Comparison of Stochastic Dynamic Programming and Sequential Decision-Dependent Operational Rules

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[0001] The invention is notably directed to a computer-implemented method of optimizing an optical sensor (1) comprising a single-walled carbon nanotube (2), or swcnt for short, which is wrapped in single-stranded sequence (3) of nucleotides (sssn). the method revolves around two or more optimization cycles. each optimization cycle includes clustering sssns of an input set of sssns to obtain clusters and selecting representative sssns of the clusters obtained. next, one accesses measurements of optical r

Mathematical programming establishes a rigorous foundation for solving complex problems in fields such as engineering, operations research, and finance. However, deploying these methods in real-time control applications, particularly Model Predictive Control (MPC), presents significant challenges regarding computational resources, solve times, and scalability. This thesis addresses these hurdles by progressing from theoretical verification methods to numerical algorithm design and experimental deployment.

Railroad diagrams (also called “syntax diagrams”) are a common, intuitive visualization of grammars, but limited tooling and a lack of formal attention to their layout mostly confines them to hand-drawn documentation. We present the first formal treatment of railroad diagram layout along with a principled, practical implementation. We characterize the problem as compiling a diagram language (specifying conceptual components and how they connect and compose) to a layout language (specifying basic graphical s

A growing body of research in the past decade has revealed that functional interaction between brain regions entail subject-specific idiosyncrasies that are highly replicable. As such, functional connectivity patterns can be seen as an individual's brain fingerprint, enabling their identification within a population, in health and disease. The conventional method involves constructing the functional connectome by treating brain regions as vertices and utilizing pairwise measures of statistical dependence, s

Matching of a test signal to a reference word hypothesis forms the core of many speech processing problems, including objective speech intelligibility assessment. This paper first shows that the comparison of two speech signals can be formulated as matching of two sequences of "uncertain" or probabilistic latent symbols, in the same manner as string matching. Based upon that, we propose a pathological speech intelligibility assessment approach that compares pathological speaker's speech to control speaker's

In recent years, new methods have been developed to synthesize complex porous and micro-structured geometry in a variety of ways. In this work, we take these approaches one step further and present these methods as an efficacious design paradigm. Specifically, complex micro-structure geometry can be synthesized while optimizing certain properties such as maximal heat exchange in heat exchangers, or minimal weight under stress specifications. By being able to adjust the geometry, the topology and/or the mate

Aerodynamic shape optimization has become of primary importance for the aerospace industry over the last years. Most of the method developed so far have been shown to be either computationally very expensive, or to have low dimensional search space. In this work we present how Geodesic Convolutional Neural Networks can be used as a surrogate model for a computational fluid dynamics solver. The trained model is shown to be capable of efficiently exploring a high dimensional shape space. We apply this method

Temperature and salinity, with largely different diffusivities, make a perfect couple that drives thermohaline flow (double diffusive convection where heat and salt are the two main concerned properties) in porous media wherever their gradients co-exist. Studies of double diffusion convection have revealed various flow regimes generated by instable and even stable vertical stratification of heat and salt including diffusive, finger, and mixed flow regimes. In the context of seawater intrusion in shallow coa

Direction reversals of intrinsic toroidal rotation have been observed in diverted Alcator C-Mod Ohmic L-mode plasmas following electron density ramps. For low density discharges, the core rotation is directed cocurrent, and reverses to countercurrent follo ...

In this project we are studying the design of district energy networks together with operation and investment optimization, as well as CO2 emissions, with different combination of available conversion technologies such as Biomass Gasification, Organic Rankine Cycle and Incinerators, including renewable resources like solar and biomass resources, to satisfy the energy demand in urban area. We use Energy Integrations concept for this purpose, as well as system modeling to simulate different conversion technol

To be profitable, a geothermal energy system must effectively and economically convert a given resource into useful energy services to be delivered to the consumer during different periods of the year. These services include electricity, district heating, and cooling. The performance of the system depends not only on the specific climate conditions and resource characteristics, but also on multiple interdependent decision variables that affect the thermodynamic and economic performance of the system. These

The selection of an hybrid MSF/RO desalination plant depends on many factors, such as capital costs, electricity consumption, water conversion rate and local requirements. In order to provide the necessary information for the decision process, stand-alone reverse osmosis or multi-stage °ash designs and alternative hybrid con¯gurations must be characterized, optimized and compared on a sound basis. The aim of this paper is to present a computer- aided method, for the design and optimization of hybrid desalin

Miniswys motor (property of Miniswys SA) is a piezoelectric, ultrasonic, linear motor intended to be integrated in an optical system for mobile phones. With the aim of continually optimizing their product, Miniswys SA has signed a collaboration agreement with the EPFL. This practical Master project is a first study realized within the framework of this collaboration.

