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Search results for: BLOOD PRESSURE, ESTIMATION, NEURAL NETWORK
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WRF-METEOPG: numerical weather forecast data for Poland - Days 274-280, Year 2021
Open Research DataWRF-METEOPG is a numerical weather forecast system developed at the Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Poland. The system was built on the basis of the Weather Research and Forecast model version 4.2 and implemented at Centre of Informatics Tricity Academic Supercomputer & Network. Physics parametrization...
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WRF-METEOPG: numerical weather forecast data for Poland - Days 246-252, Year 2021
Open Research DataWRF-METEOPG is a numerical weather forecast system developed at the Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Poland. The system was built on the basis of the Weather Research and Forecast model version 4.2 and implemented at Centre of Informatics Tricity Academic Supercomputer & Network. Physics parametrization...
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WRF-METEOPG: numerical weather forecast data for Poland - Days 288-294, Year 2021
Open Research DataWRF-METEOPG is a numerical weather forecast system developed at the Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Poland. The system was built on the basis of the Weather Research and Forecast model version 4.2 and implemented at Centre of Informatics Tricity Academic Supercomputer & Network. Physics parametrization...
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WRF-METEOPG: numerical weather forecast data for Poland - Days 260-266, Year 2021
Open Research DataWRF-METEOPG is a numerical weather forecast system developed at the Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Poland. The system was built on the basis of the Weather Research and Forecast model version 4.2 and implemented at Centre of Informatics Tricity Academic Supercomputer & Network. Physics parametrization...
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WRF-METEOPG: numerical weather forecast data for Poland - Days 232-238, Year 2021
Open Research DataWRF-METEOPG is a numerical weather forecast system developed at the Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Poland. The system was built on the basis of the Weather Research and Forecast model version 4.2 and implemented at Centre of Informatics Tricity Academic Supercomputer & Network. Physics parametrization...
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WRF-METEOPG: numerical weather forecast data for Poland - Days 204-210, Year 2021
Open Research DataWRF-METEOPG is a numerical weather forecast system developed at the Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Poland. The system was built on the basis of the Weather Research and Forecast model version 4.2 and implemented at Centre of Informatics Tricity Academic Supercomputer & Network. Physics parametrization...
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WRF-METEOPG: numerical weather forecast data for Poland - Days 197-203, Year 2021
Open Research DataWRF-METEOPG is a numerical weather forecast system developed at the Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Poland. The system was built on the basis of the Weather Research and Forecast model version 4.2 and implemented at Centre of Informatics Tricity Academic Supercomputer & Network. Physics parametrization...
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WRF-METEOPG: numerical weather forecast data for Poland - Days 190-196, Year 2021
Open Research DataWRF-METEOPG is a numerical weather forecast system developed at the Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Poland. The system was built on the basis of the Weather Research and Forecast model version 4.2 and implemented at Centre of Informatics Tricity Academic Supercomputer & Network. Physics parametrization...
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WRF-METEOPG: numerical weather forecast data for Poland - Days 183-189, Year 2021
Open Research DataWRF-METEOPG is a numerical weather forecast system developed at the Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Poland. The system was built on the basis of the Weather Research and Forecast model version 4.2 and implemented at Centre of Informatics Tricity Academic Supercomputer & Network. Physics parametrization...
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WRF-METEOPG: numerical weather forecast data for Poland - Days 176-182, Year 2021
Open Research DataWRF-METEOPG is a numerical weather forecast system developed at the Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Poland. The system was built on the basis of the Weather Research and Forecast model version 4.2 and implemented at Centre of Informatics Tricity Academic Supercomputer & Network. Physics parametrization...
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WRF-METEOPG: numerical weather forecast data for Poland - Days 218-224, Year 2021
Open Research DataWRF-METEOPG is a numerical weather forecast system developed at the Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Poland. The system was built on the basis of the Weather Research and Forecast model version 4.2 and implemented at Centre of Informatics Tricity Academic Supercomputer & Network. Physics parametrization...
