Ensemble learning of model hyperparameters and spatiotemporal data for calibration of low-cost PM2.5 sensors.

he PM2.5 air quality index (AQI) measurements from government-built supersites are accurate but cannot provide a dense coverage of monitoring areas. Low-cost PM2.5 sensors can be used to deploy a fine-grained internet-of-things (IoT) as a complement to government facilities. Calibration of low-cost sensors by reference to high-accuracy supersites is thus essential. Moreover, the imputation for missing-value in training data may affect the calibration result, the best performance of calibration model requires hyperparameter optimization, and the affecting factors of PM2.5 concentrations such as climate, geographical landscapes and anthropogenic activities are uncertain in spatial and temporal dimensions. In this paper, an ensemble learning for imputation method selection, calibration model hyperparameterization, and spatiotemporal training data composition is proposed. Three government supersites are chosen in central Taiwan for the deployment of low-cost sensors and hourly PM2.5 measurements are collected for 60 days for conducting experiments. Three optimizers, Sobol sequence, Nelder and Meads, and particle swarm optimization (PSO), are compared for evaluating their performances with various versions of ensembles. The best calibration results are obtained by using PSO, and the improvement ratios with respect to R2, RMSE, and NME, are 4.92%, 52.96%, and 56.85%, respectively

Optimized Tuning of an EKF for State and Parameter Estimation in a Semitrailer

The Extended Kalman Filter (EKF) is a well-known method for state and parameter estimation in vehicle dynamics. However, for tuning the EKF, knowledge about the process and measurement noise is needed, which is usually unknown. Tuning the noise parameters manually is very time consuming, especially for systems with many states. Automated optimization based on the filtering errors promises less application time and better estimation performance, but also requires computing resources. This work presents two approaches for estimating the noise parameters of an EKF: A particle swarm optimization (PSO) and a gradient-based optimization. The EKF is applied to a nonlinear vehicle model of a tractor-semitrailer for estimating the steering and articulation angle as well as lateral and vertical tire forces based on real measurement data with different trailer loadings. Both methods are compared to each other to achieve the best estimation performance

OPTIMISATION OF HULL FORM OF OCEAN-GOING TRAWLER

This paper proposes a method for optimising the hull form of ocean-going trawlers to decrease resistance and consequently reduce the energy consumption. The entire optimisation process was managed by the integration of computer-aided design and computational fluid dynamics (CFD) in the CAESES software. Resistance was simulated using the CFD solver and STAR-CCM+. The ocean-going trawler was investigated under two main navigation conditions: trawling and design. Under the trawling condition, the main hull of the trawler was modified using the Lackenby method and optimised by NSGA-II algorithm and Sobol + Tsearch algorithm. Under the design condition, the bulbous bow was changed using the free-form deformation method, and the trawler was optimised by NSGA-Ⅱ. The best hull form is obtained by comparing the ship resistance under various design schemes. Towing experiments were conducted to measure the navigation resistance of trawlers before and after optimisation, thus verifying the reliability of the optimisation results. The results show that the proposed optimisation method can effectively reduce the resistance of trawlers under the two navigation conditions

Optimization in Quasi-Monte Carlo Methods for Derivative Valuation

Computational complexity in financial theory and practice has seen an immense rise recently. Monte Carlo simulation has proved to be a robust and adaptable approach, well suited for supplying numerical solutions to a large class of complex problems. Although Monte Carlo simulation has been widely applied in the pricing of financial derivatives, it has been argued that the need to sample the relevant region as uniformly as possible is very important. This led to the development of quasi-Monte Carlo methods that use deterministic points to minimize the integration error. A major disadvantage of low-discrepancy number generators is that they tend to lose their ability of homogeneous coverage as the dimensionality increases. This thesis develops a novel approach to quasi-Monte Carlo methods to evaluate complex financial derivatives more accurately by optimizing the sample coordinates in such a way so as to minimize the discrepancies that appear when using lowdiscrepancy sequences. The main focus is to develop new methods to, optimize the sample coordinate vector, and to test their performance against existing quasi-Monte Carlo methods in pricing complicated multidimensional derivatives. Three new methods are developed, the Gear, the Simulated Annealing and the Stochastic Tunneling methods. These methods are used to evaluate complex multi-asset financial derivatives (geometric average and rainbow options) for dimensions up to 2000. It is shown that the two stochastic methods, Simulated Annealing and Stochastic Tunneling, perform better than existing quasi-Monte Carlo methods, Faure' and Sobol'. This difference in performance is more evident in higher dimensions, particularly when a low number of points is used in the Monte Carlo simulations. Overall, the Stochastic Tunneling method yields the smallest percentage root mean square relative error and requires less computational time to converge to a global solution, proving to be the most promising method in pricing complex derivativesImperial Users onl

