Comparison of video analysis and simulations of a drum coating process

Tablet coating is a common unit operation in the pharmaceutical industry. To improve currently established processes, it is important to understand the influence of the process parameters on the coating quality. One of the critical parameters is the tablet velocity. In this work, numerical results are compared to results obtained experimentally. Tablet movement in the drums was simulated using the Discrete Element Method (DEM). The simulation parameters were adapted to fit the simulation to the experimental data. A comparison of the experimental and simulation results showed that the simulation correctly represents the real tablet velocity. A change in the velocity over time and its dependence on the rotation rates and the baffle position in the simulation were similar to the experimental results. In summary, simulations can improve the understanding of tablet coating processes and will thus provide insights into the underlying process mechanics, which cannot be obtained via ordinary experiments.

Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control

In recent years the number of active controllable joints in electrically powered hand-prostheses has increased significantly. However, the control strategies for these devices in current clinical use are inadequate as they require separate and sequential control of each degree-of-freedom (DoF). In this study we systematically compare linear and nonlinear regression techniques for an independent, simultaneous and proportional myoelectric control of wrist movements with two DoF. These techniques include linear regression, mixture of linear experts (ME), multilayer-perceptron, and kernel ridge regression (KRR). They are investigated offline with electro-myographic signals acquired from ten able-bodied subjects and one person with congenital upper limb deficiency. The control accuracy is reported as a function of the number of electrodes and the amount and diversity of training data providing guidance for the requirements in clinical practice. The results showed that KRR, a nonparametric statistical learning method, outperformed the other methods. However, simple transformations in the feature space could linearize the problem, so that linear models could achieve similar performance as KRR at much lower computational costs. Especially ME, a physiologically inspired extension of linear regression represents a promising candidate for the next generation of prosthetic devices.