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Search results for: ARTIFICIAL NEURAL NETWORK, MODELLING,SHIP SPEED, ENGINE FUEL CONSUMPTION
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Self-Organising map neural network in the analysis of electromyography data of muscles acting at temporomandibular joint.
PublicationThe temporomandibular joint (TMJ) is the joint that via muscle action and jaw motion allows for necessary physiological performances such as mastication. Whereas mandible translates and rotates [1]. Estimation of activity of muscles acting at the TMJ provides a knowledge of activation pattern solely of a specific patient that an electromyography (EMG) examination was carried out [2]. In this work, a Self-Organising Maps (SOMs)...
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A method for determination of fuel oil consumption saving by stepvise differetiating loads on ship diesel engines running in paralel.
Publicationprzedstawiono metodykę wyznaczania oszczędniości w zużyciu paliwa dla silników pracujących na wspólną sieć w wyniku różnicowania ich obciążeń oraz stosowania metody krokowych zmian obciążenia. znajomość charakterystyk zużycia paliwa poszczególnych silników oraz histogramu rozkładu obciążeńeksploatacyjnych pozwala po zastosowaniu proponowanej metody na wyznaczanie oszczędności w zuzyciu paliwa.
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An Improved Convolutional Neural Network for Steganalysis in the Scenario of Reuse of the Stego-Key
PublicationThe 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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Digits Recognition with Quadrant Photodiode and Convolutional Neural Network
PublicationIn 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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Problems of modelling toxic compounds emitted by a marine internal combustion engine in unsteady states
PublicationContemporary engine tests are performed based on the theory of experiment. The available versions of programmes used for analysing experimental data make frequent use of the multiple regression model, which enables examining effects and interactions between input model parameters and a single output variable. The use of multi-equation models provides more freedom in analysing the measured results, as those models enable simultaneous...
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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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Impacts of Using Exhaust Gas Recirculation and Various Amount of Dimethyl Ether Premixed Ratios on Combustion and Emissions on a Dual-Fuel Compression Ignition Engine
PublicationIn the presented research, the authors dealt with the specific properties of the combustion process of dimethyl ether (DME) in a combustion car (Volkswagen Golf IV) engine AJM 1.9 TDI PDE made by Volkswagen factory. Dimethyl ether is an alternative fuel produced most often from natural gas, which can be used in compression ignition engines as a single fuel or co-burned with diesel oil. This work describes the impacts of using exhaust...
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Influence of traffic conditions on the operating fuel consumption
PublicationPrzedstawiona w pracy metoda umożliwia ocenę warunków eksploatacji pojazdu. Warunki te mogą wynikać zarówno z lokalnej specyfiki ruchu pojazdów jak również ze sposobu prowadzenia auta przez kierowcę. W pracy zamieszczono przykłady oceny zarejestrowanych w normalnej eksploatacji warunków ruchu pojazdu i ich wpływu na przebiegowe zużycie paliwa.
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Emission of CO2 and Fuel Consumption for Automotive Vehicles
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Determination of Odour Interactions in Gaseous Mixtures Using Electronic Nose Methods with Artificial Neural Networks
PublicationThis paper presents application of an electronic nose prototype comprised of eight sensors, five TGS-type sensors, two electrochemical sensors and one PID-type sensor, to identify odour interaction phenomenon in two-, three-, four- and five-component odorous mixtures. Typical chemical compounds, such as toluene, acetone, triethylamine, α-pinene and n-butanol, present near municipal landfills and sewage treatment plants were subjected...
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Modelling of some stealth features for a small navy ship at the concept design stage.
PublicationIn this paper the basic research problems associated with modelling the basic stealth features for a small navy ship at the concept design stage are introduced. Amongst the major stealth features considered are: the modification of the immersed ship hull form by a rapid change of the ship loading condition, and modification of the ship boundary layer by the hull skin cover. The other stealth features of the ship are not presented...
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Modeling and Simulation for Exploring Power/Time Trade-off of Parallel Deep Neural Network Training
PublicationIn the paper we tackle bi-objective execution time and power consumption optimization problem concerning execution of parallel applications. We propose using a discrete-event simulation environment for exploring this power/time trade-off in the form of a Pareto front. The solution is verified by a case study based on a real deep neural network training application for automatic speech recognition. A simulation lasting over 2 hours...
