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Search results for: MICROWAVE ENGINEERING, COMPUTER-AIDED DESIGN, MULTI-CRITERIAL OPTIMIZATION, MACHINE LEARNING, NEURAL NETWORKS
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Improved Modeling of Microwave Structures Using Performance-Driven Fully-Connected Regression Surrogate
PublicationFast 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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Surrogate-assisted EM-driven miniaturization of wideband microwave couplers by means of co-simulation low-fidelity models
PublicationThis article proposes a methodology for rapid design optimization of miniaturized wideband couplers. More specifically, a class of circuits is considered, in which conventional transmission lines are replaced by their abbreviated counterparts referred to as slow-wave compact cells. Our focus is on explicit reduction of the structure size as well as on reducing the CPU cost of the design process. For the sake of computational feasibility,...
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Multi-objective optimization of compact UWB impedance matching transformers using Pareto front exploration and adjoint sensitivities
PublicationIn this paper, a technique for fast multi-objective optimization of impedance matching transformers has been presented. In our approach, a set of alternative designs that represent the best possible trade-offs between conflicting objectives (here, the maximum reflection level within a frequency band of interest and the circuit size) is identified by directly exploring the Pareto front. More specifically, the subsequent Pareto-optimal...
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OPTIMIZATION AND ENGINEERING
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Monitoring the gas turbine start-up phase on the platform using a hierarchical model based on Multi-Layer Perceptron networks
PublicationVery often, the operation of diagnostic systems is related to the evaluation of process functionality, where the diagnostics is carried out using reference models prepared on the basis of the process description in the nominal state. The main goal of the work is to develop a hierarchical gas turbine reference model for the estimation of start-up parameters based on multi-layer perceptron neural networks. A functional decomposition...
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Efficient knowledge-based optimization of expensive computational models using adaptive response correction
PublicationComputer simulation has become an indispensable tool in engineering design as they allow an accurate evaluation of the system performance. This is critical in order to carry out the design process in a reliable manner without costly prototyping and physical measurements. However, high-fidelity computer simulations are computationally expensive. This turns to be a fundamental bottleneck when it comes to design automation using numerical...
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A Concept and Design Optimization of Compact Planar UWB Monopole Antenna
PublicationA novel structure concept of a compact UWB monopole antenna is introduced together with a low-cost design optimization procedure. Reduced footprint is achieved by introduction of a protruded ground plane for current path increase and a matching transformer to ensure wideband impedance matching. All geometrical parameters of the structure are optimized simultaneously by means of surrogate based optimization involving variable-fidelity...
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Sensing Direction of Human Motion Using Single-Input-Single-Output (SISO) Channel Model and Neural Networks
PublicationObject detection Through-the-Walls enables localization and identification of hidden objects behind the walls. While numerous studies have exploited Channel State Information of Multiple Input Multiple Output (MIMO) WiFi and radar devices in association with Artificial Intelligence based algorithms (AI) to detect and localize objects behind walls, this study proposes a novel non-invasive Through-the-Walls human motion direction...
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NETWORKS
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Bees Detection on Images: Study of Different Color Models for Neural Networks
PublicationThis paper presents an approach to bee detection in video streams using a neural network classifier. We describe the motivation for our research and the methodology of data acquisition. The main contribution to this work is a comparison of different color models used as an input format for a feedforward convolutional architecture applied to bee detection. The detection process has is based on a neural binary classifier that classifies...
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Engineering Graphics II, W/P, Design and Production engineering, sem. letni 2021/2022, (PG_00040167)
e-Learning CoursesCourse for Engineering Graphics II classes of Design and Production engineering students
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Farzin Kazemi Ph.D. Student at Gdansk University of Technology
PeopleHis main research areas are seismic performance assessment of structures and seismic hazard analysis in earthquake engineering. He performed a comprehensive study on the effect of pounding phenomenon and proposed modification factors to modify the seismic collapse capacity of structures or predict the seismic collapse capacity of structures which were retrofitted with linear and nonlinear Fluid Viscous Dampers (FVDs). His current...
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Computer design of materials 202324
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Predicting seismic response of SMRFs founded on different soil types using machine learning techniques
PublicationPredicting the Maximum Interstory Drift Ratio (M-IDR) of Steel Moment-Resisting Frames (SMRFs) is a useful tool for designers to approximately evaluate the vulnerability of SMRFs. This study aims to explore supervised Machine Learning (ML) algorithms to build a surrogate prediction model for SMRFs to reduce the need for complex modeling. For this purpose, twenty well-known ML algorithms implemented in Python software are trained...
