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Search results for: MICROWAVE ENGINEERING, COMPUTER-AIDED DESIGN, MULTI-CRITERIAL OPTIMIZATION, MACHINE LEARNING, NEURAL NETWORKS
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Zero-Pole Electromagnetic Optimization
PublicationA fast technique for the full-wave optimization of transmission or reflection properties of general linear timeinvariant high-frequency components is proposed. The method is based on the zeros and poles of the rational function representing the scattering parameters of the device being designed and it is the generalization of the technique developed for the design by optimization of microwave filters. The performance of the proposed...
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Towards neural knowledge DNA
PublicationIn this paper, we propose the Neural Knowledge DNA, a framework that tailors the ideas underlying the success of neural networks to the scope of knowledge representation. Knowledge representation is a fundamental field that dedicates to representing information about the world in a form that computer systems can utilize to solve complex tasks. The proposed Neural Knowledge DNA is designed to support discovering, storing, reusing,...
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Comparative study on total nitrogen prediction in wastewater treatment 1 plant and effect of various feature selection methods on machine learning algorithms performance
PublicationWastewater characteristics prediction in wastewater treatment plants (WWTPs) is valuable and can reduce the number of sampling, energy, and cost. Feature Selection (FS) methods are used in the pre-processing section for enhancing the model performance. This study aims to evaluate the effect of seven different FS methods (filter, wrapper, and embedded methods) on enhancing the prediction accuracy for total nitrogen (TN) in the WWTP...
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Fast Multi-Objective Aerodynamic Optimization Using Sequential Domain Patching and Multifidelity Models
PublicationExploration of design tradeoffs for aerodynamic surfaces requires solving of multi-objective optimization (MOO) problems. The major bottleneck here is the time-consuming evaluations of the computational fluid dynamics (CFD) model used to capture the nonlinear physics involved in designing aerodynamic surfaces. This, in conjunction with a large number of simulations necessary to yield a set of designs representing the best possible...
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Improving platelet‐RNA‐based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification
PublicationLiquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community...
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Low-cost multiband compact branch-line coupler design using response features and automated EM model fidelity adjustment
PublicationDesign closure of compact microwave components is a challenging problem because of significant electromagnetic (EM) cross-couplings in densely arranged layouts. A separate issue is a large number of designable parameters resulting from replacement of conventional transmission line sections by compact microstrip resonant cells. This increases complexity of the design optimization problem and requires employment of expensive high-fidelity...
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Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete
PublicationHigh-performance alkali-activated concrete (HP-AAC) is acknowledged as a cementless and environmentally friendly material. It has recently received a substantial amount of interest not only due to the potential it has for being used instead of ordinary concrete but also owing to the concerns associated with climate change, sustainability, reduction of CO2 emissions, and energy consumption. The characteristics and amounts of the...
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Optimization of the spindle speed during milling of large-sized structures with the use of technique of Experiment-Aided Virtual Prototyping
PublicationIn the paper are presented considerations concerning vibration suppression problems during milling of large-sized workpieces with the use of innovative method of matching the spindle speed of cutting tool. It depends on repeatable change of the spindle speed value as soon as the optimal vibration state of the workpiece approaches. The values of dominant “peaks” in the frequency spectra and the Root Mean Square (RMS) values of time...
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Low-Cost Multi-Objective Optimization of Antennas By Means Of Generalized Pareto Ranking Bisection Algorithm
PublicationThis paper introduces a generalized Pareto ranking bisection algorithm for low-cost multi-objective design optimization of antenna structures. The algorithm allows for identifying a set of Pareto optimal sets of parameters (that represent the best trade-offs between considered objectives) by iterative partitioning of the intervals connecting previously found designs and executing a Pareto-ranking-based poll search. The initial...
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Connectivity in Multi-Interface Networks
PublicationRozważano zagadnienie minimalizacji energii w sieciach bezprzewodowych bez infrastruktury, w których niektóre węzły są wyposażone w więcej, niż jeden interfejs. W przyjętym modelu sieci podano nowe algorytmy przybliżone oraz wyniki dotyczące złożoności obliczeniowej dla problemu najtańszej spójnej podsieci spinającej.
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Expedited Feature-Based Quasi-Global Optimization of Multi-Band Antenna Input Characteristics with Jacobian Variability Tracking
PublicationDesign of modern antennas relies—for reliability reasons—on full-wave electromagnetic simulation tools. In addition, increasingly stringent specifications pertaining to electrical and field performance, growing complexity of antenna topologies, along with the necessity for handling multiple objectives, make numerical optimization of antenna geometry parameters a highly recommended design procedure. Conventional algorithms, particularly...
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Distributed protection against non-cooperative node behavior in multi-hop wireless networks
PublicationAn important security problem in today's distributed data networks is the prevention of non-cooperative behavior i.e., attacks consisting in the modification of standard node operation to gain unfair advantage over other system nodes. Such a behavior is currently feasible in many types of computer networks whose communication protocols are designed to maximize the network performance assuming full node cooperation. Moreover, it...
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Blood Pressure Estimation Based on Blood Flow, ECG and Respiratory Signals Using Recurrent Neural Networks
PublicationThe estimation of systolic and diastolic blood pressure using artificial neural network is considered in the paper. The blood pressure values are estimated using pulse arrival time, and additionally RR intervals of ECG signal together with respiration signal. A single layer recurrent neural network with hyperbolic tangent activation function was used. The average blood pressure estimation error for the data obtained from 21 subjects...
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A computer system for the complete design of ship propellers
PublicationPraca zawiera opis metody obliczeniowej pozwalającej na zaprojektowanie okrętowej śruby napędowej dla również obliczeniowo wyznaczonego niejednorodnego pola prędkości za kadłubem statku o znanej geometrii i parametrach ruchu. Metoda pozwala na właściwe ujęcie efektu skali i konsekwencji obecności płetwy sterowej za projektowaną śrubą.
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Rapid and Reliable Re-Design of Miniaturized Microwave Passives by Means of Concurrent Parameter Scaling and Intermittent Local Tuning
PublicationRe-design of microwave passive components for the assumed operating frequencies or substrate parameters is an important yet a tedious process. It requires simultaneous tuning of relevant circuit variables, often over broad ranges thereof, to ensure satisfactory performance of the system. If the operating conditions at the available design are distant from the intended ones, local optimization is typically insufficient, whereas...
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Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
PublicationWetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral...
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Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning
PublicationTheophylline, a typical representative of active pharmaceutical ingredients, was selected to study the characteristics of experimental and theoretical solubility measured at 25 °C in a broad range of solvents, including neat, binary mixtures and ternary natural deep eutectics (NADES) prepared with choline chloride, polyols and water. There was a strong synergistic effect of organic solvents mixed with water, and among the experimentally...
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Fair Optimization and Networks: A Survey
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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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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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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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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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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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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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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...