Dissertations / Theses on the topic 'Analytic Network Proce'
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Winicki, Elliott. "ELECTRICITY PRICE FORECASTING USING A CONVOLUTIONAL NEURAL NETWORK." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2126.
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Many methods have been used to forecast real-time electricity prices in various regions around the world. The problem is difficult because of market volatility affected by a wide range of exogenous variables from weather to natural gas prices, and accurate price forecasting could help both suppliers and consumers plan effective business strategies. Statistical analysis with autoregressive moving average methods and computational intelligence approaches using artificial neural networks dominate the landscape. With the rise in popularity of convolutional neural networks to handle problems with large numbers of inputs, and convolutional neural networks conspicuously lacking from current literature in this field, convolutional neural networks are used for this time series forecasting problem and show some promising results.
This document fulfills both MSEE Master's Thesis and BSCPE Senior Project requirements.
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Akra, Abraham. "Modelling the Four-Party Billing Payment Scheme: The Case of BPAY." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9515.
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My thesis involves developing a detailed understanding of the dynamics of a two-sided four-party Bill Payment market utilising proprietary data obtained from BPAY. The first chapter of my thesis analyses the market from a macroeconomic perspective, whilst the second and third chapter takes a microeconomic approach. The first chapter models the demand for merchant acceptance and consumer usage of a four - party payment scheme in the Bill Payment market. Within a cointegrating framework, demand equations are estimated using vector error correction models using proprietary data between March 2003 and December 2010. Results illustrate the importance of network effects in determining consumer usage and merchant demand. Additionally, price elasticities suggest the market for payments in Australia is competitive. The second chapter exploits a unique data set that details the demographics and transactions of individuals over a 30 month observational window. Survival analysis techniques are employed to quantify the risks of individuals leaving the platform. Results suggest support for the Hayashi and Klee (2003) finding in the Bill Payment market with individuals having a credit card less likely to leave the BPAY platform at any point in time. The motivation of the third chapter was to establish whether a link existed between the usage of the BPAY platform by consumers with the adoption of prior payment method technologies, given by credit card holding. Unlike Hayashi and Klee (2003), there is an added layer of complexity as credit cards are another payment instrument individuals can use for bill payments. An ordered generalised ordered logit model is estimated to determine the influence of credit card holding on frequency of usage. The results lend support to the Hayashi and Klee (2003) hypothesis that the adoption of a technology based payment instrument is influenced by the usage of prior technologies.
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Whitehurst, Jonathan. "A network probe for the mode analysis of planar antennas." Thesis, Royal Holloway, University of London, 1988. http://repository.royalholloway.ac.uk/items/d6571ade-592e-4539-86de-a912a5a79238/1/.
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The current trend towards total circuit integration where the antenna, R.F., and baseband circuitry are incorporated on a single substrate precludes the use of measurement systems such as the network analyser since there are no suitable R.F. input or output ports. This investigation has addressed the problem of performing 'in-situ' circuit measurements on millimetre wave printed circuits using a small, capacitively coupled probe. The technique is applicable to any 'open' printed circuit geometry, and does not require any physical connection to the circuit under test. A computer controlled measurement system is described which allows either the mode of excitation of a circuit element to be determined from the EZ distribution on its top surface, or else perform circuit measurements on individual elements using a technique analogous to the waveguide slotted line. The suitability of various probe geometries for this purpose is assessed, and sources of measurement error inherent in the probe designs employed are described. Emphasis has been placed on the characterization of integrated antenna elements and their associated feed network. Examples pertaining to microstrip patch antennas, the coplanar strip vee, and the tapered slotline antenna are presented.
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Baccouche, Alexandre. "Functional analysis of artificial DNA reaction network." Thesis, Sorbonne Paris Cité, 2015. http://www.theses.fr/2015USPCB135/document.
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La gestion et transmission d’information au sein d’organismes vivants implique la production et le trafic de molécules via des voies de signalisation sutructurées en réseaux de réactions chimiques. Ces derniers varient selon leur forme, taille ainsi que la nature des molécules mises en jeu. Parmi eux, les réseaux de régulation génétiques nous ont servi de modèle pour le développement et la mise en place d’un système de programmation moléculaire in vitro. En effet, l’expression d’un gène est majoritairement dominé par des facteurs de transcription, autres protéines ou acides nucléiques, eux-mêmes exprimés par d’autres gènes. L’ensemble forme l’interactome de la cellule, carte globale des interactions entre gènes et sous-produits, où la fonction du réseau est relié à sa topologie. L’observation des noeuds et sous-architectures dénote trois mécanismes récurrents : premièrement, la nature des interactions est de type activation ou inhibition, ce qui implique que tout comportement non trivial est obtenu par une combinaison de noeuds plutôt que le développement de nouvelles interactions. Ensuite, la longévité du réseau est assurée par la stabilité chimique de l’ADN couplée à la chimiosélectivié des réactions enzymatiques. Enfin, l’aspect dynamique est maintenu par le constant anabolisme/catabolisme des intermédiaires et donc l’utilisation de combustible/énergie. C’est suivant ces observations que nous avons développé un ensemble de trois réactions enzymatiques élémentaires : la «PEN-DNA toolbox». L’architecture du réseau, à savoir les connections entre les noeuds est médiée par la séquence de brins d’ADN synthétiques (appelés matrice), et trois enzymes (polymérase, nickase, et exonucléase) assurent la catalyse des réactions chimiques. La production et dégradation des intermédiaires consomme des désoxyribonucléotides triphosphates et rejette des désoxyribonucléotides monophosphate, dissipant ainsi le potentiel chimique. Les réactions sont suivies grâce au greffage d’un fluorophore sur le brin matriciel et au «nucleobase quenching» qui intervient lorsqu’une base d’un intermédiaire se rapproche du fluorophore après hybdridation sur le brin matriciel. L’activation correspond alors à la synthèse d’un brin output en réponse à un brin input, alors que l’inhibition survient lorsqu’un brin output s’hybride sur un brin matriciel, empêchant ainsi à l’input correspondant de s’y fixer. Oscillations, bistabilité et mémoire sont des exemples de comportements implémentés en PEN-DNA toolbox, faisant appel à des architectures de plus en plus complexes. Pour cela, un réglage fin des concentrations en effecteurs (ADN et enzymes) est nécessaire, ce qui sous-tend l’existence de plusieurs comportements pour un même circuit, dépendant des conditions paramétriques. L’établissement d’une carte de chaque combinaison de paramètres avec le comportement global associé permettrait de comprendre le fonctionnement du réseau dans son ensemble, et donnerait accès à tous les comportements disponibles. Dans le cas d’un système dynamique non linéaire, une telle carte est un diagramme de bifurcation du système. Pour explorer de manière exhaustive les possibilités d’un réseau dans un cadre expérimental raisonable, nous avons développé une plateforme microfluidique capable de générer des goutelettes d’eau dans l’huile à partir de quatres canaux aqueux différents. Ce dispositif nous donne accès, grâce à un contrôle fin des contributions de chaque canal aqueux, à des goutelettes monodisperses (volume de l’ordre du picolitre) dont le contenu est différent pour chaque goutelette. Nous avons adapté notre dispositif aux contraintes matérielles (design microfluidique, génération de goutelettes à contenu différents et controllés, observation et stabilité à long terme) et techniques (tracabilité des goutelettes et stabilité/compatibilité chimique). (...)Information processing within and in between living organisms involves the production and exchange of molecules through signaling pathways organized in chemical reactions networks. They are various by their shape, size, and by the nature of the molecules embroiled. Among them, gene regulatory networks were our inspiration to develop and implement a new framework for in-vitro molecular programming. Indeed, the expression of a gene is mostly controlled by transcription factors or regulatory proteins and/or nucleic acids that are themselves triggered by other genes. The whole assembly draws a web of cross-interacting genes and their subproducts, in which the well controlled topology relates to a precise function. With a closer look at the links between nodes in such architectures, we identify three key points in the inner operating system. First, the interactions either activate or inhibit the production of the later node, meaning that non trivial behaviors are obtained by a combination of nodes rather than a specific new interaction. Second, the chemical stability of DNA, together with the precise reactivity of enzymes ensures the longevity of the network. Finally, the dynamics are sustained by the constant anabolism/catabolism of the effectors, and the subsequent use of fuel/energy. All together, these observations led us to develop an original set of 3 elementary enzymatic reactions: the PEN-DNA toolbox. The architecture of the assembly, i.e. the connectivity between nodes relies on the sequence of synthetic DNA strands (called DNA templates), and 3 enzymes (a polymerase, a nickase and an exonuclease) are taking care of catalysis. The production and degradation of intermediates consume deoxyribonucleoside triphosphates (dNTP) and produce deoxynucleotide monophosphates leading to the dissipation of chemical potential. Reactions are monitored thanks to a backbone modification of a template with a fluorophore and the nucleobase quenching effect consecutive to an input strand binding the template. The activation mechanism is then the production of an output following the triggering of an input strand, and the inhibition comes from the production of an output strand that binds the activator-producing sequence. Various behaviors such as oscillation, bistability, or switchable memory have been implemented, requiring more and more complex topologies. For that, each circuit requires a fine tuning in the amount of chemical parameters, such as templates and enzymes. This underlies the fact that a given network may lead to different demeanors depending on the set of parameters. Mapping the output of each combination in the parameter space to find out the panel of behaviors leads to the bifurcation diagram of the system. In order to explore exhaustively the possibilities of one circuit with a reasonable experimental cost, we developped a microfluidic tool generating picoliter-sized water-in-oil droplets with different contents. We overcame the technical challenges in hardware (microfluidic design, droplet generation and long-term observation) and wetware (tracability of the droplet and emulsion compatibility/stability). So far, bifurcation diagrams were calculated from mathematical models based on the enzymes kinetics and the thermodynamic properties of each reaction. The model was then fitted with experimental data taken in distant points in the parameter space. Here, millions of droplets are created, and each one encloses a given amount of parameters, becoming one point in the diagram. The parameter coordinates are barcoded in the droplet, and the output fluorescence signal is recorded by time lapse microscopy. We first applied this technique to a well-known network, and obtained the first experimental two-dimensional bifurcation diagram of the bistable system. The diagram enlightens features that were not described by the previous mathematical model. (...)