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Fast tolerance-aware design optimization of miniaturized microstrip couplers using variable-fidelity EM simulations and re-sponse features
PublicationManufacturing tolerances and other types of uncertainties may considerably affect operation and performance of microwave components and systems. Quantification of these effects is therefore an important part of the design process. It is even more important to obtain designs whose sensitivity to parameter deviations is reduced as much as possible. All of these require statistical analysis carried out at the level of electromagnetic...
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Detection of People Swimming in Water Reservoirs with the Use of Multimodal Imaging and Machine Learning
PublicationEvery year in many countries, there are fatal unintentional drownings in different water reservoirs like swimming pools, lakes, seas, or oceans. The existing threats of this type require creating a method that could automatically supervise such places to increase the safety of bathers. This work aimed to create methods and prototype solutions for detecting people bathing in water reservoirs using a multimodal imaging system and...
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Detecting Objects of Various Categories in Optical Remote Sensing Imagery Using Neural Networks
PublicationThe effective detection of objects in remote sensing images is of great research importance, so recent years have seen a significant progress in deep learning techniques in this field. However, despite much valuable research being conducted, many challenges still remain. A lot of research projects focus on detecting objects of a single category (class), while correctly detecting objects of different categories is much harder. The...
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Preference-based evolutionary multi-objective optimization in ship weather routing
PublicationIn evolutionary multi-objective optimization (EMO) the aim is to find a set of Pareto-optimal solutions. Such approach may be applied to multiple real-life problems, including weather routing (WR) of ships. The route should be optimal in terms of passage time, fuel consumption and safety of crew and cargo while taking into account dynamically changing weather conditions. Additionally it must not violate any navigational constraints...
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Visual Features for Improving Endoscopic Bleeding Detection Using Convolutional Neural Networks
PublicationThe presented paper investigates the problem of endoscopic bleeding detection in endoscopic videos in the form of a binary image classification task. A set of definitions of high-level visual features of endoscopic bleeding is introduced, which incorporates domain knowledge from the field. The high-level features are coupled with respective feature descriptors, enabling automatic capture of the features using image processing methods....
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On Accelerated Metaheuristic-Based Electromagnetic-Driven Design Optimization of Antenna Structures Using Response Features
PublicationDevelopment of present-day antenna systems is an intricate and multi-step process requiring, among others, meticulous tuning of designable (mainly geometry) parameters. Concerning the latter, the most reliable approach is rigorous numerical optimization, which tends to be re-source-intensive in terms of computing due to involving full-wave electromagnetic (EM) simu-lations. The cost-related issues are particularly pronounced whenever...
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The methodology of design of axial clearances compensation unit in hydraulic satellite displacement machine and their experimental verification
PublicationA new methodology of calculating the dimensions of the axial clearance compensation unit in the hydraulic satellite displacement machine is described in this paper. The methods of shaping the compensation unit were also proposed and described. These methods were used to calculate the geometrical dimensions of the compensation field in an innovative prototype of a satellite hydraulic motor. This motor is characterized by the fact...
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Application of a hybrid mechanistic/machine learning model for prediction of nitrous oxide (N2O) production in a nitrifying sequencing batch reactor
PublicationNitrous oxide (N2O) is a key parameter for evaluating the greenhouse gas emissions from wastewater treatment plants. In this study, a new method for predicting liquid N2O production during nitrification was developed based on a mechanistic model and machine learning (ML) algorithm. The mechanistic model was first used for simulation of two 15-day experimental trials in a nitrifying sequencing batch reactor. Then, model predictions...
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Knowledge-Based Expedited Parameter Tuning of Microwave Passives by Means of Design Requirement Management and Variable-Resolution EM Simulations
PublicationThe importance of numerical optimization techniques has been continually growing in the design of microwave components over the recent years. Although reasonable initial designs can be obtained using circuit theory tools, precise parameter tuning is still necessary to account for effects such as electromagnetic (EM) cross coupling or radiation losses. EM-driven design closure is most often realized using gradient-based procedures,...
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RESIDENTIAL FUNCTION IN MULTI-CRITERIA MULTIFUNCTIONAL BUILDING SYSTEM DESIGN PROCESS
PublicationThe paper presents the multi-criteria approach in the design process of residential structure as a part of a multifunctional building system. The purpose of work was to broaden the field of multifunctional building system design process. Background for the presented work is to define the direction of architectural growth of the modern city center area where actually are built complex and large capacity structures with a great impact...
