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Search results for: SPECTRAL ESTIMATION, MULTIVARIATE AUTOREGRESSIVEPROCESS, MODEL AVERAGING, FINAL PREDICTION ERROR
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A Universal Gains Selection Method for Speed Observers of Induction Machine
PublicationProperties of state observers depend on proper gains selection. Each method of state estimation may require the implementation of specific techniques of finding those gains. The aim of this study is to propose a universal method of automatic gains selection and perform its verification on an induction machine speed observer. The method utilizes a genetic algorithm with fitness function which is directly based on the impulse response...
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A robust sliding mode observer for non-linear uncertain biochemical systems
PublicationA problem of state estimation for a certain class of non-linear uncertain systems has been addressed in this paper. In particular, a sliding mode observer has been derived to produce robust and stable estimates of the state variables. The stability and robustness of the proposed sliding mode observer have been investigated under parametric and unstructured uncertainty in the system dynamics. In order to ensure an unambiguous non-linear...
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Color prediction from first principle quantum chemistry computations: a case of alizarin dissolved in methanol
PublicationThe electronic spectrum of alizarin (AZ) in methanol solution was measured and used as reference data for color prediction. The visible part of the spectrum was modelled by different DFT functionals within the TD-DFT framework. The results of a broad range of functionals applied for theoretical spectrum prediction were compared against experimental data by a direct color comparison. The tristimulus model of color expressed in terms...
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Machine learning-based prediction of residual drift and seismic risk assessment of steel moment-resisting frames considering soil-structure interaction
PublicationNowadays, due to improvements in seismic codes and computational devices, retrofitting buildings is an important topic, in which, permanent deformation of buildings, known as Residual Interstory Drift Ratio (RIDR), plays a crucial role. To provide an accurate yet reliable prediction model, 32 improved Machine Learning (ML) algorithms were considered using the Python software to investigate the best method for estimating Maximum...
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Michał Bernard Pietrzak dr hab.
PeopleMichal Pietrzak is head of the Department of Statistics and Econometrics at the Faculty of Economics and Management, Gdańsk University of Technology, and Deputy Editor-in-Chief for Statistical Reviewing of the journals: Oeconomia Copernicana and Equilibrium. Quarterly Journal of Economics and Economic Policy. Until October 2021, he worked as an associate professor at the Faculty of Economic Sciences and Management, Nicolaus...
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Investigating Noise Interference on Speech Towards Applying the Lombard Effect Automatically
PublicationThe aim of this study is two-fold. First, we perform a series of experiments to examine the interference of different noises on speech processing. For that purpose, we concentrate on the Lombard effect, an involuntary tendency to raise speech level in the presence of background noise. Then, we apply this knowledge to detecting speech with the Lombard effect. This is for preparing a dataset for training a machine learning-based...
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Influence of Flat Lapping Kinematics on Machinability of Ceramics
PublicationNew tools for flat grinding of ceramics are presented in the paper. Electroplated CBN tools (B64 and B107) were used in a modified single-disc lapping machine configuration. The results from experiments, such as the material removal rate and surface roughness parameters are presented and analyzed. Numerical simulations, based on the model for the shape error and tool wear estimation in machining with flat lapping kinematics, are...
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Comparative study on total nitrogen prediction in wastewater treatment 1 plant and effect of various feature selection methods on machine learning algorithms performance
PublicationWastewater characteristics prediction in wastewater treatment plants (WWTPs) is valuable and can reduce the number of sampling, energy, and cost. Feature Selection (FS) methods are used in the pre-processing section for enhancing the model performance. This study aims to evaluate the effect of seven different FS methods (filter, wrapper, and embedded methods) on enhancing the prediction accuracy for total nitrogen (TN) in the WWTP...
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A new method of wind farm active power curve estimation based on statistical approach
PublicationThe purpose of this paper is to solve the wind farm active power estimation problem, introducing the method which is based on a statistical approach and robust fitting. The proposed algorithm uses a statistical approach and compared to existing ones- includes a wind direction as well as the influence of turbine start-up procedure on the estimation. The results show that additional estimation inputs i.e. the wind direction and the...
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Towards rainfall interception capacity estimation using ALS LiDAR data
PublicationIn this study we develop a spatial model for interception capacity of vegetation based on LiDAR data. The study is conducted in the natural wetland river valley dominated meadows, reeds and small bushes. The multiple regression model was chosen to relate the field measurements of interception capacity and LiDAR statistics at 2m grid. The optimal model was chosen by stepwise selection and further manual variables selection resulting...
