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Wyniki wyszukiwania dla: convolutional neural networks
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Clothes Detection and Classification Using Convolutional Neural Networks
PublikacjaIn this paper we describe development of a computer vision system for accurate detection and classification of clothes for e-commerce images. We present a set of experiments on well established architectures of convolutional neural networks, including Residual networks, SqueezeNet and Single Shot MultiBox Detector (SSD). The clothes detection network was trained and tested on DeepFashion dataset, which contains box annotations...
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Performance Analysis of Convolutional Neural Networks on Embedded Systems
PublikacjaMachine learning is no longer confined to cloud and high-end server systems and has been successfully deployed on devices that are part of Internet of Things. This paper presents the analysis of performance of convolutional neural networks deployed on an ARM microcontroller. Inference time is measured for different core frequencies, with and without DSP instructions and disabled access to cache. Networks use both real-valued and...
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Automatic Breath Analysis System Using Convolutional Neural Networks
PublikacjaDiseases related to the human respiratory system have always been a burden for the entire society. The situation has become particularly difficult now after the outbreak of the COVID-19 pandemic. Even now, however, it is common for people to consult their doctor too late, after the disease has developed. To protect patients from severe disease, it is recommended that any symptoms disturbing the respiratory system be detected as...
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Automatic Breath Analysis System Using Convolutional Neural Networks
PublikacjaDiseases related to the human respiratory system have always been a burden for the entire society. The situation has become particularly difficult now after the outbreak of the COVID-19 pandemic. Even now, however, it is not uncommon for people to consult their doctor too late, after the disease has developed. To protect patients from severe disease, it is recommended that any symptoms disturbing the respiratory system be detected...
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Recognition of Emotions in Speech Using Convolutional Neural Networks on Different Datasets
PublikacjaArtificial Neural Network (ANN) models, specifically Convolutional Neural Networks (CNN), were applied to extract emotions based on spectrograms and mel-spectrograms. This study uses spectrograms and mel-spectrograms to investigate which feature extraction method better represents emotions and how big the differences in efficiency are in this context. The conducted studies demonstrated that mel-spectrograms are a better-suited...
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Visual Features for Improving Endoscopic Bleeding Detection Using Convolutional Neural Networks
PublikacjaThe presented paper investigates the problem of endoscopic bleeding detection in endoscopic videos in the form of a binary image classification task. A set of definitions of high-level visual features of endoscopic bleeding is introduced, which incorporates domain knowledge from the field. The high-level features are coupled with respective feature descriptors, enabling automatic capture of the features using image processing methods....
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System for monitoring road slippery based on CCTV cameras and convolutional neural networks
PublikacjaThe slipperiness of the surface is essential for road safety. The growing number of CCTV cameras opens the possibility of using them to automatically detect the slippery surface and inform road users about it. This paper presents a system of developed intelligent road signs, including a detector based on convolutional neural networks (CNNs) and the transferlearning method employed to the processing of images acquired with video...
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Using Convolutional Neural Networks for Corneal Arcus Detection Towards Familial Hypercholesterolemia Screening
PublikacjaFamilial hypercholesterolemia (FH) is a highly undiagnosed disease. Among FH patients, the onset of premature coronary artery disease is 13 times higher than in the general population. Early diagnosis and treatment is essential to prevent cardiovascular diseases and their complications, and to prolong life. One of the clinical criteria of FH is the occurrence of a corneal arcus (CA) among patients, especially those under 45 years...
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A novel approach exploiting properties of convolutional neural networks for vessel movement anomaly detection and classification
PublikacjaThe article concerns the automation of vessel movement anomaly detection for maritime and coastal traffic safety services. Deep Learning techniques, specifically Convolutional Neural Networks (CNNs), were used to solve this problem. Three variants of the datasets, containing samples of vessel traffic routes in relation to the prohibited area in the form of a grayscale image, were generated. 1458 convolutional neural networks with...
