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Search results for: smoking machine
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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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Cost-Efficient Measurement Platform and Machine-Learning-Based Sensor Calibration for Precise NO2 Pollution Monitoring
PublicationAir quality significantly impacts human health, the environment, and the economy. Precise real-time monitoring of air pollution is crucial for managing associated risks and developing appropriate short- and long-term measures. Nitrogen dioxide (NO2) stands as a common pollutant, with elevated levels posing risks to the human respiratory tract, exacerbating respiratory infections and asthma, and potentially leading to chronic lung...
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Control of induction machine supplied by a current source inverter using the multi-scalar transformation and backstepping approach
PublicationThe paper describes the voltage control technique of squire-cage induction machines supplied by a current source inverter. The control system is based on new transformation of the electric drive system (machine and inverter) state variables to the multi-scalar variables form. The backstepping approach is used to obtain the feedback control law. The control system contains the structure of the observer...
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Machine-Learning-Based Global Optimization of Microwave Passives with Variable-Fidelity EM Models and Response Features
PublicationMaximizing microwave passive component performance demands precise parameter tuning, particularly as modern circuits grow increasingly intricate. Yet, achieving this often requires a comprehensive approach due to their complex geometries and miniaturized structures. However, the computational burden of optimizing these components via full-wave electromagnetic (EM) simulations is substantial. EM analysis remains crucial for circuit...
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Efficient Calibration of Cost-Efficient Particulate Matter Sensors Using Machine Learning and Time-Series Alignment
PublicationAtmospheric 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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Development of an emulation platform for synchronous machine power generation system using a nonlinear functional level model
PublicationThe article presents the Power Hardware in the Loop (PHIL) approach for an autonomous power system analysis based on the synchronous generator model incorporating magnetic saturation effects. The model was prepared in the MATLAB/Simulink environment and then compiled into the C language for the PHIL platform implementation. The 150 kVA bidirectional DC/AC commercial-grade converter was used to emulate the synchronous generator....
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Chained machine learning model for predicting load capacity and ductility of steel fiber–reinforced concrete beams
PublicationOne of the main issues associated with steel fiber–reinforced concrete (SFRC) beams is the ability to anticipate their flexural response. With a comprehensive grid search, several stacked models (i.e., chained, parallel) consisting of various machine learning (ML) algorithms and artificial neural networks (ANNs) were developed to predict the flexural response of SFRC beams. The flexural performance of SFRC beams under bending was...
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Application of fiber optic sensors using Machine Learning algorithms for temperature measurement of lithium-ion batteries
PublicationOptical fiber sensorsusing low-coherence interferometry require processing ofthe output spectrum or interferogramto quickly and accurately determine the instantaneous value of the measured quantity, such as temperature.Methods based on machine learning are a good candidate for this application. The application of four such methods in an optical fiber temperature sensoris demonstrated.Using aZnO-coated...
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Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods
PublicationNowadays, Buckling-Restrained Brace Frames (BRBFs) have been used as lateral force-resisting systems for low-, to mid-rise buildings. Residual Interstory Drift (RID) of BRBFs plays a key role in deciding to retrofit buildings after seismic excitation; however, existing formulas have limitations and cannot effectively help civil engineers, e.g., FEMA P-58, which is a conservative estimation method. Therefore, there is a need to...
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Rapid Surrogate-Aided Multi-Criterial Optimization of Compact Microwave Passives Employing Machine Learning and ANNs
PublicationThis article introduces an innovative method for achieving low-cost and reliable multi-objective optimization (MO) of microwave passive circuits. The technique capitalizes on the attributes of surrogate models, specifically artificial neural networks (ANNs), and multi-resolution electromagnetic (EM) analysis. We integrate the search process into a machine learning (ML) framework, where each iteration produces multiple infill points...
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Ecology In Tribology: Selected Problems of Eliminating Natural Oil-Based Lubricants from Machine Friction Couples
PublicationThe elimination of mineral oil-based lubricants from machines has multiple beneficial effects on the natural environment. Firstly – these lubricants are a direct threat to the environment in the event of leaks; secondly – their elimination reduces the demand for crude oil from which they are obtained. In addition, in many cases, e.g. when replacing traditional lubricants with water, friction losses in the bearings can also be reduced...
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An algorithm for selecting a machine learning method for predicting nitrous oxide emissions in municipal wastewater treatment plants
PublicationThis study presents an advanced algorithm for selecting machine learning (ML) models for nitrous oxide (N2O) emission prediction in wastewater treatment plants (WWTPs) employing the activated sludge process. The examined ML models comprised multivariate adaptive regression spline (MARS), support vector machines (SVM), and extreme gradient boosting (XGboost). The study explores the concept that involves new criteria to select the...