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WRF-METEOPG: numerical weather forecast data for Poland - Days 239-245, Year 2021
Open Research DataWRF-METEOPG is a numerical weather forecast system developed at the Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Poland. The system was built on the basis of the Weather Research and Forecast model version 4.2 and implemented at Centre of Informatics Tricity Academic Supercomputer & Network. Physics parametrization...
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WRF-METEOPG: numerical weather forecast data for Poland - Days 225-231, Year 2021
Open Research DataWRF-METEOPG is a numerical weather forecast system developed at the Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Poland. The system was built on the basis of the Weather Research and Forecast model version 4.2 and implemented at Centre of Informatics Tricity Academic Supercomputer & Network. Physics parametrization...
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WRF-METEOPG: numerical weather forecast data for Poland - Days 267-273, Year 2021
Open Research DataWRF-METEOPG is a numerical weather forecast system developed at the Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Poland. The system was built on the basis of the Weather Research and Forecast model version 4.2 and implemented at Centre of Informatics Tricity Academic Supercomputer & Network. Physics parametrization...
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Field Calibration of Low-Cost Particulate Matter Sensors Using Artificial Neural Networks and Affine Response Correction
PublicationDue to detrimental effects of atmospheric particulate matter (PM), its accurate monitoring is of paramount importance, especially in densely populated urban areas. However, precise measurement of PM levels requires expensive and sophisticated equipment. Although low-cost alternatives are gaining popularity, their reliability is questionable, attributed to sensitivity to environmental conditions, inherent instability, and manufacturing...
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CNN-CLFFA: Support Mobile Edge Computing in Transportation Cyber Physical System
PublicationIn the present scenario, the transportation Cyber Physical System (CPS) improves the reliability and efficiency of the transportation systems by enhancing the interactions between the physical and cyber systems. With the provision of better storage ability and enhanced computing, cloud computing extends transportation CPS in Mobile Edge Computing (MEC). By inspecting the existing literatures, the cloud computing cannot fulfill...
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Economical methods for measuring road surface roughness
PublicationTwo low-cost methods of estimating the road surface condition are presented in the paper, the first one based on the use of accelerometers and the other on the analysis of images acquired from cameras installed in a vehicle. In the first method, miniature positioning and accelerometer sensors are used for evaluation of the road surface roughness. The device designed for installation in vehicles is composed of a GPS receiver and...
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Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm
PublicationSatellite image classification is widely used in various real-time applications, such as the military, geospatial surveys, surveillance and environmental monitoring. Therefore, the effective classification of satellite images is required to improve classification accuracy. In this paper, the combination of Hierarchical Framework and Ensemble Learning (HFEL) and optimal feature selection is proposed for the precise identification...
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Explainable machine learning for diffraction patterns
PublicationSerial crystallography experiments at X-ray free-electron laser facilities produce massive amounts of data but only a fraction of these data are useful for downstream analysis. Thus, it is essential to differentiate between acceptable and unacceptable data, generally known as ‘hit’ and ‘miss’, respectively. Image classification methods from artificial intelligence, or more specifically convolutional neural networks (CNNs), classify...
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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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Evaluation of aspiration problems in L2 English pronunciation employing machine learning
PublicationThe approach proposed in this study includes methods specifically dedicated to the detection of allophonic variation in English. This study aims to find an efficient method for automatic evaluation of aspiration in the case of Polish second-language (L2) English speakers’ pronunciation when whole words are analyzed instead of particular allophones extracted from words. Sample words including aspirated and unaspirated allophones...
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Combined anticancer therapy with imidazoacridinone analogue C‐1305 and paclitaxel in human lung and colon cancer xenografts—Modulation of tumour angiogenesis
PublicationThe acridanone derivative 5-dimethylaminopropylamino- 8- hydroxytriazoloacridinone (C-1305) has been described as a potent inhibitor of cancer cell growth. Its mechanism of action in in vitro conditions was attributed, among others, to its ability to bind and stabilize the microtubule network and subsequently exhibit its tumour- suppressive effects in synergy with paclitaxel (PTX). Therefore, the objective of the present study...
