Theoretical modeling and experimental validation of fluid-loaded micro rectangular plates

This paper presents a theoretical model on the vibration analysis of micro scale fluid-loaded rectangular isotropic plates, based on the Lamb's assumption of fluid-structure interaction and the Rayleigh-Ritz energy method. An analytical solution for this model is proposed, which can be applied to most cases of boundary conditions. The dynamical experimental data of a series of microfabricated silicon plates are obtained using a base-excitation dynamic testing facility. The natural frequencies and mode shapes in the experimental results are in good agreement with the theoretical simulations for the lower order modes. The presented theoretical and experimental investigations on the vibration characteristics of the micro scale plates are of particular interest in the design of microplate based biosensing devices. Copyright © 2009 by ASME.

Advances in Dynamic Modeling and Computation for Count Data

A class of multi-process models is developed for collections of time indexed count data. Autocorrelation in counts is achieved with dynamic models for the natural parameter of the binomial distribution. In addition to modeling binomial time series, the framework includes dynamic models for multinomial and Poisson time series. Markov chain Monte Carlo (MCMC) and Po ́lya-Gamma data augmentation (Polson et al., 2013) are critical for fitting multi-process models of counts. To facilitate computation when the counts are high, a Gaussian approximation to the P ́olya- Gamma random variable is developed.

Three applied analyses are presented to explore the utility and versatility of the framework. The first analysis develops a model for complex dynamic behavior of themes in collections of text documents. Documents are modeled as a “bag of words”, and the multinomial distribution is used to characterize uncertainty in the vocabulary terms appearing in each document. State-space models for the natural parameters of the multinomial distribution induce autocorrelation in themes and their proportional representation in the corpus over time.

The second analysis develops a dynamic mixed membership model for Poisson counts. The model is applied to a collection of time series which record neuron level firing patterns in rhesus monkeys. The monkey is exposed to two sounds simultaneously, and Gaussian processes are used to smoothly model the time-varying rate at which the neuron’s firing pattern fluctuates between features associated with each sound in isolation.

The third analysis presents a switching dynamic generalized linear model for the time-varying home run totals of professional baseball players. The model endows each player with an age specific latent natural ability class and a performance enhancing drug (PED) use indicator. As players age, they randomly transition through a sequence of ability classes in a manner consistent with traditional aging patterns. When the performance of the player significantly deviates from the expected aging pattern, he is identified as a player whose performance is consistent with PED use.

All three models provide a mechanism for sharing information across related series locally in time. The models are fit with variations on the P ́olya-Gamma Gibbs sampler, MCMC convergence diagnostics are developed, and reproducible inference is emphasized throughout the dissertation.

A Molecular-scale Programmable Stochastic Process Based On Resonance Energy Transfer Networks: Modeling And Applications

While molecular and cellular processes are often modeled as stochastic processes, such as Brownian motion, chemical reaction networks and gene regulatory networks, there are few attempts to program a molecular-scale process to physically implement stochastic processes. DNA has been used as a substrate for programming molecular interactions, but its applications are restricted to deterministic functions and unfavorable properties such as slow processing, thermal annealing, aqueous solvents and difficult readout limit them to proof-of-concept purposes. To date, whether there exists a molecular process that can be programmed to implement stochastic processes for practical applications remains unknown.

In this dissertation, a fully specified Resonance Energy Transfer (RET) network between chromophores is accurately fabricated via DNA self-assembly, and the exciton dynamics in the RET network physically implement a stochastic process, specifically a continuous-time Markov chain (CTMC), which has a direct mapping to the physical geometry of the chromophore network. Excited by a light source, a RET network generates random samples in the temporal domain in the form of fluorescence photons which can be detected by a photon detector. The intrinsic sampling distribution of a RET network is derived as a phase-type distribution configured by its CTMC model. The conclusion is that the exciton dynamics in a RET network implement a general and important class of stochastic processes that can be directly and accurately programmed and used for practical applications of photonics and optoelectronics. Different approaches to using RET networks exist with vast potential applications. As an entropy source that can directly generate samples from virtually arbitrary distributions, RET networks can benefit applications that rely on generating random samples such as 1) fluorescent taggants and 2) stochastic computing.

