Hybrid computer Monte-Carlo techniques

Hybrid analog-digital computer systems for Monte Carlo method application

Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping

The proliferation and ubiquity of temporal data across many disciplines has sparked interest for similarity, classification and clustering methods specifically designed to handle time series data. A core issue when dealing with time series is determining their pairwise similarity, i.e., the degree to which a given time series resembles another. Traditional distance measures such as the Euclidean are not well-suited due to the time-dependent nature of the data. Elastic metrics such as dynamic time warping (DTW) offer a promising approach, but are limited by their computational complexity, non-differentiability and sensitivity to noise and outliers. This thesis proposes novel elastic alignment methods that use parametric \& diffeomorphic warping transformations as a means of overcoming the shortcomings of DTW-based metrics. The proposed method is differentiable \& invertible, well-suited for deep learning architectures, robust to noise and outliers, computationally efficient, and is expressive and flexible enough to capture complex patterns. Furthermore, a closed-form solution was developed for the gradient of these diffeomorphic transformations, which allows an efficient search in the parameter space, leading to better solutions at convergence. Leveraging the benefits of these closed-form diffeomorphic transformations, this thesis proposes a suite of advancements that include: (a) an enhanced temporal transformer network for time series alignment and averaging, (b) a deep-learning based time series classification model to simultaneously align and classify signals with high accuracy, (c) an incremental time series clustering algorithm that is warping-invariant, scalable and can operate under limited computational and time resources, and finally, (d) a normalizing flow model that enhances the flexibility of affine transformations in coupling and autoregressive layers.Comment: PhD Thesis, defended at the University of Navarra on July 17, 2023. 277 pages, 8 chapters, 1 appendi

Excited state proton transfer in 9-aminoacridine carboxamides in water and in DNA

The 9-aminoacridine molecule is important in several different fields of chemistry. The absorption and fluorescence spectra of this compound are pH sensitive and it is this property that allowed it to be used as a pH probe in different chemical environments. The compound exhibits proton transfer reactions which are among the most fundamental of chemical reactions. The planarity of 9-aminoacridine allows it to intercalate into DNA. Intercalation is a process in which the aromatic flat surface of the intercalator inserts between adjacent base pairs of DNA The large surface area of 9-aminoacridine\u27s fused tricyclic ring system allows strong intercalative binding through van der Waals attractions. 9-Aminoacridine and many of its derivatives have been tried as possible antitumor drugs.;The cytotoxicity of an antitumor agent can be dramatically increased through the addition of one or two cationic side chains. This increase in cytotoxicity using the 9-aminoacridine compound as a parent molecule has been investigated through various derivatives with cationic side chains consisting of different number of carbon atoms between the proximal and distal N atoms. Similar derivatives varied the position of the carboxamide side chain on the aromatic ring system.;The objective of this work is to first create a baseline study of the excited state kinetics of the 9-aminoacridine carboxamides in the absence of DNA. The baseline study will allow the excited state kinetics of these antitumor drugs when placed in DNA to be more fully understood

