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Wyniki wyszukiwania dla: deep eutectic solvents
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Outlier detection method by using deep neural networks
PublikacjaDetecting outliers in the data set is quite important for building effective predictive models. Consistent prediction can not be made through models created with data sets containing outliers, or robust models can not be created. In such cases, it may be possible to exclude observations that are determined to be outlier from the data set, or to assign less weight to these points of observation than to other points of observation....
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Deep learning techniques for biometric security: A systematic review of presentation attack detection systems
PublikacjaBiometric technology, including finger vein, fingerprint, iris, and face recognition, is widely used to enhance security in various devices. In the past decade, significant progress has been made in improving biometric sys- tems, thanks to advancements in deep convolutional neural networks (DCNN) and computer vision (CV), along with large-scale training datasets. However, these systems have become targets of various attacks, with...
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Nutrients, oxygen and suspended matter - Gdansk Deep (2001-2005)
Dane BadawczeThe results show short-term changes in the concentration of nutrients (nitrates, nitrites, ammonium ions, phosphates and total forms of nitrogen and phosphorus), dissolved oxygen and suspended particulate matter - SPM and its main components (organic carbon - POC, nitrogen - PON, phosphorus - TPP) in the water column of the Gdańsk Deep (Gdańsk Bay).
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Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks
PublikacjaIn this article, the usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) is presented. The developed approach, using channel impulse response, allows higher efficiency in identifying the direct visibility conditions between nodes in off-body communication with comparison to the methods described in the literature. The effectiveness of the proposed deep feedforward neural network was checked on...
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Force transfer and stress distribution in short cantilever deep beams loaded throughout the depth with a various reinforcement
PublikacjaDeep beams used as the main reinforced concrete structural elements which taking over the load and stiffening construction are often found in high-rise buildings. The architecture of these buildings is sometimes sophisticated and varied, arouse the admiration of the majority of recipients, and thus causing an engineering challenge to correctly design the structural system and force transfer. In such structures is important to shape...
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Deep Learning-Based, Multiclass Approach to Cancer Classification on Liquid Biopsy Data
PublikacjaThe field of cancer diagnostics has been revolutionized by liquid biopsies, which offer a bridge between laboratory research and clinical settings. These tests are less invasive than traditional biopsies and more convenient than routine imaging methods. Liquid biopsies allow studying of tumor-derived markers in bodily fluids, enabling the development of more precise cancer diagnostic tests for screening, disease monitoring, and...
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Deep neural networks for data analysis 27/28
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Deep neural networks for data analysis 25/26
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Deep neural networks for data analysis 26/27
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SYNTHESIZING MEDICAL TERMS – QUALITY AND NATURALNESS OF THE DEEP TEXT-TO-SPEECH ALGORITHM
PublikacjaThe main purpose of this study is to develop a deep text-to-speech (TTS) algorithm designated for an embedded system device. First, a critical literature review of state-of-the-art speech synthesis deep models is provided. The algorithm implementation covers both hardware and algorithmic solutions. The algorithm is designed for use with the Raspberry Pi 4 board. 80 synthesized sentences were prepared based on medical and everyday...
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Deep neural network architecture search using network morphism
PublikacjaThe paper presents the results of the research on neural architecture search (NAS) algorithm. We utilized the hill climbing algorithm to search for well-performing structures of deep convolutional neural network. Moreover, we used the function preserving transformations which enabled the effective operation of the algorithm in a short period of time. The network obtained with the advantage of NAS was validated on skin lesion classification...
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Deep learning based thermal image segmentation for laboratory animals tracking
PublikacjaAutomated systems for behaviour classification of laboratory animals are an attractive alternative to manual scoring. However, the proper animals separation and tracking, especially when they are in close contact, is the bottleneck of the behaviour analysis systems. In this paper, we propose a method for the segmentation of thermal images of laboratory rats that are in close contact during social behaviour tests. For this, we are...
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Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy
PublikacjaThe diabetic retinopathy is a disease caused by long-standing diabetes. Lack of effective treatment can lead to vision impairment and even irreversible blindness. The disease can be diagnosed by examining digital color fundus photographs of retina. In this paper we propose deep learning approach to automated diabetic retinopathy screening. Deep convolutional neural networks (CNN) - the most popular kind of deep learning algorithms...
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Microbiological condition of sediments and bottom water in the area of Gdańsk Deep in Gulf of Gdańsk
Dane BadawczeThis 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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Decision making process using deep learning
PublikacjaEndüstri 4.0, dördüncü endüstri devrimi veya Endüstriyel Nesnelerin İnterneti (IIoT) olarak adlandırılan sanayi akımı, işletmelere, daha verimli, daha büyük bir esneklikle, daha güvenli ve daha çevre dostu bir şekilde üretim yapma imkanı sunmaktadır. Nesnelerin İnterneti ile bağlantılı yeni teknoloji ve hizmetler birçok endüstriyel uygulamada devrim niteliği taşımaktadır. Fabrikalardaki otomasyon, tahminleyici bakım (PdM – Predictive...
