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Search results for: MICROWAVE ENGINEERING, COMPUTER-AIDED DESIGN, MULTI-CRITERIAL OPTIMIZATION, MACHINE LEARNING, NEURAL NETWORKS
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Virtual Engineering Object / Virtual Engineering Process: A specialized form of Cyber Physical System for Industrie 4.0
PublicationThis paper reviews the theories, parallels and variances between Virtual Engineering Object (VEO) / Virtual Engineering Process (VEP) and Cyber Physical System (CPS). VEO and VEP is an experience based knowledge representation of engineering objects and processes respectively. Cyber–physical systems (CPSs) are the next generation of engineered systems in which computing, communication, and control technologies are tightly integrated....
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Fatigue criterial parameters of variable amplitude stress.
PublicationPrzedstawiono oryginalne kryterialne parametry zmęczeniowe naprężenia o zmiennej amplitudzie. Dla ich wyznaczenia założono, że wydatkowana w procesie zmęczenia energia jest proporcjonalna do energii sprężystego odkształcenia postaciowego. Jeżeli przyrost zniszczenia spowodowanego przez nj cykli naprężenia o stałej amplitudzie aj jest odniesiony do energii sprężystego odkształcenia postaciowego przekazanej jednostkowej objętości...
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Measures of region failure survivability for wireless mesh networks
PublicationWireless mesh networks (WMNs) are considered as a promising alternative to wired local, or metropolitan area networks. However, owing to their exposure to various disruptive events, including natural disasters, or human threats, many WMN network elements located close to the failure epicentre are frequently in danger of a simultaneous failure, referred to as a region failure. Therefore, network survivability, being the ability...
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Model Correction and Optimization Framework for Expedited EM-Driven Surrogate-Assisted Design of Compact Antennas
PublicationDesign of compact antennas is a numerically challenging process that heavily relies on electromagnetic (EM) simulations and numerical optimization algorithms. For reliability of simulation results, EM models of small radiators often include connectors which—despite being components with fixed dimensions—significantly contribute to evaluation cost. In this letter, a response correction method for antenna models without connector,...
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Cost assessment of computer security activities
PublicationComprehensive cost-benefit analysis plays a crucial role in the decision-making process when it comes to investments in information security solutions. The cost of breaches needs to be analysed in the context of spending on protection measures. However, no methods exist that facilitate the quick and rough prediction of true expenditures on security protection systems. Rafal Leszczyna of Gdansk University of Technology presents...
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Dispersive Delay Structures With Asymmetric Arbitrary Group-Delay Response Using Coupled-Resonator Networks With Frequency-Variant Couplings
PublicationThis article reports the design of coupled-resonatorbased microwave dispersive delay structures (DDSs) with arbitrary asymmetric-type group delay response. The design process exploits a coupling matrix representation of the DDS circuit as a network of resonators with frequency-variant couplings (FVCs). The group delay response is shaped using complex transmission zeros (TZs) created by dispersive cross-couplings. We also present an...
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Computed aided system for separation and classification of the abnormal erythrocytes in human blood
PublicationThe human peripheral blood consists of cells (red cells, white cells, and platelets) suspended in plasma. In the following research the team assessed an influence of nanodiamond particles on blood elements over various periods of time. The material used in the study consisted of samples taken from ten healthy humans of various age, different blood types and both sexes. The markings were leaded by adding to the blood unmodified...
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Design and Experimental Validation of a Metamaterial-Based Sensor for Microwave Imaging in Breast, Lung, and Brain Cancer Detection
PublicationThis study proposes an innovative geometry of a microstrip sensor for high-resolution microwave imaging (MWI). The main intended application of the sensor is early detection of breast, lung, and brain cancer. The proposed design consists of a microstrip patch antenna fed by a coplanar waveguide with a metamaterial layer-based lens implemented on the back side, and an artificial magnetic conductor (AMC) realized on as a separate...
