流动单元四维模型和剩余油预测以濮城油田为例

This study is aimed to reveal macroscopic and microscopic anisotropism by using new theories, new methods and new technology. In order to reveal the forming mechanism and distribution pattern of remaining oil, flow units 4-dimension model and realistic model was established according the data over 20 years development of the Pucheng Oil field. Based on theories of multi-discipline subject, methods and technologies, by using correspondent 4-D data body and computer, combining quantity and quality study, static and development data, macroscopic and microscopic data, the two different geneses' reservoir, eg., braided delta and lake delta, are studied. The two different geneses' reservoir flow units models were established. Main achievement of this thesis are summarized as following: The standard of parameter optimization, identification and appreciation of two different geneses' reservoir were established. Based on the standard, the reservoir were classed into four flow units class as G,E,F and P. The flow unit static models of two different geneses' reservoir were established, and the relation of geometric shape, space distribution and macroscopic remaining oil was revealed. the flow units microscopic model were established, which tells that the changes of all the microscopic factor in the development. (4) Accordig BP arithmetic method, an adapt arithmetic method were designed, and the reservoir flow units were simulated based on the new method. (5) Reservoir realistic model of flow unit were established. Based on the model the microscopic development is simulated, which reveals the oil and water seepage in the reservoir and the mechanism of the microscopic oil formation. (6) The spatial residual oil distribution patterns were summarized. The remaided oil is mainly in the places as not being affected by the injected water, high part of the structures and the place near the sealed faults. There are 3 kinds and 9 distribution modes of microscopic remaining oil. The forming mechanism and distribution rule were pointed out. The study has developed a set of theories, technology and methods for flow units study, including flow units description, characterization and prediction. The study is also an improvement of the development geology theory in continental fault depression lake basin.

Comparative analyses of seven algorithms for copy number variant identification from single nucleotide polymorphism arrays.

Determination of copy number variants (CNVs) inferred in genome wide single nucleotide polymorphism arrays has shown increasing utility in genetic variant disease associations. Several CNV detection methods are available, but differences in CNV call thresholds and characteristics exist. We evaluated the relative performance of seven methods: circular binary segmentation, CNVFinder, cnvPartition, gain and loss of DNA, Nexus algorithms, PennCNV and QuantiSNP. Tested data included real and simulated Illumina HumHap 550 data from the Singapore cohort study of the risk factors for Myopia (SCORM) and simulated data from Affymetrix 6.0 and platform-independent distributions. The normalized singleton ratio (NSR) is proposed as a metric for parameter optimization before enacting full analysis. We used 10 SCORM samples for optimizing parameter settings for each method and then evaluated method performance at optimal parameters using 100 SCORM samples. The statistical power, false positive rates, and receiver operating characteristic (ROC) curve residuals were evaluated by simulation studies. Optimal parameters, as determined by NSR and ROC curve residuals, were consistent across datasets. QuantiSNP outperformed other methods based on ROC curve residuals over most datasets. Nexus Rank and SNPRank have low specificity and high power. Nexus Rank calls oversized CNVs. PennCNV detects one of the fewest numbers of CNVs.

Wind-Induced Vibration Energy Harvesting Using Piezoelectric Transducers Coupled with Dynamic Magnification

Flexible cylindrical structures subjected to wind loading experience vibrations from periodic shedding of vortices in their wake. Vibrations become excessive when the natural frequencies of the cylinder coincide with the vortex shedding frequency. In this study, cylinder vibrations are transmitted to a beam inside the structure via dynamic magnifier system. This system amplifies the strain experienced by piezoelectric patches bonded to the beam to maximize the conversion from vibrational energy into electrical energy. Realworld applicability is tested using a wind tunnel to create vortex shedding and comparing the results to finite element modeling that shows the structural vibrational modes. A crucial part of this study is conditioning and storing the harvested energy, focusing on theoretical modeling, design parameter optimization, and experimental validation. The developed system is helpful in designing wind-induced energy harvesters to meet the necessity for novel energy resources.