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Organotin compounds in marine sediments
PublicationZwiązki cynoorganiczne są szeroko wykorzystywane w różnych sektorach gospodarki. Farby zawierające czynniki przeciwporostowe (biocydy), były wykorzystywane w ochronie przeciwkorozyjnej kadłubów statków i okrętów, są głównym źródłem emisji trójbutylo- i trójfenylocyny do środowiska morskiego.Międzynarodowa Organizacja Morska (IMO) wprowadziła w roku 2001 powszechny zakaz stosowania tych związków jako biocydów w powłokach malarskich....
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Determination of tributyltin in marine sediment.
PublicationPrzedstawiono wyniki badań międzynarodowego porównawczego, międzylaboratoryjnego oznaczania zawartości tributylocyny w osadzie morskim. Badania były organizowane przez Comite Consultatif pour la Quantite de Matiere (CCQM) pilot study P-18 international intercomparison i była to ich pierwsza część - badania pilotażowe. W badaniach uczestniczyło 11 laboratoriów. Uzyskano zadowalająco zgodne wyniki oznaczeń tributylocyny.
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On organic coatings in marine applications
PublicationZnaczna liczba konstrukcji eksploatowanych w warunkach morskich ulega zniszczeniu wskutek zmęczeniowego oddziaływania cyklicznych naprężeń mechanicznych. Również w przypadku powłok organicznych chroniących wspomniane konstrukcje obserwuje się przedwczesną degradację zmęczeniową. W poniższym artykule autorzy przedstawiają metodologię oceny wpływu cyklicznych naprężeń mechanicznych na trwałość ochronnych układów powłokowych. Badaniu...
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Seeking Optimal Nutrition for Healthy Body Mass Reduction Among Former Athletes
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Elimination of wild-type P53 mRNA in glioblastomas showing heterozygous mutations of P53
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Multigene P-value Integration Based on SNPs Investigation for Seeking Radiosensitivity Signatures
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Challenges in oscillometric blood pressure measurement in atrial fibrillation: looking for practical solutions
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Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review
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Mortality After Traumatic Brain Injury in Elderly Patients: A New Scoring System
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Stable and degradable microgels linked with cystine for storing and environmentally triggered release of drugs
PublicationEnvironmentally sensitive, degradable microgels based on poly(N-isopropylacrylamide) (pNIPA) crosslinked with the diacryloyl derivative of cystine (BISS) were synthesized by applying surfactant-free emulsion polymerization. pNIPA contributed the sensitivity to temperature to the microgels and the cross-linker made them degradable and sensitive to pH. The morphology of the microgels was investigated by using scanning and transmission...
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Shaking table experimental study on diagnosis of damage and its evaluation in steel structure
PublicationCelem artykułu jest pokazanie wyników badań eksperymentalnych przeprowadzonych na stole sejsmicznym dotyczących identyfikacji uszkodzeń modelu dwukondygnacyjnej konstrukcji stalowej. Uszkodzenie zdefiniowano poprzez spadek sztywności i symulowano poprzez zamianę słupów modelu na elementy o mniejszym przekroju poprzecznym. Wyniki badań pokazują charakterystyczny spadek wartości naturalnych częstotliwości konstrukcji wraz ze wzrostem...
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Merging extremum seeking and self-optimizing narrowband interference canceller - overdetermined case
PublicationActive cancellation systems rely on destructive interference to achieve rejection of unwanted disturbances entering the system of interest. Typical practical applications of this method employ a simple single input, single output arrangement. However, when a spatial wavefield (e.g. acoustic noise or vibration) needs to be controlled, multichannel active cancellation systems arise naturally. Among these, the so-called overdetermined...
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Exposure to cooking emitted volatile organic compounds with recirculating and extracting ventilation solutions
PublicationEnergy-efficient urban development leads to the compact design of apartments. Recirculating ventilation solutions are an attempt to minimize the space required for ventilation ducting, but more data on their performance are needed. Cooking is a major source of volatile organic compounds (VOCs) emissions. It is necessary to assess how well recirculating kitchen hoods perform in reducing the residents' exposure to cooking fumes compared...
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Looking through the past: better knowledge retention for generative replay in continual learning
PublicationIn this work, we improve the generative replay in a continual learning setting to perform well on challenging scenarios. Because of the growing complexity of continual learning tasks, it is becoming more popular, to apply the generative replay technique in the feature space instead of image space. Nevertheless, such an approach does not come without limitations. In particular, we notice the degradation of the continually trained...
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Shaking table experimental study on the effectiveness of polymer bearings for seismic isolation of structures
PublicationSeismic isolation has been recognised to be a very effective way of protecting structures from damage during earthquakes. It allows us to extend the natural period of the structure and therefore avoid resonance with the ground motion. Moreover, by increasing damping in the isolation devices, more energy can be dissipated and thus the structural response can be further reduced. The aim of this paper is to show the results of the...
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Analysis of sloping brace stiffness influence on stability and load bearing capacity of a truss
PublicationThe paper is focused on the numerical study of stability and load bearing capacity of a truss with side elastic braces. The structure is made in reality. The rotational and sliding brace stiffnesses were taken into account. Linear buckling analysis and non-linear static analysis with geometric and material nonlinearity were performed for the beam and shell model of the truss with respect to the angle of sloping braces. As a result...
