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Vibration testing in buildings and safety of their operation
PublikacjaThe paper presents the issue of vibrations in residential buildings located near roads. It describes the measurement methodology and criteria for assessing the impact of vibrations generated by passing trucks. The article specifies a method to establish the impact on the operation of the examined facilities and it promotes the idea of employing a Bayesian network to determine probabilistically the level of risk to single-family...
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Wireless LAN with noncooperative anonymous stations: QOS provisioning via war of attrition
PublikacjaMAC-layer QoS provision necessitates an admission scheme to grant a requested QoS notwithstanding subse-quent requests. For an ad hoc WLAN with anonymous stations, we assume a degree of power awareness to propose a session- rather than frame-level bidding for bandwidth. Next we analyze the underlying Bayesian war of attrition game.
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On Bayesian Tracking and Prediction of Radar Cross Section
PublikacjaWe consider the problem of Bayesian tracking of radar cross section. The adopted observation model employs the gamma family, which covers all Swerling cases in a unified framework. State dynamics are modeled using a nonstationary autoregressive gamma process. The principal component of the proposed solution is a nontrivial gamma approximation, applied during the time update recursion. The superior performance of the proposed approach...
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Medley filters - simple tools for efficient signal smoothing
PublikacjaMedley filters are defined as convex combinations of elementary smoothing filters (averaging, median) with different smoothing bandwidths. It is shown that when adaptive weights of such a mixture are evaluated using the recently proposed Bayesian rules, one obtains a tool which often outperforms the state-of-the-art wavelet-based smoothing algorithms. Additionally, unlike wavelet-based procedures, medley filters can easily cope...
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Sharp transitions in low-number quantum dots Bayesian magnetometry
PublikacjaWe consider Bayesian estimate of static magnetic field, characterized by a prior Gaussian probability distribution, in systems of a few electron quantum dot spins interacting with infinite temperature spin environment via hyperfine interaction. Sudden transitions among optimal states and measurements are observed. Usefulness of measuring occupation levels is shown for all times of the evolution, together with the role of entanglement...
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Limiting distribution of the three-state semi-Markov model of technical state transitions of ship power plant machines and its applicability in operational decision-making.
PublikacjaThe article presents the three-state semi-Markov model of the process {W(t): t 0} of state transitions of a ship power plant machine, with the following interpretation of these states: s1 – state of full serviceability, s2 – state of partial serviceability, and s3 – state of unserviceability. These states are precisely defined for the ship main engine (ME). A hypothesis is proposed which explains the possibility of application...
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On autoregressive spectrum estimation using the model averaging technique
PublikacjaThe problem of estimating spectral density of a nonstationary process satisfying local stationarity conditions is considered. The proposed solution is a two step procedure based on local autoregressive (AR) modeling. In the first step Bayesian-like averaging of AR models, differing in order, is performed. The main contribution of the paper is development of a new final-prediction-error-like statistic, which can be used to select...
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Music Data Processing and Mining in Large Databases for Active Media
PublikacjaThe aim of this paper was to investigate the problem of music data processing and mining in large databases. Tests were performed on a large data-base that included approximately 30000 audio files divided into 11 classes cor-responding to music genres with different cardinalities. Every audio file was de-scribed by a 173-element feature vector. To reduce the dimensionality of data the Principal Component Analysis (PCA) with variable...
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Comparing Arbitrary Unrooted Phylogenetic Trees Using Generalized Matching Split Distance
PublikacjaIn the paper, we describe a method for comparing arbitrary, not necessary fully resolved, unrooted phylogenetic trees. Proposed method is based on finding a minimum weight matching in bipartite graphs and can be regarded as a generalization of well-known Robinson-Foulds distance. We present some properties and advantages of the new distance. We also investigate some properties of presented distance in a common biological problem...
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Locally Adaptive Cooperative Kalman Smoothing and Its Application to Identification of Nonstationary Stochastic Systems
PublikacjaOne of the central problems of the stochastic approximation theory is the proper adjustment of the smoothing algorithm to the unknown, and possibly time-varying, rate and mode of variation of the estimated signals/parameters. In this paper we propose a novel locally adaptive parallel estimation scheme which can be used to solve the problem of fixed-interval Kalman smoothing in the presence of model uncertainty. The proposed solution...
