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Wyniki wyszukiwania dla: SURROGATE MODELING , ANTENNA DESIGN , DOMAIN CONFINEMENT , NESTED KRIGING , DEEP NEURAL NETWORKS
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Structure and computationally-efficient simulation-driven design of compact UWB monopole antenna
PublikacjaIn this letter, a structure of a small ultra-wideband (UWB) monopole antenna, its design optimization procedure as well as experimental validation are presented. According to our approach, antenna compactness is achieved by means of a meander line for current path enlargement as well as the two parameterized slits providing additional degrees of freedom that help to ensure good impedance matching. For the sake of reliability, the...
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Performance Analysis of Convolutional Neural Networks on Embedded Systems
PublikacjaMachine learning is no longer confined to cloud and high-end server systems and has been successfully deployed on devices that are part of Internet of Things. This paper presents the analysis of performance of convolutional neural networks deployed on an ARM microcontroller. Inference time is measured for different core frequencies, with and without DSP instructions and disabled access to cache. Networks use both real-valued and...
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Fast Simulation-Driven Design of a Planar UWB Dipole Antenna with an Integrated Balun
PublikacjaThe paper presents a design of an ultra-wideband (UWB) antenna with an integrated balun. A fully planar balun interfacing the microstrip input of the structure to the coplanar stripline (CPS) input of the dipole antenna is introduced. The electromagnetic (EM) model of the structure includes the dipole, the balun, and the microstrip input to account for interactions over the UWB band. The EM model is adjusted for low reflection...
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A novel approach exploiting properties of convolutional neural networks for vessel movement anomaly detection and classification
PublikacjaThe 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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Rapid Microwave Design Optimization in Frequency Domain Using Adaptive Response Scaling
PublikacjaIn this paper, a novel methodology for cost-efficient microwave design optimization in the frequency domain is proposed. Our technique, referred to as adaptive response scaling (ARS), has been developed for constructing a fast replacement model (surrogate) of the high-fidelity electromagnetic-simulated model of the microwave structure under design using its equivalent circuit (low-fidelity model). The basic principle of ARS is...
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On Nature-Inspired Design Optimization of Antenna Structures Using Variable-Resolution EM Models
PublikacjaNumerical optimization has been ubiquitous in antenna design for over a decade or so. It is indispensable in handling of multiple geometry/material parameters, performance goals, and constraints. It is also challenging as it incurs significant CPU expenses, especially when the underlying computational model involves full-wave electromagnetic (EM) analysis. In most practical cases, the latter is imperative to ensure evaluation reliability....
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Rafał Lech dr hab. inż.
OsobyIEEE Senior Member #92122578 Rafał Lech urodził się w Elblągu w 1977 roku. W roku 2001 otrzymał tytuł magistra inżyniera, w roku 2007 stopień doktora nauk technicznych (z wyróżnieniem) a w roku 2018 stopień doktora habilitowanego nauk technicznych w dyscyplinie elektronika w Politechnice Gdańskiej. Obecnie pracuje w Katedrze Inżynierii Mikrofalowej i Antenowej na Wydziale Elektroniki, Telekomunikacji i Informatyki w Politechnike...
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Expedited Machine-Learning-Based Global Design Optimization of Antenna Systems Using Response Features and Multi-Fidelity EM Analysis
PublikacjaThe design of antenna systems poses a significant challenge due to stringent per-formance requirements dictated by contemporary applications and the high com-putational costs associated with models, particularly full-wave electromagnetic (EM) analysis. Presently, EM simulation plays a crucial role in all design phases, encompassing topology development, parametric studies, and the final adjustment of antenna dimensions. The latter...
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Computationally Efficient Design Optimization of Compact Microwave and Antenna Structures
PublikacjaMiniaturization is one of the important concerns of contemporary wireless communication systems, especially regarding their passive microwave components, such as filters, couplers, power dividers, etc., as well as antennas. It is also very challenging, because adequate performance evaluation of such components requires full-wave electromagnetic (EM) simulation, which is computationally expensive. Although high-fidelity EM analysis...
