This thesis examines three decomposition techniques and their usability for economic and financial time series. The stock index DAX30 and the exchange rate from British pound to US dollar are used as representative economic time series. Additionally, autoregressive and conditional heteroscedastic simulations are analysed as benchmark processes to the real data. Discrete wavelet transform (DWT) uses wavelike functions to adapt the behaviour of time series on different time scales. The second method is the singular spectral analysis (SSA), which is applied to extract influential reconstructed modes. As a third algorithm, empirical mode decomposition (END) leads to intrinsic mode functions, who reflect the short and long term fluctuations of the time series. Some problems arise in the decomposition process, such as bleeding at the DWT method or mode mixing of multiple EMD mode functions. Conclusions to evaluate the predictability of the time series are drawn based on entropy - and recurrence - analysis. The cyclic behaviour of the decompositions is examined via the coefficient of variation, based on the instantaneous frequency. The results show rising predictability, especially on higher decomposition levels. The instantaneous frequency measure leads to low values for regular oscillatory cycles, irregular behaviour results in a high variation coefficient. The singular spectral analysis show frequency - stable cycles in the reconstructed modes, but represents the influences of the original time series worse than the other two methods, which show on the contrary very little frequency - stability in the extracted details.

Ο στόχος αυτής της εργασίας ήταν η αναγνώριση έξι βασικών κινήσεων του χεριού με χρήση δύο συστημάτων. Όντας θέμα διεπιστημονικού επιπέδου έγινε μελέτη της ανατομίας των μυών του πήχη, των βιοσημάτων, της μεθόδου της ηλεκτρομυογραφίας (ΗΜΓ) και μεθόδων αναγνώρισης προτύπων. Παράλληλα, το σήμα περιείχε αρκετό θόρυβο και έπρεπε να αναλυθεί, με χρήση του EMD, να εξαχθούν χαρακτηριστικά αλλά και να μειωθεί η διαστασιμότητά τους, με χρήση των RELIEF και PCA, για βελτίωση του ποσοστού επιτυχίας ταξινόμησης. Στο πρώτο μέρος γίνεται χρήση συστήματος ΗΜΓ της Delsys αρχικά σε ένα άτομο και στη συνέχεια σε έξι άτομα με το κατά μέσο όρο επιτυχημένης ταξινόμησης, για τις έξι αυτές κινήσεις, να αγγίζει ποσοστά άνω του 80%. Το δεύτερο μέρος περιλαμβάνει την κατασκευή αυτόνομου συστήματος ΗΜΓ με χρήση του Arduino μικροελεγκτή, αισθητήρων ΗΜΓ και ηλεκτροδίων, τα οποία είναι τοποθετημένα σε ένα ελαστικό γάντι. Τα αποτελέσματα ταξινόμησης σε αυτή την περίπτωση αγγίζουν το 75%. / The aim of this work was to identify six basic movements of the hand using two systems. Being an interdisciplinary topic, there has been conducted studying in the anatomy of forearm muscles, biosignals, the method of electromyography (EMG) and methods of pattern recognition. Moreover, the signal contained enough noise and had to be analyzed, using EMD, to extract features and to reduce its dimensionality, using RELIEF and PCA, to improve the success rate of classification. The first part uses an EMG system of Delsys initially for an individual and then for six people with the average successful classification, for these six movements at rates of over 80%. The second part involves the construction of an autonomous system EMG using an Arduino microcontroller, EMG sensors and electrodes, which are arranged in an elastic glove. Classification results in this case reached 75% of success.

本研究主要應用基於總體經驗模態分解法(EEMD)之倒傳遞類神經網路(BPNN)預測兩種不同的非線性時間序列數據，包括政大逐時用電量以及逐日歷史黃金價格。透過EEMD，這兩種資料會分別被拆解為數條具有不同物理意義的本徵模態函數(IMF)，而這讓我們可以將這些IMF視為各種影響資料的重要因子，並且可將拆解過後的IMF放入倒傳遞類神經網路中做訓練。 另外在本文中，我們也採用移動視窗法作為預測過程中的策略，另外也應用內插法和外插法於逐時用電量的預測。內插法主要是用於補點以及讓我們的數據變平滑，外插法則可以在某個範圍內準確預測後續的趨勢，此兩種方法皆對提升預測準確度占有重要的影響。 利用本文的方法，可在預測的結果上得到不錯的準確性，但為了進一步提升精確度，我們利用多次預測的結果加總平均，然後和只做一次預測的結果比較，結果發現多次加總平均後的精確度的確大幅提升，這是因為倒傳遞類神經網路訓練過程中其目標為尋找最小誤差函數的關係所致。 / In this paper, we applied the Ensemble Empirical Mode Decomposition (EEMD) based Back-propagation Neural Network (BPNN) learning paradigm to two different topics for forecasting: the hourly electricity consumption in NCCU and the historical daily gold price. The two data series are both non-linear and non-stationary. By applying EEMD, they were decomposed into a finite, small number of meaningful Intrinsic Mode Functions (IMFs). Depending on the physical meaning of IMFs, they can be regarded as important variables which are input into BPNN for training. We also use moving-window method in the prediction process. In addition, cubic spline interpolation as well as extrapolation as our strategy is applied to electricity consumption forecasting, these two methods are used for smoothing the data and finding local trend to improve accuracy of results. The prediction results using our methods and strategy resulted in good accuracy. However, for further accuracy, we used the ensemble average method, and compared the results with the data produced without applying the ensemble average method. By using the ensemble average, the outcome was more precise with a smaller error, it results from the procedure of finding minimum error function in the BPNN training.

