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Search results for: SZTUCZNA INTELIGENCJA
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Optimisation of turbine shaft heating process under steam turbine run-up conditions
PublicationAn important operational task for thermal turbines during run-up and run-down is to keep the stresses in the structural elements at a right level. This applies not only to their instantaneous values, but also to the impact of them on the engine lifetime. The turbine shaft is a particularly important element. The distribution of stresses depends on geometric characteristics of the shaft and its specific locations. This means a groove manufactured...
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Discovering Rule-Based Learning Systems for the Purpose of Music Analysis
PublicationMusic analysis and processing aims at understanding information retrieved from music (Music Information Retrieval). For the purpose of music data mining, machine learning (ML) methods or statistical approach are employed. Their primary task is recognition of musical instrument sounds, music genre or emotion contained in music, identification of audio, assessment of audio content, etc. In terms of computational approach, music databases...
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Multi-region fuzzy logic controller with local PID controllers for U-tube steam generator in nuclear power plant
PublicationIn the paper, analysis of multi-region fuzzy logic controller with local PID controllers for steam generator of pressurized water reactor (PWR) working in wide range of thermal power changes is presented. The U-tube steam generator has a nonlinear dynamics depending on thermal power transferred from coolant of the primary loop of the PWR plant. Control of water level in the steam generator conducted by a traditional PID controller...
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Inteligencja zespołowa
PublicationPrzedstawiono przegląd zespołowego przetwarzania informacji, uczenia się i podejmowania decyzji. Omówiono algorytmy ewolucyjne, roju, mrówkowe, immunologiczne, sieci neuronowe, współpracę agentów, modelowanie indywiduowe oraz przykładowe środowisko modelowania zespołowego.
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Rediscovering Automatic Detection of Stuttering and Its Subclasses through Machine Learning—The Impact of Changing Deep Model Architecture and Amount of Data in the Training Set
PublicationThis work deals with automatically detecting stuttering and its subclasses. An effective classification of stuttering along with its subclasses could find wide application in determining the severity of stuttering by speech therapists, preliminary patient diagnosis, and enabling communication with the previously mentioned voice assistants. The first part of this work provides an overview of examples of classical and deep learning...
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Attention-Based Deep Learning System for Classification of Breast Lesions—Multimodal, Weakly Supervised Approach
PublicationBreast cancer is the most frequent female cancer, with a considerable disease burden and high mortality. Early diagnosis with screening mammography might be facilitated by automated systems supported by deep learning artificial intelligence. We propose a model based on a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM) classifier able to train under data scarcity effectively. We used a private...
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Metoda i algorytmy sterowania procesami miksowania dźwięku za pomocą gestów w oparciu o analizę obrazu wizyjnego
PublicationGłównym celem rozprawy było opracowanie systemu miksowania dźwięku za pomocą gestów rąk wykonywanych w powietrzu oraz zbadanie możliwości oferowanych przez takie rozwiązanie w porównaniu ze współczesną metodą miksowania sygnałów fonicznych, wykorzystującą środowisko komputera. Opracowany system rozpoznaje zarówno dynamiczne jak i statyczne gesty rąk. Rozpoznawanie gestów dynamicznych zrealizowano w oparciu o metody logiki rozmytej...
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Jan Daciuk dr hab. inż.
PeopleJan Daciuk received his M.Sc. from the Faculty of Electronics of Gdansk University of Technology in 1986, and his Ph.D. from the Faculty of Electronics, Telecommunications and Informatics of Gdańsk University of Technology in 1999. He has been working at the Faculty from 1988. His research interests include finite state methods in natural language processing and computational linguistics including speech processing. Dr. Daciuk...
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Karol Dziedziul dr hab.
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Automated Valuation Model based on fuzzy and rough set theory for real estate market with insufficient source data
PublicationObjective monitoring of the real estate value is a requirement to maintain balance, increase security and minimize the risk of a crisis in the financial and economic sector of every country. The valuation of real estate is usually considered from two points of view, i.e. individual valuation and mass appraisal. It is commonly believed that Automated Valuation Models (AVM) should be devoted to mass appraisal, which requires a large...
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Anomaly Detection in Railway Sensor Data Environments: State-of-the-Art Methods and Empirical Performance Evaluation
PublicationTo date, significant progress has been made in the field of railway anomaly detection using technologies such as real-time data analytics, the Internet of Things, and machine learning. As technology continues to evolve, the ability to detect and respond to anomalies in railway systems is once again in the spotlight. However, railway anomaly detection faces challenges related to the vast infrastructure, dynamic conditions, aging...
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Evaluation of a company’s image on social media using the Net Sentiment Rate
PublicationVast amounts of new types of data are constantly being created as a result of dynamic digitization in all areas of our lives. One of the most important and valuable categories for business is data from social networks such as Facebook. Feedback resulting from the sharing of thoughts and emotions, expressed in comments on various products and services, is becoming the key factor on which modern business is based. This feedback is...