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Evaluating the risk of endometriosis based on patients’ self-assessment questionnaires
PublicationBackground Endometriosis is a condition that significantly affects the quality of life of about 10 % of reproductive-aged women. It is characterized by the presence of tissue similar to the uterine lining (endometrium) outside the uterus, which can lead lead scarring, adhesions, pain, and fertility issues. While numerous factors associated with endometriosis are documented, a wide range of symptoms may still be undiscovered. Methods In...
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Application of Multivariate Adaptive Regression Splines (MARSplines) for Predicting Hansen Solubility Parameters Based on 1D and 2D Molecular Descriptors Computed from SMILES String
PublicationA new method of Hansen solubility parameters (HSPs) prediction was developed by combining the multivariate adaptive regression splines (MARSplines) methodology with a simple multivariable regression involving 1D and 2D PaDEL molecular descriptors. In order to adopt the MARSplines approach to QSPR/QSAR problems, several optimization procedures were proposed and tested. The effectiveness of the obtained models was checked via standard...
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Effective Air Quality Prediction Using Reinforced Swarm Optimization and Bi-Directional Gated Recurrent Unit
PublicationIn the present scenario, air quality prediction (AQP) is a complex task due to high variability, volatility, and dynamic nature in space and time of particulates and pollutants. Recently, several nations have had poor air quality due to the high emission of particulate matter (PM2.5) that affects human health conditions, especially in urban areas. In this research, a new optimization-based regression model was implemented for effective...
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In silico modelling for predicting the cationic hydrophobicity and cytotoxicity of ionic liquids towards the Leukemia rat cell line, Vibrio fischeri and Scenedesmus vacuolatus based on molecular interaction potentials of ions
PublicationIn this study we present prediction models for estimating in silico the cationic hydrophobicity and the cytotoxicity (log [1/EC50]) of ionic liquids (ILs) towards the Leukemia rat cell line (IPC-81), the marine bacterium Vibrio fischeri and the limnic green algae Scenedesmus vacuolatus using linear free energy relationship (LFER) descriptors computed by COSMO calculations. The LFER descriptors used for the prediction model (i.e....
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Application of gas chromatographic data and 2D molecular descriptors for accurate global mobility potential prediction
PublicationMobility is a key feature affecting the environmental fate, which is of particular importance in the case of persistent organic pollutants (POPs) and emerging pollutants (EPs). In this study, the global mobility classification artificial neural networks-based models employing GC retention times (RT) and 2D molecular descriptors were constructed and validated. The high usability of RT was confirmed based on the feature selection...
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Ship Dynamic Positioning Based on Nonlinear Model Predictive Control
PublicationThe presented work explores the simulation test results of using nonlinear model predictive control algorithm for ship dynamic positioning. In the optimization task, a goal function with a penalty was proposed with a variable prediction step. The results of the proposed control algorithm were compared with backstepping and PID. The effect of estimation accuracy on the control quality with the implemented algorithms was investigated....
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Active Control of Highly Autocorrelated Machinery Noise in Multivariate Nonminimum Phase Systems
PublicationIn this paper, a novel multivariate active noise control scheme, designed to attenuate disturbances with high autocorrelation characteristics and preserve background signals, is proposed. The algorithm belongs to the class of feedback controllers and, unlike the popular feedforward FX-LMS approach, does not require availability of a reference signal. The proposed approach draws its inspiration from the iterative learning control...
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Evaluation of Response Amplitude Operator of Ship Roll Motions Based on the Experiments in White Noise Waves
PublicationEvaluation of the response amplitude operator (RAO) function for ship wave frequency motions by means of scale model tests in regular waves is a standard procedure conducted by hydrodynamic model testing institutions. The resulting RAO function allows for evaluating sufficiently reliable seakeeping predictions for low to moderate sea states. However, for standard hull forms, correct prediction of roll motion in irregular wave (and...
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Simplified, multiregional fuzzy model of a nuclear power plant steam turbine
PublicationPower systems, including steam turbines and synchronous generators, are complex nonlinear systems with parameters varying over time. The paper presents the developed simplified, multiregional fuzzy model of the steam turbine of a nuclear power plant turbine generator set and compares the results with a full nonlinear model and commonly used linear input-output model of a steam turbine. The proposed model consist of series of linear...
