We consider reduced Hsieh-Clough-Tocher basis functions with respect to a splitting into subtriangles at an arbitrary interior point of the original triangular element. This article gives a proof that the second derivatives of those functions, which in general may jump at the subtriangle boundaries, do not jump at the splitting point.:1 Introduction 2 Shape functions 3 Transformation of second derivatives 4 Second derivatives at the splitting point

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[pt] Identificar o modelo de um reservatório é o primeiro passo para interpretar corretamente os dados gerados em um teste de poços e desta forma estimar os parâmetros relacionados a esse modelo. O objetivo deste trabalho é de forma inversa, utilizar as curvas de pressão obtidas em um teste de poços, para identificar o modelo de um reservatório. Como os dados obtidos em um teste de poços podem ser ordenados ao longo do tempo, nossa abordagem será reduzir essa tarefa a um problema de classificação de séries temporais, onde cada modelo de reservatório representa uma classe. Para tanto, foi utilizada uma técnica chamada shapelet, que são subsequências de uma série temporal que representam uma classe. A partir disso, foi construído um novo feature space, onde foi medida a distância entre cada série temporal e as shapelets de cada classe. Então foi criado um comitê de votação utilizando os modelos k-nearest neighbors, decision tree, random forest, support vector machines, perceptron, multi layer perceptron e adaboost. Foram testados os pré-processamentos standard scaler, normalizer, robust scaler, power transformer and quantile transformer. Então a classificação foi feita no novo feature space pré-processado. Geramos 10 modelos de reservatório multiclass analíticos para validação. Os resultados revelam que o uso de modelos clássicos de aprendizado de máquina com shapelets, usando os pré-processamentos normalizer e quantile trasformer alcança resultados sólidos na identificação dos modelos de reservatório. / [en] Identifying a reservoir model is the first step to correctly interpret the data generated in a well-test and hence to estimate the related parameters to this model. The goal of this work is inversely to use the pressure curves, obtained in a well-test, to identify a reservoir model. Since the data obtained in a well-test can be ordered over time, we reduce this task to a problem of time series classification, where every reservoir model represents a class. For that purpose, we used a technique called shapelets, which are times series subsequences that represent a class. From that, a new feature space was built, where we measured the distance between every time series and the shapelets of every class. Then we created an ensemble using the models k-nearest neighbors, decision tree, random forest, support vector machines, perceptron, multi-layer perceptron, and adaboost. The preprocessings standard scaler, normalizer, robust scaler, power transformer, and quantile transformer were tested. Then the classification was performed on the new preprocessed feature space. We generated 10 analytical multiclass reservoir models for validation. The results reveal that the use of classical machine learning models with shapelets, using the normalizer and quantile transformer preprocessing, reaches solid results on the identification of reservoir models.

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High blood pressure, also called hypertension is one of the most common worldwide diseases afflicting humans and is a major risk factor for stroke, myocardial infarction, vascular disease, and chronic kidney disease. If blood pressure is controlled and oscillations in the hemodynamic variables are reduced, patients experience fewer complications after surgery. In clinical practice, this is usually achieved using manual drug delivery. Given that different patients have different sensitivity and reaction time to drugs, determining manually the right drug infusion rates may be difficult. This is a problem where automatic drug delivery can provide a solution, especially if it is designed to adapt to variations in the patient’s conditions. This research work presents an investigation into the development of abnormal blood pressure (hypertension) controllers for postoperative patients. Control of the drugs infusion rates is used to simultaneously regulate the hemodynamic variables such as the Mean Arterial Pressure (MAP) and the Cardiac Output (CO) at the desired level. The implementation of optimal control system is very essential to improve the quality of patient care and also to reduce the workload of healthcare staff and costs. Many researchers have conducted studies earlier on modelling and/or control of abnormal blood pressure for postoperative patients. However, there are still many concerns about smooth transition of blood pressure without any side effect. The blood pressure is classified in two categories: high blood pressure (Hypertension) and low blood pressure (Hypotension). The hypertension often occurred after cardiac surgery, and the hypotension occurred during cardiac surgery. To achieve the optimal control solution for these abnormal blood pressures, many methods are proposed, one of the common methods is infusing the drug related to blood pressure to maintain it at the desired level. There are several kinds of vasodilating drugs such as Sodium Nitroprusside (SNP), Dopamine (DPM), Nitro-glycerine (NTG), and so on, which can be used to treat postoperative patients, also used for hypertensive emergencies to keep the blood pressure at safety level. A comparative performance of two types of algorithms has been presented in chapter four. These include the Internal Model Control (IMC), and Proportional-Integral-Derivative (PID) controller. The resulting controllers are implemented, tested and verified for three sensitivity patient response. SNP is used for all three patients’ situation in order to reduce the pressure smoothly and maintain it at the desire level. A Genetic Algorithms (GAs) optimization technique has been implemented to optimise the controllers’ parameters. A set of experiments are presented to demonstrate the merits and capabilities of the control algorithms. The simulation results in chapter four have demonstrated that the performance criteria are satisfied with the IMC, and PID controllers. On the other hand, the settling time for the PID control of all three patients’ response is shorter than the settling time with IMC controller. Using multiple interacting drugs to control both the MAP and CO of patients with different sensitivity to drugs is a challenging task. A Multivariable Model Reference Adaptive Control (MMRAC) algorithm is developed using a two-input, two-output patient model. Because of the difference in patient’s sensitivity to the drug, and in order to cover the wide ranges of patients, Model Reference Adaptive Control (MRAC) has been implemented to obtain the optimal infusion rates of DPM and SNP. This is developed in chapters five and six. Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters, the simulation results have demonstrated that this algorithm cannot cover the wide range of patient’s sensitivity to drugs, due to that shortcoming, a PID controller using a Neural Network that tunes the controller parameters was designed and implemented. The parameters of the PID controller were optimised offline using Matlab genetic algorithm. The proposed Neuro-PID controller has been tested and validated to demonstrate its merits and capabilities compared to the existing approaches to cover wide range of patients. / Libyan Ministry of Higher Education scholarship