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Caufield, J. Harry. "Interactomics-Based Functional Analysis: Using Interaction Conservation To Probe Bacterial Protein Functions." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4580.
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The emergence of genomics as a discrete field of biology has changed humanity’s understanding of our relationship with bacteria. Sequencing the genome of each newly-discovered bacterial species can reveal novel gene sequences, though the genome may contain genes coding for hundreds or thousands of proteins of unknown function (PUFs). In some cases, these coding sequences appear to be conserved across nearly all bacteria. Exploring the functional roles of these cases ideally requires an integrative, cross-species approach involving not only gene sequences but knowledge of interactions among their products. Protein interactions, studied at genome scale, extend genomics into the field of interactomics. I have employed novel computational methods to provide context for bacterial PUFs and to leverage the rich genomic, proteomic, and interactomic data available for hundreds of bacterial species.
The methods employed in this study began with sets of protein complexes. I initially hypothesized that, if protein interactions reveal protein functions and interactions are frequently conserved through protein complexes, then conserved protein functions should be revealed through the extent of conservation of protein complexes and their components. The subsequent analyses revealed how partial protein complex conservation may, unexpectedly, be the rule rather than the exception. Next, I expanded the analysis by combining sets of thousands of experimental protein-protein interactions. Progressing beyond the scope of protein complexes into interactions across full proteomes revealed novel evolutionary consistencies across bacteria but also exposed deficiencies among interactomics-based approaches. I have concluded this study with an expansion beyond bacterial protein interactions and into those involving bacteriophage-encoded proteins.
This work concerns emergent evolutionary properties among bacterial proteins. It is primarily intended to serve as a resource for microbiologists but is relevant to any research into evolutionary biology. As microbiomes and their occupants become increasingly critical to human health, similar approaches may become increasingly necessary.
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Dravenstott, Ronald W. "Restaurant Industry Stock Price Forecasting Model Utilizing Artificial Neural Networks to Combine Fundamental and Technical Analysis." Ohio University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1337780178.
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Song, Fei. "Deregulated power transmission analysis and planning in congested networks." Thesis, Brunel University, 2008. http://bura.brunel.ac.uk/handle/2438/4819.
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In this thesis, methods of charging for the transmission system and optimising the expansion of the transmission network under the competitive power market are described. The first part of this thesis considers transmission tariff design. In the proposed approach, not only is all the necessary investment in the transmission system recovered, but also an absolute economic signal is offered which is very useful in the competitive power market. A fair power market opportunity is given to every participant by the new nodal-use method. The second part of this thesis considers transmission system expansion. All the tests are based on the Three Gorges Project in China. In this thesis, to optimally expand the transmission system, the LMP (Locational Marginal Price) selection method and the CBEP (Congestion-Based transmission system Expansion Planning) method are introduced. The LMP selection method is used to select optional plans for transmission system expansion. It is especially suitable for large transmission systems. The outstanding advantages of the LMP selection method are simplicity and computational efficiency. The CBEP method produces the optimal system expansion plan. For the first time, generation congestion and transmission congestion are separated within the system expansion problem. For this reason the CBEP method can be used in a supply-side power market and is suitable for the Chinese power market. In this thesis, the issue of how to relax the congestion in the transmission system have been solved. The transmission system can obtain enough income to recover the total required cost. For this reason more and more investment will come into the transmission system from investors. The risk for the independent generators is also under control in the CBEP method. Even when the system is congested, the uncertainty of LMP is taken into consideration.
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Rodrigues, Félix Carvalho. "Smoothed analysis in Nash equilibria and the Price of Anarchy." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2012. http://hdl.handle.net/10183/54866.
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São analisados nesta dissertação problemas em teoria dos jogos, com enfoque no efeito que perturbações acarretam em jogos. A análise suavizada (smoothed analysis) é utilizada para tal análise, e dois tipos de jogos são o foco principal desta dissertação, jogos bimatrizes e o problema de atribuição de tráfego (Traffic Assignment Problem.) O algoritmo de Lemke-Howson é um método utilizado amplamente para computar um equilíbrio Nash de jogos bimatrizes. Esse problema é PPAD-completo (Polynomial Parity Arguments on Directed graphs), e existem instâncias em que um tempo exponencial é necessário para terminar o algoritmo. Mesmo utilizando análise suavizada, esse problema permanece exponencial. Entretanto, nenhum estudo experimental foi realizado para demonstrar na prática como o algoritmo se comporta em casos com perturbação. Esta dissertação demonstra como as instâncias de pior caso conhecidas atualmente podem ser geradas e mostra que a performance do algoritmo nestas instâncias, quando perturbações são aplicadas, difere do comportamento esperado provado pela teoria. O Problema de Atribuição de Tráfego modela situações em uma rede viária onde usuários necessitam viajar de um nodo origem a um nodo destino. Esse problema pode ser modelado como um jogo, usando teoria dos jogos, onde um equilíbrio Nash acontece quando os usuários se comportam de forma egoísta. O custo total ótimo corresponde ao melhor fluxo de um ponto de vista global. Nesta dissertação, uma nova medida de perturbação é apresentada, o Preço da Anarquia Suavizado (Smoothed Price of Anarchy), baseada na análise suavizada de algoritmos, com o fim de analisar os efeitos da perturbação no Preço da Anarquia. Usando esta medida, são estudados os efeitos que perturbações têm no Preço da Anarquia para instâncias reais e teóricas para o Problema de Atribuição de Tráfego. É demonstrado que o Preço da Anarquia Suavizado se mantém na mesma ordem do Preço da Anarquia sem perturbações para funções de latência polinomiais. Finalmente, são estudadas instâncias de benchmark em relação à perturbação.This thesis analyzes problems in game theory with respect to perturbation. It uses smoothed analysis to accomplish such task and focuses on two kind of games, bimatrix games and the traffic assignment problem. The Lemke-Howson algorithm is a widely used algorithm to compute a Nash equilibrium of a bimatrix game. This problem is PPAD-complete (Polynomial Parity Arguments on Directed graphs), and there exists an instance which takes exponential time (with any starting pivot.) It has been proven that even with a smoothed analysis it is still exponential. However, no experimental study has been done to verify and evaluate in practice how the algorithm behaves in such cases. This thesis shows in detail how the current known worst-case instances are generated and shows that the performance of the algorithm on these instances, when perturbed, differs from the expected behavior proven in theory. The Traffic Assignment Problem models a situation in a road network where users want to travel from an origin to a destination. It can be modeled as a game using game theory, with a Nash equilibrium happening when users behave selfishly and an optimal social welfare being the best possible flow from a global perspective. We provide a new measure, which we call the Smoothed Price of Anarchy, based on the smoothed analysis of algorithms in order to analyze the effects of perturbation on the Price of Anarchy. Using this measure, we analyze the effects that perturbation has on the Price of Anarchy for real and theoretical instances for the Traffic Assignment Problem. We demonstrate that the Smoothed Price of Anarchy remains in the same order as the original Price of Anarchy for polynomial latency functions. Finally, we study benchmark instances in relation to perturbation.
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Dusílek, Adam. "Oceňování dobrovolné práce při pořádání velké sportovní akce." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-198013.
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The research problem of this thesis was to hold large-scale sports event and its organization by nonprofessional workers that must be managed. To solve the main problem, we selected two main objectives. Determining monetary value of voluntary labour during organization of big sports event based on method VŠE was the first goal. This objective was fulfilled and determines the value at CZK 118.25. The second goal was to analyse the organization of sports event and it's securing. Using network analysis was able to find the critical path of preparation activities. Gantt diagram allowed carrying out a full analysis of resources, which could be the subject of further research.
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Oujezský, Václav. "Konvergované sítě a tomografie síťového provozu s využitím evolučních algoritmů." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-320776.
