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Search results for: deep eutectic solvent
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Decision making process using deep learning
PublicationEndüstri 4.0, dördüncü endüstri devrimi veya Endüstriyel Nesnelerin İnterneti (IIoT) olarak adlandırılan sanayi akımı, işletmelere, daha verimli, daha büyük bir esneklikle, daha güvenli ve daha çevre dostu bir şekilde üretim yapma imkanı sunmaktadır. Nesnelerin İnterneti ile bağlantılı yeni teknoloji ve hizmetler birçok endüstriyel uygulamada devrim niteliği taşımaktadır. Fabrikalardaki otomasyon, tahminleyici bakım (PdM – Predictive...
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Solventless and solvent-minimized sample preparation techniques for determining currently used pesticides in water samples: A review
PublicationThe intensification of agriculture means that increasing amounts of toxic organic and inorganic compounds are entering the environment. The pesticides generally applied nowadays are regarded as some of the most dangerous contaminants of the environment. Their presence in the environment, especially in water, is hazardous because they cause human beings to become more susceptible to disease. For these reasons, it is essential to...
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Deep Instance Segmentation of Laboratory Animals in Thermal Images
PublicationIn this paper we focus on the role of deep instance segmentation of laboratory rodents in thermal images. Thermal imaging is very suitable to observe the behaviour of laboratory animals, especially in low light conditions. It is an non-intrusive method allowing to monitor the activity of animals and potentially observe some physiological changes expressed in dynamic thermal patterns. The analysis of the recorded sequence of thermal...
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Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
PublicationWe present a novel deep learning model for the detection and reconstruction of dysarthric speech. We train the model with a multi-task learning technique to jointly solve dysarthria detection and speech reconstruction tasks. The model key feature is a low-dimensional latent space that is meant to encode the properties of dysarthric speech. It is commonly believed that neural networks are black boxes that solve problems but do not...
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Microbiological condition of sediments and bottom water in the area of Gdańsk Deep in Gulf of Gdańsk
Open Research DataThis dataset contains the results of microbiological analysis of bottom water and bottom sediments in the area of Gdańsk Deep in Gulf of Gdańsk. The tested samples were collected at 5 sites on 15th of December 2007. 5 samples of bottom water and 10 samples of sediments were collected for microbiological testing. Each of these samples were analysed for...
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An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks
PublicationIn this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent...
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Deep Learning Optimization for Edge Devices: Analysis of Training Quantization Parameters
PublicationThis paper focuses on convolution neural network quantization problem. The quantization has a distinct stage of data conversion from floating-point into integer-point numbers. In general, the process of quantization is associated with the reduction of the matrix dimension via limited precision of the numbers. However, the training and inference stages of deep learning neural network are limited by the space of the memory and a...
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The impact of the shape of deep drilled well screen openings on the filtration process in full saturation conditions
PublicationThe authors propose a supplementary method of modelling seepage flow around the deep drilled well screen. The study applies 3D numerical modelling (FEM) in order to provide an in-depth analysis of the seepage process. The analysis of filtration parameters (flow distribution q(x,t) and pressure distribution p) was conducted using the ZSoil.PC software system. The analysis indicates that the shape of perforation is of secondary importance...
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Organic Acids and Polyphenols Determination in Polish Wines by Ultrasound-Assisted Solvent Extraction of Porous Membrane-Packed Liquid Samples
PublicationIn the near future, Poland is going to have more and more favorable conditions for viticulture. Organic acids and polyphenols are among the most commonly analyzed compounds due to their beneficial properties for human health and their importance in the winemaking process. In this work, a new technique involving ultrasound-assisted solvent extraction of porous membrane-packed liquid samples (UASE-PMLS) was for the first time described...
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The solvent-free thermal dehydration of hexitols on zeolites
PublicationPodczas termicznej dehydratacji heksytoli w obecności zeolitów otrzymano szereg produktów zachodzących zarówno z inwersją lub retencją konfiguracji przy asymetrycznych atomach węgla. Produkty rozdzielano i identyfikowano przy pomocy chromatografii i spektroskopii NMR. 1,4:3,6-dianhydroiditol scharakteryzowano przy pomocy rentgenowskiej analizy strukturalnej.
