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Wyniki wyszukiwania dla: TURBINE LINEAR REGRESSION MACHINE LEARNING OPTIMIZATION ORC
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Machine learning-based prediction of residual drift and seismic risk assessment of steel moment-resisting frames considering soil-structure interaction
PublikacjaNowadays, due to improvements in seismic codes and computational devices, retrofitting buildings is an important topic, in which, permanent deformation of buildings, known as Residual Interstory Drift Ratio (RIDR), plays a crucial role. To provide an accurate yet reliable prediction model, 32 improved Machine Learning (ML) algorithms were considered using the Python software to investigate the best method for estimating Maximum...
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Solubility Characteristics of Acetaminophen and Phenacetin in Binary Mixtures of Aqueous Organic Solvents: Experimental and Deep Machine Learning Screening of Green Dissolution Media
PublikacjaThe solubility of active pharmaceutical ingredients is a mandatory physicochemical characteristic in pharmaceutical practice. However, the number of potential solvents and their mixtures prevents direct measurements of all possible combinations for finding environmentally friendly, operational and cost-effective solubilizers. That is why support from theoretical screening seems to be valuable. Here, a collection of acetaminophen...
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Integrating Statistical and Machine‐Learning Approach for Meta‐Analysis of Bisphenol A‐Exposure Datasets Reveals Effects on Mouse Gene Expression within Pathways of Apoptosis and Cell Survival
PublikacjaBisphenols are important environmental pollutants that are extensively studied due to different detrimental effects, while the molecular mechanisms behind these effects are less well understood. Like other environmental pollutants, bisphenols are being tested in various experimental models, creating large expression datasets found in open access storage. The meta‐analysis of such datasets is, however, very complicated for various...
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Lead-free bismuth-based perovskites coupled with g–C3N4: A machine learning based novel approach for visible light induced degradation of pollutants
PublikacjaThe use of metal halide perovskites in photocatalytic processes has been attempted because of their unique optical properties. In this work, for the first time, Pb-free Bi-based perovskites of the Cs3Bi2X9 type (X = Cl, Br, I, Cl/Br, Cl/I, Br/I) were synthesized and subjected to comprehensive morphological, structural, and surface analyses, and photocatalytic properties in the phenol degradation reaction were examined. Furthermore,...
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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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Machine learning goes global: Cross-sectional return predictability in international stock markets
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Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines
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Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems
PublikacjaTe feld of Big Data is rapidly developing with a lot of ongoing research, which will likely continue to expand in the future. A crucial part of this is Knowledge Discovery from Data (KDD), also known as the Knowledge Discovery Process (KDP). Tis process is a very complex procedure, and for that reason it is essential to divide it into several steps (Figure 1). Some authors use fve steps to describe this procedure, whereas others...
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Machine Learning for Control Systems Security of Industrial Robots: a Post-covid-19 Overview
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Likelihood of Transformation to Green Infrastructure Using Ensemble Machine Learning Techniques in Jinan, China
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Detection of circulating tumor cells by means of machine learning using Smart-Seq2 sequencing
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Sperm segmentation and abnormalities detection during the ICSI procedure using machine learning algorithms
Publikacja(1) About 15-20% of couples struggle with the problem of infertility. 30 to 40% of these cases are caused by abnormalities in the structure and motility of sperm. Sometimes the only possibility for such people is to use the procedure of artificial insemination. CASA systems are used to increase the efficiency of this procedure by selecting the appropriate sperm cell. (2) This paper presents an approach to the sperm classification...
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SZACOWANIE ZAWARTOŚCI BENZO(a)PIRENU W PYLE ZAWIESZONYM PM10 W AGLOMERACJI TRÓJMIEJSKIEJ ZA POMOCĄ WIELOWYMIAROWEJ REGRESJI LINIOWEJ=ESTIMATION OF BENZO(A)PYRENE CONTENT IN SUSPENDED DUST PM10 IN TRI-CITY AGGLOMERATION USING MULTIDIMENSIONAL LINEAR REGRESSION
PublikacjaW pracy przedstawiono próbę oszacowania przy pomocy wielowymiarowej regresji liniowej modelu empirycznego opisującego czynniki wpływające na zawartość B(a)P w pyle zawieszonym PM10 w Aglomeracji Trójmiejskiej w latach 2008-2011. Na przestrzeni tych lat średnioroczne stężenie B(a)P w PM10 wzrosło ponad dwukrotnie i ponad trzykrotnie przewyższa poziom docelowy. Z przeprowadzonych analiz wynika, że główną przyczyną wzrostu stężenia...