Constructive approaches to quasi-Monte Carlo methods for multiple integration

Recently, quasi-Monte Carlo methods have been successfully used for approximating multiple integrals in hundreds of dimensions in mathematical finance, and were significantly more efficient than Monte Carlo methods. To understand the apparent success of quasi-Monte Carlo methods for multiple integration, one popular approach is to study worst-case error bounds in weighted function spaces in which the importance of the variables is moderated by some sequences of weights. Ideally, a family of quasi-Monte Carlo methods in some weighted function space should be strongly tractable. Strong tractability means that the minimal number of quadrature points n needed to reduce the initial error by a factor of ε is bounded by a polynomial in ε⁻¹ independently of the dimension d. Several recent publications show the existence of lattice rules that satisfy the strong tractability error bounds in weighted Korobov spaces of periodic integrands and weighted Sobolev spaces of non-periodic integrands. However, those results were non-constructive and thus give no clues as to how to actually construct these lattice rules. In this thesis, we focus on the construction of quasi-Monte Carlo methods that are strongly tractable. We develop and justify algorithms for the construction of lattice rules that achieve strong tractability error bounds in weighted Korobov and Sobolev spaces. The parameters characterizing these lattice rules are found ‘component-by-component’: the (d + 1)-th components are obtained by successive 1-dimensional searches, with the previous d components kept unchanged. The cost of these algorithms vary from O(nd²) to O(n³d²) operations. With currently available technology, they allow construction of rules easily with values of n up to several million and dimensions d up to several hundred

Metaheuristics for online drive train efficiency optimization in electric vehicles

Utilization of electric vehicles provides a solution to several challenges in today’s individual mobility. However, ensuring maximum efficient operation of electric vehicles is required in order to overcome their greatest weakness: the limited range. Even though the overall efficiency is already high, incorporating DC/DC converter into the electric drivetrain improves the efficiency level further. This inclusion enables the dynamic optimization of the intermediate voltage level subject to the current driving demand (operating point) of the drivetrain. Moreover, the overall drivetrain efficiency depends on the setup of other drivetrain components’ electric parameters. Solving this complex problem for different drivetrain parameter setups subject to the current driving demand needs considerable computing time for conventional solvers and cannot be delivered in real-time. Therefore, basic metaheuristics are identified and applied in order to assure the optimization process during driving. In order to compare the performance of metaheuristics for this task, we adjust and compare the performance of different basic metaheuristics (i.e. Monte-Carlo, Evolutionary Algorithms, Simulated Annealing and Particle Swarm Optimization). The results are statistically analyzed and based on a developed simulation model of an electric drivetrain. By applying the bestperforming metaheuristic, the efficiency of the drivetrain could be improved by up to 30% compared to an electric vehicle without the DC/DC- converter. The difference between computing times vary between 30 minutes (for the Exhaustive Search Algorithm) to about 0.2 seconds (Particle Swarm) per operating point. It is shown, that the Particle Swarm Optimization as well as the Evolutionary Algorithm procedures are the best-performing methods on this optimization problem. All in all, the results support the idea that online efficiency optimization in electric vehicles is possible with regard to computing time and success probability

