In this investigation, a new algorithm is developed for the updating of a neural network. It is concentrated in a fuzzy transition between the recursive least square and extended Kalman filter algorithms with the purpose to get a bounded gain such that a satisfactory modeling could be maintained. The advised algorithm has the advantage compared with the mentioned methods that it eludes the excessive increasing or decreasing of its gain. The gain of the recommended algorithm is uniformly stable and its convergence is found. The new algorithm is employed for the modeling of two synthetic examples.
In the present study, an alternative promising evaluation method was recommended for dead leaves of Posidonia oceanica (L.) Delile as an adsorbent for biosorption of Methylene Blue (MB). The data from batch experiments were modeled by using Artificial Neural Network (ANN). The optimal operation conditions for biosorption of MB by P. oceanica dead leaves were found for pH, adsorbent dosage, temperature and initial dye concentration as 6, 0.3 g, 303 K and 50 mg/L, respectively. The adsorption reached equilibrium after 30 minutes. According to the results of sensitivity analysis, relative importance of temperature, dye concentration, pH, adsorbent dosage and process time on the biosorption of MB were 33%, 27%, 21%, 10% and 8%, respectively. Minimum mean square error (MSE) was found as 0.0169 by ANN modeling. The present study reveals a novel strategy for adsorption studies to utilize the highly accumulated biomass of dead leaves of P. oceanica in Turkish coastlines instead of burning these dead leaves.
CFD simulation is aimed on the two-stage pump operation modeling in the pump and turbine regime. The flow analysis will be focused on the proper angle of wicket gates attack and on the separation localization. This investigation should bring the geometry modifications that will increase the hydraulic efficiency. The computational model was created in preprocessor GAMBIT and software Fluent will be used for the finite computation.
A control system architecture design for an underwater ROV, primarily Class I - Pure Observation underwater ROV is presented in this paper. A non-linear plant model was designed using SolidWorks 3D modeling tool and is imported to MATLAB as a 3D model. The non-linear modeled plant is linearized using the MATLAB linear analysis toolbox to have a linear approximate model of the system. The authors designed controllers for the linear plant model of underwater ROV. PID controllers are utilized as a controller of the modeled plant. The PID tuning tools by MATLAB are utilized to tune the controller of the plant model of underwater ROV. The researchers test the control design of underwater ROV using MATLAB Simulink by analyzing the response of the system and troubleshoot the control design to achieve the objective parameters for the control design of underwater ROV.
The design, build and test of a re-programmable neural switch (RNS) are carried out. The function of such a switch is to operate as a synaptic processor behaving in an adaptive manner and suitable to be used as a compact programmable device with other artificial neural network hardware. Interaction between constituent materials forming the switch is discussed and carrier interaction during the Programming cycles is explained. Programmability of the switch is proved to be bi-directional and reversible with hysteresis effect which is due to excess charge storage.
Large amounts of antibiotics and microplastics are used in daily life and agricultural production, which affects not only plant growth but also potentially the food safety of vegetables and other plant products. Fast detection of the presence of antibiotics and microplastics in leafy vegetables is of great interest to the public. In this work, a method was developed to detect sulfadiazine and polystyrene, commonly used antibiotics and microplastics, in vegetables by measuring and modeling photosystem II chlorophyll a fluorescence (ChlF) emission from leaves. Chrysanthemum coronarium L., a common beverage and medicinal plant, was used to verify the developed method. Scanning electron microscopy, transmission electron microscopy, and liquid chromatograph-mass spectrometer analysis were used to show the presence of the two pollutants in the samples. The developed kinetic model could describe measured ChlF variations with an average relative error of 0.6%. The model parameters estimated for the chlorophyll a fluorescence induction kinetics curve (OJIP) induction can differentiate the two types of stresses while the commonly used ChlF OJIP induction characteristics cannot. This work provides a concept to detect antibiotic pollutants and microplastic pollutants in vegetables based on ChlF.
Correct estimation of sediment volume carried by a river is very important for many water resources projects. Traditionally, artificial neural networks (ANNs) are used as black-box models without understanding what happens inside the box. The question is that, how anyone who may be unfamiliar with ANNs can apply this kind of models in any other study, while the model has not been formulated. This paper proposes an explicit neural network (ENN) formulation which is simple and can be used, by anyone who is even not familiar with ANNs, for modeling daily suspended sediment-discharge relationship. The daily streamflow and suspended sediment data from two stations on Tongue River in Montana are used as case studies. Two different sediment rating curves (SRC), multi-linear regression (MLR) and nonlinear regression (NLR) are also applied to the same data. The ENN estimates are compared with those of the SRC, MLR and NLR models. The root mean square errors (RMSE), mean absolute errors (MAE), correlation coefficient (R) and model efficiency (E) statistics are used to evaluate the performance of the models. The comparison results reveal that the suggested model performs better than the conventional SRC, MLR and NLR.
C3 photosynthesis at high light is often modeled by assuming limitation by the maximum capacity of Rubisco carboxylation (VCmax) at low CO2 concentrations, by electron transport capacity (Jmax) at higher CO2 concentrations, and sometimes by
triose-phosphate utilization rate at the highest CO2 concentrations. Net photosynthetic rate (PN) at lower light is often modeled simply by assuming that it becomes limited by electron transport (J). However, it is known that Rubisco can become deactivated at less than saturating light, and it is possible that PN at low light could be limited by the rate of Rubisco carboxylation (VC) rather than J. This could have important consequences for responses of PN to CO2 and temperature at low light. In this work, PN responses to CO2 concentration of common bean, quinoa, and soybean leaves measured over a wide range of temperatures and PPFDs were compared with rates modeled assuming either VC or J limitation at limiting light. In all cases, observed rates of PN were better predicted by assuming limitation by VC rather than J at limiting light both below and above the current ambient CO2. One manifestation of this plant response was that the relative stimulation of PN with increasing the ambient CO2 concentration from 380 to 570 µmol mol-1 did not decrease at less than saturating PPFDs. The ratio of VC to VCmax at each lower PPFD varied linearly with the ratio of PN at low PPFD to PN at high PPFD measured at 380 µmol(CO2) mol-1 in all cases. This modification of the standard C3 biochemical model was much better at reproducing observed responses of light-limited PN to CO2 concentrations from
pre-industrial to projected future atmospheric concentrations., J. A. Bunce., and Obsahuje bibliografii
Formulation of many real-life problems evolves as the problem is being solved. These changes are typically initiated by a user intervention or by changes in the environment. In this paper, we propose a formal description of a so called minimal perturbation problem that allows an “automated” modification of the (partial) solution when the problém formulation changes. Our model is defined for constraint satisfaction i)roblenis with emphasis put on finding a solution anytime even for over-constrained problems.
The applicability of neural networks for the evaluation of the lifetime of semiconductor devices is demonstrated. The neural network based method can be used as a general tool for modeling. The commonly used main-acceleratingparameter models could be obtained by the neural net reducing. The neural network based method is attractive also due to the neural net ability to process "noisy" data. The method should find wide applications in degradation modeling.