Using Inverse Modeling by HYDRUS-1D to Predict Some Soil Hydraulic Parameters from Soil Water Evaporation

The objective of this research was to assess the use of unsaturated water flow in terms of soil water evaporation, which was determined by evaluating some soil hydraulic parameters in different soil textures. The results show that the predicted values of these parameters, which were obtained through inverse modeling with the HYDRUS-1D software and depend on the change of the volumetric water content, exhibited a significant agreement with the measured values from laboratory or field simulation data for soil water evaporation at 5. 10. 20. and 45 days of measurement. At the same time, inverse simulation was conducted on soil hydraulic parameters obtained from a 5-day laboratory soil evaporation period to predict field infiltration values and water retention curve, which showed a significant agreement with measured values for all soil textures.

An Effective Antenna Array Diagnosis Method via Multivalued Neural Network Inverse Modeling Approach

Failure of element (s) in antenna arrays impair (s) symmetry and lead to unwanted distorted radiation pattern. The replacement of defective elements in aircraft antennas is a solution to the problem, but it remains a critical problem in space stations. In this paper, an antenna array diagnosis technique based on multivalued neural network (mNN) inverse modeling is proposed. Since inverse analytical input-to-output formulation is generally a challenging and important task in solving the inverse problem of array diagnosis, ANN is a compelling alternative, because it is trainable and learns from data in inverse modelling. The mNN technique proposed is an inverse modelling technique, which accommodates measurements for output model. This network takes radiation pattern samples with faults and matches it to the corresponding position or location of the faulty elements in that antenna array. In addition, we develop a new training error function, which focuses on the matching of each training sample by a value of our proposed inverse model, while the remaining values are free, and trained to match distorted radiation patterns. Thereby, mNN learns all training data by redirecting the faulty elements patterns into various values of the inverse model. Therefore, mNN is able to perform accurate array diagnosis in an automated and simpler manner.

Inverse modeling of the stewart foot

The Stewart's leg is used today in the majority of parallel robotic systems, such as the Stewart platform, but also in many other types of mechanisms and kinematic chains, in order to operate them or to transmit motion. A special character in the study of robots is the study of inverse kinematics, with the help of which the map of the motor kinematic parameters necessary to obtain the trajectories imposed on the effector can be made. For this reason, in the proposed mechanism, we will present reverse kinematic modeling in this paper. The kinematic output parameters, ie the parameters of the foot and practically of the end effector, ie those of the point marked with T, will be determined for initiating the working algorithm with the help of logical functions, "If log(ical)", with the observation that here they play the role of input parameters; it is positioned as already specified in the inverse kinematics when the output is considered as input and the input as output. The logical functions used, as well as the entire calculation program used, were written in Math Cad.

Sensitivity of Surface Currents to Bathymetry in a Partially-Mixed Estuary with Applications to Inverse Modeling

An inversion technique was tested for estimating bathymetry from observations of surface currents in a partially-mixed estuary, Mouth of the Columbia River (MCR). The methodology uses an iterative ensemble-based assimilation scheme which is found to have good skill for recovering bathymetry from observations distributed in space and time. However, the inversion skill is highly dependent on the tidal phase, location of the observations, and flow-dependent estuary dynamics. Inversion skill was found to degrade during periods of higher river discharge (up to ~ 12,000m3), or low tidal amplitude, while inversion of depth-averaged velocities instead of surface velocities caused increased skill throughout the domain. These results point to dynamical limits on inversion skill, caused by changes in estuary dynamics that affect the sensitivity of surface velocities to bathymetry. An adjoint sensitivity analysis is used to visualize these effects and is combined with data-denial experiments to explore the flow-dependent inversion skill.