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Search results for: deep learning, genetic algorithm, artificial neural networks, predictive maintenance, cost efficient maintenance
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On Computationally-Efficient Reference Design Acquisition for Reduced-Cost Constrained Modeling and Re-Design of Compact Microwave Passives
PublicationFull-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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Machine Learning Techniques in Concrete Mix Design
PublicationConcrete mix design is a complex and multistage process in which we try to find the best composition of ingredients to create good performing concrete. In contemporary literature, as well as in state-of-the-art corporate practice, there are some methods of concrete mix design, from which the most popular are methods derived from The Three Equation Method. One of the most important features of concrete is compressive strength, which...
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Ultracapacitor modeling and control with discrete fractional order artificial neural network
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Artificial-Neural-Network-Based Sensorless Nonlinear Control of Induction Motors
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On thermal and Flow Expert Systems Based on Artificial Neural Network (ANN)
PublicationZaprezentowano możliwość realizacji jednego z zadań systemów eksperckich, polegającego na określaniu rozmiaru eksploatacyjnej degradacji parametrów geometrycznych układów łopatkowych turbin. Dyskusję przeprowadzono w oparciu o zastosowanie wybranego typu sztucznej sieci neuronowej (SSN). Badano jakość i dokładność polegającą na dobrej identyfikacji rozmiaru degradacji przez tę wybraną SSN wykrywającą rozmiar degradacji geometrycznej....
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Artificial neural network controller for underwater ship hull operation robot.
PublicationZaproponowano model matematyczny pojazdu podwodnego, który w uproszczonej wersji spełnia warunki dynamiki odpowiadające głowicy roboczej podwodnego robota. Uwzględniono niektóre czynniki oddziałujące na ruch podwodnej głowicy roboczej, jak np. gęstość wody oraz siły odśrodkowe i wypornościowe. Przedstawiono układ sterowania, w którym zastosowano regulator oparty na bazie sieci neuronowych, za pomocą którego można sterować...
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Artificial Neural Network-Based Sensorless Nonlinear Control Of Induction Motors
PublicationW niniejszym artykule przedstawiono strukturę sztucznej sieci neuronowej służącej do korygowania działania układu estymacji prędkości kątowej wirnika. Odtworzona prędkość kątowa wirnika zostały wykorzystane w bezczujnikowym układzie sterowania silnikiem indukcyjnym pracującym w zamkniętej pętli sprzężenia prędkościowego.Przedstawiono wyniki badań eksperymentalnych z silnikiem o mocy 1,1kW.
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A New Heuristic Algorithm for Effective Preemption in MPLS Networks
PublicationW artykule opisano nowy algorytm wywłaszczania przeznaczony dla sieci MPLS. Umożliwia on alokację zasobów sieci dla nowej ścieżki uwzględniając koszt usuniętych jednej lub wielu istniejących już ścieżek. Proponowany heurystyczny algorytm wybiera ścieżki do wywłaszczenia wykorzystując informację o topologii sieci. Używa funkcji optymalizacji minimalizującej stratę pasma na poziomie sieci. Przedstawiono i omówiono wyniki badań symulacyjnych...
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Energy-Efficient Neural Network Inference with Microcavity Exciton Polaritons
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Hierarchical Estimation of Human Upper Body Based on 2D Observation Utilizing Evolutionary Programming and 'Genetic Memory'
PublicationNew method of the human body pose estimation based on single camera 2D observation is presented. It employs 3D model of the human body, and genetic algorithm combined with annealed particle filter for searching the global optimum of model state, best matching the object's 2D observation. Additionally, motion cost metric is employed, considering current pose and history of the body movement, favouring the estimates with the lowest...
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Free-standing TiO2 nanotubes decorated with spherical nickel nanoparticles as a cost-efficient electrocatalyst for oxygen evolution reaction
PublicationHere, we report significant activity towards the oxygen evolution reaction (OER) of spherical nickel nanoparticles (NPs) electrodeposited onto free-standing TiO2 nanotubes (TNT) via cyclic voltammetry. It has been shown that simple manipulation of processing parameters, including scan rate and number of cycles, allows for formation of the NPs in various diameters and amounts. The polarization data with respect to transmission electron...