We consider a Galerkin Finite Element approximation of the Stokes–Darcy problem which models the coupling between surface and groundwater flows. Then we propose an iterative subdomain method for its solution, inspired to the domain decomposition theory. The convergence analysis that we develop is based on the properties of the discrete Steklov–Poincaré operators associated to the given coupled problem. An optimal preconditioner for Krylov methods is proposed and analyzed.

Two of the most basic problems encountered in numerical optimization are least-squares problems and systems of nonlinear equations. The use of more and more complex simulation tools on high performance computers requires solving problems involving an increasingly large number of variables. The main thrust of this thesis the design of new algorithmic methods for solving large-scale instances of these two problems. Although they are relevant in many different applications, we concentrate specifically on real

Interplay between Identiﬁcation and Optimization in Run-to-run Schemes B. Srinivasan, D. Bonvin ´ Institut d’Automatique, Ecole Polytechnique F´d´rale de Lausanne, ee CH-1015 Lausanne, Switzerland. Optimization of batch processes has received increasing attention since its provides an uniﬁed framework to improve productivity without violating constraints on safety, selectivity, reproducability, etc. Due to uncertainties (disturbances and modeling errors) that are invariably present at the industrial level,

An analysis of unsupervised neural network models and the use of self-organizing maps in the real-time monitoring of power transformers was carried out. The application of genetic algorithms in a complex optimization problem, the allocation and parameters of FACTS devices in a power transmission system, was also studied. Intelligent systems for monitoring of equipment and for improving the loading of networks are essential tools for the operation of electric energy systems.

A comparative study has demonstrated that the formerly developed Pinch Analysis (PA) based targeting and design methods outperform (in both heat recovery and cost-effectiveness) the simplified combinatorial design methods proposed by other authors (namely the Omnium Verfahren and the Permutation Method). The PA based methods provide valuable insight in the problem, but aren’t suitable for automated design and optimization. PinchBATCH results therefore in the following advances: 1) a strategy (including guid

A correlation-based (

Hebbian'') learning rule at the spike level is formulated, mathematically analyzed, and compared with learning in a firing-rate description. As for spike coding, we take advantage of a

learning window'' that describes the effect of timing of pre- and postsynaptic spikes on synaptic weights. A differential equation for the learning dynamics is derived under the assumption that the time scales of learning and spiking dynamics can be separated. Formation of structured synapses is analy

Pb(Mg1/3Nb2/3)O-3 (PMN) alkoxide precursor solutions were synthesized and used to prepare thin layers on TiO2/Pt/TiO2/SiO2/Si substrates by spin coating. Many parameters like homogeneity of the solution and appropriate processing must be controlled to minimize the pyrochlore formation. Optimization of the processing allowed highly (111)-oriented thin films to be obtained. Dielectric measurements and TEM investigations were performed and showed that PMN thin films exhibit relaxer behavior.

Comparison of Stochastic Dynamic Programming and Sequential Decision-Dependent Operational Rules

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Optimizing optical nano-biosensors made of single-walled carbon nanotubes wrapped in single-stranded sequences of nucleotides for detection of molecules such as glucose and cancer biomarkers

Numerical Methods for Optimization and Control: From Verification to GPU-Acceleration

Automatic Layout of Railroad Diagrams

Graph learning from EEG data improves brain fingerprinting compared to correlation-based connectomes

Probabilistic Symbol Sequence Matching and its Application to Pathological Speech Intelligibility Assessment

Optimizing micro-tiles in micro-structures as a design paradigm

Aerodynamic shape optimization via surrogate modelling with convolutional neural networks

Thermohaline Flow in Seawater Intrusion in Shallow Coastal Aquifers

Rotation Reversal Bifurcation and Energy Confinement Saturation in Tokamak Ohmic L-Mode Plasmas

Multi-Objective Design and Optimization of District Energy Systems, Including Renewable energy

Integrated Thermo-economic Modeling of Geothermal Resources for Optimal Exploitation Scheme Identification

Innovative design and multi-objective optimization of hybrid reverse osmosis and multi-stage flash desalination plants

Study and optimization of a linear piezoelectric actuator

Convergence analysis of a subdomain iterative method for the finite element approximation of the coupling of Stokes and Darcy equations

New algorithmic methods for real-time transportation problems

Interplay between Identification and Optimization in Run-to-run Schemes

Application of AI techniques to monitoring of transformers and optimal allocation of FACTS in power systems

Energy Integration of Industrial Batch Processes (PinchBATCH) - Phase 2

Hebbian learning and spiking neurons

Preparation and electromechanical properties of Pb(Mg1/3Nb2/3)O-3 thin film