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Search results for: MICROWAVE ENGINEERING, COMPUTER-AIDED DESIGN, MULTI-CRITERIAL OPTIMIZATION, MACHINE LEARNING, NEURAL NETWORKS
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Forecasting energy consumption and carbon dioxide emission of Vietnam by prognostic models based on explainable machine learning and time series
PublicationThis study assessed the usefulness of algorithms in estimating energy consumption and carbon dioxide emissions in Viet- nam, in which the training dataset was used to train the models linear regression, random forest, XGBoost, and AdaBoost, allowing them to comprehend the patterns and relationships between population, GDP, and carbon dioxide emissions, energy consumption. The results revealed that random forest, XGBoost, and AdaBoost...
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High-Efficacy Global Optimization of Antenna Structures by Means of Simplex-Based Predictors
PublicationDesign of modern antenna systems has become highly dependent on computational tools, especially full-wave electromagnetic (EM) simulation models. EM analysis is capable of yielding accurate representation of antenna characteristics at the expense of considerable evaluation time. Consequently, execution of simulation-driven design procedures (optimization, statistical analysis, multi-criterial design) is severely hindered by the...
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LOS and NLOS identification in real indoor environment using deep learning approach
PublicationVisibility conditions between antennas, i.e. Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) can be crucial in the context of indoor localization, for which detecting the NLOS condition and further correcting constant position estimation errors or allocating resources can reduce the negative influence of multipath propagation on wireless communication and positioning. In this paper a deep learning (DL) model to classify LOS/NLOS...
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A bisection‐based heuristic for rapid EM‐driven multiobjective design of compact impedance transformers
PublicationDesign of microwave structures is a multiobjective task where several conflicting requirements have to be considered at the same time. For contemporary circuits characterized by complex geometries, multiobjective optimization cannot be performed using standard population‐based algorithms due to high cost of electromagnetic (EM) evaluations. In this work, we propose a deterministic approach for fast EM‐driven multiobjective design...
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Rediscovering Automatic Detection of Stuttering and Its Subclasses through Machine Learning—The Impact of Changing Deep Model Architecture and Amount of Data in the Training Set
PublicationThis work deals with automatically detecting stuttering and its subclasses. An effective classification of stuttering along with its subclasses could find wide application in determining the severity of stuttering by speech therapists, preliminary patient diagnosis, and enabling communication with the previously mentioned voice assistants. The first part of this work provides an overview of examples of classical and deep learning...
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Machine learning-based prediction of seismic limit-state capacity of steel moment-resisting frames considering soil-structure interaction
PublicationRegarding the unpredictable and complex nature of seismic excitations, there is a need for vulnerability assessment of newly constructed or existing structures. Predicting the seismic limit-state capacity of steel Moment-Resisting Frames (MRFs) can help designers to have a preliminary estimation and improve their views about the seismic performance of the designed structure. This study improved data-driven decision techniques in...
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Joint optimization of anycast and unicast flows in survivable optical networks
PublicationZnane z literatury dotychczasowe prace związane z ochroną ruchu przed awarią elementów sieci dotyczą transmisji typu unicast (jeden-do-jednego). Niniejszy artykuł jest pierwszym prezentującym rozwiązanie jednoczesnej ochrony transmisji anycast (jeden-do-jednego-z-wielu) oraz transmisji unicast. Proponowane podejście wykorzystuje model ochrony ścieżki (ang. path protection). Zagadnienie zostało sformułowane w postaci odpowiedniego...
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GPU Acceleration of Multilevel Solvers for Analysis of Microwave Components With Finite Element Method
PublicationThe letter discusses a fast implementation of the conjugate gradient iterative method with ${rm E}$-field multilevel preconditioner applied to solving real symmetric and sparse systems obtained with vector finite element method. In order to accelerate computations, a graphics processing unit (GPU) was used and significant speed-up (2.61 fold) was achieved comparing to a central processing unit (CPU) based approach. These results...
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Topology recognition and leader election in colored networks
PublicationTopology recognition and leader election are fundamental tasks in distributed computing in networks. The first of them requires each node to find a labeled isomorphic copy of the network, while the result of the second one consists in a single node adopting the label 1 (leader), with all other nodes adopting the label 0 and learning a path to the leader. We consider both these problems in networks whose nodes are equipped with...