L'objectif repose sur le traitement et l'analyse des signaux non-stationnaires, multi-composantes. <br />Pour le traitement (filtrage et débruitage), nous proposons de nouveaux outils fondés sur la Transformation de Huang (ou Décomposition modale empirique : EMD). Partant de l'opérateur de Teager-Kaiser, nous proposons un nouvel opérateur de mesure d'interaction entre deux signaux complexes. Nous établissons les liens théoriques avec les représentations temps-fréquence de la classe de Cohen. Nous montrons que c'est une mesure de similarité et qu'il est adapté à la détection de signaux. <br />Pour l'analyse, nous introduisons une nouvelle méthode temps-fréquence basée sur l'utilisation conjointe de l'EMD et de l'opérateur de Teager-Kaiser : la Transformation de Huang-Teager (THT). Pour illustrer ces concepts, des résultats de filtrage, de débruitage, de détection, d'analyse temps-fréquence de signaux sont présentés. Nous terminons par l'analyse et classification des échos de cibles sonars par THT.

La Décomposition Modale Empirique (EMD) est un outil de traitement de signal piloté par les données et dédié aux signaux non-stationnaires issus ou non de systèmes linéaires. L'idée de base de l'EMD est l'interpolation des extrema par des splines pour extraire de composantes oscillantes appelées modes empiriques intrinsèques (IMFs) et un résidu. Dans cette thèse, un nouvel algorithme de l'EMD est introduit où au lieu d'une interpolation rigide, un lissage est utilisé pour la construction des enveloppes supérieures et inférieures du signal à décomposer. Ce nouvel algorithme est plus robuste au bruit que l'EMD conventionnelle et réduit le nombre d'IMFs "artificielles" (sur-décomposition). En combinant le nouvel algorithme et la méthode de séparation d'énergie (ESA) basée sur l'Opérateur d'Energie de Teager-Kaiser (OETK), un nouveau schéma de démodulation des signaux AM-FM multi-composante appelé EMD-ESA est introduit. Différentes versions de l'EMD-ESA sont analysées en terme de performance. Pour l'analyse Temps-Fréquence (TF), une nouvelle formulation de la carte TF de l'EMD-ESA appelée Transformation de Teager-Huang (THT) est présentée. Cette nouvelle Représentation TF (RTF) ne présentant pas de termes d'interférences est comparée aux RTF classiques telles que le spectrogramme, le scalogramme, la distribution de Wigner-Ville Distribution (WVD), la Pseudo-WVD et la réallocation de la Pseudo-WVD. En combinant la nouvelle formulation de la THT et la transformée de Hough, une nouvelle méthode de détection des signaux multi-composante à modulation linéaire de fréquence dans le plan TF est présentée. Cette méthode de détection est appelée transformation de Teager-Huang-Hough (THHT). Les résultats de la THHT sont comparés à ceux de la transformée WVD-Hough. Finalement, l'analyse TF par THT et par des RTF classiques (WVD, spectrogramme, etc.) de signaux réels de rétrodiffusion par des coques cylindriques de dimensions et de caractéristiques physiques différentes est présentée. Les résultats obtenus montrent l'apport de la THT comme un outil TF.

