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Search results for: ALTERNATIVE FUELS CO-GASIFICATION DUAL-FUEL ENGINE MACHINE LEARNING RENEWABLE ENERGY OPTIMIZATION
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A dual-control strategy based on electrode material and electrolyte optimization to construct an asymmetric supercapacitor with high energy density
PublicationMetal-organic frames (MOFs) are regarded as excellent candidates for supercapacitors that have attracted much attention because of their diversity, adjustability and porosity. However, both poor structural stability in aqueous alkaline electrolytes and the low electrical conductivity of MOF materials constrain their practical implementation in supercapacitors. In this study, bimetallic CoNi-MOF were synthesized to enhance the electrical...
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Efficient sampling of high-energy states by machine learning force fields
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MACHINE LEARNING APPLICATIONS IN RECOGNIZING HUMAN EMOTIONS BASED ON THE EEG
PublicationThis study examined the machine learning-based approach allowing the recognition of human emotional states with the use of EEG signals. After a short introduction to the fundamentals of electroencephalography and neural oscillations, the two-dimensional valence-arousal Russell’s model of emotion was described. Next, we present the assumptions of the performed EEG experiment. Detail aspects of the data sanitization including preprocessing,...
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Analysis of the structure of the atomized fuel spray with marine diesel engine injector in the early stage of injection
PublicationThis paper presents the results of the experimental research of the atomized fuel spray with the marine diesel engine injector in the constant volume chamber. The specificity of the phenomena occurring in the marine engine cylinder was the reason to use the optical visualisation method in the studies – the Mie scattering technique. This work presents an analysis of the influence of different geometry of outlet orifice and opening...
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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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The Effect of Oxygenated Diesel-N-Butanol Fuel Blends on Combustion, Performance, and Exhaust Emissions of a Turbocharged CRDI Diesel Engine
PublicationThe article deals with the effects made by using various n-butanol-diesel fuel blends on the combustion history, engine performance and exhaust emissions of a turbocharged four-stroke, four-cylinder, CRDI 1154HP (85 kW) diesel engine. At first, load characteristics were taken when running an engine with normal diesel fuel (DF) to have ‘baseline’ parameters at the two ranges of speed of 1800 and 2500 rpm. Four a fossil diesel (class...
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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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Reliable renewable energy – application of electrochemical capacitors for electrical energy storage
PublicationThis paper presents electrical energy storage devices such as electrochemical capacitors, their principle of operation and electrode materials most commonly used in their manufacturing technology. Moreover, our research on development of new nanocomposite materials based on multi-walled carbon nanotubes and conducting polymer is shown. Additionally, the possibility and advantages of application of supercapacitors for accumulation...
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Assessment of Failure Occurrence Rate for Concrete Machine Foundations Used in Gas and Oil Industry by Machine Learning
PublicationConcrete machine foundations are structures that transfer loads from machines in operation to the ground. The design of such foundations requires a careful analysis of the static and dynamic effects caused by machine exploitation. There are also other substantial differences between ordinary concrete foundations and machine foundations, of which the main one is that machine foundations are separated from the building structure....
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Multimedia industrial and medical applications supported by machine learning
PublicationThis article outlines a keynote paper presented at the Intelligent DecisionTechnologies conference providing a part of the KES Multi-theme Conference “Smart Digital Futures” organized in Rome on June 14–16, 2023. It briefly discusses projects related to traffic control using developed intelligent traffic signs and diagnosing the health of wind turbine mechanisms and multimodal biometric authentication for banking branches to provide...
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Machine learning applied to acoustic-based road traffic monitoring
PublicationThe motivation behind this study lies in adapting acoustic noise monitoring systems for road traffic monitoring for driver’s safety. Such a system should recognize a vehicle type and weather-related pavement conditions based on the audio level measurement. The study presents the effectiveness of the selected machine learning algorithms in acoustic-based road traffic monitoring. Bases of the operation of the acoustic road traffic...
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Machine learning applied to acoustic-based road traffic monitoring
PublicationThe motivation behind this study lies in adapting acoustic noise monitoring systems for road traffic monitoring for driver’s safety. Such a system should recognize a vehicle type and weather-related pavement conditions based on the audio level measurement. The study presents the effectiveness of the selected machine learning algorithms in acoustic-based road traffic monitoring. Bases of the operation of the acoustic road traffic...