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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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Blended Learning in Teaching Safety of Electrical Installations
PublicationBlended learning becomes more commonly used in teaching information technology or other subjects, which involve practice in computer laboratories. In case of subjects with no access to computer rooms blended learning supports lecturing and teaching classes e.g. interactive lessons. The article presents the use of blended learning forms in Gdansk University of Technology in teaching the subject of Safety of Electrical Installations....
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Bi-GRU-APSO: Bi-Directional Gated Recurrent Unit with Adaptive Particle Swarm Optimization Algorithm for Sales Forecasting in Multi-Channel Retail
PublicationIn the present scenario, retail sales forecasting has a great significance in E-commerce companies. The precise retail sales forecasting enhances the business decision making, storage management, and product sales. Inaccurate retail sales forecasting can decrease customer satisfaction, inventory shortages, product backlog, and unsatisfied customer demands. In order to obtain a better retail sales forecasting, deep learning models...
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Interpretable deep learning approach for classification of breast cancer - a comparative analysis of multiple instance learning models
PublicationBreast cancer is the most frequent female cancer. Its early diagnosis increases the chances of a complete cure for the patient. Suitably designed deep learning algorithms can be an excellent tool for quick screening analysis and support radiologists and oncologists in diagnosing breast cancer.The design of a deep learning-based system for automated breast cancer diagnosis is not easy due to the lack of annotated data, especially...
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Assessment of student language skills in an e-learning environment
PublicationThis article presents the role of various assessment structures that can be used in a VLE. e-Learning language courses offer tutors a wide range of traditional and computer-generated formative and summative assessment procedures and tools. They help to evaluate each student’s progress, monitor their activities and provide varied support, which comes from the tutor, the course structure and materials as well as other participants....
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Deep learning techniques for biometric security: A systematic review of presentation attack detection systems
PublicationBiometric technology, including finger vein, fingerprint, iris, and face recognition, is widely used to enhance security in various devices. In the past decade, significant progress has been made in improving biometric sys- tems, thanks to advancements in deep convolutional neural networks (DCNN) and computer vision (CV), along with large-scale training datasets. However, these systems have become targets of various attacks, with...
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Tuning Ferulic Acid Solubility in Choline-Chloride- and Betaine-Based Deep Eutectic Solvents: Experimental Determination and Machine Learning Modeling
PublicationDeep eutectic solvents (DES) represent a promising class of green solvents, offering particular utility in the extraction and development of new formulations of natural compounds such as ferulic acid (FA). The experimental phase of the study undertook a systematic investigation of the solubility of FA in DES, comprising choline chloride or betaine as hydrogen bond acceptors and six different polyols as hydrogen bond donors....
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An application of the TCRBF neural network in multi-node fault diagnosis method
PublicationPrzedstawiono nową metodę samo-testowania części analogowej w systemach elektronicznych sterowanych mikrokontrolerami. Układ badany pobudzany jest przebiegiem sinusoidalnym przez generator zamontowany w systemie, a jego odpowiedź jest próbkowana w wybranych węzłach przez wewnętrzny przetwornik A/C mikrokontrolera. Detekcja i lokalizacja uszkodzenia jest dokontwana przez sieć neuronową typu TCRBF. Procedurę diagnostyczną zaimplementowano...
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MP3vec: A Reusable Machine-Constructed Feature Representation for Protein Sequences
Publication—Machine Learning (ML) methods have been used with varying degrees of success on protein prediction tasks, with two inherent limitations. First, prediction performance often depends upon the features extracted from the proteins. Second, experimental data may be insufficient to construct reliable ML models. Here we introduce MP3vec, a transferable representation for protein sequences that is designed to be used specifically for sequence-to-sequence...
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Reduced-cost surrogate modelling of compact microwave components by two-level kriging interpolation
PublicationFull-wave electromagnetic (EM) analysis is a versatile tool for evaluating the performance of high-frequency components. Its potential drawback is its high computational cost, inhibiting the execution of EM-driven tasks requiring massive simulations. The applicability of equivalent network models is limited owing to the topological complexity of compact microstrip components because of EM cross-coupling effects. Development of...
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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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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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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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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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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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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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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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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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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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