A new method for dividing flood period in the variable-parameter Muskingum models

The Muskingum routing model is favored by water engineers owing to its simplicity and accuracy. A large amount of research is done to improve the accuracy of the model. One way to do so is to consider variable hydrological parameters during the flood routing period. In this study, the random selection (RS) method was proposed to divide the flood period of the nonlinear Muskingum model into three sub-periods. The proposed method was based on RS of members in each sub-region. It was applied to rout three flood hydrographs, and the objective function was the sum of squared errors. Comparing the results from the three variable-parameter nonlinear Muskingum model with those from the variable-parameter nonlinear Muskingum models in previous studies, the proposed model optimized the objective function in these hydrographs up to 61%. The uncertainty analysis of Muskingum parameters for Wilson's hydrograph was performed by the fuzzy alpha cut method, and it was found that the uncertainty of the parameter x is greater than the uncertainty of the parameters k and m.

Autonomous Assembly Method of 3-Arm Robot to Fix the Multipin and Hole Load Plate on a Space Station

Using space stations for a large number of observation, exploration, and research is a necessary way to fully develop space technology. It is a necessary means of space experiment to install the extravehicular experimental load by using the load plate. However, the extravehicular environment is full of danger, which poses a threat to the health and even safety of astronauts. Using robots to replace astronauts to complete this task can effectively reduce the threat to astronauts. Aiming at the problem that the configurations of existing space robots have difficulty in balancing the contradiction between complexity and dexterity, our previous work proposes a 12-DOF 3-arm robot and preliminarily explores the feasibility of its large-scale ability. This paper focus on the 8-DOF redundant dexterous manipulator composed of 2 of the robot arms. In view of the difficulties in solving the inverse kinematics of the redundant manipulator, the challenges of complex environmental lighting, and difficulties of matching multiple groups of holes and pins in the load plate assembly task, the research on the autonomous assembly of the load plate is carried out. The main work is as follows: (a) A variable D-H parameter inverse kinematics solution method is proposed, which lays a foundation for humanoid dexterous operation planning of the robot. (b) An autonomous operation method based on visual guidance and variable parameter admittance control is proposed. Finally, the safety and robustness of the robot in the autonomous assembly of the load plate with multipins and holes are successfully verified by experiments.

Regulation of Temperature on Multitrays in an Indirect Solar Dryer (ISD) with Energy Storage and Three Airflow Modes

This work presents the regulation of temperature in an indirect multitrays solar dryer with oriented flux under the irradiance fluctuation. The temperature regulator using a negative temperature coefficient (NTC) as a sensor and fans is designed, and a similar device is also used to measure humidity through a sensor. Inlet and outlet dryer temperature and temperature on the three trays have been recorded with the regulation system according to different airflow modes. Irradiance and humidity have also been recorded. The model of outlet temperature with energy storage was given by using heat transfer equations. The results have shown that in the linking airflow mode, the average temperature on the three trays is 51.3 ± 1.5a°C, 52.18 ± 1.4a°C, and 51.9 ± 1.2a°C, respectively, with 52°C as setpoint temperature and NTC fixed on tray number 2. With temperature sensor in the same tray and 51°C as setpoint temperature, the average temperatures on the three trays are 51.86 ± 1.54°C, 51.60 ± 1.16°C, and 50.42 ± 1.13°C, respectively, in mixed mode, whereas in crossing airflow mode, the temperature gradient does not allow regulation on all trays. The regulation is possible when the temperature in the dryer chamber exceeds the set point temperature by more than 5%. The proportional type corrector is suitable for the temperature controller in indirect solar dryers. When the energy source is unstable, humidity which is a variable parameter is used to mark the end of drying instead of time.