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Data governance: Organizing data for trustworthy Artificial Intelligence
PublicationThe rise of Big, Open and Linked Data (BOLD) enables Big Data Algorithmic Systems (BDAS) which are often based on machine learning, neural networks and other forms of Artificial Intelligence (AI). As such systems are increasingly requested to make decisions that are consequential to individuals, communities and society at large, their failures cannot be tolerated, and they are subject to stringent regulatory and ethical requirements....
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Shape Optimisation of Kaplan Turbine Blades Using Genetic Algorithms
PublicationThis monograph is a comprehensive guide to a method of blade profile optimisation for Kaplan-type turbines. This method is based on modelling the interaction between rotor and stator blades. Additionally, the shape of the draft tube is investigated. The influence of the periodic boundary condition vs. full geometry is also discussed. Evolutionary algorithms (EA) are used as an optimisation method together with artificial neural...
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Creating a radiological database for automatic liver segmentation using artificial intelligence.
PublicationImaging in medicine is an irreplaceable stage in the diagnosis and treatment of cancer. The subsequent therapeutic effect depends on the quality of the imaging tests performed. In recent years we have been observing the evolution of 2D to 3D imaging for many medical fields, including oncological surgery. The aim of the study is to present a method of selection of radiological imaging tests for learning neural networks.
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Cost-Efficient Bi-Layer Modeling of Antenna Input Characteristics Using Gradient Kriging Surrogates
PublicationOver the recent years, surrogate modeling has been playing an increasing role in the design of antenna structures. The main incentive is to mitigate the issues related to high cost of electromagnetic (EM)-based procedures. Among the various techniques, approximation surrogates are the most popular ones due to their flexibility and easy access. Notwithstanding, data-driven modeling of antenna characteristics is associated with serious...
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Evolving gene regulatory networks controlling foraging strategies of prey and predators in an artificial ecosystem
PublicationCo-evolution of predators and prey is an example of an evolutionary arms race, leading in nature to selective pressures in positive feedback. We introduce here an artificial life ecosystem in which such positive feedback can emerge. This ecosystem consists of a 2-dimensional liquid environment and animats controlled by evolving artificial gene regulatory networks encoded in linear genomes. The genes in the genome encode chemical...
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Single-agent MOR208 salvage and maintenance therapy in a patient with refractory/relapsing diffuse large B-cell lymphoma: a case report
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Employing Subjective Tests and Deep Learning for Discovering the Relationship between Personality Types and Preferred Music Genres
PublicationThe purpose of this research is two-fold: (a) to explore the relationship between the listeners’ personality trait, i.e., extraverts and introverts and their preferred music genres, and (b) to predict the personality trait of potential listeners on the basis of a musical excerpt by employing several classification algorithms. We assume that this may help match songs according to the listener’s personality in social music networks....
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Cost-Efficient Two-Level Modeling of Microwave Passives Using Feature-Based Surrogates and Domain Confinement
PublicationA variety of surrogate modelling techniques has been utilized in high-frequency design over the last two decades. Yet, the curse of dimensionality still poses a serious challenge in setting up re-liable design-ready surrogates of modern microwave components. The difficulty of the model-ing task is only aggravated by nonlinearity of circuit responses. Consequently, constructing a practically usable surrogate model, valid across...
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Generation of microbial colonies dataset with deep learning style transfer
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Deep learning-based waste detection in natural and urban environments
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Model Predictive Super-Twisting Sliding Mode Control for An Autonomous Surface Vehicle
PublicationThis paper presents a new robust Model Predictive Control (MPC) algorithm for trajectory tracking of an Autonomous Surface Vehicle (ASV) in presence of the time-varying external disturbances including winds, waves and ocean currents as well as dynamical uncertainties. For fulfilling the robustness property, a sliding mode control-based procedure for designing of MPC and a super-twisting term are adopted. The MPC algorithm has been...
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Artificial Intelligence Aided Architectural Design
PublicationTools and methods used by architects always had an impact on the way building were designed. With the change in design methods and new approaches towards creation process, they became more than ever before crucial elements of the creation process. The automation of architects work has started with computational functions that were introduced to traditional computer-aided design tools. Nowadays architects tend to use specified tools...
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Autonomous port management based AGV path planning and optimization via an ensemble reinforcement learning framework
PublicationThe rapid development of shipping trade pushes automated container terminals toward the direction of intelligence, safety and efficiency. In particular, the formulation of AGV scheduling tasks and the safety and stability of transportation path is an important part of port operation and management, and it is one of the basic tasks to build an intelligent port. Existing research mainly focuses on collaborative operation between...