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Outlier detection method by using deep neural networks
PublicationDetecting outliers in the data set is quite important for building effective predictive models. Consistent prediction can not be made through models created with data sets containing outliers, or robust models can not be created. In such cases, it may be possible to exclude observations that are determined to be outlier from the data set, or to assign less weight to these points of observation than to other points of observation....
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Akustyczna analiza parametrów ruchu drogowego z wykorzystaniem informacji o hałasie oraz uczenia maszynowego
PublicationCelem rozprawy było opracowanie akustycznej metody analizy parametrów ruchu drogowego. Zasada działania akustycznej analizy ruchu drogowego zapewnia pasywną metodę monitorowania natężenia ruchu. W pracy przedstawiono wybrane metody uczenia maszynowego w kontekście analizy dźwięku (ang.Machine Hearing). Przedstawiono metodologię klasyfikacji zdarzeń w ruchu drogowym z wykorzystaniem uczenia maszynowego. Przybliżono podstawowe...
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Ocena wpływu drgań komunikacyjnych na budynki za pomocą maszynowego uczenia
PublicationDrgania komunikacyjne mogą powodować spękania tynków, zarysowania a nawet zawalenie się budynku. Pomiary na rzeczywistych obiektach są pracochłonne i kosztowne, a nie zawsze uzasadnione. Celem jest utworzenie modelu, dzięki któremu można przewidzieć zagrożenie szkodliwego oddziaływania drgań komunikacyjnych na budynek. Po przeprowadzeniu własnych badań pomiarowych oraz analizie literatury utworzono model oparty na Maszynach Wektorów...
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Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete
PublicationPredicting the properties of concrete before its design and application process allows for refining and optimizing its composition. However, the properties of lightweight concrete are much harder to predict than those of normal weight concrete, especially if the forecast concerns the insulating properties of concrete with artificial lightweight aggregate (LWA). It is possible to use porous aggregates and precisely modify the composition...
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Rafał Łangowski dr inż.
PeopleRafał Łangowski received the M.Sc. and the Ph.D. degrees (Hons.) in control engineering from the Faculty of Electrical and Control Engineering at the Gdańsk University of Technology in 2003 and 2015, respectively. From 2007 to 2014, he held the specialist as well as manager positions at ENERGA, one of the biggest energy enterprises in Poland. He is currently an Assistant Professor with the Department of Intelligent Control and...
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The Use of Artificial Neural Networks and Decision Trees to Predict the Degree of Odor Nuisance of Post-Digestion Sludge in the Sewage Treatment Plant Process
PublicationThis paper presents the application of artificial neural networks and decision trees for the prediction of odor properties of post-fermentation sludge from a biological-mechanical wastewater treatment plant. The input parameters were concentrations of popular compounds present in the sludge, such as toluene, p-xylene, and p-cresol, and process parameters including the concentration of volatile fatty acids, pH, and alkalinity in...
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New approach to railway noise modeling employing Genetic Algorithms
PublicationMain goal of this paper was to describe an innovative method of noise prediction based on Genetic Algorithms. First part of the paper addresses the problem of growing noise, mainly in the context of a unified method for measuring noise. Further, Genetic Algorithms are described with regards to their fundamental features. Further a description is provided as to how Genetic Algorithms were used in the area of noise modeling. Next...
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"Computing with words" concept applied to musical information retrieval
PublicationW artykule zaproponowano wykorzystanie koncepcji "przetwarzania słów języka naturalnego" do znalezienia związku pomiędzy wybranymi parametrami dźwięków muzycznych a subiektywnie postrzeganą barwą. W pierwszej kolejności przedstawiono klasyczne metody mapowania parametrów mierzalnych i ich subiektywnych odpowiedników, następnie zbudowano bazę wiedzy w oparciu o wyniki testów subiektywnych. W procesie obróbki wykorzystano metodę...
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Evaluation of the factors influencing business bankruptcy risk in Poland
PublicationThis article is devoted to the issue of assessing the causes of business failure. The presented studies answer two research questions – what are the causes of corporate bankruptcies in Poland and how to more efectively predict the scale of bankruptcies in the country. The author has conducted a study to analyze the specic endogenous and exogenous causes of company bankruptcy depending on the type of the bankruptcy with consideration...
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Intermolecular Interactions as a Measure of Dapsone Solubility in Neat Solvents and Binary Solvent Mixtures
PublicationDapsone is an effective antibacterial drug used to treat a variety of conditions. However, the aqueous solubility of this drug is limited, as is its permeability. This study expands the available solubility data pool for dapsone by measuring its solubility in several pure organic solvents: N-methyl-2-pyrrolidone (CAS: 872-50-4), dimethyl sulfoxide (CAS: 67-68-5), 4-formylmorpholine (CAS: 4394-85-8), tetraethylene pentamine (CAS:...
