Explore the vast range of titles available.

The threshing gap of the thresher device for rice combine harvester has to be adjusted in real time based on different feed rates to ensure the operation efficiency in the harvesting process. However, adjusting the threshing gap by changing the position of concave grid may result in unevenness of threshing gap of the thresher device and further impact on the fluidity of material in the thresher device; in addition, it is also unavailable to adjust the threshing gap by changing the drum diameter when the rice combine harvester is in operation. In view of the above and based on axial flow threshing drum, the design of a variable-diameter threshing drum available for overall and rapid drum diameter adjustment and the research on diameter adjustment device as well as electronic control self-locking device were introduced in this study. Besides, stress analysis was implemented to the diameter adjustment device to ensure the stability of the variable-diameter threshing drum. Field experiment was implemented to identify the difference between the impacts brought to the threshing performance (grain-entrainment loss rate, damage rate, threshing efficiency, and threshing power consumption) by both methods for threshing gap adjustment. The experiment result shows that the drum adjustment method with variable-diameter drum features higher grain-entrainment loss rate, threshing efficiency, and threshing power consumption, yet stable in terms of consumption fluctuation, but a lower damage rate than their counterparts with concave adjustment method.

Rice is a widely cultivated food crop worldwide, and threshing is one of the most important operations of combine harvesters in grain production. It is a complex, nonlinear, multi-parameter physical process. The flexible threshing device has unique advantages in reducing the grain damage rate and has already been one of the major concerns in engineering design. Using the measured test database of the flexible threshing test bench, the rotation speed of the threshing cylinder (RS), threshing clearance of the concave sieve (TC), separation clearance of the concave sieve (SC), and feeding quantity (FQ) are used as the input layer. In contrast, the crushing rate (YP), impurity rate of the threshed material (YZ), and loss rate (YS) are used in the output layer. A 4-5-3-3 artificial neural network (ANN) model, with a backpropagation learning algorithm, was developed to predict the threshing performance of the flexible threshing device. Next, we explored the degree to which the inputs affect the outputs. The results showed that the R of the threshing performance model validation set in the hidden layer reached 0.980, and the root mean square error (RMSE) and the average absolute error (MAE) were less than 0.139 and 0.153, respectively. The built neural network model predicted the performance of the flexible threshing device, and the regression determination coefficient R2 between the prediction data and the experimental data was 0.953. The results showed revealed that the data combined with the ANN method is an effective approach for predicting the threshing performance of the flexible threshing device in rice. Moreover, the sensitivity analysis showed that RS, TC, and SC were crucial factors influencing the performance of the flexible threshing device, with an average relative importance of 15.00%, 14.89%, and 14.32%, respectively. FQ had the least effect on threshing performance, with an average threshing relative importance of 11.65%. Our findings can be leveraged to optimize the threshing performance of future flexible threshing devices.

Aiming at the problems of stalks winding and poor threshing performance in the process of mechanical harvesting of oats, a type of threshing and crushing experimental device was designed. The device was composed of a belt conveyor, a tangential-flow threshing drum, an axial-flow threshing drum, a high-speed camera and a testing device. According to a regression orthogonal test method, using the rotation speed of the axial-flow drum, the horizontal center distance, and the vertical height difference of the two threshing drums as experimental factors; using the mass fraction of long stalks and the threshing rate as experimental indexes, a mathematical regression model of factors and indexes was established, and combined parameters were analyzed for the threshing quality and crushing ability. Experimental results showed that this device had the best crushing performance when the rotation speed of the axial-flow drum was 850 r/min, the horizontal center distance was 820 mm, and the vertical height difference was 10 mm, and this device had optimal threshing performance when the rotation speed of the axial-flow drum was 760 r/min, the horizontal center distance was 820 mm, and the vertical height difference was 20 mm. The parameters were optimized by Design-Expert 11 to obtain an optimization result that the rotation speed of the axial-flow drum was 800 r/min, the horizontal center distance was 820 mm, and the vertical height difference was 10 mm. A verification experiment was carried out by using a high-speed camera, and the two groups of parameters were selected for a comparative experiment. Images of material movement between the two threshing drums were captured by the high-speed camera. Experimental results showed that the optimized parameters were beneficial to improving the threshing performance and anti-winding performance of the oat threshing process. This study provides a technical basis for the research and development of oat combine harvesters.

