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Search results for: DEEP LEARNING RENEWABLE ENERGY SOURCES PHOTOVOLTAICS BUILDINGS LONG SHORT-TERM MEMORY MICRO-GRIDS
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Possibility to apply unified methodology in vibration analysis for long lasting and impulse sources, in terms of influence on people in buildings
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Voltage profiles improvement in a power network with PV energy sources – results of a voltage regulator implementation
PublicationThe constant increase in the number of photovoltaic (PV) energy sources in distribution networks is the cause of serious voltage problems. The networks built at least a dozen years ago are not provided for the installation of a large number of micro-sources. It happens that the previously properly functioning power networks are not able to provide to consumers power with the required parameters, after installing many PV sources....
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Optimized Deep Learning Model for Flood Detection Using Satellite Images
PublicationThe increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection...
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In vivo degradation of short-term implants
PublicationStan powierzchni wywiera istotny wpływ na właściwości użytkowe implantu. Stawiane wymagania, zależą od funkcji jakie ma spełniać wszczep oraz rodzaju implantowanego materiału. W przypadku implantów krótkotrwałych wymagana jest przede wszystkim odpowiednia odporność korozyjna, nie powinno tworzyć się trwałe połączenie pomiędzy wszczepem a tkanką kostną.Wszystkie biomateriały ulegają degradacji. Ważne jest to, aby produkty tej degradacji...
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Two tests for adhesive bonding long term characterization: principles and applications
PublicationThis article describes recent refinement of the traditional wedge test technique used to characterize durability of the adhesive joints. We propose two types of measuring protocols to monitor precisely and continuously the propagation of an "effective" crack during long term mode I fracture mechanic test. First method is directly derived from the traditional wedge test technique and consist in monitoring the surface strain of adherent...
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Deep eutectic solvents in analytical sample preconcentration Part B: Solid-phase (micro)extraction
PublicationOne of the key challenges of modern analytical chemistry is the monitoring of trace amounts of contaminants using sensitive and selective instrumental techniques. Due to the variety and complexity of some samples, it is often necessary to properly prepare a sample and to perform a preconcentration of trace amounts of analytes. In line with the principles of Green Analytical Chemistry (GAC), it is important for an analytical procedure...
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Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
PublicationWe present a novel deep learning model for the detection and reconstruction of dysarthric speech. We train the model with a multi-task learning technique to jointly solve dysarthria detection and speech reconstruction tasks. The model key feature is a low-dimensional latent space that is meant to encode the properties of dysarthric speech. It is commonly believed that neural networks are black boxes that solve problems but do not...
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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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A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection
PublicationComputational cost is an important consideration for memory encoding prediction models that use data from dozens of implanted electrodes. We propose a method to reduce computational expense by selecting a subset of all the electrodes to build the prediction model. The electrodes were selected based on their likelihood of measuring brain activity useful for predicting memory encoding better than chance (in terms of AUC). A logistic...
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Are deep eutectic solvents useful in chromatography? A short review
PublicationA literature update has been done concerning Deep Eutectic Solvents (DES) use in chromatography applications. The literature survey was based on the period from 2010 till 2020 and manuscripts reported in the data bases Web of Science and Scopus. The use of DES as mobile phase and mobile phase additives, stationary phases and solid phase modifiers and the use of DES as reaction solvents for chromatography use, were evaluated. Emphasis...
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Training of Deep Learning Models Using Synthetic Datasets
PublicationIn order to solve increasingly complex problems, the complexity of Deep Neural Networks also needs to be constantly increased, and therefore training such networks requires more and more data. Unfortunately, obtaining such massive real world training data to optimize neural networks parameters is a challenging and time-consuming task. To solve this problem, we propose an easy-touse and general approach to training deep learning...
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Interpretable deep learning approach for classification of breast cancer - a comparative analysis of multiple instance learning models
PublicationBreast cancer is the most frequent female cancer. Its early diagnosis increases the chances of a complete cure for the patient. Suitably designed deep learning algorithms can be an excellent tool for quick screening analysis and support radiologists and oncologists in diagnosing breast cancer.The design of a deep learning-based system for automated breast cancer diagnosis is not easy due to the lack of annotated data, especially...
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Deep learning techniques for biometric security: A systematic review of presentation attack detection systems
PublicationBiometric technology, including finger vein, fingerprint, iris, and face recognition, is widely used to enhance security in various devices. In the past decade, significant progress has been made in improving biometric sys- tems, thanks to advancements in deep convolutional neural networks (DCNN) and computer vision (CV), along with large-scale training datasets. However, these systems have become targets of various attacks, with...