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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
PublicationFollowing the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides...
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Suspended matter, composition and fluxes, Gdansk Deep, late spring 2001
Open Research DataParticulate organic carbon (POC) and nitrogen (PON) concentrations and fluxes were measured in the Gdańsk Deep (Gulf of Gdansk) from 30.05 to 06.06.2001. The vertical profiles of POC and PON were characterised by the highest values in the euphotic layer, a gradual decrease with depth, and an increase below the halocline. The hydrophysical conditions...
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Evaluation of Facial Pulse Signals Using Deep Neural Net Models
PublicationThe reliable measurement of the pulse rate using remote photoplethysmography (PPG) is very important for many medical applications. In this paper we present how deep neural networks (DNNs) models can be used in the problem of PPG signal classification and pulse rate estimation. In particular, we show that the DNN-based classification results correspond to parameters describing the PPG signals (e.g. peak energy in the frequency...
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Convenient and efficient N-methylation of secondary amines under solvent-free ball milling conditions
PublicationIn the present work, we report the development of a rapid, efcient, and solvent-free procedure for the N-methylation of secondary amines under mechanochemical conditions. After optimization of the milling parameters, a vibrational ball mill was used to synthesize 26 tertiary N-methylated amine derivatives in a short time of 20 min (30 Hz frequency) and high yields ranging from 78 to 95%. An exception was compounds having a hydroxyl...
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Tool Wear Monitoring Using Improved Dragonfly Optimization Algorithm and Deep Belief Network
PublicationIn recent decades, tool wear monitoring has played a crucial role in the improvement of industrial production quality and efficiency. In the machining process, it is important to predict both tool cost and life, and to reduce the equipment downtime. The conventional methods need enormous quantities of human resources and expert skills to achieve precise tool wear information. To automatically identify the tool wear types, deep...
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BIG DATA SIGNIFICANCE IN REMOTE MEDICAL DIAGNOSTICS BASED ON DEEP LEARNING TECHNIQUES
PublicationIn this paper we discuss the evaluation of neural networks in accordance with medical image classification and analysis. We also summarize the existing databases with images which could be used for training deep models that can be later utilized in remote home-based health care systems. In particular, we propose methods for remote video-based estimation of patient vital signs and other health-related parameters. Additionally, potential...
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Position Estimation in Mixed Indoor-Outdoor Environment Using Signals of Opportunity and Deep Learning Approach
PublicationTo improve the user's localization estimation in indoor and outdoor environment a novel radiolocalization system using deep learning dedicated to work both in indoor and outdoor environment is proposed. It is based on the radio signatures using radio signals of opportunity from LTE an WiFi networks. The measurements of channel state estimators from LTE network and from WiFi network are taken by using the developed application....
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Analysis of 2D Feature Spaces for Deep Learning-based Speech Recognition
Publicationconvolutional neural network (CNN) which is a class of deep, feed-forward artificial neural network. We decided to analyze audio signal feature maps, namely spectrograms, linear and Mel-scale cepstrograms, and chromagrams. The choice was made upon the fact that CNN performs well in 2D data-oriented processing contexts. Feature maps were employed in the Lithuanian word recognition task. The spectral analysis led to the highest word...
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Deep neural networks approach to skin lesions classification — A comparative analysis
PublicationThe paper presents the results of research on the use of Deep Neural Networks (DNN) for automatic classification of the skin lesions. The authors have focused on the most effective kind of DNNs for image processing, namely Convolutional Neural Networks (CNN). In particular, three kinds of CNN were analyzed: VGG19, Residual Networks (ResNet) and the hybrid of VGG19 CNN with the Support Vector Machine (SVM). The research was carried...
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Mechanical and physical assessment of epoxy, mineral, solvent-based, and water-soluble coating materials
PublicationThis paper assesses the behavior of mineral, epoxy (EP), solvent, and water-soluble coatings when exposed to salt and regular water for 28 days. Also, it evaluates the pull-off adhesion strength of the same coating materials applied to concrete slabs saturated with oil and water and dried with two different processes: air-dried for 28 days and air-dried for 14 days plus 14 days in the oven at 70 °C. Properties such as carbonation,...
