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Wyniki wyszukiwania dla: SZTUCZNA INTELIGENCJA
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Rotor Blade Geometry Optimisation in Kaplan Turbine
PublikacjaThe paper presents the description of method and results of rotor blade shape optimisation. The rotor blading constitutes a part ofturbine flow path. Optimisation consists in selection of the shape that minimises ratio of polytrophic loss. Shape of the blade isdefined by the mean camber line and thickness of the airfoil. Thickness is distributed around the camber line based on the ratio ofdistribution. Global optimisation was done...
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Text Documents Classification with Support Vector Machines
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Artificial intelligence for software development — the present and the challenges for the future
PublikacjaSince the time when first CASE (Computer-Aided Software Engineering) methods and tools were developed, little has been done in the area of automated creation of code. CASE tools support a software engineer in creation the system structure, in defining interfaces and relationships between software modules and, after the code has been written, in performing testing tasks on different levels of detail. Writing code is still the task...
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Introduction to the special issue on machine learning in acoustics
PublikacjaWhen we started our Call for Papers for a Special Issue on “Machine Learning in Acoustics” in the Journal of the Acoustical Society of America, our ambition was to invite papers in which machine learning was applied to all acoustics areas. They were listed, but not limited to, as follows: • Music and synthesis analysis • Music sentiment analysis • Music perception • Intelligent music recognition • Musical source separation • Singing...
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Extraction of music information based on artifical neutral networks
PublikacjaW artykule przedstawiono założenia systemu automatycznego rozpoznawania muzyki. Na podstawie przeprowadzonych eksperymentów w artykule przedstawiono efektywność zaimplementowanych algorytmów w zależności od sposobu opisu danych muzycznych. Zaimpementowany system jest oparty o sztuczne sieci neuronowe.
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How Machine Learning Contributes to Solve Acoustical Problems
PublikacjaMachine learning is the process of learning functional relationships between measured signals (called percepts in the artificial intelligence literature) and some output of interest. In some cases, we wish to learn very specific relationships from signals such as identifying the language of a speaker (e.g. Zissman, 1996) which has direct applications such as in call center routing or performing a music information retrieval task...
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Voice command recognition using hybrid genetic algorithm
PublikacjaAbstract: Speech recognition is a process of converting the acoustic signal into a set of words, whereas voice command recognition consists in the correct identification of voice commands, usually single words. Voice command recognition systems are widely used in the military, control systems, electronic devices, such as cellular phones, or by people with disabilities (e.g., for controlling a wheelchair or operating a computer...
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Erosion of privacy in computer vision systems
PublikacjaW pracy przedstawiono problemy, które mogą wystąpić, gdy technologia komputerowego wzroku zostanie zaimplementowana w urządzeniach wykorzystywanych w codziennym życiu. Przeprowadzono także dyskusję socjologicznych konsekwencji stosowania biometrii, automatycznego śledzenia ruchu i interpretacji obrazu. Omówiono też problemy wynikające z połączenia komputerowego wzroku z możliwościami oferowanymi przez Internet.
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Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods
PublikacjaOne of the important factors determining the biochemical processes in bioreactors is the quality of the wastewater inflow to the wastewater treatment plant (WWTP). Information on the quality of wastewater, sufficiently in advance, makes it possible to properly select bioreactor settings to obtain optimal process conditions. This paper presents the use of classification models to predict the variability of wastewater quality at...
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Improving Accuracy of Contactless Respiratory Rate Estimation by Enhancing Thermal Sequences with Deep Neural Networks
PublikacjaEstimation of vital signs using image processing techniques have already been proved to have a potential for supporting remote medical diagnostics and replacing traditional measurements that usually require special hardware and electrodes placed on a body. In this paper, we further extend studies on contactless Respiratory Rate (RR) estimation from extremely low resolution thermal imagery by enhancing acquired sequences using Deep...
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Automatic Rhythm Retrieval from Musical Files
PublikacjaThis paper presents a comparison of the effectiveness of two computational intelligence approaches applied to the task of retrieving rhythmic structure from musical files. The method proposed by the authors of this paper generates rhythmic levels first, and then uses these levels to compose rhythmic hypotheses. Three phases: creating periods, creating simplified hypotheses and creating full hypotheses are examined within this study....
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Computational intelligence-aided character animation
PublikacjaW pracy przedstawiono pokrótce metody animacji komputerowej, a także podano zasady oceny jakości wyniku animacji postaci. Dodatkowo dokonano przeglądu metod inteligentnych stosowanych w animacji komputerowej i w dziedzinach pokrewnych. W badaniach skupiono się na animacji ruchu w kontekście uzyskiwanej ekspresji. Podano reguły stosowane w animacji tradycyjnej oraz wyznaczono parametry opisujące fazy ruchu w odniesieniu do poszczególnych...
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Wireless Link Selection Methods for Maritime Communication Access Networks—A Deep Learning Approach
PublikacjaIn 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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Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence
PublikacjaThis work is based on a literature review (191). It mainly refers to two diagnostic methods based on artificial intelligence. This review presents new possibilities for using genetic algorithms (GAs) for diagnostic purposes in power plants transitioning to cooperation with renewable energy sources (RESs). The genetic method is rarely used directly in the modeling of thermal-flow analysis. However, this assignment proves that the...
