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Search results for: COMBINED METHOD, SEA CONDITIONS, SHIP SPEED, FUEL CONSUMPTION, POWERTRAIN LOAD, ARTIFICIAL NEURAL NETWORK (ANN)
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The Development of a Combined Method to Quickly Assess Ship Speed and Fuel Consumption at Different Powertrain Load and Sea Conditions
PublicationDecision support systems (DSS) recently have been increasingly in use during ships operation. They require realistic input data regarding different aspects of navigation. To address the optimal weather routing of a ship, which is one of the most promising field of DSS application, it is necessary to accurately predict an actually attainable speed of a ship and corresponding fuel consumption at given loading conditions and predicted...
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Applying artificial neural networks for modelling ship speed and fuel consumption
PublicationThis paper deals with modelling ship speed and fuel consumption using artificial neural network (ANN) techniques. These tools allowed us to develop ANN models that can be used for predicting both the fuel consumption and the travel time to the destination for commanded outputs (the ship driveline shaft speed and the propeller pitch) selected by the ship operator. In these cases, due to variable environmental conditions, making...
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USING ARTIFICIAL NEURAL NETWORKS FOR PREDICTING SHIP FUEL CONSUMPTION
PublicationIn 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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Heavy duty vehicle fuel consumption modelling using artificial neural networks
PublicationIn this paper an artificial neural network (ANN) approach to modelling fuel consumption of heavy duty vehicles is presented. The proposed method uses easy accessible data collected via CAN bus of the truck. As a benchmark a conventional method, which is based on polynomial regression model, is used. The fuel consumption is measured in two different tests, performed by using a unique test bench to apply the load to the engine. Firstly,...
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Heavy Duty Vehicle Fuel Consumption Modelling Based on Exploitation Data by Using Artificial Neural Networks
PublicationOne of the ways to improve the fuel economy of heavy duty trucks is to operate the combustion engine in its most efficient operating points. To do that, a mathematical model of the engine is required, which shows the relations between engine speed, torque and fuel consumption in transient states. In this paper, easy accessible exploitation data collected via CAN bus of the heavy duty truck were used to obtain a model of a diesel...
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Ship Resistance Prediction with Artificial Neural Networks
PublicationThe 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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The Analysis of Overall Ship Fuel Consumption in Acceleration Manoeuvre using Hull-Propeller-Engine Interaction Principles and Governor Features
PublicationThe problem of reduction of greenhouse gas emissions in shipping is currently addressed by many research works and related industries. There are many existing and visionary technologies and ideas, which are conceptually defined or practically realised. This goal can be achieved in different ways, and reducing fuel consumption is one of the major methods. In these circumstances, the aim of this study is to analyse the possibility...
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Impact of Propeller Emergence on Hull, Propeller, Engine, and Fuel Consumption Performance in Regular Head Waves
PublicationIn this study, the impact of propeller emergence on the performance of a ship (speed), propeller (thrust, torque, and RPM), a diesel engine (torque and RPM) and fuel consumption are analysed under severe sea conditions. The goal is to describe the variation in the system variables and fuel consumption rather than analysing the motion of the ship or the phenomenon of propeller ventilation in itself. A mathematical model of 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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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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Selection of an artificial pre-training neural network for the classification of inland vessels based on their images
PublicationArtificial neural networks (ANN) are the most commonly used algorithms for image classification problems. An image classifier takes an image or video as input and classifies it into one of the possible categories that it was trained to identify. They are applied in various areas such as security, defense, healthcare, biology, forensics, communication, etc. There is no need to create one’s own ANN because there are several pre-trained...
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Simulating Power Generation from Photovoltaics in the Polish Power System Based on Ground Meteorological Measurements—First Tests Based on Transmission System Operator Data
PublicationThe Polish power system is undergoing a slow process of transformation from coal to one that is renewables dominated. Although coal will remain a fundamental fuel in the coming years, the recent upsurge in installed capacity of photovoltaic (PV) systems should draw significant attention. Owning to the fact that the Polish Transmission System Operator recently published the PV hourly generation time series in this article, we aim...