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Convolutional Neural Networks for C. Elegans Muscle Age Classification Using Only Self-Learned Features
PublikacjaNematodes Caenorhabditis elegans (C. elegans) have been used as model organisms in a wide variety of biological studies, especially those intended to obtain a better understanding of aging and age-associated diseases. This paper focuses on automating the analysis of C. elegans imagery to classify the muscle age of nematodes based on the known and well established IICBU dataset. Unlike many modern classification methods, the proposed...
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DEEP CONVOLUTIONAL NEURAL NETWORKS AS A DECISION SUPPORT TOOL IN MEDICAL PROBLEMS – MALIGNANT MELANOMA CASE STUDY
PublikacjaThe paper presents utilization of one of the latest tool from the group of Machine learning techniques, namely Deep Convolutional Neural Networks (CNN), in process of decision making in selected medical problems. After the survey of the most successful applications of CNN in solving medical problems, the paper focuses on the very difficult problem of automatic analyses of the skin lesions. The authors propose the CNN structure...
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GPU Power Capping for Energy-Performance Trade-Offs in Training of Deep Convolutional Neural Networks for Image Recognition
PublikacjaIn the paper we present performance-energy trade-off investigation of training Deep Convolutional Neural Networks for image recognition. Several representative and widely adopted network models, such as Alexnet, VGG-19, Inception V3, Inception V4, Resnet50 and Resnet152 were tested using systems with Nvidia Quadro RTX 6000 as well as Nvidia V100 GPUs. Using GPU power capping we found other than default configurations minimizing...
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Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks
PublikacjaAge prediction from X-rays is an interesting research topic important for clinical applications such as biological maturity assessment. It is also useful in many other practical applications, including sports or forensic investigations for age verification purposes. Research on these issues is usually carried out using high-resolution X-ray scans of parts of the body, such as images of the hands or images of the chest. In this...
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Flexible Knowledge–Vision–Integration Platform for Personal Protective Equipment Detection and Classification Using Hierarchical Convolutional Neural Networks and Active Leaning
PublikacjaThis work is part of an effort to develop of a Knowledge-Vision Integration Platform for Hazard Control (KVIP-HC) in industrial workplaces, adaptable to a wide range of industrial environments. The paper focuses on hazards resulted from the non-use of personal protective equipment (PPE). The objective is to test the capability of the platform to adapt to different industrial environments by simulating the process of randomly selecting...
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Activation maps of convolutional neural networks as a tool for brain degeneration tracking in early diagnosis of dementia in Parkinson's disease based on magnetic resonance imaging
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Digits Recognition with Quadrant Photodiode and Convolutional Neural Network
PublikacjaIn this paper we have investigated the capabilities of a quadrant photodiode based gesture sensor in the recognition of digits drawn in the air. The sensor consisting of 4 active elements, 4 LEDs and a pinhole was considered as input interface for both discrete and continuous gestures. Index finger and a round pointer were used as navigating mediums for the sensor. Experiments performed with 5 volunteers...
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Characterizing the Scalability of Graph Convolutional Networks on Intel® PIUMA
PublikacjaLarge-scale Graph Convolutional Network (GCN) inference on traditional CPU/GPU systems is challenging due to a large memory footprint, sparse computational patterns, and irregular memory accesses with poor locality. Intel’s Programmable Integrated Unffied Memory Architecture (PIUMA) is designed to address these challenges for graph analytics. In this paper, a detailed characterization of GCNs is presented using the Open-Graph Benchmark...
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Deep convolutional neural network for predicting kidney tumour malignancy
PublikacjaPurpose: According to the statistics, up to 15-20% of removed solid kidney tumors turn out to be benign in postoperative histopathological examination, despite having been identified as malignant by a radiologist. The aim of the research was to limit the number of unnecessary nephrectomies of benign tumors. Methods or Background: We propose a machine-aided diagnostic system for kidney...
