Le principal objectif de ce travail est l’étude de l’influence de la pesanteur sur la mécanique respiratoire et le contrôle de la respiration, ainsi que sur les interactions cardio-respiratoires pendant les différents stades du sommeil. Le chapitre introductif présente le contexte général et les objectifs de la thèse. Des sections abordant le sommeil, la respiration, et l’interaction cardio-respiratoire y sont présentées, résumant l’état actuel des connaissances sur les effets de la pesanteur sur chacun de ces systèmes. Dans le deuxième chapitre, l’expérience “Sleep and Breathing in microgravity”, qui constitue la source des données à la base de ce travail, est présentée en détail. L’étude des signaux de longue durée requiert avant tout de disposer d’outils performants d’analyse des signaux. La première partie de la thèse présente en détail deux algorithmes : un algorithme de détection automatique d’événements respiratoires (inspiration / expiration) basé sur des réseaux neuronaux artificiels, et un algorithme de quantification de l’amplitude et de la phase de l’arythmie sinusale pendant le sommeil, utilisant la méthode des ondelettes. La validation de chaque algorithme est présentée, et leur performance évaluée. Cette partie inclut aussi des courtes introductions théoriques aux réseaux de neurones artificiels ainsi qu’aux méthodes d’analyse temps–fréquence (Fourier et ondelettes). Une approche similaire à celle utilisée pour la détection automatique d’événements respiratoires a été appliquée à la détection d’événements dans des signaux de vitesse du sang dans l’artère cérébrale moyenne, mesures obtenues par Doppler transcrânien. Ceci est le sujet de la thèse annexe. Ces deux algorithmes ont été appliqués aux données expérimentales pour extraire des informations physiologiques quant à l’impact de la pesanteur sur la mécanique respiratoire et l’interaction cardio-respiratoire. Ceci constitue la deuxième partie de la thèse. Un chapitre est consacré aux effets de l’apesanteur sur la mécanique respiratoire pendant le sommeil. Ce chapitre a mis en évidence, pour tous les stades de sommeil, une augmentation de la contribution abdominale en microgravité, suivi d’un retour progressif vers des valeurs observées avant le vol. L’augmentation initiale était attendue, mais l’adaptation progressive observée ne peut pas être expliquée par un effet purement mécanique, et nous suggère la présence d’un mécanisme d’adaptation central. Un deuxième chapitre présente les résultats comparant l’arythmie sinusale pendant le sommeil avant le vol, en apesanteur et après le retour sur terre. Le rythme cardiaque pendant le sommeil dans l’espace présente une moindre variabilité. Les différences NREM–REM observées sur terre pour les influences vagales et sympathiques sont accentuées dans l’espace. Aucun changement significatif n’est présent pour le gain et la différence de phase entre les les signaux cardiaque et respiratoire en comparant le sommeil sur terre et en apesanteur. La dissertation termine par une discussion générale du travail effectué, incluant les prin- cipales conclusions ainsi que les perspectives qui en découlent.

Intelligent decision systems have the potential to support and greatly amplify human decision-making across a number of industries and domains. However, despite the rapid improvement in the underlying capabilities of these “intelligent” systems, increasing their acceptance as decision aids in industry has remained a formidable challenge. If intelligent systems are to be successful, and their full impact on decision-making performance realized, a greater understanding of the factors that influence recommendation acceptance from intelligent machines is needed. Through an empirical experiment in the financial services industry, this study investigated the effects of perceived behavioral similarity (similarity state) on the dependent variables of recommendation acceptance, decision performance and decision efficiency under varying conditions of uncertainty (volatility state). It is hypothesized in this study that behavioral similarity as a design element will positively influence the acceptance rate of machine recommendations by human users. The level of uncertainty in the decision context is expected to moderate this relationship. In addition, an increase in recommendation acceptance should positively influence both decision performance and decision efficiency. The quantitative exploration of behavioral similarity as a design element revealed a number of key findings. Most importantly, behavioral similarity was found to positively influence the acceptance rate of machine recommendations. However, uncertainty did not moderate the level of recommendation acceptance as expected. The experiment also revealed that behavioral similarity positively influenced decision performance during periods of elevated uncertainty. This relationship was moderated based on the level of uncertainty in the decision context. The investigation of decision efficiency also revealed a statistically significant result. However, the results for decision efficiency were in the opposite direction of the hypothesized relationship. Interestingly, decisions made with the behaviorally similar decision aid were less efficient, based on length of time to make a decision, compared to decisions made with the low-similarity decision aid. The results of decision efficiency were stable across both levels of uncertainty in the decision context.

