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To improve the control precision of the nonlinear register system for flexographic printing, a feedforward active disturbance rejection control (ADRC) parameter self-tuning decoupling control strategy based on radial basis function (RBF) is proposed to address the existence of coupling interference and multiple working conditions. Firstly, according to the structure of the flexographic printing equipment system and registration principle, a nonlinear mathematical model of the global registration system is established and linearized using the small deviation method. Secondly, the decoupled controller of the register system is designed by integrating feedforward control, ADRC, and RBF, in which the feedforward control is used to eliminate the registration errors caused by modeled disturbances, the ADRC performs the estimation and compensation of unmodeled disturbances, and the RBF realizes the self-tuning of the ADRC controller parameters. Finally, different operating conditions are simulated to compare and verify the control performance of the proposed controller. Simulation results show that the designed controller has a better performance compared to traditional PID and ADRC control, and its register error peak is reduced by about 32% compared to ADRC, achieving the high accuracy control of a multi-color register system.

Study region: This study focuses on the simulation of flow and transport of potential contaminants at the riverbank filtration (RBF) site at Embaba, Cairo. RBF has a high potential along the Nile in Egypt as a pre-treatment step for drinking water production. As the site in Cairo can be affected by low water quality and spills of contaminants, the aim was to assess the RBF efficiency under various hydrological, geohydraulic and operational conditions. Study focus: MODFLOW, MT3D and MODPATH were used in this study. Three cases were considered to identify the share of bank filtrate in pumped water and the travel time. The first case focused on the river hydrograph and the permeability of the riverbed; the second on clay cap thickness, aquifer and riverbed hydraulic conductivities, and groundwater abstraction downside of the RBF scheme; and the third on different RBF well abstraction rates, depths, screen lengths and distances from the river bank. New hydrological insights for the region: The results indicate that favorable conditions for RBF sites. Good conditions for RBF include high river stage and riverbed permeability, high aquifer hydraulic conductivity and abstraction rates resulting in high portions of bank filtrate. Design and operation analysis indicate that the abstraction wells should be shallow and located close to the river bank to achieve shorter travel times, thus increasing productivity and preventing high manganese concentrations in the pumped bank filtrate. Due to the natural flow gradient from the Nile towards the RBF wells, changing conditions have impact on the portions of bank filtrate, making the site special.

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The acquisition of rotation angle and pose information of precision spherical joint is of great importance for error motion prediction analysis and motion control. In the early stage, an analytical measurement model based on the equivalent magnetic charge method has been purposed to realize the rotation direction identification and rotation angle measurement. However, several shortcomings were observed such as complicated calculations and time-consuming, and the solution accuracy of the theoretical model was decreasing with the expansion of measurement range. To improve this situation, new modeling methods based on artificial neural network have been researched. This paper employs radial basis function (RBF), extreme learning machine (ELM), and RBF–ELM hybrid neural networks to construct measurement models, then analyzes and compares their effectiveness to obtain the optimal algorithm. Analysis results show that the RBF–ELM hybrid neural network has a better calculation accuracy than the others. Finally, the experimental data are used to train and test the network model, and the error between output angle of the model and the actual measured rotation angle is calculated. The comparison results show that the measurement model based on the RBF–ELM hybrid neural network has a higher calculation accuracy and generalization capability. Within the range of ±20°, the maximum error of rotation angle around the X and Y axes are $9'~48''$ and $6'~55''$ , respectively, and the root mean squared error is $1' 59''$. [ABSTRACT FROM AUTHOR]

Reducing the disturbance of parameter fluctuations to the system is very important to the robustness of stamping product quality. In this paper, a 6-sigma robust optimization design method based on residual error and radial basis interpolation is proposed to weaken the negative influence of parameter fluctuations. Not only the key controllable parameters but also the key noise parameters are considered by grey relational analysis (GRA) to screen the parameters that have an important influence on the stamping quality. In order to reflect the relationship between key parameters and forming quality more precisely, the polynomial response surface model is improved based on residual error and radial basis function (RBF), and solved by the multi-population co-evolutionary immune algorithm (MPCEIA). Moreover, the solution of deterministic optimization is designed with 6-sigma robust optimization to improve the robustness of the system to the parameter fluctuations. Finally, the method is applied to the design of a complex automobile cover part, proving its effectiveness and reliability. [ABSTRACT FROM AUTHOR]

