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Wyniki wyszukiwania dla: ALTERNATIVE FUELS CO-GASIFICATION DUAL-FUEL ENGINE MACHINE LEARNING RENEWABLE ENERGY OPTIMIZATION
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Active Learning on Ensemble Machine-Learning Model to Retrofit Buildings Under Seismic Mainshock-Aftershock Sequence
PublikacjaThis research presents an efficient computational method for retrofitting of buildings by employing an active learning-based ensemble machine learning (AL-Ensemble ML) approach developed in OpenSees, Python and MATLAB. The results of the study shows that the AL-Ensemble ML model provides the most accurate estimations of interstory drift (ID) and residual interstory drift (RID) for steel structures using a dataset of 2-, to 9-story...
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Storing energy from renewable sources
PublikacjaOmówiono metody, urządzenia i sposoby magazynowania energii mechanicznej, elektrycznej, cieplnej i chemicznej z uwzględnieniem energii ze źródeł odnawialnych.
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ENERGY
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Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools
PublikacjaA high monetary value of the construction projects is one of the reasons of frequent disputes between a general contractor (GC) and a client. A construction site is a unique, one-time, and single-product factory with many parties involved and dependent on each other. The organizational dependencies and their complexity make any fault or mistake propagate and influence the final result (delays, cost overruns). The constant will...
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Ewa Klugmann-Radziemska prof. dr hab.
OsobyEwa Klugmann-Radziemska ukończyła studia wyższe na kierunku fizyka na Uniwersytecie Gdańskim. Od roku 1996 związana jest z Politechniką Gdańską, kiedy to rozpoczęła studia doktoranckie. Obecnie jest profesorem na Wydziale Chemicznym Politechniki Gdańskiej, od roku 2006 kierownikiem Katedry Konwersji i Magazynowania Energii. W latach 2008–2016 pełniła funkcję Prodziekana ds. współpracy i rozwoju, w latach 2016-2019 była Pełnomocnikiem...
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Study of various machine learning approaches for Sentinel-2 derived bathymetry
PublikacjaIn recent years precise and up-to-date information regarding seabed depth has become more and more important for companies and institutions that operate on coastlines. While direct, in-situ measurements are performed regularly, they are expensive, time-consuming and impractical to be performed in short time intervals. At the same time, an ever-increasing amount of satellite imaging data becomes available. With these images, it...
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Performance of microbial fuel cells operated under anoxic conditions
PublikacjaNowadays, microbial fuel cells (MFC) stand up as a promising renewable energy source. Due to the ability of the MFC to oxidize a wide spectrum of substrates, wastewater seems to be one of the most interesting fuels. Unfortunately, wastewater could contain electron acceptors such as nitrate, which could interfere with the electrical performance of the MFC. In this work, the influence of oxidised nitrogen forms on the electricity...
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Predicting emotion from color present in images and video excerpts by machine learning
PublikacjaThis work aims at predicting emotion based on the colors present in images and video excerpts using a machine-learning approach. The purpose of this paper is threefold: (a) to develop a machine-learning algorithm that classifies emotions based on the color present in an image, (b) to select the best-performing algorithm from the first phase and apply it to film excerpt emotion analysis based on colors, (c) to design an online survey...
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Analyzing the relationship between sound, color, and emotion based on subjective and machine-learning approaches
PublikacjaThe aim of the research is to analyze the relationship between sound, color, and emotion. For this purpose, a survey application was prepared, enabling the assignment of a color to a given speaker’s/singer’s voice recordings. Subjective tests were then conducted, enabling the respondents to assign colors to voice/singing samples. In addition, a database of voice/singing recordings of people speaking in a natural way and with expressed...