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Adjusted SpikeProp algorithm for recurrent spiking neural networks with LIF neurons
PublikacjaA problem related to the development of a supervised learning method for recurrent spiking neural networks is addressed in the paper. The widely used Leaky-Integrate-and-Fire model has been adopted as a spike neuron model. The proposed method is based on a known SpikeProp algorithm. In detail, the developed method enables gradient descent learning of recurrent or multi-layer feedforward spiking neural networks. The research included...
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Expedited Globalized Antenna Optimization by Principal Components and Variable-Fidelity EM Simulations: Application to Microstrip Antenna Design
PublikacjaParameter optimization, also referred to as design closure, is imperative in the development of modern antennas. Theoretical considerations along with rough dimension adjustment through supervised parameter sweeping can only yield initial designs that need to be further tuned to boost the antenna performance. The major challenges include handling of multi-dimensional parameter spaces while accounting for several objectives and...
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Surrogate Modeling and Optimization Using Shape-Preserving Response Prediction: A Review
PublikacjaComputer simulation models are ubiquitous in modern engineering design. In many cases, they are the only way to evaluate a given design with sufficient fidelity. Unfortunately, an added computa-tional expense is associated with higher fidelity models. Moreover, the systems being considered are often highly nonlinear and may feature a large number of designable parameters. Therefore, it may be impractical to solve the design problem...
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The Effectiveness of Using a Pretrained Deep Learning Neural Networks for Object Classification in Underwater Video
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On Accelerated Metaheuristic-Based Electromagnetic-Driven Design Optimization of Antenna Structures Using Response Features
PublikacjaDevelopment 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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Efficient uncertainty quantification using sequential sampling-based neural networks
PublikacjaUncertainty quantification (UQ) of an engineered system involves the identification of uncertainties, modeling of the uncertainties, and the forward propagation of the uncertainties through a system analysis model. In this work, a novel surrogate-based forward propagation algorithm for UQ is proposed. The proposed algorithm is a new and unique extension of the recent efficient global optimization using neural network (NN)-based...
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The impact of the AC922 Architecture on Performance of Deep Neural Network Training
PublikacjaPractical deep learning applications require more and more computing power. New computing architectures emerge, specifically designed for the artificial intelligence applications, including the IBM Power System AC922. In this paper we confront an AC922 (8335-GTG) server equipped with 4 NVIDIA Volta V100 GPUs with selected deep neural network training applications, including four convolutional and one recurrent model. We report...
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Applying artificial neural networks for modelling ship speed and fuel consumption
PublikacjaThis paper deals with modelling ship speed and fuel consumption using artificial neural network (ANN) techniques. These tools allowed us to develop ANN models that can be used for predicting both the fuel consumption and the travel time to the destination for commanded outputs (the ship driveline shaft speed and the propeller pitch) selected by the ship operator. In these cases, due to variable environmental conditions, making...
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Accelerated Re-Design of Antenna Structures Using Sensitivity-Based Inverse Surrogates
PublikacjaThe paper proposes a novel framework for accelerated re-design (dimension scaling) of antenna structures using inverse surrogates. The major contribution of the work is a sensitivity-based model identification procedure, which permits a significant reduction of the number of reference designs required to render the surrogate. Rigorous formulation of the approach is supplemented by its comprehensive numerical validation using a...
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Fast surrogate-assisted simulation-driven design of compact microwave hybrid couplers
PublikacjaThis work presents a robust methodology for expedited simulation-driven design optimization of compact microwave hybrid couplers. The technique relies on problem decomposition, and a bot-tom–up design strategy, starting from the level of basic building blocks of the coupler, and finishing with a tuning procedure that exploits a fast surrogate model of the entire structure. The latter is constructed by cascading local response surface...
