Subset measurement selection for globally self-optimizing control of Tennessee Eastman process

The concept of globally optimal controlled variable selection has recently been proposed to improve self-optimizing control performance of traditional local approaches. However, the associated measurement subset selection problem has not be studied. In this paper, we consider the measurement subset selection problem for globally self-optimizing control (gSOC) of Tennessee Eastman (TE) process. The TE process contains substantial measurements and had been studied for SOC with controlled variables selected from individual measurements through exhaustive search. This process has been revisited with improved performance recently through a retrofit approach of gSOC. To extend the improvement further, the measurement subset selection problem for gSOC is considered in this work and solved through a modification of an existing partially bidirectional branch and bound (PB3) algorithm originally developed for local SOC. The modified PB3 algorithm efficiently identifies the best measurement candidates among the full set which obtains the globally minimal economic loss. Dynamic simulations are conducted to demonstrate the optimality of proposed results

Retrofit self-optimizing control of Tennessee Eastman process

This paper considers near-optimal operation of the Tennessee Eastman (TE) process by using a retrofit self-optimizing control (SOC) approach. Motivated by the factor that most chemical plants in operation have already been equipped with a workable control system for regulatory control, we propose to improve the economic performance by controlling some self-optimizing controlled variables (CVs). Different from traditional SOC methods, the proposed retrofit SOC approach improves economic optimality of operation through newly added cascaded SOC loops, where carefully selected SOC CVs are maintained at constant by adjusting set-points of the existing regulatory control loops. To demonstrate the effectiveness of the retrofit SOC proposed, we adopted measurement combinations as the CVs for the TE process, so that the economic cost is further reduced comparing to existing studies where single measurements are controlled. The optimality of the designed control architecture is validated through both steady state analysis and dynamic simulations

Stiffening of Red Blood Cells Induced by Disordered Cytoskeleton Structures: A Joint Theory-experiment Study

The functions and elasticities of the cell are largely related to the structures of the cytoskeletons underlying the lipid bi-layer. Among various cell types, the Red Blood Cell (RBC) possesses a relatively simple cytoskeletal structure. Underneath the membrane, the RBC cytoskeleton takes the form of a two dimensional triangular network, consisting of nodes of actins (and other proteins) and edges of spectrins. Recent experiments focusing on the malaria infected RBCs (iRBCs) showed that there is a correlation between the elongation of spectrins in the cytoskeletal network and the stiffening of the iRBCs. Here we rationalize the correlation between these two observations by combining the worm-like chain (WLC) model for single spectrins and the Effective Medium Theory (EMT) for the network elasticity. We specifically focus on how the disorders in the cytoskeletal network affect its macroscopic elasticity. Analytical and numerical solutions from our model reveal that the stiffness of the membrane increases with increasing end-to-end distances of spectrins, but has a non-monotonic dependence on the variance of the end-to-end distance distributions. These predictions are verified quantitively by our AFM and micropipette aspiration measurements of iRBCs. The model may, from a molecular level, provide guidelines for future identification of new treatment methods for RBC related diseases, such as malaria infection.Comment: 8 pages, 4 figures; 3 supporting figure

Decentralized Event-Triggered Consensus of Linear Multi-agent Systems under Directed Graphs

An event-triggered control technique for consensus of multi-agent systems with general linear dynamics is presented. This paper extends previous work to consider agents that are connected using directed graphs. Additionally, the approach shown here provides asymptotic consensus with guaranteed positive inter-event time intervals. This event-triggered control method is also used in the case where communication delays are present. For the communication delay case we also show that the agents achieve consensus asymptotically and that, for every agent, the time intervals between consecutive transmissions is lower-bounded by a positive constant.Comment: 9 pages, 5 figures, A preliminary version of this manuscript has been submitted to the 2015 American Control Conferenc

Charge Density Wave Instability and Soft Phonon in AAPt3_3P (AA=Ca, Sr, and La)

The electronic and phonon properties of the platinum pnictide superconductors $A$Pt$_3$P ($A$=Ca, Sr, and La) were studied using first-principles calculations. The spin-orbit coupling effect is significant in LaPt$_3$P but negligible in CaPt$_3$P and SrPt$_3$P, although they all share the same anti-pevroskite structure. Moreover, SrPt$_3$P has been demonstrated to exhibit an unexpected weak charge-density-wave(CDW) instability which is neither simply related to the Fermi-surface nesting nor to the momentum-dependent electron-phonon coupling alone. The instability is absent in CaPt$_3$P and can be quickly suppressed by the external pressure, accompanied with gradual decreases in the phonon softening and BCS $T_c$. Our results suggest SrPt$_3$P as a rare example where superconductivity is enhanced by the CDW fluctuations

Distribution-based Active Learning

University of Technology Sydney. Faculty of Engineering and Information Technology.Active learning aims to maximize the learning performance of the current hypothesis by drawing as few labels as possible from an input distribution. To build a near-optimal hypothesis, halfspace learning improved the generalization of a perceptron vector over a unit sphere, presenting model guarantees for the reliable (practical) active learning, in which the error disagreement coefficient controls the hypothesis update via pruning the hypothesis class. However, this update process critically depends on the initial hypothesis and the coefficient. Their improper settings may improve the bounds on the label complexity, which estimates the label demands before achieving a desired error for the hypothesis. One question thus arises: how to reduce the label complexity bounds? In a worse situation, estimating updates of hypothesis using error lacks feasible guarantees, if the initial hypothesis is a null (insignificant) hypothesis. Another question also arises: how to control the hypothesis update without errors, when estimating the error disagreement is infeasible? For error disagreement, most of its generalizations regarding to hypothesis update, either make strong distribution assumptions such as halfspace learning, or else they are computationally prohibitive. How to improve the performance of deep active learning based on the theoretical results of active learning of halfspace? This thesis tries to answer the three questions from shattering, disagreeing, and matching over distributions. With halfspace learning, the first work presents a novel perspective of shattering the input distribution that, guaranteeing from a lower bound on Vapnik-Chervonenkis (VC) dimension, further reduces the label complexity of active learning. When estimating errors is infeasible, the second work proposes a distribution disagreement graph coefficient, which estimates hypothesis from distribution, yielding a tighter bound on typical label complexity. The constructed hyperbolic model, generalizing distribution disagreement by focal representation, shows effective improvements compared to generalization algorithms of error disagreement. On deep learning settings for active learning, the Bayesian neural network shows expressive distribution matching on the massive training parameters, which allows estimating error disagreement can work effectively. We thus integrate the error and distribution disagreements to establish a uniform framework, which matches the geometric core-set expression of the distribution, interacting with a deep learning model