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Renewable Energy Focus
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MODELLING OF TOXIC COMPOUNDS EMISSION IN MARINE DIESEL ENGINE DURING TRANSIENT STATES AT VARIABLE PRESSURE OF FUEL INJECTION
PublikacjaTransient states are an important part of the spectrum of engine loads, especially the traction engines. In the case of marine diesel engines, transient states are of particular importance in reducing the analysis of motion units for special areas and maneuvering in port, the participation of transient states in the load spectrum significantly increases, also, the emission of toxic compounds from this period increases proportionally....
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A structure and design optimization of novel compact microscrip dual-band rat-race coupler with enhanced bandwidth
PublikacjaIn the letter, a topology of a novel compact wideband dual-band rat-race coupler has been presented along with its computationally efficient design optimization procedure. Reduction of the circuit size has been achieved by meandering transmission lines of the conventional circuit. At the same time, the number of independent geometry parameters has been increased so as to secure sufficient flexibility of the circuit, necessary in...
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Laboratory station for research of the innovative dry method of exhaust gas desulfurization for an engine powered with residual fuel
PublikacjaContemporary 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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Robust and Efficient Machine Learning Algorithms for Visual Recognition
PublikacjaIn visual recognition, the task is to identify and localize all objects of interest in the input image. With the ubiquitous presence of visual data in modern days, the role of object recognition algorithms is becoming more significant than ever and ranges from autonomous driving to computer-aided diagnosis in medicine. Current models for visual recognition are dominated by models based on Convolutional Neural Networks (CNNs), which...
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Forecasting of retail prices of liquid fuels in Poland
PublikacjaMotivation: In recent years, the prices of liquid fuels in Poland have been rising , negatively affecting the country’s economy and the daily life of its inhabitants. Consequently, there is a need for effective forecasting of prices in fuel markets, as this could enable entrepreneurs and consumers to make more informed decisions. Aim: The objective of the article was to forecast the retail prices of EU95 petrol and diesel fuel...
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Evaluation of Machine Learning Methods for the Experimental Classification and Clustering of Higher Education Institutions
PublikacjaHigher education institutions have a big impact on the future of skills supplied on the labour market. It means that depending on the changes in labour market, higher education institutions are making changes to fields of study or adding new ones to fulfil the demand on labour market. The significant changes on labour market caused by digital transformation, resulted in new jobs and new skills. Because of the necessity of computer...
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Integrating Experience-Based Knowledge Representation and Machine Learning for Efficient Virtual Engineering Object Performance
PublikacjaMachine learning and Artificial Intelligence have grown significant attention from industry and academia during the past decade. The key reason behind interest is such technologies capabilities to revolutionize human life since they seamlessly integrate classical networks, networked objects and people to create more efficient environments. In this paper, the Knowledge Representation technique of Set of Experience...
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Sustainable energy system combined biogas-feedSolid Oxide Fuel Cell and Microalgae technology
PublikacjaIn the new frontier of energy and environmental safety, new efficient and clean safe energy conversion systems are required. In this sense, the present work is framed within the context of Circular Economy and proposes a multidisciplinary study for the development of more efficient, economically viable and non-polluting energy conversion systems, based on the synergetic combination of different technologies: fuel cells, biofuels,...
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Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage
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INCREASING POWER SUPPLY SAFETY IN THE ASPECT OF SUPPORTING THE RENEWABLE ENERGY SOURCES BY CONVENTIONAL AND VIRTUAL POWER STORES
PublikacjaThis paper presents characteristics and purposefulness of supporting the renewable energy sources (OZE) by means of energy stores. The main emphasis was placed on analysis of virtual energy stores available for implementation in Polish economy conditions. A role which management of Demand Side Response (DSR) may play in balancing Polish electric power system, is discussed. Implementation of such solutions together with conventional...
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Optical method supported by machine learning for urinary tract infection detection and urosepsis risk assessment
PublikacjaThe study presents an optical method supported by machine learning for discriminating urinary tract infections from an infection capable of causing urosepsis. The method comprises spectra of spectroscopy measurement of artificial urine samples with bacteria from solid cultures of clinical E. coli strains. To provide a reliable classification of results assistance of 27 algorithms was tested. We proved that is possible to obtain...