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Multi-criteria Differential Evolution for Optimization of Virtual Machine Resources in Smart City Cloud
PublikacjaIn a smart city, artificial intelligence tools support citizens and urban services. From the user point of view, smart applications should bring computing to the edge of the cloud, closer to citizens with short latency. However, from the cloud designer point of view, the trade-off between cost, energy and time criteria requires the Pareto solutions. Therefore, the proposed multi-criteria differential evolution can optimize virtual...
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Perception of Pathologists in Poland of Artificial Intelligence and Machine Learning in Medical Diagnosis—A Cross-Sectional Study
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Machine Learning Applied to Aspirated and Non-Aspirated Allophone Classification—An Approach Based on Audio "Fingerprinting"
PublikacjaThe purpose of this study is to involve both Convolutional Neural Networks and a typical learning algorithm in the allophone classification process. A list of words including aspirated and non-aspirated allophones pronounced by native and non-native English speakers is recorded and then edited and analyzed. Allophones extracted from English speakers’ recordings are presented in the form of two-dimensional spectrogram images and...
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Universal Predictors of Dental Students’ Attitudes towards COVID-19 Vaccination: Machine Learning-Based Approach
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Efficient Calibration of Cost-Efficient Particulate Matter Sensors Using Machine Learning and Time-Series Alignment
PublikacjaAtmospheric particulate matter (PM) poses a significant threat to human health, infiltrating the lungs and brain and leading to severe issues such as heart and lung diseases, cancer, and premature death. The main sources of PM pollution are vehicular and industrial emissions, construction and agricultural activities, and natural phenomena such as wildfires. Research underscores the absence of a safe threshold for particulate exposure,...
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Quantitative Soil Characterization for Biochar–Cd Adsorption: Machine Learning Prediction Models for Cd Transformation and Immobilization
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Detection of Cystic Fibrosis Symptoms Based on X-Ray Images Using Machine Learning- Pilot Study
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Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods
PublikacjaOne of the important factors determining the biochemical processes in bioreactors is the quality of the wastewater inflow to the wastewater treatment plant (WWTP). Information on the quality of wastewater, sufficiently in advance, makes it possible to properly select bioreactor settings to obtain optimal process conditions. This paper presents the use of classification models to predict the variability of wastewater quality at...
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Guest editorial: learning, scheduling, resource optimization, and evolution in smart artificial systems: challenges and support
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Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning
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Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer
PublikacjaIn this study, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting (LightGBM) algorithms were used to model-predict the drying characteristics of banana slices with an indirect solar drier. The relationships between independent variables (temperature, moisture, product type, water flow rate, and mass of product) and dependent variables (energy consumption and size reduction) were established. For energy consumption,...
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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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Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
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Personalized prediction of the secondary oocytes number after ovarian stimulation: A machine learning model based on clinical and genetic data
PublikacjaControlled ovarian stimulation is tailored to the patient based on clinical parameters but estimating the number of retrieved metaphase II (MII) oocytes is a challenge. Here, we have developed a model that takes advantage of the patient’s genetic and clinical characteristics simultaneously for predicting the stimulation outcome. Sequence variants in reproduction-related genes identified by next-generation sequencing were matched...
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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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Machine Learning Algorithm-Based Tool and Digital Framework for Substituting Daylight Simulations In Early- Stage Architectural Design Evaluation
PublikacjaThe aim of this paper is to examine the new method of obtaining the simulation-based results using backpropagation of errors artificial neural networks. The primary motivation to conduct the research was to determine an alternative, more efficient and less timeconsuming method which would serve to achieve the results of daylight simulations. Three daylight metrics: Daylight Factor, Daylight Autonomy and Daylight Glare Probability have...
