PARAMETER OPTIMIZATION OF WHOLE-STRAW RETURNING DEVICE BASED ON THE BP NEURAL NETWORK

ABSTRACT To solve the poor fitting degree of errors in multiobjective parameter optimization and low accuracy, a multiobjective optimization method based on a BP neural network was proposed. By taking the 1ZT-210 type whole-straw returning device as the research object, a BP neural network model on power consumption, straw returning rate and the influencing factors was obtained. By optimizing the model by the proposed method, the optimal parameter combination of the test factors was as follows: the advancing speed of the device was 0.65 km/h, the blade roll rotating speed was 210 rpm, the blade installation angle was 55o, the minimum power consumption was 9.82 kW and the maximum straw returning rate was 93.23%. Under such test conditions, the minimum power consumption was 10.75 kW, and the straw returning rate was 92.46%, which were all better than those obtained by the regression analysis method. Finally, a verification test was conducted on the results of BP neural network optimization. The power consumption of the test was 10.04 kW, the absolute error was 0.22 kW and the relative error was 2.24%. For a straw returning rate of 93.11%, the absolute error was -0.12% and the relative error was 0.13%. The test results indicated that the optimization method was feasible.</div

PARAMETER OPTIMIZATION OF TRACTOR'S STEERING TRAPEZOID MECHANISM BASED ON IMPROVED ADAPTIVE DIRECTION STRATEGY TEACHING-LEARNING-BASED OPTIMIZATION

ABSTRACT The parameter optimization of the tractor's steering trapezoid mechanism is a traditional optimization problem, and the teaching-learning-based optimization (TLBO) has a better solving ability for parameter optimization of the tractor's steering trapezoid. However, the teacher stage and student stage of TLBO limit the accuracy and stability and the ability to jump out of the local optimization solution. To obtain an optimal solution with an higher accuracy, an improved adaptive direction strategy teaching-learning-based optimization (IADS-TLBO) was used. This improved the feedback stage based on the adaptive direction strategy teaching-learning-based optimization (ADS-TLBO). The IADS-TLBO was verified by three different testing functions, and the results showed that the improved ideas are valid and feasible. Finally, the IADS-TLBO was used to optimize the steering trapezoid mechanism of JOHN DEERE T600. The optimal parameters obtained were as follows: the bottom angle was 35.4º, and the steering arm length was 154 mm. A verification experiment was conducted in the farm tool laboratory of Northeast Agricultural University (China). The experimental results showed that the average bottom angle was 35.48º, and the relative error between the measured and optimized bottom angles was 0.23%, which is less than 5%. This result showed that the results obtained by IADS-TLBO were reliable.</div

Additional file 1 of An integrated isotopic labeling and freeze sampling apparatus (ILSA) to support sampling leaf metabolomics at a centi-second scale

Additional file 1: Figure S1. Comparison of metabolite concentrations with previous report (Arrivault, et al., 2019). N = 6 for ILSA, N = 8 for reference (Arrivault, et al., 2019). Error bar indicated the S.E.M. Figure S2. Dynamic isotope labeling trajectories of measured metabolites. Data point with error bar (S.D. N = 3) represent the measured value, line represent the INST-MFA fitted result. M0, M1, M2, … means the mass isotopomer of each metabolites with certain number of 13C. [M6] + isotopomer of ADPG and UDPG are not be included because of the lack of labeling

Additional file 6 of An integrated isotopic labeling and freeze sampling apparatus (ILSA) to support sampling leaf metabolomics at a centi-second scale

Additional file 6: Data S5. Calculated photosynthetic flux of IR64

Additional file 4 of An integrated isotopic labeling and freeze sampling apparatus (ILSA) to support sampling leaf metabolomics at a centi-second scale

Additional file 4: Data S3. Metabolic network used for metabolic flux analysis

Additional file 5 of An integrated isotopic labeling and freeze sampling apparatus (ILSA) to support sampling leaf metabolomics at a centi-second scale

Additional file 5: Data S4. 13C labelling data of IR64

Additional file 2 of An integrated isotopic labeling and freeze sampling apparatus (ILSA) to support sampling leaf metabolomics at a centi-second scale

Additional file 2: Data S1. HPLC–MS/MS parameters used for metabolic profiling and 13C abundance of metabolites

Additional file 3 of An integrated isotopic labeling and freeze sampling apparatus (ILSA) to support sampling leaf metabolomics at a centi-second scale

Additional file 3: Data S2. Metabolic profiling of IR64

Improved Thermoelectric Performance in Nonstoichiometric Cu_2+δMn_1−δSnSe₄ Quaternary Diamondlike Compounds

A novel quaternary Cu₂MnSnSe₄ diamondlike thermoelectric material was discovered recently based on the pseudocubic structure engineering. In this study, we show that introducing off-stoichiometry in Cu₂MnSnSe₄ effectively enhances its thermoelectric performance by simultaneously optimizing the carrier concentrations and suppressing the lattice thermal conductivity. A series of nonstoichiometric Cu_2+δMn_1−δSnSe₄ (δ = 0, 0.025, 0.05, 0.075, and 0.1) samples has been prepared by the melting–annealing method. The X-ray analysis and the scanning electron microscopy measurement show that all nonstoichiometric samples are phase pure. The Rietveld refinement demonstrates that substituting part of Mn by Cu well maintains the structure distortion parameter η close to 1, but it induces obvious local distortions inside the anion-centered tetrahedrons. Significantly improved carrier concentrations are observed in these nonstoichiometric Cu_2+δMn_1−δSnSe₄ samples, pushing the power factors to the theoretical maximal value predicted by the single parabolic model. Substituting part of Mn by Cu also reduces the lattice thermal conductivity, which is well interpreted by the Callaway model. Finally, a maximal thermoelectric dimensionless figure-of-merit <i>zT</i> around 0.60 at 800 K has been obtained in Cu_2.1Mn_0.9SnSe₄, which is about 33% higher than that in the Cu₂MnSnSe₄ matrix compound