Lors d’un vol hypersonique (Mach 6, 20 km d’altitude) la couche limite se développant sur l’avant-corps d’un véhicule hypersonique est laminaire. Cet état cause un désamorçage du moteur (statoréacteur) assurant la propulsion du véhicule. Pour pallier ce problème, il faut forcer la transition de la couche limite á l’aide d’un dispositif de contrôle dont l’effet est permanent (passif) ou modulable (actif) pendant le vol. Dans ce travail, nous analysons l’efficacité d’un dispositif actif d’injection d’air á la paroi pour forcer la transition de la couche limite sur un avant-corps générique. L’interaction jet d’air/couche limite est simulée numériquement avec une approche aux grandes échelles (LES). Une étude paramétrique sur la pression d’injection permet de quantifier l’efficacité du jet á déstabiliser la couche limite. L’influence des conditions de vol (altitude, Mach) sur la transition est également étudiée. Une analyse des résultats de simulation par Décomposition en Modes Dynamiques (DMD) est menée pour comprendre quels sont les modes dynamiques responsables de la transition et les mécanismes sous-jacents. Des essais dans la soufflerie silencieuse de l’université de Purdue (BAM6QT) ont été effectués pour tester expérimentalement l’efficacité des dispositifs passifs (rugosité isolée en forme de losange) et actifs (mono-injection d’air) pour faire transitionner la couche limite. Une peinture thermo-sensible et des capteurs de pression (PCB, Kulite) ont été utilisés pour déterminer la nature de la couche limite. Les résultats de ce travail montrent qu’une injection sonique suffit pour forcer la couche limite. On observe des essais, que pour une même hauteur de pénétration, les rugosités isolées sont moins efficaces que les jets (mono injection) pour déstabiliser la couche limite. / During a hypersonic flight (Mach 6, 20 km altitude), the boundary layer developing on the forebody of a vehicle is laminar. This state may destabilize the scramjet engine propelling the vehicle. To overcome this problem during the flight, the boundary layer transition has to be forced using a control device whose effect is fixed (passive) or adjustable (active). In this work, we analyze the efficiency of a jet in crossflow in forcing the boundary layer transition on a generic forebody. The flow is computed with a Large Eddy Simulations (LES) approach. A parametric study of the injection pressure allows the efficiency of the jet in tripping the boundary layer to be quantified. The influence of flight conditions (Mach, altitude) on the transition is also studied. Dynamic Mode Decomposition (DMD) is applied to the simulation results to determine the transition leading to dynamic modes and to understand underlying transition mechanisms. Experiments in the Purdue University quiet wind tunnel (BAM6QT) were performed to quantify the efficiency of a passive transition device (diamond roughnesses) and an active transition device (single air jet) in tripping the boundary layer. A thermo-sensitive paint and pressure transducers (Kulite, PCB) were used to determine the state of the boundary layer on the generic forebody. Experimental and numerical results show a sonic injection is sufficient to induce transition. We observe from the experiments that for the same penetration height, a single roughness is less efficient than a single air jet in destabilizing the boundary layer.

This thesis aims at improving our knowledge on swirl combustors. The work presented here is based on Large Eddy Simulations (LES) coupled to an advanced combustion model: the Conditional Moment Closure (CMC). Numerical predictions have been systematically compared and validated with detailed experimental datasets. In order to analyze further the physics underlying the large numerical datasets, Proper Orthogonal Decomposition (POD) has also been used throughout the thesis. Various aspects of the aerodynamics of swirling flames are investigated, such as precession or vortex formation caused by flow oscillations, as well as various combustion aspects such as localized extinctions and flame lift-off. All the above affect flame stabilization in different ways and are explored through focused simulations. The first study investigates isothermal air flows behind an enclosed bluff body, with the incoming flow being pulsated. These flows have strong similarities to flows found in combustors experiencing self-excited oscillations and can therefore be considered as canonical problems. At high enough forcing frequencies, double ring vortices are shed from the air pipe exit. Various harmonics of the pulsating frequency are observed in the spectra and their relation with the vortex shedding is investigated through POD. The second study explores the structure of the Delft III piloted turbulent non-premixed flame. The simple configuration allows to analyze further key combustion aspects of combustors, with further insights provided on the dynamics of localized extinctions and re-ignition, as well as the pollutants emissions. The third study presents a comprehensive analysis of the aerodynamics of swirl flows based on the TECFLAM confined non-premixed S09c configuration. A periodic component inside the air inlet pipe and around the central bluff body is observed, for both the inert and reactive flows. POD shows that these flow oscillations are due to single and double helical vortices, similar to Precessing Vortex Cores (PVC), that develop inside the air inlet pipe and whose axes rotate around the burner. The combustion process is found to affect the swirl flow aerodynamics. Finally, the fourth study investigates the TECFLAM configuration again, but here attention is given to the flame lift-off evident in experiments and reproduced by the LES-CMC formulation. The stabilization process and the pollutants emission of the flame are investigated in detail.