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The structure of Al-Cu and Al-Si eutectic melts
PublicationStrukturę ciekłych stopów eutektycznych Al_{83}Cu_{17} i Al_{88}Si_{12} zbadano metodami dyfrakcyjnymi i RMC. Przeanalizowano uzyskane całkowite i cząstkowe funkcje korelacyjne i parametry strukturalne.
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Big Data from Sensor Network via Internet of Things to Edge Deep Learning for Smart City
PublicationData from a physical world is sampled by sensor networks, and then streams of Big Data are sent to cloud hosts to support decision making by deep learning software. In a smart city, some tasks may be assigned to smart devices of the Internet of Things for performing edge computing. Besides, a part of workload of calculations can be transferred to the cloud hosts. This paper proposes benchmarks for division tasks between an edge...
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Deep slot effect in the squirrel-cage induction motors with scalar (V/F) control
PublicationQualitative characteristics of the electrical drive considerably depend on identification accuracy of math model parameters. In particular, it is depend on detection accuracy of stator active resistance r1 that is used in calculation of flux linkages, rotary speed in sensorless control systems. Paper provides analysis of influence of stator deep slot effect to stator active resistance value
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Data, Information, Knowledge, Wisdom Pyramid Concept Revisited in the Context of Deep Learning
PublicationIn 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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Deep Learning Approaches in Histopathology
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Thermal Images Analysis Methods using Deep Learning Techniques for the Needs of Remote Medical Diagnostics
PublicationRemote medical diagnostic solutions have recently gained more importance due to global demographic shifts and play a key role in evaluation of health status during epidemic. Contactless estimation of vital signs with image processing techniques is especially important since it allows for obtaining health status without the use of additional sensors. Thermography enables us to reveal additional details, imperceptible in images acquired...
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Charge-based deep level transient spectroscopy of B-doped and undoped polycrystalline diamond films
PublicationThe undoped and B-doped polycrystalline diamond thin film was synthesized by hot filament chemical vapor deposition and microwave plasma, respectively. The structural characterization was performed by scanning electron microscopy, X-ray diffraction and Raman spectroscopy. The electrical properties of synthesized diamond layer were characterized by dc-conductivity method and charge deep level transient spectroscopy. The B-doped...
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Categorization of emotions in dog behavior based on the deep neural network
PublicationThe aim of this article is to present a neural system based on stock architecture for recognizing emotional behavior in dogs. Our considerations are inspired by the original work of Franzoni et al. on recognizing dog emotions. An appropriate set of photographic data has been compiled taking into account five classes of emotional behavior in dogs of one breed, including joy, anger, licking, yawning, and sleeping. Focusing on a particular...
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Deep learning-based waste detection in natural and urban environments
PublicationWaste pollution is one of the most significant environmental issues in the modern world. The importance of recycling is well known, both for economic and ecological reasons, and the industry demands high efficiency. Current studies towards automatic waste detection are hardly comparable due to the lack of benchmarks and widely accepted standards regarding the used metrics and data. Those problems are addressed in this article by...
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Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks
PublicationThe increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated...
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A closer look at how the dispersive liquid–liquid microextraction method works. Investigation of the effect of solvent mixture composition on the quality and stability of the cloudy state
PublicationThe dispersive liquid–liquid microextraction (DLLME) is one of the most popular miniaturized extraction procedures. In this paper, the degree of dispersion and dispersion stability were studied with the aim to assess the correlations of these parameters with efficiency for the selected analytical application. The dependence between the degree of dispersion (cloudy state quality) and its stability obtained by various emulsification...
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Automated detection of pronunciation errors in non-native English speech employing deep learning
PublicationDespite significant advances in recent years, the existing Computer-Assisted Pronunciation Training (CAPT) methods detect pronunciation errors with a relatively low accuracy (precision of 60% at 40%-80% recall). This Ph.D. work proposes novel deep learning methods for detecting pronunciation errors in non-native (L2) English speech, outperforming the state-of-the-art method in AUC metric (Area under the Curve) by 41%, i.e., from...