• FGF-21 and GDF-15 were significantly elevated in muscle manifesting mitochondrial disorders. • FGF-21 and GDF-15 levels were higher in patients with mitochondrial deletions and translation disorders. • Serum FGF-21, GDF-15 and combined FGF-21 and GDF-15 are all sensitive biomarkers for diagnosis of mitochondrial disorders. Mitochondrial disorders are often difficult to diagnose because of diverse clinical phenotypes. FGF-21 and GDF-15 are metabolic hormones and promising biomarkers for the diagnosis of these disorders. This study has systematically evaluated serum FGF-21 and GDF-15 levels by ELISA in a well-defined cohort of patients with definite mitochondrial disorders (n = 30), neuromuscular disease controls (n = 36) and healthy controls (n = 36) and aimed to ascertain their utility in the diagnosis of mitochondrial disorders. Both serum FGF-21 and GDF-15 were significantly elevated in patients with mitochondrial disorders, especially in those with muscle involvement. The levels were higher in patients with mitochondrial deletions (both single and multiple) and translation disorders compared to respiratory chain subunit or assembly factor defects. [ABSTRACT FROM AUTHOR]

In today's world, skin cancer is seen as the most prevalent cause of death in people. This type of cancer is not consistent or patchy, but it can occur everywhere in the body, with skin cells that usually arise on particular sections of the body that are more likely to be exposed to light. When identified early, most skin cancers can be treated. This means that a patient's life is saved by discovering skin cancer early and readily. Modern technologies have made it feasible to early detect skin cancer at an early stage. The biopsy procedure [1] is a method for diagnosing skin cancer that is done in a systematic manner. It is accomplished by harvesting skin cells, which are then sent to various laboratories for analysis. It's a long and unpleasant procedure (in terms of time). We suggested a skin cancer detection method based on svm for primitive detection of skin cancer disease. It's more beneficial to the patients. The identification approach employs a variety of image processing techniques as well as the supervised learningalgorithm Support Vector Machine (SVM). Microscopy of epiluminescience is done using an image and in particular with numerous pre-processing procedures used to reduce sound objects and improvise image quality. Certain thresholding techniques, such as OTSU, are used to segment data. To erase particular visual features, the GLCM approach must be utilised. As input, these properties are sent into the classifier. To identify data sets, the supervised learning model (SVM) is used. It assesses whether or not a photograph is malignant using various different kernel with their accuracies. [ABSTRACT FROM AUTHOR]

We construct cubature methods on scattered data via resampling on the support of known algebraic cubature formulas, by different kinds of adaptive interpolation (polynomial, RBF, PUM). This approach gives a promising alternative to other recent methods, such as direct meshless cubature by RBF or least-squares cubature formulas. [ABSTRACT FROM AUTHOR]

In this paper, a Divide-and-Cooperate Machine Learning Model (DCML) based Radial Basis Function Network (RBF) is constructed. This DCML is composed of several sub-RBF networks that take some variables as their inputs. The output of DCML is the sum of sub-RBF networks' outputs. The analysis of VC dimension of DCML shows in theory that its structural complexity is less than conventional Extended Radial Basis Function Network (ENRBF). The experimental results have verified that the DCML outperforms conventional ENRBF.

The design of load frequency control (LFC) for the hydro-hydro power system suffers from poor dynamic performance owing to large time constants associated with hydro power generations. The dynamic performance of the hydro-hydro power system can be improved using a thyristor control phase shifter (TCPS) and energy storage battery system. This paper presents a comprehensive study on LFC of an interconnected hydro-hydro power system having TCPS in series with the tie-line and redox flow battery (RBF) at the terminal of one area through few and available state technique, i.e. the output feedback technique. The LFC are designed with the help of few and available states and the performance is judged for typical demand change in the power system area and the results are matched with the LFC obtained via a technique that require all system states, i.e. full state feedback in order to justify the effectiveness of the designed LFC in terms of achieved feedback gains, eigenvalues and the dynamic system responses. Abbreviations: AC: Alternating Current; BESS: Battery Energy Storage System; FACTS: Flexible Alternating Current Transmission System; LFC: Load Frequency Control; RFB: Redox Flow Battery; SSSC: Static Synchronous Series Compensator; SMES: Superconducting Magnetic Energy Storage; TCPS: Thyristor Control Phase Shifter; UPFC: Unified Power Flow Control