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Preeclampsia Risk Prediction Using Machine Learning Methods Trained on Synthetic Data
PublikacjaThis paper describes a research study that investigates the use of machine learning algorithms on synthetic data to classify the risk of developing preeclampsia by pregnant women. Synthetic datasets were generated based on parameter distributions from three real patient studies. Four models were compared: XGBoost, Support Vector Machine (SVM), Random Forest, and Explainable Boosting Machines (EBM). The study found that the XGBoost...
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Noise profiling for speech enhancement employing machine learning models
PublikacjaThis paper aims to propose a noise profiling method that can be performed in near real-time based on machine learning (ML). To address challenges related to noise profiling effectively, we start with a critical review of the literature background. Then, we outline the experiment performed consisting of two parts. The first part concerns the noise recognition model built upon several baseline classifiers and noise signal features...
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Experimental tuning of AuAg nanoalloy plasmon resonances assisted by machine learning method
PublikacjaPlasmonic nanostructures based on AuAg nanoalloys were fabricated by thermal annealing of metallic films in an argon atmosphere. The nanoalloys were chosen because they can extend the wavelength range in which plasmon resonance occurs and thus allow the design of plasmonic platforms with the desired parameters. The influence of initial fabrication parameters and experimental conditions on the formation of nanostructures was investigated....
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Exergetic Analysis of the nCO2PP Cycle with Particular Reference to the Exergy Destruction of Sewage Sludge Due to Gasification
PublikacjaAn exergy analysis is carried out on the negative CO2 emission gas power plant (nCO2PP), which integrates the process sections of fuel preparation, power generation and carbon capture. Processes of exergy destruction are studied with particular focus on the process in the gasification unit of the fuel preparation section, where a large amount of exergy is destroyed in various chemical reactions from sewage sludge to producer gas...
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Gross final energy demand from RES, broken down by type of energy
Dane BadawczeAccording to the forecasts of the Ministry of Economy, taking into account the macroeconomic situation and legal conditions (including the EU Program "20x20x20", the Act on Renewable Energy Sources and the Energy Efficiency Act), power plants producing energy based on renewable energy will gain in importance in the coming years. energy sources.
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Real and Virtual Instruments in Machine Learning – Training and Comparison of Classification Results
PublikacjaThe continuous growth of the computing power of processors, as well as the fact that computational clusters can be created from combined machines, allows for increasing the complexity of algorithms that can be trained. The process, however, requires expanding the basis of the training sets. One of the main obstacles in music classification is the lack of high-quality, real-life recording database for every instrument with a variety...
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Evaluation of energy efficiency of vehicles powered by different fuels
PublikacjaW pracy przedstawiono nową metodę oceny efektywności energetycznej pojazdów zasilanych różnymi paliwami. Analizę można wykonać na podstawie jednego przejazdu w regularnym ruchu miejskim z rejestracją podstawowych parametrów silnika i pojazdu. Warunki eksploatacji identyfikowane są przy użyciu energochłonności jednostkowej, która uwzględnia zarówno wpływ warunków zewnętrznych jak również styl jazdy kierowcy.
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Comparison of energy efficiency of vehicles powered by different fuels
PublikacjaNajpopularniejsza metoda oceny efektywności energetycznej pojazdów samochodowych polega na porównywaniu przebiegowego zużycia paliwa osiągniętego w warunkach wybranego testu homologacyjnego. Warunki eksploatacji, zdefiniowane za pomocą przebiegów prędkości w czasie, dotyczą najczęściej tylko dwóch kategorii: jazdy miejskiej i pozamiejskiej. Problemy wynikające z takiego sposobu postępowania zostały omówione na przykładzie analizy...