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Intermolecular Interactions as a Measure of Dapsone Solubility in Neat Solvents and Binary Solvent Mixtures
PublicationDapsone is an effective antibacterial drug used to treat a variety of conditions. However, the aqueous solubility of this drug is limited, as is its permeability. This study expands the available solubility data pool for dapsone by measuring its solubility in several pure organic solvents: N-methyl-2-pyrrolidone (CAS: 872-50-4), dimethyl sulfoxide (CAS: 67-68-5), 4-formylmorpholine (CAS: 4394-85-8), tetraethylene pentamine (CAS:...
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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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Autonomous Perception and Grasp Generation Based on Multiple 3D Sensors 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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Investigation of long-range dependencies in the stochastic part of daily GPS solutions
PublicationThe long-range dependence (LRD) of the stochastic part of GPS-derived topocentric coordinates change (North, East, Up) results with relatively high autocorrelation values with a focus on self-similarity. One of the reasons for such self-similarity in the GPS time series are noises that are commonly recognised to prevail in the form of the flicker noise model. To prove the self-similarity of the stochastic part of GPS time series...
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Cost-Efficient Measurement Platform and Machine-Learning-Based Sensor Calibration for Precise NO2 Pollution Monitoring
PublicationAir quality significantly impacts human health, the environment, and the economy. Precise real-time monitoring of air pollution is crucial for managing associated risks and developing appropriate short- and long-term measures. Nitrogen dioxide (NO2) stands as a common pollutant, with elevated levels posing risks to the human respiratory tract, exacerbating respiratory infections and asthma, and potentially leading to chronic lung...
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Assessing the attractiveness of human face based on machine learning
PublicationThe attractiveness of the face plays an important role in everyday life, especially in the modern world where social media and the Internet surround us. In this study, an attempt to assess the attractiveness of a face by machine learning is shown. Attractiveness is determined by three deep models whose sum of predictions is the final score. Two annotated datasets available in the literature are employed for training and testing...
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How Integration of a Brain-Machine Interface and Obstacle Detection System Can Improve Wheelchair Control via Movement Image
PublicationThis study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation...
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EVALUATION OF LIQUID-GAS FLOW IN PIPELINE USING GAMMA-RAY ABSORPTION TECHNIQUE AND ADVANCED SIGNAL PROCESSING
PublicationLiquid-gas flows in pipelines appear in many industrial processes, e.g. in the nuclear, mining, and oil industry. The gamma-absorption technique is one of the methods that can be successfully applied to study such flows. This paper presents the use of thegamma-absorption method to determine the water-air flow parameters in a horizontal pipeline. Three flow types were studied in this work: plug, transitional plug-bubble,...
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Data fusion of sparse, heterogeneous, and mobile sensor devices using adaptive distance attention
PublicationIn environmental science, where information from sensor devices are sparse, data fusion for mapping purposes is often based on geostatistical approaches. We propose a methodology called adaptive distance attention that enables us to fuse sparse, heterogeneous, and mobile sensor devices and predict values at locations with no previous measurement. The approach allows for automatically weighting the measurements according to a priori...
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Super-resolved Thermal Imagery for High-accuracy Facial Areas Detection and Analysis
PublicationIn this study, we evaluate various Convolutional Neural Networks based Super-Resolution (SR) models to improve facial areas detection in thermal images. In particular, we analyze the influence of selected spatiotemporal properties of thermal image sequences on detection accuracy. For this purpose, a thermal face database was acquired for 40 volunteers. Contrary to most of existing thermal databases of faces, we publish our dataset...