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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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TIME-DOMAIN NUMERICAL EVALUATION OF SHIP RESISTANCE AND MOTION IN REGULAR WAVES BY USING THE CFD URANS METHOD
PublicationTaking into account the International Maritime Organisation’s (IMO) strategy to radically reduce the GHG emitted by the shipping industry towards zero emission operation, today's assessment of ship behaviour in waves, its seakeeping characteristics and resistance and their interrelation with fuel consumption and emissions are one of the most attended research subject. There are three methods to conduct this analysis, which are...
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Prediction of ship resistance with the use of Full-scale CFD simulations
PublicationIn recent years, the IMO has introduced new regulations to reduce the negative impact of ships on the natural environment. A particularly important step forcing technological innovations is the increasing requirement of ship energy efficiency. It is expressed by the Energy Efficiency Design Index (EEDI). Another important step towards green shipping is rising the required quality of fuel used for propulsion, so called Tier limits....
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TEST METHOD FOR DETERMINING THE CHEMICAL EMISSIONS OF A MARINE DIESEL ENGINE EXHAUST IN OPERATION
PublicationThe article briefly describes the problem of air pollution caused by sea-going ships and the resulting restrictions on the emission of toxic and harmful chemical compounds in the exhaust of marine engines, introduced by the International Maritime Organization (IMO) under the International Convention for the Prevention of Sea Pollution from Ships (MARPOL 73/78). Such emissions provide a significant metrological problem, not only...
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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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Exergy and Energy Analyses of Microwave Dryer for Cantaloupe Slice and Prediction of Thermodynamic Parameters Using ANN and ANFIS Algorithms
PublicationThe study targeted towards drying of cantaloupe slices with various thicknesses in a microwave dryer. The experiments were carried out at three microwave powers of 180, 360, and 540 W and three thicknesses of 2, 4, and 6 mm for cantaloupe drying, and the weight variations were determined. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were exploited to investigate energy and exergy indices of...
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Prediction of energy consumption and evaluation of affecting factors in a full-scale WWTP using a machine learning approach
PublicationTreatment of municipal wastewater to meet the stringent effluent quality standards is an energy-intensive process and the main contributor to the costs of wastewater treatment plants (WWTPs). Analysis and prediction of energy consumption (EC) are essential in designing and operating sustainable energy-saving WWTPs. In this study, the effect of wastewater, hydraulic, and climate-based parameters on the daily consumption of EC by...
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Energy consumption optimization in wastewater treatment plants: Machine learning for monitoring incineration of sewage sludge
PublicationBiomass management in terms of energy consumption optimization has become a recent challenge for developed countries. Nevertheless, the multiplicity of materials and operating parameters controlling energy consumption in wastewater treatment plants necessitates the need for sophisticated well-organized disciplines in order to minimize energy consumption and dissipation. Sewage sludge (SS) disposal management is the key stage of...
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DEEP LEARNING BASED ON X-RAY IMAGING IMPROVES COXARTHROSIS DETECTION
PublicationObjective: The purpose of the study was to create an Artificial Neural Network (ANN) based on X-ray images of the pelvis, as an additional tool to automate and improve the diagnosis of coxarthrosis. The research is focused on joint space narrowing, which is a radiological symptom showing the thinning of the articular cartilage layer, which is translucent to X-rays. It is the first and the most important of the radiological signs...
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Design and Analysis of Artificial Neural Network (ANN) Models for Achieving Self-Sustainability in Sanitation
PublicationThe present study investigates the potential of using fecal ash as an adsorbent and demonstrates a self-sustaining, optimized approach for urea recovery from wastewater streams. Fecal ash was prepared by heating synthetic feces to 500 °C and then processing it as an adsorbent for urea adsorption from synthetic urine. Since this adsorption approach based on fecal ash is a promising alternative for wastewater treatment, it increases...
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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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Piotr Jasiński prof. dr hab. inż.
PeoplePiotr Jasinski obtained MSc in electronics in 1992 from the Gdansk University of Technology (GUT), Poland. Working at GUT, he received PhD in 2000 and DSc in 2009. Between 2001 and 2004 Post Doctoral Fellow at Missouri University of Science and Technology, while between 2008 and 2010 an Assistant Research Professor. Currently is an Associate Professor at Gdansk University of Technology working in the field of electronics, biomedical...