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Evolving neural network as a decision support system — Controller for a game of “2048” case study
PublicationThe paper proposes an approach to designing the neuro-genetic self-learning decision support system. The system is based on neural networks being adaptively learned by evolutionary mechanism, forming an evolved neural network. Presented learning algorithm enables for a selection of the neural network structure by establishing or removing of connections between the neurons, and then for a finding the beast suited values of the network...
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Artificial Neural Networks for Comparative Navigation
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Application of road map of operating condition for estimation of fuel and electric energy consumption from city transport
PublicationThe paper presents procedure of data collecting and generation of road map of operating condition in the selected urban area. This map allows forecasting the selected vehicle operating parameters for the assumed road. The main parameters calculated using the road maps of operating conditions are: total energy spent to drive the selected vehicle, consumed fuel, travel time, average speed of travel, CO2 emissions. Presented example...
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Categorization of emotions in dog behavior based on the deep neural network
PublicationThe aim of this article is to present a neural system based on stock architecture for recognizing emotional behavior in dogs. Our considerations are inspired by the original work of Franzoni et al. on recognizing dog emotions. An appropriate set of photographic data has been compiled taking into account five classes of emotional behavior in dogs of one breed, including joy, anger, licking, yawning, and sleeping. Focusing on a particular...
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Combined Close Range Photogrammetry and Terrestrial Laser Scanning for Ship Hull Modelling
PublicationThe paper addresses the fields of combined close-range photogrammetry and terrestrial laser scanning in the light of ship modelling. The authors pointed out precision and measurement accuracy due to their possible complex application for ship hulls inventories. Due to prescribed vitality of every ship structure, it is crucial to prepare documentation to support the vessel processes. The presented methods are directed, combined...
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Diagnosing wind turbine condition employing a neural network to the analysis of vibroacoustic signals
PublicationIt is important from the economic point of view to detect damage early in the wind turbines before failures occur. For this purpose, a monitoring device was built that analyzes both acoustic signals acquired from the built-in non-contact acoustic intensity probe, as well as from the accelerometers, mounted on the internal devices in the nacelle. The signals collected in this way are used for long-term training of the autoencoder...
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EPILEPTIC BEHAVIOR WITH A DISTINGUISHED PREICTAL PERIOD IN A LARGE-SCALE NEURAL NETWORK MODEL
PublicationWe present a neural network model capable of reproducing focal epileptic behavior. An important property of our model is the distinguished preictal state. This novel feature may shed light on the pathologi-cal mechanisms of seizure generation and, in perspective, help develop new therapeutic strategies to manage refractory partial epilepsy.
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Investigations of the Working Process in a Dual-Fuel Low-Emission Combustion Chamber for an FPSO Gas Turbine Engine
PublicationThis investigation is devoted to an analysis of the working process in a dual-fuel low-emission combustion chamber for a floating vessel’s gas turbine. The low-emission gas turbine combustion chamber with partial pre-mixing of fuel and air inside the outer and inner radial-axial swirlers was chosen as the object of research. When modelling processes in a dual-flow low-emission gas turbine combustion chamber, a generalized method...
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Enhancing Renal Tumor Detection: Leveraging Artificial Neural Networks in Computed Tomography Analysis
PublicationRenal 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...
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Evolutionary sets of safe ship trajectories with speed reduction manoeuvres within traffic separation schemes
PublicationIn the previous paper the author presented the evolutionary ship trajectory planning method designed to support Traffic Separation Schemes (TSS). This time the extensions of this method are described which allow to combine evolutionary trajectory planning with speed reduction manoeuvres. On TSS regions with higher than usual density of traffic and smaller distances between ships, the course alterations alone are not always sufficient...
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Neural network simulator's application to reference performance determination of turbine blading in the heat-flow diagnostics.
PublicationIn the paper, the possibility of application of artificial neural networks to perform the fluid flow calculations through both damaged and undamaged turbine blading was investigated. Preliminary results are presented and show the potentiality of further development of the method for the purpose of heat-flow diagnostics.
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NETWORK-COMPUTATION IN NEURAL SYSTEMS
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Wind-wave variability in a shallow tidal sea—Spectral modelling combined with neural network methods
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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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Assesment of operation of ship main diesel engine using the theory of semi-markovian and markov processes.
PublicationTo precisely determine the task it is necessary to specify also its duration time, apart from conditions in which it will be realized. When considering propulsion engine, i.e. the main element of ship propulsion system, especially important becomes not only the problem which amount of energy could be at one's disposal but also within which time interval it could be delivered. Therefore apart from applying the commonly used reliability...