Because the threshing device of a combine harvester determines the harvesting level and threshing separation performance of a combine harvester, the analysis and study of the threshing device of a combine harvester is key to improving its performance. Based on the threshing device of a half-feed combine harvester, the simulation model of a discrete element threshing device is established in this paper. With the threshing drum rotation speed, feed volume, and concave sieve vibration frequency as the variable factors, the BP neural network model and linear regression equation model established for the loss rate and impurity content for two kinds of threshing performance indicators, respectively, and through the discrete element threshing performance test, two kinds of methods of threshing performance prediction are analyzed. The results show that the neural network and linear regression can be used for the threshing performance indicators, however, the BP neural network prediction effect has a better prediction precision, better reliability, and the trained neural network can be used in the general case of the threshing performance indicators. This provides a new idea for improving the threshing performance of a combine harvester.

Threshing is the most important function of grain harvester. Grain loss and damage in harvesting are significantly related to threshing theory and technology. There are four kinds of threshing principles including impact, rubbing, combing and grinding. Four types of contact models between grain and threshing components have been constructed correspondently. Grain damage can be regarded as a function of peripheral velocity and contact pattern of impacting. Grain loss can be regarded as a function of contact pattern of rasp bars. Grain loss coming from cleaning and separation in the subsequent process of combing threshing was significantly decreased. Tangential and axial threshing technologies have been applied in grain threshing system widely. It showed that in the combined application, tangential rolls are used to accelerate grain flow, and axial rolls are used to increase threshing quality especially lower loss and damage. Conical concave may take the place of the traditional cylindrical one. With the development of sensor technology and communication technology, intelligent harvesting robot and automatic threshing system will be integrated together to improve grain quality and operation comfort.

In order to solve the problem of the threshing performance of a large combine harvester being reduced due to the non-adjustable diameter of the threshing drum, a variable-diameter threshing drum with movable radial plates based on the principle of concentric regulation was studied. It was mainly composed of a mechanism for adjusting the diameter by moving the radial plates, six fixed threshing tooth rods, six retractable threshing tooth rods and the single piston rod hollow hydraulic cylinder. The threshing gap can be adjusted by a stepless change of the drum diameter. By using RecurDyn simulation and field performance tests, the adjustable ranges of diameter and gap of the movable variable-diameter threshing drum were 670~710 mm and 10~30 mm. Based on the feed amount of the combine, the rotation speed of the threshing drum and the threshing gap (the diameter of the drum) as the influencing parameters, and the grain entrainment loss rate, grain un-threshed rate and grain breakage rate as the evaluation indexes, the three-factor and three-level response surface tests were carried out, and the result data were analyzed using Design-Expert 13.0. The optimal threshing gap and rotation speed of the threshing drum were determined under different feeding quantities. A comparative test was carried out to adjust and fix the threshing gap and rotation speed of the threshing drum in real time according to the change in feeding amount. The results showed that when the working parameter combination under different feeding amounts was adjusted in real time, the entrainment loss rate was 0.65%, the un-threshed rate was 0.063% and the breakage rate was 0.47%. Compared with the threshing gap and the rotation speed of the threshing drum being fixed, the entrainment loss rate, the un-threshed rate and the breakage rate were reduced by 44.9%, 27.6% and 34.1%, respectively. A threshing drum with variable diameter was provided for a large multi-crop harvesting combine to realize the concentric stepless adjustment of the threshing gap.

In this article we consider estimation of sparse covariance matrices and propose a thresholding procedure that is adaptive to the variability of individual entries. The estimators are fully data-driven and demonstrate excellent performance both theoretically and numerically. It is shown that the estimators adaptively achieve the optimal rate of convergence over a large class of sparse covariance matrices under the spectral norm. In contrast, the commonly used universal thresholding estimators are shown to be suboptimal over the same parameter spaces. Support recovery is discussed as well. The adaptive thresholding estimators are easy to implement. The numerical performance of the estimators is studied using both simulated and real data. Simulation results demonstrate that the adaptive thresholding estimators uniformly outperform the universal thresholding estimators. The method is also illustrated in an analysis on a dataset from a small round blue-cell tumor microarray experiment. A supplement to this article presenting additional technical proofs is available online.