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Wykorzystanie algorytmów ewolucyjnych do doboru wzmocnień rozszerzonego obserwatora prędkości maszyny indukcyjnej
PublicationW pracy opisano sposób doboru wzmocnień rozszerzonego obserwatora prędkości maszyny indukcyjnej przy wykorzystaniu algorytmów ewolucyjnych. Zaproponowano funkcję celu opartą na rozkładzie biegunów obserwatora. Ze względu na wpływ prędkości maszyny na dynamikę obserwatora zaproponowano dobór wzmocnień obserwatora dla różnych przedziałów prędkości. Dla poszczególnych przedziałów zaprezentowano wyniki doboru wzmocnień w postaci tabel...
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Predicting seismic response of SMRFs founded on different soil types using machine learning techniques
PublicationPredicting the Maximum Interstory Drift Ratio (M-IDR) of Steel Moment-Resisting Frames (SMRFs) is a useful tool for designers to approximately evaluate the vulnerability of SMRFs. This study aims to explore supervised Machine Learning (ML) algorithms to build a surrogate prediction model for SMRFs to reduce the need for complex modeling. For this purpose, twenty well-known ML algorithms implemented in Python software are trained...
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A predictive estimation based control strategy for a quasi-resonant dc-link inverter
PublicationIn this paper the predictive estimation based control strategy for a quasi-resonant dc link inverter (PQRDCLI) is developed. Instead of direct measurement of dc link input inverter current – its estimation with one step prediction is applied. The PQRDCLI fed induction motor, controlled with a predictive current estimation stabilized inverter output voltage slopes independently of load. Moreover, reduction of overvoltage spikes...
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Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods
PublicationNowadays, Buckling-Restrained Brace Frames (BRBFs) have been used as lateral force-resisting systems for low-, to mid-rise buildings. Residual Interstory Drift (RID) of BRBFs plays a key role in deciding to retrofit buildings after seismic excitation; however, existing formulas have limitations and cannot effectively help civil engineers, e.g., FEMA P-58, which is a conservative estimation method. Therefore, there is a need to...
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Long-term hindcast simulation of sea ice in the Baltic Sea
Open Research DataThe data set contains the results of numerical modeling of sea ice over a period of 50 years (1958-2007) in the Baltic Sea. A long-term hindcast simulation was performed using a three-dimensional hydrodynamic model PM3D (Kowalewski and Kowalewska-Kalkowska, 2017), a new version of the M3D model (Kowalewski, 1997). A numerical dynamic-thermodynamic model...
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Acoustic heating produced in the boundary layer
Publication: Instantaneous acoustic heating of a viscous fluid flow in a boundary layer is the subject of investigation. The governing equation of acoustic heating is derived by means of a special linear combination of conservation equations in the differential form, which reduces all acoustic terms in the linear part of the final equation but preserves terms belonging to the thermal mode. The procedure of decomposition is valid in a weakly...
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Audio codec employing frequency-derived tonality measure
PublicationA transform codec employing efficient algorithm for detection of spectral tonal components is presented. The tonality measure used in MPEG psychoacoustic model is replaced with the method providing adequate tonality estimates even if the tonal components are deeply frequency modulated. The reliability of hearing threshold estimated using psychoacoustic model with standardized tonality measure and the proposed one is investigated...
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Justyna Płotka-Wasylka dr hab. inż.
PeopleUrodziła się w Słupsku (24.03.1986).W 2005 roku ukończyła I Liceum Ogólnokształcące im. Jana II Sobieskiego w Wejherowie i rozpoczęła studia na Wydziale Chemicznym Politechniki Gdańskiej. Po ich ukończeniu w 2010 rozpoczęła pracę naukową na tej uczelni, uzyskując w 2014 roku stopień doktora nauk chemicznych. Tematem jej rozprawy doktorskiej, wykonywanej pod kierunkiem prof. Marka Biziuka oraz dr Caluma Morrisona (Uniwersytet w...
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Hourly GNSS-derived integrated moisture in the global tropics for the years 2001-2018
Open Research DataGlobal tropics are essential in formulating weather patterns and climate across various latitudes through atmospheric teleconnections. Since water vapour is an essential parameter in atmospheric convection and, thus, latent heat release, its tropical variability on different time scales is crucial in understanding weather and climate changes. The provided...
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Prediction of metal deformation due to line heating; an alternative method of mechanical bending, based on artificial neural network approach
PublicationLine heating is one of the alternative methods of forming metals and this kind of forming uses the heating torch as a source of heat input. During the process, many parameters are considered like the size of the substrate, thickness, cooling method, source power intensity, the travel speed of the power source, the sequence of heating, and so on. It is important to analyze the factors affecting the...
