Filtry
wszystkich: 448
wybranych: 442
-
Katalog
Filtry wybranego katalogu
Wyniki wyszukiwania dla: deep l
-
Optimized Deep Learning Model for Flood Detection Using Satellite Images
PublikacjaThe increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection...
-
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...
-
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....
-
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...
-
Analysis-by-synthesis paradigm evolved into a new concept
PublikacjaThis work aims at showing how the well-known analysis-by-synthesis paradigm has recently been evolved into a new concept. However, in contrast to the original idea stating that the created sound should not fail to pass the foolproof synthesis test, the recent development is a consequence of the need to create new data. Deep learning models are greedy algorithms requiring a vast amount of data that, in addition, should be correctly...
-
Nowoczesne technologie uszczelniania wałów przeciwpowodziowych.
PublikacjaOpisano zastosowanie wąskoszczelinowych przesłon przeciwfiltracyjnych wykonywanych w technologii WIPS wibracyjnie iniektowana przesłona szczelinowa oraz DSM ( ang. deep soil mixing - mieszanie wgłębne gruntu). Przedstawiono przykłady zastosowania w Polsce.
-
Selected Technical Issues of Deep Neural Networks for Image Classification Purposes
PublikacjaIn recent years, deep learning and especially Deep Neural Networks (DNN) have obtained amazing performance on a variety of problems, in particular in classification or pattern recognition. Among many kinds of DNNs, the Convolutional Neural Networks (CNN) are most commonly used. However, due to their complexity, there are many problems related but not limited to optimizing network parameters, avoiding overfitting and ensuring good...
-
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...
-
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...
-
Low-frequency noise in ZrS3 van der Waals semiconductor nanoribbons
PublikacjaWe report the results of the investigation of low-frequency electronic noise in ZrS3 van der Waals semiconductor nanoribbons. The test structures were of the back-gated field-effect-transistor type with a normally off n-channel and an on-to-off ratio of up to four orders of magnitude. The current–voltage transfer characteristics revealed significant hysteresis owing to the presence of deep levels. The noise in ZrS3 nanoribbons...
-
Plant-based meat substitute analysis using microextraction with deep eutectic solvent followed by LC-MS/MS to determine acrylamide, 5-hydroxymethylfurfural and furaneol
PublikacjaFor the analysis of plant-based meat substitutes and the determination of Maillard reaction products such as acrylamide, 5-hydroxymethylfurfural and furaneol, a novel and effective procedure based on hydrophobic natural deep eutectic solvent and liquid chromatography coupled with tandem mass spectrometry was developed for the first time. The 49 compositions of the deep eutectic solvents were designed and screened to select the...
-
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
PublikacjaThis 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...
-
Effect of temperature and composition on physical properties of deep eutectic solvents based on 2-(methylamino)ethanol – measurement and prediction
PublikacjaNovel deep eutectic solvents were synthesized using 2-(methylamino)ethanol as hydrogen bond donor with tetrabutylammonium bromide or tetrabutylammonium chloride or tetraethylammonium chloride as hydrogen bond acceptors. Mixtures were prepared at different molar ratios of 1:6, 1:8 and 1:10 salt to alkanolamine and then Fourier Transform Infrared Spectroscopy measurements were performed to confirm hydrogen bonds interactions between...
-
Experience-Oriented Knowledge Management for Internet of Things
PublikacjaIn this paper, we propose a novel approach for knowledge management in Internet of Things. By utilizing Decisional DNA and deep learning technologies, our approach enables Internet of Things of experiential knowledge discovery, representation, reuse, and sharing among each other. Rather than using traditional machine learning and knowledge discovery methods, this approach focuses on capturing domain’s decisional events via Decisional...
-
Explainable AI for Inspecting Adversarial Attacks on Deep Neural Networks
PublikacjaDeep Neural Networks (DNN) are state of the art algorithms for image classification. Although significant achievements and perspectives, deep neural networks and accompanying learning algorithms have some important challenges to tackle. However, it appears that it is relatively easy to attack and fool with well-designed input samples called adversarial examples. Adversarial perturba-tions are unnoticeable for humans. Such attacks...
-
Impact of Visual Image Quality on Lymphocyte Detection Using YOLOv5 and RetinaNet Algorithms
PublikacjaLymphocytes, a type of leukocytes, play a vital role in the immune system. The precise quantification, spatial arrangement and phenotypic characterization of lymphocytes within haematological or histopathological images can serve as a diagnostic indicator of a particular lesion. Artificial neural networks, employed for the detection of lymphocytes, not only can provide support to the work of histopathologists but also enable better...
