Search results for: DEEP EUTECTIC SOLVENTS
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Performance and Energy Aware Training of a Deep Neural Network in a Multi-GPU Environment with Power Capping
PublicationIn this paper we demonstrate that it is possible to obtain considerable improvement of performance and energy aware metrics for training of deep neural networks using a modern parallel multi-GPU system, by enforcing selected, non-default power caps on the GPUs. We measure the power and energy consumption of the whole node using a professional, certified hardware power meter. For a high performance workstation with 8 GPUs, we were...
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Changes in conditions of acoustic wave propagation in the Gdansk deep as an effect of climate changes in the Baltic Sea region
PublicationThe article presents the results from a research project investigating acoustic climate changes in the Gdansk Deep based on data extending from 1902 to 2019. This part of the southern Gotland Basin, is rarely discussed in the scientific literature. The speed of sound in the seawater is a function of temperature, salinity, and depth. In such shallow sea as Baltic Sea, the impact of depth is not substantial. The other two factors...
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Recent developments in the determination of residual solvents in pharmaceutical products by microextraction methods
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Usefulness of the Free Length Theory for assessment of the self-association of the pure solvents
PublicationWykonano pomiary szybkości rozchodzenia się dźwięku i gęstości metanolu, acetonitrylu, N,N-dimetyloformamidu, N,N-dimetyloacetamidu, dimetylosulfotlenku i fosforanu trietylu w zakresie temperatur 294 - 333 K . W oparciu o wyznaczone ściśliwości adiabatyczne zastosowano teorię FLT do oceny wzajemnej asocjacji cząsteczek. Uzyskane wyniki przedyskutowano na tle innych sposobów klasyfikacji rozpuszczalników.
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Apparent molar volumes and compressibilities of tetrabutyl-ammonium bromide in organic solvents
PublicationW oparciu o pomiary gęstości i szybkości rozchodzenia się dźwięku w niewodnych roztworach bromku czterobutyloamoniowego obliczono pozorne objętości molowe w przedziale temperatur od 288,15 K do 323,15 K oraz pozorne ściśliwości w temperaturze 298,15 K. Graniczne wielkości pozornej objętości molowej i ściśliwości wyznaczono w oparciu o równanie Massona, jak i równanie Redlicha, Rosenfelda i Mayera oraz z wykorzystaniem teorii Pitzera....
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Abdominal Aortic Aneurysm segmentation from contrast-enhanced computed tomography angiography using deep convolutional networks
PublicationOne of the most common imaging methods for diagnosing an abdominal aortic aneurysm, and an endoleak detection is computed tomography angiography. In this paper, we address the problem of aorta and thrombus semantic segmentation, what is a mandatory step to estimate aortic aneurysm diameter. Three end-to-end convolutional neural networks were trained and evaluated. Finally, we proposed an ensemble of deep neural networks with underlying...
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Changes in conditions of acoustic wave propagation in the Gdansk deep as an effect of climate changes in the Baltic Sea region
PublicationThe article presents the results from a research project investigating acoustic climate changes in the Gdansk Deepbased on data extending from 1902 to 2019. This part of the southern Gotland Basin, is rarely discussed in thescientific literature.The speed of sound in the seawater is a function of temperature, salinity, and depth. In such shallow sea asBaltic Sea, the impact of depth is not substantial....
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Deep-Learning-Based Precise Characterization of Microwave Transistors Using Fully-Automated Regression Surrogates
PublicationAccurate models of scattering and noise parameters of transistors are instrumental in facilitating design procedures of microwave devices such as low-noise amplifiers. Yet, data-driven modeling of transistors is a challenging endeavor due to complex relationships between transistor characteristics and its designable parameters, biasing conditions, and frequency. Artificial neural network (ANN)-based methods, including deep learning...
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Deep neural networks for human pose estimation from a very low resolution depth image
PublicationThe work presented in the paper is dedicated to determining and evaluating the most efficient neural network architecture applied as a multiple regression network localizing human body joints in 3D space based on a single low resolution depth image. The main challenge was to deal with a noisy and coarse representation of the human body, as observed by a depth sensor from a large distance, and to achieve high localization precision....
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Rediscovering Automatic Detection of Stuttering and Its Subclasses through Machine Learning—The Impact of Changing Deep Model Architecture and Amount of Data in the Training Set
PublicationThis work deals with automatically detecting stuttering and its subclasses. An effective classification of stuttering along with its subclasses could find wide application in determining the severity of stuttering by speech therapists, preliminary patient diagnosis, and enabling communication with the previously mentioned voice assistants. The first part of this work provides an overview of examples of classical and deep learning...