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An adaptive-noise Augmented Kalman Filter approach for input-state estimation in structural dynamics
PublikacjaThe establishment of a Digital Twin of an operating engineered system can increase the potency of Structural Health Monitoring (SHM) tools, which are then bestowed with enhanced predictive capabilities. This is particularly relevant for wind energy infrastructures, where the definition of remaining useful life is a main driver for assessing the efficacy of these systems. In order to ensure a proper representation of the physical...
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A Bayesian regularization-backpropagation neural network model for peeling computations
PublikacjaA Bayesian regularization-backpropagation neural network (BRBPNN) model is employed to predict some aspects of the gecko spatula peeling, viz. the variation of the maximum normal and tangential pull-off forces and the resultant force angle at detachment with the peeling angle. K-fold cross validation is used to improve the effectiveness of the model. The input data is taken from finite element (FE) peeling results. The neural network...
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Identification of nonstationary multivariate autoregressive processes– Comparison of competitive and collaborative strategies for joint selection of estimation bandwidth and model order
PublikacjaThe problem of identification of multivariate autoregressive processes (systems or signals) with unknown and possibly time-varying model order and time-varying rate of parameter variation is considered and solved using parallel estimation approach. Under this approach, several local estimation algorithms, with different order and bandwidth settings, are run simultaneously and compared based on their predictive performance. First,...
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A probabilistic-driven framework for enhanced corrosion estimation of ship structural components
PublikacjaThe work proposes a probabilistic-driven framework for enhanced corrosion estimation of ship structural components using Bayesian inference and limited measurement data. The new approach for modelling measurement uncertainty is proposed based on the results of previous corrosion tests that incorporate the non-uniform character of the corroded surface of structural components. The proposed framework's basic features are outlined,...
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Genre-Based Music Language Modeling with Latent Hierarchical Pitman-Yor Process Allocation
PublikacjaIn 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
PublikacjaIn 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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On reliability assessment of ship machinery system in different autonomy degree; A Bayesian-based approach
PublikacjaAnalyzing the reliability of autonomous ships has recently attracted attention mainly due to epistemic uncertainty (lack of knowledge) integrated with automatic operations in the maritime sector. The advent of new random failures with unrecognized failure patterns in autonomous ship operations requires a comprehensive reliability assessment specifically aiming at estimating the time in which the ship can be trusted to be left unattended....
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The mechanisms of technological innovation in SMEs: a Bayesian Network Analysis of EU regional policy impact on Polish firms.
PublikacjaWe study the underlying mechanisms of technological innovation in SMEs in the context of ex-post evaluation of European Union’s regional policy. Our aim is to explain the observed change in firms’ innovativeness after receiving EU support for technological investment. To do so, we take an approach that is novel in innovation studies: a Bayesian Network Analysis to assess the effectiveness of EU policy instrument for technological...
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Coupling between Blood Pressure and Subarachnoid Space Width Oscillations during Slow Breathing
PublikacjaThe precise mechanisms connecting the cardiovascular system and the cerebrospinal fluid (CSF) are not well understood in detail. This paper investigates the couplings between the cardiac and respiratory components, as extracted from blood pressure (BP) signals and oscillations of the subarachnoid space width (SAS), collected during slow ventilation and ventilation against inspiration resistance. The experiment was performed on...
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Adaptive Hyperparameter Tuning within Neural Network-based Efficient Global Optimization
PublikacjaIn this paper, adaptive hyperparameter optimization (HPO) strategies within the efficient global optimization (EGO) with neural network (NN)-based prediction and uncertainty (EGONN) algorithm are proposed. These strategies utilize Bayesian optimization and multiarmed bandit optimization to tune HPs during the sequential sampling process either every iteration (HPO-1itr) or every five iterations (HPO-5itr). Through experiments using...
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Design and Analysis of Artificial Neural Network (ANN) Models for Achieving Self-Sustainability in Sanitation
PublikacjaThe present study investigates the potential of using fecal ash as an adsorbent and demonstrates a self-sustaining, optimized approach for urea recovery from wastewater streams. Fecal ash was prepared by heating synthetic feces to 500 °C and then processing it as an adsorbent for urea adsorption from synthetic urine. Since this adsorption approach based on fecal ash is a promising alternative for wastewater treatment, it increases...