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Predicting the peak structural displacement preventing pounding of buildings during earthquakes
PublicationThe aim of the present paper is to verify the effectiveness of the artificial neural network (ANN) in predicting the peak lateral displacement of multi-story building during earthquakes, based on the peak ground acceleration (PGA) and building parameters. For the purpose of the study, the lumped-mass multi-degree-of-freedom structural model and different earthquake records have been considered. Firstly, values of stories mass and...
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Feature Weighted Attention-Bidirectional Long Short Term Memory Model for Change Detection in Remote Sensing Images
PublicationIn remote sensing images, change detection (CD) is required in many applications, such as: resource management, urban expansion research, land management, and disaster assessment. Various deep learning-based methods were applied to satellite image analysis for change detection, yet many of them have limitations, including the overfitting problem. This research proposes the Feature Weighted Attention (FWA) in Bidirectional Long...
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Pedestrian detection in low-resolution thermal images
PublicationOver one million people die in car accidents worldwide each year. A solution that will be able to reduce situations in which pedestrian safety is at risk has been sought for a long time. One of the techniques for detecting pedestrians on the road is the use of artificial intelligence in connection with thermal imaging. The purpose of this work was to design a system to assist the safety of people and car intelligence with the use...
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Audio Feature Analysis for Precise Vocalic Segments Classification in English
PublicationAn approach to identifying the most meaningful Mel-Frequency Cepstral Coefficients representing selected allophones and vocalic segments for their classification is presented in the paper. For this purpose, experiments were carried out using algorithms such as Principal Component Analysis, Feature Importance, and Recursive Parameter Elimination. The data used were recordings made within the ALOFON corpus containing audio signal...
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Survey on fuzzy logic methods in control systems of electromechanical plants
PublicationРассмотрены алгоритмы управления электромеханическими системами с использованием теории нечеткой логики, приводятся основные положения их синтеза, рассматриваются методы анализа их устойчивости на основе нечетких функций Ляпунова. Эти алгоритмы чаще всего реализуются в виде различных регуляторов, применение которых целесообразно в системах, математическая модель которых не известна, не детерминирована или является строго нелинейной,...
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Synteza układu sterowania statkiem morskim dynamicznie pozycjonowanym w warunkach niepewności
PublicationNiniejsza monografia obejmuje zagadnienia związane z syntezą układu dynamicznego pozycjonowania statku w środowisku morskim z zastosowaniem wybranych nieliniowych metod sterowania. W ramach pracy autorka rozważała struktury sterowania z zastosowaniem wektorowej adaptacyjnej metody backstep oraz metod jej pokrewnych, takich jak regulatory MSS (ang. multiple surface sliding), DSC (ang. dynamic surface control), NB (ang. neural backstepping)....
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Supercomputing Grid-Based Services for Hearing Protection and Acoustical Urban Planning, Research and Education
PublicationSpecific computational environments, so-called domain grids, are developed within the PLGrid Plus project in order to prepare specialized IT solutions, i.e., dedicated software implementations and hardware (infrastructure adaptation), suited for particular research group demands. One of the PLGrid Plus domain grids, presented in this paper, is Acoustics. The article describes in detail two kinds of the acoustic domain services....
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Fast Design Closure of Compact Microwave Components by Means of Feature-Based Metamodels
PublicationPrecise tuning of geometry parameters is an important consideration in the design of modern microwave passive components. It is mandatory due to limitations of theoretical design methods unable to quantify certain phenomena that are important for the operation and performance of the devices (e.g., strong cross-coupling effects in miniaturized layouts). Consequently, the initial designs obtained using analytical or equivalent network...
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Effect of free water on the quasi‑static compression behavior of partially‑saturated concrete with a fully coupled DEM/CFD approach.
PublicationThe work aims to numerically investigate the quasi-static response of partially fluid-saturated concrete under two-dimensional uniaxial compression at the mesoscale. We investigated how the impact of free pore fluid content (water and gas) affected the quasi-static strength of concrete. The totally and partially fluid-saturated concrete behavior was simulated using an improved pore-scale hydro-mechanical model based on DEM/CFD....