Composite materials are widely used in the aerospace industry because of their high strength-to-weight ratio. Although they have many advantages, their inhomogeneity and anisotropy pose problems. Because of these properties, machining of composites, unlike conventional metal working, needs more investigation. Conventional drilling of composites is one such field that requires extensive study and research. Among various parameters that determine the quality of a drilled hole, surface finish is of vital importance. The surface finish of a drilled hole depends on speed, feed-rate, material of the work piece, and geometry of the drill bit. This project studied the effect of speed and feed on surface finish and also the optimization of these parameters. Experiments were conducted based on Design of Experiment (DOE) and qualitative verification using Artificial Neural Network (ANN). Relevant behavior of surface finish was also studied. In this project, holes were drilled using a conventional twist drill at different cutting speeds (2,000 to 5,000 rpm) and feed rate was varied from 0.001 to 0.01 ipr for solid carbon fiber laminate (composite material). The other material drilled is BMS 8-276 form 3 (toughened resin system). Also five different drill bits were used to conduct experiments on BMS 8-276 form 3. Speed values were 5,000, 3,000, and 2,000 rpm and feed rates were 0.004, 0.006, and 0.01 ipr. The effect of speed, feed rate, and different drill geometries was analyzed with respect to surface finish in the drilled composites. / Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Mechanical Engineering. / "July 2006." / Includes bibliographic references (leaves 79-81).

<p>An on-line identifier using Functional Link Network (FLN) and Pole-shift (PS) controller for power system stabilizer (PSS) application are presented in this thesis. To have the satisfactory performance of the PSS controller, over a wide range of operating conditions, it is desirable to adapt PSS parameters in real time. Artificial Neural Networks (ANNs) transform the inputs in a low-dimensional space to high-dimensional nonlinear hidden unit space and they have the ability to model the nonlinear characteristics of the power system. The ability of ANNs to learn makes them more suitable for use in adaptive control techniques.</p> <p>On-line identification obtains a mathematical model at each sampling period to track the dynamic behavior of the plant. The ANN identifier consisting of a Functional link Network (FLN) is used for identifying the model parameters. A FLN model eliminates the need of hidden layer while retaining the nonlinear mapping capability of the neural network by using enhanced inputs. This network may be conveniently used for function approximation with faster convergence rate and lesser computational load.</p> <p>The most commonly used Pole Assignment (PA) algorithm for adaptive control purposes assign the pole locations to fixed locations within the unit circle in the z-plane. It may not be optimum for different operating conditions. In this thesis, PS type of adaptive control algorithm is used. This algorithm, instead of assigning the closed-loop poles to fixed locations within the unit circle in the z-plane, this algorithm assumes that the pole characteristic polynomial of the closed-loop system has the same form as the pole characteristic of the open-loop system and shifts the open-loop poles radially towards the centre of the unit circle in the z-plane by a shifting factor &alpha; according to some rules. In this control algorithm, no coefficients need to be tuned manually, so manual parameter tuning (which is a drawback in conventional power system stabilizer) is minimized. The PS control algorithm uses the on-line updated ARMA parameters to calculate the new closed-loop poles of the system that are always inside the unit circle in the z-plane.</p> <p>Simulation studies on a single-machine infinite bus and on a multi-machine power system for various operating condition changes, verify the effectiveness of the combined model of FLN identifier and PS control in damping the local and multi-mode oscillations occurring in the system. Simulation studies prove that the APSSs have significant benefits over conventional PSSs: performance improvement and no requirement for parameter tuning.</p>