A Hybrid Forward Algorithm for RBF Neural Network Construction

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness

A novel continuous forward algorithm for RBF neural modelling

A continuous forward algorithm (CFA) is proposed for nonlinear modelling and identification using radial basis function (RBF) neural networks. The problem considered here is simultaneous network construction and parameter optimization, well-known to be a mixed integer hard one. The proposed algorithm performs these two tasks within an integrated analytic framework, and offers two important advantages. First, the model performance can be significantly improved through continuous parameter optimization. Secondly, the neural representation can be built without generating and storing all candidate regressors, leading to significantly reduced memory usage and computational complexity. Computational complexity analysis and simulation results confirm the effectiveness.

Effects of process parameters upon the shape memory and pseudo-elastic behaviors of laser-welded NiTi thin foil

This article discusses the effects of laser welding parameters such as power, welding speed, and focus position on the weld bead profile, microstructure, pseudo-elasticity (PE), and shape memory effect (SME) of NiTi foil with thickness of 250 um using 100W CW fiber laser. The parameter settings to produce the NiTi welds for analysis in this article were chosen from a fractional factorial design to ensure the welds produced were free of any apparent defect. The welds obtained were mainly of cellular dendrites with grain sizes ranging from 2.5 to 4.8 um at the weld centerline. A small amount of Ni3Ti was found in the welds. The onset of transformation temperatures (As and Ms) of the NiTi welds shifted to the negative side as compared to the as-received NiTi alloy. Ultimate tensile stress of the NiTi welds was comparable to the as received NiTi alloy, but a little reduction in the pseudo-elastic property was noted. Full penetration welds with desirable weld bead profiles and mechanical properties were successfully obtained in this study.