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Accurate Modeling of Antenna Structures by Means of Domain Confinement and Pyramidal Deep Neural Networks
PublicationThe importance of surrogate modeling techniques has been gradually increasing in the design of antenna structures over the recent years. Perhaps the most important reason is a high cost of full-wave electromagnetic (EM) analysis of antenna systems. Although imperative in ensuring evaluation reliability, it entails considerable computational expenses. These are especially pronounced when carrying out EM-driven design tasks such...
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AGAR a Microbial Colony Dataset for Deep Learning Detection
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Detecting Lombard Speech Using Deep Learning Approach
PublicationRobust Lombard speech-in-noise detecting is challenging. This study proposes a strategy to detect Lombard speech using a machine learning approach for applications such as public address systems that work in near real time. The paper starts with the background concerning the Lombard effect. Then, assumptions of the work performed for Lombard speech detection are outlined. The framework proposed combines convolutional neural networks...
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Musical Instrument Identification Using Deep Learning Approach
PublicationThe work aims to propose a novel approach for automatically identifying all instruments present in an audio excerpt using sets of individual convolutional neural networks (CNNs) per tested instrument. The paper starts with a review of tasks related to musical instrument identification. It focuses on tasks performed, input type, algorithms employed, and metrics used. The paper starts with the background presentation, i.e., metadata...
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Attention-Based Deep Learning System for Classification of Breast Lesions—Multimodal, Weakly Supervised Approach
PublicationBreast cancer is the most frequent female cancer, with a considerable disease burden and high mortality. Early diagnosis with screening mammography might be facilitated by automated systems supported by deep learning artificial intelligence. We propose a model based on a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM) classifier able to train under data scarcity effectively. We used a private...
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Spatiotemporal Assessment of Satellite Image Time Series for Land Cover Classification Using Deep Learning Techniques: A Case Study of Reunion Island, France
PublicationCurrent Earth observation systems generate massive amounts of satellite image time series to keep track of geographical areas over time to monitor and identify environmental and climate change. Efficiently analyzing such data remains an unresolved issue in remote sensing. In classifying land cover, utilizing SITS rather than one image might benefit differentiating across classes because of their varied temporal patterns. The aim...
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Metrological parameters of sulphur dioxide amperometric sensor containing addition of aprotic solvents
PublicationW publikacji zaprezentowano wyniki badań nad dodatku rozpuszczalnika aprotycznego do roztworu elektrolitu wewnętrznego czujnika na parametry metrologiczne prototypowego czujnika ditlenku siarki z membraną nafionową. Porównywano właściwości pięciu czujników różniących się składem elektrolitu. W każdym przypadku elektroda pracująca wykonana była ze złota, próżniowo napylonego na powierzchnię membrany. Roztwory elektrolitów zawierały...
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Possible application of new class of solvents in the purification of water used in hydraulic fracturing
PublicationNowym źródłem energii niekonwencjonalnej w Polsce może być gaz, który jest uwięziony w łupkach. Podczas poszukiwań gazu łupkowego, duże ilości wody wykorzystywanej w procesie szczelinowania hydraulicznego powracają na powierzchnię zanieczyszczone. Istnieje wiele metod usuwania śladowych ilości węglowodorów tj. fizyczne czyli filtracja czy chemiczne czyli zakwaszanie, ale mogą one być nieskuteczne. Pożądana metoda to monitorowanie...
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Donor properties of water in organic solvents derived from infrared spectraof HDO
PublicationPrzedyskutowano niektóre ilościowe aspekty kooperatywności wiązań wodorowych wody. Zaproponowano skalę własności elektronodonorowych wody w środowisku aprotycznych rozpuszczalników organicznych, pochodną w stosunku do skali liczb donorowych Gutmanna.
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Distinguishing of cocrystals from simple eutectic mixtures: phenolic acids as potential pharmaceutical coformers
PublicationThe multiparameter model comprising 1D and 2D QSPR/QSAR descriptors was proposed and validated for phenolic acid binary systems. This approach is based on the optimization of regression coefficients for maximization of the percentage of true positives in the pool of systems comprising either simple binary eutectics or cocrystals. The training set consisted of 58 eutectics and 168 cocrystals. The solid dispersions collection used...