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Analysis of start energy of Stirling engine type alpha
PublicationThe Stirling engine type alpha is composed of two cylinders (expansion space E and compression space C), regenerator that forms the space between the cylinders and the buffer space (under the pistons). Before the start-up and as a result of long-term operation, the average pressure in the working space (above the pistons) and in the buffer space is the same. However, in the initial phase of operation, the average pressure in the...
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Solar Photovoltaic Energy Optimization and Challenges
PublicationThe study paper focuses on solar energy optimization approaches, as well as the obstacles and concerns that come with them. This study discusses the most current advancements in solar power generation devices in order to provide a reference for decision-makers in the field of solar plant construction throughout the world. These technologies are divided into three groups: photovoltaic, thermal, and hybrid (thermal/photovoltaic)....
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Alternative Energy: Photovoltaic Modules and Systems
PublicationUse of solar energy does not contribute to global warming. The light-to-current conversion (photovoltaic conversion) takes place within solar cells, which in most cases are made of silicon. Solar module consists of many solar cells, which are electrically connected and placed between glass or Tedlar® and framed by an aluminium frame. A number of solar modules and other components form photovoltaic systems. In this entry, a brief...
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MARKAL LONG-TERM POWER GENERATION SCENARIOS FOR POLAND: INCREASING THE SHARE OF RENEWABLE ENERGY SOURCES BY 2040
PublicationIn this paper, renewable energy sources (RES) support mechanisms in Poland was presented with perspectives of proposed support system modifications, discussed in the project of Renewable Energy Act. In addition, MARKAL model of RES support mechanism was presented, taking into account technology-specific multiplication factors. Two model runs with emission trading system in place and two additional runs without emission trade were...
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A novel dual-band rectifier circuit with enhanced bandwidth for RF energy harvesting applications
PublicationIn recent years, a rapid development of low-power sensor networks, enabling machine-to-machine communication in applications such as environmental monitoring, has been observed. Contemporary sensors are normally supplied by an external power source, typically in a form of a battery, which limits their lifespan and increases the maintenance costs. This problem can be addressed by harvesting and converting ambient RF energy into...
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COMPARISON OF THE CONVENTIONAL AND ALTERNATIVE GRANULAR MATERIALS FOR DUAL-MEDIA FILTRATION OF GROUNDWATER: PILOT PLANT TESTING
PublicationNowadays, occurrence of abnormal mineral or organic natural (geogenic) compounds concentrations, in ground and infiltration water, but also quite often in surface waters, is now a common problem encountered in Poland, Europe and many other countries throughout the world. The most concern is usually paid on the removal of iron (Fe) and manganese (Mn) as well as anthropogenic compounds (in particular referring to the organic compounds...
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Modeling lignin extraction with ionic liquids using machine learning approach
PublicationLignin, next to cellulose, is the second most common natural biopolymer on Earth, containing a third of the organic carbon in the biosphere. For many years, lignin was perceived as waste when obtaining cellulose and hemicellulose and used as a biofuel for the production of bioenergy. However, recently, lignin has been considered a renewable raw material for the production of chemicals and materials to replace petrochemical resources....
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MARKAL long-term power generation scenarios for Poland: Increasing the share of renewable energy sources by 2040
PublicationIn this paper, renewable energy sources (RES) support mechanisms in Poland was presented with perspec-tives of proposed support system modifications, discussed in the project of Renewable Energy Act. In ad-dition, MARKAL model of RES support mechanism was presented, taking into account technology-specific multiplication factors. Two model runs with emission trading system in place and two additional runs without emission trade...
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Concrete mix design using machine learning
PublicationDesigning a concrete mix is a process of synthesizing many components, it is not a simple process and requires extensive technical knowledge. The design process itself focuses on obtaining the required strength of concrete. Very often designing a concrete mix takes into account the need to maintain the proper water-demand and frost-resistance features. The parameters that influence the concrete class most significantly are the...
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Predictions of cervical cancer identification by photonic method combined with machine learning
PublicationCervical cancer is one of the most commonly appearing cancers, which early diagnosis is of greatest importance. Unfortunately, many diagnoses are based on subjective opinions of doctors—to date, there is no general measurement method with a calibrated standard. The problem can be solved with the measurement system being a fusion of an optoelectronic sensor and machine learning algorithm to provide reliable assistance for doctors...
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Energy Transition in Poland—Assessment of the Renewable Energy Sector
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Active learning on stacked machine learning techniques for predicting compressive strength of alkali-activated ultra-high-performance concrete
PublicationConventional ultra-high performance concrete (UHPC) has excellent development potential. However, a significant quantity of CO2 is produced throughout the cement-making process, which is in contrary to the current worldwide trend of lowering emissions and conserving energy, thus restricting the further advancement of UHPC. Considering climate change and sustainability concerns, cementless, eco-friendly, alkali-activated UHPC (AA-UHPC)...