Uncommon Problems in Phylogenetic Inference

Die Phylogenetik ist die Lehre der Entwicklung des Lebens auf der Erde. Das Auf- decken alter evolutionärer Beziehungen zwischen lebenden Arten ist von großem Wert, da sie zu wichtigen Entdeckungen in der Biologie führte, wie beispielsweise zur Entwicklung neuer Medikamente, zur Nachverfolgung der Dynamik einer globa- len Pandemie sowie zu Erkenntnissen über den Ursprung der Menschheit. Heutzu- tage werden phylogenetische Analysen typischerweise mit Hilfe statistischer Modelle durchgeführt, wobei Sequenzdaten, in der Regel molekulare Sequenzen, als Einga- bedaten verwendet werden. Basierend auf diesen statistischen Modellen wird die wahrscheinlichste Erklärung für die Eingabedaten berechnet. Das heißt, der (ver- meintlich) korrekte phylogenetische Baum ist der Baum, der gemäß eines bestimm- ten Modells der Sequenzentwicklung am wahrscheinlichsten ist. Die rasche Zunahme verfügbarer Daten in den letzten Jahren ermöglicht wesentlich kompliziertere phylogenetische Analysen. Paradoxerweise hat diese massive Zunah- me der für die Analyse verfügbaren Daten nicht in allen Fällen zu einer endgültigen Schlussfolgerung geführt, d. h. das verwendete Modell ist unsicher bezüglich der wahrscheinlichsten Schlussfolgerung. Dies kann auf eine Vielzahl von Faktoren zu- rückzuführen sein, wie beispielsweise hochkomplexe Modelle, Rauschen in einigen oder allen Daten sowie physikalische Prozesse, die durch das Modell nicht angemes- sen berücksichtigt werden. Schwierigkeiten aufgrund von Ungewissheit sind weder in der Phylogenetik noch in der Wissenschaft im Allgemeinen neu, doch die Entwick- lung komplizierterer Analysemethoden fordert neue Methoden zur Angabe, Analyse und Integration von Unsicherheiten. Die vorliegende Arbeit präsentiert drei Beiträge zur Verbesserung der Unsicherheits- bewertung. Der erste Beitrag betrifft die Bestimmung der Wurzel von ungewurzelten phylogenetischen Bäumen. Phylogenetische Bäume sind entweder bezüglich der Zeit orientiert, in diesem Fall nennt man sie verwurzelt, oder sie haben keine Orientie- rung, in diesem Fall sind sie unverwurzelt. Die meisten Programme zur Bestimmung phylogenetischer Bäume erzeugen aus rechnerischen Gründen einen ungewurzelten phylogenetischen Baum. Ich habe das Open-Source-Softwaretool RootDigger entwi- ckelt, das sowohl einen ungewurzelten phylogenetischen Baum, als auch eine Vertei- lung der wahrscheinlichen Wurzeln berechnet. Darüber hinaus verfügt RootDigger über ein Parallelisierungsschema mit verteiltem Speicher, welches auch die Analyse großer Datensätze erlaubt, wie beispielsweise die Bestimmung eines phylogenetischen Baumes aus 8736 SARS-CoV-2-Virussequenzen. Mein zweiter Beitrag in der vorliegenden Arbeit ist das Open-Source-Softwaretool Phylourny zur Berechnung des wahrscheinlichsten Gewinners eines Knock-out-Turniers. Der Algorithmus in Phylourny ist angelehnt an den Felsenstein Pruning Algorith- mus, einen dynamischen Programmierungsalgorithmus zur Berechnung der Wahr- scheinlichkeit eines phylogenetischen Baums. Die Verwendung dieses Algorithmus erlaubt eine erhebliche Beschleunigung der Berechnung im Vergleich zu Standard- Turniersimulationen. Mit dieser beschleunigten Methode untersucht Phylourny auch den Parameterraum des Modells mit Hilfe einer MCMC-Methode, um Ergebnisse zu bewerten und zusammenzufassen, die eine ähnliche Wahrscheinlichkeit des Auftre- tens haben. Diese Ergebnisse weichen oft erheblich vom wahrscheinlichsten Ergebnis ab. In der vorliegenden Arbeit präsentiere ich die Performanz von Phylourny anhand zweier realer Fußball- und Basketballturniere. Der finale Beitrag in dieser Arbeit ist die Neugestaltung und Neuimplementierung eines bekannten Tools für historische Biogeografie, mit dem sich Rückschlüsse auf die Verteilung der angestammten Verbreitungsgebiete ziehen lassen. Ein Hauptin- teresse der Biogeographie besteht in der Bestimmung der Verbreitungsgebiete von Arten. Die historische Biogeografie befasst sich daher häufig mit der Ableitung des Verbreitungsgebiets der Vorfahren lebender Arten. Diese Verteilungen des Verbrei- tungsgebiets der Vorfahren sind ein häufiges Ergebnis von biogeografischen Studien, die oft mit einem Modell abgeleitet werden, das zahlreiche Ähnlichkeiten mit Mo- dellen der Sequenzevolution aufweist. Meine neue Version, Lagrange-NG, berechnet die Ergebnisse bis zu 50 Mal schneller als die vorherige Version und bis zu zwei Grö- ßenordnungen schneller als das beliebte analoge Tool BioGeoBEARS. Darüber hinaus habe ich eine neue Abstandsmetrik entwickelt, die es erlaubt Ergebnisse alternativer Tools und Algorithmen zu vergleichen

