Deep Adversarial Transition Learning using Cross-Grafted Generative Stacks

Current deep domain adaptation methods used in computer vision have mainly focused on learning discriminative and domain-invariant features across different domains. In this paper, we present a novel "deep adversarial transition learning" (DATL) framework that bridges the domain gap by projecting the source and target domains into intermediate, transitional spaces through the employment of adjustable, cross-grafted generative network stacks and effective adversarial learning between transitions. Specifically, we construct variational auto-encoders (VAE) for the two domains, and form bidirectional transitions by cross-grafting the VAEs' decoder stacks. Furthermore, generative adversarial networks (GAN) are employed for domain adaptation, mapping the target domain data to the known label space of the source domain. The overall adaptation process hence consists of three phases: feature representation learning by VAEs, transitions generation, and transitions alignment by GANs. Experimental results demonstrate that our method outperforms the state-of-the art on a number of unsupervised domain adaptation benchmarks.Comment: 12 pages, 8 figure

Variational Transfer Learning using Cross-Domain Latent Modulation

To successfully apply trained neural network models to new domains, powerful transfer learning solutions are essential. We propose to introduce a novel cross-domain latent modulation mechanism to a variational autoencoder framework so as to achieve effective transfer learning. Our key idea is to procure deep representations from one data domain and use it to influence the reparameterization of the latent variable of another domain. Specifically, deep representations of the source and target domains are first extracted by a unified inference model and aligned by employing gradient reversal. The learned deep representations are then cross-modulated to the latent encoding of the alternative domain, where consistency constraints are also applied. In the empirical validation that includes a number of transfer learning benchmark tasks for unsupervised domain adaptation and image-to-image translation, our model demonstrates competitive performance, which is also supported by evidence obtained from visualization.Comment: Under review. arXiv admin note: substantial text overlap with arXiv:2012.1172

Extraction and Purification of Inulin from Jerusalem Artichoke with Response Surface Method and Ion Exchange Resins

Inulin is used as an important food ingredient, widely used for its fiber content. In this study the operational extraction variables to obtain higher yields of inulin from Jerusalem artichoke tubers, as well as the optimal conditions, were studied. Response surface methodology and Box-Behnken design were used for optimization of extraction steps. The optimal extraction conditions were as follows: extraction temperature 74 degrees C, extraction time 65 min, and ratio of liquid to solid 4 mL/g. Furthermore, series connection of ion-exchange resins were used to purify the extraction solution where the optimal resin combinations were D202 strongly alkaline anion resin, HD-8 strongly acidic cation resin, and D315 weakly alkaline resin while the decolorization rate and decreased salinity reached 99.76 and 93.68, respectively. Under these conditions, the yield of inulin was 85.4 +/- 0.5%

Calibration of Small-Grain Seed Parameters Based on a BP Neural Network: A Case Study with Red Clover Seeds

In order to enhance the accuracy of discrete element numerical simulations in the processing of small-seed particles, it is essential to calibrate the parameters of seeds within the discrete element software. This study employs a series of physical tests to obtain the physical and contact parameters of red clover seeds. A discrete element model of red clover seeds is established. Plackett–Burman Design, steepest ascent, and Central Composite Design experiments are sequentially conducted. The simulation deviation of the resting angle of red clover seeds is employed as the evaluation criterion for parameter optimization. The results indicate that the coefficients of static friction between red clover seeds, the coefficients of rolling friction between red clover seeds, and the coefficients of static friction between red clover seeds and the steel plates significantly influence the resting angle. Modeling was performed using a backpropagation neural network, a genetic algorithm–optimized BP network, particle swarm optimization, and simulated annealing. It was found that GA-BP ensured both accuracy and stability. Compared to the traditional response surface methodology, GA-BP showed better fitting performance. For the optimized red clover seed simulation, the error between the angle of repose and the physical experiment was 0.98%. This research provides new insights into the calibration of small-grain seed parameters, demonstrating the value of GA-BP for precision modeling

Numerical Simulation and Experiment on Pill Coating of Red Clover Seeds under the Action of Vibrating Force Field

In order to solve the problem of the low qualification rate of the pilling and coating of small-grain forage seeds, a vibration force field is introduced to the traditional vertical disk coating machine to promote the mixing of materials and improve the qualification rate of the pilling. Using the typical small-grain forage seed red clover as an example, we used the vibration force field after adding seed powder particles to a coating pot for the theoretical analysis of the force situation, using the discrete element software EDEM to construct a red clover seed simulation model with the coefficient of discretization as the evaluation index. We studied the effects of the rotational speed of the coating pot, the vibration frequency of the pot, the amplitude of the vibration of the pot, and the other operating parameters of the pot on the uniformity of the seed powder mixing, with the pelletization of the pass rate as the physical evaluation standard, using a one-way test to study the effect of operating parameters on the pelletization pass rate. We used the qualified rate as the physical evaluation standard, through a single-factor test, to study the influence of the working parameters on the qualification rate of the pilling. The results show that the simulation test evaluation index of the discrete coefficient and the physical test evaluation index of the pilling qualification rate with the change rule of the working parameters is consistent with the discrete coefficient, and can be used as an indirect evaluation index of the quality of pilling. To further determine the optimal combination of working parameters, a quadratic regression orthogonal design test was conducted with the discrete coefficients as the evaluation index, and the second-order regression equations of the red clover seeds were established and analyzed by ANOVA using Design-Expert software. The study shows that, when the rotational speed of the coating pot is 307.204 rpm, the vibration frequency is 2.526 Hz, and the vibration amplitude of the coating pot is 5.843 mm, the predicted coefficient of dispersion at this time is 8.1%. Simulation using the best combination of parameters to obtain the average value of the dispersion coefficient of 8.4%, relative to the predicted value of 3.7%, indicated that the optimization of the experimental regression model is accurate, and the results obtained for the vibration of small seeds under the conditions of the design of the pellet granulation coating machine and the optimization of the pelletization coating process parameters have a certain degree of reference significance

Calibration and Experimental Studies on the Mixing Parameters of Red Clover Seeds and Coated Powders

The physical and mechanical properties of the materials in the swirling fluidized-bed seed pelleting unit affect the mixing degree of the materials in the pelleting and coating process, which is of great significance to research on pelleting and coating. The problem of discrete particle model parameters affecting CFD-DEM simulation results is addressed. In this paper, red clover seeds (referred to as seeds) and pelletized coating powder (referred to as powder) were used as the research objects, and the JKR. model was selected to calibrate the contact parameters between seeds and powder based on particle amplification theory. With the powder rest angle as the response value, a simulation calibration test was conducted; the parameters with significant effects on the response value were screened based on the Plackett–Burman test, and the steepest climb test determined the range of factor levels of essential parameters. The Box–Behnken test was used to establish the curvilinear response surface and quadratic regression equation to determine the best combination of simulation parameters for the powder. The discrete element rest angle was conducted with the best combination of parameters. The error of the test compared with the physical examination was 1%. The particles calibrated by simulation were subjected to the pneumatic suspension velocity test and particle mixing test. The test results matched the physical test results, which further verified the accuracy and applicability of the established discrete element model and parameters for coated powders