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DEEP-SEA RESEARCH PART I-OCEANOGRAPHIC RESEARCH PAPERS
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Reliable computationally-efficient behavioral modeling of microwave passives using deep learning surrogates in confined domains
PublicationThe importance of surrogate modeling techniques has been steadily growing over the recent years in high-frequency electronics, including microwave engineering. Fast metamodels are employed to speedup design processes, especially those conducted at the level of full-wave electromagnetic (EM) simulations. The surrogates enable massive system evaluations at nearly EM accuracy and negligible costs, which is invaluable in parameter...
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GPU Power Capping for Energy-Performance Trade-Offs in Training of Deep Convolutional Neural Networks for Image Recognition
PublicationIn the paper we present performance-energy trade-off investigation of training Deep Convolutional Neural Networks for image recognition. Several representative and widely adopted network models, such as Alexnet, VGG-19, Inception V3, Inception V4, Resnet50 and Resnet152 were tested using systems with Nvidia Quadro RTX 6000 as well as Nvidia V100 GPUs. Using GPU power capping we found other than default configurations minimizing...
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Deep brain stimulation: new possibilities for the treatment of mental disorders
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Generation of microbial colonies dataset with deep learning style transfer
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Deep learning-based waste detection in natural and urban environments
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Efficiency of deep bed filtration in treatment of swimming pool water
PublicationPrzebadano efektywność filtracji wody w filtrach ze złożem żwirowo-piaskowym w instalacji basenu rehabilitacyjnego. Obok analizy instrumentalnej wody, w badaniach uwzględniono rozkład wielkości cząstek i analizę termiczną osadu zgromadzonego w złożu piaskowym filtrów wgłębnych i usuwanego podczas płukania.
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The Impact of Dredging Deep Pits on Organic Matter Decomposition in Sediments
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Problem of selection of reference plane with deep and wide valleys analysis
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TiO2 Processed by pressurized hot solvents as a novel photocatalyst for photocatalytic reduction of carbon dioxide
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Fluorescence Lifetimes Study of α-Tocopherol and Biological Prenylquinols in Organic Solvents and Model Membranes
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Determination of solvents residues in vegetable oils and pharmaceuticals by headspace analysis and capillary gas chromatography.
PublicationPrzedstawiono wyniki zastosowania techniki analizy fazy nadpowierzchniowej w połączeniu z kapilarną chromatografią gazową do oznaczania wybranych rozpuszczalników organicznych w próbkach olejów jadalnych i produktów farmaceutycznych. Oznaczane rozpuszczalniki to: benzen, toluen, heksan oraz chlorowcopochodne węglowodorów alifatycznych. Opracowaną procedurę poddano walidacji. Wyznaczono granicę wytrzymalności, oznaczalności, liniowość...
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Real-Time Facial Features Detection from Low Resolution Thermal Images with Deep Classification Models
PublicationDeep networks have already shown a spectacular success for object classification and detection for various applications from everyday use cases to advanced medical problems. The main advantage of the classification models over the detection models is less time and effort needed for dataset preparation, because classification networks do not require bounding box annotations, but labels at the image level only. Yet, after passing...
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Release Kinetics Studies of Early-Stage Volatile Secondary Oxidation Products of Rapeseed Oil Emitted during the Deep-Frying Process
PublicationThe research concerns the use of proton transfer reaction mass spectrometer to track real-time emissions of volatile secondary oxidation products released from rapeseed oil as a result of deep-frying of potato cubes. Therefore, it was possible to observe a sudden increase of volatile organic compound (VOC) emissions caused by immersion of the food, accompanied by a sudden release of steam from a potato cube and a decrease of the...
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Improvement of speech intelligibility in the presence of noise interference using the Lombard effect and an automatic noise interference profiling based on deep learning
PublicationThe Lombard effect is a phenomenon that results in speech intelligibility improvement when applied to noise. There are many distinctive features of Lombard speech that were recalled in this dissertation. This work proposes the creation of a system capable of improving speech quality and intelligibility in real-time measured by objective metrics and subjective tests. This system consists of three main components: speech type detection,...
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Stability studies of selected polycyclic aromatic hydrocarbons in different organic solvents and identification of their transformation products
PublicationGłównym problemem w laboratoriach analitycznych jest konserwacja próbek i ekstraktów przeznaczonych do analizy. Rozpatrując procesy degradacji z analitycznego punktu widzenia i związanej z tym wiarygodności uzyskiwanych wyników pod uwagę należy wziąć wpływ procesów degradacji w badanym elemencie środowiska na poziom stężenia składników śladowych; wpływ tych samych procesów na poziom składników śladowych w już pobranych próbkach...