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Neural networks and deep learning
PublikacjaIn this chapter we will provide the general and fundamental background related to Neural Networks and Deep Learning techniques. Specifically, we divide the fundamentals of deep learning in three parts, the first one introduces Deep Feed Forward Networks and the main training algorithms in the context of optimization. The second part covers Convolutional Neural Networks (CNN) and discusses their main advantages and shortcomings...
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An Improved Convolutional Neural Network for Steganalysis in the Scenario of Reuse of the Stego-Key
PublikacjaThe topic of this paper is the use of deep learning techniques, more specifically convolutional neural networks, for steganalysis of digital images. The steganalysis scenario of the repeated use of the stego-key is considered. Firstly, a study of the influence of the depth and width of the convolution layers on the effectiveness of classification was conducted. Next, a study on the influence of depth and width of fully connected...
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From Linear Classifier to Convolutional Neural Network for Hand Pose Recognition
PublikacjaRecently gathered image datasets and the new capabilities of high-performance computing systems have allowed developing new artificial neural network models and training algorithms. Using the new machine learning models, computer vision tasks can be accomplished based on the raw values of image pixels instead of specific features. The principle of operation of deep neural networks resembles more and more what we believe to be happening...
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Highlighting interlanguage phoneme differences based on similarity matrices and convolutional neural network
PublikacjaThe goal of this research is to find a way of highlighting the acoustic differences between consonant phonemes of the Polish and Lithuanian languages. For this purpose, similarity matrices are employed based on speech acoustic parameters combined with a convolutional neural network (CNN). In the first experiment, we compare the effectiveness of the similarity matrices applied to discerning acoustic differences between consonant...
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Neural Networks and the Evolution of Environmental Change
PublikacjaZmiany środowiskowe na Ziemii są odwieczne i liczą około 4 miliardy lat. Homo sapiens wpłynął na każdy aspekt środowiska ziemskiego w wyniku rozwoju ludzkości na przestrzeni ostatnich milionów lat. Ale nic tak nie wpłynęło na wzrost i szybkość zmian na Ziemi jak ludzka aktywność w ciągu ostatnich dwóch stuleci. Po raz pierwszy zmiany ekosystemów były tak intensywne i zachodziły na tka wielką skalę i z taką szybkością jak nigdy...
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Artificial Neural Networks for Comparative Navigation
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The influence of image masks definition onsegmentation results of histopathological imagesusing convolutional neural network
PublikacjaAbstract—In the era of collecting large amounts of tissue materials, assisting the work of histopathologists with various electronic and information IT tools is an undeniable fact. The traditional interaction between a human pathologist and the glass slide is changing to interaction between an AI pathologist with a whole slide images. One of the important tasks is the segmentation of objects (e.g. cells) in such images. In this...
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Robustness in Compressed Neural Networks for Object Detection
PublikacjaModel compression techniques allow to significantly reduce the computational cost associated with data processing by deep neural networks with only a minor decrease in average accuracy. Simultaneously, reducing the model size may have a large effect on noisy cases or objects belonging to less frequent classes. It is a crucial problem from the perspective of the models' safety, especially for object detection in the autonomous driving...
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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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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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A survey of neural networks usage for intrusion detection systems
PublikacjaIn recent years, advancements in the field of the artificial intelligence (AI) gained a huge momentum due to the worldwide appliance of this technology by the industry. One of the crucial areas of AI are neural networks (NN), which enable commer‐ cial utilization of functionalities previously not accessible by usage of computers. Intrusion detection system (IDS) presents one of the domains in which neural networks are widely tested...
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Outlier detection method by using deep neural networks
PublikacjaDetecting 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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Diagnosis of damages in family buildings using neural networks
PublikacjaThe article concerns a problem of damages in family buildings, which result from traffic-induced vibrations. These vibrations arise from various causes and their size is influenced by many factors. The most important is the type of a road, type and weight of vehicles that run on the road, type and condition of the road surface, the distance from the house to the source of vibrations and many others which should be taken into account....