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Emotion Recognition from Physiological Channels Using Graph Neural Network
PublicationIn recent years, a number of new research papers have emerged on the application of neural networks in affective computing. One of the newest trends observed is the utilization of graph neural networks (GNNs) to recognize emotions. The study presented in the paper follows this trend. Within the work, GraphSleepNet (a GNN for classifying the stages of sleep) was adjusted for emotion recognition and validated for this purpose. The...
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Field Calibration of Low-Cost Particulate Matter Sensors Using Artificial Neural Networks and Affine Response Correction
PublicationDue 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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Deep convolutional neural network for predicting kidney tumour malignancy
PublicationPurpose: 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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Tyre rolling resistance and its influence on fuel consumption
PublicationRolling resistance of tyres is one of the major resistive forces acting on any wheeled vehicle. Unfortunately, it is also one of the forces very difficult to measure. It is estimated that in certain traffic conditions (like for example constant speed driving with slow or moderate speed) so called Rolling Resistance Impact Factor may be as high as 0.3. This means that reduction of rolling resistance by 50% would lead to 15% of energy...
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Modelling relation between oxidation resistance and tribological properties of non-toxic lubricants with the use of artificial neural networks
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Predicting Performance of Lightweight Concrete with Granulated Expanded Glass and Ash Aggregate by Means of Using Artificial Neural Networks
PublicationLightweight concrete (LWC) is a group of cement composites of the defined physical, mechanical, and chemical performance. The methods of designing the composition of LWC with the assumed density and compressive strength are used most commonly. The purpose of using LWC is the reduction of the structure’s weight, as well as the reduction of thermal conductivity index. The highest possible strength, durability and low thermal conductivity...
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Modelling of some stealth features for a small navy ship at the concept design stage - part II
PublicationIn the paper a few problems associated with modelling the basic stealth features for a small ship at the concept design stage are introduced. One problem concerns the modification of the immersed ship hull using the rapid change of the ship loading condition. The second is associated with the modification of the ship boundary layer by the hull skin cover. The other stealth features of the ship are not presented in this paper. The...
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A methodology for ultimate strength assessment of ship hull girder accounting for enhanced corrosion degradation modelling
PublicationThe presented work shows a methodology for the ultimate strength assessment of a ship hull, considering enhanced corrosion modelling. The approach is based on the classical Smith method. However, the recent findings regarding the impact of corrosion degradation on ultimate strength are incorporated. To this end, the stress–strain relationships for particular elements composing ship hull cross-section are modified using a specially...
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Neural network based algorithm for hand gesture detection in a low-cost microprocessor applications
PublicationIn this paper the simple architecture of neural network for hand gesture classification was presented. The network classifies the previously calculated parameters of EMG signals. The main goal of this project was to develop simple solution that is not computationally complex and can be implemented on microprocessors in low-cost 3D printed prosthetic arms. As the part of conducted research the data set EMG signals corresponding...
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ReFlexeNN - the Wearable EMG Interface with Neural Network Based Gesture Classification
PublicationThe electromyographic activity of muscles was measured using a wireless biofeedback device. The aim of the study was to examine the possibility of creating an automatic muscle tension classifier. Several measurement series were conducted and the participant performed simple physical exercises - forcing the muscle to increase its activity accordingly to the selected scale. A small wireless device was attached to the electrodes placed...
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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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Optical Sensor Based Gestures Inference Using Recurrent Neural Network in Mobile Conditions
PublicationIn this paper the implementation of recurrent neural network models for hand gesture recognition on edge devices was performed. The models were trained with 27 hand gestures recorded with the use of a linear optical sensor consisting of 8 photodiodes and 4 LEDs. Different models, trained off-line, were tested in terms of different network topologies (different number of neurons and layers) and different effective sampling frequency...
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New Two-center Ellipsoidal Basis Function Neural Network for Fault Diagnosis of Analog Electronic Circuits
PublicationIn the paper a new fault diagnosis-oriented neural network and a diagnostic method for localization of parametric faults in Analog Electronic Circuits (AECs) with tolerances is presented. The method belongs to the class of dictionary Simulation Before Test (SBT) methods. It utilizes dictionary fault signatures as a family of identification curves dispersed around nominal positions by component tolerances of the Circuit Under Test...