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RECSYS CHALLENGE 2015: a BUY EVENT PREDICTION IN THE E-COMMERCE DOMAIN
PublicationIn this paper we present our approach to RecSys Challenge 2015. Given a set of e-commerce events, the task is to predict whether a user will buy something in the current session and, if yes, which of the item will be bought. We show that the data preparation and enrichment are very important in finding the solution for the challenge and that simple ideas and intuitions could lead to satisfactory results. We also show that simple...
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Things You Might Not Know about the k-Nearest Neighbors Algorithm
PublicationRecommender Systems aim at suggesting potentially interesting items to a user. The most common kind of Recommender Systems is Collaborative Filtering which follows an intuition that users who liked the same things in the past, are more likely to be interested in the same things in the future. One of Collaborative Filtering methods is the k Nearest Neighbors algorithm which finds k users who are the most similar to an active user...
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Online sound restoration system for digital library applications
PublicationAudio signal processing algorithms were introduced to the new online non-commercial service for audio restoration intended to enhance the content of digitized audio repositories. Missing or distorted audio samples are predicted using neural networks and a specific implementation of the Jannsen interpolation method based on the autoregressive model (AR) combined with the iterative restoring of missing signal samples. Since the distortion...
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Adam Inglot dr inż.
PeopleAdam Inglot (born October 3, 1984), studied Environmental Engineering and Geodesy and Cartography at the Faculty of Mine Surveying and Environmental Engineering of AGH University of Science and Technology in Krakow. He graduated in 2011 as a M.Sc. in Environmental Engineering, defending his thesis "Verification of usefulness of GeoMod model for prediction of the urbanization process in Cracow agglomeration" under the guidance of...
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A Novel Approach of Using Selected Unconventional Geodesic Methods of Estimation on VTS Areas
PublicationThe Vessel Traffic Service (VTS) systems belong to the fundamental tools used in ensuring a high level of safety across sea basins with heavy traffic, where the presence of navigational hazards poses a great risk of collision or a ship running aground. In order to determine the mutual location of ships, VTS systems obtain information from different facilities, such as coastal radar stations, AIS, and vision systems. Fixing a ship’s...
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Muneer Ahmed Mr
PeopleLooking for an organization where I can better utilize my scientific and technical skills in the arena of research or work as a faculty person. Besides, I am also willing to work as professional engineer with dedication and diligence in dynamic environment. Likewise, I've been a committed and tenacious candidate throughout my career. I am aware of the level of commitment, dedication and strength required. Despite the fact that...
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MAPPING SOUTH BALTIC NEAR-SHORE BATHYMETRY USING SENTINEL-2 OBSERVATIONS
PublicationOne of the most promising new applications of remote observation satellite systems (RO) is the near-shore bathymetry estimation based on spaceborn multispectral imageries. In recent years, many experiments aiming to estimate bathymetry in optically shallow water with the use of remote optical observations have been presented. In this paper, optimal models of satellite derived bathymetry (SDB) for relatively turbid waters of the...
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N-point estimators of the Instantaneous Complex Frequency
PublicationIn this paper estimators of the instantaneous complex frequency (ICF) are presented and discussed. The differential approach for the estimation of the ICF is used, therefore the estimators are based on maximally flat N-point FIR filters: differential and delay. The investigation of the filter performance includes static characteristics of ICF estimation and the error of the ICF estimation in the discrete frequency domain.W pracy...
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Estimation of Average Speed of Road Vehicles by Sound Intensity Analysis
PublicationConstant monitoring of road traffic is important part of modern smart city systems. The proposed method estimates average speed of road vehicles in the observation period, using a passive acoustic vector sensor. Speed estimation based on sound intensity analysis is a novel approach to the described problem. Sound intensity in two orthogonal axes is measured with a sensor placed alongside the road. Position of the apparent sound...
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Simultaneous grouping and ranking with combination of SOM and TOPSIS for selection of preferable analytical procedure for furan determination in food
PublicationNovel methodology for grouping and ranking with application of self-organizing maps and multicriteria decision analysis is presented. The dataset consists of 22 objects that are analytical procedures applied to furan determination in food samples. They are described by 10 variables, referred to their analytical performance, environmental and economic aspects. Multivariate statistics analysis allows to limit the amount of input...
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Sampling-based novel heterogeneous multi-layer stacking ensemble method for telecom customer churn prediction
PublicationIn recent times, customer churn has become one of the most significant issues in business-oriented sectors with telecommunication being no exception. Maintaining current customers is particularly valuable due to the high degree of rivalry among telecommunication companies and the costs of acquiring new ones. The early prediction of churned customers may help telecommunication companies to identify the causes of churn and design...
