Nonlinear models in the height description of the Rhino sunflower cultivar

ABSTRACT: Sunflower produces achenes and oil of good quality, besides serving for production of silage, forage and biodiesel. Growth modeling allows knowing the growth pattern of the crop and optimizing the management. The research characterized the growth of the Rhino sunflower cultivar using the Logistic and Gompertz models and to make considerations regarding management based on critical points. The data used come from three uniformity trials with the Rhino confectionery sunflower cultivar carried out in the experimental area of the Federal University of Santa Maria - Campus Frederico Westphalen in the 2019/2020 agricultural harvest. In the first, second and third trials 14, 12 and 10 weekly height evaluations were performed on 10 plants, respectively. The data were adjusted for the thermal time accumulated. The parameters were estimated by ordinary least square’s method using the Gauss-Newton algorithm. The fitting quality of the models to the data was measured by the adjusted coefficient of determination, Akaike information criterion, Bayesian information criterion, and through intrinsic and parametric nonlinearity. The inflection points (IP), maximum acceleration (MAP), maximum deceleration (MDP) and asymptotic deceleration (ADP) were determined. Statistical analyses were performed with Microsoft Office Excel® and R software. The models satisfactorily described the height growth curve of sunflower, providing parameters with practical interpretations. The Logistics model has the best fitting quality, being the most suitable for characterizing the growth curve. The estimated critical points provide important information for crop management. Weeds must be controlled until the MAP. Covered fertilizer applications must be carried out between the MAP and IP range. ADP is an indicator of maturity, after reaching this point, the plants can be harvested for the production of silage without loss of volume and quality.

EXPERIMENTAL TEST AND FINITE ELEMENT ANALYSIS OF POTATO IMPACT ACCELERATION

To analyze the maximum acceleration (amax) of a potato colliding with different objects, both experimental test and finite element analysis (FEA) methods were used. Results showed that when potatoes were collided with the single rod, the steel plate and the double rods, the average discrepancies of FEA and experimental test values were 5.3%, 3.95% and 5.04%. The maximum acceleration increased with the increase of potato drop height, and decreased with the increase of potato mass. Under the same conditions, the maximum acceleration decreased in turn when the potatoes were collided with the steel plate, the single rod and the double rods. The FEA results showed that the maximum acceleration in collision with the steel plate was 60.78% to 96.29% higher than that with the double rods. The maximum acceleration in collision with the steel plate was 53.89% to 83.27% higher than that with the double rods. The maximum acceleration in collision with the single rod covered with soil was 37.65% and 31.54% lower than that without soil. The research methods and conclusions of this article provided a basis for the analysis of impact mechanics and damage mechanism of potatoes, and contributed to further researches related to solid-like agricultural and food products.

Enterprise Performance Management following Big Data Analysis Technology under Multisource Information Fusion

With the development of the Internet, big data collection, analysis, and processing are flourishing. The study aims to explore the performance management of power enterprises based on multisource information fusion and big data. First, the application of big data to enterprise management is analyzed. Second, the multisource information fusion method is introduced, and the multisource information fusion model is implemented. Finally, the fuzzy language algorithm is used to evaluate the performance management of power enterprises. The results show that the proposed multisource information fusion algorithm has high efficiency in evaluating enterprise performance management. The evaluation result is closer to the actual value than other algorithms, and the maximum acceleration ratio can reach 7, indicating that the algorithm is suitable for processing big data. The performance evaluation shows that enterprises pay most attention to the quality of their products; the weight reached 0.414; and the index weight difference is large. This study promotes the reform of the performance management mode and improves the management efficiency of enterprises through the proposed enterprise performance management strategy. It provides a great reference for the application of big data and information fusion technology.

Both Prey and Predator Features Determine Predation Risk and Survival of Schooling Prey

Predation is one of the main evolutionary drivers of social grouping. While it is well appreciated that predation risk is likely not shared equally among individuals within groups, its detailed quantification has remained difficult due to the speed of attacks and the highly-dynamic nature of collective prey response. Here, using high-resolution tracking of solitary predators (Northern pike) hunting schooling fish (golden shiners), we not only provide detailed insights into predator decision-making but show which key spatial and kinematic features of predator and prey influence individual's risk to be targeted and survive attacks. Pike tended to stealthily approach the largest groups, and were often already inside the school when launching their attack, making prey in this frontal "strike zone" the most vulnerable to be targeted. From the prey's perspective, those fish in central locations, but relatively far from, and less aligned with, neighbours, were most likely to be targeted. While the majority of attacks (70%) were successful, targeted individuals that did manage to avoid capture exhibited a higher maximum acceleration response just before the attack and were further away from the pike's head. Our results highlight the crucial interplay between predators' attack strategy and response of prey in determining predation risk in mobile animal groups.