-
Deep Eutectic Solvents and Their Uses for Air Purification
PublikacjaChemical compounds released into the air by the activities of industrial plants and emitted from many other sources, including in households (paints, waxes, cosmetics, disinfectants, plastic (PVC) flooring), may affect the environment and human health. Thus, air purification is an important issue in the context of caring for the condition of the environment. Deep eutectic solvents (DESs) as liquids with environmentally friendly...
-
Green adsorbents and solvents in food analysis
PublikacjaGreen analytical chemistry aims to minimize the negative impact of analytical procedures on the environment and human health. This can be achieved through the use of non-toxic and environmentally friendly reagents. Classical green solvents include water, ethanol, acetone, and supercritical fluids. Water has been used for the extraction of water-soluble compounds (sugars, amino acids). Ethanol and acetone have been used for the...
-
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...
-
Spatiotemporal Assessment of Satellite Image Time Series for Land Cover Classification Using Deep Learning Techniques: A Case Study of Reunion Island, France
PublikacjaCurrent 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...
-
Federated Learning in Healthcare Industry: Mammography Case Study
PublikacjaThe paper focuses on the role of federated learning in a healthcare environment. The experimental setup involved different healthcare providers, each with their datasets. A comparison was made between training a deep learning model using traditional methods, where all the data is stored in one place, and using federated learning, where the data is distributed among the workers. The experiment aimed to identify possible challenges...
-
Investigation of the vertical distribution of the sound speed of the Gulf of Gdansk in the years 2000-2010
PublikacjaThe conditions of the acoustic wave propagation in the southern Baltic are much more complex than in other shallow waters. In the typical shallow water, seasonal changes in acoustical conditions in the upper layer, of the depth of about 60-70 m, are observed. They are caused by variation of the annual meteorological conditions. Most often, in the deep water layer, acoustical conditions are stable throughout the year. However, in...
-
User Orientation Detection in Relation to Antenna Geometry in Ultra-Wideband Wireless Body Area Networks Using Deep Learning
PublikacjaIn this paper, the issue of detecting a user’s position in relation to the antenna geometry in ultra-wideband (UWB) off-body wireless body area network (WBAN) communication using deep learning methods is presented. To measure the impulse response of the channel, a measurement stand consisting of EVB1000 devices and DW1000 radio modules was developed and indoor static measurement scenarios were performed. It was proven that for...
-
Investigating Feature Spaces for Isolated Word Recognition
PublikacjaThe study addresses the issues related to the appropriateness of a two-dimensional representation of speech signal for speech recognition tasks based on deep learning techniques. The approach combines Convolutional Neural Networks (CNNs) and time-frequency signal representation converted to the investigated feature spaces. In particular, waveforms and fractal dimension features of the signal were chosen for the time domain, and...
-
THE ROLE OF INFERENCE IN MOBILE MEDICAL APPLICATION DESIGN
PublikacjaIn the early 21st century, artificial intelligence began to be used to process medical information. However, before this happened, predictive models used in healthcare could only consider a limited number of variables, and only in properly structured and organised medical data. Today, advanced tools based on machine learning techniques - which, using artificial neural networks, can explore extremely complex relationships - and...
-
On-line assessment of oil quality during deep frying using an electronic nose and proton transfer reaction mass spectrometry
PublikacjaWe describe a novel method for the quality assessment of oil utilized for deep frying. The method is based on the analysis of frying fumes using a custom electronic nose. The quality score could be obtained after less than 3 min of analysis and without interrupting the frying process or sampling the oil directly. The obtained results were correlated with the peroxide value using a multivariate linear regression model. The most...
-
Electrostatic interface recombination in the system of disordered materials characterized by different permittivities
PublikacjaWe report on the analysis of an electrostatic interface recombination in a system consisting of disordered organic materials. This process is a consequence of the polarization effect which takes place at the interface of two phases characterized by different permittivities. In this paper, the impact of tail and deep localized states on the recombination order is demonstrated. We also discuss the influence of temperature on this...
-
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
-
Magnetic deep eutectic solvents – Fundamentals and applications
PublikacjaMagnetic deep eutectic solvents (MDES), a relatively new subclass of conventional deep eutectic solvents (DES) containing additional paramagnetic components in their structure. MDES exhibit a strong response toward external magnetic fields, thus they can improve many industrial and analytical applications. In addition, this new group of solvents present unique physicochemical properties that can be easily tuned by selecting the...