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Application of Doubly Connected Dominating Sets to Safe Rectangular Smart Grids
PublicationSmart grids, together with the Internet of Things, are considered to be the future of the electric energy world. This is possible through a two-way communication between nodes of the grids and computer processing. It is necessary that the communication is easy and safe, and the distance between a point of demand and supply is short, to reduce the electricity loss. All these requirements should be met at the lowest possible cost....
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Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
PublicationThe field of cancer diagnostics has been revolutionized by liquid biopsies, which offer a bridge between laboratory research and clinical settings. These tests are less invasive than traditional biopsies and more convenient than routine imaging methods. Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and...
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Palm Oil Fuel Ash-Based Eco-Efficient Concrete: A Critical Review of the Short-Term Properties
PublicationThe huge demand for concrete is predicted to upsurge due to rapid construction developments. Environmental worries regarding the large amounts of carbon dioxide emanations from cement production have resulted in new ideas to develop supplemental cementing materials, aiming to decrease the cement volume required for making concrete. Palm-oil-fuel-ash (POFA) is an industrial byproduct derived from palm oil waste’s incineration in...
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Visualization of short-term heart period variability with network tools as a method for quantifying autonomic drive
PublicationWe argue that network methods are successful in detecting nonlinear properties in the dynamics of autonomic nocturnal regulation in short-term variability. Two modes of visualization of networks constructed from RR-increments are proposed. The first is based on the handling of a state space. The state space of RR-increments can be modified by a bin size used to code a signal and by the role of a given vertex as the representation...
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A model of damaged media used for describing the process of non-stationary creep and long-term strength of polycrystalline structural alloys
PublicationThe main laws of the processes of creep and long-term strength of polycrystalline structural alloys are considered. From the viewpoint of continuum damaged media (CDM), a mathematical model is developed that describes the processes of viscoplastic deformation and damage accumulation under creep. The problem of determining material parameters and scalar functions of the developed constitutive relations based on the results of specially...
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Potential negative effect of long-term exposure to nitrofurans on bacteria isolated from wastewater
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Short-term Price Reaction to Involuntary Bankruptcies Filed in Bad Faith: Empirical Evidence from Poland
PublicationPurpose: Assessing the reaction of the prices of shares of companies listed in the Warsaw Stock Exchange to the public disclosure of information about the filing a bankruptcy petition in bad faith by creditors. Design/Methodology/Approach: Event study analysis. Findings: It can therefore be assumed that the filing of an unfounded bankruptcy petition does not, in the short term, have a statistically significant negative impact on...
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Deep learning based thermal image segmentation for laboratory animals tracking
PublicationAutomated systems for behaviour classification of laboratory animals are an attractive alternative to manual scoring. However, the proper animals separation and tracking, especially when they are in close contact, is the bottleneck of the behaviour analysis systems. In this paper, we propose a method for the segmentation of thermal images of laboratory rats that are in close contact during social behaviour tests. For this, we are...
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POSSIBILITIES OF ELECTRICAL ENERGY GENERATION IN PHOTOVOLTAIC SYSTEMS INSTALLED IN CENTRAL EUROPE
PublicationNowadays, fossil fuels are the main sources of energy from which electricity is obtained. But these sources will not last forever, so in due course renewable energies will have to replace them in this role. One of these new sources is solar energy. To generate electricity from sunlight, solar (photovoltaic - PV) cells and modules are used. The increasing interest in PV cells and modules worldwide is due mainly to the fact that...
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Modelling Long‐Term Transition from Coal‐Reliant to Low‐Emission Power Grid and District Heating Systems in Poland
PublicationEnergy systems require technological changes towards climate neutrality. In Poland, where the power system is dominated by outdated coal-fired power plants, efforts to minimize the environmental impact are associated with high costs. Therefore, optimal paths for the development of the energy sector should be sought in order to achieve ambitious long-term strategic goals, while minimizing the negative impact on the consumers’ home...
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Latest Insights on Novel Deep Eutectic Solvents (DES) for Sustainable Extraction of Phenolic Compounds from Natural Sources
PublicationPhenolic compounds have long been of great importance in the pharmaceutical, food, and cosmetic industries. Unfortunately, conventional extraction procedures have a high cost and are time consuming, and the solvents used can represent a safety risk for operators, consumers, and the environment. Deep eutectic solvents (DESs) are green alternatives for extraction processes, given their low or non-toxicity, biodegradability, and reusability....