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MACHINE LEARNING–BASED ANALYSIS OF ENGLISH LATERAL ALLOPHONES
PublikacjaAutomatic classification methods, such as artificial neural networks (ANNs), the k-nearest neighbor (kNN) and selforganizing maps (SOMs), are applied to allophone analysis based on recorded speech. A list of 650 words was created for that purpose, containing positionally and/or contextually conditioned allophones. For each word, a group of 16 native and non-native speakers were audio-video recorded, from which seven native speakers’...
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Support Vector Machines in Biomedical and Biometrical Applications
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Training of Deep Learning Models Using Synthetic Datasets
PublikacjaIn order to solve increasingly complex problems, the complexity of Deep Neural Networks also needs to be constantly increased, and therefore training such networks requires more and more data. Unfortunately, obtaining such massive real world training data to optimize neural networks parameters is a challenging and time-consuming task. To solve this problem, we propose an easy-touse and general approach to training deep learning...
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Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools
PublikacjaA high monetary value of the construction projects is one of the reasons of frequent disputes between a general contractor (GC) and a client. A construction site is a unique, one-time, and single-product factory with many parties involved and dependent on each other. The organizational dependencies and their complexity make any fault or mistake propagate and influence the final result (delays, cost overruns). The constant will...
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Data governance: Organizing data for trustworthy Artificial Intelligence
PublikacjaThe 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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Social media for e-learning of citizens in smart city
PublikacjaThe rapid development of social media can be applied for citizens’ e-learning in a smart city. Big cities have to cope with several open issues like a growing population or a traffic congestion. Especially, a home and public space is supposed to be used in more efficient way. Sustainable homes and buildings can be planned with using some modern techniques. Even currently, there is a huge problem with a lack of key resources like...
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Integrating Experience-Based Knowledge Representation and Machine Learning for Efficient Virtual Engineering Object Performance
PublikacjaMachine learning and Artificial Intelligence have grown significant attention from industry and academia during the past decade. The key reason behind interest is such technologies capabilities to revolutionize human life since they seamlessly integrate classical networks, networked objects and people to create more efficient environments. In this paper, the Knowledge Representation technique of Set of Experience...
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Wielowymiarowe techniki analizy danych pomiarowych - przykłady z zakresu analityki i monitoringu środowiska.
PublikacjaPrzedstawiono techniki obróbki wielowymiarowych zbiorów wyników pomiarowych. Na podstawie danych literaturowych zaprezentowano możliwość wykorzystania w analityce i moitoringu środowiskowym takich technik jak: analiza wariancji (ANOVA), analiza szeregów czasowych, analiza czynnikowa, sztuczne sieci neuronowe.
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Field Calibration of Low-Cost Particulate Matter Sensors Using Artificial Neural Networks and Affine Response Correction
PublikacjaDue to detrimental effects of atmospheric particulate matter (PM), its accurate monitoring is of paramount importance, especially in densely populated urban areas. However, precise measurement of PM levels requires expensive and sophisticated equipment. Although low-cost alternatives are gaining popularity, their reliability is questionable, attributed to sensitivity to environmental conditions, inherent instability, and manufacturing...
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Machine Learning and Electronic Noses for Medical Diagnostics
PublikacjaThe need for noninvasive, easy-to-use, and inexpensive methods for point-of-care diagnostics of a variety of ailments motivates researchers to develop methods for analyzing complex biological samples, in particular human breath, that could aid in screening and early diagnosis. There are hopes that electronic noses, that is, devices based on arrays of semiselective or nonselective chemical sensors, can fill this niche. Electronic...
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Soft computing based automatic recognition of musical instrument classes.
PublikacjaW artykule przedstawiono wyniki eksperymentów dotyczących automatycznego rozpoznawania klas instrumentów muzycznych. Proces klasyfikacji zrealizowano w oparciu o sztuczne sieci neuronowe, zaś wektor cch został oparty o parametry obliczane w wyniku analizy falkowej dźwięków instrumentów muzycznych.
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Application of fuzzy logic to determine the odour intensity of model gas mixtures using electronic nose
PublikacjaThe paper presents the possibility of application of fuzzy logic to determine the odour intensity of model, ternary gas mixtures (α-pinene, toluene and triethylamine) using electronic nose prototype. The results obtained using fuzzy logic algorithms were compared with the values obtained using multiple linear regression (MLR) model and sensory analysis. As the results of the studies, it was found the electronic nose prototype along...
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Analysis of the Capability of Deep Learning Algorithms for EEG-based Brain-Computer Interface Implementation
PublikacjaMachine learning models have received significant attention for their exceptional performance in classifying electroencephalography (EEG) data. They have proven to be highly effective in extracting intricate patterns and features from the raw signal data, thereby contributing to their success in EEG classification tasks. In this study, we explore the possibilities of utilizing contemporary machine learning algorithms in decoding...
