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Global Surrogate Modeling by Neural Network-Based Model Uncertainty
PublicationThis work proposes a novel adaptive global surrogate modeling algorithm which uses two neural networks, one for prediction and the other for the model uncertainty. Specifically, the algorithm proceeds in cycles and adaptively enhances the neural network-based surrogate model by selecting the next sampling points guided by an auxiliary neural network approximation of the spatial error. The proposed algorithm is tested numerically...
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Neural Network-Based Sequential Global Sensitivity Analysis Algorithm
PublicationPerforming global sensitivity analysis (GSA) can be challenging due to the combined effect of the high computational cost, but it is also essential for engineering decision making. To reduce this cost, surrogate modeling such as neural networks (NNs) are used to replace the expensive simulation model in the GSA process, which introduces the additional challenge of finding the minimum number of training data samples required to...
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Cellular network quality evaluation at a university campus on the eve of 5G
PublicationThanks to the availability of mobile devices and the spread of broadband access around the world, the number of network users continues to grow. This has raised user awareness when it comes to the quality of content they consume. Many service providers and operators focus on monitoring QoN (Quality of Network) and QoS (Quality of Service) parameters, particularly those influenced by bandwidth and latency. However, for most end-users,...
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Technology brands in the digitally-based and network economy. Setting the background
PublicationThis chapter defines and draws a general picture on Digital Technologies and Technology Brands’ meaning in the current economy. It contextualises the role and channels of digital technology’s impact on their shaping technology brands in network economy. It briefly explains why digital, innovative technologies constitute a perfect background and environment for technology brands of global scope. It discusses the place of technology...
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Categorization of emotions in dog behavior based on the deep neural network
PublicationThe aim of this article is to present a neural system based on stock architecture for recognizing emotional behavior in dogs. Our considerations are inspired by the original work of Franzoni et al. on recognizing dog emotions. An appropriate set of photographic data has been compiled taking into account five classes of emotional behavior in dogs of one breed, including joy, anger, licking, yawning, and sleeping. Focusing on a particular...
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The Performance of ASON/GMPLS Network with Hierarchical Control Plane Structure
PublicationThe paper regards the problem of ASON/GMPLS network performance with hierarchical control plane structure in condition of incomplete domain network information. The authors propose the hierarchical ASON/GMPLS control plane architecture, which fulfills the requirements of modern optical networks and allows to control the multidomain network with requirement quality of service. The authors examine the scalability and properties of...
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A Bayesian regularization-backpropagation neural network model for peeling computations
PublicationA 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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Application of a fuzzy neural network for river water quality prediction
PublicationMonitoring i modelowanie zmian w jakości wód powierzchniowych stanowią jeden z kluczowych elementów monitoringu i zarządzania ochroną środowiska na skalę globalną. Kontrolowanie tak złożonych i nieliniowych w swojej charakterystyce obiektów, jakimi są rzeki, jest trudnym zadaniem. Zazwyczaj do tego celu wykorzystuje się modele matematyczne, jednak czasem wymagają one bardzo dużej ilości danych, lub czas oczekiwania na odpowiedź...
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SNAIL Promotes Metastatic Behavior of Rhabdomyosarcoma by Increasing EZRIN and AKT Expression and Regulating MicroRNA Networks
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A novel class-based protection algorithm providing fast service recovery in IP/WDM networks
PublicationW artykule rozważa się warstwową strukturę sieci IP-MPLS/WDM. Węzły sieci mają funkcjonalność zarówno optycznych krotnic transferowych (OXC), jak i routerów IP. Dowolne dwa routery IP mogą być ze sobą połączone poprzez logiczne łącze IP realizowane przez ścieżkę optyczną WDM. Zaproponowano metodę klasową doboru tras przeżywalnych zapewniającą szybkie odtwarzanie uszkodzonych strumieni ruchu zarówno w warstwie WDM jak i IP-MPLS....
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Multiservice Optical Fibre Local Area Networks For Data Transmission And Telemetry In The Electric Power Industry
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Optimum Choice of Randomly Oriented Carbon Nanotube Networks for UV-Assisted Gas Sensing Applications
PublicationWe investigated the noise and photoresponse characteristics of various optical transparencies of nanotube networks to identify an optimal randomly oriented network of carbon nanotube (CNT)-based devices for UV-assisted gas sensing applications. Our investigation reveals that all of the studied devices demonstrate negative photoconductivity upon exposure to UV light. Our studies confirm the effect of UV irradiation on the electrical...
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Efficient handover scheme for Mobile IPv4 over IEEE 802.11 networks with IEEE 802.21 triggers.
PublicationEfektywność przełączania jest bardzo istotnym parametrem, decydującym o pracy sieci bezprzewodowych, realizujacych usługi multimedialne na wysokim poziomie jakości. Użytkownicy takich sieci oczekują ciągłej obsługi podczas procesu przemieszczania się. Okazuje się, że istotnym źródlem opóźnień są nieefektywne procedury przełączania w warstwach drugiej i trzeciej, wynikający częściowo z postulatu o separacji funkcji realizowanych...