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Artificial Neural Networks in Microwave Components and Circuits Modeling
PublikacjaArtykuł dotyczy wykorzystania sztucznych sieci neuronowych (SNN) w projektowaniu i optymalizacji układów mikrofalowych.Zaprezentowano podstawowe zasady i założenia modelowania z użyciem SNN. Możliwości opisywanej metody opisano wykorzystując przykładowyprojekt anteny łatowej. Przedstawiono różne strategie modelowania układów, które wykorzystują możliwości opisywanej metody w połączeniu zwiedzą mikrofalową. Porównano również dokładność...
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Neural networks in the diagnostics of induction motor rotor cages.
PublikacjaW środowisku Lab VIEW została stworzona aplikacja służąca do pomiaru, prezentacji i zapisu przebiegów widma prądu stojana z uwzględnieniem potrzeb pomiarowych występujących podczas badania wirników silników indukcyjnych przy użyciu sieci neuronowych. Utworzona na bazie zbioru uczącego sieć Kohonena z powodzeniem rozwiązała stawiany przed nią problem klasyfikacji widm prądu stojana, a co za tym idzie również diagnozy stanu...
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Applications of neural networks and perceptual masking to audio restoration
PublikacjaOmówiono zastosowania algorytmów uczących się w dziedzinie rekonstruowania nagrań fonicznych. Szczególną uwagę zwrócono na zastosowanie sztucznych sieci neuronowych do usuwania zakłócających impulsów. Ponadto opisano zastosowanie inteligentnego algorytmu decyzyjnego do sterowania maskowaniem perceptualnym w celu redukowania szumu.
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Application of neural networks for turbine rotor trajectory investigation.
PublikacjaW pracy przedstawiono rezultaty badań sieci neuronowych przewidujących trajektorię wirnika turbinowego uzyskanych ze stanowiska turbiny modelowej. Badania wykazały, iż sieci neuronowe wydają się być z powodzeniem zastosowane do przewidywania trajektorii ruchu wirnika turbiny. Najważniejszym zadaniem wydaje się poprawne określenie wektorów sygnałów wejściowych oraz wyjściowych jak również prawidłowe stworzenie sieci neuronowej....
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Problems in toxicity analysis - application of fuzzy neural networks
PublikacjaPraca dotyczy zastosowania sztucznych sieci neuronowych do przygotowywania danych do szacowania toksyczności (wody powierzchniowe). Przygotowanie to polega na sztucznym zagęszczaniu zbioru danych, które następnie mogą być wykorzystane do szacowania/modelowania wartości toksyczności na ich podstawie.
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Classification of Covid-19 using Differential Evolution Chaotic Whale Optimization based Convolutional Neural Network
PublikacjaCOVID-19, also known as the Coronavirus disease-2019, is an transferrable disease that spreads rapidly, affecting countless individuals and leading to fatalities in this worldwide pandemic. The precise and swift detection of COVID-19 plays a crucial role in managing the pandemic's dissemination. Additionally, it is necessary to recognize COVID-19 quickly and accurately by investigating chest x-ray images. This paper proposed a...
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Explainable AI for Inspecting Adversarial Attacks on Deep Neural Networks
PublikacjaDeep Neural Networks (DNN) are state of the art algorithms for image classification. Although significant achievements and perspectives, deep neural networks and accompanying learning algorithms have some important challenges to tackle. However, it appears that it is relatively easy to attack and fool with well-designed input samples called adversarial examples. Adversarial perturba-tions are unnoticeable for humans. Such attacks...
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A MODEL FOR FORECASTING PM10 LEVELS WITH THE USE OF ARTIFICIAL NEURAL NETWORKS
PublikacjaThis 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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USING ARTIFICIAL NEURAL NETWORKS FOR PREDICTING SHIP FUEL CONSUMPTION
PublikacjaIn marine vessel operations, fuel costs are major operating costs which affect the overall profitability of the maritime transport industry. The effective enhancement of using ship fuel will increase ship operation efficiency. Since ship fuel consumption depends on different factors, such as weather, cruising condition, cargo load, and engine condition, it is difficult to assess the fuel consumption pattern for various types...