Improving the microbial production of biofuels through metabolic engineering

Les défis conjoints du changement climatique d'origine anthropique et la diminution des réserves de combustibles fossiles sont le moteur de recherche intense pour des sources d'énergie alternatives. Une avenue attrayante est d'utiliser un processus biologique pour produire un biocarburant. Parmi les différentes options en matière de biocarburants, le bio-hydrogène gazeux est un futur vecteur énergétique attrayant en raison de son efficacité potentiellement plus élevé de conversion de puissance utilisable, il est faible en génération inexistante de polluants et de haute densité d'énergie. Cependant, les faibles rendements et taux de production ont été les principaux obstacles à l'application pratique des technologies de bio-hydrogène. Des recherches intensives sur bio-hydrogène sont en cours, et dans les dernières années, plusieurs nouvelles approches ont été proposées et étudiées pour dépasser ces inconvénients. À cette fin, l'objectif principal de cette thèse était d'améliorer le rendement en hydrogène moléculaire avec un accent particulier sur l'ingénierie métabolique et l’utilisation de bioprocédés à variables indépendantes. Une de nos hypothèses était que la production d’hydrogène pourrait être améliorée et rendue plus économiquement viable par ingénierie métabolique de souches d’Escherichia coli producteurs d’hydrogène en utilisant le glucose ainsi que diverses autres sources de carbone, y compris les pentoses. Les effets du pH, de la température et de sources de carbone ont été étudiés. La production maximale d'hydrogène a été obtenue à partir de glucose, à un pH initial de 6.5 et une température de 35°C. Les études de cinétiques de croissance ont montré que la μmax était 0.0495 h-1 avec un Ks de 0.0274 g L-1 lorsque le glucose est la seule source de carbone en milieu minimal M9. .Parmi les nombreux sucres et les dérivés de sucres testés, les rendements les plus élevés d'hydrogène sont avec du fructose, sorbitol et D-glucose; 1.27, 1.46 et 1.51 mol H2 mol-1 de substrat, respectivement. En outre, pour obtenir les interactions entre les variables importantes et pour atteindre une production maximale d'hydrogène, un design 3K factoriel complet Box-Behnken et la méthodologie de réponse de surface (RSM) ont été employées pour la conception expérimentale et l'analyse de la souche d'Escherichia coli DJT135. Le rendement en hydrogène molaire maximale de 1.69 mol H2 mol-1 de glucose a été obtenu dans les conditions optimales de 75 mM de glucose, à 35°C et un pH de 6.5. Ainsi, la RSM avec un design Box-Behken était un outil statistique utile pour atteindre des rendements plus élevés d'hydrogène molaires par des organismes modifiés génétiquement. Ensuite, l'expression hétérologue de l’hydrogénases soluble [Ni-Fe] de Ralstonia eutropha H16 (l'hydrogénase SH) a tenté de démontrer que la mise en place d'une voie capable de dériver l'hydrogène à partir de NADH pourrait surpasser le rendement stoechiométrique en hydrogène.. L’expression a été démontrée par des tests in vitro de l'activité enzymatique. Par ailleurs, l'expression de SH a restaurée la croissance en anaérobie de souches mutantes pour adhE, normalement inhibées en raison de l'incapacité de réoxyder le NADH. La mesure de la production d'hydrogène in vivo a montré que plusieurs souches modifiées métaboliquement sont capables d'utiliser l'hydrogénase SH pour dériver deux moles d’hydrogène par mole de glucose consommé, proche du maximum théorique. Une autre stratégie a montré que le glycérol brut pourrait être converti en hydrogène par photofermentation utilisant Rhodopseudomonas palustris par photofermentation. Les effets de la source d'azote et de différentes concentrations de glycérol brut sur ce processus ont été évalués. À 20 mM de glycérol, 4 mM glutamate, 6.1 mol hydrogène / mole de glycérol brut ont été obtenus dans des conditions optimales, un rendement de 87% de la théorie, et significativement plus élevés que ce qui a été réalisé auparavant. En prolongement de cette étude, l'optimisation des paramètres a également été utilisée. Dans des conditions optimales, une intensité lumineuse de 175 W/m2, 30 mM glycérol et 4.5 mM de glutamate, 6.69 mol hydrogène / mole de glycérol brut ont été obtenus, soit un rendement de 96% de la valeur théorique. La détermination de l'activité de la nitrogénase et ses niveaux d'expression ont montré qu'il y avait relativement peu de variation de la quantité de nitrogénase avec le changement des variables alors que l'activité de la nitrogénase variait considérablement, avec une activité maximale (228 nmol de C2H4/ml/min) au point central optimal. Dans la dernière section, la production d'hydrogène à partir du glucose via la photofermentation en une seule étape a été examinée avec la bactérie photosynthétique Rhodobacter capsulatus JP91 (hup-). La méthodologie de surface de réponse avec Box-Behnken a été utilisée pour optimiser les variables expérimentales de façon indépendante, soit la concentration de glucose, la concentration du glutamate et l'intensité lumineuse, ainsi que d'examiner leurs effets interactifs pour la maximisation du rendement en hydrogène moléculaire. Dans des conditions optimales, avec une intensité lumineuse de 175 W/m2, 35 mM de glucose, et 4.5 mM de glutamate,, un rendement maximal d'hydrogène de 5.5 (± 0.15) mol hydrogène /mol glucose, et un maximum d'activité de la nitrogénase de 246 (± 3.5) nmol C2H4/ml/min ont été obtenus. L'analyse densitométrique de l'expression de la protéine-Fe nitrogenase dans les différentes conditions a montré une variation significative de l'expression protéique avec un maximum au point central optimisé. Même dans des conditions optimales pour la production d'hydrogène, une fraction significative de la protéine Fe a été trouvée dans l'état ADP-ribosylée, suggérant que d'autres améliorations des rendements pourraient être possibles. À cette fin, un mutant amtB dérivé de Rhodobacter capsulatus JP91 (hup-) a été créé en utilisant le vecteur de suicide pSUP202. Les résultats expérimentaux préliminaires montrent que la souche nouvellement conçue métaboliquement, R. capsulatus DG9, produit 8.2 (± 0.06) mol hydrogène / mole de glucose dans des conditions optimales de cultures discontinues (intensité lumineuse, 175 W/m2, 35 mM de glucose et 4.5 mM glutamate). Le statut d'ADP-ribosylation de la nitrogénase-protéine Fe a été obtenu par Western Blot pour la souche R. capsulatus DG9. En bref, la production d'hydrogène est limitée par une barrière métabolique. La principale barrière métabolique est due au manque d'outils moléculaires possibles pour atteindre ou dépasser le rendement stochiométrique en bio-hydrogène depuis les dernières décennies en utilisant les microbes. À cette fin, une nouvelle approche d’ingénierie métabolique semble très prometteuse pour surmonter cette contrainte vers l'industrialisation et s'assurer de la faisabilité de la technologie de la production d'hydrogène. Dans la présente étude, il a été démontré que l’ingénierie métabolique de bactéries anaérobiques facultatives (Escherichia coli) et de bactéries anaérobiques photosynthétiques (Rhodobacter capsulatus et Rhodopseudomonas palustris) peuvent produire de l'hydrogène en tant que produit majeur à travers le mode de fermentation par redirection métabolique vers la production d'énergie potentielle. D'autre part, la méthodologie de surface de réponse utilisée dans cette étude représente un outil potentiel pour optimiser la production d'hydrogène en générant des informations appropriées concernant la corrélation entre les variables et des producteurs de bio-de hydrogène modifiés par ingénierie métabolique. Ainsi, un outil d'optimisation des paramètres représente une nouvelle avenue pour faire un pont entre le laboratoire et la production d'hydrogène à l'échelle industrielle en fournissant un modèle mathématique potentiel pour intensifier la production de bio-hydrogène. Par conséquent, il a été clairement mis en évidence dans ce projet que l'effort combiné de l'ingénierie métabolique et la méthodologie de surface de réponse peut rendre la technologie de production de bio-hydrogène potentiellement possible vers sa commercialisation dans un avenir rapproché.