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Wzorzec poprawnej pracy wymienników regeneracyjnych oparty o sztuczne sieci neuronowe
PublikacjaArtykuł opisuje probę stworzenia wzorca poprawnej pracy wymiennikow regeneracyjnych silowni turbo parowej o mocy 20mw przy pomocy sztucnych sieci neurnowych (SSN). Stworzony model pracy wymienników w zmiennych warunkachruchu silowni może zostać wykorzystany do diagnostki tych wlasnie urządzeń jaki i również do diagnostyki calego systemu silowni turbo parowej. Model neuronowy ma zastapic skomplikowane i czasochlonne obliczenia bilansowe...
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Machine Learning and data mining tools applied for databases of low number of records
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Idea zastosowania sztucznej inteligencji w prognozowaniu wpływu drgań komunikacyjnych na odpowiedź dynamiczną budynków mieszkalnych
PublikacjaW poniższym artykule autorzy analizują wpływ drgań komunikacyjnych na budynki mieszkalne oraz metodykę pomiarową według PN-85 B-02170 [1]. Problemem badawczym jest opracowanie prostej metody prognozowania wpływu drgań na budynki mieszkalne w taki sposób, aby nie było konieczne przeprowadzanie pracochłonnych i kosztownych pomiarów polowych. W tym celu wykonano analizę przy użyciu algorytmów opartych na sztucznej inteligencji oraz...
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Machine learning system for estimating the rhythmic salience of sounds.
PublikacjaW artykule przedstawiono badania dotyczące wyszukiwania danych rytmicznych w muzyce. W pracy przedstawiono postać funkcji rankingujacej poszczególnych dźwięków frazy muzycznej. Opracowano metodę tworzenia wszystkich możliwych hierarchicznych struktur rytmicznych, zwanych hipotezami rytmicznymi. Otrzymane hipotezy są następnie porządkowane w kolejności malejącej wartości funkcji rankingującej, aby ustalić, która ze znalezionych...
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Metody neuronowe do prognozowania finansowego
PublikacjaSztuczne sieci neuronowe mogą być stosowane do prognozowania kursów akcji na giełdzie, oceny wiarygodności kredytobiorców czy prognozowania kryzysów bankowych. W referacie omówiono zasady współpracy sieci neuronowych z algorytmami ewolucyjnymi oraz metodą wektorów wspierających. Ponadto, odniesiono się do pozostałych metod sztucznej inteligencji, które stosowane są w finansach.
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Integration of natural and artificial intelligence in production systems
PublikacjaIntegration processes play an increasingly important role in modern economy, and seriously co-decide about the effectiveness of the company. Integration phase occurs in the system life cycle by preceding the final stages of its implementation and activation. In turn, used in software engineering (SE) iteration-evolutionary models, such as spiral model make that the integration activities can occur in varying degrees in all phases...
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INFLUENCE OF DATA NORMALIZATION ON THE EFFECTIVENESS OF NEURAL NETWORKS APPLIED TO CLASSIFICATION OF PAVEMENT CONDITIONS – CASE STUDY
PublikacjaIn recent years automatic classification employing machine learning seems to be in high demand for tele-informatic-based solutions. An example of such solutions are intelligent transportation systems (ITS), in which various factors are taken into account. The subject of the study presented is the impact of data pre-processing and normalization on the accuracy and training effectiveness of artificial neural networks in the case...
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Optymalizacja treningu i wnioskowania sieci neuronowych
PublikacjaSieci neuronowe są jedną z najpopularniejszych i najszybciej rozwijających się dziedzin sztucznej inteligencji. Ich praktyczne wykorzystanie umożliwiło szersze użycie komputerów w wielu obszarach komunikacji, przemysłu i transportu. Dowody tego są widoczne w elektronice użytkowej, medycynie, a nawet w zastosowaniach militarnych. Wykorzystanie sztucznej inteligencji w wielu przypadkach wymaga jednak znacznej mocy obliczeniowej,...
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Deep learning techniques for biometric security: A systematic review of presentation attack detection systems
PublikacjaBiometric technology, including finger vein, fingerprint, iris, and face recognition, is widely used to enhance security in various devices. In the past decade, significant progress has been made in improving biometric sys- tems, thanks to advancements in deep convolutional neural networks (DCNN) and computer vision (CV), along with large-scale training datasets. However, these systems have become targets of various attacks, with...
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Modelowanie przeplywu pary przez okołodźwiękowe wieńce turbinowe przy użyciu sztucznych sieci neuronowych
PublikacjaNiniejszy artykul stanowi opis modelu przepływu pary przez okołodźwiękowe stopnie turbinowe, stworzonego w oparciu o sztuczne sieci neuronowe (SSN). Przedstawiony model neuronowy pozwala na wyznaczenie rozkladu wybranych parametrów w analizowanym przekroju kanalu przeplywowego turbiny, dla rozpatrywanego zakresu wartości ciśnienia wlotowego.
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Unsupervised Learning for Biomechanical Data Using Self-organising Maps, an Approach for Temporomandibular Joint Analysis
PublikacjaWe proposed to apply a specific machine learning technique called Self-Organising Maps (SOM) to identify similarities in the performance of muscles around human temporomandibular joint (TMJ). The performance was assessed by measuring muscle activation with the use of surface electromyography (sEMG). SOM algorithm used in the study was able to find clusters of data in sEMG test results. The SOM analysis was based on processed sEMG...
