In the present work, a simple intelligence-based computation of artificial neural networks with the Levenberg-Marquardt backpropagation algorithm is developed to analyze the new ferromagnetic hybrid nanofluid flow model in the presence of a magnetic dipole within the context of flow over a stretching sheet. A combination of cobalt and iron (III) oxide (Co-Fe2O3) is strategically selected as ferromagnetic hybrid nanoparticles within the base fluid, water. The initial representation of the developed ferromagnetic hybrid nanofluid flow model, which is a system of highly nonlinear partial differential equations, is transformed into a system of nonlinear ordinary differential equations using appropriate similarity transformations. The reference data set of the possible outcomes is obtained from bvp4c for varying the parameters of the ferromagnetic hybrid nanofluid flow model. The estimated solutions of the proposed model are described during the testing, training, and validation phases of the backpropagated neural network. The performance evaluation and comparative study of the algorithm are carried out by regression analysis, error histograms, function fitting graphs, and mean squared error results. The findings of our study analyze the increasing effect of the ferrohydrodynamic interaction parameter β to enhance the temperature and velocity profiles, while increasing the thermal relaxation parameter α decreases the temperature profile. The performance on MSE was shown for the temperature and velocity profiles of the developed model about 9.1703e-10, 7.1313ee-10, 3.1462e-10, and 4.8747e-10. The accuracy of the artificial neural networks with the Levenberg-Marquardt algorithm method is confirmed through various analyses and comparative results with the reference data. The purpose of this study is to enhance understanding of ferromagnetic hybrid nanofluid flow models using artificial neural networks with the Levenberg-Marquardt algorithm, offering precise analysis of key parameter effects on temperature and velocity profiles. Future studies will provide novel soft computing methods that leverage artificial neural networks to effectively solve problems in fluid mechanics and expand to engineering applications, improving their usefulness in tackling real-world problems.

• Klíčová slova
• Artificial neural networks, Dimensionless parameters, Heat transfer, Hybrid nanoparticles, Levenberg-Marquardt algorithm, Magnetic dipole,
• Publikační typ
• časopisecké články MeSH

High-capacity impulse-radio ultra-wideband (IR-UWB) indoor localization systems are typically based on the time difference of arrival (TDoA) principle. When the fixed and synchronized localization infrastructure, the anchors, transmit precisely timestamped messages, a virtually unlimited number of user receivers (tags) are able to estimate their position based on differences in the time of arrival of those messages. However, the drift of the tag clock causes systematic errors at a sufficiently high magnitude to effectively deny the positioning, if left uncorrected. Previously, the extended Kalman filter (EKF) has been used to track and compensate for the clock drift. In this article, the utilization of a carrier frequency offset (CFO) measurement for suppressing the clock-drift related error in anchor-to-tag positioning is presented and compared to the filtered solution. The CFO is readily available in the coherent UWB transceivers, such as Decawave DW1000. It is inherently related to the clock drift, since both carrier and timestamping frequencies are derived from the identical reference oscillator. The experimental evaluation shows that the CFO-aided solution performs worse than the EKF-based solution in terms of accuracy. Nonetheless, with CFO-aiding it is possible to obtain a solution based on measurements from a single epoch, which is favorable especially for power-constrained applications.

• Klíčová slova
• Levenberg–Marquardt algorithm, carrier frequency offset (CFO), clock drift compensation, indoor localization, synchronization, time difference of arrival (TDoA), ultra-wideband (UWB),
• Publikační typ
• časopisecké články MeSH

OBJECTIVE: T2 maps are more vendor independent than other MRI protocols. Multi-echo spin-echo signal decays to a non-zero offset due to imperfect refocusing pulses and Rician noise, causing T2 overestimation by the vendor's 2-parameter algorithm. The accuracy of the T2 estimate is improved, if the non-zero offset is estimated as a third parameter. Three-parameter Levenberg-Marquardt (LM) T2 estimation takes several minutes to calculate, and it is sensitive to initial values. We aimed for a 3-parameter fitting algorithm that was comparably accurate, yet substantially faster. METHODS: Our approach gains speed by converting the 3-parameter minimisation problem into an empirically unimodal univariate problem, which is quickly minimised using the golden section line search (GS). RESULTS: To enable comparison, we propose a novel noise-masking algorithm. For clinical data, the agreement between the GS and the LM fit is excellent, yet the GS algorithm is two orders of magnitude faster. For synthetic data, the accuracy of the GS algorithm is on par with that of the LM fit, and the GS algorithm is significantly faster. The GS algorithm requires no parametrisation or initialisation by the user. DISCUSSION: The new GS T2 mapping algorithm offers a fast and much more accurate off-the-shelf replacement for the inaccurate 2-parameter fit in the vendor's software.