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Assessment of Fuel Cells’ State of Health by Low-Frequency Noise Measurements
PublicationWe proposed applying low-frequency (flicker) noise in proton-exchange membrane fuel cells under selected loads to assess their state of health. The measurement set-up comprised a precise data acquisition board and was able to record the DC voltage and its random component at the output. The set-up estimated the voltage noise power spectral density at frequencies up to a few hundred mHz. We observed the evolution of the electrical...
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Driver’s Condition Detection System Using Multimodal Imaging and Machine Learning Algorithms
PublicationTo this day, driver fatigue remains one of the most significant causes of road accidents. In this paper, a novel way of detecting and monitoring a driver’s physical state has been proposed. The goal of the system was to make use of multimodal imaging from RGB and thermal cameras working simultaneously to monitor the driver’s current condition. A custom dataset was created consisting of thermal and RGB video samples. Acquired data...
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Hydrodynamic reanalysis of sea level in the Baltic Sea using the PM3D model
Open Research DataThe data set contains the results of numerical modelling of sea level fluctuations in the Baltic Sea in the Baltic Sea since 1998. A long-term reanalysis was performed using a three-dimensional hydrodynamic model PM3D (Kowalewski and Kowalewska-Kalkowska, 2017), a new version of the M3D model (Kowalewski, 1997).
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ZASTOSOWANIE FILTRACJI CZĄSTECZKOWEJ DO ESTYMACJI POŁOŻENIA W SYSTEMIE LOKALIZACYJNYM UWB
PublicationNiniejszy artykuł dotyczy kwestii poprawy dokładności estymacji położenia w systemie lokalizacji wewnątrzbudynkowej, bazującym na radiowych pomiarach odległości realizowanych przez modemy UWB. Proponuje się zastosowanie metody filtracji cząsteczkowej do zmniejszenia błędu wyznaczania pozycji obiektu przy braku bezpośredniej widoczności ze stacją referencyjną. W artykule opisano algorytm filtru cząsteczkowego, jego przykładową implementację...
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Dynamical description of quantum computing: generic nonlocality of quantumnoise
PublicationWe develop a dynamical non-Markovian description of quantum computing in the weak-coupling limit, in the lowest-order approximation. We show that the long-range memory of the quantum reservoir (such as the 1/t4 one exhibited by electromagnetic vacuum) produces a strong interrelation between the structure of noise and the quantum algorithm, implying nonlocal attacks of noise. This shows that the implicit assumption of quantum error...
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Evaluating Accuracy of Respiratory Rate Estimation from Super Resolved Thermal Imagery
PublicationNon-contact estimation of Respiratory Rate (RR) has revolutionized the process of establishing the measurement by surpassing some issues related to attaching sensors to a body, e.g. epidermal stripping, skin disruption and pain. In this study, we perform further experiments with image processing-based RR estimation by using various image enhancement algorithms. Specifically, we employ Super Resolution (SR) Deep Learning (DL) network...
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Calibration of precipitation estimation algorithm with particular emphasis on the Pomeranian region using high performance computing
PublicationFast and accurate precipitation estimation is an important element of remote atmosphere monitoring, as it allows, for example, to correct short-term weather forecasts and the prediction of several types of meteorological threats. The paper presents methodology for calibrating precipitation estimation algorithm based on MSG SEVIRI sensor data, and Optimal Cloud Analysis product available via EumetCast transmission. Calibration is...
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Online sound restoration system for digital library applications.
PublicationAudio signal processing algorithms were introduced to the new online non-commercial service for audio restoration intended to enhance the content of digitized audio repositories. Missing or distorted audio samples are predicted using neural networks and a specific implementation of the Jannsen interpolation method based on the autoregressive model (AR) combined with the iterative restoring of missing signal samples. Since the distortion...
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Speech codec enhancements utilizing time compression and perceptual coding
PublicationA method for encoding wideband speech signal employing standardized narrowband speech codecs is presented as well as experimental results concerning detection of tonal spectral components. The speech signal sampled with a higher sampling rate than it is suitable for narrowband coding algorithm is compressed in order to decrease the amount of samples. Next, the time-compressed representation of a signal is encoded using a narrowband...
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Automated Reduced Model Order Selection
PublicationThis letter proposes to automate generation of reduced-order models used for accelerated -parameter computation by applying a posteriori model error estimators. So far,a posteriori error estimators were used in Reduced Basis Method (RBM) and Proper Orthogonal Decomposition (POD) to select frequency points at which basis vectors are generated. This letter shows how a posteriori error estimators can be applied to automatically select...