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Automatic singing quality recognition employing artificial neural networks
PublikacjaCelem 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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Gesture Recognition With the Linear Optical Sensor and Recurrent Neural Networks
PublikacjaIn this paper, the optical linear sensor, a representative of low-resolution sensors, was investigated in the multiclass recognition of near-field hand gestures. The recurrent neural network (RNN) with a gated recurrent unit (GRU) memory cell was utilized as a gestures classifier. A set of 27 gestures was collected from a group of volunteers. The 27 000 sequences obtained were divided into training, validation, and test subsets....
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Application of Artificial Neural Networks in Investigations of Steam Turbine Cascades
PublikacjaZaprezentowano 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
PublikacjaW 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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Using neural networks to examine trending keywords in Inventory Control
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Application of neural networks for description of pressure distribution in slide bearing.
PublikacjaBadano rozkład ciśnienia hydrodynamicznego w łożysku ślizgowym dla wybranych wariantów łożyska. Wykazano, że zastosowanie sieci neuronowych umożliwia opis rozkładu ciśnienia hydrodynamicznego z uwzględnieniem zmian geometrycznych (bezwymiarowa długość - L) i mechanicznych (mimośrodowość względem H) łożyska.
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Identification of slide bearing main parameters using neural networks.
PublikacjaWykazano, że sieci neuronowe jak najbardziej nadają się do identyfikacji głównych parametrów geometrycznych i ruchowych hydrodynamicznych łożysk ślizgowych.
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Neural networks based NARX models in nonlinear adaptive control
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Estimation the rhythmic salience of sound with association rules and neural networks
PublikacjaW referacie przedstawiono eksperymenty mające na celu automatyczne wyszukiwanie wartości rytmicznych we frazie muzycznej. W tym celu wykorzystano metody data mining i sztuczne sieci neuronowe.
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Deep neural networks approach to skin lesions classification — A comparative analysis
PublikacjaThe 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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Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures
PublikacjaAbstract Background In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments...
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Abdominal Aortic Aneurysm segmentation from contrast-enhanced computed tomography angiography using deep convolutional networks
PublikacjaOne of the most common imaging methods for diagnosing an abdominal aortic aneurysm, and an endoleak detection is computed tomography angiography. In this paper, we address the problem of aorta and thrombus semantic segmentation, what is a mandatory step to estimate aortic aneurysm diameter. Three end-to-end convolutional neural networks were trained and evaluated. Finally, we proposed an ensemble of deep neural networks with underlying...
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Selected Technical Issues of Deep Neural Networks for Image Classification Purposes
PublikacjaIn recent years, deep learning and especially Deep Neural Networks (DNN) have obtained amazing performance on a variety of problems, in particular in classification or pattern recognition. Among many kinds of DNNs, the Convolutional Neural Networks (CNN) are most commonly used. However, due to their complexity, there are many problems related but not limited to optimizing network parameters, avoiding overfitting and ensuring good...
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Bees Detection on Images: Study of Different Color Models for Neural Networks
PublikacjaThis paper presents an approach to bee detection in video streams using a neural network classifier. We describe the motivation for our research and the methodology of data acquisition. The main contribution to this work is a comparison of different color models used as an input format for a feedforward convolutional architecture applied to bee detection. The detection process has is based on a neural binary classifier that classifies...
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Applying artificial neural networks for modelling ship speed and fuel consumption
PublikacjaThis 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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Use of Neural Networks in Diagnostics of Rolling-Element Bearing of the Induction Motor
PublikacjaBearing defect is statistically the most frequent cause of an induction motor fault. The research described in the paper utilized the phenomenon of the current change in the induction motor with bearing defect. Methods based on the analysis of the supplying current are particularly useful when it is impossible to install diagnostic devices directly on the motor. The presented method of rolling-element bearing diagnostics used indirect...