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Smart heating system for home extending utilization of renewable energy sources
PublikacjaIn the paper a modern approach to smart home heating is presented. Proposed solution utilizes at least two low-polluting energy source technologies. The main idea is to connect well known ecological energy sources in a way that they can support each other and minimize risks of failure when using single system or even both of them but managed in separate way. Considered energy technologies, used separatelly, have disadvan-tages,...
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Application of road map of operating condition for estimation of fuel and electric energy consumption from city transport
PublikacjaThe 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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Janusz Cieśliński prof. dr hab. inż.
OsobyUrodził się 15 kwietnia 1954 r. w Słupsku. Jest absolwentem Wydziału Budowy Maszyn Politechniki Gdańskiej (1978), z którą związał całe swoje życie zawodowe. W 1986 r. obronił doktorat, w 1997 r. – habilitację, w 2006 r. – uzyskał tytuł profesora. Pełnił funkcje prodziekana ds. nauki Wydziału Mechanicznego przez dwie kadencje (2002–2008) oraz kierownika: Katedry Maszyn Przemysłu Spożywczego (2002–2006), Katedry Ekoinżynierii i...
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Machine learning-based prediction of preplaced aggregate concrete characteristics
PublikacjaPreplaced-Aggregate Concrete (PAC) is a type of preplaced concrete where coarse aggregate is placed in the mold and a Portland cement-sand grout with admixtures is injected to fill the voids. Due to the complex nature of PAC, many studies were conducted to determine the effects of admixtures and the compressive and tensile strengths of PAC. Considering that a prediction tool is needed to estimate the compressive and tensile...
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Machine learning-based prediction of preplaced aggregate concrete characteristics
PublikacjaPreplaced-Aggregate Concrete (PAC) is a type of preplaced concrete where coarse aggregate is placed in the mold and a Portland cement-sand grout with admixtures is injected to fill the voids. Due to the complex nature of PAC, many studies were conducted to determine the effects of admixtures and the compressive and tensile strengths of PAC. Considering that a prediction tool is needed to estimate the compressive and tensile strengths...
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Platelet RNA Sequencing Data Through the Lens of Machine Learning
PublikacjaLiquid biopsies offer minimally invasive diagnosis and monitoring of cancer disease. This biosource is often analyzed using sequencing, which generates highly complex data that can be used using machine learning tools. Nevertheless, validating the clinical applications of such methods is challenging. It requires: (a) using data from many patients; (b) verifying potential bias concerning sample collection; and (c) adding interpretability...
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Optimization of Train Energy Cooperation Using Scheduled Service Time Reserve
PublikacjaThe main aim of the paper was to develop an innovative approach to the preliminary estimation possibility of train energy cooperation based on data from timetables, without traction calculations. The article points out the need to strive for sustainable and environmentally friendly transport. It was pointed out that rail transport using electric traction is one of the more ecological branches of transport. It also offers a number...
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DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors
PublikacjaWe present a physics-based machine learning approach to predict in vitro transcription factor binding affinities from structural and mechanical DNA properties directly derived from atomistic molecular dynamics simulations. The method is able to predict affinities obtained with techniques as different as uPBM, gcPBM and HT-SELEX with an excellent performance, much better than existing algorithms. Due to its nature, the method can...
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Renewable and Sustainable Energy: Current State and Prospects
PublikacjaThe last two decades of the twentieth century represented a period of above-average, systematic growth of formal and informal interdependencies between economies of different countries and between world markets. The intensity, magnitude, and diversity of these interdependencies have never been recorded before in economic history, and the market transformations taking place have been referred to in the literature as the process...
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Alternative Energy: Photovoltaic Modules and Systems
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Alternative Energy: Photovoltaic Solar Cells
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Solubility of dapsone in deep eutectic solvents: Experimental analysis, molecular insights and machine learning predictions
PublikacjaBackground. Dapsone (DAP) is an anti-inflammatory and antimicrobial active pharmaceutical ingredient used to treat, e.g., AIDS-related diseases. However, low solubility is a feature hampering its efficient use. Objectives. First, deep eutectic solvents...