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Nowadays, the traffic tomography represents an integral component in converged networks and systems for detecting their behavioral characteristics. The dissertation deals with research of its implementation with the use of evolutionary algorithms. The research was mainly focused on innovation and solving behavioral detection data flows in networks and network anomalies using tomography and evolutionary algorithms. Within the dissertation has been proposed a new algorithm, emerging from the basics of the statistical method survival analysis, combined with a genetics’ algorithm. The proposed algorithm was tested in a model of a self-created network probe using the Python programming language and Cisco laboratory network devices. Performed tests have shown the basic functionality of the proposed solution.
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Kusch, Katharina. "Beyond customer perception of price discrimination: A consumer behavior analysis and its implications on aviation revenue management." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-262169.
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The purpose of this paper is to assess consumer behavior in the airline industry from a perspective beyond the effects of price discrimination. First the consequences of dynamic pricing will be assessed before looking at the role of social media and offline social influences, consumer satisfaction and airline equilibrium networks and their effects on consumer loyalty. Final implications on aviation revenue management will be drawn.
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Hadachi, Amnir. "Travel Time Estimation Using Sparsely Sampled Probe GPS Data in Urban Road Networks Context." Phd thesis, INSA de Rouen, 2013. http://tel.archives-ouvertes.fr/tel-00800203.
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This dissertation is concerned with the problem of estimating travel time per links in urban context using sparsely sampled GPS data. One of the challenges in this thesis is use the sparsely sampled data. A part of this research work, i developed a digital map with its new geographic information system (GIS), dealing with map-matching problem, where we come out with an enhancement tecnique, and also the shortest path problem.The thesis research work was conduct within the project PUMAS, which is an avantage for our research regarding the collection process of our data from the real world field and also in making our tests. The project PUMAS (Plate-forme Urbaine de Mobilité Avancée et Soutenable / Urban Platform for Sustainable and Advanced Mobility) is a preindustrial project that has the objective to inform about the traffic situation and also to develop an implement a platform for sustainable mobility in order to evaluate it in the region, specifically Rouen, France. The result is a framework for any traffic controller or manager and also estimation researcher to access vast stores of data about the traffic estimation, forecasting and status.
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Benhamiche, Amal. "Designing optical multi-band networks : polyhedral analysis and algorithms." Thesis, Paris 9, 2013. http://www.theses.fr/2013PA090075/document.
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Dans cette thèse, on s'intéresse à deux problèmes de conception de réseaux, utilisant la technologie OFDM multi-bandes. Le premier problème concerne la conception d'un réseau mono-couche avec contraintes spécifiques. Nous donnons une formulation en PLNE pour ce problème et étudions le polyèdre associé à sa restriction sur un arc. Nous introduisons deux familles d'inégalités valides définissant des facettes et développons un algorithme de coupes et branchements pour le problème. Nous étudions la variante multicouche du problème précédent et proposons plusieurs PLNE pour le modéliser. Nous identifions plusieurs familles de facettes et discutons des problèmes de séparation associés. Nous développons un algorithme de coupes et branchements utilisant l'ensemble des contraintes identifiées. Enfin, une formulation compacte et deux formulations basées sur des chemins sont proposées pour le problème. Nous présentons deux algorithmes de génération de colonnes et branchements pour le problèmeIn this thesis we consider two capacitated network design (CND) problems, using OFDM multi-band technology. The first problem is related to single-layer network design with specific requirements. We give an ILP formulation for this problem and study the polyhedra associated with its arc-set restriction. We describe two families of facet defining inequalities. We devise a Branch-and-Cut algorithm for the problem. Next, we investigate the multilayer version of CND using OFDM technology. We propose several ILP formulations and study the polyhedron associated with the first (cut) formulation. We identify several classes of facets and discuss the related separation problem. We devise a Branch-and-Cut algorithm embedding valid inequalities of both single-layer and multilayer problems. The second formulation is compact, and holds a polynomial number of constraints and variables. Two further path formulations are given which yield two efficient Branch-and-Price algorithms for the problem
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Ott, Marion [Verfasser], and S. [Akademischer Betreuer] Berninghaus. "Second-price proxy auctions in bidder-seller networks : a game theoretic and experimental analysis / Marion Ott. Betreuer: S. Berninghaus." Karlsruhe : KIT-Bibliothek, 2009. http://d-nb.info/1014223121/34.
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Farníková, Jana. "Studie průběhu zakázky podnikem." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2013. http://www.nusl.cz/ntk/nusl-224179.
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This thesis focusses on how a specific order is processed in a specific company. The analysis is more specifically focussed on heat meters, which make up a substantial part of the company sales. It contains suggestions on how the company could process these orders using a program called Microsoft Project, which could benefit the company in many respects.
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Doiron, Meghan. "Information access, market trade and rural livelihoods in the Peruvian amazon: an analysis of communication networks and price uncertainty in riverine communities." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=117178.
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Information is an important part of livelihood decision making for rural peasants throughout the developing world. In recent times, the role of market information has become a topic of particular interest in development circles, given the global rise and increasing accessibility of information, brought about by technological innovations, notably the internet and mobile phones. In this study we examine the traditional social structures that facilitate access to market price information among ribereño producers in four villages along the Tahuayo River in the northeastern Peruvian Amazon. Further, we assess the factors that contribute to market price information uncertainty and the implications for household livelihood strategies. Methods include summary statistics, network analysis using NetDraw, and Probit and OLS regression models. Data were collected between June and August, 2011 and included informal interviews with the three Tahuayo River boat operators, participant observation, daily notation of market prices for four products of high regional importance: yuca, plantain, aguaje and charcoal, and semi-structured household interviews (n=70) with heads of households in the four study villages. Additionally, we use household survey data collected between June and November, 2010 from the same sampled households (n=70), and historic market price data from 1994, 1995 and 1998. Our results show that produce prices in Belén are highly variable as a result of unstable and, sometimes unpredictable market supply, which contributes to information uncertainty among ribereño producers. Information about market prices is primarily communicated by word of mouth from other members of the village; knowledge of market prices is variable and dependant on who has been to the market that week. Finally, telephone ownership did not appear to improve household knowledge of market price information, probably due to other issues such as lack of access to electricity supply, low affordability of telecommunication, and lack of an informant to call in the markets of Iquitos.La connaissance des prix du marché est un élément important du processus décisionnel au sujet des moyens de subsistance pour les paysans en milieu rural à travers les pays en voie de développement. Ces derniers temps, son rôle est devenu omniprésent ces milieux, compte tenu du développement mondiale et l'accroissement de l'accessibilité de l'information, provoquée par les innovations technologiques, notamment internet et les téléphones mobiles. Dans cette étude, nous examinons les structures sociales traditionnelles qui facilitent l'accès aux prix du marché entre les producteurs ribereño dans quatre villages longeant la rivière Tahuayo dans le nord-est de l'Amazonie péruvienne. De plus, nous évaluons les facteurs qui contribuent à l'incertitude de l'information sur le marché des prix et les implications sur les stratégies de subsistance des ménages. La méthodologie comprend des statistiques sommaires, des analyses de réseau en utilisant NetDraw et Probit et des modèles de régression OLS. Les données ont été recueillies au cours de Juin et Août 2011 et comprenaient des entretiens informels avec les trois exploitants de bateaux de transport par la rivière Tahuayo, des observations des participants, des relevés quotidiens des prix du marché pour quatre produits de haute importance régionale (le yuca, la banane plantain, le aguaje et le charbon de bois) et des entrevues semi-structurées auprès des chefs de ménages (n = 70) dans les quatre villages études. Nous utilisons aussi des données sur les ménages recueillies entre Juin et Novembre 2010 auprès des mêmes ménages échantillonnés (n = 70), et des données historiques des prix du marché entre 1994 et 1996. Nos résultats démontrent que les prix des produits du marché de Belén sont très variables en raison de l'approvisionnement instable du marché, voire parfois imprévisible, ce qui contribue à l'incertitude de l'information au sein des producteurs ribereño. L'information sur les prix du marché est principalement transmise de bouche à oreille par les autres membres du village. La connaissance des prix du marché est variable et dépend de la personne qui a été au marché pour cette semaine. Enfin, posséder un téléphone n'a pas amélioré la connaissance des ménages des prix du marché, probablement en raison d'autres problèmes sous-jacents liés à l'utilisation d'un téléphone comme le manque d'infrastructures électriques, l'accessibilité des télécommunications et de l'absence d'un informateur à appeler à Belén.
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Wang, Nancy. "Spectral Portfolio Optimisation with LSTM Stock Price Prediction." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273611.