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Perception of Pathologists in Poland of Artificial Intelligence and Machine Learning in Medical Diagnosis—A Cross-Sectional Study
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Wspomaganie decyzji przy utrzymaniu nawierzchni kolejowej.
PublikacjaW artykule przedstawiono koncepcje wspomagania decyzji przy utrzymaniu nawierzchni kolejowej. Zaprezentowano problemy decyzyjne, użytkowane już systemy wspomagania decyzji oraz ewolucyjny proces powstawania takich systemów.
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An evaluation of effectiveness of fuzzy logic model in predicting the business bankruptcy
PublikacjaW artykule sprawdzono skuteczność pojedynczego modelu logiki rozmytej w prognozowaniu ryzyka upadłości przedsiębiorstw w Polsce. W badaniach wykorzystano wartości 14 wskaźników finansowych oraz ich dynamikę zmiany między pierwszym a drugim, drugim a trzecim oraz trzecim a czwartym rokiem objętymi analizą. We wnioskach omówiono różnicę w skutecznościach modelu uzyskanego na wartościach statycznych oraz dynamicznych wskaźników finansowych....
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Widzenie komputerowe oparte na mnogości widoków
PublikacjaArtykuł poświęcony jest tematowi tworzenia map głębokości na podstawie obrazów z wielu kamer. Zwykle mapy głębokości oparte na widzeniu stereoskopowym wyznaczane są na podstawie obrazów z dwóch kamer. Artykuł przedstawia możliwości wykorzystania większej liczby kamer w celu zwiększenia dokładności map głębokości. Badania przedstawione w artykule ukierunkowane są na zastosowanie w autonomicznych robotach, będących w stanie samodzielnie...
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Monitoring the BTEX Volatiles during 3D Printing with Acrylonitrile Butadiene Styrene (ABS) Using Electronic Nose and Proton Transfer Reaction Mass Spectrometry
PublikacjaWe describe a concept study in which the changes of concentration of benzene, toluene, ethylbenzene, and xylene (BTEX) compounds and styrene within a 3D printer enclosure during printing with different acrylonitrile butadiene styrene (ABS) filaments were monitored in real-time using a proton transfer reaction mass spectrometer and an electronic nose. The quantitative data on the concentration of the BTEX compounds, in particular...
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Evaluation of Facial Pulse Signals Using Deep Neural Net Models
PublikacjaThe reliable measurement of the pulse rate using remote photoplethysmography (PPG) is very important for many medical applications. In this paper we present how deep neural networks (DNNs) models can be used in the problem of PPG signal classification and pulse rate estimation. In particular, we show that the DNN-based classification results correspond to parameters describing the PPG signals (e.g. peak energy in the frequency...
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Application of case based reasoning to hybrid expert system for electronic filter design
PublikacjaPrzedstawiono koncepcję i przykład praktycznej realizacji obiektowo zorientowanego hybrydowego systemu ekspertowego wykorzystującego rozumowanie sytuacyjne. System wykorzystuje algorytmy najbliższego sąsiada i sztuczne sieci neuronowe. System został przetestowany jako klasyfikator decyzyjny w projektowaniu filtrów elektronicznych. W budowie systemu został wykorzystany obiektowy system CLIPS, rozszerzony o wiele dodatkowych funkcji...
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Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects
PublikacjaMachine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects Hammed A. Mojeed & Rafal Szlapczynski Conference paper First Online: 14 September 2023 161 Accesses Part of the Lecture Notes in Computer Science book series (LNAI,volume 14125) Abstract Software development project requires proper planning to mitigate risk and...
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Comparative Analysis of Text Representation Methods Using Classification
PublikacjaIn our work, we review and empirically evaluate five different raw methods of text representation that allow automatic processing of Wikipedia articles. The main contribution of the article—evaluation of approaches to text representation for machine learning tasks—indicates that the text representation is fundamental for achieving good categorization results. The analysis of the representation methods creates a baseline that cannot...
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Ontology-based text convolution neural network (TextCNN) for prediction of construction accidents
PublikacjaThe construction industry suffers from workplace accidents, including injuries and fatalities, which represent a significant economic and social burden for employers, workers, and society as a whole.The existing research on construction accidents heavily relies on expert evaluations,which often suffer from issues such as low efficiency, insufficient intelligence, and subjectivity.However, expert opinions provided in construction...
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Survey on fuzzy logic methods in control systems of electromechanical plants
PublikacjaРассмотрены алгоритмы управления электромеханическими системами с использованием теории нечеткой логики, приводятся основные положения их синтеза, рассматриваются методы анализа их устойчивости на основе нечетких функций Ляпунова. Эти алгоритмы чаще всего реализуются в виде различных регуляторов, применение которых целесообразно в системах, математическая модель которых не известна, не детерминирована или является строго нелинейной,...