By using RET networks between chromophores to implement fluorescent taggants with temporally coded signatures, the taggant design is not constrained by resolvable dyes and has a significantly larger coding capacity than spectrally or lifetime coded fluorescent taggants. Meanwhile, the taggant detection process becomes highly efficient, and the Maximum Likelihood Estimation (MLE) based taggant identification guarantees high accuracy even with only a few hundred detected photons.

Meanwhile, RET-based sampling units (RSU) can be constructed to accelerate probabilistic algorithms for wide applications in machine learning and data analytics. Because probabilistic algorithms often rely on iteratively sampling from parameterized distributions, they can be inefficient in practice on the deterministic hardware traditional computers use, especially for high-dimensional and complex problems. As an efficient universal sampling unit, the proposed RSU can be integrated into a processor / GPU as specialized functional units or organized as a discrete accelerator to bring substantial speedups and power savings.

Visualization and prediction of spatial deformation using thin-plate splines in the context of scoliosis

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Formal Modeling and Analysis Techniques for High Level Petri Nets

Petri Nets are a formal, graphical and executable modeling technique for the specification and analysis of concurrent and distributed systems and have been widely applied in computer science and many other engineering disciplines. Low level Petri nets are simple and useful for modeling control flows but not powerful enough to define data and system functionality. High level Petri nets (HLPNs) have been developed to support data and functionality definitions, such as using complex structured data as tokens and algebraic expressions as transition formulas. Compared to low level Petri nets, HLPNs result in compact system models that are easier to be understood. Therefore, HLPNs are more useful in modeling complex systems. There are two issues in using HLPNs - modeling and analysis. Modeling concerns the abstracting and representing the systems under consideration using HLPNs, and analysis deals with effective ways study the behaviors and properties of the resulting HLPN models. In this dissertation, several modeling and analysis techniques for HLPNs are studied, which are integrated into a framework that is supported by a tool. For modeling, this framework integrates two formal languages: a type of HLPNs called Predicate Transition Net (PrT Net) is used to model a system's behavior and a first-order linear time temporal logic (FOLTL) to specify the system's properties. The main contribution of this dissertation with regard to modeling is to develop a software tool to support the formal modeling capabilities in this framework. For analysis, this framework combines three complementary techniques, simulation, explicit state model checking and bounded model checking (BMC). Simulation is a straightforward and speedy method, but only covers some execution paths in a HLPN model. Explicit state model checking covers all the execution paths but suffers from the state explosion problem. BMC is a tradeoff as it provides a certain level of coverage while more efficient than explicit state model checking. The main contribution of this dissertation with regard to analysis is adapting BMC to analyze HLPN models and integrating the three complementary analysis techniques in a software tool to support the formal analysis capabilities in this framework. The SAMTools developed for this framework in this dissertation integrates three tools: PIPE+ for HLPNs behavioral modeling and simulation, SAMAT for hierarchical structural modeling and property specification, and PIPE+Verifier for behavioral verification.

New Developments in Tourism and Hotel Demand Modeling and Forecasting

Abstract Purpose The purpose of the study is to review recent studies published from 2007-2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field. Design/Methodology/approach Articles on tourism and hotel demand modeling and forecasting published in both science citation index (SCI) and social science citation index (SSCI) journals were identified and analyzed. Findings This review found that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, while disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area. Research limitations/implications The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting. Practical implications This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices. Originality/value The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

Gas Solubility and Vapor Pressure of Electrolytes Based on Alkylcarbonates Containing Litfsi, Lifap, and LiPF6 Lithium Salt: Measurement and Prediction

Most liquid electrolytes used in commercial lithium-ion batteries are composed by alkylcarbonate mixture containing lithium salt. The decomposition of these solvents by oxidation or reduction during cycling of the cell, induce generation of gases (CO2, CH4, C2H4, CO …) increasing of pressure in the sealed cell, which causes a safety problem [1]. The prior understanding of parameters, such as structure and nature of salt, temperature pressure, concentration, salting effects and solvation parameters, which influence gas solubility and vapor pressure of electrolytes is required to formulate safer and suitable electrolytes especially at high temperature.