• Klíčová slova
• Algorithms, Least-squares analysis, Software,
• MeSH
• algoritmy MeSH
• časové faktory MeSH
• fantomy radiodiagnostické MeSH
• interpretace obrazu počítačem metody   MeSH
• lidé MeSH
• magnetická rezonanční tomografie * MeSH
• metoda nejmenších čtverců MeSH
• nádory prostaty diagnostické zobrazování   MeSH
• počítačové zpracování obrazu metody   MeSH
• poměr signál - šum MeSH
• pravděpodobnost MeSH
• regresní analýza MeSH
• reprodukovatelnost výsledků MeSH
• software MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

This paper deals with the design of a neural network-based biomass concentration estimation system. This system is enhanced by the incorporation of information about the actual metabolism of the microorganism cultivated, which is taken from an on-line knowledge-based system. Two different design approaches have been investigated using the fed-batch cultivation of baker's yeast as the model process. In the first, metabolic state (MS) data were passed as additional input to the neural network; in the second, these data were used to select a neural network suitable for the specific MS. Two neural network types--feed-forward (Levenberg-Marquardt) and cascade correlation--were applied to this system and tested, and the performances of these neural networks were compared.

• MeSH
• algoritmy * MeSH
• biologické modely * MeSH
• biomasa MeSH
• energetický metabolismus fyziologie   MeSH
• neuronové sítě * MeSH
• on-line systémy MeSH
• počítačová simulace MeSH
• proliferace buněk MeSH
• Saccharomyces cerevisiae růst a vývoj   metabolismus   MeSH
• spotřeba kyslíku fyziologie   MeSH
• ukládání a vyhledávání informací metody   MeSH
• Publikační typ
• časopisecké články MeSH
• hodnotící studie MeSH
• srovnávací studie MeSH
• validační studie MeSH

This paper shows an influence of chemical composition of used electrolyte, such as amount of sulphuric acid in electrolyte, amount of aluminium cations in electrolyte and amount of oxalic acid in electrolyte, and operating parameters of process of anodic oxidation of aluminium such as the temperature of electrolyte, anodizing time, and voltage applied during anodizing process. The paper shows the influence of those parameters on the resulting thickness of aluminium oxide layer. The impact of these variables is shown by using central composite design of experiment for six factors (amount of sulphuric acid, amount of oxalic acid, amount of aluminium cations, electrolyte temperature, anodizing time, and applied voltage) and by usage of the cubic neural unit with Levenberg-Marquardt algorithm during the results evaluation. The paper also deals with current densities of 1 A · dm(-2) and 3 A · dm(-2) for creating aluminium oxide layer.

• MeSH
• elektrolyty MeSH
• neuronové sítě * MeSH
• oxid hlinitý chemie   MeSH
• teoretické modely MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• elektrolyty MeSH
• oxid hlinitý MeSH