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Prediction of energy consumption and evaluation of affecting factors in a full-scale WWTP using a machine learning approach
PublikacjaTreatment of municipal wastewater to meet the stringent effluent quality standards is an energy-intensive process and the main contributor to the costs of wastewater treatment plants (WWTPs). Analysis and prediction of energy consumption (EC) are essential in designing and operating sustainable energy-saving WWTPs. In this study, the effect of wastewater, hydraulic, and climate-based parameters on the daily consumption of EC by...
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A note on the applications of artificial intelligence in the hospitality industry: preliminary results of a survey
PublikacjaIntelligent technologies are widely implemented in different areas of modern society but specific approaches should be applied in services. Basic relationships refer to supporting customers and people responsible for services offering for these customers. The aim of the paper is to analyze and evaluate the state-of-the art of artificial intelligence (AI) applications in the hospitality industry. Our findings show that the major...
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Sensors and System for Vehicle Navigation
PublikacjaIn recent years, vehicle navigation, in particular autonomous navigation, has been at the center of several major developments, both in civilian and defense applications. New technologies, such as multisensory data fusion, big data processing, or deep learning, are changing the quality of areas of applications, improving the sensors and systems used. Recently, the influence of artificial intelligence on sensor data processing and...
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Upadłość przedsiębiorstw a wykorzystanie sztucznej inteligencji
PublikacjaMonografia jest jedną z nielicznych publikacji, która ukazuje problematykę upadłości przedsiębiorstw zarówno z punktu widzenia prawa jak i ekonomii. Dodatkowo autorzy znaczną część tego opracowania poświęcili problematyce prognozowania zagrożenia przedsiębiorstw upadłością, ze szczególnym uwzględnienim takich metod jak sztuczne sieci neuronowe oraz liniowa wielowymiarowa analiza dyskryminacyjna.
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Application of autoencoder to traffic noise analysis
PublikacjaThe aim of an autoencoder neural network is to transform the input data into a lower-dimensional code and then to reconstruct the output from this code representation. Applications of autoencoders to classifying sound events in the road traffic have not been found in the literature. The presented research aims to determine whether such an unsupervised learning method may be used for deploying classification algorithms applied to...
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Evolutionary Planning of Safe Ship Tracks in Restricted Visibility
PublikacjaThe paper presents the continuation of the author's research on ship track planning by means of Evolutionary Algorithms (EA). The presented method uses EA to search for an optimal set of safe tracks for all ships involved in an encounter. Until now the method assumed good visibility – compliance with standard rules of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS, 1972). However, in restricted...
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Towards the 4th industrial revolution: networks, virtuality, experience based collective computational intelligence, and deep learning
PublikacjaQuo vadis, Intelligent Enterprise? Where are you going? The authors of this paper aim at providing some answers to this fascinating question addressing emerging challenges related to the concept of semantically enhanced knowledge-based cyber-physical systems – the fourth industrial revolution named Industry 4.0.
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Data, Information, Knowledge, Wisdom Pyramid Concept Revisited in the Context of Deep Learning
PublikacjaIn this paper, the data, information, knowledge, and wisdom (DIKW) pyramid is revisited in the context of deep learning applied to machine learningbased audio signal processing. A discussion on the DIKW schema is carried out, resulting in a proposal that may supplement the original concept. Parallels between DIWK pertaining to audio processing are presented based on examples of the case studies performed by the author and her collaborators....
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Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
PublikacjaMany studies have been performed to put quantifying uncertainties into the seismic risk assessment of reinforced concrete (RC) buildings. This paper provides a risk-assessment support tool for purpose of retrofitting and potential design strategies of RC buildings. Machine Learning (ML) algorithms were developed in Python software by innovative methods of hyperparameter optimization, such as halving search, grid search, random...
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Experience-Oriented Knowledge Management for Internet of Things
PublikacjaIn this paper, we propose a novel approach for knowledge management in Internet of Things. By utilizing Decisional DNA and deep learning technologies, our approach enables Internet of Things of experiential knowledge discovery, representation, reuse, and sharing among each other. Rather than using traditional machine learning and knowledge discovery methods, this approach focuses on capturing domain’s decisional events via Decisional...
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Design and optimisation of combinational digital circuits using modified evolutionary algorithm.Projektowanie i optymalizacja kombinacyjnych układów cyfrowych przy użyciu zmodyfikowanego algorytmu ewolucyjnego.
PublikacjaW pracy przedstawiono możliwości projektowania i optymalizacji układów kombinacyjnych przy użyciu zmodyfikowanych algorytmów ewolucyjnych. Modyfikacja algorytmów polega na wprowadzeniu chromosomów wielowarstwowych i operatorów działających na nich. Wyniki projektowania czterech układów kombinacyjnych uzyskanych uzyskane tą metodą porównano z następującymi metodami opisanymi w literaturze jak: Mapy Karnaugh, metoda Quine-McCluskey...
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Machine Learning Techniques in Concrete Mix Design
PublikacjaConcrete 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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Projektowanie symetryzatorów planarnych dla pasma UWB z wykorzystaniem sztucznych sieci neuronowych
PublikacjaW pracy zaprezentowano nową metodę projektowania planarnych symetryzatorów szerokopasmowych, wykorzystującą sztuczne sieci neuronowe oraz zasady modelowania elektromagnetycznego. Metoda zakłada wykorzystanie projektu wzorcowego, jego przeskalowanie dla nowego podłoża z wykorzystaniem zasad modelowania elektromagnetycznego oraz optymalizację końcową w oparciu o odpowiednio nauczoną sieć neuronową. Poprawność działania algorytmu zweryfikowano...