RWater – A Cyber Enabled Tool For Hydrologic Modeling And Analysis

Existing distributed hydrologic models are complex and computationally demanding for using as a rapid-forecasting policy-decision tool, or even as a class-room educational tool. In addition, platform dependence, specific input/output data structures and non-dynamic data-interaction with pluggable software components inside the existing proprietary frameworks make these models restrictive only to the specialized user groups. RWater is a web-based hydrologic analysis and modeling framework that utilizes the commonly used R software within the HUBzero cyber infrastructure of Purdue University. RWater is designed as an integrated framework for distributed hydrologic simulation, along with subsequent parameter optimization and visualization schemes. RWater provides platform independent web-based interface, flexible data integration capacity, grid-based simulations, and user-extensibility. RWater uses RStudio to simulate hydrologic processes on raster based data obtained through conventional GIS pre-processing. The program integrates Shuffled Complex Evolution (SCE) algorithm for parameter optimization. Moreover, RWater enables users to produce different descriptive statistics and visualization of the outputs at different temporal resolutions. The applicability of RWater will be demonstrated by application on two watersheds in Indiana for multiple rainfall events.

A New Approach for Nontechnical Losses Detection Based on Optimum-Path Forest

Nowadays, fraud detection is important to avoid nontechnical energy losses. Various electric companies around the world have been faced with such losses, mainly from industrial and commercial consumers. This problem has traditionally been dealt with using artificial intelligence techniques, although their use can result in difficulties such as a high computational burden in the training phase and problems with parameter optimization. A recently-developed pattern recognition technique called optimum-path forest (OPF), however, has been shown to be superior to state-of-the-art artificial intelligence techniques. In this paper, we proposed to use OPF for nontechnical losses detection, as well as to apply its learning and pruning algorithms to this purpose. Comparisons against neural networks and other techniques demonstrated the robustness of the OPF with respect to commercial losses automatic identification.

Modeling and analysis of artificial neural networks applied in operations research

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements. Systems based on artificial neural networks have high computational rates due to the use of a massive number of these computational elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving problems related to operations research. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.

Control motion approach of a lower limb orthosis to reduce energy consumption

By analysing the dynamic principles of the human gait, an economic gait‐control analysis is performed, and passive elements are included to increase the energy efficiency in the motion control of active orthoses. Traditional orthoses use position patterns from the clinical gait analyses (CGAs) of healthy people, which are then de‐normalized and adjusted to each user. These orthoses maintain a very rigid gait, and their energy cosT is very high, reducing the autonomy of the user. First, to take advantage of the inherent dynamics of the legs, a state machine pattern with different gains in eachstate is applied to reduce the actuator energy consumption. Next, different passive elements, such as springs and brakes in the joints, are analysed to further reduce energy consumption. After an off‐line parameter optimization and a heuristic improvement with genetic algorithms, a reduction in energy consumption of 16.8% is obtained by applying a state machine control pattern, and a reduction of 18.9% is obtained by using passive elements. Finally, by combining both strategies, a more natural gait is obtained, and energy consumption is reduced by 24.6%compared with a pure CGA pattern.