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Nobel Prize-winning modern portfolio theory (MPT) has been considered to be one of the most important and influential economic theories within finance and investment management. MPT assumes investors to be riskaverse and uses the variance of asset returns as a proxy of risk to maximise the performance of a portfolio. Successful portfolio management reply, thus on accurate risk estimate and asset return prediction. Risk estimates are commonly obtained through traditional asset pricing factor models, which allow the systematic risk to vary over time domain but not in the frequency space. This approach can impose limitations in, for instance, risk estimation. To tackle this shortcoming, interest in applications of spectral analysis to financial time series has increased lately. Among others, the novel spectral portfolio theory and the spectral factor model which demonstrate enhancement in portfolio performance through spectral risk estimation [1][11]. Moreover, stock price prediction has always been a challenging task due to its non-linearity and non-stationarity. Meanwhile, Machine learning has been successfully implemented in a wide range of applications where it is infeasible to accomplish the needed tasks traditionally. Recent research has demonstrated significant results in single stock price prediction by artificial LSTM neural network [6][34]. This study aims to evaluate the combined effect of these two advancements in a portfolio optimisation problem and optimise a spectral portfolio with stock prices predicted by LSTM neural networks. To do so, we began with mathematical derivation and theoretical presentation and then evaluated the portfolio performance generated by the spectral risk estimates and the LSTM stock price predictions, as well as the combination of the two. The result demonstrates that the LSTM predictions alone performed better than the combination, which in term performed better than the spectral risk alone.Den nobelprisvinnande moderna portföjlteorin (MPT) är utan tvekan en av de mest framgångsrika investeringsmodellerna inom finansvärlden och investeringsstrategier. MPT antar att investerarna är mindre benägna till risktagande och approximerar riskexponering med variansen av tillgångarnasränteavkastningar. Nyckeln till en lyckad portföljförvaltning är därmed goda riskestimat och goda förutsägelser av tillgångspris. Riskestimering görs vanligtvis genom traditionella prissättningsmodellerna som tillåter risken att variera i tiden, dock inte i frekvensrummet. Denna begränsning utgör bland annat ett större fel i riskestimering. För att tackla med detta har intresset för tillämpningar av spektraanalys på finansiella tidsserier ökat de senast åren. Bland annat är ett nytt tillvägagångssätt för att behandla detta den nyintroducerade spektralportföljteorin och spektralfak- tormodellen som påvisade ökad portföljenprestanda genom spektralriskskattning [1][11]. Samtidigt har prediktering av aktierpriser länge varit en stor utmaning på grund av dess icke-linjära och icke-stationära egenskaper medan maskininlärning har kunnat använts för att lösa annars omöjliga uppgifter. Färska studier har påvisat signifikant resultat i aktieprisprediktering med hjälp av artificiella LSTM neurala nätverk [6][34]. Detta arbete undersöker kombinerade effekten av dessa två framsteg i ett portföljoptimeringsproblem genom att optimera en spektral portfölj med framtida avkastningar predikterade av ett LSTM neuralt nätverk. Arbetet börjar med matematisk härledningar och teoretisk introduktion och sedan studera portföljprestation som genereras av spektra risk, LSTM aktieprispredikteringen samt en kombination av dessa två. Resultaten visar på att LSTM-predikteringen ensam presterade bättre än kombinationen, vilket i sin tur presterade bättre än enbart spektralriskskattningen.
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Djervbrant, Karl-Johan, and Andreas Häggström. "A Study on Fingerprinting of Locally Assigned MAC-Addresses." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-39749.
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The number of WiFi Devices is increasing every day, most people traveling hasa device with a WiFi network card enabled. This is something EffectSoft AB in Halmstad utilizes in their service Flow, to track and count devices. The accuracy of counted devices was however not accurate enough for a commercial use and this is where this candidate Thesis will continue research on how to improve the accuracy. It introduces the fundamental problem on how one cannot directly count transmitted MAC-Addresses to count present devices, since the manufacturers implement features against this such as MAC-Address randomization. It covers how manufacturers are not consistent in their implementation of the IEEE 802.11 standard, how this can be utilized to estimate how many devices are present in the networkwith three different approaches. It also concludes that Control Frame Attacks is not a viable approach any longer to count devices and the best method for counting devices are a combination of Passive Probe Request Analysis techniques.Mängden enheter som kommunicerar över WiFi ökar dagligen och idag bär de flesta människor en enhet med ett aktiverat WiFi-nätverkskort. Detta använder EffectSoft AB, ett företag i Halmstad till sin teknik Flow för att räkna mobila enheter. Noggrannheten för beräkningen är dock inte tillräckligt bra för att produkten ska kunna vara applicerbar på marknaden och därav handlar denna kandidatuppsatsen om beräkning av mobila enheter. Denna rapport presenterar de problem som man stöter på vid beräkning av mobila enheter som till exempel randomisering av MAC-Adresser. Den täcker även hur tillverkare inte är konsekventa i sin implementation av IEEE 802.11 standarden och hur detta kan utnyttjas genom tre metoderför beräkning av antal mobila enheter. Det fastställs att Control Frame Attack inte längre är en möjlig metod för syftet samt att den bästa metoden för beräkning avantalet mobila enheter är en kombination av olika passiva Probe Request analyser.
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Queiroz, Leonardo Mendonça Oliveira de. "Estimação e analise das perdas tecnicas na distribuição de energia eletrica." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260679.
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Orientadores: Christiano Lyra Filho, Celso CavellucciTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: Este trabalho estuda a estimação de perdas técnicas na distribuição de energia elétrica e apresenta uma análise para a definição dos níveis adequados dessas perdas. Ambas as abordagens são focadas na regulação. É apresentada uma metodologia de estimação das perdas técnicas de energia baseada no valor médio e na variância dos pontos da curva de carga. Essa metodologia pode ser aplicada alternativamente aos métodos baseados na perda de potência máxima, que inserem imprecisões desnecessárias para a estimativa das perdas de energia. Foram desenvolvidos modelos de regressão para a estimação das perdas técnicas em redes de distribuição de média e baixa tensão, objetivando utilizar o mínimo de informações possíveis para uma precisão adequada. Uma metodologia de geração de redes foi desenvolvida para o estudo desses modelos, de forma a disponibilizar redes com características semelhantes às redes reais. Também são propostos aprimoramentos na estimativa das perdas em transformadores e ramais de ligação. Adicionalmente, é apresentada uma análise dos níveis adequados de perdas técnicas na distribuição, utilizando-se técnicas de engenharia e benchmarking. As propostas deste trabalho sugerem aprimoramentos na regulação das perdas técnicas, tornando o método de estimação das perdas mais preciso e introduzindo a análise de eficiência das distribuidoras.
Abstract: This work studies technical losses estimation in power distribution systems and analyses the adequacy of the losses. Both approaches are carried in a regulatory perspective. It is presented a methodology to estimate energy losses from the mean and the variance of the load curve points. This methodology can be applied in substitution of methods based on maximum power losses, which inserts unnecessary inaccuracy to the procedure. Regression models were developed to estimate technical losses in medium and low voltage distribution networks, aiming to require less information as possible to meet an appropriate accuracy. A methodology of networks generation was developed to make available networks with characteristics similar to the ones presented by real networks. Improvements in transformers and service conductors losses estimation were also proposed. Engineering and benchmarking techniques were applied to analyze technical losses adequacy. The proposals presented in this work may improve technical losses regulation, making the estimation of losses more accurate and introducing efficiency analysis of power distribution companies.
Doutorado
Automação
Doutor em Engenharia Elétrica
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Radoš, Daniel. "Algoritmické obchodování na burze s využitím umělých neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363869.
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This master's thesis is focused on algorithmic trading on the forex market using artificial neural networks. In the introduction, there are generally described terms concerning the trading. Subsequently, in the following chapters, the thesis describes the theory of neural networks and their possible use. The practical part contains designed business strategies with neural networks. Inputs used in the network are indicators of technical analysis or directly price level. Business strategies have been implemented and tested. In the conclusion, there are summarized findings of individual business models.
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Habtemariam, Mesay. "Bioinformatics Approach to Probe Protein-Protein Interactions: Understanding the Role of Interfacial Solvent in the Binding Sites of Protein-Protein Complexes;Network Based Predictions and Analysis of Human Proteins that Play Critical Roles in HIV Pathogenesis." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/2997.
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The thesis work contains two projects under the same umbrella. The first project is to provide a detailed analysis on the behavior of interfacial water molecules at protein-protein complexes, in this case focusing on homodimeric complexes, and to investigate their effect with respect to different residue types. For that reason the homodimeric data-set, which includes high-resolution (≤ 2.30 Å) X-ray crystal structures of 252 (140 Biological & 112 Non-biological) protein complexes was chosen to explore fundamental differences between interfaces that Nature has “engineered” vs. compared to interfaces found under man-made conditions. The data set was comprised of 5391 water molecules where a maximum of 4 Å from both interfacing proteins. Our analysis is applied a suite of modeling tools based on HINT, a program for hydropathic analysis developed in our laboratory. HINT is based on the experimental measurement of the hydrophobic effect. The second project is designed to explore various means of suppressing the expression of human genes that play critical role in HIV pathogenesis. To achieve this aim, a data set of Affymetrix Human HG Focus Target Array, which measures the expression levels of HIV seronegative and seropositive individuals in human PBMCs, was analyzed with Pathway Studio 9.0 software. This work gives insight into the elucidation of the important mechanisms of human proteins interactions in HIV seropositive individuals and their implications. Hence, we found the kind and types of microRNAs that are suppressing the human genes which have great role for HIV replication in a cell.