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A novel genetic approach to provide differentiated levels of service resilience in IP-MPLS/WDM networks
PublicationThis paper introduces a novel class-based method of survivable routing for connection-oriented IP-MPLS/WDM networks, called MLS-GEN-H. The algorithm is designed to provide differentiated levels of service survivability in order to respond to varying requirements of end-users. It divides the complex problem of survivable routing in IP-MPLS/WDM networks into two subproblems, one for each network layer, which enables finding the...
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Deformation Analysis of Geodetic Networks by Applying M split Estimation with Conditions Binding the Competitive Parameters
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Set membership estimation of parameters and variables in dynamic networks by recursive algorithms with moving measurment window
PublicationW artykule rozważana jest łączna estymacja przedziałowa zmiennych i parametrów w złożonej sieci dynamicznej w oparciu niepewne modele parametryczne i ograniczoną liczbę pomiarów. Opracowany został rekursywny algorytm estymacji z przesuwnym oknem pomiarowym, odpowiedni dla monitorowania sieci on-line. Okno pomiarowe pozwala na stabilizowanie klasycznego algorytmu rekurencyjnego estymacji i znacznie poprawienie obcisłości estymat....
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Testbed analysis of video and VoIP transsmission performance in IEEE 802.11 b/g/n networks
PublicationThe aim of the work is to analyze capabilities and limitations of different implementations of IEEE 802.11 technologies (IEEE 802.11 b/g/n), utilized for both video streaming and VoIP calls directed to mobile devices. Our preliminary research showed that results obtained with currently popular simulation tools can be drastically different than these possible in real-world environment, so, in order to correctly evaluate performance...
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Quantum superadditivity in linear optics networks: Sending bits via multiple-access Gaussian channels
PublicationSuperadditivity effects of communication capacities are known in the case of discrete variable quantum channels. We describe the continuous variable analog of one of these effects in the framework of Gaussian multiple access channels (MACs). Classically, superadditivity-type effects are strongly restricted: For example, adding resources to one sender is never advantageous to other senders in sending their respective information...
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Long-distance quantum communication over noisy networks without long-time quantum memory
PublicationThe problem of sharing entanglement over large distances is crucial for implementations of quantum cryptography. A possible scheme for long-distance entanglement sharing and quantum communication exploits networks whose nodes share Einstein-Podolsky-Rosen (EPR) pairs. In Perseguers et al. [Phys. Rev. A 78, 062324 (2008)] the authors put forward an important isomorphism between storing quantum information in a dimension D and transmission...
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Efficient Usage of Dense GNSS Networks in Central Europe for the Visualization and Investigation of Ionospheric TEC Variations
PublicationThe technique of the orthogonal projection of ionosphere electronic content variations for mapping total electron content (TEC) allows us to visualize ionospheric irregularities. For the reconstruction of global ionospheric characteristics, numerous global navigation satellite system (GNSS) receivers located in different regions of the Earth are used as sensors. We used dense GNSS networks in central Europe to detect and investigate...
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Nodal cooperation equilibrium analysis in multi-hop wireless ad hoc networks with a reputation system
PublicationMotivated by the concerns of cooperation security, this work examines selected principles of state-of-the-art reputation systems for multi-hop ad hoc networks and their impact upon optimal strategies for rational nodes. An analytic framework is proposed and used for identification of effective cooperation-enforcement schemes. It is pointed out that optimum rather than high reputation can be expected to be sought by rational nodes.
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When Neural Networks Meet Decisional DNA: A Promising New Perspective for Knowledge Representation and Sharing
PublicationABSTRACT In this article, we introduce a novel concept combining neural network technology and Decisional DNA for knowledge representation and sharing. Instead of using traditional machine learning and knowledge discovery methods, this approach explores the way of knowledge extraction through deep learning processes based on a domain’s past decisional events captured by Decisional DNA. We compare our approach with kNN (k-nearest...
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Evidence-Based Risk Management for Civil Engineering Projects Using Bayesian Belief Networks (BBN)
PublicationThe 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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Assessment of Therapeutic Progress After Acquired Brain Injury Employing Electroencephalography and Autoencoder Neural Networks
PublicationA method developed for parametrization of EEG signals gathered from participants with acquired brain injuries is shown. Signals were recorded during therapeutic session consisting of a series of computer assisted exercises. Data acquisition was performed in a neurorehabilitation center located in Poland. The presented method may be used for comparing the performance of subjects with acquired brain injuries (ABI) who are involved...
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Application of Bayesian Networks in risk diagnostics arising from the degree of urban regeneration area degradation
PublicationUrban 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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Determination of Odour Interactions in Gaseous Mixtures Using Electronic Nose Methods with Artificial Neural Networks
PublicationThis paper presents application of an electronic nose prototype comprised of eight sensors, five TGS-type sensors, two electrochemical sensors and one PID-type sensor, to identify odour interaction phenomenon in two-, three-, four- and five-component odorous mixtures. Typical chemical compounds, such as toluene, acetone, triethylamine, α-pinene and n-butanol, present near municipal landfills and sewage treatment plants were subjected...