Ice ridge density signatures in high resolution SAR images

The statistics of ice ridging signatures was studied using a high (1.25 m) and a medium (20 m) resolution SAR image over the Baltic sea ice cover, acquired in 2016 and 2011, respectively. Ice surface profiles measured by a 2011 Baltic campaign was used as ground truth data for both. The images did not delineate well individual ridges as linear features. This was assigned to the random, intermittent occurrence of ridge rubble block arrangements with bright SAR return. Instead, the ridging signature was approached in terms of the density of bright pixels and relations with the corresponding surface profile quantity, ice ridge density, were studied. In order to apply discrete statistics, these densities were quantified by counting bright pixel numbers (BPN) in pixel blocks of side length L, and by counting ridge sail numbers (RSN) in profile segments of length L. The scale L is a variable parameter of the approach. The other variable parameter is the pixel intensity threshold defining bright pixels, equivalently bright pixel percentage (BPP), or the ridge sail height threshold used to select ridges from surface profiles, respectively. As a sliding image operation the BPN count resulted in enhanced ridging signature and better applicability of SAR in ice information production. A distribution model for BPN statistics was derived by considering how the BPN values change in BPP changes. The model was found to apply over wide range of values for BPP and L. The same distribution model was found to apply to RSN statistics. This reduces the problem of correspondence between the two density concepts to connections between the parameters of the respective distribution models. The correspondence was studied for the medium resolution image for which the 2011 surface data set has close temporal match. The comparison was done by estimating ridge rubble coverage in 1 km2 squares from surface profile data and, on the other hand, assuming that the bright pixel density can be used as a proxy for ridge rubble coverage. Apart from a scaling factor, both were found to follow the presented distribution model.

Electron localization induced by intrinsic anion disorder in a transition metal oxynitride

Anderson localization derived from randomness plays a crucial role in various kinds of phase transitions. Although treated as a free variable parameter in theory, randomness in electronic materials is hard to control experimentally owing to the coexisting Coulomb interaction. Here we demonstrate that the intrinsic anion disorder in a mixed-anion system of SrNbO2N induces a significant random potential that overwhelms the Coulomb potential while maintaining the lattice structure. A metal-to-insulator transition is triggered by a chemical modulation of the electron density where the critical electron density is more than three orders of magnitude greater than that predicted by the well-known Mott criterion. The localized electrons show characteristic electrical properties such as temperature-dependent multiple crossovers of conduction mechanisms and a positive magnetoresistance above 50% at low temperature. The large magnetoresistance is attributed to wave-function shrinkage of the localized states and clearly visualizes the anisotropy in the band structure, which indicates a compatibility of the periodicity and randomness.

Calibration of X-Band Radar for Extreme Events in a Spatially Complex Precipitation Region in North Peru: Machine Learning vs. Empirical Approach

Cost-efficient single-polarized X-band radars are a feasible alternative due to their high sensitivity and resolution, which makes them well suited for complex precipitation patterns. The first horizontal scanning weather radar in Peru was installed in Piura in 2019, after the devastating impact of the 2017 coastal El Niño. To obtain a calibrated rain rate from radar reflectivity, we employ a modified empirical approach and draw a direct comparison to a well-established machine learning technique used for radar QPE. For both methods, preprocessing steps are required, such as clutter and noise elimination, atmospheric, geometric, and precipitation-induced attenuation correction, and hardware variations. For the new empirical approach, the corrected reflectivity is related to rain gauge observations, and a spatially and temporally variable parameter set is iteratively determined. The machine learning approach uses a set of features mainly derived from the radar data. The random forest (RF) algorithm employed here learns from the features and builds decision trees to obtain quantitative precipitation estimates for each bin of detected reflectivity. Both methods capture the spatial variability of rainfall quite well. Validating the empirical approach, it performed better with an overall linear regression slope of 0.65 and r of 0.82. The RF approach had limitations with the quantitative representation (slope = 0.44 and r = 0.65), but it more closely matches the reflectivity distribution, and it is independent of real-time rain-gauge data. Possibly, a weighted mean of both approaches can be used operationally on a daily basis.

Correlations of geotechnical monitoring data in open pit slope back-analysis -A mine case study

Geotechnical monitoring plays an important role in the detection of operational safety issues in the slopes of open pits. Currently, monitoring companies offer several solutions involving robust technologies that boast highly reliable data and the ability to control risky conditions. The monitoring data must be processed and analysed so as to allow the results to be used for several purposes, thereby providing information that can be used to manage operational actions and optimize mining plans or engineering projects. In this work we analysed monitoring data (pore pressure and displacement) and its correlation with the tension and displacement of the mass of an established failure slope calculated using the finite element method. To optimize the back-analysis, a Python language routine was developed using input data (point coordinates, parameter matrix, and critical section) to use software with the rock mass parameters (cohesion, friction angle, Young's modulus, and Poisson's ratio). For the back-analysis, the Mohr-Coulomb criterion was applied with the shear strength reduction technique to obtain the strength reduction factor. The results were consistent with both the measured displacements and the maximum deformation contours, revealing the possible failure mechanism, allowing the strength parameters to be calibrated according to the slope failure conditions, and providing information about the contribution of each variable (parameter) to the slope failure in the study area.