In this study, the radial basis function (RBF) which has good performance for nonlinear problem is introduced to approximate the implicit relationships between EDM parameters and performance responses for 304 steel. The fitting precision of RBF is compared with the second order polynomial response surface (PRS), support vector regression (SVR) and Kriging model (KRG) using the multiple correlation coefficient (R2) based cross validation error method. Then the RBF model is called to conduct multi-objective optimization using non-dominated sorting genetic algorithm II (NSGA-II) method. The energy consumption index unit energy consumption (UEC) and the air-pollution indices PM2.5 and PM10 are considered in proposed multi-objective optimization model. UEC is considered as the objective function to reduce the machining cost and the PM indices are termed as the constraints to protect the operators' health. The pulse current, time period and duty cycle are considered as the main factors affecting the EDM responses. According to the Pareto plots of multi-objective optimization model, conclusion can be drawn that SR and PM10 play significant roles in multi-optimization and PM2.5 has less influence on optimization results. The results of the present study reveal that using maximum material removal rate (MRR) and minimum UEC as objective and using surface roughness (SR), PM2.5 and PM10 as constraints can be an effective method to provide appropriate process parameters reference for EDM machining.

The meshless radial basis function collocation method is an efficient numerical technique for solving partial differential equations. The multiquadric is the most widely utilized radial function for this purpose; but it contains a shape parameter, which has a significant effect on the performance of the method. In this study, the meshless collocation method employing multiquadric as the radial function with optimum shape parameters is applied to the numerical solution of the multigroup neutron diffusion equation. The optimization of the shape parameter is performed by minimizing the Madych-Nelson function. One external and two fission source problems are solved to investigate the performance of the method. The results show that the meshless collocation method with optimized shape parameters yield a high level of accuracy with an exponential convergence rate.

With the increasing demand for electric vehicles, the high voltage safety of electric vehicles has attracted significant attention. More than 30% of electric vehicle accidents are caused by the battery system; hence, it is vital to investigate the fault diagnosis method of lithium-ion battery packs. The fault types of lithium-ion battery packs for electric vehicles are complex, and the treatment is cumbersome. This paper presents a fault diagnosis method for the electric vehicle power battery using the improved radial basis function (RBF) neural network. First, the fault information of lithium-ion battery packs was collected using battery test equipment, and the fault levels were then determined. Subsequently, the improved RBF neural networks were employed to identify the fault of the lithium-ion battery pack system using the experimental data. The diagnosis test results showed that the improved RBF neural networks could effectively identify the fault diagnosis information of the lithium-ion battery packs, and the diagnosis accuracy was about 100%.

The paper presents the five variables higher order shear deformation model for bending analysis of sandwich plate under uniformly line load using radial basis function (RBF) based mesh free method. The governing differential equations (GDEs) is obtained using energy principle. Multiquadric RBF (MQRBF) is used to discretize GDEs. The present results are compared with the available 3D elasticity solutions and the other results available in the literature. The effect of transverse loads with location of applied load, stiffness ratio 'R', and the core thickness to plate thickness ratio is studied. Some new results for sandwich plate considered which may serve as a benchmark for future investigations. [ABSTRACT FROM AUTHOR]

This paper presents a novel framework for stochastic analysis of linear elastic fracture problems. Monte Carlo simulation (MCs) is adopted to address themulti-dimensional uncertainties, whose computation cost is reduced by combination of Proper Orthogonal Decomposition (POD) and the Radial Basis Function (RBF). In order to avoid re-meshing and retain the geometric exactness, isogeometric boundary element method (IGABEM) is employed for simulation, in which the Non-Uniform Rational B-splines (NURBS) are employed for representing the crack surfaces and discretizing dual boundary integral equations. The stress intensity factors (SIFs) are extracted byM integral method. The numerical examples simulate several cracked structures with various uncertain parameters such as load effects, materials, geometric dimensions, and the results are verified by comparison with the analytical solutions. [ABSTRACT FROM AUTHOR]