Approximation of Bayesian Efficiency in Experimental Choice Designs

This paper compares different types of simulated draws over a range of number of draws in generating Bayesian efficient designs for stated choice studies. The paper examines how closely pseudo Monte Carlo, quasi Monte Carlo and polynomial cubature methods are able to replicate the true levels of Bayesian efficiency for SC designs of various dimensions. The authors conclude that the predominantly employed method of using pseudo Monte Carlo draws is unlikely to result in leading to truly Bayesian efficient SC designs. The quasi Monte Carlo methods analyzed here (Halton, Sobol, and Modified Latin Hypercube Sampling) all clearly outperform the pseudo Monte Carlo draws. However, the polynomial cubature method examined in this paper, incremental Gaussian quadrature, outperforms all, and is therefore the recommended approximation method for the calculation of Bayesian efficiency of stated choice designs

Anomalous phenomena and spectral tailoring in photonic crystals

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 98-109).Photonic crystals are recently discovered meta-materials whose optical properties arise from periodic refractive index variations. In this thesis I examine various aspects of photonic crystals including a self-assembled photonic crystal, anomalous properties arising from periodicity, and tailoring absorption and emission spectra. Fabricating photonic crystals with the desired properties in the infrared and optical frequencies, including a complete photonic bandgap, is an experimental challenge. Self-assembly can provide a solution. In Chapter 2, I examine a new type of colloidal photonic crystal of tetrahedral building blocks in a fcc lattice that is found to possess a robust and complete bandgap. In Chapter 3, I explore the photonic states that exist around a zero-group velocity point. Motivated by negative refraction, a measure of the effective wavevector is constructed that distinguishes various types of zerogroup velocity modes. Around one type of zero-group velocity mode, an anomalous region of backward effective wavevector is found that enables superior light confining properties of a mirror-less cavity. In the last two chapters I look at the problem of efficiently converting radiant energy to electrical power. In Chapter 4, I explore the extent to which ID multi-layer thin films can enhance the short circuit current of a 2 [mu]-thick silicon solar cell. Though such cells are limited by their size, for two front-layers a relative boost of 45% is possible. Finally, in Chapter 5, motivated by the problem of low efficiency in thermophotovoltaics, I look at selective emissivity of a 2D metallic photonic crystal. A semi-analytical theory is developed using only the material dispersion and geometrical parameters. Applications of the selective emitter, including power generation and lighting, are discussed.by Michael Ghebrebrhan.Ph.D

A systems biology approach to the Arabidopsis circadian clock

Circadian clocks involve feedback loops that generate rhythmic expression of key genes. Molecular genetic studies in the higher plant Arabidopsis theliene have revealed a complex clock network. We begin by modelling the first part of the Arabidopsis clock network to be identified, a transcriptional feedback loop comprising TIMING OF CAB EXPRESSION 1 (TOCl), LATE ELONGATED HYPOCOTYL (LHY) and CIRCADIAN CLOCK ASSOCIATED 1 (CCA1). As for many biological systems, there are no experimental values for the parameters in our model, and the data available for parameter fitting is noisy and varied. To tackle this we construct a cost function, which quantifies the agreement between our model and various key experimental features. We then undertake a global search of parameter space, to test whether the proposed circuit can fit the experimental data. Our optimized solution for the Arabidopsis clock model is unable to account for significant experimental data. Thanks to our search of parameter space, we are able to interpret this as a failure of the network architecture. We develop an extended clock model that is based upon a wider range of data and accurately predicts additional experimental results. The model comprises two interlocking feedback loops comparable to those identified experimentally in other circadian systems. We propose that each loop receives input signals from light, and that each loop includes a hypothetical component that had not been explicitly identified. Analysis of the model predicts the properties of these components, including an acute light induction at dawn that is rapidly repressed by LHY and CCAL We find this unexpected regulation in RNA levels of the evening-expressed gene GIGANTEA (GI), supporting our proposed network and making GI a strong candidate for this component. We go on to develop reduced models of the Arabidopsis clock to aid conceptual understanding, and add a further proposed feedback loop to develop a 3-loop model of the circadian clock. This 3-loop model is able to reproduce further key experimental data.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Off-the-rack instead of tailor-made module-based plant design at equipment level