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Abdominal Aortic Aneurysm segmentation from contrast-enhanced computed tomography angiography using deep convolutional networks
PublikacjaOne of the most common imaging methods for diagnosing an abdominal aortic aneurysm, and an endoleak detection is computed tomography angiography. In this paper, we address the problem of aorta and thrombus semantic segmentation, what is a mandatory step to estimate aortic aneurysm diameter. Three end-to-end convolutional neural networks were trained and evaluated. Finally, we proposed an ensemble of deep neural networks with underlying...
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Traffic Modeling in IMS-based NGN Networks
PublikacjaIn the modern world the need for accurate and quickly delivered information is becoming more and more essential. In order to fulfill these requirements, next generation telecommunication networks should be fast introduced and correctly dimensioned. For this reason proper traffic models must be identified, which is the subject of this paper. In the paper standardization of IMS (IP Multimedia Subsystem) concept and IMS-based NGN...
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Design of a Coplanar Waveguide-Fed Wideband Compact-Size Circularly Polarized Antenna and polarization-sense alteration
PublikacjaThis paper presents the design and validation of a geometrically simple circularly polarized(CP) structure featuring flat gain in the sub-6 GHz 5th generation spectrum. The proposed structure is based on coplanar-waveguide-fed, modified wide slot etched in the ground plane. For generating CP waves, the coplanar ground planes are designed with slight asymmetry in both the horizontal and vertical directions. Furthermore, the ground...
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Patch size setup and performance/cost trade-offs in multi-objective EM-driven antenna optimization using sequential domain patching
PublikacjaPurpose This paper aims to assess control parameter setup and its effect on computational cost and performance of deterministic procedures for multi-objective design optimization of expensive simulation models of antenna structures. Design/methodology/approach A deterministic algorithm for cost-efficient multi-objective optimization of antenna structures has been assessed. The algorithm constructs a patch connecting extreme Pareto-optimal...
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Clothes Detection and Classification Using Convolutional Neural Networks
PublikacjaIn this paper we describe development of a computer vision system for accurate detection and classification of clothes for e-commerce images. We present a set of experiments on well established architectures of convolutional neural networks, including Residual networks, SqueezeNet and Single Shot MultiBox Detector (SSD). The clothes detection network was trained and tested on DeepFashion dataset, which contains box annotations...
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Antenna Optimization Using Machine Learning with Reduced-Dimensionality Surrogates
PublikacjaIn modern times, antenna design has become more demanding than ever. The escalating requirements for performance and functionality drive the development of intricately structured antennas, where parameters must be meticulously adjusted to achieve peak performance. Often, global adjustments to geometry are necessary for optimal results. However, direct manipulation of antenna responses evaluated with full-wave electromagnetic (EM)...
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Design optimization of novel compact circular polarization antenna
PublikacjaThe paper describes a structure and a design optimization procedure of a miniaturized circular polarization antenna with elliptical ground plane slots and feed line with stepped-impedance stubs. Constrained optimization of all antenna parameters is executed in order to explicitly reduce the antenna size while maintaining required impedance axial ratio bandwidth of 5 GHz to 7 GHz at the same time. The size of the optimized antenna...
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Kriging-assisted hybrid reliability design and optimization of offshore wind turbine support structure based on a portfolio allocation strategy
PublikacjaIn recent years, offshore wind power generation technology has developed rapidly around the world, making important contributions to the further development of renewable energy. When designing an Offshore Wind Turbine (OWT) system, the uncertainties in parameters and different types of constraints need to be considered to find the optimal design of these systems. Therefore, the Reliability-Based Design Optimization (RBDO) method...
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A MODEL FOR FORECASTING PM10 LEVELS WITH THE USE OF ARTIFICIAL NEURAL NETWORKS
PublikacjaThis work presents a method of forecasting the level of PM10 with the use of artificial neural networks. Current level of particulate matter and meteorological data was taken into account in the construction of the model (checked the correlation of each variable and the future level of PM10), and unidirectional networks were used to implement it due to their ease of learning. Then, the configuration of the network (built on the...
