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- Publikacje 2019 wyników po odfiltrowaniu
- Czasopisma 45 wyników po odfiltrowaniu
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Wyniki wyszukiwania dla: NEURAL NETWORKS, SURROGATE-BASED OPTIMIZATION, HYPERPARAMETER OPTIMIZATION, SEQUENTIAL SAMPLING
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Parametric optimization of sandwich composite footbridge with U-shaped cross-section
PublikacjaParametric optimization of sandwich composite footbridge was presented in the paper. Composite footbridge has 14,5 m long and has U-shaped cross-section with inner dimensions 2,6 × 1,3 m. The sandwich structure in made from GFRP laminate as a faces and PET foam as a core. The aim of analysis was to minimize the mass of the new footbridge that can lead to minimize the cost of structure. After optimization was conducted, the new...
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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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Expedited Yield Optimization of Narrow- and Multi-Band Antennas Using Performance-Driven Surrogates
PublikacjaUncertainty quantification is an important aspect of engineering design, also pertaining to the development and performance evaluation of antenna systems. Manufacturing tolerances as well as other types of uncertainties, related to material parameters (e.g., substrate permittivity) or operating conditions (e.g., bending) may affect the antenna characteristics. In the case of narrow- or multi-band antennas, this usually leads to...
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An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks
PublikacjaIn this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent...
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Modelling, Simulation and Optimization of the Wavemaker in a Towing Tank
PublikacjaThe paper analyses the problem of experimental identification (frequency response), modelling and optimization of the towing tank wavemaker in the Scilab/Xcos environment. The experimental identification of the objects (the towing tank wavemaker placed in the hydrodynamic laboratory of the CTO S.A. Ship Design and Research Centre (CTO)) and the implementation of the models in the simulation environment, enable to perform: 1. tuning...
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Globalized parametric optimization of microwave components by means of response features and inverse metamodels
PublikacjaSimulation-based optimization of geometry parameters is an inherent and important stage of microwave design process. To ensure reliability, the optimization process is normally carried out using full-wave electromagnetic (EM) simulation tools, which entails significant computational overhead. This becomes a serious bottleneck especially if global search is required (e.g., design of miniaturized structures, dimension scaling over...
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Approximate Quality Criteria for Difficult Multi-Objective Optimization Problems
PublikacjaThis paper introduces approximate analytic quality criteria useful in assessing the efficiency of evolutionary multi-objective optimization (EMO) procedures. We present a summary of extensive research into computing. In the performed comparative study we take into account the various approaches of the state-of-the-art, in order to objectively assess the EMO performance in highly dimensional spaces; where some executive criteria,...
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Approximate Quality Criteria for Difficult Multi-Objective Optimization Problems
PublikacjaThis paper introduces approximate analytic quality criteria useful in assessing the efficiency of evolutionary multi-objective optimization (EMO) procedures. We present a summary of extensive research into computing. In the performed comparative study we take into account the various approaches of the state-of-the-art, in order to objectively assess the EMO performance in highly dimensional spaces; where some executive criteria,...
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Solving highly-dimensional multi-objective optimization problems by means of genetic gender
PublikacjaPaper presents a computational optimization study using a genetic gender approach for solving multi-objective optimization problems of detection observers. In this methodology the information about an individual gender of all the considered solutions is applied for the purpose of making distinction between different groups of objectives. This information is drawn out of the fitness of individuals and applied during a current parental...
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Solving highly-dimensional multi-objective optimization problems by means of genetic gender
PublikacjaPaper presents a computational optimization study using a genetic gender approach for solving multi-objective optimization problems of detection observers. In this methodology the information about an individual gender of all the considered solutions is applied for the purpose of making distinction between different groups of objectives. This information is drawn out of the fitness of individuals and applied during a current parental...
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Expedited optimization of antenna input characteristics with adaptive Broyden updates
PublikacjaSimulation-driven adjustment of geometry and/or material parameters is a necessary step in the design of contemporary antenna structures. Due to their topological complexity, other means, such as supervised parameter sweeping, does not usually lead to satisfactory results. On the other hand, rigorous numerical optimization is computationally expensive due to a high cost of underlying full-wave electromagnetic (EM) analyses, otherwise...
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Rotational Design Space Reduction for Cost-Efficient Multi-Objective Antenna Optimization
PublikacjaCost-efficient multi-objective design of antenna structures is presented. Our approach is based on design space reduction algorithm using auxiliary single-objective optimization runs and coordinate system rotation. The initial set of Pareto-optimal solutions is obtained by optimizing a response surface approximation model established in the reduced space using coarse-discretization EM simulation data. The optimization engine is...
