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Search results for: CONVOLUTIONAL NEURAL NETWORK
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Designing of an effective structure of system for the maintenance of a technical object with the using information from an artificial neural network
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Biotrickling filtration of n-butanol vapors: process monitoring using electronic nose and artificial neural network
PublicationBiotrickling filtration is one of the techniques used to reduce odorants in the air. It is based on the aerobic degradation of pollutants by microorganisms located in the filter bed. The research presents the possibility of using the electronic nose prototype combined with artificial neural network for biofiltration process monitoring in terms of reduction in n-butanol concentration and odour intensity of treated air. The study...
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Mutual Coupling Reduction in Antenna Arrays Using Artificial Intelligence Approach and Inverse Neural Network Surrogates
PublicationThis paper presents a novel approach to reduce undesirable coupling in antenna arrays using custom-designed resonators and inverse surrogate modeling. To illustrate the concept, two stand-ard patch antenna cells with 0.07λ edge-to-edge distance are designed and fabricated to operate at 2.45 GHz. A stepped-impedance resonator is applied between the antennas to suppress their mutual coupling. For the first time, the optimum values...
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Generalized regression neural network and fitness dependent optimization: Application to energy harvesting of centralized TEG systems
PublicationThe thermoelectric generator (TEG) system has attracted extensive attention because of its applications in centralized solar heat utilization and recoverable heat energy. The operating efficiency of the TEG system is highly affected by operating conditions. In a series-parallel structure, due to diverse temperature differences, the TEG modules show non-linear performance. Due to the non-uniform temperature distribution (NUTD) condition,...
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Food Classification from Images Using a Neural Network Based Approach with NVIDIA Volta and Pascal GPUs
PublicationIn the paper we investigate the problem of food classification from images, for the Food-101 dataset extended with 31 additional food classes from Polish cuisine. We adopted transfer learning and firstly measured training times for models such as MobileNet, MobileNetV2, ResNet50, ResNet50V2, ResNet101, ResNet101V2, InceptionV3, InceptionResNetV2, Xception, NasNetMobile and DenseNet, for systems with NVIDIA Tesla V100 (Volta) and...
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<title>Recurrent neural network application to image filtering: 2-D Kalman filtering approach</title>
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Modelling of a medium-term dynamics in a shallow tidal sea, based on combined physical and neural network methods
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Safety assessment of ships in critical conditions using a knowledge-based system for design and neural network system
PublicationW pracy opisano wybrane elementy metody oceny bezpieczeństwa statków w stanie uszkodzonym, ukierunkowanej na ocenę osiągów statku i ocenę ryzyka. Metoda analizy osiągów i zachowania się statku w stanie uszkodzonym została wykorzystana do oceny charakterystyk hydromechanicznych statku uszkodzonego. Do oceny ryzyka wykorzystano elementy metodyki Formalnej Oceny Bezpieczeństwa. System ekspertowy został wykorzystany do analziy podziału...
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DIAGNOSIS OF MALIGNANT MELANOMA BY NEURAL NETWORK ENSEMBLE-BASED SYSTEM UTILISING HAND-CRAFTED SKIN LESION FEATURES
PublicationMalignant melanomas are the most deadly type of skin cancer but detected early have high chances for successful treatment. In the last twenty years, the interest of automated melanoma recognition detection and classification dynamically increased partially because of public datasets appearing with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task...
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Artificial neural network prophecy of ion exchange process for Cu (II) eradication from acid mine drainage
PublicationThe removal of heavy metal ions from wastewater was found to be significant when the cation exchange procedure was used effectively. The model of the cation exchange process was built using an artificial neural network (ANN). The acid mine drainage waste’s Cu(II) ion was removed using Indion 730 cation exchange resin. Experimental data from 252 cycles were recorded. In a column study, 252 experimental observations validated the...
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Performance analysis of an rfid-based 3d indoor positioning system combining scene analysis and neural network methods
PublicationThe main purpose of this research is to improve localization accuracy of an active Radio Frequency Identification, RFID tag, in 3D indoor space. The paper presents a new RFID based 3D Indoor Positioning System which shows performance improvement. The proposed positioning system combines two methods: the Scene Analysis technique and Artificial Neural Network. The results of both simulation using Log-Distance Path Loss Model and...