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Benchmarking Deep Neural Network Training Using Multi- and Many-Core Processors
PublicationIn the paper we provide thorough benchmarking of deep neural network (DNN) training on modern multi- and many-core Intel processors in order to assess performance differences for various deep learning as well as parallel computing parameters. We present performance of DNN training for Alexnet, Googlenet, Googlenet_v2 as well as Resnet_50 for various engines used by the deep learning framework, for various batch sizes. Furthermore,...
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Laboratory station for research of the innovative dry method of exhaust gas desulfurization for an engine powered with residual fuel
PublicationContemporary methods of exhaust gas desulfurization in marine engines are all expensive methods (4-5 million euro). This is, among other reasons, due to the limited market audience, but primarily due to the monop-olized position of manufacturers offering fabrication and assembly of this type of marine ship installations. Proposed as part of a research project financed by the Regional Fund for Environmental Protection and Maritime...
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Experimental study on the selected aspects of bow thruster generated flow field at ship zero-speed conditions
PublicationThe paper presents the results of experimental study on the interaction between the bow thrusters understood as the flow field changes generated by bow tunnel thruster in deep water conditions operated as a single and twin units. The research was limited to zero-speed case for the ship dead in the water. The influence of the hull form and jet spread between the neighbouring thrusters for several combinations of the applied bow...
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Dataset Related Experimental Investigation of Chess Position Evaluation Using a Deep Neural Network
PublicationThe idea of training Articial Neural Networks to evaluate chess positions has been widely explored in the last ten years. In this paper we investigated dataset impact on chess position evaluation. We created two datasets with over 1.6 million unique chess positions each. In one of those we also included randomly generated positions resulting from consideration of potentially unpredictable chess moves. Each position was evaluated...
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Chapter 8 : Possibilities of operating fuel consumption estimation of vehicles
PublicationPrzedstawiona w pracy metoda umożliwia ocenę eksploatacyjnego zużycia paliwa pojazdu samochodowego dzięki porównaniu zarejestrowanego zużycia paliwa z referencyjnym dla tych samych warunków eksploatacji. Warunki te mogą wynikać zarówno z lokalnej specyfiki ruchu pojazdów jak również ze sposobu prowadzenia auta przez kierowcę. Zgodnie z przyjętą metodą warunki te mogą zostać w bardzo prosty sposób zarejestrowane w czasie codziennej...
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A model of fuel combustion process in the marine reciprocating engine work space taking into account load and wear of crankshaft-piston assembly and the theory of semi-Markov processes
PublicationThe ar ticle analyses the operation of reciprocal internal combu stion engines, with mar ine engines u sed a s an example. The analysis takes into account types of energy conversion in the work spaces (cylinders) of these engines, loads of their crankshaft-piston assemblies, and types of fuel combustion which can take place in these spaces during engine operation. It is highlighted that the analysed time-dependent loads of marine...
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Problems of modelling toxic compounds emitted by a marine internal combustion engine for the evaluation of its structure parameters
PublicationThe paper presents the possibility of using an analytical study of the engine exhaust ignition to evaluate the technical condition of the selected components. Software tools available for the analysis of experimental data commonly use multiple regression model that allows the study of the effects and iterations between model input quantities and one output variable. The use of multi-equation models gives a lot of freedom in the...
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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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Highlighting interlanguage phoneme differences based on similarity matrices and convolutional neural network
PublicationThe 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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Performance and Energy Aware Training of a Deep Neural Network in a Multi-GPU Environment with Power Capping
PublicationIn this paper we demonstrate that it is possible to obtain considerable improvement of performance and energy aware metrics for training of deep neural networks using a modern parallel multi-GPU system, by enforcing selected, non-default power caps on the GPUs. We measure the power and energy consumption of the whole node using a professional, certified hardware power meter. For a high performance workstation with 8 GPUs, we were...
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Compressive Sensing Approach to Harmonics Detection in the Ship Electrical Network
PublicationThe contribution of this paper is to show the opportunities for using the compressive sensing (CS) technique for detecting harmonics in a frequency sparse signal. The signal in a ship’s electrical network, polluted by harmonic distortions, can be modeled as a superposition of a small number of sinusoids and the discrete Fourier transform (DFT) basis forms its sparse domain. According to the theory of CS, a signal may be reconstructed...