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Extremal thermal loading of a bifurcation pipe
PublicationThe subject of considerations is a spherical bifurcation pipe of a live steam made of steel P91, which is an element of a block of coal-fired power plant working with a 18K370 turbine. As experience shows, it is a very sensitive element of the boiler pipelines. An extreme work scenario for such a block has been adopted, in which the turbine is shutting down to a warm state three times in 24 hours. This is an action dictated by...
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The Optimal Location of Ground-Based GNSS Augmentation Transceivers
PublicationModern Global Navigation Satellite Systems (GNSS) allow for positioning with accuracies ranging from tens of meters to single millimeters depending on user requirements and available equipment. A major disadvantage of these systems is their unavailability or limited availability when the sky is obstructed. One solution is to use additional range measurements from ground-based nodes located in the vicinity of the receiver. The highest...
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On the Handling of Outliers in the GNSS Time Series by Means of the Noise and Probability Analysis
PublicationThe data pre-analysis plays a significant role in the noise determination. The most important issue is to find an optimum criterion for outliers removal, since their existence can affect any further analysis. The noises in the GNSS time series are characterized by spectral index and amplitudes that can be determined with a few different methods. In this research, the Maximum Likelihood Estimation (MLE) was used. The noise amplitudes...
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Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design
PublicationThe design of concrete mixtures is crucial in concrete technology, aiming to produce concrete that meets specific quality and performance criteria. Modern standards require not only strength but also eco-friendliness and production efficiency. Based on the Three Equation Method, conventional mix design methods involve analytical and laboratory procedures but are insufficient for contemporary concrete technology, leading to overengineering...
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Melanoma skin cancer detection using mask-RCNN with modified GRU model
PublicationIntroduction: Melanoma Skin Cancer (MSC) is a type of cancer in the human body; therefore, early disease diagnosis is essential for reducing the mortality rate. However, dermoscopic image analysis poses challenges due to factors such as color illumination, light reflections, and the varying sizes and shapes of lesions. To overcome these challenges, an automated framework is proposed in this manuscript. Methods: Initially, dermoscopic...
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An ANN-Based Approach for Prediction of Sufficient Seismic Gap between Adjacent Buildings Prone to Earthquake-Induced Pounding
PublicationEarthquake-induced structural pounding may cause major damages to structures, and therefore it should be prevented. This study is focused on using an artificial neural network (ANN) method to determine the sufficient seismic gap in order to avoid collisions between two adjacent buildings during seismic excitations. Six lumped mass models of structures with a different number of stories (from one to six) have been considered in...
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Heavy Duty Vehicle Fuel Consumption Modelling Based on Exploitation Data by Using Artificial Neural Networks
PublicationOne of the ways to improve the fuel economy of heavy duty trucks is to operate the combustion engine in its most efficient operating points. To do that, a mathematical model of the engine is required, which shows the relations between engine speed, torque and fuel consumption in transient states. In this paper, easy accessible exploitation data collected via CAN bus of the heavy duty truck were used to obtain a model of a diesel...
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Application of a hybrid mechanistic/machine learning model for prediction of nitrous oxide (N2O) production in a nitrifying sequencing batch reactor
PublicationNitrous oxide (N2O) is a key parameter for evaluating the greenhouse gas emissions from wastewater treatment plants. In this study, a new method for predicting liquid N2O production during nitrification was developed based on a mechanistic model and machine learning (ML) algorithm. The mechanistic model was first used for simulation of two 15-day experimental trials in a nitrifying sequencing batch reactor. Then, model predictions...
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Balanced Spider Monkey Optimization with Bi-LSTM for Sustainable Air Quality Prediction
PublicationA reliable air quality prediction model is required for pollution control, human health monitoring, and sustainability. The existing air quality prediction models lack efficiency due to overfitting in prediction model and local optima trap in feature selection. This study proposes the Balanced Spider Monkey Optimization (BSMO) technique for effective feature selection to overcome the local optima trap and overfitting problems....