In this paper, we study the design aspects of an indoor visible light positioning (VLP) system that uses an artificial neural network (ANN) for positioning estimation by considering a multipath channel. Previous results usually rely on the simplistic line of sight model with limited validity. The study considers the influence of noise as a performance indicator for the comparison between different design approaches. Three different ANN algorithms are considered, including Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient algorithms, to minimize the positioning error (εp) in the VLP system. The ANN design is optimized based on the number of neurons in the hidden layers, the number of training epochs, and the size of the training set. It is shown that, the ANN with Bayesian regularization outperforms the traditional received signal strength (RSS) technique using the non-linear least square estimation for all values of signal to noise ratio (SNR). Furthermore, in the inner region, which includes the area of the receiving plane within the transmitters, the positioning accuracy is improved by 43, 55, and 50% for the SNR of 10, 20, and 30 dB, respectively. In the outer region, which is the remaining area within the room, the positioning accuracy is improved by 57, 32, and 6% for the SNR of 10, 20, and 30 dB, respectively. Moreover, we also analyze the impact of different training dataset sizes in ANN, and we show that it is possible to achieve a minimum εp of 2 cm for 30 dB of SNR using a random selection scheme. Finally, it is observed that εp is low even for lower values of SNR, i.e., εp values are 2, 11, and 44 cm for the SNR of 30, 20, and 10 dB, respectively.

• Klíčová slova
• Bayesian regularization, artificial neural network (ANN), multipath reflections, non-linear least square, visible light communication (VLC), visible light positioning,
• MeSH
• algoritmy * MeSH
• Bayesova věta MeSH
• metoda nejmenších čtverců MeSH
• neuronové sítě * MeSH
• světlo MeSH
• Publikační typ
• časopisecké články MeSH

During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time.

• MeSH
• biologické modely * MeSH
• lidé MeSH
• mechanika dýchání * MeSH
• nádory plic patologie   patofyziologie   radioterapie   MeSH
• neuronové sítě * MeSH
• pohyb těles * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

A variety of models are available for the estimation of parameters of the human growth curve. Several have been widely and successfully used with longitudinal data that are reasonably complete. On the other hand, the modeling of data for a limited number of observation points is problematic and requires the interpolation of the interval between points and often an extrapolation of the growth trajectory beyond the range of empirical limits (prediction). This study tested a new approach for fitting a relatively limited number of longitudinal data using the normal variation of human empirical growth curves. First, functional principal components analysis was done for curve phase and amplitude using complete and dense data sets for a reference sample (Brno Growth Study). Subsequently, artificial curves were generated with a combination of 12 of the principal components and applied for fitting to the newly analyzed data with the Levenberg-Marquardt optimization algorithm. The approach was tested on seven 5-points/year longitudinal data samples of adolescents extracted from the reference sample. The samples differed in their distance from the mean age at peak velocity for the sample and were tested by a permutation leave-one-out approach. The results indicated the potential of this method for growth modeling as a user-friendly application for practical applications in pediatrics, auxology and youth sport.

• Klíčová slova
• Sitar, functional data analysis, growth modelling, human growth,
• Publikační typ
• časopisecké články MeSH

PURPOSE: Leksell Gamma Knife (LGK) installations replace their Co-60 sources every 5-10 years corresponding to one two Co-60 half-lives. Between source replacements the dose rate gradually declines. The purpose of this study was to assess whether the decreasing dose rates associated with radioactive decay of Co-60 may affect the radiobiological response of a given dose delivered to 9L rat gliosarcoma cells. METHOD AND MATERIALS: 9L rat gliosarcoma cells were irradiated using LGK U, LGK 4C, and LGK Perfexion providing three different dose rates of 0.770 Gy/ min (sources reloaded 12.0 years ago), 1.853 Gy/min (sources reloaded 5.0 years ago) and 2.937 Gy/min (sources reloaded 1.6 years ago), respectively. After irradiation of cell samples to 4.0 Gy, 8.0 Gy and 16.0 Gy using each of the LGK units, the irradiated cells were plated into petri dishes. Two weeks later cell colonies with greater than 50 cells were counted. The survival of cells was plotted as a function of dose over the range of delivered doses and fitted to a linear quadratic function of the form SD = e-αD-βD2 , where α and β are terms fit using the Levenberg-Marquardt algorithm. CONCLUSIONS: This study demonstrated no difference in tumor cell kill in the range of dose rates when using actual LGK unit with new sources or with sources decayed even for two half lives. This study focused on tumor cells. In future studies we will reassess the dose rate effect on cultured neurons to simulate response of normal healthy brain tissue to different dose rates.

• Klíčová slova
• Dose Rate Effect, Gamma Knife, Radiosurgery, Relative Biological Effectiveness,
• Publikační typ
• časopisecké články MeSH