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Architectural Modifications to Enhance Steganalysis with Convolutional Neural Networks
PublikacjaThis paper investigates the impact of various modifications introduced to current state-of-the-art Convolutional Neural Network (CNN) architectures specifically designed for the steganalysis of digital images. Usage of deep learning methods has consistently demonstrated improved results in this field over the past few years, primarily due to the development of newer architectures with higher classification accuracy compared to...
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Novel structure and design of compact UWB slot antenna
PublikacjaIn this paper, a novel structure of a compact UWB slot antenna is presented along with a simulation-driven design optimization algorithm for adjusting geometry parameters of the device. Our primary objective is to obtain small footprint of the structure while maintaining its acceptable electrical performance. It is achieved by introducing sufficiently large number of geometry degrees of freedom, including increased number of parameterized...
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Rapid design closure of linear microstrip antenna array apertures using response features
PublikacjaA simple yet reliable approach to a rapid design closure of linear antenna array apertures at the electromagnetic (EM)-simulation level is proposed. Our methodology exploits an underlying array factor (AF) model suitably corrected by means of characteristic points (angles and levels) of the radiation pattern of the EM model of the antenna array aperture. This conveniently allows for controlling both the side lobe levels...
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Deep Learning
PublikacjaDeep learning (DL) is a rising star of machine learning (ML) and artificial intelligence (AI) domains. Until 2006, many researchers had attempted to build deep neural networks (DNN), but most of them failed. In 2006, it was proven that deep neural networks are one of the most crucial inventions for the 21st century. Nowadays, DNN are being used as a key technology for many different domains: self-driven vehicles, smart cities,...
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Ship Resistance Prediction with Artificial Neural Networks
PublikacjaThe paper is dedicated to a new method of ship’s resistance prediction using Artificial Neural Network (ANN). In the initial stage selected ships parameters are prepared to be used as a training and validation sets. Next step is to verify several network structures and to determine parameters with the highest influence on the result resistance. Finally, other parameters expected to impact the resistance are proposed. The research utilizes...
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Performance-driven yield optimization of high-frequency structures by kriging surrogates
PublikacjaUncertainty quantification is an important aspect of engineering design, as manufacturing toler-ances may affect the characteristics of the structure. Therefore, quantification of these effects is in-dispensable for adequate assessment of the design quality. Toward this end, statistical analysis is performed, for reliability reasons, using full-wave electromagnetic (EM) simulations. Still, the computational expenditures associated...
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Expedited Simulation-Driven Multi-Objective Design Optimization of Quasi-Isotropic Dielectric Resonator Antenna
PublikacjaMajority of practical engineering design problems require simultaneous handling of several criteria. Although many of design tasks can be turned into single-objective problems using sufficient formulations, in some situations, acquiring comprehensive knowledge about possible trade-offs between conflicting objectives may be necessary. This calls for multi-objective optimization that aims at identifying a set of alternative, Pareto-optimal...
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Use of Neural Networks in Diagnostics of Rolling-Element Bearing of the Induction Motor
PublikacjaBearing defect is statistically the most frequent cause of an induction motor fault. The research described in the paper utilized the phenomenon of the current change in the induction motor with bearing defect. Methods based on the analysis of the supplying current are particularly useful when it is impossible to install diagnostic devices directly on the motor. The presented method of rolling-element bearing diagnostics used indirect...
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A Novel Iterative Decoding for Iterated Codes Using Classical and Convolutional Neural Networks
PublikacjaForward error correction is crucial for communication, enabling error rate or required SNR reduction. Longer codes improve correction ratio. Iterated codes offer a solution for constructing long codeswith a simple coder and decoder. However, a basic iterative code decoder cannot fully exploit the code’s potential, as some error patterns within its correction capacity remain uncorrected.We propose two neural network-assisted decoders:...
