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Search results for: SURROGATE MODELING , ANTENNA DESIGN , DOMAIN CONFINEMENT , NESTED KRIGING , DEEP NEURAL NETWORKS
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Low-Cost Yield-Driven Design of Antenna Structures Using Response-Variability Essential Directions and Parameter Space Reduction
PublicationQuantifying the effects of fabrication tolerances and uncertainties of other types is fundamental to improve antenna design immunity to limited accuracy of manufacturing procedures and technological spread of material parameters. This is of paramount importance especially for antenna design in the industrial context. Degradation of electrical and field properties due to geometry parameter deviations often manifests itself as, e.g.,...
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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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Simulation-driven design of compact ultra-wideband antenna structures
PublicationPurpose–The purpose of this paper is to investigate strategies and algorithms for expedited designoptimization and explicit size reduction of compact ultra-wideband (UWB) antennas.Design/methodology/approach–Formulation of the compact antenna design problem aiming atexplicit size reduction while maintaining acceptable electrical performance is presented. Algorithmicframeworks are described suitable for handling various design situations...
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Data-Driven Surrogate-Assisted Optimization of Metamaterial-Based Filtenna Using Deep Learning
PublicationIn this work, a computationally efficient method based on data driven surrogate models is pro-posed for the design optimization procedure of a Frequency Selective Surface (FSS)-based filtering antenna (Filtenna). A Filtenna acts as a as module that simultaneously pre-filters unwanted sig-nals, and enhances the desired signals at the operating frequency. However, due to a typically large number of design variables of FSS unit elements,...
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Low-cost data-driven modelling of microwave components using domain confinement and PCA-based dimensionality reduction
PublicationFast data-driven surrogate models can be employed as replacements of computationally demanding full-wave electromagnetic simulations to facilitate the microwave design procedures. Unfortunately, practical application of surrogate modelling is often hindered by the curse of dimensionality and/or considerable nonlinearity of the component characteristics. This paper proposes a simple yet reliable approach to cost-efficient modelling...
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GPU Power Capping for Energy-Performance Trade-Offs in Training of Deep Convolutional Neural Networks for Image Recognition
PublicationIn the paper we present performance-energy trade-off investigation of training Deep Convolutional Neural Networks for image recognition. Several representative and widely adopted network models, such as Alexnet, VGG-19, Inception V3, Inception V4, Resnet50 and Resnet152 were tested using systems with Nvidia Quadro RTX 6000 as well as Nvidia V100 GPUs. Using GPU power capping we found other than default configurations minimizing...
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Scalability of surrogate-assisted multi-objective optimization of antenna structures exploiting variable-fidelity electromagnetic simulation models
PublicationMulti-objective optimization of antenna structures is a challenging task due to high-computational cost of evaluating the design objectives as well as large number of adjustable parameters. Design speedup can be achieved by means of surrogate-based optimization techniques. In particular, a combination of variable-fidelity electromagnetic (EM) simulations, design space reduction techniques, response surface approximation (RSA) models,...
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Optimal Design of Transmitarray Antennas via Low-Cost Surrogate Modelling
PublicationOver the recent years, reflectarrays and transmitarrays have been drawing a considerable attention due to their attractive features, including a possibility of realizing high gain and pencil-like radiation patterns without the employment of complex feeding networks. Among the two, transmitarrays seem to be superior over reflectarrays in terms of achieving high radiation efficiency without the feed blockage. Notwithstanding, the...
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Reduced-Cost Constrained Modeling of Microwave and Antenna Components: Recent Advances
PublicationElectromagnetic (EM) simulation models are ubiquitous in the design of microwave and antenna components. EM analysis is reliable but CPU intensive. In particular, multiple simulations entailed by parametric optimization or uncertainty quantification may considerably slow down the design processes. In order to address this problem, it is possible to employ fast metamodels. Here, the popular solution approaches are approximation...
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Artificial Neural Networks in Microwave Components and Circuits Modeling
PublicationArtykuł dotyczy wykorzystania sztucznych sieci neuronowych (SNN) w projektowaniu i optymalizacji układów mikrofalowych.Zaprezentowano podstawowe zasady i założenia modelowania z użyciem SNN. Możliwości opisywanej metody opisano wykorzystując przykładowyprojekt anteny łatowej. Przedstawiono różne strategie modelowania układów, które wykorzystują możliwości opisywanej metody w połączeniu zwiedzą mikrofalową. Porównano również dokładność...
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Enhancing Performance of Switched Parasitic Antenna for Localization in Wireless Sensor Networks
PublicationThis paper presents an Electronically Steerable Parasitic Array Radiator (ESPAR) antenna with enhanced performance of estimating the incoming signal direction. Designed antenna is dedicated for 2.4 GHz ISM applications with emphasis on Wireless Sensor Networks (WSN). The limitations of the existing design approach are illustrated, as well as perspectives and challenges of the proposed solution in relation to the localization in...