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Ship Dynamic Positioning Based on Nonlinear Model Predictive Control
PublicationThe presented work explores the simulation test results of using nonlinear model predictive control algorithm for ship dynamic positioning. In the optimization task, a goal function with a penalty was proposed with a variable prediction step. The results of the proposed control algorithm were compared with backstepping and PID. The effect of estimation accuracy on the control quality with the implemented algorithms was investigated....
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Flexible Knowledge–Vision–Integration Platform for Personal Protective Equipment Detection and Classification Using Hierarchical Convolutional Neural Networks and Active Leaning
PublicationThis work is part of an effort to develop of a Knowledge-Vision Integration Platform for Hazard Control (KVIP-HC) in industrial workplaces, adaptable to a wide range of industrial environments. The paper focuses on hazards resulted from the non-use of personal protective equipment (PPE). The objective is to test the capability of the platform to adapt to different industrial environments by simulating the process of randomly selecting...
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Predicting emotion from color present in images and video excerpts by machine learning
PublicationThis work aims at predicting emotion based on the colors present in images and video excerpts using a machine-learning approach. The purpose of this paper is threefold: (a) to develop a machine-learning algorithm that classifies emotions based on the color present in an image, (b) to select the best-performing algorithm from the first phase and apply it to film excerpt emotion analysis based on colors, (c) to design an online survey...
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Speed sensorless induction motor drive with predictive current controller
PublicationToday, speed sensorless modes of operation are becoming standard solutions in the area of electric drives. This paper presents a speed sensorless control system of an induction motor with a predictive current controller. A closed-loop estimation system with robustness against motor parameter variation is used for the control approach. The proposed algorithm has been implemented using field-programmable gate arrays (FPGAs) and a...
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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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Machine-Learning-Powered EM-Based Framework for Efficient and Reliable Design of Low Scattering Metasurfaces
PublicationPopularity of metasurfaces has been continuously growing due to their attractive properties including the ability to effectively manipulate electromagnetic (EM) waves. Metasurfaces comprise optimized geometries of unit cells arranged as a periodic lattice to obtain a desired EM response. One of their emerging application areas is the stealth technology, in particular, realization of radar cross section (RCS) reduction. Despite...
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Efficient list cost coloring of vertices and/or edges of some sparse graphs
PublicationRozważane jest kolorowanie wierzchołków i krawędzi grafów w modelach klasycznym, totalnym i pseudototalnym z uwzględnieniem dodatkowego ograniczenia w postaci list dostępnych kolorów. Proponujemy wielomianowy algorytm oparty na paradygmacie programowania dynamicznego dla grafów o strukturze drzewa. Wynik ten można uogólnić na grafy o liczbie cyklomatycznej ograniczonej z góry przez dowolnie wybraną stała.
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Efficient list cost coloring of vertices and/or edges of bounded cyclicity graphs
PublicationW artykule rozważamy listowo-kosztowe kolorowanie wierzchołków i krawędzi grafu w modelu wierzchołkowym, krawędziowym, totalnym i pseudototalnym. Stosujemy programowanie dynamiczne w celu otrzymania algorytmów wielomianowych dla drzew. Następnie uogólniamy to podejście na dowolne grafy z ograniczonymi liczbami cyklomatycznymi i na ich multikolorowania.
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Efficient list cost coloring of vertices and/or edges of bounded cyclicity graphs
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Efficient List Cost Coloring of Vertices and∕or Edges of Some Sparse Graphs
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Collaborative Data Acquisition and Learning Support
PublicationWith the constant development of neural networks, traditional algorithms relying on data structures lose their significance as more and more solutions are using AI rather than traditional algorithms. This in turn requires a lot of correctly annotated and informative data samples. In this paper, we propose a crowdsourcing based approach for data acquisition and tagging with support for Active Learning where the system acts as an...
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Assessing the attractiveness of human face based on machine learning
PublicationThe attractiveness of the face plays an important role in everyday life, especially in the modern world where social media and the Internet surround us. In this study, an attempt to assess the attractiveness of a face by machine learning is shown. Attractiveness is determined by three deep models whose sum of predictions is the final score. Two annotated datasets available in the literature are employed for training and testing...
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Agent-Based Population Learning Algorithm for RBF Network Tuning
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Practical issues for the implementation of survivability and recovery techniques in optical networks
PublicationFailures in optical networks are inevitable. They may occur during work being done for the maintenance of other infrastructures, or on a larger scale as the result of an attack or large-scale disaster. As a result, service availability, an important aspect of Quality of Service (QoS), is often degraded. Appropriate fault recovery techniques are thus crucial to meet the requirements set by the Service Level Agreements (SLAs) between...