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An Automated Method for Biometric Handwritten Signature Authentication Employing Neural Networks
PublikacjaHandwriting biometrics applications in e-Security and e-Health are addressed in the course of the conducted research. An automated graphomotor analysis method for the dynamic electronic representation of the handwritten signature authentication was researched. The developed algorithms are based on dynamic analysis of electronically handwritten signatures employing neural networks. The signatures were acquired with the use of the...
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Comparative study of neural networks used in modeling and control of dynamic systems
PublikacjaIn this paper, a diagonal recurrent neural network that contains two recurrent weights in the hidden layer is proposed for the designing of a synchronous generator control system. To demonstrate the superiority of the proposed neural network, a comparative study of performances, with two other neural network (1_DRNN) and the proposed second-order diagonal recurrent neural network (2_DRNN). Moreover, to confirm the superiority...
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Application of Artificial Neural Networks to Predict Insulation Properties of Lightweight Concrete
PublikacjaPredicting 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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Modeling of Surface Roughness in Honing Processes by UsingFuzzy Artificial Neural Networks
PublikacjaHoning 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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Application of the neural networks for developing new parametrization of the Tersoff potential for carbon
PublikacjaPenta-graphene (PG) is a 2D carbon allotrope composed of a layer of pentagons having sp2- and sp3-bonded carbon atoms. A study carried out in 2018 has shown that the parameterization of the Tersoff potential proposed in 2005 by Ehrhart and Able (T05 potential) performs better than other potentials available for carbon, being able to reproduce structural and mechanical properties of the PG. In this work, we tried to improve the...
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Efficient uncertainty quantification using sequential sampling-based neural networks
PublikacjaUncertainty quantification (UQ) of an engineered system involves the identification of uncertainties, modeling of the uncertainties, and the forward propagation of the uncertainties through a system analysis model. In this work, a novel surrogate-based forward propagation algorithm for UQ is proposed. The proposed algorithm is a new and unique extension of the recent efficient global optimization using neural network (NN)-based...
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Constrained aerodynamic shape optimization using neural networks and sequential sampling
PublikacjaAerodynamic shape optimization (ASO) involves computational fluid dynamics (CFD)-based search for an optimal aerodynamic shape such as airfoils and wings. Gradient-based optimization (GBO) with adjoints can be used efficiently to solve ASO problems with many design variables, but problems with many constraints can still be challenging. The recently created efficient global optimization algorithm with neural network (NN)-based prediction...
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Extended Hopfield models of neural networks for combinatorial multiobjective optimization problems
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Use of Neural Networks in Diagnostics of Rolling-Element Bearing of the Induction Motor
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Analysis of electrical patterns activity in artificial multi-stable neural networks
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Automatic singing voice recognition employing neural networks and rough sets
PublikacjaCelem prac opisanych w referacie jest automatyczne rozpoznawanie głosów śpiewaczych. Do tego celu utworzona została baza nagrań próbek śpiewu profesjonalnego i amatorskiego. Próbki poddane zostały parametryzacji parametrami zaproponowanymi przez autorów ściśle do tego celu. Sposób wyznaczenia parametrów i ich interpretacja fizyczna przedstawione są w referacie. Parametry wprowadzane są do systemów decyzyjnych, klasyfikatorów opartych...
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Neural Networks in the Diagnostics Process of Low-Power Solar Plant Devices
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Optimization of a three-bed adsorption chiller by genetic algorithms and neural networks
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The concept of application of artificial neural networks for cultivation controlof cartilages in bioreactors.
PublikacjaNowym 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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Musical phrase representation and recognition by means of neural networks and rough sets.