Indian rivers carry large sediment loads. During monsoon season when river discharge is high, rivers carry suspended particles, silt, clay, and organic substances, causing river water turbidity that is not suitable for drinking. Riverbank filtration (RBF) is a natural process in which one can abstract good quality water from a highly polluted river through abstraction wells. As RBF water is a mixture of riverbank filtered water and groundwater, assuming no turbidity was picked up by the river water as it travelled through the soil matrix to reach the pumping well. A strategy-A was developed in which flow of groundwater contribution toward pumping well was neglected, while simultaneously measuring source water and abstracted water quality; it was observed that the variation of K, the kinetic coefficient, captured the variation in ln(C0), natural logarithm of influent quality, with R2 equal to 0.974. The kinetic coefficient, K, also captured the variation with ln(C0) of turbidity after travel times of 14 days and 21 days, having R2 equal to 0.991 and 0.989, respectively. A strategy-B was developed using the mass balance relationship, wherein the flow of groundwater contribution toward pumping well was utilized in refining the abstracted water quality. It was observed that K, the kinetic coefficient, of strategy-B captured the variation with ln(C0), the natural logarithm of the influent quality of turbidity with R2 equal to 0.972. If both the strategies were to be compared, strategy-B was found to be superior to strategy-A. However, one additional data of measurement on the groundwater turbidity was required for strategy-B. Further, strategy-C was developed with the assumption that there was no turbidity in groundwater, as a low value of turbidity was observed in groundwater. The variation of K, the kinetic coefficient, with ln(C0), the natural logarithm of influent quality of turbidity in strategy-C, showed that when the groundwater turbidity was ignored, the agreement between observed and computed turbidity was adversely affected. The average contribution of groundwater toward pumping well was observed nearly 12.43% and the average contribution of river water toward the pumping well was observed nearly 87.57%, considering the depth of river water. In strategy-C also, the agreement between simulated and observed values of turbidity was satisfactory. [ABSTRACT FROM AUTHOR]

In the present study, classical and proposed methods were used to investigate the monthly precipitation characteristics of 30 stations in the southeastern United States during 1968-2018. Maximal overlap discrete wavelet transform (MODWT) as preprocessing method and K-means clustering method were used. First, the monthly precipitation time series of stations were decomposed into several subseries using MODWT and considering db as the mother wavelet. Then, the energy values of theses subseries were calculated and used as inputs in K-means and radial basis functions (RBF) methods. The optimum number of clusters obtained for the considered stations in both classical and proposed methods was five clusters. In order to use the data as the input of the RBF method, the data correlation was evaluated by variogram. Based on the results of clustering and in accordance with the latitude and longitude variations of the stations, it was found that with increasing the energy of the clusters, the amount of precipitation in the stations decreased and vice versa. The silhouette coefficient of clustering for the classical method obtained was 0.3 and for the proposed method it was 0.8, which indicates better clustering of the selected area using the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we develop the numerical solution of nonlinear Klein-Gordon equation (NKGE) using the meshless methods. The finite difference scheme and the radial basis functions (RBFs) collocation methods are used to discretize time derivative and spatial derivatives, respectively. Numerical results are given to confirm the accuracy and efficiency of the presented schemes. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate labeling efficiency of pseudo‐continuous arterial spin labeling (PCASL) and to find the gradient parameters that increase PCASL robustness for renal perfusion measurements. Methods: Aortic blood flow was characterized in 3 groups: young healthy volunteers (YHV1), chronic kidney disease (CKD) patients (CKDP), and healthy controls (HCO). PCASL inversion efficiency was evaluated through numeric simulations considering the measured pulsatile flow velocity profiles and off‐resonance effects for a wide range of gradient parameters, and the results were assessed in vivo. The most robust PCASL implementation was used to measure renal blood flow (RBF) in CKDP and HCO. Results: Aortic blood velocities reached peak values of 120 cm/s in YHV1, whereas for elderly subjects values were lower by approximately a factor of 2. Simulations and experiments showed that by reducing the gradient average (Gave) and the selective to average gradient ratio (Gmax/Gave), labeling efficiency was maximized and PCASL robustness to off‐resonance was improved. The study in CKDP and HCO showed significant differences in RBF between groups. Conclusion: An efficient and robust PCASL scheme for renal applications requires a Gmax/Gave ratio of 6‐7 and a Gave value that depends on the aortic blood flow velocities (0.5 mT/m being appropriate for CKDP and HCO). [ABSTRACT FROM AUTHOR]