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TOXIC GASES IDENTIFICATION USING SINGLE ELECTROCATALYTIC SENSOR RESPONSES AND ARTIFICIAL NEURAL NETWORK
PublicationThe need for precise detection of toxic gases drives development of new gas sensors structures and methods of processing the output signals from the sensors. In literature, artificial neural networks are considered as one of the most effective tool for the analysis of gas sensors or sensors arrays responses. In this paper a method of toxic gas components identification using a electrocatalytic gas sensor as a detector and an artificial...
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Automatic singing quality recognition employing artificial neural networks
PublicationCelem artykułu jest udowodnienie możliwości automatycznej oceny jakości technicznej głosów śpiewaczych. Pokrótce zaprezentowano w nim stworzoną bazę danych głosów śpiewaczych oraz zaimplementowane parametry. Przy pomocy sztucznych sieci neuronowych zaprojektowano system decyzyjny, który oceniono w pięciostopniowej skali jakość techniczną głosu. Przy pomocy metod statystycznych udowodniono, że wyniki generowane przez ten system...
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Application of Artificial Neural Networks in Investigations of Steam Turbine Cascades
PublicationZaprezentowano wyniki badań numerycznych zastosowania sieci neuronowych przy obliczeniach przepływów w palisadach turbin parowych. Na podstawie uzyskanych wyników wykazano, że sieci neuronowe mogą być używane do szacowania przestrzennego rozkładu parametrów przepływu, takich jak entalpia, entropia, ciśnienie czy prędkość czynnika w kanale przepływowym. Omówiono również zastosowania tego typu metod przy projektowaniu palisad, stopni...
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Forecasting of currency exchange rates using artificial neural networks
PublicationW rozdziale tym autor przedstawił wyniki swoich badań nad wykorzystaniem sztucznych sieci neuronowych do prognozowania kursu walut (na przykładzie pary walutowej PLN-USD).Głównym celem badań było porównanie skuteczności przewidywania kursu złotówki w latach 1997 - 2005 przy pomocy różnych rodzajów sieci neuronowych.
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A MODEL FOR FORECASTING PM10 LEVELS WITH THE USE OF ARTIFICIAL NEURAL NETWORKS
PublicationThis work presents a method of forecasting the level of PM10 with the use of artificial neural networks. Current level of particulate matter and meteorological data was taken into account in the construction of the model (checked the correlation of each variable and the future level of PM10), and unidirectional networks were used to implement it due to their ease of learning. Then, the configuration of the network (built on the...
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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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Optymalizacja struktur i obliczeń w sieciach neuronowych - 2023
e-Learning Courses3 semestr studiów II stopnia, kierunek Informatyka, specjalność Uczenie Maszynowe
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Optymalizacja struktur i obliczeń w sieciach neuronowych
e-Learning Courses3 semestr studiów II stopnia, kierunek Informatyka, specjalność Uczenie Maszynowe
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Optymalizacja struktur i obliczeń w sieciach neuronowych - 2024
e-Learning Courses3 semestr studiów II stopnia, kierunek Informatyka, specjalność Uczenie Maszynowe
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Model-free and Model-based Reinforcement Learning, the Intersection of Learning and Planning
PublicationMy doctoral dissertation is intended as the compound of four publications considering: structure and randomness in planning and reinforcement learning, continuous control with ensemble deep deterministic policy gradients, toddler-inspired active representation learning, and large-scale deep reinforcement learning costs.
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Algorytmy klasyfikacji i uczenia w rozpoznawaniu treści
PublicationZadanie klasyfikacji treści może zostać podzielone na dwa etapy: ekstrakcji cech istotnych dla podziału na klasy oraz etapu klasyfikacji na podstawie cech wyznaczonych w poprzednim etapie. Dzięki takiemu podziałowi, możliwe jest użycie w drugim etapie standardowych algorytmów budowy (uczenia) klasyfikatorów, takich klasyfikator bayesowski, drzewa decyzyjne, sztuczne sieci neuronowe czy metoda wektorów wspierających (SVM). Przy...
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Extracting concepts from the software requirements specification using natural language processing
PublicationExtracting concepts from the software require¬ments is one of the first step on the way to automating the software development process. This task is difficult due to the ambiguity of the natural language used to express the requirements specification. The methods used so far consist mainly of statistical analysis of words and matching expressions with a specific ontology of the domain in which the planned software will be applicable....
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Energy-Aware Scheduling for High-Performance Computing Systems: A Survey
PublicationHigh-performance computing (HPC), according to its name, is traditionally oriented toward performance, especially the execution time and scalability of the computations. However, due to the high cost and environmental issues, energy consumption has already become a very important factor that needs to be considered. The paper presents a survey of energy-aware scheduling methods used in a modern HPC environment, starting with the...