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Resource constrained neural network training
PublicationModern applications of neural-network-based AI solutions tend to move from datacenter backends to low-power edge devices. Environmental, computational, and power constraints are inevitable consequences of such a shift. Limiting the bit count of neural network parameters proved to be a valid technique for speeding up and increasing efficiency of the inference process. Hence, it is understandable that a similar approach is gaining...
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Neural network model of ship magnetic signature for different measurement depths
PublicationThis paper presents the development of a model of a corvette-type ship’s magnetic signature using an artificial neural network (ANN). The capabilities of ANNs to learn complex relationships between the vessel’s characteristics and the magnetic field at different depths are proposed as an alternative to a multi-dipole model. A training dataset, consisting of signatures prepared in finite element method (FEM) environment Simulia...
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Features of Machine Cyclogram Optimization with the Account of Interaction of Mechanism Links with Stops
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Neural Approximators for Variable-Order Fractional Calculus Operators (VO-FC)
PublicationThe paper presents research on the approximation of variable-order fractional operators by recurrent neural networks. The research focuses on two basic variable-order fractional operators, i.e., integrator and differentiator. The study includes variations of the order of each fractional operator. The recurrent neural network architecture based on GRU (Gated Recurrent Unit) cells functioned as a neural approximation for selected...
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ProSIL software for computer aided functional Safety management = Program komputerowy ProSIL do wspomagania zarządzaniem bezpieczeństwa funkcjonalnego
Publication..
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A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multi-Head Convolutional Attention
PublicationTogether with fast advancement of the Internet of Things (IoT), smart healthcare applications and systems are equipped with increasingly more wearable sensors and mobile devices. These sensors are used not only to collect data, but also, and more importantly, to assist in daily activity tracking and analyzing of their users. Various human activity recognition (HAR) approaches are used to enhance such tracking. Most of the existing...
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A complete design of contra-rotating propellers using the new computer system
PublicationPraca przedstawia system komputerowy do projektowania okrętowych układów śrub przeciwbieżnych oparty na modelu linii nośnej i powierzchni nośnej. System uwzględnia niestacjonarne oddziaływania hydrodynamiczne pomiędzy przednią a tylną śrubą układu, a także oddziaływania z kadłubem statku. System umożliwia optymalizację projektu z punktu widzenia minimalizacji kawitacji, niestacjonarnych sił na wale napędowym oraz generowanych pulsacji...
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CBR methodology application in an expert system for aided design ship's engine room automation
PublicationW artykule przedstawiono metody obliczania podobieństwa na podstawie przypadków, zastosowane w opracowanym systemie ekspertowym do wspomagania projektowania siłowni okrętowej. Do realizacji metody na przykładzie automatyki napędu głównego zastosowano oprogramowanie bazy danych i system ekspertowy. Uzyskane wyniki wyszukiwania w bazie danych statków podobnych zostały porównane i przeanalizowane. Do weryfikacji zastosowanych w bazie...
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Designing RBF Networks Using the Agent-Based Population Learning Algorithm
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Smart Knowledge Engineering for Cognitive Systems: A Brief Overview
PublicationCognition in computer sciences refers to the ability of a system to learn at scale, reason with purpose, and naturally interact with humans and other smart systems, such as humans do. To enhance intelligence, as well as to introduce cognitive functions into machines, recent studies have brought humans into the loop, turning the system into a human–AI hybrid. To effectively integrate and manipulate hybrid knowledge, suitable technologies...
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Study of Multi-Class Classification Algorithms’ Performance on Highly Imbalanced Network Intrusion Datasets
PublicationThis paper is devoted to the problem of class imbalance in machine learning, focusing on the intrusion detection of rare classes in computer networks. The problem of class imbalance occurs when one class heavily outnumbers examples from the other classes. In this paper, we are particularly interested in classifiers, as pattern recognition and anomaly detection could be solved as a classification problem. As still a major part of...
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Arterial cannula shape optimization by means of the rotational firefly algorithm
PublicationThe article presents global optimization results of arterial cannula shapes by means of the newly modified firefly algorithm. The search for the optimal arterial cannula shape is necessary in order to minimize losses and prepare the flow that leaves the circulatory support system of a ventricle (i.e. blood pump) before it reaches the heart. A modification of the standard firefly algorithm, the so-called rotational firefly algorithm,...