We present in this work the CO2, CH4, C2H4, CO solubility in different pure alkyl-carbonate solvents (PC, DMC, EMC, DEC) and their binary or ternary mixtures as well as the effect of temperature and lithium salt LiX (X = LiPF6, LiTFSI or LiFAP) structure and concentration on these properties. Furthermore, in order to understand parameters that influence the choice of the structure of the solvents and their ability to dissolve gas through the addition of a salt, we firstly analyzed experimentally the transport properties (Self diffusion coefficient (D), fluidity (h-1), and conductivity (s) and lithium transport number (tLi) using the Stock-Einstein, and extended Jones-Dole equations [2]. Furthermore, measured data for the of CO2, C2H4, CH4 and CO solubility in pure alkylcarbonates and their mixtures containing LiPF6; LiFAP; LiTFSI salt, are reported as a function of temperature and concentration in salt. Based on experimental solubility data, the Henry’s law constant of gases in these solvents and electrolytes was then deduced and compared with values predicted by using COSMO-RS methodology within COSMOthermX software. From these results, the molar thermodynamic functions of dissolution such as the standard Gibbs energy, the enthalpy, and the entropy, as well as the mixing enthalpy of the solvents and electrolytes with the gases in its hypothetical liquid state were calculated and discussed [3]. Finally, the analysis of the CO2 solubility variations with the salt addition was then evaluated by determining specific ion parameters Hi by using the Setchenov coefficients in solution. This study showed that the gas solubility is entropy driven and can been influenced by the shape, charge density, and size of the anions in lithium salt.

References

[1] S.A. Freunberger, Y. Chen, Z. Peng, J.M. Griffin, L.J. Hardwick, F. Bardé, P. Novák, P.G. Bruce, Journal of the American Chemical Society 133 (2011) 8040-8047.

[2] P. Porion, Y.R. Dougassa, C. Tessier, L. El Ouatani, J. Jacquemin, M. Anouti, Electrochimica Acta 114 (2013) 95-104.

[3] Y.R. Dougassa, C. Tessier, L. El Ouatani, M. Anouti, J. Jacquemin, The Journal of Chemical Thermodynamics 61 (2013) 32-44.

Modeling and simulation of bulk gallium nitride power semiconductor devices

Bulk gallium nitride (GaN) power semiconductor devices are gaining significant interest in recent years, creating the need for technology computer aided design (TCAD) simulation to accurately model and optimize these devices. This paper comprehensively reviews and compares different GaN physical models and model parameters in the literature, and discusses the appropriate selection of these models and parameters for TCAD simulation. 2-D drift-diffusion semi-classical simulation is carried out for 2.6 kV and 3.7 kV bulk GaN vertical PN diodes. The simulated forward current-voltage and reverse breakdown characteristics are in good agreement with the measurement data even over a wide temperature range.

Posterior Probability Modeling and Image Classification for Archaeological Site Prospection: Building a Survey Efficacy Model for Identifying Neolithic Felsite Workshops in the Shetland Islands

The application of custom classification techniques and posterior probability modeling (PPM) using Worldview-2 multispectral imagery to archaeological field survey is presented in this paper. Research is focused on the identification of Neolithic felsite stone tool workshops in the North Mavine region of the Shetland Islands in Northern Scotland. Sample data from known workshops surveyed using differential GPS are used alongside known non-sites to train a linear discriminant analysis (LDA) classifier based on a combination of datasets including Worldview-2 bands, band difference ratios (BDR) and topographical derivatives. Principal components analysis is further used to test and reduce dimensionality caused by redundant datasets. Probability models were generated by LDA using principal components and tested with sites identified through geological field survey. Testing shows the prospective ability of this technique and significance between 0.05 and 0.01, and gain statistics between 0.90 and 0.94, higher than those obtained using maximum likelihood and random forest classifiers. Results suggest that this approach is best suited to relatively homogenous site types, and performs better with correlated data sources. Finally, by combining posterior probability models and least-cost analysis, a survey least-cost efficacy model is generated showing the utility of such approaches to archaeological field survey.

Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks

The focus of this work is to develop the knowledge of prediction of the physical and chemical properties of processed linear low density polyethylene (LLDPE)/graphene nanoplatelets composites. Composites made from LLDPE reinforced with 1, 2, 4, 6, 8, and 10 wt% grade C graphene nanoplatelets (C-GNP) were processed in a twin screw extruder with three different screw speeds and feeder speeds (50, 100, and 150 rpm). These applied conditions are used to optimize the following properties: thermal conductivity, crystallization temperature, degradation temperature, and tensile strength while prediction of these properties was done through artificial neural network (ANN). The three first properties increased with increase in both screw speed and C-GNP content. The tensile strength reached a maximum value at 4 wt% C-GNP and a speed of 150 rpm as this represented the optimum condition for the stress transfer through the amorphous chains of the matrix to the C-GNP. ANN can be confidently used as a tool to predict the above material properties before investing in development programs and actual manufacturing, thus significantly saving money, time, and effort.