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Analysis of odour interactions in model gas mixtures using electronic nose and fuzzy logic
PublicationMeasurement and monitoring of air quality in terms of odour nuisance is an important problem. Although the source of these nuisances is different (e.g. wastewater treatment plants, municipal landfills), their common feature is that they are a complex mixture of odorants with different odour thresholds. An additional problem is occurrence of the odour interactions between mixture components. From a practical point of view, it would...
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Chemometric Evaluation of WWTPs’ Wastewaters and Receiving Surface Waters in Bulgaria
PublicationWastewater treatment plant (WWTP) installations are designed and operated to reduce the quantity of pollutants emitted to surface waters receiving treated wastewaters. In this work, we used classical instrumental studies (to determine chemicals and parameters under obligations put with Directive 91/271/EEC), ecotoxicological tools (Sinapis alba root growth inhibition (SA-RG) and Heterocypris incongruens mortality (MORT) and growth...
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Application of gas chromatographic data and 2D molecular descriptors for accurate global mobility potential prediction
PublicationMobility is a key feature affecting the environmental fate, which is of particular importance in the case of persistent organic pollutants (POPs) and emerging pollutants (EPs). In this study, the global mobility classification artificial neural networks-based models employing GC retention times (RT) and 2D molecular descriptors were constructed and validated. The high usability of RT was confirmed based on the feature selection...
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Graph Neural Networks and Structural Information on Ionic Liquids: A Cheminformatics Study on Molecular Physicochemical Property Prediction
PublicationIonic liquids (ILs) provide a promising solution in many industrial applications, such as solvents, absorbents, electrolytes, catalysts, lubricants, and many others. However, due to the enormous variety of their structures, uncovering or designing those with optimal attributes requires expensive and exhaustive simulations and experiments. For these reasons, searching for an efficient theoretical tool for finding the relationship...
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Intelligent Audio Signal Processing − Do We Still Need Annotated Datasets?
PublicationIn this paper, intelligent audio signal processing examples are shortly described. The focus is, however, on the machine learning approach and datasets needed, especially for deep learning models. Years of intense research produced many important results in this area; however, the goal of fully intelligent signal processing, characterized by its autonomous acting, is not yet achieved. Therefore, a review of state-of-the-art concerning...
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Widzenie Komputerowe 2023/2024 semestr zimowy
e-Learning CoursesKurs do przedmiotu Widzenie Komputerowe prowadzonego w semestrze zimowym 2023/2024
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Knowledge representation of motor activity of patients with Parkinson’s disease
PublicationAn approach to the knowledge representation extraction from biomedical signals analysis concerning motor activity of Parkinson disease patients is proposed in this paper. This is done utilizing accelerometers attached to their body as well as exploiting video image of their hand movements. Experiments are carried out employing artificial neural networks and support vector machine to the recognition of characteristic motor activity...
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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
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Wykorzystanie sieci neuronowych do diagnostyki poprawności wykonania płytek drukowanych
PublicationArtykuł opisuje stanowisko badawcze do diagnostyki optycznej poprawności wykonania płytek drukowanych przesuwających się po taśmie produkcyjnej. Diagnostyka optyczna dokonywana jest poprzez kamerę. Obraz z kamery przekazywany jest do komputera PC, gdzie trafia do zaprojektowanego systemu diagnostycznego, zaimplementowanego w środowisku Matlab. Po odpowiednim przetworzeniu obrazy kierowane są do właściwego systemu diagnostycznego...
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Automatic music set organizatio based on mood of music / Automatyczna organizacja bazy muzycznej na podstawie nastroju muzyki
PublicationThis work is focused on an approach based on the emotional content of music and its automatic recognition. A vector of features describing emotional content of music was proposed. Additionally, a graphical model dedicated to the subjective evaluation of mood of music was created. A series of listening tests was carried out, and results were compared with automatic mood recognition employing SOM (Self Organizing Maps) and ANN (Artificial...
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Collaborative approach to WordNet and Wikipedia integration
PublicationIn this article we present a collaborative approach tocreating mappings between WordNet and Wikipedia. Wikipediaarticles have been first matched with WordNet synsets in anautomatic way. Then such associations have been evaluated andcomplemented in a collaborative way using a web application.We describe algorithms used for creating automatic mappingsas well as a system for their collaborative development. Theoutcome enables further...
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A fuzzy logic model for forecasting exchange rates
PublicationThis article is devoted to the issue of forecasting exchange rates. The objective of the conducted research is to develop a predictive model with the use of an innovative methodology - fuzzy logic theory - and to evaluate its effectiveness in times of prosperity and during the financial crisis. The model is based on sets of rules written by the author in the form of IF-THEN, where expert knowledge is stored. This model is the result...