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Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from Cracow and machine learning
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Determinants of anxiety levels among young males in a threat of experiencing military conflict–Applying a machine-learning algorithm in a psychosociological study
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Statistical Data Pre-Processing and Time Series Incorporation for High-Efficacy Calibration of Low-Cost NO2 Sensor Using Machine Learning
PublikacjaAir pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given...
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Machine-learning-based precise cost-efficient NO2 sensor calibration by means of time series matching and global data pre-processing
PublikacjaAir pollution remains a considerable contemporary challenge affecting life quality, the environment, and economic well-being. It encompasses an array of pollutants—gases, particulate matter, biological molecules—emanating from sources such as vehicle emissions, industrial activities, agriculture, and natural occurrences. Nitrogen dioxide (NO2), a harmful gas, is particularly abundant in densely populated urban areas. Given its...
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Example of Using Particle Swarm Optimization Algorithm with Nelder–Mead Method for Flow Improvement in Axial Last Stage of Gas–Steam Turbine
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The dynamic performance analysis of the Micro-turbine's rotor supparted on slide and roling element bearings
PublikacjaThis article presents the results of investigations of dynamic characteristics of a small-dimension rotor with slide and rolling element bearings. The object of investigations was the rotor-bearing system designed for the ORC based low-power steam micro-turbine. The investigations were performed using MESWIR series programs, as well as commercial FEA software ABAQUS and MADYN 2000. The results of modal analysis of the micro-rotor...
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Looking for a minimum exergy destruction in hierarchical cycle
PublikacjaThe paper presents results of energy analysis, complemented with an exergy balance, of hierarchical thermodynamic cycle. Proposed cycle is a binary vapour cycle based on a model of real supercritical steam power plant. Energy analysis is used to preliminary optimization of the cycle and the exergy losses analysis is proposed to perform optimization of heat transfer processes, which are essential for hierarchical cycles. Proposed...
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Management of low-temperature heat source by ORC aided by additional heat source
PublikacjaThe presented work is aimed at utilisation of waste heat in the manner to produce electricity in ORC installation. Therefore the precondition of the study was to use the waste heat available in the form of a stream of hot water at 90°C. Such low enthalpy heat source is rather insufficient to produce a good quality vapour to feed the ORC turbine. That was the incentive to search for the ways of increasing the temperature of the...
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Combined operation of 900MW power plant with the ORC through the bleed steam extraction point and CO2 recovery system
PublikacjaThe work presented here is aimed at utylisation of waste heat in the reference supercritical power plant in the manner to produce electricity in ORC installation. The waste heat is available in the form of a stream of hot water at 90 C, recovered from the exhaust gases in the amount of 200MW. Such low enthalpy heat source is rather insufficient to produce a good quality vapour to feed the ORC turbine. Therefore an original approach...
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A commercial gas boiler fitted with the ORC module as perspective solution for households
PublikacjaIn this paper the applicability of the commercial gas boiler (De Dietrich DTG X23N) coupled with the laboratory prototype micro ORC setup was experimentally studied. The main objective was to determine the working fluid capability to obtain required temperatures of vapour prior to the turbine, attainable heat rates and efficiencies of the whole system. The boiler thermal power was 25 kW. Tests were carried out with a single stage...
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Organic Supercritical Thermodynamic Cycles with Isothermal Turbine
PublikacjaOrganic Rankine cycles (ORC) are quite popular, but the overall efficiencies of these plants are rather very low. Numerous studies have been conducted in many scientific centers and research centers to improve the efficiency of such cycles. The research concerns both the modification of the cycle and the increase in the parameters of the medium at the inlet to the turbine. However, the efficiency of even these modified cycles rarely...
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Double-stage ORC system based on various temperature waste heat sources of the negative CO2 power plant
PublikacjaAnalysed is the modification of the thermodynamic cycle with the negative CO2 power plant concept by its combination with the organic Rankine cycle. The analysed power plant operates on a gas produced from the gasification of sewage sludge. The negative emission term comes from the aggregated CO2 balance resulting from the capture of the CO2, while the sewage sludge is one of the inevitable environmental sources of CO2 to be avoided....