Artificial intelligence techniques can unravel clinically consistent information in clinical data which in turn assist in decision-making. With the help of state-of-the-art techniques, diagnostic systems are used to identify various diseases by using different machine learning (ML) techniques. In this paper, a combination of ML techniques such as Radial Basis Function (RBF) and Multiple Layer Perceptron (MLP) was used to predict cell decisions (cell survival/death) of AKT protein. AKT signalling networks have various downstream consequences on cellular metabolism either directly through the regulation of nutrient transporters, metabolic enzymes or indirectly through the control of transcription factors that regulate the expression of metabolic pathways which determine cell survival, cell growth, and cell death. Experimental analysis was performed in this work to examine the signalling networks that determine cell survival/death decisions by using an amalgamation of three proteins for ten different combinations in 13 different slices for a period of 0–24 h. P-P plot, Q-Q plot, and histogram tests were used for data visualization to determine which distribution the data fits. In addition, goodness of fit test was also employed using distribution functions such as Weibull, Exponential, and Normal distribution to determine whether the data fits a distribution of a certain population. The results were validated by calculating the MTTF values. The results of the analysis performed show that the Weibull distribution yields remarkable results. Also the results obtained with the Multiple Layer Perceptron, MLP 10-8-1 was found to perform better than other techniques giving an accuracy of 99.33% when the exponential activation function was used. The results of the experimental study indicate that it is possible to create self-consistent cell-signalling compendia based on AKT protein data that have been computationally simulated to provide valuable insights for cell survival/death regulation.

Proper bond between rebar and concrete ensures efficient load transfer between rebar and surrounding concrete in reinforced concrete (RC) element. Corrosion of rebar reduces bond strength of RC element. Retained bond factor (RBF) describes bond retention capacity of RC element subjected to corrosion. The present experimental work investigates the influence of rebar corrosion on RBF-slip relationship of RC element with various ratios of concrete cover to rebar diameter (c/d ratios). From the experimental results it was found that corrosion of rebar and c/d ratio have significant influence on RBF-slip behaviour of RC element.

Staphylococcus aureus is a gram-positive pathogenic bacterium and is capable of secreting numerous toxins interfering directly with the host to cause acute infections. Rbf, a transcriptional regulator of AraC/XylS family, has been reported to promote biofilm formation in polysaccharide intercellular adhesion (PIA) mediated manner to cause chronic infections. In this study, we revealed the new virulence-mediated role of Rbf that can negatively regulate the hemolytic activity. Furthermore, Rbf can specifically bind to the hla and psmα promoters to repress their expression, resulting in significantly decreased production of phenol-soluble modulins α (PSMα) and alpha-toxin. Accordingly, the rbf mutant strain exhibited the increased pathogenicity compared to the wild-type (WT) strain in a mouse subcutaneous abscess model, representing a type of acute infection by S. aureus. Collectively, our results provide a novel insight into the virulence regulation and acute infections mediated by Rbf in S. aureus.

Epilepsy is a chronic recurrent transient brain dysfunction syndrome. It is characterized by recurrent epilepsy caused by abnormal discharge of brain neurons. Epilepsy is one of the common diseases in nervous system. The analysis of EEG signals is a hot topic in current research. In order to solve the problem of epileptic EEG signals classification accurately, we carry out in-depth research on epileptic EEG signals, analyze features from linear and non-linear perspectives, input them into the improved RBF model to dynamically extract effective features, and introduce one against one strategy classifier to reduce the probability of error classification. Experiments show that the proposed algorithm has strong robustness and high epileptic signal recognition rate.

Currently, epilepsy disease (ED) is considered to be one of the gradual diseases in brain function over a period of several months or years. Seizure status is the primary common cause of ED. The main goal of this paper is to discover the seizure and epilepsy status using the prediction algorithm on the test results received from patient medical reports. This paper proposed an automatic epilepsy diagnostic method based on a self-organization map (SOM) method using a radial basis function (RBF) neural networks approach. The hybrid technique sought to enhance epilepsy diagnosis precision and to decrease the misdiagnosis of seizure disease. The SOM algorithm was employed to differentiate the unknown patterns of the seizure and epilepsy dataset. The experiments were performed on various RBF neural network algorithms with integrated SOM algorithms to predict and classify the standard epilepsy disease dataset. The hybrid method was tested on the UCI epilepsy dataset. The overall detection accuracy with 10-fold cross validation using SOM-RBF method achieved 97.47%. The results were compared with other modern classification techniques for seizure prediction and detection in terms of the evaluation factor.