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Inverse modeling for fast design optimization of small-size rat-race couplers incorporating compact cells
PublikacjaIn the paper, a framework for computationally-efficient design optimization of compact rat-race couplers (RRCs) is discussed. A class of hybrid RRCs with variable operating conditions is investigated, whose size reduction is obtained by replacing ordinary transmission lines with compact microstrip resonant cells (CMRCs). Our approach employs a bottom-up design strategy leading to the development of compact RRCs through rapid design...
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Particle Swarm Optimization Based Optimal Design of Six-Phase Induction Motor for Electric Propulsion of Submarines
PublikacjaRecent research reveals that multi-phase motors in electric propulsion systems are highly recommended due to their improved reliability and efficiency over traditional three phase motors. This research presented a comparison of optimal model design of a six phase squirrel cage induction motor (IM) for electric propulsion by using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). A six phase squirrel cage induction...
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Particle Swarm Optimization Based Optimal Design of Six-Phase Induction Motor for Electric Propulsion of Submarines
PublikacjaAbstract: Recent research reveals that multi-phase motors in electric propulsion systems are highly recommended due to their improved reliability and efficiency over traditional three phase motors. This research presented a comparison of optimal model design of a six phase squirrel cage induction motor (IM) for electric propulsion by using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). A six phase squirrel cage induction...
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Testing Stability of Digital Filters Using Optimization Methods with Phase Analysis
PublikacjaIn this paper, novel methods for the evaluation of digital-filter stability are investigated. The methods are based on phase analysis of a complex function in the characteristic equation of a digital filter. It allows for evaluating stability when a characteristic equation is not based on a polynomial. The operation of these methods relies on sampling the unit circle on the complex plane and extracting the phase quadrant of a function...
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Gender approach to multi-objective optimization of detection systems by pre-selection of criteria
PublikacjaA novel idea of performing evolutionary computations for solving highly-dimensional multi-objective optimization (MOO) problems is proposed. The information about individual genders is applied. This information is drawn out of the fitness of individuals and applied during the parental crossover in the evolutionary multi-objective optimization (EMO) processes. The paper introduces the principles of the genetic-gender approach (GGA)...
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Gender approach to multi-objective optimization of detection systems by pre-selection of criteria
PublikacjaA novel idea of performing evolutionary computations for solving highly-dimensional multi-objective optimization (MOO) problems is proposed. The information about individual genders is applied. This information is drawn out of the fitness of individuals and applied during the parental crossover in the evolutionary multi-objective optimization (EMO) processes. The paper introduces the principles of the genetic-gender approach (GGA)...
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Gender approach to multi-objective optimization of detection systems by pre-selection of criteria
PublikacjaA novel idea of performing evolutionary computations for solving highly-dimensional multi-objective optimization (MOO) problems is proposed. The information about individual genders is applied. This information is drawn out of the fitness of individuals and applied during the parental crossover in the evolutionary multi-objective optimization (EMO) processes. The paper introduces the principles of the genetic-gender approach (GGA)...
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Neural Networks Based on Ultrafast Time-Delayed Effects in Exciton Polaritons
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Dynamically positioned ship steering making use of backstepping method and artificial neural networks
PublikacjaThe article discusses the issue of designing a dynamic ship positioning system making use of the adaptive vectorial backstepping method and RBF type arti cial neural networks. In the article, the backstepping controller is used to determine control laws and neural network weight adaptation laws. e arti cial neural network is applied at each time instant to approximate nonlinear functions containing parametric uncertainties....
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Approximate Criteria for the Evaluation of Truly Multi-Dimensional Optimization Problems
PublikacjaIn this paper we propose new improved approximate quality criteria useful in assessing the efficiency of evolutionary multi-objective optimization (EMO). In the performed comparative study we take into account the various EMO algorithms of the state-of-the-art, in order to objectively assess the EMO performance in highly dimensional spaces. It is well known that useful executive criteria, such as those based on the true Pareto...
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Hybrid of Neural Networks and Hidden Markov Models as a modern approach to speech recognition systems
PublikacjaThe aim of this paper is to present a hybrid algorithm that combines the advantages ofartificial neural networks and hidden Markov models in speech recognition for control purpos-es. The scope of the paper includes review of currently used solutions, description and analysis of implementation of selected artificial neural network (NN) structures and hidden Markov mod-els (HMM). The main part of the paper consists of a description...
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Low-cost multi-objective optimization of antennas using Pareto front exploration and response features
PublikacjaIn the paper, a procedure for low-cost multi-objective optimization of antenna structures is presented. Our approach is based on exploration of the Pareto front representing the best possible trade-offs between conflicting objectives, here, the structure size and its electrical performance. Starting from the design representing the best in-band reflection level, subsequent Pareto-optimal designs are identified through local constrained...