Large marine vessels carry their loads all over the world. It can be a container ship carrying over 10 000 containers filled with foods, textiles and electronics or a bulk freighter carrying 400 000 tons of coal. Vessels usually have a ballast system that pumps water into ballast tanks to stabilize the vessel. The ballast system can be used to change the vessel’s trim and list angles. Trim and list are the ship equivalents of pitch and roll. By changing the trim angle the water resistance can be reduced and thus also the fuel consumption. Since the vessel is consuming a couple of hundred tons of fuel per day, a small reduction in fuel consumption can save a considerable amount of money, and it is good for the environment. In this thesis, the ship’s power consumption has been estimated using an artificial neural network, which is a mathematical model based on data. The name refers to certain structural similarities with the neural synapse system in animals. The idea with neural networks has been to create brain-like systems. For applications such as learning to interpret sensor data, artificial neural networks are an effective learning method. The goal is to estimate the ship power using a artificial neural network and then use it to calculate the trim angle, to be able to save fuel. The data used in the artificial neural network come from sensor systems mounted on a container ship sailing between Europe and Asia. The sensor data have been thoroughly preprocessed and this includes for example removing the parts when the ship is docked in harbour, data patching and synchronisation and outlier detection based on a Kalman filter. A physical model of a marine craft including wind, wave, hydrodynamic and hydrostatic effects, has also been introduced to help analyse the performance and behaviour of the artificial neural network. The artificial neural network developed in this thesis could successfully estimate the power consumption of the ship. Based on the developed networks it can be seen that the fuel consumption is reduced by trimming the ship by bow, i.e., the ship is angled so the bow is closer to the water line than the stern. The method introduced here could also be applied on other marine vessels, such as bulk freighters or tank ships.

<p>An on-line identifier using Functional Link Network (FLN) and Pole-shift (PS) controller for power system stabilizer (PSS) application are presented in this thesis. To have the satisfactory performance of the PSS controller, over a wide range of operating conditions, it is desirable to adapt PSS parameters in real time. Artificial Neural Networks (ANNs) transform the inputs in a low-dimensional space to high-dimensional nonlinear hidden unit space and they have the ability to model the nonlinear characteristics of the power system. The ability of ANNs to learn makes them more suitable for use in adaptive control techniques.</p> <p>On-line identification obtains a mathematical model at each sampling period to track the dynamic behavior of the plant. The ANN identifier consisting of a Functional link Network (FLN) is used for identifying the model parameters. A FLN model eliminates the need of hidden layer while retaining the nonlinear mapping capability of the neural network by using enhanced inputs. This network may be conveniently used for function approximation with faster convergence rate and lesser computational load.</p> <p>The most commonly used Pole Assignment (PA) algorithm for adaptive control purposes assign the pole locations to fixed locations within the unit circle in the z-plane. It may not be optimum for different operating conditions. In this thesis, PS type of adaptive control algorithm is used. This algorithm, instead of assigning the closed-loop poles to fixed locations within the unit circle in the z-plane, this algorithm assumes that the pole characteristic polynomial of the closed-loop system has the same form as the pole characteristic of the open-loop system and shifts the open-loop poles radially towards the centre of the unit circle in the z-plane by a shifting factor &alpha; according to some rules. In this control algorithm, no coefficients need to be tuned manually, so manual parameter tuning (which is a drawback in conventional power system stabilizer) is minimized. The PS control algorithm uses the on-line updated ARMA parameters to calculate the new closed-loop poles of the system that are always inside the unit circle in the z-plane.</p> <p>Simulation studies on a single-machine infinite bus and on a multi-machine power system for various operating condition changes, verify the effectiveness of the combined model of FLN identifier and PS control in damping the local and multi-mode oscillations occurring in the system. Simulation studies prove that the APSSs have significant benefits over conventional PSSs: performance improvement and no requirement for parameter tuning.</p>