In order to further reduce the damage rate in threshing seed corn, a seed corn threshing testbed with variable diameter and spacing that can realize dynamic adjustment of parameters, such as feed quantity, rotating speed of the threshing device, threshing spacing of the threshing units, was designed in this research. The software of finite element analysis ANSYS Workbench was applied to do modal analysis on the threshing axis designed for variable diameter and spacing of seed corn. The first 8 orders of natural frequencies were distributed in 201.12-1640.20 Hz, with corresponding vibration amplitude in 5.86-27.04 mm, showing reasonable structural design of the threshing axis, which could realize effective seed corn threshing and conveying. Discrete element method was applied to do simulation analysis on the seed corn threshing and conveying process with variable diameter and spacing. Under the condition of different feed quantity, different rotating speed of the thresher, the moving speed of corn clusters and contact force among clusters were measured through simulation, and the working characteristics of the threshing testbed for low-damage and dynamic threshing and conveying of seed corn with variable diameter and spacing were revealed. Working performance test results of the testbed of seed corn with variable diameter and spacing showed that, when the rotating speed of the threshing axis was 190-290 r/min, feed quantity was 1.80-3.80 kg/s, the damage rate of seed corn was 0.32%-0.63%, threshing rate was 99.20%-99.82%, and content impurity rate was 4.23%-5.86%, the mass of threshed corn grains first increased and then decreased along the axial direction. The test verification process was in line with the simulation results; thus, the test results could satisfy the requirements in design and actual operation.

The minimal depth of a maximal subtree is a dimensionless order statistic measuring the predictiveness of a variable in a survival tree. We derive the distribution of the minimal depth and use it for high-dimensional variable selection using random survival forests. In big p and small n problems (where p is the dimension and n is the sample size), the distribution of the minimal depth reveals a \"ceiling effect\" in which a tree simply cannot be grown deep enough to properly identify predictive variables. Motivated by this limitation, we develop a new regularized algorithm, termed RSF-Variable Hunting. This algorithm exploits maximal subtrees for effective variable selection under such scenarios. Several applications are presented demonstrating the methodology, including the problem of gene selection using microarray data. In this work we focus only on survival settings, although our methodology also applies to other random forests applications, including regression and classification settings. All examples presented here use the R-software package randomSurvivalForest.

Peanut is an important produce in the global food chain because of their high-quality oil and protein content. Due to the significant value of its production in Iran, a threshing machine was developed for high-quality harvesting, to reduce harvesting costs and labor effort. In the course of a number of field experiments to evaluate the performance of the machine, the rotational speed of the thresher was adopted at three levels of 150, 200, and 300 rpm. Other experimental factors included the distance of the concave from the thresher (2, 6, and 8 cm) and the product feeding rate of 750, 850, and 950 kg·h−1. Regarding the measurements, the threshing efficiency, the separation rate, and the percentage of the crushed product were calculated and evaluated. The results revealed that as the rotational speed of the thresher, the increment feeding rate of the product and the distance between the thresher and the concave grate increased, the thresher efficiency decreased. The maximum threshing efficiency of 95% was obtained at a rotational speed of 150 rpm and a distance of 2 cm. Also, with increasing the rotational speed of 300 rpm and a distance of 8 cm, the threshing efficiency decreased to 75%. The separation rate decreased intensely as the distance between the thresher and the concave increased. In addition, the separation rate decreases with increasing rotational speed of the thresher. At a rotational speed of 150 rpm and a distance of 2 cm, the separation rate was 96%, but the separation rate decreased to 76% as rotational speed increased to 300 rpm and distance increased to 8 cm. With increasing rotational speed and feeding rate, the percentage of crushed pods increased. The maximum of 16% was obtained at a rotational speed of 300 rpm, a feeding rate of 950 kg·h−1 and a distance of 2 cm.