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Solubility Characteristics of Acetaminophen and Phenacetin in Binary Mixtures of Aqueous Organic Solvents: Experimental and Deep Machine Learning Screening of Green Dissolution Media
PublicationThe solubility of active pharmaceutical ingredients is a mandatory physicochemical characteristic in pharmaceutical practice. However, the number of potential solvents and their mixtures prevents direct measurements of all possible combinations for finding environmentally friendly, operational and cost-effective solubilizers. That is why support from theoretical screening seems to be valuable. Here, a collection of acetaminophen...
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Solubility of dapsone in deep eutectic solvents: Experimental analysis, molecular insights and machine learning predictions
PublicationBackground. Dapsone (DAP) is an anti-inflammatory and antimicrobial active pharmaceutical ingredient used to treat, e.g., AIDS-related diseases. However, low solubility is a feature hampering its efficient use. Objectives. First, deep eutectic solvents...
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Cost-efficient design optimization of compact patch antennas with improved bandwidth
PublicationIn this letter, a surrogate-assisted optimization procedure for fast design of compact patch antennas with enhanced bandwidth is presented. The procedure aims at addressing a fundamental challenge of the design of antenna structures with complex topologies, which is simultaneous adjustment of numerous geometry parameters. The latter is necessary in order to find a truly optimum design and cannot be executed-at the level of high-fidelity...
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Agnieszka Landowska dr hab. inż.
PeopleAgnieszka Landowska works for Gdansk University of Technology, FETI, Department of Software Engineering. Her research concentrates on usability, accessibility and technology adoption, as well as affective computing methods. She initiated Emotions in HCI Research Group and conducts resarch on User eXperiene evaluation of applications and other technologies.
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Cost-Efficient Measurement Platform and Machine-Learning-Based Sensor Calibration for Precise NO2 Pollution Monitoring
PublicationAir quality significantly impacts human health, the environment, and the economy. Precise real-time monitoring of air pollution is crucial for managing associated risks and developing appropriate short- and long-term measures. Nitrogen dioxide (NO2) stands as a common pollutant, with elevated levels posing risks to the human respiratory tract, exacerbating respiratory infections and asthma, and potentially leading to chronic lung...
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Control of induction machine supplied by a current source inverter using the multi-scalar transformation and backstepping approach
PublicationThe paper describes the voltage control technique of squire-cage induction machines supplied by a current source inverter. The control system is based on new transformation of the electric drive system (machine and inverter) state variables to the multi-scalar variables form. The backstepping approach is used to obtain the feedback control law. The control system contains the structure of the observer...
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Computer-aided analysis of resonance risk in power system with Static Var Compensators
PublicationStatic Var Compensators operation in a power system may significantly improve voltage profiles in nodes and the reactive power balance, as well as ensure greater system stability in emergency conditions. However these devices may be a cause of a resonance in the system. The aim of this paper is to call attention to the need to include resonance phenomena in a compensator’s location evaluation process. The analysis performed in...
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Computer-Aided Saturation Mutagenesis of Arabidopsis thaliana Ent-Copalyl Diphosphate Synthase
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Fabrication Of Scaffolds From Ti6Al4V Powders Using The Computer Aided Laser Method
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Application of fiber optic sensors using Machine Learning algorithms for temperature measurement of lithium-ion batteries
PublicationOptical fiber sensorsusing low-coherence interferometry require processing ofthe output spectrum or interferogramto quickly and accurately determine the instantaneous value of the measured quantity, such as temperature.Methods based on machine learning are a good candidate for this application. The application of four such methods in an optical fiber temperature sensoris demonstrated.Using aZnO-coated...
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Piotr Szczuko dr hab. inż.
PeoplePiotr Szczuko received his M.Sc. degree in 2002. His thesis was dedicated to examination of correlation phenomena between perception of sound and vision for surround sound and digital image. He finished Ph.D. studies in 2007 and one year later completed a dissertation "Application of Fuzzy Rules in Computer Character Animation" that received award of Prime Minister of Poland. His interests include: processing of audio and video, computer...
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Rapid simulation-driven design of miniaturised dual-band microwave couplers by means of adaptive response scaling
PublicationOne of the major challenges in the design of compact microwave structures is the necessity of simultaneous handling of several objectives and the fact that expensive electromagnetic (EM) analysis is required for their reliable evaluation. Design of multi-band circuits where performance requirements are to be satisfied for several frequencies at the same time is even more difficult. In this work, a computationally efficient design...