This thesis examines three decomposition techniques and their usability for economic and financial time series. The stock index DAX30 and the exchange rate from British pound to US dollar are used as representative economic time series. Additionally, autoregressive and conditional heteroscedastic simulations are analysed as benchmark processes to the real data. Discrete wavelet transform (DWT) uses wavelike functions to adapt the behaviour of time series on different time scales. The second method is the singular spectral analysis (SSA), which is applied to extract influential reconstructed modes. As a third algorithm, empirical mode decomposition (END) leads to intrinsic mode functions, who reflect the short and long term fluctuations of the time series. Some problems arise in the decomposition process, such as bleeding at the DWT method or mode mixing of multiple EMD mode functions. Conclusions to evaluate the predictability of the time series are drawn based on entropy - and recurrence - analysis. The cyclic behaviour of the decompositions is examined via the coefficient of variation, based on the instantaneous frequency. The results show rising predictability, especially on higher decomposition levels. The instantaneous frequency measure leads to low values for regular oscillatory cycles, irregular behaviour results in a high variation coefficient. The singular spectral analysis show frequency - stable cycles in the reconstructed modes, but represents the influences of the original time series worse than the other two methods, which show on the contrary very little frequency - stability in the extracted details.:1. Einleitung 2. Datengrundlage 2.1. Auswahl und Besonderheiten ökonomischer Zeitreihen 2.2. Simulationsstudie mittels AR-Prozessen 2.3. Simulationsstudie mittels GARCH-Prozessen 3. Zerlegung mittels modernen Techniken der Zeitreihenanalyse 3.1. Diskrete Wavelet Transformation 3.2. Singulärsystemanalyse 3.3. Empirische Modenzerlegung 4. Bewertung der Vorhersagbarkeit 4.1. Entropien als Maß der Kurzzeit-Vorhersagbarkeit 4.2. Rekurrenzanalyse 4.3. Frequenzstabilität der Zerlegung 5. Durchführung und Interpretation der Ergebnisse 5.1. Visuelle Interpretation der Zerlegungen 5.2. Beurteilung mittels Charakteristika 6. Fazit