Contributions to the Resilience Management in the Internet of Things

El auge del "Internet de las Cosas" (IoT, "Internet of Things") y sus tecnologías asociadas han permitido su aplicación en diversos dominios de la aplicación, entre los que se encuentran la monitorización de ecosistemas forestales, la gestión de catástrofes y emergencias, la domótica, la automatización industrial, los servicios para ciudades inteligentes, la eficiencia energética de edificios, la detección de intrusos, la gestión de desastres y emergencias o la monitorización de señales corporales, entre muchas otras. La desventaja de una red IoT es que una vez desplegada, ésta queda desatendida, es decir queda sujeta, entre otras cosas, a condiciones climáticas cambiantes y expuestas a catástrofes naturales, fallos de software o hardware, o ataques maliciosos de terceros, por lo que se puede considerar que dichas redes son propensas a fallos. El principal requisito de los nodos constituyentes de una red IoT es que estos deben ser capaces de seguir funcionando a pesar de sufrir errores en el propio sistema. La capacidad de la red para recuperarse ante fallos internos y externos inesperados es lo que se conoce actualmente como "Resiliencia" de la red. Por tanto, a la hora de diseñar y desplegar aplicaciones o servicios para IoT, se espera que la red sea tolerante a fallos, que sea auto-configurable, auto-adaptable, auto-optimizable con respecto a nuevas condiciones que puedan aparecer durante su ejecución. Esto lleva al análisis de un problema fundamental en el estudio de las redes IoT, el problema de la "Conectividad". Se dice que una red está conectada si todo par de nodos en la red son capaces de encontrar al menos un camino de comunicación entre ambos. Sin embargo, la red puede desconectarse debido a varias razones, como que se agote la batería, que un nodo sea destruido, etc. Por tanto, se hace necesario gestionar la resiliencia de la red con el objeto de mantener la conectividad entre sus nodos, de tal manera que cada nodo IoT sea capaz de proveer servicios continuos, a otros nodos, a otras redes o, a otros servicios y aplicaciones. En este contexto, el objetivo principal de esta tesis doctoral se centra en el estudio del problema de conectividad IoT, más concretamente en el desarrollo de modelos para el análisis y gestión de la Resiliencia, llevado a la práctica a través de las redes WSN, con el fin de mejorar la capacidad la tolerancia a fallos de los nodos que componen la red. Este reto se aborda teniendo en cuenta dos enfoques distintos, por una parte, a diferencia de otro tipo de redes de dispositivos convencionales, los nodos en una red IoT son propensos a perder la conexión, debido a que se despliegan en entornos aislados, o en entornos con condiciones extremas; por otra parte, los nodos suelen ser recursos con bajas capacidades en términos de procesamiento, almacenamiento y batería, entre otros, por lo que requiere que el diseño de la gestión de su resiliencia sea ligero, distribuido y energéticamente eficiente. En este sentido, esta tesis desarrolla técnicas auto-adaptativas que permiten a una red IoT, desde la perspectiva del control de su topología, ser resiliente ante fallos en sus nodos. Para ello, se utilizan técnicas basadas en lógica difusa y técnicas de control proporcional, integral y derivativa (PID - "proportional-integral-derivative"), con el objeto de mejorar la conectividad de la red, teniendo en cuenta que el consumo de energía debe preservarse tanto como sea posible. De igual manera, se ha tenido en cuenta que el algoritmo de control debe ser distribuido debido a que, en general, los enfoques centralizados no suelen ser factibles a despliegues a gran escala. El presente trabajo de tesis implica varios retos que conciernen a la conectividad de red, entre los que se incluyen: la creación y el análisis de modelos matemáticos que describan la red, una propuesta de sistema de control auto-adaptativo en respuesta a fallos en los nodos, la optimización de los parámetros del sistema de control, la validación mediante una implementación siguiendo un enfoque de ingeniería del software y finalmente la evaluación en una aplicación real. Atendiendo a los retos anteriormente mencionados, el presente trabajo justifica, mediante una análisis matemático, la relación existente entre el "grado de un nodo" (definido como el número de nodos en la vecindad del nodo en cuestión) y la conectividad de la red, y prueba la eficacia de varios tipos de controladores que permiten ajustar la potencia de trasmisión de los nodos de red en respuesta a eventuales fallos, teniendo en cuenta el consumo de energía como parte de los objetivos de control. Así mismo, este trabajo realiza una evaluación y comparación con otros algoritmos representativos; en donde se demuestra que el enfoque desarrollado es más tolerante a fallos aleatorios en los nodos de la red, así como en su eficiencia energética. Adicionalmente, el uso de algoritmos bioinspirados ha permitido la optimización de los parámetros de control de redes dinámicas de gran tamaño. Con respecto a la implementación en un sistema real, se han integrado las propuestas de esta tesis en un modelo de programación OSGi ("Open Services Gateway Initiative") con el objeto de crear un middleware auto-adaptativo que mejore la gestión de la resiliencia, especialmente la reconfiguración en tiempo de ejecución de componentes software cuando se ha producido un fallo. Como conclusión, los resultados de esta tesis doctoral contribuyen a la investigación teórica y, a la aplicación práctica del control resiliente de la topología en redes distribuidas de gran tamaño. Los diseños y algoritmos presentados pueden ser vistos como una prueba novedosa de algunas técnicas para la próxima era de IoT. A continuación, se enuncian de forma resumida las principales contribuciones de esta tesis: (1) Se han analizado matemáticamente propiedades relacionadas con la conectividad de la red. Se estudia, por ejemplo, cómo varía la probabilidad de conexión de la red al modificar el alcance de comunicación de los nodos, así como cuál es el mínimo número de nodos que hay que añadir al sistema desconectado para su re-conexión. (2) Se han propuesto sistemas de control basados en lógica difusa para alcanzar el grado de los nodos deseado, manteniendo la conectividad completa de la red. Se han