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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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Improvement of speech intelligibility in the presence of noise interference using the Lombard effect and an automatic noise interference profiling based on deep learning
PublicationThe Lombard effect is a phenomenon that results in speech intelligibility improvement when applied to noise. There are many distinctive features of Lombard speech that were recalled in this dissertation. This work proposes the creation of a system capable of improving speech quality and intelligibility in real-time measured by objective metrics and subjective tests. This system consists of three main components: speech type detection,...
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Modeling of Surface Roughness in Honing Processes by UsingFuzzy Artificial Neural Networks
PublicationHoning processes are abrasive machining processes which are commonly employed to improve the surface of manufactured parts such as hydraulic or combustion engine cylinders. These processes can be employed to obtain a cross-hatched pattern on the internal surfaces of cylinders. In this present study, fuzzy artificial neural networks are employed for modeling surface roughness parameters obtained in finishing honing operations. As...
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Processing of musical data employing rough sets and artificial neural networks
PublicationArtykuł opisuje założenia systemu automatycznej identyfikacji muzyki i dźwięków muzycznych. Dokonano przeglądu standardu MPEG-7, ze szczególnym naciskiem na parametry opisowe dźwięku. Przedyskutowano problemy analizy danych audio, związane z zastosowaniami wykorzystującymi MPEG-7. W oparciu o eksperymenty przedstawiono efektywność deskryptorów niskiego poziomu w automatycznym rozpoznawaniu dźwięków instrumentów muzycznych. Przedyskutowano...
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Artificial Neural Networks for Prediction of Antibacterial Activity in Series of Imidazole Derivatives
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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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The concept of application of artificial neural networks for cultivation controlof cartilages in bioreactors.
PublicationNowym elementem niniejszej pracy jest omówienie problemów związanych z możliwością sterowania parametrami hydrodynamicznymi hodowanej w bioreaktorze chrząstki stawowej przy wykorzystaniu sztucznych sieci neuronowych. Przedstawiona została architektura strategii sterowania hodowlą tkanki z zastosowaniem tych sieci.
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Processing of musical data employing rough sets and artificial neural networks
PublicationArtykuł opisuje założenia systemu automatycznej identyfikacji muzyki i dźwięków muzycznych. Dokonano przeglądu standardu MPEG-7, ze szczególnym naciskiem na parametry opisowe dźwięku. Przedyskutowano problemy analizy danych audio, związane z zastosowaniami wykorzystującymi MPEG-7. W oparciu o eksperymenty przedstawiono efektywność deskryptorów niskiego poziomu w automatycznym rozpoznawaniu dźwięków instrumentów muzycznych. Przedyskutowano...
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DECISION SUPPORT SYSTEMS
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Self-Supervised Learning to Increase the Performance of Skin Lesion Classification
PublicationTo successfully train a deep neural network, a large amount of human-labeled data is required. Unfortunately, in many areas, collecting and labeling data is a difficult and tedious task. Several ways have been developed to mitigate the problem associated with the shortage of data, the most common of which is transfer learning. However, in many cases, the use of transfer learning as the only remedy is insufficient. In this study,...
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Role patterns in IT projects teams - design of a selection module using fuzzy logic techniques
PublicationPresented paper introduces an approach based on usage of role patterns and modelling by the use of fuzzy logic tool for selection process with limitation to the area of IT projects environment. The article shows a concept of role patterns structures and their further usage in process of forming a fuzzy model dedicated to candidate assessment process support
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Fuzzy logic for control Peltier module
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ALS Data Filtration with Fuzzy Logic
PublicationEkstrakcji DTM pozyskanego z użyciem ALS (Airborne Laser Scanning) z chmury punktów, jest złożonym zadaniem, które wymaga wielu algorytmów i procedur numerycznych. Jednym z pierwszych kroków jest filtracja danych. Istnieje wiele różnych metod filtrowania i algorytmów. W tym artykule autorzy proponują metodę filtracji w oparciu o logikę rozmytą. Prezentują podstawowe informacje dotyczące logiki rozmytej, projekt reguł rozmytych...
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Combining MUSHRA Test and Fuzzy Logic in the Evaluation of Benefits of Using Hearing Prostheses
PublicationAssessing the effectiveness of hearing aid fittings based on the benefits they provide is crucial but intricate. While objective metrics of hearing aids like gain, frequency response, and distortion are measurable, they do not directly indicate user benefits. Hearing aid performance assessment encompasses various aspects, such as compensating for hearing loss and user satisfaction. The authors suggest enhancing the widely used...