<p>Probing the subsurface for evidence related to the degradation of porous mediums and the evolution of damage mechanisms has been a long-standing challenge in geophysics. As such imaging and predicting fracture network development has remained a difficult area for subsurface science for decades despite the seminal and significant works put forward by many researchers. While this has provide great understanding about the behaviours and properties of natural porous media, there is still much that needs to be explored particularly in regard to the mineralogical composition and chemistry of clay-rich rocks. Despite the fact that argillaceous rocks which consist of different types of clays and varied mineral composition are ubiquitous in nature and are often the target of several technologies (e.g. geotechnical engineering, nuclear waste storage and disposal,hydrocarbon exploration and extraction, carbon capture and sequestration, etc.), many studies focus primarily on the bulk properties or the percentage of components in the matrix. For these reason and due to the problems that can be encountered with natural rocks that contain a swelling clay component whether randomly distirbuted or localized in consolidated globs in zones of the matrix, the influence of clay chemistry in relation to fracture development which is not well characterized, especially during desaturation is investigated with analogue rock samples which were systematically fabricated for this purpose.</p> <p><br></p> <p>The research performed in this dissertation investigated, the applicability of  the fabrication protocol for developing synthetic rocks with desirable rock like features and behavior, the impact and relationship between the rock properties, the microstructural composition, water loss, and the macroscopic behavior of the analogue rocks, focusing on the structure and chemistry of the constituent clay materials. Synthetic rocks were fashioned with the necessary geometries, properties, and material compositions. On the macroscopic scale the fracture and drying behavior of the synthetic rocks were examined with 3D X-ray microscopy and further evaluated through the utility of acoustic emission monitoring, water loss monitoring, and unconfined compressive testing. On the finer scale (nano-microscale), the chemical and mechanical properties, and behavior of select clays was explored by exploiting several methods of material characterization which also included cation exchange experiments coupled with inductively coupled plasma – optical emission spectrometry (ICP-OES). </p> <p><br></p> <p>For the finer scale, experiments verified that calcined kaolinite clay had a different mineral structure and negligible to non-existence shrinkage abilities. In contrast, the montmorillonite clays possessed higher and similar moisture contents but, owing to the different principal cations these clays interacted a bit differently in the highly akaline environment, experienced varying degrees of shrinkage, and had observedly minor structural dissimilarities. For the relatively larger scale, the emergence of damage, extent of the damage network, and the patterns of the crack network mainly depended on the microstructural composition of the analogue rocks, particularly it's clay chemistry and/ or distribution. The location of damage depended on the emplacement and percentage of swelling clay in the matrix, and numerical investigations with peridynamics revealed that the observed damage was a consequence of the action of the swelling and non-swelling components of the matrix. Furthermore, if the microstructure consisted of no clay or calcined kaolinite the AE activity was solely attributed to interfacial processes that occurred during fluid front movement. If the microstructure consisted of a particular montmorillonite, the cracks propagated in the direction of the drying front. Conversely, for montmorillonite clay predominated by a different principal cation, the crack network developed and propagated differently during water loss. Additionally, on the laboratory core scale, properties and behavior similar to natural rocks were confirmed and the rock strength, porosity, AE activity, and velocities were primarily affected by the microstructural composition of the analogue rocks. </p> <p><br></p> <p>An added challenge for investigating and monitoring evolving systems and processes, whether on the laboratory or field scale, is the problem of extracting useful information from the physical data that can be used to identify signatures of developing processes, and changes in the properties or the behavior of a system. Here, data driven machine learning modeling and clustering techniques were undertaken to build a mechanistic understanding of the AE activity generated during drying. The intent is for this work to add to the fundamental research aimed at developing methods that will robustly detect and extract signatures related to evolutionary processes or features in the AE signals, and group them according to some degree of similarity. Such research will support reliable interpretations of the physical data for predictions of the behavior of systems, development of engineering controls, and improvement of the understanding of intrinsic dynamics related to complex processes particularly those that occur in clay-rich systems.</p> <p><br></p> <p>Combined chemical and mechanical investigations have great potential for unraveling practical challenges in subsurface science, especially regarding damage processes in clay-rich rock systems, and identifying and interpreting the presence of discontinuities from geophysical data. The present findings are useful for establishing a link between the constituent clay and observed damage, and improving our understanding of the development of damage in clay-bearing systems. These results provide insight on the influence of swelling clay and the chemistry of such clays on the generation of cracks and crack networks in rock like materials which can be useful for the characterization of damage in both laboratory and the field. The work presented here can also be a basis for further experiments that aim to uncover methods and protocols that will help with the indirect characterization of evolutionary processes, damage mechanisms, and damage in clay rich porous media. Additionally support for the use of analogue rocks in experimental rock physics, architected with specific material compositions, pore structures, crack systems, or clay fractions, is provided here. </p>

金融市場是個變化莫測的環境，看似隨機，在隨機中卻隱藏著某些特性與關係。不論是自然現象中的氣象預測或是金融領域中對下一時刻價格的預測, 都有相似的複雜性。 時間序列的預測一直都是許多領域中重要的項目之一, 金融時間序列的預測也不例外。在本論文中我們針對金融時間序列的非線性與非穩態關係引入類神經網路(ANNs) 與集合經驗模態分解法(EEMD), 藉由ANNs處理非線性問題的能力與EEMD處理時間序列信號的優點，並進一步與傳統上使用於金融時間序列分析的自回歸滑動平均模型(ARMA)進行複合式的模型建構，引入燭型圖概念嘗試進行高頻下的台指期貨TAIEX交易。在不計交易成本的績效測試下本研究的高頻交易模型有突出的績效，證明以ANNs、EEMD方法與ARMA組成的混合式模型在高頻時間尺度交易下有相當的發展潛力，具有進一步發展的價值。在處理高頻時間尺度下所產生的大型數據方面，引入平行運算架構SPMD(single program, multiple data)以增進其處理大型資料下的運算效率。本研究亦透過分析高頻時間尺度的本質模態函數(IMFs)探討在高頻尺度下影響台指期貨價格的因素。 / Financial market is complex, unstable and non-linear system, it looks like have some principle but the principle usually have exception. The forecasting of time series always an issue in several field include finance. In this thesis we propose several version of hybrid models, they combine Ensemble Empirical Mode Decomposition (EEMD), Back-Propagation Neural Networks(BPNN) and ARMA model, try to improve the forecast performance of financial time series forecast. We also found the physical means or impact factors of IMFs under high-frequency time-scale. For processing the massive data generated by high-frequency time-scale, we pull in the concept of big data processing, adopt parallel computing method ”single program, multiple data (SPMD)” to construct the model improve the computing performance. As the result of backtesting, we prove the enhanced hybrid models we proposed outperform the standard EEMD-BPNN model and obtain a good performance. It shows adopt ANN, EEMD and ARMA in the hybrid model configure for high-frequency trading modeling is effective and it have the potential of development.