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Method of selecting the LS-SVM algorithm parameters in gas detection process
PublikacjaIn this paper we showed the method of resistive gas sensors data processing. The UV irradiation and temperature modulation was applied to improve gas sensors’ selectivity and sensitivity. Noise voltage across the sensor’s terminals (proportional to its resistance fluctuations) was recorded to estimate power spectral density. This function was an input data vector for LS-SVM (least squares – support vector machine) algorithm, which...
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Assessment, optimisation and working fluid comparison of organic rankine cycle combined with negative CO2 gas power plant system
PublikacjaThis study aims to investigate the application of the Organic Rankine Cycle (ORC) as an alternative to low-pressure expansion in the negative CO2 power plant (nCO2PP). The reason for this study is that a detailed analysis of nCO2PP indicates a certain amount of waste heat present in the exhaust gas from the high-to-intermediate pressure gas turbine. Some of this energy can be used by the application of the expansion in a low-pressure...
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Organic Rankine as bottoming cycle to a combined Brayton and Clausius-Rankine cycle
PublikacjaThis paper presents an enhanced approach, as it will be considered here that the ORC installation could be extra-heated with the bleed steam, a concept presented by the authors. In such way the efficiency of the bottoming cycle can be increased and an amount of electricity generated increases. A thermodynamic analysis and a comparative study of the cycle efficiency for a simplified steam cycle cooperating with ORC cycle will be...
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When Neural Networks Meet Decisional DNA: A Promising New Perspective for Knowledge Representation and Sharing
PublikacjaABSTRACT In this article, we introduce a novel concept combining neural network technology and Decisional DNA for knowledge representation and sharing. Instead of using traditional machine learning and knowledge discovery methods, this approach explores the way of knowledge extraction through deep learning processes based on a domain’s past decisional events captured by Decisional DNA. We compare our approach with kNN (k-nearest...
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Operation of the 900 MW power plant with the ORC supplied from three heat sources
PublikacjaThe chapter is aimed at utilisation of waste heat in the reference supercritical power plant in the manner to produce electricity in ORC installation. The waste heat is available in the form of a stream of hot water at 90°C, recovered from the exhaust gases in the amount of 200MW. Such low enthalpy heat source is rather insufficient to produce a good quality vapour to feed the ORC turbine. Therefore an original approach to increase...
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Computing methods for fast and precise body surface area estimation of selected body parts
PublikacjaCurrently used body surface area (BSA) formulas give satisfactory results only for individuals with typical physique, while for elderly, obese or anorectic people accurate results cannot be expected. Particularly noteworthy are the results for individuals with severe obesity (body-mass index greater than 35 kg/m2), for which BSA estimation errors reached 80%. The main goal of our study is the development of precise BSA models for...
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Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models
PublikacjaThis work addresses artificial-intelligence-based buried object characterization using 3-D full-wave electromagnetic simulations of a ground penetrating radar (GPR). The task is to characterize cylindrical shape, perfectly electric conductor (PEC) object buried in various dispersive soil media, and in different positions. The main contributions of this work are (i) development of a fast and accurate data driven surrogate modeling...
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Selected aspects of performance of organic Rankine cycles incorporated into bioenergy with carbon capture and storage using gasification of sewage sludge
PublikacjaThe study aims to investigate the application of the Organic Rankine Cycle (ORC) in the bioenergy with carbon capture and storage (BECCS) using gasification of sewage sludge. The tool used in the investigation is the Aspen Plus software with REFPROP property methods for calculating fluid properties. The reason for this study is that a detailed analysis of the proposed BECCS process flow diagram indicates that a certain amount of...
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Increase of power and efficiency of the 900 MW supercritical power plant through incorporation of the ORC
PublikacjaThe objective of the paper is to analyse thermodynamical and operational parameters of the supercritical power plant with reference conditions as well as following the introduction of the hybrid system incorporating ORC. In ORC the upper heat source is a stream of hot water from the system of heat recovery having temperature of 90 °C, which is additionally aided by heat from the bleeds of the steam turbine. Thermodynamical analysis...