AMA, Agricultural Mechanization in Asia, Africa and Latin America (AMA) (issn: 00845841) is a peer reviewed journal first published online after indexing scopus in 1982. AMA is published by Farm Machinery Industrial Research Corp and Shin-Norinsha Co. AMA publishes every subjects of general engineering and agricultural engineering. Azerbaijan Medical Journal Gongcheng Kexue Yu Jishu/Advanced Engineering Science Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Interventional Pulmonology Zhenkong Kexue yu Jishu Xuebao/Journal of Vacuum Science and Technology Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) Zhonghua yi shi za zhi (Beijing, China : 1980)
AMA, Agricultural Mechanization in Asia, Africa and Latin America (ISSN: 00845841) is a peer-reviewed journal. The journal covers Agricultural and Biological Sciences and all sort of engineering topic. the journal's scopes are in the following fields but not limited to:
This paper presents the results of a study on optimizing Electrical discharge machining (EDM) when machining cylindrical parts made of 90CrSi tool steel to achieve the minimum surface roughness (SR). To do that, an experiment was performed. The Taguchi method in Minitab 19 software was used to design the experiment and analyze its results. The effects of the input process parameters, including the pulse on time, the pulse off time, the servo current, and the servo voltage on the surface roughness were investigated. Furthermore, optimal EDM parameters for minimal SR was proposed.
Aiming at the positioning and clamping control problem of the push-and-swing camellia fruit picking machine, the dynamic model of the push-and-swing camellia fruit picking manipulator and the dynamic model of the hydraulic servo actuator were established. The state space equation of the control object was deduced based on these two dynamic models. Based on the traditional sliding mode variable structure control (abbreviated as SMVS control in this paper), the RBF neural network fuzzy sliding mode variable structure adaptive controller (abbreviated as NNFSMVS controller in this paper) is designed, which was proved to be stable by Lyapunov's theorem. Then the manipulator control system was simulated with MATLAB/Simulink, and a SMVS controller was used to contrast with it. The simulation results show that the NNFSMVS controller has a faster response speed, and its maximum trajectory tracking error is 0.0026rad/mm smaller than the maximum trajectory tracking error of the SMVS controller, and it can significantly reduce the control system chattering. Finally, after field experiments, the control response speed of the NNFSMVS controller is between 0.8-1s, which can meet the positioning and clamping requirements of the camellia fruit picking machine.
This paper presents the results of an optimization problem on determining optimal gear ratios of a two-stage bevel helical gearbox to achieve the minimum gearbox cost. To solve this problem, a simulation experiment was performed by building a computer program with the use of the Minitab 19 software to design experiments and analyze experimental results. The influence of the maine design parameters including the total gear ratio, the face width coefficients of the bevel gear set and the helical gear set, the allowable contact stress of the bevel and helical gear sets, the output torque, and the component costs have been evaluated. In particular, the cost of rolling bearing has been taken into account in this study. Moreover, a regression model to find the optimum gear ratio has been proposed.
In the present study a diallel set of 9 x 9 was attempted by crossing nine bread wheat genotypes in all possible combinations excluding reciprocals. The mean squares of nine diverse parents and 36F1s due to GCA and SCA component were significant for all the thirteen traits. These outcomes show the importance of additive variance in the inheritance of all the traits. The comparative importance of additive and non-additive components was revealed by checking the components of variance (s²g and s²s), heritability in broad-sense (Hb), narrow-sense (Hn) and gca/sca ratio. The magnitude of GCA component (s²g) and gca/sca ratio was higher for plant height and peduncle length, indicating that these two traits were under the control of additive genetic variance and all the others traits were controlled by non-additive genetic component. Based on general combining ability effects and per se performance, parents WH1184, HD3086 and HD3059 were found the good general combiners for grain yield per plant. On the basis of per se performance and SCA effects the crosses viz., HD2967 × WH1184 and HD3059 × Raj3765 were found as good specific cross combination. These crosses can be extensively used in further breeding programmes to develop superior pure lines.
In the analysis of high-dimensional data the challenging problem is selecting a useful set of variables among the set of large number of variables. Feature selection reduces the dimensionality of feature space, removes redundant, irrelevant, or noisy data. In this study, comparisons between different variable selection methods were performed. These methods include four methods such as Raoˊs F test, Wilkˊs lambda (Backward and Forward) and Random Forests. A Monte Carlo Simulation study was conducted to compare the performance of various methods of variable selection for classification and discrimination. Random samples with varying sizes (50, 100, 200, 500) were generated using Monte Carlo simulation using means and variance covariance matrices of groups formed on the basis of seed yield and oil content of the 310 genotypes of Indian mustard data set. For samples generated on the basis of seed yield of equal size three methods viz Rao's F test, Wilkˊs lambda (Backward) and Wilkˊs lambda (Forward) were found to have equal performance for (N1=200, N2=200) with least error rate of 18.50 per cent. On comparing the equal sized samples ((N1=50, N2=50), (N1=100, N2=100), (N1=200, N2=200) and (N1=500, N2=500) the most suitable methods for selection of variables affecting oil content with least leave one out cross validation 31.50 percent error rate are Wilkˊs lambda (Backward) and Wilkˊs lambda (Forward) for sample size (N1=100, N2=100).