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Interior Point Method Evaluation for Reactive Power Flow Optimization in the Power System
PublikacjaThe paper verifies the performance of an interior point method in reactive power flow optimization in the power system. The study was conducted on a 28 node CIGRE system, using the interior point method optimization procedures implemented in Power Factory software.
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Use of AMI Meters in the Process of Low Voltage Grid Optimization
PublikacjaThis is a report of a project involving, inter alia, low voltage grid performance optimization with data from AMI meters . The study was supported with a European UPGRID grant, and executed by a consortium of companies from seven European countries, including Poland .
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PLC-based Implementation of Stochastic Optimization Method in the Form of Evolutionary Strategies for PID, LQR, and MPC Control
PublikacjaProgrammable logic controllers (PLCs) are usually equipped with only basic direct control algorithms like proportional-integral-derivative (PID). Modules included in engineering software running on a personal computer (PC) are usually used to tune controllers. In this article, an alternative approach is considered, i.e. the development of a stochastic optimizer based on the (μ,λ) evolution strategy (ES) in a PLC. For this purpose,...
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Generalized Formulation of Response Features for Reliable Optimization of Antenna Input Characteristics
PublikacjaElectromagnetic (EM)-driven parameter adjustment has become imperative in the design of modern antennas. It is necessary because the initial designs rendered through topology evolution, parameter sweeping, or theoretical models, are often of poor quality and need to be improved to satisfy stringent performance requirements. Given multiple objectives, constraints, and a typically large number of geometry parameters, the design closure...
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Design and Optimization of a Compact Planar Radiator for UWB Applications and Beyond
PublikacjaA compact monopole antenna for ultra-wideband (UWB) and beyond applications has been proposed. The radiator is based on the monopole topology. The super-wideband behavior has been achieved using a combination of spline-based modifications applied to the driven element, as well as utilization of a tapered feed and a slot-modified ground plane. The electrical performance of the structure has been tuned using a numerical optimization...
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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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A High-Efficient Measurement System With Optimization Feature for Prototype CMOS Image Sensors
PublikacjaIn this paper, a gray-scale CMOS image sensor (CIS) characterization system with an optimization feature has been proposed. By using a very fast and precise control of light intensity, based on the pulsewidth-modulation method, it is avoided to measure the illuminance every time. These features accelerate the multicriteria CIS optimization requiring many thousands of measurements. The system throughput is 2.5 Gb/s, which allows...
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Some Optimization Methods for Simulations in Volunteer and Grid Systems
PublikacjaIn this chapter, some optimization methods have been presented for improving performance of simulations in the volunteer and grid computing system called Comcute. Some issues related to the cloud computing can be solved by presented approaches as well as the Comcute platform can be used to simulate execution of expensive and energy consuming long-term tasks in the cloud environment. In particular, evolutionary algorithms as well...
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Optimization of the efficiency of braking energy recovery in rail transport by changing arrival time
PublikacjaThe article refers to the previous work of the authors, in which the model of traffic organization of cooperating trains including the optimization of the use of energy returned to the catenary was presented. In the presented article, the model was modified by changing the main control variable, which affects the efficient use of energy. Departure time was changed for the arrival time of the train to the stop or station. The optimization...
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Bees Detection on Images: Study of Different Color Models for Neural Networks
PublikacjaThis 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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Recognition of Emotions in Speech Using Convolutional Neural Networks on Different Datasets
PublikacjaArtificial Neural Network (ANN) models, specifically Convolutional Neural Networks (CNN), were applied to extract emotions based on spectrograms and mel-spectrograms. This study uses spectrograms and mel-spectrograms to investigate which feature extraction method better represents emotions and how big the differences in efficiency are in this context. The conducted studies demonstrated that mel-spectrograms are a better-suited...
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Aerodynamic Shape Optimization for Delaying Dynamic Stall of Airfoils by Regression Kriging
PublikacjaThe phenomenon of dynamic stall produce adverse aerodynamic loading which can adversely affect the structural strength and life of aerodynamic systems. Aerodynamic shape optimization (ASO) provides an effective approach for delaying and mitigating dynamic stall characteristics without the addition of auxiliary system. ASO, however, requires multiple evaluations time-consuming computational fluid dynamics models. Metamodel-based...
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Improved-Efficacy Optimization of Compact Microwave Passives by Means of Frequency-Related Regularization
PublikacjaElectromagnetic (EM)-driven optimization is an important part of microwave design, especially for miniaturized components where the cross-coupling effects in tightly arranged layouts make traditional (e.g., equivalent network) representations grossly inaccurate. Efficient parameter tuning requires reasonably good initial designs, which are difficult to be rendered for newly developed structures or when re-design for different operating...