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How high-tech solutions support the fight against IUU and ghost fishing: a review of innovative approaches, methods, and trends
PublikacjaIllegal, Unreported, and Unregulated fishing is a major threat to human food supply and marine ecosystem health. Not only is it a cause of significant economic loss but also its effects have serious long-term environmental implications, such as overfishing and ocean pollution. The beginning of the fight against this problem dates since the early 2000s. From that time, a number of approaches and methods have been developed and reported....
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Artificial Neural Networks for Comparative Navigation
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Open-Set Speaker Identification Using Closed-Set Pretrained Embeddings
PublikacjaThe paper proposes an approach for extending deep neural networks-based solutions to closed-set speaker identification toward the open-set problem. The idea is built on the characteristics of deep neural networks trained for the classification tasks, where there is a layer consisting of a set of deep features extracted from the analyzed inputs. By extracting this vector and performing anomaly detection against the set of known...
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Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
PublikacjaThe field of cancer diagnostics has been revolutionized by liquid biopsies, which offer a bridge between laboratory research and clinical settings. These tests are less invasive than traditional biopsies and more convenient than routine imaging methods. Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and...
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Assessing the attractiveness of human face based on machine learning
PublikacjaThe 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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Detecting Lombard Speech Using Deep Learning Approach
PublikacjaRobust Lombard speech-in-noise detecting is challenging. This study proposes a strategy to detect Lombard speech using a machine learning approach for applications such as public address systems that work in near real time. The paper starts with the background concerning the Lombard effect. Then, assumptions of the work performed for Lombard speech detection are outlined. The framework proposed combines convolutional neural networks...
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Automatic labeling of traffic sound recordings using autoencoder-derived features
PublikacjaAn approach to detection of events occurring in road traffic using autoencoders is presented. Extensions of existing algorithms of acoustic road events detection employing Mel Frequency Cepstral Coefficients combined with classifiers based on k nearest neighbors, Support Vector Machines, and random forests are used. In our research, the acoustic signal gathered from the microphone placed near the road is split into frames and converted...
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Machine learning-based seismic response and performance assessment of reinforced concrete buildings
PublikacjaComplexity and unpredictability nature of earthquakes makes them unique external loads that there is no unique formula used for the prediction of seismic responses. Hence, this research aims to implement the most well-known Machine Learning (ML) methods in Python software to propose a prediction model for seismic response and performance assessment of Reinforced Concrete Moment-Resisting Frames (RC MRFs). To prepare 92,400 data...
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Production planning and control methods in the intelligent manufacturing systems
PublikacjaNiniejszy rozdział prezentuje zagadnienia związane z planowaniem i sterowaniem wytwarzaniem w kontekście budowy i działania inteligentnych systemów produkcyjnych (ISP). Architektura ISP, będąca rozwinięciem elastycznych systemów produkcyjnych, integruje systemy wspomagania decyzji ze strukturami bazodanowymi oraz dodatkowymi modułami komunikacyjnymi. Systemy wspomagania decyzji budowane są w oparciu o mechanizmy tzw. inteligencji...
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Klasyfikacja sygnału EKG przy użyciu konwolucyjnych sieci neuronowych
PublikacjaAutomation and improvement of diagnostic process is a vital element of medicine development and patient’s condition self-control. For a long time different ECG signal classification methods exist and are successfully applied, nevertheless their accuracy is not always satisfying enough. The lack of identification of an existing abnormality, which is very similar to a normal heartbeat is the biggest issue - for example premature...
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Klasyfikacja sygnału EKG przy użyciu konwolucyjnych sieci neuronowych
PublikacjaAutomation and improvement of diagnostic process is a vital element of medicine development and patient’s condition self-control. For a long time different ECG signal classification methods exist and are successfully applied, nevertheless their accuracy is not always satisfying enough. The lack of identification of an existing abnormality, which is very similar to a normal heartbeat is the biggest issue - for example premature...
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Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams
PublikacjaOne of the main issues associated with steel fiber–reinforced concrete (SFRC) beams is the ability to anticipate their flexural response. With a comprehensive grid search, several stacked models (i.e., chained, parallel) consisting of various machine learning (ML) algorithms and artificial neural networks (ANNs) were developed to predict the flexural response of SFRC beams. The flexural performance of SFRC beams under bending was...
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Rozdział 4. Cieplno-przepływowe relacje diagnostyczne ustabilizowanych cieplnie bloków energetycznych wykorzystujące sztuczne sieci neuronowe (SSN)
PublikacjaPodano przykłady relacji diagnostycznych budowanych dla bloków energetycznych pracujących w warunkach stabilizacji cieplnej. Należą one do metod off-line. Dobrze sprawdzają się w nich sztuczne sieci neuronowe. Przy modułowej strukturze relacji diagnostycznych wykorzystywane są z powodzeniem SSN zarówno z ciągłymi jak i skokowymi funkcjami przejścia, w zależności od oczekiwanego wyniku obliczeń neuronowych.