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Sognestrand, Johanna, and Matilda Österberg. "KOLLEKTIVTRAFIKENS GEOGRAFISKA VARIATIONER I TID OCH KOSTNAD – HUR PÅVERKAR DETTA BOSTADSPRISERNA? : Fallstudie Uppsala län med pendlingsomland." Thesis, University of Gävle, Ämnesavdelningen för samhällsbyggnad, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-5881.
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The distance between home and work has increased in recent decades. By the development of infrastructure and public transport, jobs farther from home have become more accessible and this development has in turn increased commuting. Commuting travellers often pass over administrative boundaries which often serve as borders for public transport pricing. Also the market control prices. Research shows that travel times and costs significantly affect commuting choice. Many people have an upper limit of 60 minutes commuting distance between home and work. How commuting costs affect the individual's choice of commuting will vary depending on the individual's income and housing costs. The aim of our study was to see how public transport costs and travel times may vary geographically. GIS, Geographic Information System was used to make a network analysis which showed time distances and travel costs on maps. We also examined whether there was a link between towns accessibility by public transport and housing market which we did with help of correlation and regression analysis. In order to answer our questions we started from a study area consisting of Uppsala County with its surrounding commuting area. The maps showed how accessibility to larger towns varies among the smaller towns. The access is often best between bigger towns while there is less accessibility between smaller towns. The distance to bus stops or railway station also has a significant effect on how long the total travel time will be. Urban areas with access to rail services had the best opportunities to reach larger cities and that give also better access to labour market. From our study of the Uppsala County with a monocentric structure, we could indicate a link between accessibility to the bigger cities and housing prices in the surrounding towns. The higher commuting costs and longer travel time to the central place the lower the housing prices. A similar study of Stockholm which has a polycentric structure showed that the relationship between accessibility and house prices not are applicable to all regions. Here we can conclude that housing markets depends on many other factors than access to rapid public transport. House prices can depend on things like closeness to nature and water.
Avståndet mellan bostad och arbete har ökat under de senaste decennierna. Utvecklingen av infrastruktur och kollektivtrafik har lett till att arbetsplatser längre från hemmet har blivit mer tillgängliga och denna utveckling har i sin tur bidragit till en ökad arbetspendling i samhället. Pendlingsresenärer passerar ofta över administrativa gränser och dessa gränser styr ofta över kollektivtrafikens prissättning men även efterfrågan kan styra priset. Forskning visar att restider och kostnader i hög grad påverkar pendlingsvalet. Många människor föredrar ett pendlingsavstånd, mellan hem och arbete på högst 60 minuter. Hur pendlingskostnader påverkar individens val till pendling varierar bland annat beroende på individens inkomst och boendekostnader.
Syftet med vår studie var att se hur kollektivtrafikens kostnader och restider kan variera geografiskt. GIS, Geografiska Informationssystem, användes vid utförandet av en nätverks- och kostnadsanalys vilket visade tidsmässigt avstånd och kostnad på kartor. Vi undersökte också om det fanns ett samband mellan orters tillgänglighet med kollektivtrafik och bostadsmarknaden genom att utföra korrelations- och regressionsanalyser. För att svara på våra frågeställningar utgick vi från ett undersökningsområde bestående av Uppsala län med pendlingsomland.
Kartbilderna visade tydligt hur tillgängligheten till större städer varierar mellan olika orter och att tillgängligheten ofta är bäst mellan större tätorter medan det är sämre tillgänglighet mellan mindre tätorter. Avståndet till hållplatser har också betydande påverkan på hur lång den totala restiden blir. Tätorter med tillgång till järnvägstrafik hade det bästa möjligheterna att nå större tätorter och därmed blir arbetsmarknaden större för dessa orter. Från vår studie över Uppsala län som kan anses ha monocentrisk struktur kunde vi även tyda ett samband mellan tätorters tillgänglighet till centralorten och orternas bostadspriser. Ju högre pendlingskostnad och längre restid till centralorten desto lägre var orternas bostadspriser. En likadan studie över Stockholm som har en mer polycentrisk struktur visade dock att detta samband mellan tillgänglighet och bostadspriser inte gäller för alla regioner. Här kan vi dra den slutsatsen att bostadsmarknaden styrs av många andra faktorer än tillgång till snabb kollektivtrafik och att vissa områdens bostadspriser mer styrs av exempelvis närhet till natur och vatten.
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Chasák, Petr. "Analýza informačního systému firmy a návrh změn." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2010. http://www.nusl.cz/ntk/nusl-222630.
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Nowadays the information system of dynamically developing companies has an increasingly important role. The aim of this thesis is to evaluate current information system used in Brněnské komunikace a.s., analyze its drawbacks and weak spots and then propose steps leading to its improvement and thus to increase the company´s performance.
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Chloupek, Pavel. "Posouzení informačního systému firmy a návrh změn." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2011. http://www.nusl.cz/ntk/nusl-222836.
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Nowadays the information system of dynamically developing companies has an increasingly important role. IS MFG PRO of Fei Company will not be easy to criticize. Thesis will try to direct on coincident state. Thesis will try to find out it´s positives and negatives and according to this informations it will contained changes suggestions whitch can be profitable in state of effectivity to company.
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Šulc, Martin. "Návrh marketingového plánu pro firmu specializovanou na informační zboží." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236393.
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The subject of this diploma thesis "The proposal of a marketing plan for a company specialized in information products" is to suggest a system based on the theoretical background that generates the marketing plan from the input data. The important requirement for this system is to respect the specialization of the company, which operates in the field of information products. The thesis contains the detailed proposal of a prototype system, in which the focus is on the user-friendliness and the complexity considering the scale of the issue analyzed. The proposal also contains the possibilities and the limitations which are the results from the mentioned solution. The proposed prototype is implemented successfully. The work also includes the demonstration of all implemented functions and the description of the improvements that lead to its use also in other areas of business than the market of information products.
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Maršová, Eliška. "Predikce hodnot v čase." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255333.
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This work deals with the prediction of numerical series whose application is suitable for prediction of stock prices. They explain the procedures for analysis and works with price charts. Also explains the methods of machine learning. Knowledge is used to build a program that finds patterns in numerical series for estimation.
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Tupý, Maroš. "Rizika v oblasti řízení marketingových nástrojů." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2018. http://www.nusl.cz/ntk/nusl-382727.
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Cholachue, Ngounou Christel. "Caractérisation des blindages électromagnétiques des câbles et faisceaux aéronautiques." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMR025.
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Ces dernières années, la multiplication des loisirs à bord des nouveaux avions a connu une croissance exponentielle. Dans un appareil comme l’A380, chaque siège intègre plusieurs fonctions (jeux vidéo, musique, etc. ..) et chaque fonction est connectée à l’aide d’au moins un câble électrique. Ce qui nécessite un nombre important de kilomètres de câbles pour établir toutes les connexions électriques à bord d’un tel appareil. En plus l’électrification progressive pour des raisons de sécurité des fonctions auparavant mécaniques, hydrauliques ou pneumatiques, a augmenté les exigences de câblage notamment en compatibilité électro- magnétique (CEM). La cohabitation de tous ces kilomètres de câbles dans un espace aussi réduit a accru les exigences en termes de blindage électromagnétique (EM). Les nombreuses méthodes d’analyse du blindage des câbles et faisceaux sont limitées en termes de rapidité, d’analyse des systèmes multiports. Les outils de simulations eux sont très onéreux (prévoir environ18Ke pour une licence), et nécessitent des compétences avancées et beaucoup de temps pour la caractérisation du blindage des câbles et faisceaux de câbles. Avec un outil de simulation EM 2D/3D comme HFSSR d’ANSYSR, il faut prévoir environ trois heures pour créer le modèle d’une gaine de blindage tressé, et également prévoir un temps de simulation moyen de 20 minutes en utilisant un PC équipé d’un processeur mono-cœur Intel RXeon RCPUE5-1620v4@ 3,50 GHz et 32 Go de RAM physique avec 64 bits Windows 10. En plus, les méthodes et techniques de caractérisation du blindage des câbles et faisceaux de câbles aéronautiques ont montré leurs limites à l’instar du banc triaxial avec lequel il est difficile d’effectuer des mesures d’impédance de transfert au-delà de 100 MHz. Les travaux effectués dans cette thèse ont pour but de dépasser ces limites. Nous avons développé une nouvelle méthode d’extraction rapide des paramètres S des systèmes multiports. La connaissance analytique des paramètres S permet de remonter à des caractéristiques intrinsèques des structures de blindage tubulaire. Par exemple, nos calculs des paramètres S d’une structure de câble coaxial sont prometteurs pour déterminer l’impédance de transfert par rapport à la géométrie et d’autres paramètres physiques des gaines. L’originalité de ce travail de recherche consiste à se familiariser à une méthode analytique et semi-hybride de modélisation des structures de blindage tubulaire en exploitant le formalisme de l’Analyse Tensorielle des Réseaux (ATR) à base de la méthode de Kron. Ce formalisme offre un avantage notable pour l’analyse rapide avec une précision relativement importante des systèmes électriques et électroniques complexes comme le cas des éléments de blindage. La rapidité de la méthode a été évaluée avec différentes structures sur une bande de fréquence allant de 0 Hz à quelques gigahertz en menant à des expressions dont les applications numériques se font sur un temps de calcul ne dépassant pas les millisecondes. D’autre part, nous avons aussi développé nos savoir-faire sur l’analyse des blindages électromagnétiques des câbles et faisceaux de câbles. Une approche a été élaborée pour déterminer l’efficacité de blindage (EB) d’une gaine via une configuration