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Optimal selection of input features and an acompanying neural network structure for the classification purposes - skin lesions case study
PublicationMalignant melanomas are the most deadly type of skin cancers however detected early enough give a high chances for successful treatment. The last years saw the dynamic growth of interest of automatic computer-aided skin cancer diagnosis. Every month brings new research results on new approaches to this problem, new methods of preprocessing, new classifiers, new ideas to follow etc. In particular, the rapid development of dermatoscopy,...
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Mathematical modeling and prediction of pit to crack transition under cyclic thermal load using artificial neural network
PublicationThe formation of pitting is a major problem in most metals, which is caused by extremely localized corrosion that creates small holes in metal and subsequently, it changes into cracks under mechanical load, thermo-mechanical stress, and corrosion process factors. This research aims to study pit to crack transition phenomenon of steel boiler heat tubes under cyclic thermal load, and mathematical modeling...
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Optimal Selection of Input Features and an Acompanying Neural Network Structure for the Classification Purposes - Skin Lesions Case Study
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Prediction of metal deformation due to line heating; an alternative method of mechanical bending, based on artificial neural network approach
PublicationLine heating is one of the alternative methods of forming metals and this kind of forming uses the heating torch as a source of heat input. During the process, many parameters are considered like the size of the substrate, thickness, cooling method, source power intensity, the travel speed of the power source, the sequence of heating, and so on. It is important to analyze the factors affecting the...
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A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels
PublicationBiodiesel has been emerging as a potential and promising biofuel for the strategy of reducing toxic emissions and improving engine performance. Computational methods aiming to offer numerical solutions were inevitable as a study methodology which was sometimes considered the only practical method. Artificial neural networks (ANN) were data-processing systems, which were used to tackle many issues in engineering and science, especially...
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A new analyzer based on pellistor sensor with neural network data postprocessing for measurement of hydrocarbons in lower explosive limit range
PublicationW pracy przedstawiono rezultaty pierwszego etapu badań nad nowym typem analizatora do oznaczania stężenia wodoru i lotnych węglowodorów w zakresie dolnej granicy wybuchowości. Analizator ten zbudowano w oparciu o pojedynczy czujnik pelistorowy z układem przetwarzania danych wykorzystującym sztuczną sieć neuronową.
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Recurrent Neural Network Based Adaptive Variable-Order Fractional PID Controller for Small Modular Reactor Thermal Power Control
PublicationThis paper presents the synthesis of an adaptive PID type controller in which the variable-order fractional operators are used. Due to the implementation difficulties of fractional order operators, both with a fixed and variable order, on digital control platforms caused by the requirement of infinite memory resources, the fractional operators that are part of the discussed controller were approximated by recurrent neural networks...
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Energy Management for PV Powered Hybrid Storage System in Electric Vehicles Using Artificial Neural Network and Aquila Optimizer Algorithm
PublicationIn an electric vehicle (EV), using more than one energy source often provides a safe ride without concerns about range. EVs are powered by photovoltaic (PV), battery, and ultracapacitor (UC) systems. The overall results of this arrangement are an increase in travel distance; a reduction in battery size; improved reaction, especially under overload; and an extension of battery life. Improved results allow the energy to be used efficiently,...
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Application of Generalized Regression Neural Network and Gaussian Process Regression for Modelling Hybrid Micro-Electric Discharge Machining: A Comparative Study
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Prediction of skin color, tanning and freckling from DNA in Polish population: linear regression, random forest and neural network approaches
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Artificial neural network model of hardness, porosity and cavitation erosion wear of APS deposited Al2O3 -13 wt% TiO2 coatings
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Artificial Neural Network based fatigue life assessment of riveted joints in AA2024 aluminum alloy plates and optimization of riveted joints parameters
PublicationThe objective of this paper is to provide the fatigue life of riveted joints in AA2024 aluminum alloy plates and optimization of riveted joints parameters. At first, the fatigue life of the riveted joints in AA2024 aluminum alloy plates is obtained by experimental tests. Then, an artificial neural network is applied to estimate the fatigue life of riveted lap joints based on the number of lateral and longitudinal holes, punch pressure,...