Global Simultaneous Optimization of Oil, Hysteretic and Inertial Dampers Using Real-Valued Genetic Algorithm and Local Search

A method for global simultaneous optimization of oil, hysteretic and inertial dampers is proposed for building structures using a real-valued genetic algorithm and local search. Oil dampers has the property that they can reduce both displacement and acceleration without significant change of natural frequencies and hysteretic dampers possess the characteristic that they can absorb energy efficiently and reduce displacement effectively in compensation for the increase of acceleration. On the other hand, inertial dampers can change (prolong) the natural periods with negative stiffness and reduce the effective input and the maximum acceleration in compensation for the increase of deformation. By using the proposed simultaneous optimization method, structural designers can select the best choice of these three dampers from the viewpoints of cost and performance indices (displacement, acceleration). For attaining the global optimal solution which cannot be attained by the conventional sensitivity-based approach, a method including a real-valued genetic algorithm and local search is devised. In the first stage, a real-valued genetic algorithm is used for searching an approximate global optimal solution. Then a local search procedure is activated for enhancing the optimal character of the solutions by reducing the total quantity of three types of dampers. It is demonstrated that a better design from the viewpoint of global optimality can be obtained by the proposed method and the preference of damper selection strongly depends on the design target (displacement, acceleration). Finally, a multi-objective optimization for the minimum deformation and acceleration is investigated.

Effects of Strong Ground Motion with Identical Response Spectra and Different Duration on Pile Support Mechanism and Seismic Resistance of Spherical Gas Holders on Soft Ground

Safety measures are required for spherical gas holders to prevent them from malfunctioning even after a large earthquake. In this study, considering the strong nonlinearity of the ground and damage to the pile during an earthquake, a three-dimensional seismic response analysis of the holder–pile–ground interaction system was conducted for an actual gas holder on the soft ground consisting of alternating layers of sand and clay. In the analysis, the seismic response of the structure to strong ground motions of different durations with the same acceleration response spectrum was verified. The results show that the piles were relatively effective in controlling the settlement when the duration of the earthquake motion was long. This is because the axial force acting on the pile increased due to the redistribution of the holder load caused by the lowering of the effective confining pressure of the sand and clay layers during the earthquake, which increased the bearing capacity of the pile. In contrast, when the duration of the seismic motion was short, the piles had little effect on the reduction in the settlement because the maximum acceleration was higher than that in the former case, and the piles immediately lost their support function.

A large-stroke rapid motion controller for servo applications with speed and control constraints

Large-stroke rapid motion is required in many industrial servo systems (e.g. pick-and-place applications), on which constraints of control input and motion speed are usually imposed. This paper presents a hybrid control scheme for large-stroke rapid motion in such systems. The controller resorts to the time-optimal control (TOC) for maximum acceleration initially and then switches into a linear control law to achieve smooth settling. A speed regulation stage is inserted in the tracking process to prevent violating the speed constraint. To tackle the unmeasured speed and unknown disturbance, an extended state observer (ESO) can be included in the controller. The controller is applied to a permanent magnet synchronous motor (PMSM) servo system for large-stroke position regulation. Digital simulation via MATLAB is conducted first, followed by an experimental verification using a TMS320F28335 board. The results confirm that the proposed controller can track a wide range of target references with desirable performance under speed constraint and load disturbance and has a competitive advantage over the popular active disturbance-rejection control (ADRC) scheme.

Improved A-Star Algorithm for Long-Distance Off-Road Path Planning Using Terrain Data Map

To overcome the limitation of poor processing times for long-distance off-road path planning, an improved A-Star algorithm based on terrain data is proposed in this study. The improved A-Star algorithm for long-distance off-road path planning tasks was developed to identify a feasible path between the start and destination based on a terrain data map generated using a digital elevation model. This study optimised the algorithm in two aspects: data structure, retrieval strategy. First, a hybrid data structure of the minimum heap and 2D array greatly reduces the time complexity of the algorithm. Second, an optimised search strategy was designed that does not check whether the destination is reached in the initial stage of searching for the global optimal path, thus improving execution efficiency. To evaluate the efficiency of the proposed algorithm, three different off-road path planning tasks were examined for short-, medium-, and long-distance path planning tasks. Each group of tasks corresponded to three different off-road vehicles, and nine groups of experiments were conducted. The experimental results show that the processing efficiency of the proposed algorithm is significantly better than that of the conventional A-Star algorithm. Compared with the conventional A-Star algorithm, the path planning efficiency of the improved A-Star algorithm was accelerated by at least 4.6 times, and the maximum acceleration reached was 550 times for long-distance off-road path planning. The simulation results show that the efficiency of long-distance off-road path planning was greatly improved by using the improved algorithm.

Determination of Bearing Quality Using Frequency Vibration Analysis

The paper proposes two methods for determining the quality of industrially manufactured bearings, where the vibro-acoustic energy is expressed by measuring the vibration acceleration. The determined criteria were verified and 100 % agreement was reached in determining the required quality of the bearings. Unwanted bearing noise emissions and its faults can be monitored over the maximum acceleration value determined from the time history, the frequency distribution and the r.m.s. value of the determined frequency range.

Computer intelligent dynamic simulation and optimization for structural platform of water treatment subsystem on the spacecraft

In order to reduce the load response condition of space-borne equipment, an optimized design scheme of carbon fiber composites sandwich panel was proposed in this paper. The state before and after optimization was analyzed and compared trough simulation calculation. The comparison shows that after the honeycomb sandwich structure was optimized, its fundamental frequency increased by 75%, the drawing force of embedded parts decreased by 50%, and the maximum acceleration response also decreased. Finally, the response under sinusoidal vibration was evaluated by test, and the results show that the optimization of the structure is reasonable and feasible, it can be the reference of other similar products.