de couplage entre un câble coaxial blindé et une sonde boucle. Une méthode de modélisation innovante a été développée à l’aide de la théorie des circuits pour déterminer l’efficacité de blindage en étudiant le couplage entre un câble nu (conducteur interne) et un câble tressé en parallèle par la méthode de Kron. Nous avons également développé un banc de caractérisation des structures tubulaire de blindage électromagnétique dans le but de fonctionner jusqu’à 300 MHz. Les résultats dans ces études ont été confirmés par des simulations 3D et des mesuresDuring the last decade, the proliferation of on-board leisure activities in the new aircrafts have been growing exponentially. In the airplane like A380, each seat integrates several functions (video games, music, etc. ..) Additionally, each function must be connected using at least one electric cable. This system requires a significant number of kilometers of cables to establish all the on-board electrical connections. Furthermore, for reasons of safety and security related to mechanical, hydraulic or pneumatic functions, the wiring EMC requirements associated to the massive progressive electrification becomes considerably stricter. The coexistence of kilometer lengths of cables system in such a small space has increased the requirements in terms of electromagnetic (EM) shielding. Most of existing numerous methods for analyzing the shielding of cables and harnesses are limited in terms of computation speed, design process and in accuracies for the multiport systems analysis. Moreover, most of popular simulation and commercial tools are very expensive (for example with license cost can be more than 18K€). The use of commercial tools requires advanced skills and a lot of time to characterize the shielding of cables and harnesses. For example, with a simulation tool like HFSSR from ANSYSR , the computation time may cost approximately 3 hours to create a design model of a braided shields heath. Then, the computed results can be generated during an average simulation time of 20 minutes using a PC equipped with an Intel single-core processor RXeon RCPU E5-1620 v4 @ 3.50 GHz and 32 GB of physical RAM with 64-bit Windows 10. Most of available methods and techniques for characterizing the shielding of aeronautical cables and cable harnesses have shown their limits. For example, most of existing triaxial benches are particularly difficult to deploy for the transfer impedance measurements and they cannot operate beyond 100 MHz. The present PhD thesis aims to overcome these technical limits. Doing this, an original analytical method is developed for extracting S-parameters from multiport systems under fast computation speed and design process. An innovative methodology of EMC modelling was proposed. The knowledge of S-parameters is helpful to determine the broadband EM intrinsic parameters of the cabling as coaxial system. The developed analytical and semi-hybrid model is based on the unfamiliar formalism using tensorial analysis of networks (TAN) based on the Kron’s method. The model offers an outstanding possibility to analyze complex systems with deep knowledge of physical phenomenal behind the EM shielding. Thanks to the TAN formalism, an innovative method of circuit theory has been developed to determine the shielding efficiency (SE) of shielded cable. The feasibility of this multiport S-parameter approach was verified with the consideration of EM coupling between a nude cable constituting an internal conductor and a braided cable placed in parallel. More importantly, an advanced study of shielding efficiency (SE) with respect to the EM coupling configuration between a shielded coaxial cable and a loop probe is performed. Substantially, it was noteworthy that the TAN formalism provides an illuminating know-how on the theoretical, numerical and experimental analyses of cables and bundles EM shielding, and transfer impedances of the shielding sheath. Moreover, the TAN modelling effectiveness was confirmed with different applications with computation time which does not exceed milliseconds. Finally, the TAN model was also used to develop a SE characterization bench for tubular EM shielding structures up to 300 MHz. Emphatically, broadband SE and transfer impedance results in good correlation between 3D simulations and measurements were obtained
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Silva, Ana Catarina Lopes da. "Pricing network analysis of a supermarket chain in Portugal." Master's thesis, 2022. http://hdl.handle.net/10362/133847.
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Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementWith the growth of digital transformation, the business is now conducted in the digital age, opening new markets and new business opportunities with a simple click, sitting at home. Along with it, as the internet grows, more data is exposed, which leads us to competitive advantages, especially during the pandemic and in the post-pandemic period. This work explores daily price data from the online website of one of the largest supermarket chains in Portugal, covering the period between February 2020 to March of 2021. To observe the market and the competition behavior, we applied the science of networks, a data mining technique that provides deeper insight into the structure of the retailer market. We used the Maximum Spanning Tree and added the most relevant links from the correlation matrix to represent the retail market. As a starting point, we were able to build five networks that represent the daily price changes for products, categories, and brands. Moreover, we study the network interactions using centrality measures, namely: degree, betweenness, closeness, and eigenvector. These outputs were useful by creating better visibility of the market using real data from online retailing platforms and making available the first input for future work and further research to improve the pricing strategies.
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Yarish, David. "Utilizing Crowd Sourced Analytics for Building Smarter Mobile Infrastructure and Achieving Better Quality of Experience." Thesis, 2015. http://hdl.handle.net/1828/7000.
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There is great power in knowledge. Having insight into and predicting network events can be both informative and profitable. This thesis aims to assess how crowd-sourced network data collected on smartphones can be used to improve the quality of experience for users of the network and give network operators insight into how the networks infrastructure can also be improved.
Over the course of a year, data has been collected and processed to show where networks have been performing well and where they are under-performing. The results of this collection aim to show that there is value in the collection of this data, and that this data cannot be adequately obtained without a device side presence. The various graphs and histograms demonstrate that the quantities of measurements and speeds recorded vary by both the location and time of day. It is these variations that cannot be determined via traditional network-side measurements. During the course of this experiment, it was observed that certain times of day have much greater numbers of people using the network and it is likely that the quantities of users on the network are correlated with the speeds observed at those times. Places of gathering such as malls and public areas had a higher user density, especially around noon which could is a normal time when people would take a break from the work day. Knowing exactly where and when an Access Point (AP) is utilized is important information when trying to identify how users are utilizing the network.Graduate
davidyarish@gmail.com
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LIU, CHUN-YU, and 劉峻宇. "Applying Technical Analysis and Neural Networks for Stock Price Prediction." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/st87nz.
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碩士國立臺北大學
電機工程學系
106
The stock market forecasting is an important issue in financial engineering. An accurate forecasting system helps investors obtain high profit margin. With the development of technologies and the evolution of big data, the stock market investment will no longer be directly performed by the human; instead, intelligent investment will provide investors more accurate strategy analysis and more effective investment decisions. Therefore, this study proposed to combine the technical analysis pointers with the back propagation neural network. The technical analysis provided several useful functions such as stock price analysis, forecasting, and obtaining the key data in the stock price. We used the technical pointers instead of the raw data as the input variables of neural networks and verified if the pre-processing data can achieve more accurate stock price prediction. The technical analysis indicator package was written in the R language. The four major indexes of U.S stock market, Dow Jones Industrial Average, Philadelphia Semiconductor Index, Standard & Poor's 500 Index, NASDAQ Composite Index and sixteen listed companies serve as the sample data. Several kinds of pre-processing models were introduced. Through looking into the experimental results, the proposed package helped the neural networks achieve better performance. The proposed package passed a comprehensive R archive network (CRAN) check and made contribution to R in the field of stock data analysis.
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"Application of neural network to study share price volatility." 1999. http://library.cuhk.edu.hk/record=b5896263.
Full textAbstract:
by Lam King Wan.Thesis (M.B.A.)--Chinese University of Hong Kong, 1999.
Includes bibliographical references (leaves 72-73).
ABSTRACT --- p.ii.
TABLE OF CONTENTS --- p.iv.
Section
Chapter I. --- OBJECTIVE --- p.1
Chapter II. --- INTRODUCTION --- p.3
The principal investment risk --- p.3
Effect of risk on investment --- p.4
Investors' concern for investment risk --- p.6
Chapter III. --- THE INPUT PARAMETERS --- p.9
Chapter IV. --- LITERATURE REVIEW --- p.15
What is an artificial neural network? --- p.15
What is a neuron? --- p.16
Biological versus artificial neuron --- p.16
Operation of a neural network --- p.17
Neural network paradigm --- p.20
Feedforward as the most suitable form of neural network --- p.22
Capability of neural network --- p.23
The learning process --- p.25
Testing the network --- p.29
Neural network computing --- p.29
Neural network versus conventional computer --- p.30
Neural network versus a knowledge based system --- p.32
Strength of neural network --- p.34
Weaknesses of neural network --- p.35
Chapter V. --- NEURAL NETWORK AS A TOOL FOR INVESTMENT ANALYSIS --- p.38
Neural network in financial applications --- p.38
Trading in the stock market --- p.41
Why neural network could outperform in the stock market? --- p.43
Applications of neural network --- p.45
Chapter VI. --- BUILDING THE NEURAL NETWORK MODEL --- p.47
Implementation process --- p.48
Step 1´ؤ Problem specification --- p.49
Step 2 ´ؤ Data collection --- p.51
Step 3 ´ؤ Data analysis and transformation --- p.55
Step 4 ´ؤ Training data set extraction --- p.58
Step 5 ´ؤ Selection of network architecture --- p.60
Step 6 ´ؤ Selection of training algorithm --- p.62
Step 7 ´ؤ Training the network --- p.64
Step 8 ´ؤ Model deployment --- p.65
Chapter 7 --- RESULT AND CONCLUSION --- p.67
Result --- p.67
Conclusion --- p.69
BIBLIOGRAPHY --- p.72
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Wang, Kung-Piao, and 王光麃. "The Analysis of Monopoly’s Optimal Quality and Price Under Network Externality Surroundings." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/99708225443111255093.