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Shaking table experimental study on models of steel buildings with different types of joints
PublicationThe aim of this paper is to study the response of models of steel buildings with destroyed and non-destroyed joints. The study was conducted experimentally using the shaking table tests. Two steel models were considered. Several types of joints were taken into account: totally destroyed joints, partially destroyed joints, welded joints and joints stiffened with additional metal. Six ground motions were taken into account. The acceleration...
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Changes in the EPA and DHA content and lipids quality parameters of rainbow trout (Oncorhynchus mykiss, Walbaum) and carp (Cyprinus carpio, L.) at individual stages of hot smoking
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Integrated Processing: Quality Assurance Procedure of the Surface Layer of Machine Parts during the Manufacturing Step "Diamond Smoothing"
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Molecular Simulations Using Boltzmann’s Thermally Activated Diffusion - Implementation on ARUZ – Massively-parallel FPGA-based Machine
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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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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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THE EFFECT OF WOOD DRYING METHOD ON THE GRANULARITY OF SAWDUST OBTAINED DURING THE SAWING PROCESS USING THE FRAME SAWING MACHINE
PublicationThe experimental results of the study focused on the effect of drying processes of warm air drying at the temperature of 6580°C and warm air-steam mixture drying at the temperature of 105°C of pine and beech wood to the size of sawdust grains created by cutting using RPW 15M frame saw is presented in the paper. Particle size analysis of dry sawdust was performed using two methods - screening method and optical method based on...
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Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
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The Influence of Permanent Magnet Length and Magnet Type on Flux-control of Axial Flux Hybrid Excited Electrical Machine
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A pilot study to assess manufacturing processes using selected point measures of vibroacoustic signals generated on a multitasking machine
PublicationThe article presents the method for the evaluation of selected manufacturing processes using the analysis of vibration and sound signals. This method is based on the use of sensors installed outside the machining zone, allowing to be used quickly and reliably in real production conditions. The article contains a developed measurement methodology based on the specific location of microphones and vibration transducers mounted on...
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The chemistry, properties and performance of flame-retardant rubber composites: Collecting, analyzing, categorizing, machine learning modeling, and visualizing
PublicationRubbers combine the flexibility with mechanical strength, supporting myriad applications, but suffer from inherent flammability. Formulation and production of flame-retardant rubber composites (FRRCs) have intensively been practiced over years, but not comprehensively reviewed. This necessity has outlined collecting, analyzing, screening, classifying, and interpreting the literature with the aim of classifying the FRRCs. We quantified...
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Improving platelet‐RNA‐based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification
PublicationLiquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community...
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Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls
PublicationEfficient seismic risk assessment aids decision-makers in formulating citywide risk mitigation plans, providing insights into building performance and retrofitting costs. The complexity of modeling, analysis, and post-processing of the results makes it hard to fast-track the seismic probabilities, and there is a need to optimize the computational time. This research addresses seismic probability and risk assessment of reinforced...
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Development of advanced machine learning for prognostic analysis of drying parameters for banana slices using indirect solar dryer
PublicationIn 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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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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Cost-Efficient Multi-Objective Design of Miniaturized Microwave Circuits Using Machine Learning and Artificial Neural Network
PublicationDesigning microwave components involves managing multiple objectives such as center frequencies, impedance matching, and size reduction for miniaturized structures. Traditional multi-objective optimization (MO) approaches heavily rely on computationally expensive population-based methods, especially when exe-cuted with full-wave electromagnetic (EM) analysis to guarantee reliability. This paper introduces a novel and cost-effective...
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Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
PublicationWetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral...
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Hybrid Process Equipment: Improving the Efficiency of the Integrated Metalworking Machines Initial Designing
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1 kapitola: Status report on frame sawing machines for narrow-kerf sawing
PublicationW opracowaniu przedstawiono genezę pilarek ramowych oraz przecinania drewna cienkimi piłami. Omówiono ekonomiczne i ekologiczne zalety tego typu obróbki. Zaprezentowano kinematykę współczesnych pilarek dostępnych na rynkach światowych: Orbit, Mamuth oraz DSG Sonic. Szczegółowo opisano układ napędowy pilarek DTPX-30 i PRW15 z hybrydowym napędem głównym i całkowitym wyrównoważeniem dynamicznym układu.
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An overview of torque meters and new devices development for condition monitoring of mining machines
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Dynamic Analysis of an Enhanced Multi-Frequency Inertial Exciter for Industrial Vibrating Machines
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Scheduling of identical jobs with bipartite incompatibility graphs on uniform machines. Computational experiments
PublicationWe consider the problem of scheduling unit-length jobs on three or four uniform parallel machines to minimize the schedule length or total completion time. We assume that the jobs are subject to some types of mutual exclusion constraints, modeled by a bipartite graph of a bounded degree. The edges of the graph correspond to the pairs of jobs that cannot be processed on the same machine. Although the problem is generally NP-hard,...