Validation of stress indicators for the assessment of animal welfare and prediction of pork meat quality variation at commercial level

Les procédures appliquées avant l’abattage des animaux influencent directement la qualité de la viande en modulant l’état physiologique des porcs; ainsi, l’augmentation de la température corporelle, les taux élevés de lactate sanguin et l’épuisement des réserves de glycogène entre autres, occasionnent la majorité des baisses de qualité. L’objectif de cette thèse était de valider des outils indicateurs de stress porcin pour les fermes et les abattoirs. Ceux-ci seraient appliqués à la surveillance du bien-être animal et à la prédiction de variation de qualité de la viande porcine au niveau commercial. Premierement, les résultats de la thèse ont permis de conclure qu’un des outils développés (analyseur portatif de lactate) mesure la variation du niveau de lactate sanguin associé à l’état physiologique des porcs dans la phase péri-mortem et aide à expliquer la variation de la qualité de la viande chez le porc à l’abattoir, en particulier dans les muscles du jambon. Deuxièmement, les résultats des audits du bien-être animal appliqués de la ferme à l’abattoir ont démontré que la qualité du système d’élevage à la ferme d’origine et les compétences du chauffeur de camion sont d’importants critères affectant la réponse comportementale des porcs à la manipulation avant l’abattage. Ces résultats ont également démontré que les conditions de logement à la ferme (la faible densité et l’enrichissement dans les enclos), le comportement des porcs en période pré-abattage (glissade), ainsi que les interventions du manipulateur (utilisation du bâton électrique) dans la zone d’étourdissement de l’abattoir affectent négativement la variation de la qualité de la viande. L’application des protocoles d’audits dans la filière porcine a également démontré que le respect des critères de bien-être animal fixés par un outil de vérification est primordiale et permet de contrôler les conditions de bien-être des porcs à chaque étape de la période pré-abattage, de produire une viande de qualité supérieure et de réduire les pertes. Les audits de bien-être animal sont donc un outil qui apporte des resultats très pertinents pour aider a éviter les variations de la qualité de la viande chez le porc. Troisièmement, la thermographie infrarouge s’est avéré être une technique prometteuse permettant d’évaluer la variation de température corporelle de l’animal pendant et après un stress physique, en particulier lorsque cette mesure est prise derrière les oreilles. En conclusion, les outils validés à travers cette thèse représentent des méthodologies non invasives et potentiellement complémentaires à d’autres approches d’évaluation de l’état physiologique et du bien-être animal par rapport au stress, permettant de réduire les pertes de qualité de viande (par exemple en utilisation conjointe avec le niveau de lactate sanguin et les indicateurs de stress comportemental, entre autres).

Flexible modeling and estimation for high-dimensional graphs

Thesis (Ph.D.)--University of Washington, 2016-08

Modeling and control algorithms for product phasing using smart belts

Nella seguente tesi è descritto il principio di sviluppo di una macchina industriale di alimentazione. Il suddetto sistema dovrà essere installato fra due macchine industriali. L’apparato dovrà mettere al passo e sincronizzare con la macchina a valle i prodotti che arriveranno in input. La macchina ordina gli oggetti usando una serie di nastri trasportatori a velocità regolabile.
Lo sviluppo è stato effettuato al Laboratorio Liam dopo la richiesta dell’azienda Sitma. Sitma produceva già un tipo di sistema come quello descritto in questa tesi. Il deisderio di Sitma è quindi quello di modernizzare la precedente applicazione poiché il dispositivo che le permetteva di effettuare la messa al passo di prodotti era un PLC Siemens che non è più commercializzato. La tesi verterà sullo studio dell’applicazione e la modellazione tramite Matlab-Simulink per poi proseguire ad una applicazione, seppure non risolutiva, in TwinCAT 3.

Designing for User-facing Uncertainty in Everyday Sensing and Prediction

Thesis (Ph.D.)--University of Washington, 2016-07