Full textAbstract:
碩士國立高雄第一科技大學
運輸倉儲營運所
91
Katz and Shapiro (1985) deemed that network externality originated from increment of product consumption which emanates more demand quantity. It is possible that network is from network externality and both of them belong to positive relation, meaning that increment of consumption can raise network externality. Based on classical quality demand theory, quality is viewed as exogenous variable .In the other hand, this study manipulate quality as a ingenuous variable and therefore, when pricing optimally, firms need to think about positive and negative effects caused by quality at the same time. This study employs monopoly and duopoly models to analyses firm’s optimal price and quality in comparative static analysis. Results reveal that in duopoly model, one firm is located on 1/3 and the other is located on .2/3, which is distributed averagely in the market. According to monopoly structure, equilibrium price and quantity are under increasing condition and in addition, quality demand elasticity and price demand elasticity display decreasing phenomenon. Ultimately, monopoly model is revised to dynamic optimal control theory with one state variable (reputation) ,one control variable (quality).and space economic idea proposed by Hotelling In dynamic control policy, monopoly structure lying in autonomous system is under saddle point equilibrium. In the non-autonomous system, quality and reputation reveals a stable equilibrium.
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Li, Kuo-Cheng, and 李國成. "Prediction on Stock Price Variations Using Keyword Analysis and Recurrent Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/yxt87r.
Full textAbstract:
碩士國立臺灣科技大學
資訊管理系
106
Nowadays, the stock is still the most popular investment tool in our society. To find the time of trading for a stock is the most important issue that concerns all the investors. When selecting an investment target and finding the time of trading, most of investors take advantage of technical analysis and fundamental analysis. However, the influence of financial news on the stock market should also be taken into account since the frequently released financial news carry large amount of information that influences the expectation of an investor on a stock. As such, the financial news may affect the trading behavior of the investor. This thesis aims at constructing a prediction model on the variations of a stock price based on the company-related news and technical analysis of the stock. To this end, we collected financial news of several listed stocks in Taiwan Stock Exchange (TWSE). We first use text mining algorithm to filter out important keywords from the news and calculate their corresponding scores of importance. Then, we train to find the representing vectors of the important words using Word2Vec model. Finally, the vectors of important words of the daily news of a stock are multiplied by their corresponding scores of importance to generate a news vector for the stock. With the daily news vector and several financial variables, we construct a prediction model on the selected stocks using the long short-term memory (LSTM) recurrent neural network. We conducted several experiments on the proposed model, termed Text-LSTM, a model without considering the daily news, termed LSTM, and a model constructed using ARIMA model. The experimental results showed that the Text-LSTM achieved an average of 61.8% accuracy and a maximum of 69.3% accuracy in Directional Symmetry (DS) on the predictions. The accuracy of the Text-LSTM outperforms those of the pure LSTM and the ARIMA by 6.2% and 13.4%, respectively.
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Yu-DeLin and 林宇德. "Text Analysis for Prediction of Bitcoin Price by Sequence Neural Network Model." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/r8hw6e.
Full textAbstract:
碩士國立成功大學
資訊工程學系
106
With the accelerated development of artificial intelligence, some people want to use it to predict market trends. Simultaneously, digital currency, headed by Bitcoin and Ethereum, caught people’s attention because of its soaring price in last year. The reputation of digital currency get higher and higher in social media and traditional media. People certainly hope to use AI to predict the digital currency market. In this research, we use Twitter posts as training data and vectored method to represent the tweet information (day vector) per day. After cleaning Twitter raw data, we converted the tweets in the giving day as day vector and feed the day vector to sequence to Sequence model use to predict the change of Bitcoin price. The entire system uses attention model in day vector model and the sequence to sequence model, respectively. The experiments show that the prediction accuracy rise slightly by increasing day vector dimension and the attention model of the SequenceDecoder model can significantly improve the accuracy. Finally, we analyzed the 7-day predicted results individually and found that the accuracy decrease when predicting latter day. This meet our understanding that it is harder to predict the latter day than the near day.
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Gullapalli, Sneha. "Learning to predict cryptocurrency price using artificial neural network models of time series." 2018. http://hdl.handle.net/2097/38867.
Full textAbstract:
Master of ScienceDepartment of Computer Science
William H. Hsu
Cryptocurrencies are digital currencies that have garnered significant investor attention in the financial markets. The aim of this project is to predict the daily price, particularly the daily high and closing price, of the cryptocurrency Bitcoin. This plays a vital role in making trading decisions. There exist various factors which affect the price of Bitcoin, thereby making price prediction a complex and technically challenging task. To perform prediction, we trained temporal neural networks such as time-delay neural networks (TDNN) and recurrent neural networks (RNN) on historical time series – that is, past prices of Bitcoin over several years. Features such as the opening price, highest price, lowest price, closing price, and volume of a currency over several preceding quarters were taken into consideration so as to predict the highest and closing price of the next day. We designed and implemented TDNNs and RNNs using the NeuroSolutions artificial neural network (ANN) development environment to build predictive models and evaluated them by computing various measures such as the MSE (mean square error), NMSE (normalized mean square error), and r (Pearson’s correlation coefficient) on a continuation of the training data from each time series, held out for validation.
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莊文慶. "The Analysis of Stock Price from Macro-Economic Factors : -- Apply Artificial Neural Network." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/57337091607566322842.
Full text
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HUNG, YUNG-YI, and 洪詠譯. "Jet Fuel Price Forecasting with Time Series Analysis and Artificial Neural Networks." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/88449903669522035173.
Full textAbstract:
碩士國立高雄第一科技大學
運籌管理所
97
Abstract This study forecasts jet fuel price with time series analysis and artificial neural networks. The fuel price of aviation fluctuates violently, the fuel cost accounts for more than 50% of airline''s total cost at the time of the peak, cause the enormous business losses. At the time of fast drop, because of fuel hedge direction mistake, it has to withdraw huge amortization of non-operating loss also. So airlines who can make a profit or suffer the loss continuously depend on its fuel supply chain stability and success of fuel hedge definitely. Hence, predict the tendency of the jet fuel price, not only the focal point of reducing expenditure, but also could obtain non-operating income. As to fuel hedge operations and energy supply chain stability, the prediction ability of the future price is important keys. So this research combines some time series models, for instance: ARIMA, GARCH, VAR, VECM and structural change point method, with artificial neural networks (ANN) to forecast the Singapore jet fuel spot price. This Research looks for oil price volatile factors and historical datum with relevant documents at first, and then selects proper parameters and prediction models of time series analysis and artificial neural networks. With EIA Petroleum (2009) data from June of 1986 to November of 2008 , in empirical research, finally succeed in to gain three models of the good average accuracy of out sample prediction, are respectively to 89.83%, 91.87% and 95.85%. So, these models of this research could be offered to the airlines as reference on forecasting decisions of the jet fuel price.
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WEN-HWA, CHEN, and 陳文華. "THE SENSITIVITY ANALYSIS FOR VARIABLES OF FORECASTING GOVERNMENT BOND PRICE VIA USING NEURAL NETWORK." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/05018225584382357818.
Full textAbstract:
碩士國立臺灣大學
國際企業學研究所
89
This research aims to explore (1)the important variables for forecasting government bond price and the leading periods in which those variables influence government bond price. (2)whether the relative importance of every variables would change with time.(3)to fine tune the input variables for neural network and to examine the improvement of prediction performance ,according to the importance of variables in every interval. The key characteristics of this research methodology include (1)using unit root test for input variables in order to avoid spurious regression. (2)using adjusted Granger causality test to screen input variables and simultaneously to find out the leading periods of every variables. (3)dividing the variables into major and minor variables. If the major variables couldn’t be selected, they would be kept as the input variables to avoid the pitfall of the linear model for unpurposely screening out the important un-linear related variables. (4)designing the dynamic Sensitivity Analysis and Granger Causality test and illustrating the time track of the importance of every input variables. (5)constructing the neural network by adjusting dynamically the set of input variables. This study contributes to both academic and application researching in the following four aspects.(1)when using neural network for forecasting, latest variables should not be directly input but to consider the periods the input variables lead output variables. (2)whether the importance of variables will change in different interval should be considered. If the importance of every variable changes with time, the same variables should not be input for a long time during forecasting.(3)Improving the prediction performance by capturing the change of the importance of the input variables. (4)describing every variables in influencing government bond price (ex. positive or negative relation between them or mainly long term influence or mainly short term influence)
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OU, YAO-LUN, and 歐曜綸. "Integration of Artificial Neural Network and Technical Analysis for Stock Price Prediction in Taiwan." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/t5844d.