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Data Mining Applications and Methods in Medicine
PublicationIn this paper we describe the research area of data mining and its applications in medicine. The origins of data mining and its crucial features are shortly presented. We discuss the specificity of medicine as an application area for computer systems. Characteristic features of the medical data are investigated. Common problems in the area are also presented as well as the strengths and capabilities of the data mining methods....
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Structure and Randomness in Planning and Reinforcement Learning
PublicationPlanning in large state spaces inevitably needs to balance the depth and breadth of the search. It has a crucial impact on the performance of a planner and most manage this interplay implicitly. We present a novel method \textit{Shoot Tree Search (STS)}, which makes it possible to control this trade-off more explicitly. Our algorithm can be understood as an interpolation between two celebrated search mechanisms: MCTS and random...
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Mirosław Włas dr inż.
PeopleWykształcenie : 1987-1991 – Liceum Ogólnokształcące im. Jana Bażyńskiego w Ostródzie – profil matematyczno-fizyczny 1991-1996 Politechnika Gdańska – studia na Wydziale Elektrycznym 1997-2002 Politechnika Gdańska – studia doktoranckie na Wydziale Elektrotechniki i Automatyki 1998- 1999 Politechnika Gdańska – kurs ekonomii na Wydziale Zarządzania i Ekonomii PG 1997-2000 – kurs j. angielskiego - Zespół Lektorów BEST – Gdańsk 2003...
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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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Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
PublicationBackground: In the context of Warehouse Management Systems, knowledge related to motion trajectory prediction methods utilizing machine learning techniques seems to be scattered and fragmented. Objective: This study seeks to fill this research gap by using a systematic literature review approach. Methods: Based on the data collected from Google Scholar, a systematic literature review was performed, covering the period from 2016...
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Application of artificial neural networks (ANN) as multiple degradation classifiers in thermal and flow diagnostics
PublicationPrzedyskutowano problem zwiększenia dokładności rozpoznawania wielokrotnych degradacji eksploatacyjnych urządzeń składowych dużych obiektów energetycznych. Zastosowani sieć neuronową (SSN) o skokowych funkcjach przejścia. Sprawdzono możliwości przyspieszenia treningu sieci neuronowych. Zastosowano modułową metodę budowy SSN, polegającą na dedykowaniu pojedynczej sieci do rozpoznawania tylko jednego typu degradacji.
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Model neuronowy jako alternatywa dla numerycznego modelu okołodźwiękowego przepływu pary przez palisadę turbinową.
PublicationWystępowanie skośnej fali uderzeniowej w przepływie pary przez palisadę turbinową stanowi zagrożenie dla bezpiecznej pracy turbiny oraz dla jej elementów konstrukcyjnych. Detekcja oraz lokalizacja fali uderzeniowej, a także rozpoznanie przyczyny jej powstawania, nie są możliwe do osiągnięcia na drodze pomiarowej. Analizę zjawisk zachodzących wewnątrz kanału przepływowego umożliwiają natomiast modele numeryczne oraz neuronowe. Zaletą...
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Algorytmy ewolucyjne o wielowarswowych chromosomach i ich zastosowania w elektronice
PublicationW artykule przedstawiono koncepcje chromosomów wielowarstwowych w algorytmach ewolucyjnych. Ukazano strukturę chromosomu wielowarstwowego oraz opisano możliwe zastosowania algorytmów ewolucyjnych z jego wykorzystaniem. Omówiono zastosowanie algorytmu ewolucyjnego z wielowarstwowym chromosomem do: projektowania i optymalizacji kombinacyjnych układów cyfrowych budowanych zarówno w oparciu o bramki napięciowe jak i bramki prądowe,...
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Metaheurystyczne metody optymalizacji dyskretnej w problemie układania rozkładów zajęć dla szkół wyższych.
PublicationW pracy rozważany jest problem układania rozkładów zajęć dla szkoły wyższej. Do rozwiązania tego zagadnienia wykorzystane zostały następujące metody lokalnego i globalnego przeszukiwania przestrzeni możliwych rozwiązań: symulowane wyżarzenie, przeszukiwanie tabu oraz algorytmy genetyczne.
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Deep neural networks approach to skin lesions classification — A comparative analysis
PublicationThe paper presents the results of research on the use of Deep Neural Networks (DNN) for automatic classification of the skin lesions. The authors have focused on the most effective kind of DNNs for image processing, namely Convolutional Neural Networks (CNN). In particular, three kinds of CNN were analyzed: VGG19, Residual Networks (ResNet) and the hybrid of VGG19 CNN with the Support Vector Machine (SVM). The research was carried...
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Data Mining 2022/2023
e-Learning CoursesThis is a web page for our course in Data Mining.
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Data Mining 2021/2022
e-Learning CoursesThis is a web page for our course in Data Mining.