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OPTICAL STRAIN MEASUREMENT OF CONCRETE VERSUS MANUAL EXTENSOMETER MEASUREMENT BASED ON THE TEST RC DEEP BEAM IN A COMPLEX STATE OF STRESS
PublicationThe purpose of this study is to compare the strain measurement techniques of concrete in R-C element subjected to the monotonic load up to the failure. In the analysis manual extensometer methods of measurements and the optical system ARAMIS for non-contact three-dimensional measurements of deformation was used. The test sample was a cantilever deep beam loaded throughout the depth which was a part of the reinforced concrete deep...
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Green aspects of sample preparation - a need for solvent reduction
PublicationWzrastające zainteresowanie ochroną środowiska zmusza chemików, łącznie z chemikami analitykami, do modyfikacji chemicznej aktywności w taki sposób aby była ona prowadzona zgodnie z zasadami zielonej chemii. W artykule przedstawiono przegląd problemów zielonej chemii w dziedzinie przygotowania próbek do analizy, koncentrując się szczególnie na zaletach tzw. bezrozpuszczalnikowych technik przygotowania próbek. Przedstawiono techniki...
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Pervaporation Zeolite-Based Composite Membranes for Solvent Separations
PublicationThanks to their well-defined molecular sieving and stability, zeolites have been proposed in selective membrane separations, such as gas separation and pervaporation. For instance, the incorporation of zeolites into polymer phases to generate composite (or mixed matrix) membranes revealed important advances in pervaporation. Therefore, the goal of this review is to compile and elucidate the latest advances (over the last 2-3 years)...
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Optimization of vortex-assisted supramolecular solvent-based liquid liquid microextraction for the determination of mercury in real water and food samples
PublicationA novel method was developed for sample preparation for spectrophotometric determination of Hg(II) in water and food samples. The method was based on vortex-assisted supramolecular solvent-assisted liquid-liquid microextraction (VA-SUPRASs-LLME). Analytical parameters such as pH, chelating agent, solvent type and volume, vortex time and salting out effect were optimized. Surface and normal probability plots were drawn for the variables...
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Bimodal deep learning model for subjectively enhanced emotion classification in films
PublicationThis research delves into the concept of color grading in film, focusing on how color influences the emotional response of the audience. The study commenced by recalling state-of-the-art works that process audio-video signals and associated emotions by machine learning. Then, assumptions of subjective tests for refining and validating an emotion model for assigning specific emotional labels to selected film excerpts were presented....
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A Comprehensive Analysis of Deep Neural-Based Cerebral Microbleeds Detection System
PublicationMachine learning-based systems are gaining interest in the field of medicine, mostly in medical imaging and diagnosis. In this paper, we address the problem of automatic cerebral microbleeds (CMB) detection in magnetic resonance images. It is challenging due to difficulty in distinguishing a true CMB from its mimics, however, if successfully solved it would streamline the radiologists work. To deal with this complex three-dimensional...
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Methods of deep modification of low-bearing soil for the foundation of new and spare air runways
PublicationAfter analyzing the impact of aircraft on the airport pavement (parking spaces, runways, startways), it was considered advisable to consider the problem of deep improvement or strengthening of its subsoil. This is especially true for low-bearing soil. The paper presents a quick and effective method of strengthening the subsoil intended for the construction of engineering structures used for civil...
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Face with Mask Detection in Thermal Images Using Deep Neural Networks
PublicationAs the interest in facial detection grows, especially during a pandemic, solutions are sought that will be effective and bring more benefits. This is the case with the use of thermal imaging, which is resistant to environmental factors and makes it possible, for example, to determine the temperature based on the detected face, which brings new perspectives and opportunities to use such an approach for health control purposes. The...
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Application of ultrasound-assisted solvent extraction of porous membrane packed liquid samples for polyphenols determination in wine samples
PublicationPolyphenols play a crucial role in a proper human health maintenance as well as their presence very often correspond to the quality assessment of producs like wine. Thus, their monitoring is of high interest. However, as they occur in a complex matrices their extraction is very often necessary prior the analysis. Herein, a new ultrasound-assisted solvent extraction of porous membrane packed liquid sample technique has been optimized...