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Overcoming “Big Data” Barriers in Machine Learning Techniques for the Real-Life Applications
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Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches
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Variable-fidelity shape optimization of dual-rotor wind turbines
PublicationPurpose Dual-rotor wind turbines (DRWTs) are a novel type of wind turbines that can capture more power than their single-rotor counterparts. Because their surrounding flow fields are complex, evaluating a DRWT design requires accurate predictive simulations, which incur high computational costs. Currently, there does not exist a design optimization framework for DRWTs. Since the design optimization of DRWTs requires numerous model...
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Design and Analysis of Artificial Neural Network (ANN) Models for Achieving Self-Sustainability in Sanitation
PublicationThe 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...
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Developments in manufacturing engineering and machining process planning
PublicationCurrent research conducted in the Department of Manufacturing Engineering and Automation is presented in the paper. This includes: abrasive cutting off, grinding with lapping kinematics, rapid prototyping and machining procrss planning. Experimental results and computer simulation are presented and analysed.
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Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests
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Designing the Composition of Cement Stabilized Rammed Earth Using Artificial Neural Networks
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Self-organizing Artificial Neural Networks into Hydrographic Big Data Reduction Process
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Aerodynamic excitations generated in turbine shroud clearance determined bymeans of neural networks
PublicationSiły aerodynamiczne generowane w uszczelnieniach turbinowych z reguły opisywane są modelem liniowym. Przy dużych drganiach wirnika sposób ten daje niezbyt dokładne wyniki. Zaproponowano wykorzystanie sieci neuronowych do określania sił ciśnieniowych powstających w uszczelnieniu. Wyniki porównano z badaniami eksperymentalnymi.
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Heavy Duty Vehicle Fuel Consumption Modelling Based on Exploitation Data by Using Artificial Neural Networks
PublicationOne of the ways to improve the fuel economy of heavy duty trucks is to operate the combustion engine in its most efficient operating points. To do that, a mathematical model of the engine is required, which shows the relations between engine speed, torque and fuel consumption in transient states. In this paper, easy accessible exploitation data collected via CAN bus of the heavy duty truck were used to obtain a model of a diesel...
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Design of Optical Wireless Networks with Fair Traffic Flows
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Design of Weather Disruption-Tolerant Wireless Mesh Networks
PublicationZ uwagi na wysoki koszt realizacji sieci teleinformatycznych wykorzystujących przewodową transmisję światłowodową, bezprzewodowe sieci kratowe (WMN) oferujące transmisję rzędu 1-10 Gb/s (przy wykorzystaniu pasma millimeter-wave - 71-86 GHz), wydają się być obiecującą alternatywą dla przewodowych sieci MAN. Jednakże z uwagi na właściwości transmisji bezprzewodowej w oparciu o łącza wysokiej częstotliwości, łącza te są bardzo wrażliwe...
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Low-Cost Modeling of Microwave Components by Means of Two-Stage Inverse/Forward Surrogates and Domain Confinement
PublicationFull-wave electromagnetic (EM) analysis is one of the most important tools in the design of modern microwave components and systems. EM simulation permits reliable evaluation of circuits at the presence of cross-coupling effects or substrate anisotropy, as well as for accounting for interactions with the immediate environment. However, repetitive analyses required by EM-driven procedures, such as parametric optimization or statistical...
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Machine learning-based prediction of residual drift and seismic risk assessment of steel moment-resisting frames considering soil-structure interaction
PublicationNowadays, due to improvements in seismic codes and computational devices, retrofitting buildings is an important topic, in which, permanent deformation of buildings, known as Residual Interstory Drift Ratio (RIDR), plays a crucial role. To provide an accurate yet reliable prediction model, 32 improved Machine Learning (ML) algorithms were considered using the Python software to investigate the best method for estimating Maximum...
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Performance Evaluation of an Axial Flux Machine with a Hybrid Excitation Design
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Work Safety and Ergonomics - L-15/C-0/L-0/P-0, Design and Production Engineering, WIMiO, undergraduate studies, engineering studies, full-time (stationary) studies, 2021/2022, se04, (M:32013W0), summer semester 2023/2024
e-Learning CoursesWydział Mechaniczny Mechanika i budowa maszyn (w języku angielskim). Kurs: Specjalność: Design and Production Engineering (WM), I stopnia - inżynierskie, stacjonarne, 2018/2019 - zimowy (obecnie sem. 4), Semestr: 2019/2020 - letni. Student explains the concepts of ergonomics. Describes its goals and area of application. Defines the human - machine - environment system. Designs the human working environment taking into account...