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Fuzzy Control of Waves Generation in a Towing Tank
PublicationThis paper presents the results of research related to the transformation of electrical energy into potential and kinetic energy of waves generated on the water surface. The waves are generated to model the environmental conditions for the needs of the model tests. The model tests are performed on model-scale objects to predict the features of full-scale maritime objects. It is done to improve human safety and the survivability...
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DESIGN LOGICAL LINGUISTIC MODELS TO CALCULATE NECESSITY IN TRUCKS DURING AGRICULTURAL CARGOES LOGISTICS USING FUZZY LOGIC
Publication: The study is aimed to develop the logic-linguistic models to design a number of rules for the correct calculation of the vehicles needed, taking into account the technical, technological, and weather and climate conditions of the harvesting and transport complex. The article has shown that the construction of the design of logic-linguistic models was not performed earlier to solve the problem of the agro-industrial production...
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Farzin Kazemi Ph.D. Student at Gdansk University of Technology
PeopleHis main research areas are seismic performance assessment of structures and seismic hazard analysis in earthquake engineering. He performed a comprehensive study on the effect of pounding phenomenon and proposed modification factors to modify the seismic collapse capacity of structures or predict the seismic collapse capacity of structures which were retrofitted with linear and nonlinear Fluid Viscous Dampers (FVDs). His current...
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Adaptive CAD-Model Construction Schemes
PublicationTwo advanced surrogate model construction techniques are discussed in this paper. The models employ radial basis function (RBF)interpolation scheme or artificial neural networks (ANN) with a new training algorithm. Adaptive sampling technique is applied withrespect to all variables. Histograms showing the quality of the models are presented. While the quality of RBF models is satisfactory, theperformance of the ANN models obtained...
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Analysis of Learning Outcomes in Medical Education with the Use of Fuzzy Logic
PublicationThe national curricula of the EU member states are structured around learning outcomes, selected according to Bloom’s Taxonomy. The authors of this paper claim that using Bloom’s Taxonomy to phrase learning outcomes in medical education in terms of students’ achievements is difficult and unclear. This paper presents an efficient method of assessing course learning outcomes using Fuzzy Logic.
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Experience Based Clinical Decision Support Systems: An Overview and Case Studies
PublicationThis chapter briefly overviews the evolution of the application of the Decisional DNA and the Set of Experience Knowledge Structure (SOEKS) in the medical domain and in particular in the specific case of the experience-based decision support systems. Decisional DNA, as a knowledge representation structure, offers great possibilities on gathering explicit knowledge of formal decision events as well as a tool for decision making...
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Wprowadzenie do sztucznej inteligencji - 2023/2024
e-Learning CoursesCelem przedmiotu jest zaznajomienie studentów z podstawami sztucznej inteligencji, rozumianej jako inteligentne techniki obliczeniowe, systemy uczące się, systemy decyzyjne czy systemy ekspertowe. Dodatkowo istotnym elementem wykładu jest zapoznanie studentów z wybranymi metodami inteligentnego przetwarzania sygnałów i danych oraz procesem przetwarzania wiedzy w oparciu o wnioskowanie formułowane w postaci reguł decyzyjnych. Uzyskana...
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Towards Improving Optimised Ship Weather Routing
PublicationThe aim of the paper is to outline a project focusing on the development of a new type of ship weather routing solution with improved uncertainty handling, through better estimation of ship performance and responses to sea conditions. Ensemble forecasting is considered to take into account the uncertainty levels that are typical of operations in a stochastic environment. Increased accuracy of weather prediction is achieved through...
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Algorithmic Human Resources Management - Perspectives and Challenges
PublicationTheoretical background: Technology – most notably processes of digitalisation, the use of artificial intelligence, machine learning, big data and prevalence of remote work due to pandemic – changes the way organizations manage human resources. One of the increasing trends is the use of so-called “algorithmic management”. It is notably different than previous e-HRM or HRIS (human resources information systems) applications, as it...
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Podstawy uczenia maszynowego AI
e-Learning CoursesPodstawy uczenia maszynowego. Machine Learning fundamentals.
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Supply current signal and artificial neural networks in the induction motor bearings diagnostics
PublicationThis paper contains research results of the diagnostics of induction motor bearings based on measurement of the supply current with usage of artificial neural networks. Bearing failure amount is greater than 40% of all engine failures, which makes their damage-free operation crucial. Tests were performed on motors with intentionally made bearings defects. Chapter 2 introduces the concept of artificial neural networks. It presents...
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Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests
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Designing the Composition of Cement Stabilized Rammed Earth Using Artificial Neural Networks
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The Influence of Input Data Standardization Method on Prediction Accuracy of Artificial Neural Networks
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Self-organizing Artificial Neural Networks into Hydrographic Big Data Reduction Process
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Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening
PublicationBeta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural...