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Application of PSO-artificial neural network and response surface methodology for removal of methylene blue using silver nanoparticles from water samples
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Using Deep Neural Network Methods for Forecasting Energy Productivity Based on Comparison of Simulation and DNN Results for Central Poland—Swietokrzyskie Voivodeship
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Using Deep Neural Network Methods for Forecasting Energy Productivity Based on Comparison of Simulation and DNN Results for Central Poland – Swietokrzyskie Voivodeship
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Particle swarm optimization–artificial neural network modeling and optimization of leachable zinc from flour samples by miniaturized homogenous liquid–liquid microextraction
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Artificial neural network based fatigue life assessment of friction stir welding AA2024-T351 aluminum alloy and multi-objective optimization of welding parameters
PublicationIn this paper, the fracture behavior and fatigue crack growth rate of the 2024-T351 aluminum alloy has been investigated. At first, the 2024-T351 aluminum alloys have been welded using friction stir welding procedure and the fracture toughness and fatigue crack growth rate of the CT specimens have been studied experimentally based on ASTM standards. After that, in order to predict fatigue crack growth rate and fracture toughness,...
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Zastosowanie algorytmu ewolucyjnego do uczenia neuronowego regulatora napięcia generatora synchronicznego. Evolutionary algorithm for training a neural network of synchronous generator voltage controller
PublicationNajpopularniejsza metoda uczenia wielowarstwowych sieci neuronowych -metoda wstecznej propagacji błędu - charakteryzuje się słabą efektywnością. Z tego względu podejmowane są próby stosowania innych metod do uczenia sieci. W pracy przedstawiono wyniki uczenia sieci realizującej regulator neuronowy, za pomocą algorytmu ewolucyjnego. Obliczenia symulacyjne potwierdziły dobrą zbieżność algorytmu ewolucyjnego w tym zastosowaniu.
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Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms
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Neural networks and deep learning
PublicationIn 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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1D convolutional context-aware architectures for acoustic sensing and recognition of passing vehicle type
PublicationA network architecture that may be employed to sensing and recognition of a type of vehicle on the basis of audio recordings made in the proximity of a road is proposed in the paper. The analyzed road traffic consists of both passenger cars and heavier vehicles. Excerpts from recordings that do not contain vehicles passing sounds are also taken into account and marked as ones containing silence....
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Selected Technical Issues of Deep Neural Networks for Image Classification Purposes
PublicationIn 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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Deep neural networks for human pose estimation from a very low resolution depth image
PublicationThe work presented in the paper is dedicated to determining and evaluating the most efficient neural network architecture applied as a multiple regression network localizing human body joints in 3D space based on a single low resolution depth image. The main challenge was to deal with a noisy and coarse representation of the human body, as observed by a depth sensor from a large distance, and to achieve high localization precision....
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Fusion-based Representation Learning Model for Multimode User-generated Social Network Content
PublicationAs mobile networks and APPs are developed, user-generated content (UGC), which includes multi-source heterogeneous data like user reviews, tags, scores, images, and videos, has become an essential basis for improving the quality of personalized services. Due to the multi-source heterogeneous nature of the data, big data fusion offers both promise and drawbacks. With the rise of mobile networks and applications, UGC, which includes...
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Bees Detection on Images: Study of Different Color Models for Neural Networks
PublicationThis paper presents an approach to bee detection in video streams using a neural network classifier. We describe the motivation for our research and the methodology of data acquisition. The main contribution to this work is a comparison of different color models used as an input format for a feedforward convolutional architecture applied to bee detection. The detection process has is based on a neural binary classifier that classifies...
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An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks
PublicationIn 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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Towards bees detection on images: study of different color models for neural networks
PublicationThis 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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Analysis of 2D Feature Spaces for Deep Learning-based Speech Recognition
Publicationconvolutional neural network (CNN) which is a class of deep, feed-forward artificial neural network. We decided to analyze audio signal feature maps, namely spectrograms, linear and Mel-scale cepstrograms, and chromagrams. The choice was made upon the fact that CNN performs well in 2D data-oriented processing contexts. Feature maps were employed in the Lithuanian word recognition task. The spectral analysis led to the highest word...
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Detecting type of hearing loss with different AI classification methods: a performance review
PublicationHearing is one of the most crucial senses for all humans. It allows people to hear and connect with the environment, the people they can meet and the knowledge they need to live their lives to the fullest. Hearing loss can have a detrimental impact on a person's quality of life in a variety of ways, ranging from fewer educational and job opportunities due to impaired communication to social withdrawal in severe situations. Early...