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Influence of Thermal Imagery Resolution on Accuracy of Deep Learning based Face Recognition
PublicationHuman-system interactions frequently require a retrieval of the key context information about the user and the environment. Image processing techniques have been widely applied in this area, providing details about recognized objects, people and actions. Considering remote diagnostics solutions, e.g. non-contact vital signs estimation and smart home monitoring systems that utilize person’s identity, security is a very important factor....
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Pursuing the Deep-Learning-Based Classification of Exposed and Imagined Colors from EEG
PublicationEEG-based brain-computer interfaces are systems aiming to integrate disabled people into their environments. Nevertheless, their control could not be intuitive or depend on an active external stimulator to generate the responses for interacting with it. Targeting the second issue, a novel paradigm is explored in this paper, which depends on a passive stimulus by measuring the EEG responses of a subject to the primary colors (red,...
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LOS and NLOS identification in real indoor environment using deep learning approach
PublicationVisibility conditions between antennas, i.e. Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) can be crucial in the context of indoor localization, for which detecting the NLOS condition and further correcting constant position estimation errors or allocating resources can reduce the negative influence of multipath propagation on wireless communication and positioning. In this paper a deep learning (DL) model to classify LOS/NLOS...
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Ultrasound-assisted solvent extraction of porous membrane packed solid samples: A new approach for extraction of target analytes from solid samples
PublicationFor the first time, a porous membrane-based method is proposed for the extraction of target analytes directly from the solid samples. This method involves the packing of solid sample inside a porous polypropylene membrane sheet whose edges are heat-sealed to fabricate a bag. This bag is immersed in a suitable solvent and the analytes are extracted by the application of ultrasound energy. The various factors that affect the extraction...
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Deep convolutional neural network for predicting kidney tumour malignancy
PublicationPurpose: According to the statistics, up to 15-20% of removed solid kidney tumors turn out to be benign in postoperative histopathological examination, despite having been identified as malignant by a radiologist. The aim of the research was to limit the number of unnecessary nephrectomies of benign tumors. Methods or Background: We propose a machine-aided diagnostic system for kidney...
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A hierarchical porous composite magnetic sorbent of reduced graphene oxide embedded in polyvinyl alcohol cryogel for solvent‐assisted‐solid phase extraction of polycyclic aromatic hydrocarbons
PublicationA hierarchical porouscomposite magnetic sorbent was fabricated and applied tothe dispersive solvent-assisted solid-phase extraction of five polycyclic aromatichydrocarbons. A sorbent was first prepared by incorporating graphene oxide,calcium carbonate, and magnetite nanoparticles into a polyvinyl alcohol cryo-gel. The graphene oxide was converted to reduced graphene oxide using ascorbicacid and a hierarchical porous structure was...
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Improving Accuracy of Contactless Respiratory Rate Estimation by Enhancing Thermal Sequences with Deep Neural Networks
PublicationEstimation of vital signs using image processing techniques have already been proved to have a potential for supporting remote medical diagnostics and replacing traditional measurements that usually require special hardware and electrodes placed on a body. In this paper, we further extend studies on contactless Respiratory Rate (RR) estimation from extremely low resolution thermal imagery by enhancing acquired sequences using Deep...
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Detection of Alzheimer's disease using Otsu thresholding with tunicate swarm algorithm and deep belief network
PublicationIntroduction: Alzheimer’s Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is necessary to reduce the mortality rate through slowing down its progression. The prevention and detection of AD is the emerging research topic for many researchers. The structural Magnetic Resonance Imaging (sMRI) is an extensively used imaging technique in detection of AD, because...
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Deep learning for ultra-fast and high precision screening of energy materials
PublicationSemiconductor materials for energy storage are the core and foundation of modern information society and play important roles in photovoltaic system, integrated circuit, spacecraft technology, lighting applications, and other fields. Unfortunately, due to the long experiment period and high calculation cost, the high-precision band gap (the basic characteristic parameter) of semiconductor is difficult to obtain, which hinders the...