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Predicting the impact of traffic–induced vibrations on buildings using artificial neural networks
PublicationTraffic–induced vibrations may constitute a considerable load to a building, cause cracking of plaster, cracks in load–bearing elements or even a global structural collapse of the whole structure [1-4]. Vibrations measurements of real structures are costly and laborious, not justified in all cases. The aim of the paper is to create an original algorithm, to predict the negative dynamic impact on the examined residential building...
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Heavy duty vehicle fuel consumption modelling using artificial neural networks
PublicationIn this paper an artificial neural network (ANN) approach to modelling fuel consumption of heavy duty vehicles is presented. The proposed method uses easy accessible data collected via CAN bus of the truck. As a benchmark a conventional method, which is based on polynomial regression model, is used. The fuel consumption is measured in two different tests, performed by using a unique test bench to apply the load to the engine. Firstly,...
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Intelligent decision-making system for autonomous robots
PublicationAdaptacja może być oparta na różnych koncepcjach cybernetycznych (takich jak algorytmy genetyczne, rojowe, sieci neuronowe, itp.). Główna myśl opisywanej pracy opiera się na wykorzystaniu modeli psychologii żywych istot (człowieka) w konstrukcji systemów sterowania jednostek autonomicznych w celu adaptacji jednostki do zmiennego środowiska (kluczowe dla projektu jest traktowanie elementów motywacyjnych jako problemu osobowości)....
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Fuzzy logic based sensorless control of induction motors.
PublicationW artykule przedstawiono układ sterowania bezczujnikowego silnikiem indukcyjnym wykorzystujący logikę rozmytą. W układzie wykorzystano metodę sterowania polowo zorientowanego. Prędkość kątowa estymowana jest za pomocą obserwatora i pomiarów mocy. Układ działa w szerokim zakresie prędkości. Zaprezentowano wyniki badań symulacyjnych.
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Neural network training with limited precision and asymmetric exponent
PublicationAlong with an extremely increasing number of mobile devices, sensors and other smart utilities, an unprecedented growth of data can be observed in today’s world. In order to address multiple challenges facing the big data domain, machine learning techniques are often leveraged for data analysis, filtering and classification. Wide usage of artificial intelligence with large amounts of data creates growing demand not only for storage...
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Metal–Organic Frameworks (MOFs) for Cancer Therapy
PublicationMOFs exhibit inherent extraordinary features for diverse applications ranging from catalysis, storage, and optics to chemosensory and biomedical science and technology. Several procedures including solvothermal, hydrothermal, mechanochemical, electrochemical, and ultrasound techniques have been used to synthesize MOFs with tailored features. A continued attempt has also been directed towards functionalizing MOFs via “post-synthetic...
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Tomasz Maria Boiński dr inż.
PeopleI’m associated with the University since the year 2000 when I started my studies in Computer Science on the Faculty of Electronics, Telecommunications and Informatics. After graduating with honors in 2005, I applied for doctoral studies. During his studies and immediately afterward I was involved in cooperation with Hogart from Warsaw, in the implementation of business solutions in Gdynia company Elektronika SA (Infor FMS SunSystems)...
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Waldemar Korłub dr inż.
PeopleWaldemar Korłub obtained an Eng. degree in 2011, MSc.Eng. degree in 2012 and PhD in Computer Science in 2017 granted by the Faculty of Electronics, Telecommunications and Informatics at Gdansk University of Technology. His research interests include: distributed systems mainly grid and cloud computing platforms, autonomous systems capable of self-optimization, self-management, self-healing and self-protection, artificial intelligence...
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Using Isolation Forest and Alternative Data Products to Overcome Ground Truth Data Scarcity for Improved Deep Learning-based Agricultural Land Use Classification Models
PublicationHigh-quality labelled datasets represent a cornerstone in the development of deep learning models for land use classification. The high cost of data collection, the inherent errors introduced during data mapping efforts, the lack of local knowledge, and the spatial variability of the data hinder the development of accurate and spatially-transferable deep learning models in the context of agriculture. In this paper, we investigate...
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Ship Evolutionary Trajectory Planning Method with Application of Polynomial Interpolation
PublicationPaper presents the application of evolutionary algorithms and polynomial interpolation in ship evolutionary trajectory planning method. Evolutionary algorithms allows to find a coIlision free trajectory in real time, while polynomial interpolation allows to model smooth trajectory which keeps continuity of velocity and acceleration values along path. Combination of this two methods allows to find trajectory, which under some assumptions,...
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From Sequential to Parallel Implementation of NLP Using the Actor Model
PublicationThe article focuses on presenting methods allowing easy parallelization of an existing, sequential Natural Language Processing (NLP) application within a multi-core system. The actor-based solution implemented with the Akka framework has been applied and compared to an application based on Task Parallel Library (TPL) and to the original sequential application. Architectures, data and control flows are described along with execution...