Microwave detection techniques based on the theory of perturbation of cavity resonators are commonly used to measure the permittivity and permeability of objects of dielectric and ferrite materials at microwave frequencies. When a small object is introduced into a microwave cavity resonator, the resonant frequency is perturbed. Since it is possible to measure the change in frequency with high accuracy, this provides a valuable method for measuring the electric and magnetic properties of the object. Likewise, these microwave resonators can be used as sensors for sorting dielectric objects. Techniques based upon this principle are in common use for measuring the dielectric and magnetic properties of materials at microwave frequencies for variety of applications. This thesis presents an approach of using Artificial Neural Networks to detect material change in a rectangular cavity. The method is based on the theory of the perturbation of cavity resonators where a change in the resonant frequencies of the cavity is directly proportional to the dielectric constant of the inserted objects. A rectangular cavity test fixture was built and excited with a monopole antenna. The cavity was filled with different materials, and the reflection coefficient of each material was measured over a wide range of frequencies. An intelligent systems approach using an artificial neural network (ANN) methodology was implemented for the automatic material change detection. To develop an automatic detection model, a multi-layer perceptron (MLP) was designed with one hidden layer and gradient descent back-propagation (BP) learning algorithm was used for the ANN training. The network training process was performed in an off-line mode, and after the training process was accomplished, the model was able to learn the rules without knowing any algorithm for automatic detection.

An isolated hybrid system comprised of a dispatchable and a non-dispatchable power generation sources, is proposed to supply the load of a remote village in the west coast region of The Gambia. The thesis presents an artificial neural network (ANN) based approach to tune the parameters of the frequency regulator in hybrid wind/diesel power system for isolated area power supply. The multi-layer feed-forward ANN with the error back-propagation training is employed to tune the frequency regulator in the simulation of hybrid system under different load and wind conditions. Using MATLAB/Simulink, dynamic simulations are performed to investigate the interaction between these two power sources for the load management, and the voltage and frequency behaviors during wind speed and load variations. Simulation results show that the wind turbine and the diesel generator can be operated suitably in parallel. During simulation, the frequency and voltage regulators used in the proposed hybrid system performed fairly well under wind speed variations and load changing conditions. A good frequency regulator interface, which is around 50Hz is observed for nearly the entire period of operation.

Reflection cracking is one of the main distresses in hot-mix asphalt (HMA) overlays. It has been a serious concern since early in the 20th century. Since then, several models have been developed to predict the extent and severity of reflection cracking in HMA overlays. However, only limited research has been performed to evaluate and calibrate these models. In this dissertation, mechanistic-based models are calibrated to field data of over 400 overlay test sections to produce a design process for predicting reflection cracks. Three cracking mechanisms: bending, shearing traffic stresses, and thermal stress are taken into account to evaluate the rate of growth of the three increasing levels of distress severity: low, medium, and high. The cumulative damage done by all three cracking mechanisms is used to predict the number of days for the reflection crack to reach the surface of the overlay. The result of this calculation is calibrated to the observed field data (severity and extent) which has been fitted with an S-shaped curve. In the mechanistic computations, material properties and fracture-related stress intensity factors are generated using efficient Artificial Neural Network (ANN) algorithms. In the bending and shearing traffic stress models, the traffic was represented by axle load spectra. In the thermal stress model, a recently developed temperature model was used to predict the temperature at the crack tips. This process was developed to analyze various overlay structures. HMA overlays over either asphalt pavement or jointed concrete pavement in all four major climatic zones are discussed in this dissertation. The results of this calculated mechanistic approach showed its ability to efficiently reproduce field observations of the growth, extent, and severity of reflection cracking. The most important contribution to crack growth was found to be thermal stress. The computer running time for a twenty-year prediction of a typical overlay was between one and four minutes.

Now Taiwan is not only the country that facts the corruption threat. The greedy politician and never satisfied merchant unceasingly perform the scandal in the whole world. The national economy and the people¡¦s wealth are also injured. The topic of this research is how to choose the important variable from the corruption case. In recent years the Data Mining technique application in the behavioral analysis of shopping, customer relations management, crime investigation is in fashion; however the Data Mining technique application in politics and social domain is still not enough. In this research, we attempt to introduce the concepts and techniques of Data Mining and use Data Mining technique to set up a selective model for the consideration for the government in the corruption preventing. It attempts to explore the opportunity for the social sciences research.