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Selected Artificial Intelligence Methods in the Risk Analysis of Damage to Masonry Buildings Subject to Long-Term Underground Mining Exploitation
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Energy Transition in Poland—Assessment of the Renewable Energy Sector
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Data, Information, Knowledge, Wisdom Pyramid Concept Revisited in the Context of Deep Learning
PublicationIn this paper, the data, information, knowledge, and wisdom (DIKW) pyramid is revisited in the context of deep learning applied to machine learningbased audio signal processing. A discussion on the DIKW schema is carried out, resulting in a proposal that may supplement the original concept. Parallels between DIWK pertaining to audio processing are presented based on examples of the case studies performed by the author and her collaborators....
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Reducing Air Pollutant Emissions from the Residential Sector by Switching to Alternative Energy Sources in Single-Family Homes
PublicationThe paper discusses a scenario for adapting residential buildings to the requirements of the EU climate and energy package. It analyzes the option of reducing pollutant emissions to ambient air by switching to alternative energy sources in a typical single-family residential building. The most common sources of energy in central heating and ventilation systems and water heating systems were compared, and the analyzed energy carriers...
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Long-term outcomes of stapled hemorrhoidopexy
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Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks
PublicationIn this article, the usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) is presented. The developed approach, using channel impulse response, allows higher efficiency in identifying the direct visibility conditions between nodes in off-body communication with comparison to the methods described in the literature. The effectiveness of the proposed deep feedforward neural network was checked on...
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Entropic Measures of Complexity of Short-Term Dynamics of Nocturnal Heartbeats in an Aging Population
PublicationTwo entropy-based approaches are investigated to study patterns describing differences in time intervals between consecutive heartbeats. The first method explores matrices arising from networks of transitions constructed following events represented by a time series. The second method considers distributions of ordinal patterns of length three, whereby patterns with repeated values are counted as different patterns. Both methods provide...
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NEW ORGANIC MATERIALS FOR PHOTOVOLTAICS
PublicationThis report presents the results of investigations carried out for polymer- and low-weightmolecular materials prepared in the form of thin films by various methods, namely the thermalvacuum evaporation and chemical vapour deposition. The influence of the technologicalconditions on the structure, surface morphology, and electronic properties of selected organic thinfilms, including polyazomethines, phthalocyanines, and perylene...
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Boundary conditions for non-residential buildings from the user’s perspective: literature review
PublicationBackground and objective: This paper aims to review the boundary conditions (B/C) in specific categories (energy, building use, and lighting) within non-residential buildings to pave the way to a better understanding of users’ requirements and needs of the built environment. For this paper, B/C are understood as unique preconditions, specific characteristics for use, determining specific features of buildings, enabling an accurate...
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Low-power device simulator for micro-energy measurement methods testing
PublicationThe paper presents low-power device simulator designed for testing of micro-energy measurement methods. Such kind methods can be used for evaluation of power systems based on Energy Harvesting. The device allows to set the current consumption characteristic over the time, thus making possible playing the role of different types of low-power devices e.g. simulating sensor network node.
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Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks
PublicationThe increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated...
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Effect of long term service at elevated temperatures on mechanical properties of Manaurite XM reformer tubes
PublicationMicrostructure transformations occur in the Manaurite XM cast steel tubes during long-term operation in the reformer furnace were revealed and described. The rela tionship between mechanical properties, an increase of internal diameter of the tube and microstructure degradation is discussed. Static tensile test was performed on two types of samples with different shapes. It has been shown differences in the results of tests...
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Deep learning for recommending subscription-limited documents
PublicationDocuments recommendation for a commercial, subscription-based online platform is important due to the difficulty in navigation through a large volume and diversity of content available to clients. However, this is also a challenging task due to the number of new documents added every day and decreasing relevance of older contents. To solve this problem, we propose deep neural network architecture that combines autoencoder with...
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Synergistic Effects of Bitumen Plasticization and Microwave Treatment on Short-Term Devulcanization of Ground Tire Rubber
PublicationGround tire rubber (GTR) was mechano-chemically modified with road bitumen 160/220 and subsequently treated using a microwave radiation. The combined impact of bitumen 160/220 content and microwave treatment on short-term devulcanization of GTR was studied by thermal camera, wavelength dispersive X-ray fluorescence spectrometry (WD-XRF), static headspace, and gas chromatography-mass spectrometry (SHS-GC-MS), thermogravimetric analysis...