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Neural Networks, Support Vector Machine and Genetic Algorithms for Autonomous Underwater Robot Support
PublikacjaIn this paper, artificial neural networks, a classification technique called support vector machine and meta-heuristics genetic algorithm have been considered for development in autonomous underwater robots. Artificial neural networks have been used for seabed modelling as well as support vector machine has been applied for the obstacles classification to avoid some collision problems. Moreover, genetic algorithm has been applied...
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On Computationally-Efficient Reference Design Acquisition for Reduced-Cost Constrained Modeling and Re-Design of Compact Microwave Passives
PublikacjaFull-wave electromagnetic (EM) analysis has been playing a major role in the design of microwave components for the last few decades. In particular, EM tools allow for accurate evaluation of electrical performance of miniaturized structures where strong cross-coupling effects cannot be adequately quantified using equivalent network models. However, EM-based design procedures (parametric optimization, statistical analysis) generate...
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An Automated Method for Biometric Handwritten Signature Authentication Employing Neural Networks
PublikacjaHandwriting biometrics applications in e-Security and e-Health are addressed in the course of the conducted research. An automated graphomotor analysis method for the dynamic electronic representation of the handwritten signature authentication was researched. The developed algorithms are based on dynamic analysis of electronically handwritten signatures employing neural networks. The signatures were acquired with the use of the...
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Surrogate-Assisted Design of Checkerboard Metasurface for Broadband Radar Cross-Section Reduction
PublikacjaMetasurfaces have been extensively exploited in stealth applications to reduce radar cross section (RCS). They rely on the manipulation of backward scattering of electromagnetic (EM) waves into various oblique angles. However, arbitrary control of the scattering properties poses a significant challenge as a design task. Yet it is a principal requirement for making RCS reduction possible. This article introduces a surrogate-based...
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A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks
PublikacjaThis article proposes the use of a feedforward neural network (FNN) to select the starting point for the first iteration in well-known iterative location estimation algorithms, with the research objective of finding the minimum size of a neural network that allows iterative position estimation algorithms to converge in an example positioning network. The selected algorithms for iterative position estimation, the structure of the...
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3rd International Workshop on Reliable Networks Design and Modeling (RNDM 2011)
Publikacjaartykuł sprawozdawczy z konferencji
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Fourth International Workshop on Reliable Networks Design and Modeling (RNDM 2012)
Publikacjaartykuł sprawozdawczy z konferencji
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Rapid antenna design optimization using shape-preserving response prediction
PublikacjaAn approach to rapid optimization of antennas using the shape-preserving response-prediction (SPRP) technique and coarsediscretization electromagnetic (EM) simulations (as a low-fidelity model) is presented. SPRP allows us to estimate the response of the high-fidelity EM antenna model, e.g., its reflection coefficient versus frequency, using the properly selected set of so-called characteristic points of the low-fidelity model...
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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
PublikacjaFollowing the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides...
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Wireless Link Selection Methods for Maritime Communication Access Networks—A Deep Learning Approach
PublikacjaIn 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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Supply current signal and artificial neural networks in the induction motor bearings diagnostics
PublikacjaThis paper contains research results of the diagnostics of induction motor bearings based on measurement of the supply current with usage of artificial neural networks. Bearing failure amount is greater than 40% of all engine failures, which makes their damage-free operation crucial. Tests were performed on motors with intentionally made bearings defects. Chapter 2 introduces the concept of artificial neural networks. It presents...
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Design of Microstrip UWB Balun Using Quasi-TEM Approach Aided by the Artificial Neural Network
PublikacjaThe design procedure for UWB balun realized in the microstrip technology is proposed in the paper. The procedure applies Artificial Neural Network which corrects the dimensions of the approximate design found by appropriate scaling of the dimensions of the prototype. The scale coefficients for longitudinal and transverse dimensions of microstrip lines are determined from electromagnetic modeling based on transmission line equations....
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Visual Features for Improving Endoscopic Bleeding Detection Using Convolutional Neural Networks
PublikacjaThe 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....