Designing hypercyclic replicating networks

In the last 20 years there has been a number of synthetic and natural product based molecular replicators published in the literature. The majority of these systems have focused on the minimal model with only a few examples of cross-catalytic or reciprocal replication. Of the cross-catalytic systems investigated the majority focus around the use of natural products, oligonucleotides, peptides etc. This thesis will investigate the design, synthesis and kinetic analysis of both synthetic minimal and reciprocal replicating systems, and how these two forms of replication interact in a complex hypercyclic network. Chapter 1 introduces key concepts such as molecular recognition, intramolecularity/ enzyme kinetic, bisubstrate systems and the work conducted into replication systems to date. Chapter 2 describes the design, synthesis and kinetic analysis of a reciprocal replicating system, based on Diels-Alder and 1,3-dipolar cycloadditions, before going on to discuss what we have learned and how this system can be improved. Chapter 3 focuses on the design, synthesis and kinetic analysis of a replicating network (minimal and reciprocal replication), based on 1,3-dipolar cycloadditions. Initial individual systems are examined in isolation to determine their behavior and nature. After which the systems are combined to observe how each species interacts in a potential complex hypercyclic network. Chapter 4 investigates the redesign of the replicating network in Chapter 3 in order to overcome the problems identified from its kinetic analysis. Chapter 5 introduces the shift in direction away from kinetically controlled replicating networks towards systems in thermodynamic equilibrium

Ion-exchange resins as heterogeneous catalysts in biodiesel production from triolein and canola oil

Biodiesel is an alternative to petroleum diesel produced from renewable sources. A heterogeneous solid acid catalyst is required to circumvent the issues associated with the continued use of homogeneous catalysts in the production of biodiesel. Ion-exchange resins can be used as catalysts in transesterification. The objective of this research was to identify an ion-exchange resin as an effective heterogeneous catalyst for the production of biodiesel. Commercial ion-exchange resins from various sources were tested in the transesterification of oils to fatty acid methyl esters (biodiesel). Triolein was used as a model oil feedstock for catalyst screening and statistical optimization of the operating conditions. Amberlyst 15 was the most active ion-exchange resin tested during catalyst screening. Optimized reactor variables were 200˚C, 13 wt% catalyst loading and 1:24 oil to alcohol molar ratio. Conversion of triolein to products at 2 hours was 97 mol%. The acid value of the products was 56 mg KOH/g sample. Water was added to the reactants up to 2 wt% to determine if a hydrolysis reaction was responsible for this increase in acid value and to determine whether water would have a hindering effect on transesterification. Water addition did not have a measurable effect on the reaction products up to 1 wt%. At 2 wt%, conversion to products decreased slightly. Free fatty acid addition up to 15 wt% to simulate low quality feedstock had a negligible impact on conversion to products. From the water and acid value testing it was determined that the catalyst was performing the hydrolysis, esterification and transesterification reactions. In longevity experiments, the catalyst was reused once without an impact on conversion to products. Use of canola oil from green seed as a low cost and low quality feedstock demonstrated similar reaction results compared to results using triolein as feedstock. The reaction kinetics of Amberlyst 15 in transesterification were studied at temperatures lower than the optimal temperature to minimize the effects of the hydrolysis and esterification side reactions. Alcohol to oil molar ratio was increased in order to increase conversion to products at the lower temperatures. In the kinetic study, the temperatures examined were 100˚C, 110˚C and 120˚C. Additional reaction parameters were: catalyst loading of 13 wt%, 1:77 oil to alcohol molar ratio, 600 RPM stirring speed and 50 grams of canola oil. This experiment demonstrated a conversion to products of 79 mol% after 72 hours. The rate constants of the three reversible reactions were calculated using a MATLab program to simulate transesterification reaction kinetics. Reaction rate constants for the forward reactions at 120˚C for TG to DG, DG to MG and MG to GL were 0.08, 0.22 and 6.5 L/mol/day, respectively. The activation energy for the rate limiting step (TG to DG) was 120 kJ/mol. Diffusion and internal mass transfer limitations were neglected during the kinetic study due to the results from experiments with a crushed catalyst, the large pore size of Amberlyst 15, the rate of agitation and the high activation energy calculated from experimental results