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Enhanced uniform data sampling for constrained data‐driven modeling of antenna input characteristics
PublicationData-driven surrogates are the most popular replacement models utilized in many fields of engineering and science, including design of microwave and antenna structures. The primary practical issue is a curse of dimensionality which limits the number of independent parameters that can be accounted for in the modelling process. Recently, a performance-driven modelling technique has been proposed where the constrained domain of the...
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Expedited Yield-Driven Design of High-Frequency Structures by Kriging Surrogates in Confined Domains
PublicationUncertainty quantification is an important aspect of engineering design, also pertaining to the development and performance evaluation of high-frequency structures 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 characteristics of antennas or microwave devices. For example, in the case...
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Response Feature Technology for High-Frequency Electronics. Optimization, Modeling, and Design Automation
PublicationThis book discusses response feature technology and its applications to modeling, optimization, and computer-aided design of high-frequency structures including antenna and microwave components. By exploring the specific structure of the system outputs, feature-based approaches facilitate simulation-driven design procedures, both in terms of improving their computational efficiency and reliability. These benefits are associated...
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Exploring Neural Networks for Musical Instrument Identification in Polyphonic Audio
PublicationThe purpose of this paper is to introduce neural network-based methods that surpass state-of-the-art (SOTA) models, either by training faster or having simpler architecture, while maintaining comparable effectiveness in musical instrument identification in polyphonic music. Several approaches are presented, including two authors’ proposals, i.e., spiking neural networks (SNN) and a modular deep learning model named FMCNN (Fully...
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A survey of neural networks usage for intrusion detection systems
PublicationIn recent years, advancements in the field of the artificial intelligence (AI) gained a huge momentum due to the worldwide appliance of this technology by the industry. One of the crucial areas of AI are neural networks (NN), which enable commer‐ cial utilization of functionalities previously not accessible by usage of computers. Intrusion detection system (IDS) presents one of the domains in which neural networks are widely tested...
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Computationally Efficient Surrogate-Assisted Design of Pyramidal-Shaped 3D Reflectarray Antennas
PublicationReflectarrays (RAs) have been attracting considerable interest in the recent years due to their appealing features, in particular, a possibility of realizing pencil-beam radiation patterns, as in the phased arrays, but without the necessity of incorporating the feeding networks. These characteristics make them attractive solutions, among others, for satellite communications or mobile radar antennas. Notwithstanding, available microstrip...
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Simulation-Driven Design of Microstrip Antenna Subarrays
PublicationA methodology for computationally efficient simulation-driven design of microstrip antenna subarrays is presented. Our approach takes into account the effect of the feed (e.g., a corporate network) on the subarray side lobe level and allows adjusting both radiation and reflection responses of the structure under design within a single automated process. This process is realized as surrogate-based optimization that produces designs...
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Multi-objective design optimization of antenna structures using sequential domain patching with automated patch size deter-mination
PublicationIn this paper, a simple yet efficient and reliable technique for fully automated multi-objective design optimization of antenna structures using sequential domain patching (SDP) is discussed. The optimization procedure according to SDP is a two-step process: (i) obtaining the initial set of Pareto-optimal designs representing the best possible trade-offs between considered conflicting objectives, and (ii) Pareto set refinement...
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Benchmarking Deep Neural Network Training Using Multi- and Many-Core Processors
PublicationIn the paper we provide thorough benchmarking of deep neural network (DNN) training on modern multi- and many-core Intel processors in order to assess performance differences for various deep learning as well as parallel computing parameters. We present performance of DNN training for Alexnet, Googlenet, Googlenet_v2 as well as Resnet_50 for various engines used by the deep learning framework, for various batch sizes. Furthermore,...
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A structure and design of a novel compact UWB MIMO antenna
PublicationIn the paper, a concept and design procedure of a novel compact MIMO slot antenna is presented. In order to achieve a better filling of available space, individual antennas are constrained to a triangular shape and optimized for a reduced size. The MIMO structure is then assembled using the two of previously designed antennas in orthogonal arrangement. Surrogate-assisted numerical optimization involving variable-fidelity electromagnetic...
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Reliable computationally-efficient behavioral modeling of microwave passives using deep learning surrogates in confined domains
PublicationThe importance of surrogate modeling techniques has been steadily growing over the recent years in high-frequency electronics, including microwave engineering. Fast metamodels are employed to speedup design processes, especially those conducted at the level of full-wave electromagnetic (EM) simulations. The surrogates enable massive system evaluations at nearly EM accuracy and negligible costs, which is invaluable in parameter...
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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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Robustness in Compressed Neural Networks for Object Detection
PublicationModel compression techniques allow to significantly reduce the computational cost associated with data processing by deep neural networks with only a minor decrease in average accuracy. Simultaneously, reducing the model size may have a large effect on noisy cases or objects belonging to less frequent classes. It is a crucial problem from the perspective of the models' safety, especially for object detection in the autonomous driving...