The suspension system of Maglev vehicle needs strong robustness and anti-jamming ability in the process of operation and fluctuation control. In order to solve the open-loop instability and strong nonlinearity of the mechanical equation of the suspension system of the Maglev vehicle, the non-linear dynamic equation is established. At present, the research based on the single electromagnet and the rigid track is the most common. However, based on this method, it is impossible to study the coupled vibration caused by track factors. Therefore, based on the dynamic equation of a single span simply supported beam of flexible track and the non-linear equation of the suspended electromagnet itself, an overall control model is needed to discuss the control strategy. Based on this dynamic model, the singularities of the system are solved according to Hurwitz criterion, and the characteristic equation corresponding to the Jacobian matrix is obtained. The stability analysis shows that the system is unstable. At the same time, the necessity of using a feedback control method to control the air gap has been proved. On this basis, a sliding mode adaptive state feedback controller for the maglev system is designed based on the RBF network approximation principle. The corresponding simulation and experimental results are given. The simulation and experimental results show that the controller can ensure the vehicle's stable suspension and effectively suppress external interference. Compared with the traditional PID and fuzzy controllers, the controller can guarantee a faster dynamic response, stronger robustness, and smaller overshoot while considering the flexible track and external disturbances.

There are two important issues in the construction of a radial basis function (RBF) neural network. The first one is to select suitable RBF centers. The second one is that the resultant RBF network should be with good fault tolerance. This paper proposes an algorithm that is able to select RBF centers and to train fault tolerant RBF networks simultaneously. The proposed algorithm borrows the concept from sparse approximation. In our formulation, we first define a fault tolerant objective function based on all input vectors from the training samples. We then introduce the minimax concave penalty (MCP) function, which is an approximation of ℓ0-norm, into the objective function. The MCP term is able to force some unimportant RBF weights to zero. Hence the RBF node selection process can be achieved during training. As the MCP function is nondifferentiable and nonconvex, traditional gradient descent based algorithms are still unable to minimize the modified objective function. Based on the alternating direction method of multipliers (ADMM) framework, we develop an algorithm, called ADMM-MCP, to minimize the modified objective function. The convergent proof of the proposed ADMM-MCP algorithm is also presented. Simulation results show that the proposed ADMM-MCP algorithm is superior to many existing center selection algorithms under the concurrent fault situation.

The correct and proper selection of coolant and lubricant fluids in the dimension hard rocks’ cutting process is one of the most important factors in the improvement of the cutting process, and in the increase of economic efficiency and quality of stone production. Therefore, this present study aims to study the role of coolant and lubricant fluids in the cutting performance of dimension hard rocks. In this study, 10 granite stone samples were collected from Iranian quarries and their physical and mechanical properties including their uniaxial compressive strength, Mohs hardness, Schimazek’s F-abrasiveness factors and Young modulus were measured and determined. According to the three types of coolant and lubricant fluids, including the two types of soap water with ratios of 1 to 40 and 1 to 20 and one type of lubricant and coolant powder, and using two different operational parameters such as feed rate (Fr) and depth of cut (Dc), 160 laboratory tests for each of these fluids were studied and measured in order to determine the maximum electrical current consumed by the machine. The obtained results were statistically and intelligently evaluated and analyzed through the radial basis function (RBF). Based upon the results obtained from both the analytical methods, the lubricant and coolant powder with ratios of 1 to 20 had the maximum and minimum effects on the reduction of the maximum consumed electrical current in the hard rocks’ cutting process.

In this paper, a calibration algorithm for forecasting the significant wave height (SWH) in nearshore areas is proposed, based on artificial neural networks. The algorithm has two features: first, it is based on SOM−BRFnn (self−organizing map–radial basis function neural network) to better reflect the clustering characteristics of the input parameters regarding wind and wave. In addition, the high-frequency variation part and the low-frequency variation part of SWH are separated by a threshold of 24 h to better describe the diurnal variation of SWH under the influence of tidal current. The algorithm is applied to the nearshore region of Nan-ao Island in the northeastern South China Sea. The results show that the algorithm can effectively correct the modeling results of nearshore SWH. Compared with the original outputs of the ERA5 model, the correlation coefficient is increased from 0.472 to 0.774, the root mean square error is reduced from 0.252 m to 0.103 m, and the mean relative error is reduced from 41% to 17.6%, respectively. Further analysis indicates that the frequency division is crucial in realizing the correction of the high-frequency variation of SWH. The results have reference significance for the application of wave numerical models in coastal areas.