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Deep learning for ultra-fast and high precision screening of energy materials
PublicationSemiconductor materials for energy storage are the core and foundation of modern information society and play important roles in photovoltaic system, integrated circuit, spacecraft technology, lighting applications, and other fields. Unfortunately, due to the long experiment period and high calculation cost, the high-precision band gap (the basic characteristic parameter) of semiconductor is difficult to obtain, which hinders the...
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On Design Optimization of Miniaturized Microscrip Dual-Band Rat-Race Coupler with Enhanced Bandwidth
PublicationIn the paper, a novel topology of a miniaturized wideband dual-band rat-race coupler has been presented. Small size of the circuit has been obtained by meandering transmission lines of the conventional circuit. At the same time, the number of independent geometry parameters has been increased in order to secure sufficient circuit flexibility in the context of its design optimization for dual-band operation. Optimum dimensions of...
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A Proposed Machine Learning Model for Forecasting Impact of Traffic-Induced Vibrations on Buildings
PublicationTraffic-induced vibrations may cause various damages to buildings located near the road, including cracking of plaster, cracks in load-bearing elements or even collapse of the whole structure. Measurements of vibrations of real buildings are costly and laborious. Therefore the aim of the research is to propose the original numerical algorithm which allows us to predict, with high probability, the nega-tive dynamic impact of traffic-induced...
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Model-Based Adaptive Machine Learning Approach in Concrete Mix Design
PublicationConcrete mix design is one of the most critical issues in concrete technology. This process aims to create a concrete mix which helps deliver concrete with desired features and quality. Contemporary requirements for concrete concern not only its structural properties, but also increasingly its production process and environmental friendliness, forcing concrete producers to use both chemically and technologically complex concrete...
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Comparison of Downdraft and Updraft Gasification of Biomass in a Fixed Bed Reactor
PublicationThe aim of this study was to compare and analyze the gasification process of beech wood. The experimental investigation was conducted inside a gasifier, which can be operated in downdraft and updraft gasification system. The most important operating parameter studied in this paper was the influence of the amount of supply air on the temperature distribution, biomass consumption and syngas calorific value. The results show that...
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Explainable machine learning for diffraction patterns
PublicationSerial crystallography experiments at X-ray free-electron laser facilities produce massive amounts of data but only a fraction of these data are useful for downstream analysis. Thus, it is essential to differentiate between acceptable and unacceptable data, generally known as ‘hit’ and ‘miss’, respectively. Image classification methods from artificial intelligence, or more specifically convolutional neural networks (CNNs), classify...
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Active Learning on Ensemble Machine-Learning Model to Retrofit Buildings Under Seismic Mainshock-Aftershock Sequence
PublicationThis 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
PublicationOmówiono metody, urządzenia i sposoby magazynowania energii mechanicznej, elektrycznej, cieplnej i chemicznej z uwzględnieniem energii ze źródeł odnawialnych.
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Quantitative Risk Assessment in Construction Disputes Based on Machine Learning Tools
PublicationA 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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Study of various machine learning approaches for Sentinel-2 derived bathymetry
PublicationIn 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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Predicting emotion from color present in images and video excerpts by machine learning
PublicationThis 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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Performance of microbial fuel cells operated under anoxic conditions
PublicationNowadays, 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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Analyzing the relationship between sound, color, and emotion based on subjective and machine-learning approaches
PublicationThe 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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MODELLING OF TOXIC COMPOUNDS EMISSION IN MARINE DIESEL ENGINE DURING TRANSIENT STATES AT VARIABLE PRESSURE OF FUEL INJECTION
PublicationTransient 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
PublicationIn 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
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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Robust and Efficient Machine Learning Algorithms for Visual Recognition
PublicationIn 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
PublicationMotivation: 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
PublicationHigher 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
PublicationMachine 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
PublicationIn 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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Optical method supported by machine learning for urinary tract infection detection and urosepsis risk assessment
PublicationThe 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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INCREASING POWER SUPPLY SAFETY IN THE ASPECT OF SUPPORTING THE RENEWABLE ENERGY SOURCES BY CONVENTIONAL AND VIRTUAL POWER STORES
PublicationThis 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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Preeclampsia Risk Prediction Using Machine Learning Methods Trained on Synthetic Data
PublicationThis 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
PublicationThis 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...