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Halucynacje chatbotów a prawda: główne nurty debaty i ich interpretacje
PublikacjaGeneratywne systemy sztucznej inteligencji (SI) są w stanie tworzyć treści medialne poprzez zastosowanie uczenia maszynowego do dużych ilości danych szkoleniowych. Te nowe dane mogą obejmować tekst (np. Bard firmy Google, LLaMa firmy Meta lub ChatGPT firmy OpenAI) oraz elementy wizualne (np. Stable Diffusion lub DALL-E OpenAI) i dźwięk (np. VALL-E firmy Micro- soft). Stopień zaawansowania tych treści może czynić je nieodróżnialnymi...
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Spatiotemporal Assessment of Satellite Image Time Series for Land Cover Classification Using Deep Learning Techniques: A Case Study of Reunion Island, France
PublikacjaCurrent Earth observation systems generate massive amounts of satellite image time series to keep track of geographical areas over time to monitor and identify environmental and climate change. Efficiently analyzing such data remains an unresolved issue in remote sensing. In classifying land cover, utilizing SITS rather than one image might benefit differentiating across classes because of their varied temporal patterns. The aim...
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Speaker Recognition Using Convolutional Neural Network with Minimal Training Data for Smart Home Solutions
PublikacjaWith the technology advancements in smart home sector, voice control and automation are key components that can make a real difference in people's lives. The voice recognition technology market continues to involve rapidly as almost all smart home devices are providing speaker recognition capability today. However, most of them provide cloud-based solutions or use very deep Neural Networks for speaker recognition task, which are...
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Neural network simulator's application to reference performance determination of turbine blading in the heat-flow diagnostics.
PublikacjaIn 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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LOS and NLOS identification in real indoor environment using deep learning approach
PublikacjaVisibility conditions between antennas, i.e. Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) can be crucial in the context of indoor localization, for which detecting the NLOS condition and further correcting constant position estimation errors or allocating resources can reduce the negative influence of multipath propagation on wireless communication and positioning. In this paper a deep learning (DL) model to classify LOS/NLOS...
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Predicting bankruptcy with the use of macroeconomic variables
PublikacjaRegarding the current global financial crisis, the firms can expect the increased uncertainty of their existence. The relevant literature includes extensive studies on bankruptcy prediction. Studies show that the most popular method used for prediction of firms' failures are discriminant analyses (30,3% of all models), then logit and probit models (21,3%), which all three are parametric models. The nature, the structure of the...
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When Neural Networks Meet Decisional DNA: A Promising New Perspective for Knowledge Representation and Sharing
PublikacjaABSTRACT In this article, we introduce a novel concept combining neural network technology and Decisional DNA for knowledge representation and sharing. Instead of using traditional machine learning and knowledge discovery methods, this approach explores the way of knowledge extraction through deep learning processes based on a domain’s past decisional events captured by Decisional DNA. We compare our approach with kNN (k-nearest...
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Prediction of Overall In Vitro Microsomal Stability of Drug Candidates Based on Molecular Modeling and Support Vector Machines. Case Study of Novel Arylpiperazines Derivatives
PublikacjaOther than efficacy of interaction with the molecular target, metabolic stability is the primary factor responsible for the failure or success of a compound in the drug development pipeline. The ideal drug candidate should be stable enough to reach its therapeutic site of action. Despite many recent excellent achievements in the field of computational methods supporting drug metabolism studies, a well-recognized procedure to model...
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Multi-criterion decision making in distributed systems by quantum evolutionary algorithms
PublikacjaDecision making by the AQMEA (Adaptive Quantum-based Multi-criterion Evolutionary Algorithm) has been considered for distributed computer systems. AQMEA has been extended by a chromosome representation with the registry of the smallest units of quantum information. Evolutionary computing with Q-bit chromosomes has been proofed to characterize by the enhanced population diversity than other representations, since individuals represent...
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Skuteczność systemu eksperckiego i sztucznej inteligencji w prognozowaniu upadłości firm
PublikacjaArtykuł ten dotyczy prognozowania upadłości przedsiębiorstw w Polsce. W artykule tym porównano dwie metody prognozowania zagrożeń firm upadłością: sztuczne sieci neuronowe oraz logikę rozmytą. W badaniach autor wykorzystał dane dotyczące 185 spółek notowanych na Warszawskiej Giełdzie Papierów Wartościowych. Populacja ta została podzielona na próbę uczącą i testową. Każde z analizowanych przedsiębiorstw opisanych zostało za pomocą...
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Algorytmy ewolucyjne w projektowaniu sieci MPLS
PublikacjaNiniejszy artykuł opisuje zrealizowane narzędzie, które umożliwia projektowanie sieci MPLS za pomocą Algorytmów Ewolucyjnych. Narzędzie to generuje ścieżki i optymalizuje alokację na nich przepływności żądań zapotrzebowań z uwzględnieniem klas obsługi strumieni ruchu z gwarancją zróżnicowanego QoS. Może także wybierać ścieżki do alokacji spośród danych wejściowych tak, aby wykorzystanie sieci było optymalne. Narzędzie to zostało...