Module-based plant design facilitates a paradigm shift in chemical and biochemical industry to decrease the time needed for plant design. Instead of a tailored design of apparatuses for a target production rate, modules are selected off-the-rack to set up a production plant. Within the scope of this thesis, four important areas of module-based plant design at equipment level are investigated. First, the determination of a plants’ overall operating window, a prerequisite for equipment module selection and evaluation is improved by considering the so far neglected non-linear dependency between the operating constraints and the production rate of a plant. Second, the currently accepted view that investment costs are determining the decision on the use of equipment modules for different process units is disproved and novel preselection approaches are proposed, applied and evaluated. A preselection approach based on investment and operating costs is rated most suitable to decide on the use of equipment modules for a case study. The third area explored is equipment module selection for a constant market demand, aiming at flexibility in production rate at low investment costs, as well as for a market demand development. It is shown by case studies that modular production plants offer a promising alternative to conventionally designed plants. Finally, an approach to design equipment modules for flexibility in production rate is introduced and applied. For the case study of a heat exchanger it is shown that a four times larger operating window can be obtained at only 14 % higher total annual costs compared to a conventionally designed heat exchanger. Hence, this work investigates four key areas in module-based plant design at equipment level beyond current state of the art contributing to a paradigm shift in plant design.Modulbasierte Anlagenplanung ermöglicht einen Paradigmenwechsel in der chemischen und biochemischen Industrie, um die Zeit der Anlagenplanung zu verkürzen. Anstelle einer maßgeschneiderten Auslegung von Apparaten für einen Auslegungspunkt werden Module von der Stange ausgewählt, um eine Produktionsanlage zu errichten. Im Rahmen dieser Arbeit werden vier wichtige Bereiche der modulbasierten Anlagenplanung auf Equipmentebene untersucht. Erstens wird die Bestimmung des Gesamtbetriebsfensters einer Anlage, eine Voraussetzung für die Auswahl von Equipmentmodulen und Bewertung von modularen Anlagen, durch die Berücksichtigung der bisher vernachlässigten und nichtlinearen Abhängigkeit zwischen den Betriebsgrenzen und der Produktionsrate einer Anlage verbessert. Zweitens werden aktuelle Entscheidungskriterien für den Einsatz von Equipmentmodulen für verschiedene Prozesseinheiten in Frage gestellt und neue Vorauswahlmethoden vorgeschlagen, angewendet und bewertet. Dabei wird die derzeit akzeptierte Ansicht, dass Investitionskosten bestimmend sind, widerlegt. Eine Vorauswahlmethode, um über die Verwendung von Equipmentmodulen zu entscheiden, die auf Investitions- und Betriebskosten basiert, wird für eine Fallstudie als am geeignetsten bewertet. Der dritte untersuchte Bereich behandelt die Auswahl von Equipmentmodulen für eine konstante Marktnachfrage, mit dem Ziel einer hohen Flexibilität in der Produktionsrate bei niedrigen Investitionskosten, sowie für eine Marktnachfrageentwicklung. Anhand von Fallstudien wird gezeigt, dass modulare Produktionsanlagen eine vielversprechende Alternative zu konventionell ausgelegten Anlagen darstellen. Abschließend wird ein Ansatz zur Auslegung von Equipmentmodulen für eine hohe Flexibilität in der Produktionsrate vorgestellt und angewendet. Am Beispiel eines Wärmeübertragers wird gezeigt, dass ein viermal größeres Betriebsfenster für nur 14 % höhere jährliche Gesamtkosten im Vergleich zu einem konventionell ausgelegten Wärmeübertrager erreicht werden kann. Somit untersucht diese Arbeit vier wichtige Bereiche der modulbasierten Anlagenplanung auf Equipmentebene über den aktuellen Stand der Technik hinaus und liefert ihren Beitrag für einen Paradigmenwechsel in der Anlagenplanung