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Wyniki wyszukiwania dla: deep learning renewable energy sources photovoltaics buildings long short-term memory micro-grids
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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
PublikacjaThe 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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Efficiency of Biomass Energy used for Heating Purposes in a Residential Building in Comparison with other Energy Sources
PublikacjaThis 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
PublikacjaDeep 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
PublikacjaAbout 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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Machine Learning and Deep Learning Methods for Fast and Accurate Assessment of Transthoracic Echocardiogram Image Quality
PublikacjaHigh-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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Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches
PublikacjaExamining 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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Using phase of short-term Fourier transform for evaluation of spectrogram performance
PublikacjaThe 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
PublikacjaThis 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
PublikacjaThis 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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Data augmentation for improving deep learning in image classification problem
PublikacjaThese 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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Energy policy and the role of bioenergy in Poland
PublikacjaPrzedstawiono 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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A Proposed Machine Learning Model for Forecasting Impact of Traffic-Induced Vibrations on Buildings
PublikacjaTraffic-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
PublikacjaThis 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
PublikacjaThis 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
PublikacjaEndüstri 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
PublikacjaPurpose 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
PublikacjaThis 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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Optimized Deep Learning Model for Flood Detection Using Satellite Images
PublikacjaThe 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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Voltage profiles improvement in a power network with PV energy sources – results of a voltage regulator implementation
PublikacjaThe 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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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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Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
PublikacjaWe 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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Two tests for adhesive bonding long term characterization: principles and applications
PublikacjaThis 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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In vivo degradation of short-term implants
PublikacjaStan 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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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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Are deep eutectic solvents useful in chromatography? A short review
PublikacjaA 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
PublikacjaIn 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
PublikacjaBreast 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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JOURNAL OF POWER SOURCES
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A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection
PublikacjaComputational 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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Deep learning techniques for biometric security: A systematic review of presentation attack detection systems
PublikacjaBiometric 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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Deep eutectic solvents in analytical sample preconcentration Part B: Solid-phase (micro)extraction
PublikacjaOne 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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Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
PublikacjaThe 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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Application of Doubly Connected Dominating Sets to Safe Rectangular Smart Grids
PublikacjaSmart 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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Palm Oil Fuel Ash-Based Eco-Efficient Concrete: A Critical Review of the Short-Term Properties
PublikacjaThe 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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Poland’s energy dependence - economic context
Dane BadawczePoland 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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Visualization of short-term heart period variability with network tools as a method for quantifying autonomic drive
PublikacjaWe 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
PublikacjaThe 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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Deep learning based thermal image segmentation for laboratory animals tracking
PublikacjaAutomated 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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Potential negative effect of long-term exposure to nitrofurans on bacteria isolated from wastewater
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POSSIBILITIES OF ELECTRICAL ENERGY GENERATION IN PHOTOVOLTAIC SYSTEMS INSTALLED IN CENTRAL EUROPE
PublikacjaNowadays, 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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Short-term Price Reaction to Involuntary Bankruptcies Filed in Bad Faith: Empirical Evidence from Poland
PublikacjaPurpose: 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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Latest Insights on Novel Deep Eutectic Solvents (DES) for Sustainable Extraction of Phenolic Compounds from Natural Sources
PublikacjaPhenolic 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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Modelling Long‐Term Transition from Coal‐Reliant to Low‐Emission Power Grid and District Heating Systems in Poland
PublikacjaEnergy 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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Data, Information, Knowledge, Wisdom Pyramid Concept Revisited in the Context of Deep Learning
PublikacjaIn 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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Energy Transition in Poland—Assessment of the Renewable Energy Sector
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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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Reducing Air Pollutant Emissions from the Residential Sector by Switching to Alternative Energy Sources in Single-Family Homes
PublikacjaThe 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...