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Comparative study of learning methods for artificial network
PublicationW artykule przedstawiono wyniki badań porównawczych metod uczenia sieci neuronowych takich jak: metoda propagacji wstecznej błędów, rekurencyjna metoda najmniejszych kwadratów, metoda Zangwill'a, metoda algorytmów ewolucyjnych. Celem tych badań jest dobieranie najefektywniejszej metody uczenia do projektowania adaptacyjnego neuronowego regulatora napięcia generatora synchronicznego.metody uczenia, sieć neuronowa, neuronowy regulator...
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The Use of Artificial Neural Networks and Decision Trees to Predict the Degree of Odor Nuisance of Post-Digestion Sludge in the Sewage Treatment Plant Process
PublicationThis paper presents the application of artificial neural networks and decision trees for the prediction of odor properties of post-fermentation sludge from a biological-mechanical wastewater treatment plant. The input parameters were concentrations of popular compounds present in the sludge, such as toluene, p-xylene, and p-cresol, and process parameters including the concentration of volatile fatty acids, pH, and alkalinity in...
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Artificial Neural Networks in Microwave Components and Circuits Modeling
PublicationArtykuł 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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Classification of Covid-19 using Differential Evolution Chaotic Whale Optimization based Convolutional Neural Network
PublicationCOVID-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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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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Speaker Recognition Using Convolutional Neural Network with Minimal Training Data for Smart Home Solutions
PublicationWith 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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On application of some artificial intelligence methods in ship design
PublicationWprowadzenie odrębnego etapu badań własności strukturalnych do analizy i syntezy układów sterowania o złożonej strukturze, umożliwia wyznaczenie i analizę nieprzesuwnych biegunów układów. Te bieguny charakteryzują się zerową wrażliwością na zmianę szeregu parametrów modelu układu.
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APPLICATION OF STATISTICAL FEATURES AND MULTILAYER NEURAL NETWORK TO AUTOMATIC DIAGNOSIS OF ARRHYTHMIA BY ECG SIGNALS
PublicationAbnormal electrical activity of heart can produce a cardiac arrhythmia. The electrocardiogram (ECG) is a non-invasive technique which is used as a diagnostic tool for cardiac diseases. Non-stationarity and irregu- larity of heartbeat signal imposes many difficulties to clinicians (e.g., in the case of myocardial infarction arrhythmia). Fortunately, signal processing algorithms can expose hidden information within ECG signal contaminated...
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A semi-Markov model of fuel combustion process in a Diesel engine
PublicationW artykule przedstawiono czterostanowy model procesu spalania w przestrzeniach roboczych (cylindrach) silników o zapłonie samoczynnym w formie procesu semimarkowskiego, dyskretnego w stanach i ciągłego w czasie. Wartościami tego procesu są stany odpowiadające powszechnie akceptowanym rodzajom spalania w tego rodzaju silnikach a mianowicie takie stany procesu jak: spalanie pełne (całkowite i zupełne), spalanie niezupełne, spalanie...
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Simulation of ammonia combustion in dual-fuel compression-ignition engine
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Influence of the use of ethanol fuel on selected parameters of the gasoline engine
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Jerzy Kowalski dr hab. inż.
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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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Application of Artificial Neural Networks in Investigations of Steam Turbine Cascades
PublicationZaprezentowano wyniki badań numerycznych zastosowania sieci neuronowych przy obliczeniach przepływów w palisadach turbin parowych. Na podstawie uzyskanych wyników wykazano, że sieci neuronowe mogą być używane do szacowania przestrzennego rozkładu parametrów przepływu, takich jak entalpia, entropia, ciśnienie czy prędkość czynnika w kanale przepływowym. Omówiono również zastosowania tego typu metod przy projektowaniu palisad, stopni...
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Forecasting of currency exchange rates using artificial neural networks
PublicationW rozdziale tym autor przedstawił wyniki swoich badań nad wykorzystaniem sztucznych sieci neuronowych do prognozowania kursu walut (na przykładzie pary walutowej PLN-USD).Głównym celem badań było porównanie skuteczności przewidywania kursu złotówki w latach 1997 - 2005 przy pomocy różnych rodzajów sieci neuronowych.
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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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Identification of the Contamination Source Location in the Drinking Water Distribution System Based on the Neural Network Classifier
PublicationThe contamination ingression to the Water Distribution System (WDS) may have a major impact on the drinking water consumers health. In the case of the WDS contamination the data from the water quality sensors may be efficiently used for the appropriate disaster management. In this paper the methodology based on the Learning Vector Quantization (LVQ) neural network classifier for the identification of the contamination source location...