evaluado diferentes tipos de controladores basados en lógica difusa mediante simulaciones, y los resultados se han comparado con otros algoritmos representativos. (3) Se ha investigado más a fondo, dando un enfoque más simple y aplicable, el sistema de control de doble bucle, y sus parámetros de control se han optimizado empleando algoritmos heurísticos como el método de la entropía cruzada (CE, "Cross Entropy"), la optimización por enjambre de partículas (PSO, "Particle Swarm Optimization"), y la evolución diferencial (DE, "Differential Evolution"). (4) Se han evaluado mediante simulación, la mayoría de los diseños aquí presentados; además, parte de los trabajos se han implementado y validado en una aplicación real combinando técnicas de software auto-adaptativo, como por ejemplo las de una arquitectura orientada a servicios (SOA, "Service-Oriented Architecture"). ABSTRACT The advent of the Internet of Things (IoT) enables a tremendous number of applications, such as forest monitoring, disaster management, home automation, factory automation, smart city, etc. However, various kinds of unexpected disturbances may cause node failure in the IoT, for example battery depletion, software/hardware malfunction issues and malicious attacks. So, it can be considered that the IoT is prone to failure. The ability of the network to recover from unexpected internal and external failures is known as "resilience" of the network. Resilience usually serves as an important non-functional requirement when designing IoT, which can further be broken down into "self-*" properties, such as self-adaptive, self-healing, self-configuring, self-optimization, etc. One of the consequences that node failure brings to the IoT is that some nodes may be disconnected from others, such that they are not capable of providing continuous services for other nodes, networks, and applications. In this sense, the main objective of this dissertation focuses on the IoT connectivity problem. A network is regarded as connected if any pair of different nodes can communicate with each other either directly or via a limited number of intermediate nodes. More specifically, this thesis focuses on the development of models for analysis and management of resilience, implemented through the Wireless Sensor Networks (WSNs), which is a challenging task. On the one hand, unlike other conventional network devices, nodes in the IoT are more likely to be disconnected from each other due to their deployment in a hostile or isolated environment. On the other hand, nodes are resource-constrained in terms of limited processing capability, storage and battery capacity, which requires that the design of the resilience management for IoT has to be lightweight, distributed and energy-efficient. In this context, the thesis presents self-adaptive techniques for IoT, with the aim of making the IoT resilient against node failures from the network topology control point of view. The fuzzy-logic and proportional-integral-derivative (PID) control techniques are leveraged to improve the network connectivity of the IoT in response to node failures, meanwhile taking into consideration that energy consumption must be preserved as much as possible. The control algorithm itself is designed to be distributed, because the centralized approaches are usually not feasible in large scale IoT deployments. The thesis involves various aspects concerning network connectivity, including: creation and analysis of mathematical models describing the network, proposing self-adaptive control systems in response to node failures, control system parameter optimization, implementation using the software engineering approach, and evaluation in a real application. This thesis also justifies the relations between the "node degree" (the number of neighbor(s) of a node) and network connectivity through mathematic analysis, and proves the effectiveness of various types of controllers that can adjust power transmission of the IoT nodes in response to node failures. The controllers also take into consideration the energy consumption as part of the control goals. The evaluation is performed and comparison is made with other representative algorithms. The simulation results show that the proposals in this thesis can tolerate more random node failures and save more energy when compared with those representative algorithms. Additionally, the simulations demonstrate that the use of the bio-inspired algorithms allows optimizing the parameters of the controller. With respect to the implementation in a real system, the programming model called OSGi (Open Service Gateway Initiative) is integrated with the proposals in order to create a self-adaptive middleware, especially reconfiguring the software components at runtime when failures occur. The outcomes of this thesis contribute to theoretic research and practical applications of resilient topology control for large and distributed networks. The presented controller designs and optimization algorithms can be viewed as novel trials of the control and optimization techniques for the coming era of the IoT. The contributions of this thesis can be summarized as follows: (1) Mathematically, the fault-tolerant probability of a large-scale stochastic network is analyzed. It is studied how the probability of network connectivity depends on the communication range of the nodes, and what is the minimum number of neighbors to be added for network re-connection. (2) A fuzzy-logic control system is proposed, which obtains the desired node degree and in turn maintains the network connectivity when it is subject to node failures. There are different types of fuzzy-logic controllers evaluated by simulations, and the results demonstrate the improvement of fault-tolerant capability as compared to some other representative algorithms. (3) A simpler but more applicable approach, the two-loop control system is further investigated, and its control parameters are optimized by using some heuristic algorithms such as Cross Entropy (CE), Particle Swarm Optimization (PSO), and Differential Evolution (DE). (4) Most of the designs are evaluated by means of simulations, but part of the proposals are implemented and tested in a real-world application by combining the self-adaptive software technique and the control algorithms which are presented in this thesis.