PublikacjaW artykule przedstawiono podstawowe definicje dotyczące frazy muzycznej. W eksperymentach posłużono się zapisem parametrycznym. W celu wzmocnienia procesu rozpoznawania wykorzystano kodowanie entropijne muzyki. W eksperymentach klasyfikacji oparto się o sztuczne sieci neuronowe i metodę zbiorów przybliżonych. Słowa kluczowe: fraza muzyczna, klasyfikacja, sztuczne sieci neuronowe, metoda zbiorów przybliżonych
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Processing of musical data employing rough sets and artificial neural networks
PublikacjaArtykuł 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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Comparison of effectiveness of musical sound separation algorithms employing neural networks.
PublikacjaNiniejszy referat przedstawia kilka algorytmów służących do separacji dźwięków instrumentów muzycznych. Zaproponowane podejście do dekompozycji miksów dźwiękowych opiera się na założeniu, że wysokość dźwięków w miksie jest znana, tzn. wejściem dla algorytmów jest przebieg zmian wysokości dźwięków składowych miksu. Proces estymacji fazy i amplitudy składowych harmonicznych wykorzystuje dopasowywanie zespolonych przebiegów harmonicznych...
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Artificial Neural Networks for Prediction of Antibacterial Activity in Series of Imidazole Derivatives
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Neural Networks Based on Ultrafast Time-Delayed Effects in Exciton Polaritons
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Face with Mask Detection in Thermal Images Using Deep Neural Networks
PublikacjaAs the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it possible, for example, to determine the temperature based on the detected face, which brings new perspectives and opportunities to use such an approach for health control purposes. The...
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Processing of musical data employing rough sets and artificial neural networks
PublikacjaArtykuł 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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Estimation of musical sound separation algorithm effectiveness employing neural networks.
PublikacjaŚlepa separacja dźwięków sygnałów muzycznych zawartych w zmiksowanym materiale jest trudnym zadaniem. Jest to spowodowane tym, że dźwięki znajdujące się w relacjach harmonicznych mogą zawierać kolidujące składowe sinusoidalne (składowe harmoniczne). Ewaluacja wyników separacji jest również problematyczna, gdyż analiza błędu energetycznego często nie odzwierciedla subiektywnej jakości odseparowanych sygnałów. W tej publikacji zostały...
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Towards bees detection on images: study of different color models for neural networks
PublikacjaThis paper presents an approach to bee detection in videostreams using a neural network classifier. We describe the motivationfor our research and the methodology of data acquisition. The maincontribution to this work is a comparison of different color models usedas an input format for a feedforward convolutional architecture appliedto bee detection. The detection process has is based on a neural...
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Physics-guided neural networks (PGNNs) to solve differential equations for spatial analysis
PublikacjaNumerous examples of physically unjustified neural networks, despite satisfactory performance, generate contradictions with logic and lead to many inaccuracies in the final applications. One of the methods to justify the typical black-box model already at the training stage and lead to many inaccuracies in the final applications. One of the methods to justify the typical black-box model already at the training stage involves extending...
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Supply current signal and artificial neural networks in the induction motor bearings diagnostics
PublikacjaThis 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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Predicting the impact of traffic–induced vibrations on buildings using artificial neural networks
PublikacjaTraffic–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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The application of neural networks in forecasting the influence of traffic-induced vibrations on residential buildings
PublikacjaTraffic-induced vibrations may cause the cracking of plaster, damage to structural elements and, in extreme cases, may even lead to the structural collapse of residential buildings. The aim of this article is to analyse the effectiveness of a method of forecasting the impact of vibrations on residential buildings using the concept of artificial intelligence. The article presents several alternative forecasting systems for which...
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Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening
PublikacjaBeta-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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Multi-Camera Vehicle Tracking Using Local Image Features and Neural Networks
PublikacjaA method for tracking moving objects crossing fields of view of multiple cameras is presented. The algorithm utilizes Artificial Neural Networks (ANNs). Each ANN is trained to recognize images of one moving object acquired by a single camera. Local image features calculated in the vicinity of automatically detected interest points are used as object image parameters. Next, ANNs are employed to identify the same objects captured...