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The effect of long-term service at elevated temperatures on structure and mechanical properties of Cr-Mo-V steel
PublicationPurpose: of this paper is to reveal the microstructural changes in 13HMF steel exposed to long-term service at elevated temperatures. The degradation of bainite structure was determined and carbides morphology has been examined. The influence of carbides evolution was discussed in dependence of creep rupture strength and mechanical properties of the steel.
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Application of MARKAL model to optimisation of electricity generation structure in Poland in the long-term time horizon Part I - concept of the model
PublicationIn this paper, which inaugurates a series of papers on this subject, a concept is proposed of a power system development model with regard to the technological structure of electricity generation in Poland, in the long-term time perspective – until 2060. The model is based on the mathematical structure of the MARKAL optimization package. The paper presents a brief description of the tool used in the model research. In addition,...
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Bimodal deep learning model for subjectively enhanced emotion classification in films
PublicationThis research delves into the concept of color grading in film, focusing on how color influences the emotional response of the audience. The study commenced by recalling state-of-the-art works that process audio-video signals and associated emotions by machine learning. Then, assumptions of subjective tests for refining and validating an emotion model for assigning specific emotional labels to selected film excerpts were presented....
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Position Estimation in Mixed Indoor-Outdoor Environment Using Signals of Opportunity and Deep Learning Approach
PublicationTo improve the user's localization estimation in indoor and outdoor environment a novel radiolocalization system using deep learning dedicated to work both in indoor and outdoor environment is proposed. It is based on the radio signatures using radio signals of opportunity from LTE an WiFi networks. The measurements of channel state estimators from LTE network and from WiFi network are taken by using the developed application....
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Big Data from Sensor Network via Internet of Things to Edge Deep Learning for Smart City
PublicationData from a physical world is sampled by sensor networks, and then streams of Big Data are sent to cloud hosts to support decision making by deep learning software. In a smart city, some tasks may be assigned to smart devices of the Internet of Things for performing edge computing. Besides, a part of workload of calculations can be transferred to the cloud hosts. This paper proposes benchmarks for division tasks between an edge...
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Deep Learning Approaches in Histopathology
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Pathological brain network activity: memory impairment in epilepsy
PublicationOur thinking, memory and cognition in general, relies upon precisely timed interactions among neurons forming brain networks that support cognitive processes. The surgical evaluation of drug-resistant epilepsy using intracranial electrodes provides a unique opportunity to record directly from human brain and to investigate the coordinated activity of cognitive networks. In this issue of Neurology®, Kleen and colleagues1 implicate...
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Energy roadmaps for the city of Gdańsk
PublicationThe paper presents energy roadmaps for Gdansk in three time perspectives: short-term (the year 2012), medium-term (2020) and long-term (2050). The paper is a result of the research carried out under the PATH-TO-RES project, supported by European Commission programme SAVE Altener Intelligent Energy Europe.
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Decision support system for design of long distance heat transportation system
PublicationDistrict Heating (DH) systems are commonly supplied using local heat sources. Nowadays, modern insulation materials allow for effective and economically viable heat transportation over long distances (over 20 km). The paper proposes a Decision Support System (DSS) for optimized selection of design and operating parameters of a long distance Heat Transportation System (HTS). The method allows for evaluation of feasibility and effectiveness...
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LOS and NLOS identification in real indoor environment using deep learning approach
PublicationVisibility conditions between antennas, i.e. Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) can be crucial in the context of indoor localization, for which detecting the NLOS condition and further correcting constant position estimation errors or allocating resources can reduce the negative influence of multipath propagation on wireless communication and positioning. In this paper a deep learning (DL) model to classify LOS/NLOS...
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Long-term Hindcast Simulation of Currents, Sea Level, Water Temperature and Salinity in the Baltic Sea
PublicationThis dataset contains the results of numerical modelling of currents, sea level, water temperature and salinity over a period of 50 years (1958–2007) in the Baltic Sea. A long-term hindcast simulation was performed using a three-dimensional hydrodynamic model (PM3D) based on the Princeton Ocean Model (POM). The spatial resolution was 3 nautical miles, i.e. about 5.5 km. Currents, water temperature, and salinity were recorded...
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Machine learning-based seismic response and performance assessment of reinforced concrete buildings
PublicationComplexity and unpredictability nature of earthquakes makes them unique external loads that there is no unique formula used for the prediction of seismic responses. Hence, this research aims to implement the most well-known Machine Learning (ML) methods in Python software to propose a prediction model for seismic response and performance assessment of Reinforced Concrete Moment-Resisting Frames (RC MRFs). To prepare 92,400 data...