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INFLUENCE OF DATA NORMALIZATION ON THE EFFECTIVENESS OF NEURAL NETWORKS APPLIED TO CLASSIFICATION OF PAVEMENT CONDITIONS – CASE STUDY
PublicationIn recent years automatic classification employing machine learning seems to be in high demand for tele-informatic-based solutions. An example of such solutions are intelligent transportation systems (ITS), in which various factors are taken into account. The subject of the study presented is the impact of data pre-processing and normalization on the accuracy and training effectiveness of artificial neural networks in the case...
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A novel approach exploiting properties of convolutional neural networks for vessel movement anomaly detection and classification
PublicationThe article concerns the automation of vessel movement anomaly detection for maritime and coastal traffic safety services. Deep Learning techniques, specifically Convolutional Neural Networks (CNNs), were used to solve this problem. Three variants of the datasets, containing samples of vessel traffic routes in relation to the prohibited area in the form of a grayscale image, were generated. 1458 convolutional neural networks with...
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Deep neural networks for human pose estimation from a very low resolution depth image
PublicationThe work presented in the paper is dedicated to determining and evaluating the most efficient neural network architecture applied as a multiple regression network localizing human body joints in 3D space based on a single low resolution depth image. The main challenge was to deal with a noisy and coarse representation of the human body, as observed by a depth sensor from a large distance, and to achieve high localization precision....
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Classification of objects in the LIDAR point clouds using Deep Neural Networks based on the PointNet model
PublicationThis work attempts to meet the challenges associated with the classification of LIDAR point clouds by means of deep learning. In addition to achieving high accuracy, the designed system should allow the classification of point clouds covering an area of several dozen square kilometers within a reasonable time interval. Therefore, it must be characterized by fast processing and efficient use of memory. Thus, the most popular approaches...
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Improving Accuracy of Contactless Respiratory Rate Estimation by Enhancing Thermal Sequences with Deep Neural Networks
PublicationEstimation of vital signs using image processing techniques have already been proved to have a potential for supporting remote medical diagnostics and replacing traditional measurements that usually require special hardware and electrodes placed on a body. In this paper, we further extend studies on contactless Respiratory Rate (RR) estimation from extremely low resolution thermal imagery by enhancing acquired sequences using Deep...
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Formation of Protein Networks between Mucins: Molecular Dynamics Study Based on the Interaction Energy of the System
PublicationMolecular dynamics simulations have been performed for a model aqueous solution of mucin. As mucin is a central part of lubricin, a key component of synovial fluid, we investigate its ability to form cross-linked networks. Such network formation could be of major importance for the viscoelastic properties of the soft-matter system and crucial for understanding the lubrication mechanism in articular cartilage. Thus,the inter- and...
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Adrian Bekasiewicz dr hab. inż.
PeopleAdrian Bekasiewicz received the MSc, PhD, and DSc degrees in electronic engineering from Gdansk University of Technology, Poland, in 2011, 2016, and 2020, respectively. In 2014, he joined Engineering Optimization & Modeling Center where he held a Research Associate and a Postdoctoral Fellow positions, respectively. Currently, he is an Associate Professor with Gdansk University of Technology, Poland. His research interests include...
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International Journal of Computer Network and Information Security
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International Journal of Mobile Network Design and Innovation
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Network Modeling and Analysis in Health Informatics and Bioinformatics
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Journal of the International Network for Korean Language and Culture
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International Journal of Distributed Sensor Networks
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Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications
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Event driven MPC for networked control systems
PublicationBecause of variable delays and stochastic data packets loss networked control systems require suitable algorithms to ensure stability of the control system and guarantee desired control performance. This paper presents the idea of an event driven approach with MPC controller. In opposition to network compensation, where standard regulators are used, the presented solution integrates network with plant. A MPC based controller is...
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Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers
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High-resolution microscopy assisted mechanical modeling of ultrafine electrospun network
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Ultracapacitor modeling and control with discrete fractional order artificial neural network
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Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer
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<title>Intranet and Internet metrological network with photonic sensors and transmission</title>
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Using Probabilistic Availability Measures for Predicting Targeted Attacks on Network Nodes
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Network Protection Against Node Attacks Based on Probabilistic Availability Measures
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Artificial-Neural-Network-Based Sensorless Nonlinear Control of Induction Motors
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Network relationship’s transformation as the effect of the development of trust in cooperation with a foreign partner
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Metal heksacyanoferrate network synthesized inside polymer matrix for electrochemical capacitors
PublicationPrzedmiotem publikacji jest charakterystyka elektrochemiczna polimeru przewodzącego (poli(3,4-etylenodioksytiofenu)) modyfikowanego błękitem pruskim oraz jego pochodnymi, pochodną kobaltową oraz pochodną niklową. Materiały są stabilne elektrochemicznie, a uzyskane wartości pojemności elektrycznej pozwalają na wykorzystanie ich przy konstrukcji kondensatorów elektrochemicznych.