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Mixed integer nonlinear optimization of biological processes in wastewater sequencing batch reactor
PublikacjaWastewater treatment plays a key role for humanity. The waste entering lakes, rivers, and seas deteriorates daily quality of life. Therefore, it is very important to improve the efficiency of wastewater treatment. From a control point of view, a biological wastewater treatment plant is a complex, non-linear, multidimensional, hybrid control system. The paper presents the design of the optimizing hierarchical control system applied...
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Numerically efficient algorithm for compact microwave device optimization with flexible sensitivity updating scheme
PublikacjaAn efficient trust-region algorithm with flexible sensitivity updating management scheme for electromagnetic (EM)-driven design optimization of compact microwave components is proposed. During the optimization process, updating of selected columns of the circuit response Jacobian is performed using a rank-one Broyden formula (BF) replacing finite differentiation (FD). The FD update is omitted for directions sufficiently well aligned...
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Approximation of Fractional Order Dynamic Systems Using Elman, GRU and LSTM Neural Networks
PublikacjaIn the paper, authors explore the possibility of using the recurrent neural networks (RNN) - Elman, GRU and LSTM - for an approximation of the solution of the fractional-orders differential equations. The RNN network parameters are estimated via optimisation with the second order L-BFGS algorithm. It is done based on data from four systems: simple first and second fractional order LTI systems, a system of fractional-order point...
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Accurate Modeling of Antenna Structures by Means of Domain Confinement and Pyramidal Deep Neural Networks
PublikacjaThe importance of surrogate modeling techniques has been gradually increasing in the design of antenna structures over the recent years. Perhaps the most important reason is a high cost of full-wave electromagnetic (EM) analysis of antenna systems. Although imperative in ensuring evaluation reliability, it entails considerable computational expenses. These are especially pronounced when carrying out EM-driven design tasks such...
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Estimation of structural stiffness with the use of Particle Swarm Optimization
PublikacjaThe paper presents the theoretical background and four applications examples of the new method for the estimation of support stiffness coefficients of complex structures modelled discretely (e.g. with the use of the Finite Element Model (FEM) method based on the modified Particle Swarm Optimization (PSO) algorithm. In real-life cases, exact values of the supports’ stiffness coefficients may change for various reasons...
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Efficient Multi-Fidelity Design Optimization of Microwave Filters Using Adjoint Sensitivity
PublikacjaA simple and robust algorithm for computationally efficient design optimiza-tion of microwave filters is presented. Our approach exploits a trust-region (TR)-based algorithm that utilizes linear approximation of the filter response obtained using adjoint sensitivity. The algorithm is sequentially executed on a family of electromagnetic (EM)-simulated models of different fidelities, starting from a coarse-discretization one, and...
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Diagnosis of damages in family buildings using neural networks
PublikacjaThe article concerns a problem of damages in family buildings, which result from traffic-induced vibrations. These vibrations arise from various causes and their size is influenced by many factors. The most important is the type of a road, type and weight of vehicles that run on the road, type and condition of the road surface, the distance from the house to the source of vibrations and many others which should be taken into account....
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International Journal of Neural Networks
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IEEE TRANSACTIONS ON NEURAL NETWORKS
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The application of neural networks in forecasting the influence of traffic-induced vibrations on residential buildings
PublikacjaTraffic-induced vibrations may cause the cracking of plaster, damage to structural elements and, in extreme cases, may even lead to the structural collapse of residential buildings. The aim of this article is to analyse the effectiveness of a method of forecasting the impact of vibrations on residential buildings using the concept of artificial intelligence. The article presents several alternative forecasting systems for which...
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Gradient-based optimization of filters using FD-TD software
PublikacjaW artykule opisane jest nowe podejście do zagadnienia optymalizacji filtrów mikrofalowych pasmowo-przepustowych. Optymalizacja prowadzona jest z wykorzystaniem metody gradientowej poszukiwania minimum wartości funkcji celu oraz przy założeniu pełnofalowej symulacji obwodów metodą różnic skończonych w dziedzinie czasu. W artykule pokazane jest, że stosując zaawansowane techniki cyfrowego przetwarzania sygnałów możliwa jest optymalizacja...
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Heavy Duty Vehicle Fuel Consumption Modelling Based on Exploitation Data by Using Artificial Neural Networks
PublikacjaOne 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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Accurate simulation-driven modeling and design optimization of compact microwave structures
PublikacjaCost efficient design optimization of microwave structures requires availability of fast yet reliable replacement models so that multiple evaluations of the structure at hand can be executed in reasonable timeframe. Direct utilization of full-wave electromagnetic (EM) simulations is often prohibitive. On the other hand, accurate data-driven modeling normally requires a very large number of training points and it is virtually infeasible...