-
Deep eutectic solvents in analytical sample preconcentration Part B: Solid-phase (micro)extraction
PublikacjaOne of the key challenges of modern analytical chemistry is the monitoring of trace amounts of contaminants using sensitive and selective instrumental techniques. Due to the variety and complexity of some samples, it is often necessary to properly prepare a sample and to perform a preconcentration of trace amounts of analytes. In line with the principles of Green Analytical Chemistry (GAC), it is important for an analytical procedure...
-
Data augmentation for improving deep learning in image classification problem
PublikacjaThese days deep learning is the fastest-growing field in the field of Machine Learning (ML) and Deep Neural Networks (DNN). Among many of DNN structures, the Convolutional Neural Networks (CNN) are currently the main tool used for the image analysis and classification purposes. Although great achievements and perspectives, deep neural networks and accompanying learning algorithms have some relevant challenges to tackle. In this...
-
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...
-
Effect of choline chloride based natural deep eutectic solvents on aqueous solubility and thermodynamic properties of acetaminophen
PublikacjaIn this work, natural deep eutectic solvents (NADESs) containing choline chloride as hydrogen bond acceptor and 1,2-propanediol, malic acid and tartaric acid as hydrogen bond donors have been synthesized and applied to enhance the aqueous solubility of model sparingly water-soluble drug – acetaminophen. The results indicate that the greatest impact on the solubility of acetaminophen have deep eutectic solvents based on 1,2-propanediol...
-
Spiral Search Grasshopper Features Selection with VGG19-ResNet50 for Remote Sensing Object Detection
PublikacjaRemote sensing object detection plays a major role in satellite imaging and is required in various scenarios such as transportation, forestry, and the ocean. Deep learning techniques provide efficient performance in remote sensing object detection. The existing techniques have the limitations of data imbalance, overfitting, and lower efficiency in detecting small objects. This research proposes the spiral search grasshopper (SSG)...
-
An air-assisted dispersive liquid phase microextraction method based on a hydrophobic magnetic deep eutectic solvent for the extraction and preconcentration of melamine from milk and milk-based products
PublikacjaIn the current research, a fast and sustainable air-assisted hydrophobic magnetic deep eutectic solvent-based dispersive liquid phase microextraction followed by UV–Vis spectrophotometry measurements was optimized for the extraction and determination of melamine in milk and milk-based products. The central composite design was applied for the optimization of factors affecting the recovery of melamine. Quantitative extraction of...
-
The impact of the AC922 Architecture on Performance of Deep Neural Network Training
PublikacjaPractical deep learning applications require more and more computing power. New computing architectures emerge, specifically designed for the artificial intelligence applications, including the IBM Power System AC922. In this paper we confront an AC922 (8335-GTG) server equipped with 4 NVIDIA Volta V100 GPUs with selected deep neural network training applications, including four convolutional and one recurrent model. We report...
-
Comprehensive evaluation of physical properties and carbon dioxide capacities of new 2-(butylamino)ethanol-based deep eutectic solvents
PublikacjaThe aim of this research was to assess the impact of the components of alkanolamine deep eutectic solvents (DESs) on the physical properties of those DESs and their carbon dioxide capacity. To achieve this goal, novel deep eutectic solvents were synthesized by using 2-(butylamino)ethanol (BAE) as the hydrogen bond donor (HBD), along with tetrabutylammonium bromide TBAB), tetrabutylammonium chloride (TBAC), or tetraethy- lammonium...
-
Remarks on use of the term “deep eutectic solvent” in analytical chemistry
PublikacjaAbout 20 years ago, Abbott and co-workers researched new solvents that were based on mixtures of choline chloride with urea and carboxylic acids and that were liquid at ambient temperature. The term “deep eutectic solvent” (DES) was later adopted for similar mixtures. As DESs have a number of interesting features, they quickly attracted the attention of researchers and found application in various branches of chemical and materials...
-
Modelowanie dokładności radiolokalizowania w różnych warunkach środowiskowych przy wykorzystaniu interfejsu radiowego 5G-NR
PublikacjaW artykule przedstawiono wyniki eksperymentalnych badań dokładności estymacji położenia terminala użytkownika korzystającego~z interfejsu radiowego 5G-NR. W środowisku miejskim dokonano rejestracji rzeczywistych sygnałów sieci 5G, a następnie przeprowadzono badania numeryczne. Celem było zweryfikowanie różnic dokładności estymacji położenia w trzech różnych środowiskach: wewnątrz- i zewnątrzbudynkowym oraz tzw. deep-indoor.