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Artificial Neural Networks as an architectural design tool- generating new detail forms based on the Roman Corinthian order capital
PublicationThe following paper presents the results of the research in the field of the machine learning, investigating the scope of application of the artificial neural networks algorithms as a tool in architectural design. The computational experiment was held using the backward propagation of errors method of training the artificial neural network, which was trained based on the geometry of the details of the Roman Corinthian order capital....
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Accelerated design optimization of miniaturized microwave passives by design reusing and Kriging interpolation surrogates
PublicationElectromagnetic (EM) analysis has become ubiquitous in the design of microwave components and systems. One of the reasons is the increasing topological complexity of the circuits. Their reliable evaluation—at least at the design closure stage—can no longer be carried out using analytical or equivalent network representations. This is especially pertinent to miniaturized structures, where considerable EM cross-coupling effects occurring...
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On Decomposition-Based Surrogate-Assisted Optimization of Leaky Wave Antenna Input Characteristics for Beam Scanning Applications
PublicationRecent years have witnessed a growing interest in reconfigurable antenna systems. Travelling wave antennas (TWAs) and leaky wave antennas (LWAs) are representative examples of structures featuring a great level of flexibility (e.g., straightforward implementation of beam scanning), relatively simple geometrical structure, low profile, and low fabrication cost. Notwithstanding, the design process of TWAs/LWAs is a challenging endeavor...
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Categorization of emotions in dog behavior based on the deep neural network
PublicationThe aim of this article is to present a neural system based on stock architecture for recognizing emotional behavior in dogs. Our considerations are inspired by the original work of Franzoni et al. on recognizing dog emotions. An appropriate set of photographic data has been compiled taking into account five classes of emotional behavior in dogs of one breed, including joy, anger, licking, yawning, and sleeping. Focusing on a particular...
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Rapid tolerance‐aware design of miniaturized microwave passives by means of confined‐domain surrogates
PublicationThe effects of uncertainties, primarily manufacturing tolerances but also incomplete information about operating conditions or material parameters, can be detrimental to the performance of microwave components. Quantification of such effects is essential to ensure a meaningful evaluation of the structure, in particular, its reliability under imperfect fabrication procedures. The improvement of the circuit robustness can be achieved...
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Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks
PublicationIn this article, the usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) is presented. The developed approach, using channel impulse response, allows higher efficiency in identifying the direct visibility conditions between nodes in off-body communication with comparison to the methods described in the literature. The effectiveness of the proposed deep feedforward neural network was checked on...
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A structure and simulation-driven design of compact CPW-fed UWB antenna
PublicationIn this letter, a structure of a miniaturized ultra-wideband CPW-fed antenna and its design proce-dure are presented. The antenna is a modified version of the design previously proposed in the literature, with additional degrees of freedom introduced in order to improve the structure flexibility. The small size is achieved by executing a rigorous optimization procedure that consists of two stages: (i) smart random search carried...
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Novel Structure and EM-Driven Design of Small UWB Monopole Antenna
PublicationA novel structure of a small UWB monopole antenna is presented. In our approach, a compact size is achieved by means of a meander line for current path enlargement as well as the two parameterized slits that introduce additional degrees of freedom helping to ensure good impedance matching. The antenna design is carried out using surrogate-based optimization involving variable-fidelity EM simulations. This allows us to simultaneously...
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Physics-guided neural networks (PGNNs) to solve differential equations for spatial analysis
PublicationNumerous examples of physically unjustified neural networks, despite satisfactory performance, generate contradictions with logic and lead to many inaccuracies in the final applications. One of the methods to justify the typical black-box model already at the training stage and lead to many inaccuracies in the final applications. One of the methods to justify the typical black-box model already at the training stage involves extending...
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Structure and computationally-efficient simulation-driven design of compact UWB monopole antenna
PublicationIn 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
PublicationMachine 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
PublicationThe 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
PublicationThe 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
PublicationIn 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
PublicationNumerical 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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Expedited Machine-Learning-Based Global Design Optimization of Antenna Systems Using Response Features and Multi-Fidelity EM Analysis
PublicationThe 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
PublicationMiniaturization 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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Expedited Globalized Antenna Optimization by Principal Components and Variable-Fidelity EM Simulations: Application to Microstrip Antenna Design
PublicationParameter 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
PublicationComputer 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
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 impact of the AC922 Architecture on Performance of Deep Neural Network Training
PublicationPractical 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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Efficient uncertainty quantification using sequential sampling-based neural networks
PublicationUncertainty 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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Accelerated Re-Design of Antenna Structures Using Sensitivity-Based Inverse Surrogates
PublicationThe 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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Applying artificial neural networks for modelling ship speed and fuel consumption
PublicationThis 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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Abdominal Aortic Aneurysm segmentation from contrast-enhanced computed tomography angiography using deep convolutional networks
PublicationOne 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...