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Deep Instance Segmentation of Laboratory Animals in Thermal Images
PublikacjaIn 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
PublikacjaWe present a novel deep learning model for the detection and reconstruction of dysarthric speech. We train the model with a multi-task learning technique to jointly solve dysarthria detection and speech reconstruction tasks. The model key feature is a low-dimensional latent space that is meant to encode the properties of dysarthric speech. It is commonly believed that neural networks are black boxes that solve problems but do not...
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An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks
PublikacjaIn 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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The impact of the shape of deep drilled well screen openings on the filtration process in full saturation conditions
PublikacjaThe 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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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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Suspended matter, composition and fluxes, Gdansk Deep, late spring 2001
Dane BadawczeParticulate 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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Ionic liquids - greener solvents or environmental threat?
PublikacjaZe względu na niemierzalnie niską prężność par ciecze jonowe były uważane za 'zielone rozpuszczalniki'. Ich zastosowanie w technologiach (bio)chemicznych przynosi korzyści nie tylko ekologiczne ale również ekonomiczne. Niemniej jednak zanieczyszczenie środowiska poprzez przedostanie się cieczy jonowych do gleb i wód gruntowych wraz ze ściekami/odciekami przemysłowymi lub w wyniku przypadkowych rozlewów stanowi realne zagrożenie....
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The potential anti-tumor activity of neoteric solvents
PublikacjaCiecze jonowe stały się obiektem zainteresowania naukowców. Znajdują one coraz to szersze zastosowanie. Dlatego też ważne jest określenie ich właściwości Eko-toksycznych. Aktywność anty-nowotworowa i cytotoksyczna cieczy jonowych stała się zatem ważnym aspektem badań. Artykuł podsumowuje najnowsze wyniki badań nad cytotoksycznością tych rozpuszczalników
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Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning
PublikacjaFollowing 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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Evaluation of Facial Pulse Signals Using Deep Neural Net Models
PublikacjaThe 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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Tool Wear Monitoring Using Improved Dragonfly Optimization Algorithm and Deep Belief Network
PublikacjaIn 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
PublikacjaIn 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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Chromatographic lipophilicity determination using large volume injections of the solvents non-miscible with the mobile phase
PublikacjaA new perspective in the lipophilicity evaluation through RP-HPLC is permitted by analysis of the retentionfactor (k) obtained by injecting large volumes of test samples prepared in solvents immiscible withmobile phase. The experiment is carried out on representative groups of compounds with increasedtoxicity (mycotoxins and alkaloids) and amines with important biological activity (naturally occurringmonoamine compounds and related...
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Position Estimation in Mixed Indoor-Outdoor Environment Using Signals of Opportunity and Deep Learning Approach
PublikacjaTo 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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Deep neural networks approach to skin lesions classification — A comparative analysis
PublikacjaThe 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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Analysis of 2D Feature Spaces for Deep Learning-based Speech Recognition
Publikacjaconvolutional 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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The structure of Al-Cu and Al-Si eutectic melts
PublikacjaStrukturę 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
PublikacjaData 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
PublikacjaQualitative 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
PublikacjaIn this paper, the data, information, knowledge, and wisdom (DIKW) pyramid is revisited in the context of deep learning applied to machine learningbased audio signal processing. A discussion on the DIKW schema is carried out, resulting in a proposal that may supplement the original concept. Parallels between DIWK pertaining to audio processing are presented based on examples of the case studies performed by the author and her collaborators....
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Deep Learning Approaches in Histopathology
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Thermal Images Analysis Methods using Deep Learning Techniques for the Needs of Remote Medical Diagnostics
PublikacjaRemote 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
PublikacjaThe 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
PublikacjaThe 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
PublikacjaWaste 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
PublikacjaThe 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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Automated detection of pronunciation errors in non-native English speech employing deep learning
PublikacjaDespite 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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Monitoring of occupational exposure to volatile organohalogen solvents (VOXs) in human urine samples of dry-cleaner workers by TLHS-DAI-GC-ECD procedure
PublikacjaChlorinated hydrocarbon solvents are often used for dry-cleaning clothes in the laundry industry. The object of this study was to monitor the occupational exposure of dry-clean employees coming into contact with VOXs. 25 workers collected their urine samples before the work shift, after 4 hours of work and after the work shift. The analyses of urine samples and solvents used in dry-cleaning were performed using TLHS-DAI-GC-ECD....
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Trends in the new generation of green solvents in extraction processes
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New Insights into Thermodynamics of Solutes in Neat and Complex Solvents
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Effect of neoteric solvents on the activated sewage sludge activity
PublikacjaCiecze jonowe (ILs) cieszą sie rosnącym zainteresowaniem z uwagi na fakt, iż znajdują coraz więcej zastosowań jako alternatywa dla rozpuszczalników organicznych. W związku z zaistnieniem możliwości zastosowania ILs w nowych czystych technologiach nastała potrzeba zbadania ich toksyczności w stosunku do organizmów żywych i środowiska. Celem badań było oszacowanie wpływu cieczy jonowych na wyselekcjonowane mikroorganizmy osadu czynnego....
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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
PublikacjaThe 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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Bimodal deep learning model for subjectively enhanced emotion classification in films
PublikacjaThis 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
PublikacjaMachine 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
PublikacjaAfter 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...