-
Exploration of the Solubility Hyperspace of Selected Active Pharmaceutical Ingredients in Choline- and Betaine-Based Deep Eutectic Solvents: Machine Learning Modeling and Experimental Validation
PublikacjaDeep eutectic solvents (DESs) are popular green media used for various industrial, pharmaceutical, and biomedical applications. However, the possible compositions of eutectic systems are so numerous that it is impossible to study all of them experimentally. To remedy this limitation, the solubility landscape of selected active pharmaceutical ingredients (APIs) in choline chloride- and betaine-based deep eutectic solvents was...
-
Self-Supervised Learning to Increase the Performance of Skin Lesion Classification
PublikacjaTo successfully train a deep neural network, a large amount of human-labeled data is required. Unfortunately, in many areas, collecting and labeling data is a difficult and tedious task. Several ways have been developed to mitigate the problem associated with the shortage of data, the most common of which is transfer learning. However, in many cases, the use of transfer learning as the only remedy is insufficient. In this study,...
-
Closer look into the structures of tetrabutylammonium bromide–glycerol-based deep eutectic solvents and their mixtures with water
PublikacjaIn recent years, deep eutectic solvents (DES) and it’s mixture with water have become more and more attention as green solvents used in chemistry. However, there are only a few theoretical studies on the mechanisms of pure DES and DES-water complex formation. Therefore, the structural properties of tetrabutylammonium bromide–glycerol-based deep eutectic solvents and their mixtures with water have been investigated by means of Molecular...
-
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...
-
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...
-
Enhanced trap-assisted recombination in organic semiconductors
PublikacjaAn analytical model to describe the interaction of excitons and charge transfer states with deep traps is formulated for the case of molecular materials. Here, we have considered the influence of a trap-assisted recombination on this phenomenon. The final expression for the effective recombination rate has been derived from the Shockley–Read–Hall theory and kinetic equations which characterize different photophysical processes....
-
Application of the laser diode with central wavelength 975 nm for the therapy of neurofibroma and hemangiomas
PublikacjaThis paper presents newly developed dermatological laser (with central wavelength 975 nm) for application in therapies requiring deep penetration of tissue, e.g. cutaneous (dermal) neurofibroma (Recklinghausen disease) and hemangiomas. This laser can work either in pulse or continues wave mode. Laser radiation is transmitted toward the application region by optical fiber with a diameter of 0.6 mm. The compact design of the laser...
-
SIGNIFICANT GUIDELINE FOR THE DAMAGE INDICES APPLIED TO REINFORCED CONCRETE STRUCTURES
PublikacjaIn this paper, based on a deep overview and literature study, the different formulas proposed for damage indices (DIs) applied to reinforced concrete structures under monotonic or cyclic loading are classified and presented. The DIs are applied to quantify the damages to structures, ranging from zero to one. Normally, they are applied to make a decision for repairing or demolition of the structures in the post-earthquake phases. Keywords:...
-
Comparative model tests of SDP and CFA pile groups in non-cohesive soil
PublikacjaThe research topic relates to the subject of deep foundations supported on continuous flight auger (CFA) piles and screw displacement piles (SDP). The authors have decided to conduct model tests of foundations supported on the group of piles mentioned above and also the tests of the same piles working alone. The tests are ongoing in Geotechnical Laboratory of Gdańsk University of Technology. The description of test procedure, interpretation...
-
Autonomous port management based AGV path planning and optimization via an ensemble reinforcement learning framework
PublikacjaThe rapid development of shipping trade pushes automated container terminals toward the direction of intelligence, safety and efficiency. In particular, the formulation of AGV scheduling tasks and the safety and stability of transportation path is an important part of port operation and management, and it is one of the basic tasks to build an intelligent port. Existing research mainly focuses on collaborative operation between...
-
Designing acoustic scattering elements using machine learning methods
PublikacjaIn the process of the design and correction of room acoustic properties, it is often necessary to select the appropriate type of acoustic treatment devices and make decisions regarding their size, geometry, and location of the devices inside the room under the treatment process. The goal of this doctoral dissertation is to develop and validate a mathematical model that allows predicting the effects of the application of the scattering...