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Search results for: deep learning renewable energy sources photovoltaics buildings long short-term memory micro-grids
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RAPESEED PELLET - A BYPRODUCT OF BIODIESEL PRODUCTION - AS AN EXCELLENT RENEWABLE ENERGY SOURCE
PublicationVegetable oils are renewable feedstock currently being used for production of biofuels from sustainable biomass resources. The existing technology for producing diesel fuel from plant oils, such as rapeseed, soybean, canola and palm oil are largely centered on transesterification of oils with methanol to produce fatty acid methyl esters (FAME) or biodiesel. Rapeseed pellet - crushed seed residue from oil extraction is a byproduct...
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Scrutiny of power grids by penetrating PV energy in wind farms: a case study of the wind corridor of Jhampir, Pakistan
PublicationThis study examines the problems caused by intermittent renewable energy sources, especially wind farms, and suggests a different solar energy penetration strategy to improve their loading capacity. The study uses real-time data from a wind farm in Jhampir, Pakistan, to analyse and assess various aspects of grid stations connected to wind farms. Electrical Transient Analyzer Program is used to validate the results by linking...
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Scrutiny of power grids by penetrating PV energy in wind farms: a case study of the wind corridor of Jhampir, Pakistan
PublicationThis study examines the problems caused by intermittent renewable energy sources, especially wind farms, and suggests a different solar energy penetration strategy to improve their loading capacity. The study uses real-time data from a wind farm in Jhampir, Pakistan, to analyse and assess various aspects of grid stations connected to wind farms. Electrical Transient Analyzer Program is used to validate the results by linking these...
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Renewable Energy in the Pomerania Voivodeship—Institutional, Economic, Environmental and Physical Aspects in Light of EU Energy Transformation
PublicationIn the era of globalization and rapid economic growth, affecting most world economies, increased production and consumption are leading to higher levels of energy production and consumption. The growing demand for energy means that energy resources from conventional sources are not sufficient; moreover, its production generates high costs and contributes to the emission of greenhouse gases and waste. In view of the above, many...
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Cleaner energy for sustainable future using hybrid photovoltaics-thermoelectric generators system under non-static conditions using machine learning based control technique
PublicationIn addition to the load demand, the temperature difference between the hot and cold sides of the thermoelectric generator (TEG) module determines the output power for thermoelectric generator systems. Maximum power point tracking (MPPT) control is needed to track the optimal global power point as operating conditions change. The growing use of electricity and the decline in the use of fossil fuels have sparked interest in photovoltaic-TEG...
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Learning & Memory
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LEARNING & MEMORY
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Long-distance quantum communication over noisy networks without long-time quantum memory
PublicationThe problem of sharing entanglement over large distances is crucial for implementations of quantum cryptography. A possible scheme for long-distance entanglement sharing and quantum communication exploits networks whose nodes share Einstein-Podolsky-Rosen (EPR) pairs. In Perseguers et al. [Phys. Rev. A 78, 062324 (2008)] the authors put forward an important isomorphism between storing quantum information in a dimension D and transmission...
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ENERGY SOURCES
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Basics of Deep Learning 24/25
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Breast MRI segmentation by deep learning: key gaps and challenges
PublicationBreast MRI segmentation plays a vital role in early diagnosis and treatment planning of breast anomalies. Convolutional neural networks with deep learning have indicated promise in automating this process, but significant gaps and challenges remain to address. This PubMed-based review provides a comprehensive literature overview of the latest deep learning models used for breast segmentation. The article categorizes the literature...
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Efficiency of Biomass Energy used for Heating Purposes in a Residential Building in Comparison with other Energy Sources
PublicationThis paper discusses the results of analyses investigating the energy efficiency of biomass in comparison with other popular energy carriers used for heating, ventilation and water heating in residential buildings. The compared energy sources were lignite, natural gas, heating oil and electricity produced by conventional and integrated power generation plants. The most efficient variant relying on biomass and the least efficient...
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Force transfer and stress distribution in short cantilever deep beams loaded throughout the depth with a various reinforcement
PublicationDeep beams used as the main reinforced concrete structural elements which taking over the load and stiffening construction are often found in high-rise buildings. The architecture of these buildings is sometimes sophisticated and varied, arouse the admiration of the majority of recipients, and thus causing an engineering challenge to correctly design the structural system and force transfer. In such structures is important to shape...
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Remarks on use of the term “deep eutectic solvent” in analytical chemistry
PublicationAbout 20 years ago, Abbott and co-workers researched new solvents that were based on mixtures of choline chloride with urea and carboxylic acids and that were liquid at ambient temperature. The term “deep eutectic solvent” (DES) was later adopted for similar mixtures. As DESs have a number of interesting features, they quickly attracted the attention of researchers and found application in various branches of chemical and materials...
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Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches
PublicationExamining the game-changing possibilities of explainable machine learning techniques, this study explores the fast-growing area of biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization of training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified and enhanced the forecasting of biochar output and composition from various biomass...