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Optimized Deep Learning Model for Flood Detection Using Satellite Images
PublikacjaThe increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection...
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Algorytmy genetyczne w wielokryterialnej optymalizacji obserwatorów detekcyjnych.
PublikacjaW rozdziale przedstawia się możliwości zastosowania podejścia genetycznego do zagadnień wielokryterialnej optymalizacji w przestrzeniach wielowymiarowych z wykorzystaniem koncepcji optymalności w sensie Pareto. Jako przykład ilustrujący rozważane podejście daje się zadanie syntezy obserwatorów stanu służących wykrywaniu błędów występujących w układzie sterowania bezzałogowego statku latającego oraz w układzie napędowym jednostki...
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The Implementation of Fuzzy Logic in Forecasting Financial Ratios
PublikacjaThis paper is devoted to the issue of forecasting financial ratios. The objective of the conducted research is to develop a predictive model with the use of an innovative methodology, i.e., fuzzy logic theory, and to evaluate its effectiveness. Fuzzy logic has been widely used in machinery, robotics and industrial engineering. This paper introduces the use of fuzzy logic for the financial analysis of enterprises. While many current...
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Predicting Compressive Strength of Cement-Stabilized Rammed Earth Based on SEM Images Using Computer Vision and Deep Learning
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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
PublikacjaFollowing the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides...
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Wpływ ruchu pojazdów ciężarowych na zniszczenia domów zlokalizowanych w pobliżu dróg przejazdowych
PublikacjaW niniejszym artykule przestawiono problem występowania drgań w budynkach, spowodowanych poruszającymi się pojazdami oraz ideę rozwiązania problemu pracochłonnych i kosztownych pomiarów takiego zjawiska. Autorzy starają się ukazać czynniki mające wpływ na wielkość drgań w świetle obowiązujących przepisów i norm. Główny wysiłek skupiają na ich pomiarze i interpretacji otrzymanych wyników w świetle propozycji budowy aplikacji wykorzystującej...
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Sensors and Sensor’s Fusion in Autonomous Vehicles
PublikacjaAutonomous vehicle navigation has been at the center of several major developments, both in civilian and defense applications. New technologies such as multisensory data fusion, big data processing, and deep learning are changing the quality of areas of applications, improving the sensors and systems used. New ideas such as 3D radar, 3D sonar, LiDAR, and others are based on autonomous vehicle revolutionary development. The Special...
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Ship Resistance Prediction with Artificial Neural Networks
PublikacjaThe paper is dedicated to a new method of ship’s resistance prediction using Artificial Neural Network (ANN). In the initial stage selected ships parameters are prepared to be used as a training and validation sets. Next step is to verify several network structures and to determine parameters with the highest influence on the result resistance. Finally, other parameters expected to impact the resistance are proposed. The research utilizes...
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Machine learning approach to packaging compatibility testing in the new product development process
PublikacjaThe paper compares the effectiveness of selected machine learning methods as modelling tools supporting the selection of a packaging type in new product development process. The main goal of the developed model is to reduce the risk of failure in compatibility tests which are preformed to ensure safety, durability, and efficacy of the finished product for the entire period of its shelf life and consumer use. This kind of testing...
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An algorithm for selecting a machine learning method for predicting nitrous oxide emissions in municipal wastewater treatment plants
PublikacjaThis study presents an advanced algorithm for selecting machine learning (ML) models for nitrous oxide (N2O) emission prediction in wastewater treatment plants (WWTPs) employing the activated sludge process. The examined ML models comprised multivariate adaptive regression spline (MARS), support vector machines (SVM), and extreme gradient boosting (XGboost). The study explores the concept that involves new criteria to select the...
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Exploring the influence of personal factors on physiological responses to mental imagery in sport
PublikacjaImagery is a well-known technique in mental training which improves performance efficiency and influences physiological arousal. One of the biomarkers indicating the amount of physiological arousal is skin conductance level (SCL). The aim of our study is to understand how individual differences in personality (e.g. neuroticism), general imagery and situational sport anxiety are linked to arousal measuring with SCL in situational...
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Knowledge Base Suitable for Answering Questions in Natural Language
PublikacjaThis paper presents three knowledge bases widely used by researchers coping with natural language processing: OpenCyc, DBpedia and YAGO. They are characterized from the point of view of questions answering system. In this paper a short description of the aforementioned system implementation is also presented.
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Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models
PublikacjaThis work addresses artificial-intelligence-based buried object characterization using 3-D full-wave electromagnetic simulations of a ground penetrating radar (GPR). The task is to characterize cylindrical shape, perfectly electric conductor (PEC) object buried in various dispersive soil media, and in different positions. The main contributions of this work are (i) development of a fast and accurate data driven surrogate modeling...
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A new library for construction of automata
PublikacjaWe present a new library of functions that construct minimal, acyclic, deterministic, finite-state automata in the same format as the author's fsa package, and also accepted by the author's fadd library of functions that use finite-state automata as dictionaries in natural language processing.