Maintenance Strategies for used equipment under lease

Abstract: Purpose – The aim of this research is to determine the optimal upgrade and preventive maintenance actions that minimize the total expected cost (maintenance costs+penalty costs). Design/methodology/approach – The problem is a four-parameter optimization with two parameters being k-dimensional. The optimal solution is obtained by using a four-stage approach where at each stage a one-parameter optimization is solved. Findings – Upgrading action is an extra option before the lease of used equipment, in addition to preventive maintenance action. Upgrading action makes equipment younger and preventive maintenance action lowers the ROCOF. Practical implications – There is a growing trend towards leasing equipment rather than owning it. The lease contract contains penalties if the equipment fails often and repairs are done within reasonable time period. This implies that the lessor needs to look at optimal preventive maintenance strategies in the case of new equipment lease, and upgrade actions plus preventive maintenance in the case of used equipment lease. The paper deals with this topic and is of great significant to business involved with leasing equipment. Originality/value – Nowadays many organizations are interested in leasing equipment and outsourcing maintenance. The model in this paper addresses the preventive maintenance problem for leased equipment. It provides an approach to dealing with this problem.

Optimisation of two-stage raman converter based on phosphosilicate core fibre:Modelling and experiment

Parameter optimization of a two-stage Raman fibre converters (RFC) based on phosphosilicate core fiber was presented. The optimal operational regime was determined and tolerance of the converter against variations of laser parameters was analyzed. Converter was pumped by ytterbium-doped double-clad fibre laser with a maximum output power of 3.8W at 1061 nm. A phosphosilicate-core RFC with enhanced performance was fabricated using the results of numerical modelling.

A population dynamical model of Operophtera brumata, L. extended by climatic factors

Setting out from the database of Operophtera brumata, L. in between 1973 and 2000 due to the Light Trap Network in Hungary, we introduce a simple theta-logistic population dynamical model based on endogenous and exogenous factors, only. We create an indicator set from which we can choose some elements with which we can improve the fitting results the most effectively. Than we extend the basic simple model with additive climatic factors. The parameter optimization is based on the minimized root mean square error. The best model is chosen according to the Akaike Information Criterion. Finally we run the calibrated extended model with daily outputs of the regional climate model RegCM3.1, regarding 1961-1990 as reference period and 2021-2050 with 2071-2100 as future predictions. The results of the three time intervals are fitted with Beta distributions and compared statistically. The expected changes are discussed.