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Dynamically positioned ship steering making use of backstepping method and artificial neural networks
PublicationThe 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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TIME-DOMAIN NUMERICAL EVALUATION OF SHIP RESISTANCE AND MOTION IN REGULAR WAVES BY USING THE CFD URANS METHOD
PublicationTaking into account the International Maritime Organization's (IMO) strategy to radically reduce the...
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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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On thermal and Flow Expert Systems Based on Artificial Neural Network (ANN)
PublicationZaprezentowano możliwość realizacji jednego z zadań systemów eksperckich, polegającego na określaniu rozmiaru eksploatacyjnej degradacji parametrów geometrycznych układów łopatkowych turbin. Dyskusję przeprowadzono w oparciu o zastosowanie wybranego typu sztucznej sieci neuronowej (SSN). Badano jakość i dokładność polegającą na dobrej identyfikacji rozmiaru degradacji przez tę wybraną SSN wykrywającą rozmiar degradacji geometrycznej....
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Investigating Fuel Injection Strategies to Enhance Ship Energy Efficiency in Wave Conditions
PublicationThe prediction of fuel consumption and resulting transportation costs is a crucial stage in ship design, particularly for conditions involving motion in waves. This study investigates the real-time fuel consumption of a container ship when sailing in waves. The overall ship performance is evaluated using a novel non-linear coupled hull-engine-propeller interaction model. A series of towing tank experiments for hull resistance in...
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Artificial Neural Network (ANN)-Based Voltage Stability Prediction of Test Microgrid Grid
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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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Electromagnetic Modeling of Microstrip Elements Aided with Artificial Neural Network
PublicationThe electromagnetic modeling principle aided withartificial neural network to designing the microwave widebandelements/networks prepared in microstrip technology is proposedin the paper. It is assumed that the complete information is knownfor the prototype design which is prepared on certain substratewith certain thickness and electric permittivity. The longitudinaland transversal dimensions of new design...
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TOXIC GASES IDENTIFICATION USING SINGLE ELECTROCATALYTIC SENSOR RESPONSES AND ARTIFICIAL NEURAL NETWORK
PublicationThe need for precise detection of toxic gases drives development of new gas sensors structures and methods of processing the output signals from the sensors. In literature, artificial neural networks are considered as one of the most effective tool for the analysis of gas sensors or sensors arrays responses. In this paper a method of toxic gas components identification using a electrocatalytic gas sensor as a detector and an artificial...
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Acoustic journal bearing – Performance under various load and speed conditions speed conditions
PublicationThe paper presents results of experimental testing aiming at finding out what effect system of piezo-electric actuators (PZTs)attached to an aerodynamic journal bearing has on the magnitude of shaft's motion within the bearing operating at specified speed and load. The results clearly demonstrate effectiveness of PZTs in mitigating the shaft's motion thus contributing to the increased stability of the bearing. This stabilizing...
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Analysis of different tillage implements with respect to reduced fuel consumption, tractor operating speed and its wheel slippage
PublicationNowadays the prices of agricultural farm produced products are increasing with ant-speed and the prices of input for raising crops are increasing with rocket-speed. Therefore, there is a dire need to conduct some important studies to innovate the techniques which can lower the costs of crop production. In this context, this study was conducted to evaluate the consumption of fuel at different tillage implements, tractor operating...
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Artificial Neural Network in Forecasting the Churn Phenomena Among Costumers of IT and Power Supply Services
PublicationThis paper presents an attempt to use an artificial neural network to investigate the churn phenomenon among the customers of a telecommunications operator. An attempt was made to create a data model based on the customer lifetime value (CLV) rather than on activity alone. A multilayered artificial neural network was used for the experiments. The results yielded a 99% successful identification rate for customers in no danger of...
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Artificial neural network controller for underwater ship hull operation robot.
PublicationZaproponowano model matematyczny pojazdu podwodnego, który w uproszczonej wersji spełnia warunki dynamiki odpowiadające głowicy roboczej podwodnego robota. Uwzględniono niektóre czynniki oddziałujące na ruch podwodnej głowicy roboczej, jak np. gęstość wody oraz siły odśrodkowe i wypornościowe. Przedstawiono układ sterowania, w którym zastosowano regulator oparty na bazie sieci neuronowych, za pomocą którego można sterować...