연소 환경 진단을 위한 고속 및 고정확도 화염 자발광 분광법에 관한 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2023. 2. 도형록.최근 연소의 효율성, 안정성 및 성능을 유지하면서 환경 문제에 대응하기 위해 많은 연소 기술이 개발되었다. 이러한 기술이 적용된 연소기를 최적으로 작동하기 위해 연소기 내부 화학 반응 영역의 가스 특성은 빠르고 정확하게 감시되고 즉각적으로 제어되어야 한다. 가스 특성을 측정하기 위해 화염 자발광 분광신호를 사용하는 화염 방출 분광법(FES, Flame Emission Spectroscopy)은 실시간으로 정확한 가스 특성 측정을 제공할 수 있는 기법의 하나다. 이는 화염 방출 분광법이 자발광을 이용한 비침입식 광학 측정으로 정확도가 높으며 광 검출 장비만을 사용하는 실험 특성으로 인해 고속 측정이 가능하기 때문이다. 그러나 정보 수집 속도를 높이기 위해 광 검출 장치의 노출 시간이 짧아짐에 따라, 화염의 자발광 분광신호의 신호 대 잡음비(SNR, Signal to Noise Ratio)가 낮아지고 FES 측정의 정확도가 감소한다. 그 뿐만 아니라 신호 대 잡음비가 높은 신호 수집이 가능하여도, 자발광으로 가스 특성을 직접 예측하는 것은 화학 발광의 완전한 화학 반응 경로 모델링이 요구되어 어렵다. 이에 따라 분광 신호로부터 가스 특성을 예측하는 것은 자발광과 가스 특성을 상호 연관시키는 보정 절차(Calibration process)에 의해 달성된다. 기존의 방법은 들뜬 상태인 화학종의 분광신호 면적 비율과 같은 국부적인 분광신호 특징을 추출하고, 이 특징의 변화를 가스 특성과 연관 지어 일대일 보정 곡선을 이용한다. 그렇지만 분광신호 특징의 변화가 항상 단조로운 것은 아니므로 보정 프로세스가 간단하지 않다. 본 논문에서는 화염 방출 분광법을 이용한 가스 특성 예측의 시간 분해능과 정확도를 개선하기 위한 체계를 제안하였다. 1) 합성곱 신경망(CNN, Convolutional Neural Network) 구조에 기반한 신호 처리와 2) 적합 직교 분해(POD, Proper Orthogonal Decomposition) 및 크리깅 기법(Kriging Method)을 포함한 차수 축소 모델(ROM, Reduced Order Model) 보정을 결합한 데이터 기반 보정 체계 기법이다. 분광신호의 적합 직교 분해 기저를 포함한 손실 함수 및 잡음이 있는 데이터와 깨끗한 신호의 데이터 쌍으로 학습되는 딥 러닝 신경망은 정보 손실을 최소화하면서 신호 대 잡음비를 향상할 수 있다. 처리된 화염 분광신호를 가스 특성에 높은 연관성을 가지는 분광신호의 직교 적합 분해 기저의 계수로 차수를 축소하고, 이 계수로 크리깅 모델을 활용하여 가스 특성 예측을 하였다. 결과적으로 제안된 보정 체계는 짧은 노출 시간을 가지는 신호를 사용하여 압력 및 연료 당량비 같은 다중 가스 특성의 예측 정확도를 향상할 수 있다. 제안된 새로운 기법을 실제 고압 메탄-공기 화염 신호에 적용하여, 고정확도 감지기로 측정된 실험값과 비교 및 제안된 기법의 정확도 및 정밀도를 분석하였다. 압력 및 연료 당량비의 특성 예측 정확도 및 정밀도는 임의의 실시간 측정을 나타내는 평가 분광 데이터(보정 체계 학습에 사용되지 않음)의 가스 특성 예측 평균 상대 오차(REP, average Relative Errors of Prediction) 및 평균 상대 표준 편차(RSD, average Relative Standard deviation)를 사용하여 정량화되었다. 제안된 방법은 메탄-공기 화염의 당량비(0.8 – 1.2)와 압력(1 – 10 bar)의 넓은 시험 범위에서 짧은 노출 시간(0.05, 0.2, 0.4 초)의 화염 자발광 신호를 이용하여 연소 조건의 가스 특성을 정확하게 예측하였다. 이를 통해 제안된 체계가 높은 정확도와 높은 시간 분해능의 화염 방출 분광법을 가능하게 할 수 있음을 확인하였다.Remarkable combustion techniques have been developed to deal with environmental issues while maintaining the efficiency, stability, and performance of combustion. To operate recent combustors optimally, the gas properties in the reaction zones should be monitored quickly and accurately as well as instantaneously controlled. Flame emission spectroscopy (FES) is one of the candidate solutions for providing accurate gas properties measurements in real-time. This is because FES is a non-intrusive optical method that uses spontaneous and instantaneous emission spectra to estimate gas properties with the simplest experimental setup utilizing only detector systems. However, as the exposure time is reduced to increase the data acquisition rate, the signal-to-noise ratio (SNR) of the flame emission spectrum decreases as well reducing the accuracy of the FES measurements. On the other hand, even if it is possible to collect signals with a high SNR, it is challenging to predict gas properties directly from the signals because of the requirement for complete chemical reaction path modeling of chemiluminescence. Therefore, predicting gas properties from emission spectra is achieved by a calibration process that correlates flame emission with gas properties. Conventional methods utilize one-to-one calibration functions by extracting local spectral features, such as band intensity ratio, and matching the features to gas properties. Nevertheless, the variations of spectral features are not always monotonic which makes the calibration process not straightforward. This study mainly discusses the framework for improving the temporal resolution and accuracy of FES for predicting gas properties. A data-driven calibration framework that combines 1) deep learning-based denoising based on the convolutional neural network (CNN) architecture as a signal preprocessor, and 2) data-driven calibration technique using a reduced order model (ROM) consisting of proper orthogonal decomposition (POD) and Kriging model is proposed. A deep learning neural network supervised