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The porosity and morphology of PU foams prepared by solvent casting/salt leaching method with different solvents
PublicationIn this study, the polyurethane (PU) system based on poly(ethylene-butylene) adipate diol, 1,6-hexamethylene diisocyanate, 1,4-butanediol and ascorbic acid is used to prepare a foamed material. Polymer foams were created using solvent casting/salt-particle leaching (SC/PL) method. The influence of the PU concentration in different solutions [either in a DMF or in DMF with THF as a co-solvent] on the morphology and porosity of the...
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Analysis of the enthalpies of transfer of Co(II) ion in mixed solvents by means of the theory of preferential solvation.
PublicationZa pomocą teorii solwatacji preferencyjnej zanalizowano entalpie przeniesienia jonu Co(II) w kilku mieszaninach wodno-organicznych i organicznych, wyznaczając parametry, które w sposób ilościowy opisują solwatację preferencyjną jonu i jego wpływ na oddziaływania między cząsteczkami rozpuszczalnika. Przeprowadzono porównanie właściwości solwatacyjnych mieszanin. Wyznaczono także średni skład sfery solwatacyjnej jonu w zależności...
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Deep learning approach for delamination identification using animation of Lamb waves
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Deep learning super-resolution for the reconstruction of full wavefield of Lamb waves
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OmicSelector: automatic feature selection and deep learning modeling for omic experiments
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Simulation of backup rolls quenching with experimental study of deep cryogenic treatment
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Deep sea habitats in the chemical warfare dumping areas of the Baltic Sea
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Deep Data Analysis of a Large Microarray Collection for Leukemia Biomarker Identification
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Influence of parameters of deep grinding on nanohardness and surface roughness of C45 steel
PublicationPrzedstawiono wyniki badań wpływu głębokości współbieżnego szlifowania powierzchni płaskich na chropowatość i nanotwardość warstwy wierzchniej stali C45 o strukturze ferrytyczno-perlitycznej i średniej wielkości ziarna 20 μm. Dla wszystkich wartości głębokości szlifowania uzyskano znaczny wzrost twardości warstwy wierzchniej przedmiotu obrabianego.
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Deep Features Class Activation Map for Thermal Face Detection and Tracking
PublicationRecently, capabilities of many computer vision tasks have significantly improved due to advances in Convolutional Neural Networks. In our research, we demonstrate that it can be also used for face detection from low resolution thermal images, acquired with a portable camera. The physical size of the camera used in our research allows for embedding it in a wearable device or indoor remote monitoring solution for elderly and disabled...
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Autonomous pick-and-place system based on multiple 3Dsensors and deep learning
PublicationGrasping objects and manipulating them is the main way the robot interacts with its environment. However, for robots to operate in a dynamic environment, a system for determining the gripping position for objects in the scene is also required. For this purpose, neural networks segmenting the point cloud are usually applied. However, training such networks is very complex and their results are unsatisfactory. Therefore, we propose...
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Deep learning approach on surface EEG based Brain Computer Interface
PublicationIn this work we analysed the application of con-volutional neural networks in motor imagery classification for the Brain Computer Interface (BCI) purposes. To increase the accuracy of classification we proposed the solution that combines the Common Spatial Pattern (CSP) with convolutional network (ConvNet). The electroencephalography (EEG) is one of the modalities we try to use for controlling the prosthetic arm. Therefor in this...
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DEEP LEARNING BASED ON X-RAY IMAGING IMPROVES COXARTHROSIS DETECTION
PublicationObjective: The purpose of the study was to create an Artificial Neural Network (ANN) based on X-ray images of the pelvis, as an additional tool to automate and improve the diagnosis of coxarthrosis. The research is focused on joint space narrowing, which is a radiological symptom showing the thinning of the articular cartilage layer, which is translucent to X-rays. It is the first and the most important of the radiological signs...
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Using deep learning to increase accuracy of gaze controlled prosthetic arm
PublicationThis paper presents how neural networks can be utilized to improve the accuracy of reach and grab functionality of hybrid prosthetic arm with eye tracing interface. The LSTM based Autoencoder was introduced to overcome the problem of lack of accuracy of the gaze tracking modality in this hybrid interface. The gaze based interaction strongly depends on the eye tracking hardware. In this paper it was presented how the overall the...