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Classifying Emotions in Film Music - A Deep Learning Approach
PublicationThe paper presents an application for automatically classifying emotions in film music. A model of emotions is proposed, which is also associated with colors. The model created has nine emotional states, to which colors are assigned according to the color theory in film. Subjective tests are carried out to check the correctness of the assumptions behind the adopted emotion model. For that purpose, a statistical analysis of the...
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Musical Instrument Identification Using Deep Learning Approach
PublicationThe work aims to propose a novel approach for automatically identifying all instruments present in an audio excerpt using sets of individual convolutional neural networks (CNNs) per tested instrument. The paper starts with a review of tasks related to musical instrument identification. It focuses on tasks performed, input type, algorithms employed, and metrics used. The paper starts with the background presentation, i.e., metadata...
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Style Transfer for Detecting Vehicles with Thermal Camera
PublicationIn this work we focus on nighttime vehicle detection for intelligent traffic monitoring from the thermal camera. To train a Convolutional Neural Network (CNN) detector we create a stylized version of COCO (Common Objects in Context) dataset using Style Transfer technique that imitates images obtained from thermal cameras. This new dataset is further used for fine-tuning of the model and as a result detection accuracy on images...
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Semantic segmentation training using imperfect annotations and loss masking
PublicationOne of the most significant factors affecting supervised neural network training is the precision of the annotations. Also, in a case of expert group, the problem of inconsistent data annotations is an integral part of real-world supervised learning processes, well-known to researchers. One practical example is a weak ground truth delineation for medical image segmentation. In this paper, we have developed a new method of accurate...
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CNN Architectures for Human Pose Estimation from a Very Low Resolution Depth Image
PublicationThe paper is dedicated to proposing and evaluating a number of convolutional neural network architectures for calculating a multiple regression on 3D coordinates of human body joints tracked in a single low resolution depth image. The main challenge was to obtain a high precision in case of a noisy and coarse scan of the body, as observed by a depth sensor from a large distance. The regression network was expected to reason about...
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Pose-Invariant Face Detection by Replacing Deep Neurons with Capsules for Thermal Imagery in Telemedicine
PublicationAbstract— The aim of this work was to examine the potential of thermal imaging as a cost-effective tool for convenient, non- intrusive remote monitoring of elderly people in different possible head orientations, without imposing specific behavior on users, e.g. looking toward the camera. Illumination and pose invariant head tracking is important for many medical applications as it can provide information, e.g. about vital signs, sensory...
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Deep learning approach on surface EEG based Brain Computer Interface
PublicationIn this work we analysed the application of con-volutional neural networks in motor imagery classification for the Brain Computer Interface (BCI) purposes. To increase the accuracy of classification we proposed the solution that combines the Common Spatial Pattern (CSP) with convolutional network (ConvNet). The electroencephalography (EEG) is one of the modalities we try to use for controlling the prosthetic arm. Therefor in this...
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User Orientation Detection in Relation to Antenna Geometry in Ultra-Wideband Wireless Body Area Networks Using Deep Learning
PublicationIn this paper, the issue of detecting a user’s position in relation to the antenna geometry in ultra-wideband (UWB) off-body wireless body area network (WBAN) communication using deep learning methods is presented. To measure the impulse response of the channel, a measurement stand consisting of EVB1000 devices and DW1000 radio modules was developed and indoor static measurement scenarios were performed. It was proven that for...
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Architektury klasyfikatorów obrazów
PublicationKlasyfikacja obrazów jest zagadnieniem z dziedziny widzenia komputerowego. Polega na całościowej analizie obrazu i przypisaniu go do jednej lub wielu kategorii (klas). Współczesne rozwiązania tego problemu są w znacznej części realizowane z wykorzystaniem konwolucyjnych głębokich sieci neuronowych (convolutional neural network, CNN). W tym rozdziale opisano przełomowe architektury CNN oraz ewolucję state-of-the-art w klasyfikacji...
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Intelligent Autonomous Robot Supporting Small Pets in Domestic Environment
PublicationIn this contribution, we present preliminary results of the student project aimed at the development of an intelligent autonomous robot supporting small pets in a domestic environment. The main task of this robot is to protect a freely moving small pets against accidental stepping on them by home residents. For this purpose, we have developed the mobile robot which follows a pet and makes an alarm signal when a human is approaching....