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Neural networks based NARX models in nonlinear adaptive control
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Using neural networks to examine trending keywords in Inventory Control
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Application of neural networks for description of pressure distribution in slide bearing.
PublicationBadano rozkład ciśnienia hydrodynamicznego w łożysku ślizgowym dla wybranych wariantów łożyska. Wykazano, że zastosowanie sieci neuronowych umożliwia opis rozkładu ciśnienia hydrodynamicznego z uwzględnieniem zmian geometrycznych (bezwymiarowa długość - L) i mechanicznych (mimośrodowość względem H) łożyska.
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Identification of slide bearing main parameters using neural networks.
PublicationWykazano, że sieci neuronowe jak najbardziej nadają się do identyfikacji głównych parametrów geometrycznych i ruchowych hydrodynamicznych łożysk ślizgowych.
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Estimation the rhythmic salience of sound with association rules and neural networks
PublicationW referacie przedstawiono eksperymenty mające na celu automatyczne wyszukiwanie wartości rytmicznych we frazie muzycznej. W tym celu wykorzystano metody data mining i sztuczne sieci neuronowe.
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Experimental and Machine-Learning-Assisted Design of Pharmaceutically Acceptable Deep Eutectic Solvents for the Solubility Improvement of Non-Selective COX Inhibitors Ibuprofen and Ketoprofen
PublicationDeep eutectic solvents (DESs) are commonly used in pharmaceutical applications as excellent solubilizers of active substances. This study investigated the tuning of ibuprofen and ketoprofen solubility utilizing DESs containing choline chloride or betaine as hydrogen bond acceptors and various polyols (ethylene glycol, diethylene glycol, triethylene glycol, glycerol, 1,2-propanediol, 1,3-butanediol) as hydrogen bond donors. Experimental...
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Experimental and Machine-Learning-Assisted Design of Pharmaceutically Acceptable Deep Eutectic Solvents for the Solubility Improvement of Non-Selective COX Inhibitors Ibuprofen and Ketoprofen
PublicationDeep eutectic solvents (DESs) are commonly used in pharmaceutical applications as excellent solubilizers of active substances. This study investigated the tuning of ibuprofen and ketoprofen solubility utilizing DESs containing choline chloride or betaine as hydrogen bond acceptors and various polyols (ethylene glycol, diethylene glycol, triethylene glycol, glycerol, 1,2-propanediol, 1,3-butanediol) as hydrogen bond donors. Experimental...
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Efficient Usage of Dense GNSS Networks in Central Europe for the Visualization and Investigation of Ionospheric TEC Variations
PublicationThe technique of the orthogonal projection of ionosphere electronic content variations for mapping total electron content (TEC) allows us to visualize ionospheric irregularities. For the reconstruction of global ionospheric characteristics, numerous global navigation satellite system (GNSS) receivers located in different regions of the Earth are used as sensors. We used dense GNSS networks in central Europe to detect and investigate...
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Genetic programming extension to APF-based monocular human body pose estimation
PublicationNew method of the human body pose estimation based on a single camera 2D observation is presented, aimed at smart surveillance related video analysis and action recognition. It employs 3D model of the human body, and genetic algorithm combined with annealed particle filter for searching the global optimum of model state, best matching the object's 2D observation. Additionally, new motion cost metric is employed, considering current...
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Prediction of maximum tensile stress in plain-weave composite laminates with interacting holes via stacked machine learning algorithms: A comparative study
PublicationPlain weave composite is a long-lasting type of fabric composite that is stable enough when being handled. Open-hole composites have been widely used in industry, though they have weak structural performance and complex design processes. An extensive number of material/geometry parameters have been utilized for designing these composites, thereby an efficient computational tool is essential for that purpose. Different Machine Learning...
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Neural network simulator's application to reference performance determination of turbine blading in the heat-flow diagnostics.
PublicationIn the paper, the possibility of application of artificial neural networks to perform the fluid flow calculations through both damaged and undamaged turbine blading was investigated. Preliminary results are presented and show the potentiality of further development of the method for the purpose of heat-flow diagnostics.
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Leveraging Training Strategies of Artificial Neural Network for Classification of Multiday Electromyography Signals
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