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Wireless Link Selection Methods for Maritime Communication Access Networks—A Deep Learning Approach
PublicationIn recent years, we have been witnessing a growing interest in the subject of communication at sea. One of the promising solutions to enable widespread access to data transmission capabilities in coastal waters is the possibility of employing an on-shore wireless access infrastructure. However, such an infrastructure is a heterogeneous one, managed by many independent operators and utilizing a number of different communication...
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Study of the Effectiveness of Model Order Reduction Algorithms in the Finite Element Method Analysis of Multi-port Microwave Structures
PublicationThe purpose of this paper is to investigate the effectiveness of model order reduction algorithms in finite element method analysis of multi-port microwave structures. Consideration is given to state of the art algorithms, i.e. compact reduced-basis method (CRBM), second-order Arnoldi method for passive-order reduction (SAPOR), reduced-basis methods (RBM) and subspace-splitting moment-matching MOR (SSMM-MOR)
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Mariusz Deja dr hab. inż.
PeopleAssistant Professor in the Department of Manufacturing Engineering and Automation. He graduated in 1993, obtaining Master degree at the Faculty of Mechanical Engineering, Gdańsk University of Technology in the field of study: Mechanical Engineering, specialization: Technological Process Design. After graduation he worked for the Department of Manufacturing Engineering and Automation and his main research area was associated with...
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Fast geometry scaling of miniaturized microwave couplers with power split correction
PublicationRedesigning a microwave circuit for various operating conditions is a practically important yet challenging problem. The purpose of this article is development and presentation of a technique for fast geometry scaling of miniaturized microwave couplers with respect to operating frequency. Our approach exploits an inverse surrogate model constructed using several reference designs that are optimized for a set of operating frequencies...
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Implementation of multicriteria decision analysis in design of experiment for dispersive liquid-liquid microextraction optimization for chlorophenols determination
PublicationA novel and efficient approach to optimization of extraction step prior the chromatographic determination of nine chlorinated phenols is described. It is based on the combination of design of experiments and multicriteria decision analysis. Such an approach is used to optimize dispersive liquid-liquid microextraction procedure for the determination of 9 chlorophenols in water samples. Three parameters are optimized – sample volume,...
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Gas Detection Using Resistive Gas Sensors And Radial Basis Function Neural Networks
PublicationWe present a use of Radial Basis Function (RBF) neural networks and Fluctuation Enhanced Sensing (FES) method in gas detection system utilizing a prototype resistive WO3 gas sensing layer with gold nanoparticles. We investigated accuracy of gas detection for three different preprocessing methods: no preprocessing, Principal Component Analysis (PCA) and wavelet transformation. Low frequency noise voltage observed in resistive gas...
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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
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Face with Mask Detection in Thermal Images Using Deep Neural Networks
PublicationAs the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it possible, for example, to determine the temperature based on the detected face, which brings new perspectives and opportunities to use such an approach for health control purposes. The...
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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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EM-Driven Multi-Objective Optimization of a Generic Monopole Antenna by Means of a Nested Trust-Region Algorithm
PublicationAntenna structures for modern applications are characterized by complex and unintuitive topologies that are difficult to develop when conventional experience-driven techniques are of use. In this work, a method for automatic generation of antenna geometries in a multi-objective setup has been proposed. The approach involves optimization of a generic spline-based radiator with adjustable number of parameters using a nested trust-region-based...
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Journal of Computer Aided Chemistry
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On Fast Multi-objective Optimization of Antenna Structures Using Pareto Front Triangulation and Inverse Surrogates
PublicationDesign of contemporary antenna systems is a challenging endeavor, where conceptual developments and initial parametric studies, interleaved with topology evolution, are followed by a meticulous adjustment of the structure dimensions. The latter is necessary to boost the antenna performance as much as possible, and often requires handling several and often conflicting objectives, pertinent to both electrical and field properties...
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A design framework for rigorous constrained EM-driven optimization of miniaturized antennas with circular polarization
PublicationCompact radiators with circular polarization are important components of modern mobile communication systems. Their design is a challenging process which requires maintaining simultaneous control over several performance figures but also the structure size. In this work, a novel design framework for multi-stage constrained miniaturization of antennas with circular polarization is presented. The method involves sequential optimization...
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User -friendly E-learning Platform: a Case Study of a Design Thinking Approach Use
PublicationE-learning systems are very popular means to support the teaching process today. These systems are mainly used by universities as well as by commercial training centres. We analysed several popular e-learning platforms used in Polish universities and find them very unfriendly for the users. For this reason, the authors began the work on the creation of a new system that would be not only useful, but also usable for students, teachers...