on data pairs of noisy and clean signals with a loss function that utilizes POD of the spectrum can enhance the SNR of the short-gated spectra with minimal information loss. Then, the POD method with a Kriging model mapping flame emission spectrum to the target gas properties predicts the gas properties from the processed spectra. To sum up, the proposed calibration method can improve prediction accuracy of gas properties such as equivalence ratio and pressure using short-gated noisy signals. The proposed combustion diagnosis method was applied to actual spontaneous flame emission spectra in high-pressure conditions. The gas property predictions of the proposed method were compared with experimental values measured by high-precision and high-accuracy sensors to estimate the accuracy and precision of the proposed method. The prediction accuracy and precision of the proposed method were evaluated using the average relative errors of prediction (REP) and the average relative standard deviation (RSD) of the gas predictions from the test spectrum data (not used for model training). The proposed method was investigated under combustion conditions in broad test ranges of equivalence ratio (0.8 – 1.2) and pressure (1 – 10 bar) of methane-air flame, taking a short-gated (0.05, 0.2, and 0.4 s) flame emission spectrum with low SNR as input. It was confirmed that the proposed framework enables flame emission spectroscopy to achieve high accuracy and fast temporal resolution.Chapter 1. Introduction 1 1.1 Background and Motivation 1 1.2 Previous studies 8 1.2.1 Mechanism of flame emission 8 1.2.2 Calibration process of flame emission spectroscopy (FES) 16 1.2.3 Deep learning-based denoising 17 1.3 Outline of the Dissertation 22 1.4 Contributions 24 Chapter 2. Experimental result of flame emission 26 2.1 Flame emission measurement 26 2.1.1 Experimental setup 26 2.1.2 Uncertainty quantification of experimental measurement 32 2.1.3 Noise analysis of spectral signal 34 2.2 Computational simulation of flame properties 37 2.3 Characteristics of flame emission 39 Chapter 3. Development of Data-driven Calibration Process 47 3.1 Overview of data-driven calibration process 47 3.2 Calibration framework based on data-driven approach 49 3.2.1 Training and test dataset 52 3.2.2 Proper orthogonal decomposition (POD) 53 3.2.3 Kriging model 56 3.2.4 Global sensitivity analysis (GSA): Sobol sensitivity indices 59 3.2.5 Evaluation of accuracy and precision of calibration process 61 3.3 Validation of data-driven calibration process 63 3.3.1 POD of flame emission spectra 63 3.3.2 Parametric study using Global sensitivity analysis (GSA) 68 3.3.3 Validation of Kriging model 71 3.4 Results of data-driven calibration process 73 3.4.1 Calibration result on experimental data 73 3.4.2 Wavelength range effect on calibration accuracy 78 3.4.3 Calibration result on simulation data 80 Chapter 4. Development of Deep Learning-Based Denoising 83 4.1 Overview of fast time-resolved FES 83 4.2 Deep learning-based denoising process 86 4.2.1 Training and test dataset 86 4.2.2 Neural network architecture 88 4.2.3 Loss function 93 4.3 Results of data processing 94 4.3.1 Denoising with the proposed CNN 94 4.3.2 Neural network architecture and loss function 100 4.3.3 Computational Efficiency 107 Chapter 5. Framework for fast time-resolved and high accuracy FES 111 5.1 Overview of proposed framework 111 5.2 Fast time-resolved and high accuracy FES 115 5.2.1 Calibration and prediction of gas properties 115 5.2.2 Neural network architecture and loss function 118 5.2.3 Hyperparameter search 123 5.2.4 Noise level sensitivity analysis 128 5.2.5 Exposure time 131 Chapter 6. Conclusions 133 APPENDIX A 137 A.1 Result of computational simulation 137 A.2 Denoising performance of neural networks with MSE loss 140 A.3 Denoising performance of neural networks with MSE and POD loss 143 A.4 Calibration and prediction performance of neural network architecture trained by MSE and POD loss 146 REFERENCES 153 ABSTRACT (KOREAN) 160박