学位プログラム名: 京都大学大学院思修館 / 京都大学 / 新制・課程博士 / 博士(総合学術) / 甲第24056号 / 総総博第25号 / 新制||総総||4(附属図書館) / 京都大学大学院総合生存学館総合生存学専攻 / (主査)教授 山敷 庸亮, 教授 寶 馨, 教授 池田 裕一, 佐藤 達彦 / 学位規則第4条第1項該当 / Doctor of Philosophy / Kyoto University / DFAM

Graphene, an allotrope of carbon, has demonstrated exceptional mechanical, thermal, electronic, and optical properties. Complementary to such innate properties, structural modification through chemical functionalization or defect engineering can significantly enhance the properties and functionality of graphene and its derivatives. Hence, understanding structure-property relationships in graphene-based metamaterials has garnered much attention in recent years. In this thesis, we present molecular dynamics studies aimed at elucidating structure-property relationships that govern the thermomechanical response of interlayer-bonded graphene bilayers. First, we present a systematic and thorough analysis of thermal transport in interlayer-bonded twisted bilayer graphene (IB-TBG). We find that the introduction of interlayer C-C bonds in these bilayer structures causes an abrupt drop in the in-plane thermal conductivity of pristine, non-interlayer-bonded bilayer graphene, while further increase in the interlayer C-C bond density (2D diamond fraction) leads to a monotonic increase in the in-plane thermal conductivity of the resulting superstructures approaching the high in-plane thermal conductivity of 2D diamond (diamane). We also find a similar trend in the in-plane thermal conductivity of interlayer-bonded graphene bilayers with randomly distributed individual interlayer C-C bonds (RD-IBGs) as a function of interlayer C-C bond density, but with the in-plane thermal conductivity of the IB-TBG 2D diamond superstructures consistently exceeding that of RD-IBGs at a given interlayer bond density. We analyze the simulation results employing effective medium and percolation theories and explain the predicted dependence of in-plane thermal conductivity on interlayer bond density on the basis of lattice distortions induced in the bilayer structures as a result of interlayer bonding. Our findings demonstrate that the in-plane thermal conductivity of IB-TBG 2D diamond superstructures and RD-IBGs can be precisely tuned by controlling interlayer C-C bond density with important implications for the thermal management applications of interlayer-bonded few-layer graphene derivatives. Secondly, we report results on the mechanical and structural response to shear deformation of nanodiamond superstructures in interlayer-bonded twisted bilayer graphene (IB-TBG) and interlayer-bonded graphene bilayers with randomly distributed individual interlayer C-C bonds (RD-IBGs). We find that IB-TBG nanodiamond superstructures subjected to shear deformation undergo a brittle-to-ductile transition (BDT) with increasing interlayer bond density (nanodiamond fraction). However, RD-IBG bilayer sheets upon shear deformation consistently undergo brittle failure without exhibiting a BDT. We identify, explain, and characterize in atomic-level detail the different failure mechanisms of the above bilayer structures. We also report the dependence of the mechanical properties, such as shear strength, crack initiation strain, toughness, and shear modulus, of these graphene bilayer sheets on their interlayer bond density and find that these properties differ significantly between IB-TBG nanodiamond superstructures and RD-IBG sheets. Our findings show that the mechanical properties of interlayer-bonded bilayer graphene sheets, including their ductility and the type of failure they undergo under shear deformation, can be systematically tailored by controlling interlayer bond density and distribution. These findings contribute significantly to our understanding of these 2D graphene-based materials as mechanical metamaterials.

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