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Dimethyl ether (DME) as potential environmental friendly fuel
PublikacjaIn recent years, there has been a growing interest in replacing petroleum fuels with so-called second generation environmental friendly fuels. Compared to traditional petroleum fuels dimethyl ether (DME) could be used as a clean high-efficiency compression ignition fuel with reduced particulate matter (PM), sulfur oxides (SOx), hydrocarbons (HC), carbon monoxide (CO) as well as combustion noise. Compared to some of the other leading...
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Hybrid Multi-Criteria Method of Analyzing the Location of Distributed Renewable Energy Sources
PublikacjaThis paper presents the development and the application of a hybrid multi-criteria method, the combination of the Analytic Hierarchy Process (AHP), and numerical taxonomy (NT),to support the decision making on the location of distributed renewable energy sources meetingvarious types of assessment criteria. Finding criteria weights, using the AHP method, eliminates thedisadvantage of NT—which, in current form, is defined by its...
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Forewarned Is Forearmed: Machine Learning Algorithms for the Prediction of Catheter-Induced Coronary and Aortic Injuries
PublikacjaCatheter-induced dissections (CID) of coronary arteries and/or the aorta are among the most dangerous complications of percutaneous coronary procedures, yet the data on their risk factors are anecdotal. Logistic regression and five more advanced machine learning techniques were applied to determine the most significant predictors of dissection. Model performance comparison and feature importance ranking were evaluated. We identified...
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Assessment Of the Relevance of Best Practices in The Development of Medical R&D Projects Based on Machine Learning
PublikacjaMachine learning has emerged as a fundamental tool for numerous endeavors within health informatics, bioinformatics, and medicine. However, novices among biomedical researchers and IT developers frequently lack the requisite experience to effectively execute a machine learning project, thereby increasing the likelihood of adopting erroneous practices that may result in common pitfalls or overly optimistic predictions. The paper...
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Power and heat from renewable energy sources on energy market in Poland
PublikacjaPrzedstawiono aktualne uwarunkowania rynków energii w Polsce w odniesieniu do źródeł odnawialnych. Przedstawiono perspektywy rozwojowe. Omówiono wyniki opłacalności budowy małego bloku skojarzonego opalanego słomą.
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Proximal primal–dual best approximation algorithm with memory
PublikacjaWe propose a new modified primal–dual proximal best approximation method for solving convex not necessarily differentiable optimization problems. The novelty of the method relies on introducing memory by taking into account iterates computed in previous steps in the formulas defining current iterate. To this end we consider projections onto intersections of halfspaces generated on the basis of the current as well as the previous...
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Reinforcement Learning Algorithm and FDTD-based Simulation Applied to Schroeder Diffuser Design Optimization
PublikacjaThe aim of this paper is to propose a novel approach to the algorithmic design of Schroeder acoustic diffusers employing a deep learning optimization algorithm and a fitness function based on a computer simulation of the propagation of acoustic waves. The deep learning method employed for the research is a deep policy gradient algorithm. It is used as a tool for carrying out a sequential optimization process the goal of which is...
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Asphalt pavement structure optimization with alternative materials
PublikacjaThe paper briefly describes modern method assessment of the pavement structure based on the simplified viscoelastic continuum damage (S-VECD) model. The method was used to compare two types of pavement structures. There were analysed classical cstructures with asphalt concretes with neat bitumen and innovative one- or two layered structures with SMA 16 with highly polymer modified bitumen (HiMA). Pavement structures using SMA 16...
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Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
PublikacjaMany studies have been performed to put quantifying uncertainties into the seismic risk assessment of reinforced concrete (RC) buildings. This paper provides a risk-assessment support tool for purpose of retrofitting and potential design strategies of RC buildings. Machine Learning (ML) algorithms were developed in Python software by innovative methods of hyperparameter optimization, such as halving search, grid search, random...