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Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality
PublicationHigh-quality echocardiogram images are the cornerstone of accurate and reliable measurements of the heart. Therefore, this study aimed to develop, validate and compare machine learning and deep learning algorithms for accurate and automated assessment of transthoracic echocardiogram image quality. In total, 4090 single-frame two-dimensional transthoracic echocardiogram...
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Using phase of short-term Fourier transform for evaluation of spectrogram performance
PublicationThe concept of spectrogram performance evaluation which exploits information on phase of short-term Fourier transform (STFT) is presented. A spectrograph which is time-frequency analyzing tool, is compared to a filter bank that demultiplexes a signal. Local group delay (LGD) and channelized instantaneous frequency (CIF) is obtained for each filtered component signal. In presented solution the performance is evaluated using so-called...
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LONG-TERM RISK CLASS MIGRATIONS OF NON-BANKRUPT AND BANKRUPT ENTERPRISES
PublicationThis paper investigates how the process of going bankrupt can be recognized much earlier by enterprises than by traditional forecasting models. The presented studies focus on the assessment of credit risk classes and on determination of the differences in risk class migrations between non-bankrupt enterprises and future insolvent firms. For this purpose, the author has developed a model of a Kohonen artificial neural network to...
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Advancing Solar Energy: Machine Learning Approaches for Predicting Photovoltaic Power Output
PublicationThis research is primarily concentrated on predicting the output of photovoitaic power, an essential field in the study of renewable energy. The paper comprehensively reviews various forecasting methodologies, transitioning from conventional physical and statistical methods to advanced machine learning (ML) techniques. A significant shift has been observed from traditional point forecasting to machine learning-based forecasting...
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Energy policy and the role of bioenergy in Poland
PublicationPrzedstawiono sytuacje energetyczną kraju. Omówiono poszczególne sektory wytwarzania energii oraz zapotrzebowania na energię i paliwa. Omówiono przekształcenia sektorów energetycznych w Polsce na przestrzeni lat 1990-2002. Na tym tle przedstawiono techniczne, ekonomiczne, społeczne i prawne aspekty rozwoju energetyki źródeł odnawialnych a w szczególności opartych o bioenergię. Przedstawiono wnioski dla Polski płynące z dotychczasowych...
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Data augmentation for improving deep learning in image classification problem
PublicationThese days deep learning is the fastest-growing field in the field of Machine Learning (ML) and Deep Neural Networks (DNN). Among many of DNN structures, the Convolutional Neural Networks (CNN) are currently the main tool used for the image analysis and classification purposes. Although great achievements and perspectives, deep neural networks and accompanying learning algorithms have some relevant challenges to tackle. In this...
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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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Short-Term Price Reaction to Filing for Bankruptcy and Restructuring Proceedings—The Case of Poland
PublicationThis study aims to check market reaction to filing for bankruptcy and restructuring proceedings and to verify the short-term effect of a price reversal in the Polish market in the years 2004–2019. The research was conducted by dividing the analysed companies according to the procedure (bankruptcy and restructuring) and market (the main market and the NewConnect market). The research methodology used in the study is the event analysis...
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Deep Learning Optimization for Edge Devices: Analysis of Training Quantization Parameters
PublicationThis paper focuses on convolution neural network quantization problem. The quantization has a distinct stage of data conversion from floating-point into integer-point numbers. In general, the process of quantization is associated with the reduction of the matrix dimension via limited precision of the numbers. However, the training and inference stages of deep learning neural network are limited by the space of the memory and a...
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Decision making process using deep learning
PublicationEndüstri 4.0, dördüncü endüstri devrimi veya Endüstriyel Nesnelerin İnterneti (IIoT) olarak adlandırılan sanayi akımı, işletmelere, daha verimli, daha büyük bir esneklikle, daha güvenli ve daha çevre dostu bir şekilde üretim yapma imkanı sunmaktadır. Nesnelerin İnterneti ile bağlantılı yeni teknoloji ve hizmetler birçok endüstriyel uygulamada devrim niteliği taşımaktadır. Fabrikalardaki otomasyon, tahminleyici bakım (PdM – Predictive...
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Evaluation of reformer tubes degradation after long term operation
PublicationPurpose of this paper is to show the effect of long-term service at elevated temperatures on microstructural changes of cast steel reformer tubes made of the alloy IN-519 (24%Cr, 24%Ni, Nb). The relationship between mechanical properties and microstructure degradation is discussed. Investigations were performed on five tubes taken from ammonia reformer furnace. Tubes worked at temperature of 880C and 3,2 MPa pressure. The operation...
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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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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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JOURNAL OF POWER SOURCES
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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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Poland’s energy dependence - economic context
Open Research DataPoland does not have vast resources of non-renewable energy and no nuclear power plant, therefore the issue of the energy dependence of the state, which affects the level of energy security of the country, is an extremely important factor. It depends on both the volume of imports of energy raw materials and the policy of diversification of sources of...
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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...