Multiple Input-Multiple Output Cycle-to-Cycle Control of Manufacturing Processes

Cycle-to-cycle control is a method for using feedback to improve product quality for processes that are inaccessible within a single processing cycle. This limitation stems from the impossibility or the prohibitively high cost of placing sensors and actuators that could facilitate control during, or within, the process cycle. Our previous work introduced cycle to cycle control for single input-single output systems, and here it is extended to multiple input-multiple output systems. Gain selection, stability, and process noise amplification results are developed and compared with those obtained by previous researchers, showing good agreement. The limitation of imperfect knowledge of the plant model is then imposed. This is consistent with manufacturing environments where the cost and number of tests to determine a valid process model is desired to be minimal. The implications of this limitation are modes of response that are hidden from the controller. Their effects on system performance and stability are discussed.Singapore-MIT Alliance (SMA

Forced dynamic dewetting of structured surfaces: Influence of surfactants

We analyse the dewetting of printing plates for gravure printing with well-defined gravure cells. The printing plates were mounted on a rotating horizontal cylinder that is half immersed in an aqueous solution of the anionic surfactant sodium 1-decanesulfonate. The gravure plates and the presence of surfactants serve as one example of a real-world dewetting situation. When rotating the cylinder, a liquid meniscus was partially drawn out of the liquid forming a dynamic contact angle at the contact line. The dynamic contact angle is decreased on a structured surface as compared to a smooth one. This is due to contact line pinning at the borders of the gravure cells. Additionally, surfactants tend to decrease the dynamic receding contact angle. We consider the interplay between these two effects. We compare the height differences of the meniscus on the structured and unstructured area as function of dewetting speeds. The height difference increases with increasing dewetting speed. With increasing size of the gravure cells this height difference and the induced changes in the dynamic contact angle increased. By adding surfactant, the height difference and the changes in the contact angle for the same surface decreased. We further note that although the liquid dewets the printing plates some liquid is always left in the gravure cell. At high enough surfactant concentrations or high enough dewetting speed, the dynamic contact angles in the structured surface approach those in flat surfaces. We conclude that surfactant reduces the influence of surface structure on dynamic dewetting

Differentially Private Model Selection with Penalized and Constrained Likelihood

In statistical disclosure control, the goal of data analysis is twofold: The released information must provide accurate and useful statistics about the underlying population of interest, while minimizing the potential for an individual record to be identified. In recent years, the notion of differential privacy has received much attention in theoretical computer science, machine learning, and statistics. It provides a rigorous and strong notion of protection for individuals' sensitive information. A fundamental question is how to incorporate differential privacy into traditional statistical inference procedures. In this paper we study model selection in multivariate linear regression under the constraint of differential privacy. We show that model selection procedures based on penalized least squares or likelihood can be made differentially private by a combination of regularization and randomization, and propose two algorithms to do so. We show that our private procedures are consistent under essentially the same conditions as the corresponding non-private procedures. We also find that under differential privacy, the procedure becomes more sensitive to the tuning parameters. We illustrate and evaluate our method using simulation studies and two real data examples