The article discusses the issue of designing a dynamic ship positioning system making use of the adaptive vectorial backstepping method and RBF type artificial neural networks. In the article, the backstepping controller is used to determine control laws and neural network weight adaptation laws. The artificial neural network is applied at each time instant to approximate nonlinear functions containing parametric uncertainties. The proposed control system does not require precise knowledge of the model of ship dynamics and external disturbances, it also eliminates the problem of analytical determination of the regression matrix when designing the control law with the aid of the adaptive backstepping procedure.

Spectral Reflectance of suspended sediment concentration (SSC) remotely sensed by satellite images is an alternative and economically efficient method to measure SSC in inland waters such as rivers and lakes, coastal waters, and oceans. This paper retrieved SSC from satellite remote sensing imagery using radial basis function networks (RBF). In-situ measurement of SSC, water flow data, as well as MODIS band 1 and band ratio of band 2 to 1 were the inputs of the RBF. A multi-regression method was also used to make a relationship between the in-situ data and the water reflectance data retrieved from MODIS bands. The results showed that RBF had the best SSC prediction error (RMSE=0.19), as compared to the multi-regression and sediment rating curve methods, with the RMSE of 0.29 and 0.21, respectively.

We consider the steady, laminar, convection ow in a long channel of 2D rectangular constricted cross-section under the inuence of an applied magnetic field. The Navier-Stokes equations including Lorentz and buoyancy forces are coupled with the temperature equation and are solved by using linear radial basis function (RBF) approximations in terms of the velocity, pressure and the temperature of the fluid. RBFs are used in the approximation of the particular solution which becomes also the approximate solution of the problem. Results are obtained for several values of Grashof number (Gr), Hartmann number (M) and the constriction ratios (CR) to see the effects on the ow and isotherms for fixed values of Reynolds number and Prandtl number. As M increases, the ow is flattened. An increase in Gr increases the magnitude of the ow in the channel. Isolines undergo an inversion at the center of the channel indicating convection dominance due to the strong buoyancy force, but this inversion is retarded with the increase in the strength of the applied magnetic field. When both Hartmann number and constriction ratio are increased, ow is divided into more loops symmetrically with respect to the axes.

In this paper, a new fault diagnosis and fault tolerant control algorithm for manipulators with actuator multiplicative fault is proposed. The dynamic model of the manipulator with disturbance is taken as the research object. When faults occur in the actuator, a nonlinear observer based on radial basis function (RBF) neural network is used to estimate the fault information. After the fault information is obtained, an adaptive back-stepping sliding mode controller is used to control the manipulator to reach the desired trajectory. At last, an illustrated example is given to demonstrate the efficiency of the proposed algorithm, and satisfactory results have been obtained. [ABSTRACT FROM AUTHOR]

The trapezoidal rule uses function values at equispaced nodes. It is very accurate for integrals over periodic intervals, but is usually quite inaccurate in nonperiodic cases. Commonly used improvements, such as Simpson's rule and the Newton--Cotes formulas, are not much (if at all) better than the even more classical quadrature formulas described by James Gregory in 1670. For increasing orders of accuracy, these methods all suffer from the Runge phenomenon (the fact that polynomial interpolants on equispaced grids become violently oscillatory as their degree increases). In the context of quadrature methods on equispaced nodes, and for orders of accuracy around 10 or higher, this leads to weights of oscillating signs and large magnitudes. This article develops further a recently discovered approach for avoiding these adverse effects. [ABSTRACT FROM AUTHOR]

This paper presents the radial basis function (RBF) approximation for the numerical solution of Stokes and Navier-Stokes equations in a constricted enclosure under the effect of magnetic field with different orientations. RBFs are used for the approximation of the particular solution which becomes also the approximate solution of the problem satisfying the boundary conditions. Numerical results are obtained for several values of Hartmann number and constriction ratio. As the strength of the horizontally applied magnetic field increases, Stokes flow extends covering the whole pipe. Applied magnetic field in the pipe-axis direction generates the electric potential exhibiting behavior similar to streamlines. When the constriction ratio increases, flow squeezes through the left wall regardless of the direction of the magnetic field. [ABSTRACT FROM AUTHOR]