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Olgun Aydin dr
PeopleOlgun Aydin finished his PhD by publishing a thesis about Deep Neural Networks. He works as a Principal Machine Learning Engineer in Nike, and works as Assistant Professor in Gdansk University of Technology in Poland. Dr. Aydin is part of editorial board of "Journal of Artificial Intelligence and Data Science" Dr. Aydin served as Vice-Chairman of Why R? Foundation and is member of Polish Artificial Intelligence Society. Olgun is...
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Development of a tropical disease diagnosis system using artificial neural network and GIS
PublicationExpert systems for diagnosis of tropical diseases have been developed and implemented for over a decade with varying degrees of success. While the recent introduction of artificial neural networks has helped to improve the diagnosis accuracy of such systems, this aspect is still negatively affected by the number of supported diseases. A large number of supported diseases usually corresponds to a high number of overlapping symptoms,...
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EXPERIMENTAL RESEARCH ON INFLUENCE OF THE FUEL NOZZLE GEOMETRY ON THE FUEL CONSUMPTION OF THE MARINE 4-STROKE DIESEL ENGINE
PublicationThe article presents experimental research that has been carried out on a marine, 4-stroke, 3-cylinder, turbocharged engine. During testing, the engine operated at a constant rotational speed of 750 rpm and a load from 0 kW to 280 kW. The engine was fuelled by diesel oil of known specification and loaded by electric generator with water resistance. The fuel consumption was measured during the engine operation with fuel nozzles...
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Artificial neural network based sensorless control ofinduction motor.
PublicationW artykule przedstawiono bezczujnikowy układ sterowania silnikiem indukcyjnym wykorzystujący sztuczne sieci neuronowe (ANN). Sieć neuronową wykorzystano w regulatorze prędkości silnika. Zaprezentowano wyniki badań symulacyjnych.
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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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Simulation of ship spiral test for ballast and full load conditions
Open Research DataThe data show the results of the spiral test for the simplified ship model, taking into account two states of loading: ballast and full load. The data set contains the results of time simulation for sea state 10 on the Beaufort scale: changes in rudder angle delta_tab[deg]; angular velocity r_tab[deg/s]; surge velocity u_tab (m/s); sway velocity v_tab...
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Investigating the sustainability, utilisation, consumption and conservation of sea mammals – A systematic review
PublicationAlmost 80 % of the oceans, especially the Arctic and Subarctic are primarily inhabited by marine mammals. Marine species depend mostly on sea ice for food, raising their young ones and safeguarding themselves from predators. Consumption of marine mammals has always been recommended as healthy, but the truth is that it can be detrimental for human health because of sea water pollution from trash and chemicals. This systematic review...
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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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Design of Microstrip UWB Balun Using Quasi-TEM Approach Aided by the Artificial Neural Network
PublicationThe design procedure for UWB balun realized in the microstrip technology is proposed in the paper. The procedure applies Artificial Neural Network which corrects the dimensions of the approximate design found by appropriate scaling of the dimensions of the prototype. The scale coefficients for longitudinal and transverse dimensions of microstrip lines are determined from electromagnetic modeling based on transmission line equations....
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Controlling computer by lip gestures employing neural network
PublicationResults of experiments regarding lip gesture recognition with an artificial neural network are discussed. The neural network module forms the core element of a multimodal human-computer interface called LipMouse. This solution allows a user to work on a computer using lip movements and gestures. A user face is detected in a video stream from a standard web camera using a cascade of boosted classifiers working with Haar-like features....
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A method of the UMTS-FDD network design based on universal load characteristics
PublicationIn the paper an original method of the UMTS radio network design was presented. The method is based on simple way of capacity-coverage trade-off estimation for WCDMA/FDD radio interface. This trade-off is estimated by using universal load characteristics and normalized coverage characteristics. The characteristics are useful for any propagation environment as well as for any service performance requirements. The practical applications...
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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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Wind-wave variability in a shallow tidal sea—Spectral modelling combined with neural network methods
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