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Maximizing SDN resilience to node‐targeted attacks through joint optimization of the primary and backup controllers placements
PublicationIn Software Defined Networks (SDN) packet data switches are configured by a limited number of SDN controllers, which respond to queries for packet forwarding decisions from the switches. To enable optimal control of switches in real time the placement of controllers at network nodes must guarantee that the controller-to-controller and switch-to-controller communications delays are bounded. Apart from the primary controllers that...
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Inverse and forward surrogate models for expedited design optimization of unequal-power-split patch couplers
PublicationIn the paper, a procedure for precise and expedited design optimization of unequal power split patchcouplers is proposed. Our methodology aims at identifying the coupler dimensions that correspond to thecircuit operating at the requested frequency and featuring a required power split. At the same time, thedesign process is supposed to be computationally efficient. The proposed methodology involves two typesof auxiliary models (surrogates):...
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Detecting Lombard Speech Using Deep Learning Approach
PublicationRobust Lombard speech-in-noise detecting is challenging. This study proposes a strategy to detect Lombard speech using a machine learning approach for applications such as public address systems that work in near real time. The paper starts with the background concerning the Lombard effect. Then, assumptions of the work performed for Lombard speech detection are outlined. The framework proposed combines convolutional neural networks...
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Computer-assisted pronunciation training—Speech synthesis is almost all you need
PublicationThe 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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Rapid multi-objective antenna design using point-by-point Pareto set identification and local surrogate models
PublicationAntenna design is inherently a multicriterial problem.Determination of the best possible tradeoffs between conflicting objectives (a so-called Pareto front), such as reflection response, gain, and antenna size, is indispensable from the designer’s point of view, yet challenging when high-fidelity electromagnetic (EM) simulations are utilized for performance evaluation. Here, a novel and computationally...
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Low-Cost and Precise Automated Re-Design of Antenna Structures Using Interleaved Geometry Scaling and Gradient-Based Optimization
PublicationDesign of contemporary antennas is an intricate endeavor involving multiple stages, among others, tuning of geometry parameters. In particular, re-designing antennas to different operating frequencies, makes parametric optimization imperative to ensure the best achievable system performance. If the center frequency at the current design is distant from the target one, local tuning methods generally fail, whereas global algorithms...
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Integrating Statistical and Machine‐Learning Approach for Meta‐Analysis of Bisphenol A‐Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival
PublicationBisphenols are important environmental pollutants that are extensively studied due to different detrimental effects, while the molecular mechanisms behind these effects are less well understood. Like other environmental pollutants, bisphenols are being tested in various experimental models, creating large expression datasets found in open access storage. The meta‐analysis of such datasets is, however, very complicated for various...
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Optimization of a Fabry-Perot Sensing Interferometer design for an optical fiber sensor of hematocrit level
PublicationContinuous measurement of the hemato crit level in blo o d can p otentially b e p erformed using optical bre sensors. The FabryPerot interferometric sensors are a promising candidate in this application. The most imp ortant step in the design of the sensor is design of the sensing interferometer. Adequate cavity length and high interference contrast are two most imp ortant requirements in this application. The design metho d of...
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Efficient Calibration of Cost-Efficient Particulate Matter Sensors Using Machine Learning and Time-Series Alignment
PublicationAtmospheric particulate matter (PM) poses a significant threat to human health, infiltrating the lungs and brain and leading to severe issues such as heart and lung diseases, cancer, and premature death. The main sources of PM pollution are vehicular and industrial emissions, construction and agricultural activities, and natural phenomena such as wildfires. Research underscores the absence of a safe threshold for particulate exposure,...
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e-Learning - user's guide for students
e-Learning Coursese-Learning - user's guide for students
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Finite State Machine Based Modelling of Discrete Control Algorithm in LAD Diagram Language With Use of New Generation Engineering Software
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Multi-response optimization on the effect of wet and eco-friendly cryogenic turning of D2 steel using Taguchi-based grey relational analysis
PublicationMaterial removal processes, including turning and milling, are still commonly used operations for manufacturing most of mechanical components in modern industry. Apart from the cutting parameters, the cooling method has the great impact on the technological efects and, above all, on the environmental friendliness of production. In this study, multi-response optimization on the efect of wet and cryogenic machining is performed during...
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Design of dimensionally stable composites using efficient global optimization method
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