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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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Application of Generalized Regression Neural Network and Gaussian Process Regression for Modelling Hybrid Micro-Electric Discharge Machining: A Comparative Study
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Neural Modelling of Steam Turbine Control Stage
PublicationThe paper describes possibility of steam turbine control stage neural model creation. It is of great importance because wider application of green energy causes severe conditions for control of energy generation systems operation Results of chosen steam turbine of 200 MW power measurements are applied as an example showing way of neural model creation. They serve as training and testing data of such neural model. Relatively simple...
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Analysis of the Influence of Fuel Sulphur Content on Diesel Engine Particulate Emissions
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Neural network breast cancer relapse time prognosis
PublicationPrzedstawiono architekturę i wyniki testowania sztucznej sieci neuronowej w prognozowaniu czasu nawrotu choroby u kobiet chorych na raka piersi. Sieć neuronowa uczona była na danych zgromadzonych przez 20 lat. Dane opisują grupę 439 pacjentów za pomocą 40 parametrów. Spośród tych parametrów wybrano 6 najistotniejszych: liczbę przerzutowych węzłów chłonnych, wielkość guza, wiek, skalę według Blooma oraz stan receptorów estrogenowych...
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Neural-Network-Based Parameter Estimations of Induction Motors
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Neural Network - Based Parameters Estimations Of Induction Motors
PublicationW artykule przedstwaiono algorytmy estymacji rezystancji wirnika i indukcyjności wzajemnej w zamkniętym układzie sterowania prędkości silnika indukcyjnego klatkowego. Do wyznaczenia rezystancji wykorzystano algorytm oparty na porównaniu modelu napięciowego i prądowego silnika. Do wyznaczania indukcyjności wykorzystano, znaną z literatury, zależność modelu multiskalarnego. Wyznaczane w stanie ustalonym parametry zapisywane są w...
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Cellular neural network application to moire pattern filtering
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Measurement of engine and vehicle parameters using onboard CAN network
PublicationWspółczesne samochody wyposażane są w dużą liczbę układów elektronicznych, które wymagają wzajemnej wymiany informacji. Techniczne problemy niezależnego łączenia ze sobą dużej liczby sterowników doprowadziły do powstania pokładowych sieci wymiany danych. Do najpopularniejszych z nich zalicza się obecnie system CAN. W pracy przedstawiono metodę rozpoznawania ramek wiadomości transmitowanych w pokładowej sieci CAN pojazdu przez sterowniki....
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The use of fast molecular descriptors and artificial neural networks approach in organochlorine compounds electron ionization mass spectra classification
PublicationDeveloping of theoretical tools can be very helpful for supporting new pollutant detection. Nowadays, a combination of mass spectrometry and chromatographic techniques are the most basic environmental monitoring methods. In this paper, two organochlorine compound mass spectra classification systems were proposed. The classification models were developed within the framework of artificial neural networks (ANNs) and fast 1D and...
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Optimization of quadrilateral mesh for ship hull modelling
PublicationArtykuł przedstawia metodę poprawy jakości czworokątnej siatki elementów skończonych, które najlepiej nadają się do modelowania kadłuba statku dla potrzeb analizy MES. W metodzie zakłada się poprawną topologię siatki i stosuje gradient funkcji celu do poszukiwania optymalnego rozwiązania. Funkcja celu może być dowolną funkcją, opisująca jakość elementu (metryka), odpowiednią dla wymagań metody elementów skończonych. W artykule...
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Processing of musical data employing rough sets and artificial neural networks
PublicationArtykuł opisuje założenia systemu automatycznej identyfikacji muzyki i dźwięków muzycznych. Dokonano przeglądu standardu MPEG-7, ze szczególnym naciskiem na parametry opisowe dźwięku. Przedyskutowano problemy analizy danych audio, związane z zastosowaniami wykorzystującymi MPEG-7. W oparciu o eksperymenty przedstawiono efektywność deskryptorów niskiego poziomu w automatycznym rozpoznawaniu dźwięków instrumentów muzycznych. Przedyskutowano...
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The concept of application of artificial neural networks for cultivation controlof cartilages in bioreactors.