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Heavy duty vehicle fuel consumption modelling using artificial neural networks
PublikacjaIn 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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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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Adsorption chiller in a combined heating and cooling system: simulation and optimization by neural networks
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Aerodynamic excitations generated in turbine shroud clearance determined bymeans of neural networks
PublikacjaSiły aerodynamiczne generowane w uszczelnieniach turbinowych z reguły opisywane są modelem liniowym. Przy dużych drganiach wirnika sposób ten daje niezbyt dokładne wyniki. Zaproponowano wykorzystanie sieci neuronowych do określania sił ciśnieniowych powstających w uszczelnieniu. Wyniki porównano z badaniami eksperymentalnymi.
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Self-organizing Artificial Neural Networks into Hydrographic Big Data Reduction Process
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An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks
PublikacjaIn this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent...
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Neural Networks, Support Vector Machine and Genetic Algorithms for Autonomous Underwater Robot Support
PublikacjaIn this paper, artificial neural networks, a classification technique called support vector machine and meta-heuristics genetic algorithm have been considered for development in autonomous underwater robots. Artificial neural networks have been used for seabed modelling as well as support vector machine has been applied for the obstacles classification to avoid some collision problems. Moreover, genetic algorithm has been applied...
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A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks
PublikacjaThis article proposes the use of a feedforward neural network (FNN) to select the starting point for the first iteration in well-known iterative location estimation algorithms, with the research objective of finding the minimum size of a neural network that allows iterative position estimation algorithms to converge in an example positioning network. The selected algorithms for iterative position estimation, the structure of the...
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Dynamically positioned ship steering making use of backstepping method and artificial neural networks
PublikacjaThe article discusses the issue of designing a dynamic ship positioning system making use of the adaptive vectorial backstepping method and RBF type arti cial neural networks. In the article, the backstepping controller is used to determine control laws and neural network weight adaptation laws. e arti cial neural network is applied at each time instant to approximate nonlinear functions containing parametric uncertainties....
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Hybrid of Neural Networks and Hidden Markov Models as a modern approach to speech recognition systems
PublikacjaThe aim of this paper is to present a hybrid algorithm that combines the advantages ofartificial neural networks and hidden Markov models in speech recognition for control purpos-es. The scope of the paper includes review of currently used solutions, description and analysis of implementation of selected artificial neural network (NN) structures and hidden Markov mod-els (HMM). The main part of the paper consists of a description...
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Approximation of Fractional Order Dynamic Systems Using Elman, GRU and LSTM Neural Networks
PublikacjaIn the paper, authors explore the possibility of using the recurrent neural networks (RNN) - Elman, GRU and LSTM - for an approximation of the solution of the fractional-orders differential equations. The RNN network parameters are estimated via optimisation with the second order L-BFGS algorithm. It is done based on data from four systems: simple first and second fractional order LTI systems, a system of fractional-order point...
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Gas Detection Using Resistive Gas Sensors And Radial Basis Function Neural Networks
PublikacjaWe present a use of Radial Basis Function (RBF) neural networks and Fluctuation Enhanced Sensing (FES) method in gas detection system utilizing a prototype resistive WO3 gas sensing layer with gold nanoparticles. We investigated accuracy of gas detection for three different preprocessing methods: no preprocessing, Principal Component Analysis (PCA) and wavelet transformation. Low frequency noise voltage observed in resistive gas...
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Enhancing Renal Tumor Detection: Leveraging Artificial Neural Networks in Computed Tomography Analysis
PublikacjaRenal cell carcinoma is one of the most common cancers in Europe, with a total incidence rate of 18.4 cases per 100 000 population. There is currently significant overdiagnosis (11% to 30.9%) at times of planned surgery based on radiological studies. The purpose of this study was to create an artificial neural network (ANN) solution based on computed tomography (CT) images as an additional tool to improve the differentiation of...