Using MPC to control middle-vessel continuous distillation columns

The use of model predictive control (MPC) in middle-vessel continuous distillation column (MVCC) is discussed. It is shown that using a 5 × 5 MPC implementation (where all levels are included in MPC as integral process variables) allows using a smaller middle-vessel, particularly when disturbances can be measured: a good performance is ensured without having the middle vessel drained or overfilled. Also, it is shown that MPC practically circumvents the issue of tuning the middle-vessel level controller. Furthermore, the MVCC design makes conventional decentralised control perform comparably to MPC.     

Dealing with logical omniscience: Expressiveness and pragmatics

We examine four approaches for dealing with the logical omniscience problem and their potential applicability: the syntactic approach, awareness, algorithmic knowledge, and impossible possible worlds. Although in some settings these approaches are equi-expressive and can capture all epistemic states, in other settings of interest (especially with probability in the picture), we show that they are not equi-expressive. We then consider the pragmatics of dealing with logical omniscience—how to choose an approach and construct an appropriate model.     

Making random permutations from physically unclonable constants

The recent availability of reliable schemes for physically unclonable constants (PUC) opens interesting possibilities in the field of security. In this paper, we explore the possibility of using PUCs to embed in a chip random permutations to be used, for example, as building blocks in cryptographic constructions such as sponge functions, substitution–permutation networks, and so on. We show that the most difficult part is the generation of random integers using as the only randomness source the bit-string produced by the PUC. In order to solve the integer generation problem, we propose a partial rejection method that allows the designer to trade-off between entropy and efficiency. The results show that the proposed schemes can be implemented with reasonable complexity.     

Shape from Radiological Density

In this paper we propose a strategy to solve the problem of recovering the 3-D shape of anatomical structures from single X-ray images, i.e., the problem of Shape from Radiological Density (SFRD). In order to overcome the noninvertibility of the process of image generation, we formulate a minimal set of physical assumptions that are used to constrain SFRD and to transform it into a well-posed problem. Our shape recovery strategy requires the solution of four problems: (a) linearization of the process of X-ray image generation, (b) image segmentation, (c) estimation of a map of the local thickness of each anatomical structure of interest, and (d) recovery of the 3-D shape of each structure from its boundaries and thickness map. In this paper we assume that problems (a) and (b) have already been faced, and propose a solution for problems (c) and (d). Experimental results on synthetic images, X-ray images of phantoms, and real radiograms are reported.     

Modeling and control of an airbrake electro‐hydraulic smart actuator

In this paper, an accurate model of an airbrake electro‐hydraulic smart actuator is obtained by physical considerations, and then different control strategies (variable‐gain proportional control, PT₁ control with switching integrator, and second order sub‐optimal sliding mode control) are proposed and analyzed. This application is innovative in the avionic field, and is one of the first attempts to realize a fly‐by‐wire system for airbrakes, oriented to its immediate employment and installation on current aircraft. The project was carried on with the participation of the Italian Ministry of Defense, and was commissioned to MAG, a leading provider of integrated systems and aviation services for aerospace. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

PointProNets: Consolidation of Point Clouds with Convolutional Neural Networks

With the widespread use of 3D acquisition devices, there is an increasing need of consolidating captured noisy and sparse point cloud data for accurate representation of the underlying structures. There are numerous algorithms that rely on a variety of assumptions such as local smoothness to tackle this ill‐posed problem. However, such priors lead to loss of important features and geometric detail. Instead, we propose a novel data‐driven approach for point cloud consolidation via a convolutional neural network based technique. Our method takes a sparse and noisy point cloud as input, and produces a dense point cloud accurately representing the underlying surface by resolving ambiguities in geometry. The resulting point set can then be used to reconstruct accurate manifold surfaces and estimate surface properties. To achieve this, we propose a generative neural network architecture that can input and output point clouds, unlocking a powerful set of tools from the deep learning literature. We use this architecture to apply convolutional neural networks to local patches of geometry for high quality and efficient point cloud consolidation. This results in significantly more accurate surfaces, as we illustrate with a diversity of examples and comparisons to the state‐of‐the‐art.     

The New Mind: thinking beyond the head

Throughout much of the modern period, the human mind has been regarded as a property of the brain and therefore something confined to the inside of the head—a view commonly known as ‘internalism’. But recent works in cognitive science, philosophy, and anthropology, as well as certain trends in the development of technology, suggest an emerging view of the mind as a process not confined to the brain but spread through the body and world—an outlook covered by a family of views labelled ‘externalism’. In this paper, we will suggest there is now sufficient momentum in favour of externalism of various kinds to mark a historical shift in the way the mind is understood. We dub this emerging externalist tendency the ‘New Mind’. Key properties of the New Mind will be summarised and some of its implications considered in areas such as art and culture, technology, and the science of consciousness.     

Dynamic analyses of information encoding in neural ensembles

Neural spike train decoding algorithms and techniques to compute Shannon mutual information are important methods for analyzing how neural systems represent biological signals. Decoding algorithms are also one of several strategies being used to design controls for brain-machine interfaces. Developing optimal strategies to design decoding algorithms and compute mutual information are therefore important problems in computational neuroscience. We present a general recursive filter decoding algorithm based on a point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. We derive from the algorithm new instantaneous estimates of the entropy, entropy rate, and the mutual information between the signal and the ensemble spiking activity. We assess the accuracy of the algorithm by computing, along with the decoding error, the true coverage probability of the approximate 0.95 confidence regions for the individual signal estimates. We illustrate the new algorithm by reanalyzing the position and ensemble neural spiking activity of CA1 hippocampal neurons from two rats foraging in an open circular environment. We compare the performance of this algorithm with a linear filter constructed by the widely used reverse correlation method. The median decoding error for Animal 1 (2) during 10 minutes of open foraging was 5.9 (5.5) cm, the median entropy was 6.9 (7.0) bits, the median information was 9.4 (9.4) bits, and the true coverage probability for 0.95 confidence regions was 0.67 (0.75) using 34 (32) neurons. These findings improve significantly on our previous results and suggest an integrated approach to dynamically reading neural codes, measuring their properties, and quantifying the accuracy with which encoded information is extracted.     

A decision-support system for the design and management of warehousing systems

The issue of material handling involves the design and operative control of warehousing systems (i.e., distribution centres), which allow matching vendors and demands, smoothing with seasonality, consolidating products and arranging distribution activities. Warehousing systems play a crucial role in providing efficiency and customer satisfaction. The warehouse design entails a wide set of decisions, which involve layout constraints and operative issues that seriously affect the performances and the overall logistics costs.