PublicationNowym elementem niniejszej pracy jest omówienie problemów związanych z możliwością sterowania parametrami hydrodynamicznymi hodowanej w bioreaktorze chrząstki stawowej przy wykorzystaniu sztucznych sieci neuronowych. Przedstawiona została architektura strategii sterowania hodowlą tkanki z zastosowaniem tych sieci.
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Processing of musical data employing rough sets and artificial neural networks
PublicationArtykuł opisuje założenia systemu automatycznej identyfikacji muzyki i dźwięków muzycznych. Dokonano przeglądu standardu MPEG-7, ze szczególnym naciskiem na parametry opisowe dźwięku. Przedyskutowano problemy analizy danych audio, związane z zastosowaniami wykorzystującymi MPEG-7. W oparciu o eksperymenty przedstawiono efektywność deskryptorów niskiego poziomu w automatycznym rozpoznawaniu dźwięków instrumentów muzycznych. Przedyskutowano...
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Analysis of electrical patterns activity in artificial multi-stable neural networks
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Artificial Neural Networks for Prediction of Antibacterial Activity in Series of Imidazole Derivatives
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Traffic Speed Deflectometer for Network-Level Pavement Management in Tennessee
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Creating neural models using an adaptive algorithm for optimal size of neural network and training set.
PublicationZaprezentowano adaptacyjny algorytm generujący modele neuronowe liniowych układów mikrofalowych, zdolny do oszacowania optymalnego rozmiaru zbiory uczącego i sieci neuronowej. Stworzono kilka modeli nieciągłości falowodowych i mokropaskowych, a następnie zweryfikowano ich poprawność porównując wyniki analiz metodą dopasowania rodzajów i metodą momentów filtrów pasmowo-przepustowych.
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The effect of a low ambient temperature on the cold-start emissions and fuel consumption of passenger cars
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Failure model of main elements of the ship engine crankshaft-piston assembly
PublicationThe paper presents a failure model of main elements of the crankshaft-piston assembly, based on failures ofcrankshaft-piston assembly and timing gear system of the Sulzer RD engines, retrieved from the equipment reliabilitydata of selected ships.
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Zdzisław Kowalczuk prof. dr hab. inż.
PeopleZdzislaw Kowalczuk received his M.Sc. degree in 1978 and Ph.D. degree in 1986, both in Automatic Control from Technical University of Gdańsk (TUG), Gdańsk, Poland. In 1993 he received his D.Sc. degree (Dr Habilitus) in Automatic Control from Silesian Technical University, Gliwice, Poland, and the title of Professor from the President of Poland in 2003. Since 1978 he has been with Faculty of Electronics, Telecommunications and Informatics...
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The fuzzy neural network: application for trends in river pollution prediction
PublicationPraca przedstawia zastosowanie rozmytych sieci neuronowych do przygotowywania prognoz zmian w stężeniu zanieczyszczeń w rzekach. Opisane są pokrótce inne narzędzia stosowane w tym celu.
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Application of a fuzzy neural network for river water quality prediction
PublicationMonitoring i modelowanie zmian w jakości wód powierzchniowych stanowią jeden z kluczowych elementów monitoringu i zarządzania ochroną środowiska na skalę globalną. Kontrolowanie tak złożonych i nieliniowych w swojej charakterystyce obiektów, jakimi są rzeki, jest trudnym zadaniem. Zazwyczaj do tego celu wykorzystuje się modele matematyczne, jednak czasem wymagają one bardzo dużej ilości danych, lub czas oczekiwania na odpowiedź...
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Neural Network-Based Sequential Global Sensitivity Analysis Algorithm
PublicationPerforming global sensitivity analysis (GSA) can be challenging due to the combined effect of the high computational cost, but it is also essential for engineering decision making. To reduce this cost, surrogate modeling such as neural networks (NNs) are used to replace the expensive simulation model in the GSA process, which introduces the additional challenge of finding the minimum number of training data samples required to...
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Energy-Efficient Neural Network Inference with Microcavity Exciton Polaritons
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Neural network approach to 2D Kalman filtering in image processing
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Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests
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Designing the Composition of Cement Stabilized Rammed Earth Using Artificial Neural Networks
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The Influence of Input Data Standardization Method on Prediction Accuracy of Artificial Neural Networks
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Self-organizing Artificial Neural Networks into Hydrographic Big Data Reduction Process
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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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Excess Emissions and Fuel Consumption of Modern Spark Ignition Passenger Cars at Low Ambient Temperatures
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