Full textAbstract:
碩士中國文化大學
財務金融學系
107
Following the development of artificial intelligence, practitioners are gradually using artificial intelligence to handle complex huge amounts of data (big data). This study uses a neural network that is also a kind of artificial intelligence technology and combines with technical analysis data as the predictive variables of artificial neural network. First, we compare the predicted performance of different hidden layer and different neuron number combinations, and then compare the performance of GARCH volatility as the predictive variable of artificial neural network. We use the historical share price data of MSCI Taiwan Index for the period of 2014-2018 and view the predicted performance through historical share price data. The artificial neural network with excellent fault tolerance, even if there is noise data also can produce more accurate prediction results. Many previous studies and literature used artificial neural networks as a research tool to improve predictive performance. This study can be used as a reference basis for the prediction of the price, volatility, etc. by using artificial neural networks. In particularly, the technical analysis of data as a variable for price forecasting research.
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Batsukh, Tushigmaa, and 圖史格. "Chaos for Rare Earth Elements Price forecasting-An Analysis of Artificial Neural Networks." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/56124205566294384217.
Full textAbstract:
碩士中原大學
企業管理研究所
100
This study aims to predict the price of the Rare Earth Elements (REE), which are key components of green energy technologies and other high technology applications. The use of REEs in modern technology has increased dramatically over the past years. Based on the growing global demand for REEs and the limited supply, there is growing concern that the world may soon face a shortage of the materials. As a result, prices have risen significantly. A rise in prices in REEs market creates challenging for mining and manufacturing companies in pricing of products and services because of high cost raw material. To reduce REE explosion risk and make hedging, future investment and evaluation decision will depend on accurately forecasting future price trend. This study applied three different approaches, Brock-Dechert Scheinkman test, Rescaled range analysis and Correlation Dimension Analysis for detection of chaotic phenomena. Next this study utilizes the artificial neural networks, including back propagation network (BPN) and Time delay recurrent neural network (TDRNN) to make a prediction for the price of REEs associated with the inputs such as The Broad Index, Baltic Dry Index, Commodity Research Bureau Futures Price Index, PHLX Semiconductor Sector index, NASDAQ Computer Index and The London Interbank Offered Rate. The result will explore the price behavior behind the REEs and to provide investors by the valuable information, this study compares which network forecast is more accurate. The simulation resulted that the chaos effect is exists in REE prices and suggest that employing TDRNN in price data of REE is more effective than BPN and the best performance is attained by the TDRNN. Therefore, REEs associated inputs, The Broad Index, Baltic Dry Index, Commodity Research Bureau Futures Price Index, PHLX Semiconductor Sector index, NASDAQ Computer Index and The London Interbank Offered Rate would be good indicator for forecasting REEs.
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Liao, Wen-Jung, and 廖文榮. "A neural network system implementation of stocks price forecasting model based on MA analysis technique." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/87159872231585678404.
Full textAbstract:
碩士國立中興大學
電子商務研究所
96
This paper develops a model of a trading system by using neuro-fuzzy framework in order to better predict the stock market index. This research is proposed to integrate every component in the stock analysis system. For this reason, the analysis system is a flexible and adjustable through calling by each component. Dynamic parameters can be automatically analyzed the model developed in the research. The empirical results show strong evidence of nonlinearity in the stock market index by using moving average based technical trading rules. Theoretically our approach shows that the neuro-fuzzy model may allow investors to earn higher returns in the dynamic model when profit making opportunities exist and can be exploited with an efficient information processing and learning system strategically.
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Cheng, Chi-Wei, and 鄭志偉. "A Neural Network Approach for Prediction and Analysis in the Taiwan Stock Market --- Stock Price and TSEWPI." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/84882368526384217481.
Full textAbstract:
碩士淡江大學
資訊管理學系
85
This study attempts to select the significant ones from various offinancial ratios and indicators about sale events from the viewpoint offundamental analysis . After interviewing with investment experts and referencing the results from related studies , some indicators withcorrelation were eliminated and a group of indicators was reserved . Back-Propagation Network (BPN) approach is used in this study. We collectretrospective data as BPN''s training samples and testing samples from four listed companies that are with similar industrial architectures . To prevent the noise from the effects of whole market and sector (consists of companies which have similar products) , we proposed amodule which is used to predict the relative trends between individual stock price and sector it belongs . Results indicate that the hit ratiosare about 59% in the individual stock module and about 67% in the sector-relative module on the end of the first month in which companiesopen their accounting information , and there are no obvious predictingcapacities after the second and the third month . This research also present a model that discusses the mutualrelations among GNP growth rate , interest rate and weighted EPR(Earning-Price Ratio) of whole listed companies on Taiwan Stock Exchange Company .We adopted neural network approach to identifying the forecasting - capacities of this model on long-term fluctuation of TSEWPI . The modelwe have proposed use retrospective date to discuss the relations betweenGNP growth rate and aggregate profits of whole listed company , the cyclic phenomenon within interest rate and PER of listed company , andtime-lagging effect . We hope this model can be applied on real world like TSEWPI trend prediction , for example , forecasting the peak and trough of TSEWPI curve .
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Wang, Hwa-Yee, and 王華頤. "The Analysis of The Financial Volatility Against Stock Price Based on Neural Network Under Client-Sever Infrastructure." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/46694516894705557155.
Full textAbstract:
碩士國立交通大學
資訊管理研究所
85
To the investors and speculators in the uncertain stock market, the most they desire to know is the analysis point of stock volatility. However, the volatility is critically related to profits and strategies of companies they invested. Therefore, if we can further hold and make use of the financial reports of individual industry to predict the future operation performance which will become the basis of comparison and analysis.This research is based on the neural network to control the behavior mode of volatility rate under important financial index, and use the volatility rate of financial index as a factor to verify if the volatility is ahead of the financial reports announced.At the same time, the research is also from the viewpoint of system integration to combine the database with the outcome of behavior mode described as above and apply to the internet under the modified infrastructure of client-sever database and the World- Wide-Web for the public to operate this analysis system and access what they need.We attribute two consequences after research 1、By the way of financial volatility rate this month to learn the stock volatility rate this month, the hit rate is the highest in predicting stock volatility. However, on the other side, we get the lowest hit rate by this month to predict next month. This result shows there is not much relativity between share price and financial information after financial reports announced. In other words, share prices have fluctuated before financial reports announced.2、For the complex and fast- updating environment under the client-server database system, we use the communicators and publishers and subscribers to modify the client-server infrastructure, not only to reduce the load of core database, but also enhance the performance of system, moreover, we can provide the more convenient and easier way for system conversion and data exchange.
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Jou, Fa, and 周發. "The Study of Real Estate Price Indicator in Northern Taiwan-An Analysis of Chaos and Artificial Neural Network." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/81144995681851080557.
Full textAbstract:
碩士中原大學
企業管理研究所
100
Real estate is the one of the investment targets for public favorite in Taiwan. Land as wealth was relatively important for investors. And the real estate not only provided demand of living, but also revealed a symbol of wealth and status. The house prices changed caused by the rarity of land. Demand and supply reflecting the the cyclical trend are two important interactive factors of the real estate market to make choose. Through the analysis results provided the appropriate investment opportunities and enhanced investing profit for self-occupied or investment occupied purchases. This study utilizes BDS test, R/S analysis, and Correlation Dimension Analysis to examine whether Leading Indicators, Coincident Indicators, Cathay Home Price Index, and the Lutheran Home Price Index have the chaos phenomenon. This paper uses a suitable nonlinear prediction of Back-Propagation Neural Network (BPNN) and Time-Delay Recurrent Neural Network (TDRNN), joining ten independent variables (such as Number of the country's population growth, economic growth rate, construction stocks index, gross domestic product, consumer price index, home mortgage rates, the license of construction permits, land value-added tax, and the money supply) and compares the predict performance of the models. The samples are divided into 40 groups. The period was from first quarter of 2001 to the fourth quarter of 2011. The results of BDS test and R/S analysis showed that are significant, and the outputs of Correlation Dimension Analysis are convergence. This indicated that the Leading Indicators, Coincident Indicators, Cathay Home Price Index, and the Lutheran Home Price Index have the chaos phenomenon, suggesting that the real estate price was predictability. The results of neural network indicated that population growth, economic growth rate, construction stocks index, gross domestic product, consumer price index, home mortgage rates, the license of construction permits, land value-added tax, and the money supply were suitable for real estate price forecasting. The Back-Propagation Neural Network (BPNN) processed the predict ability to have the better performance for Leading indicators, Coincident Indicators, Cathay Home Price Index and the Lutheran Home Price Index . In comparison of the results for national level of Leading Indicators, Coincident Indicators, Cathay House Price Index and the Lutheran Home Price Index, and Taipei, new Taipei city, Taoyuan County of the Cathay House Price Index with regional level for the Lutheran Home Price Index samples, this paper found that the Coincident indicators have better forecast performance. In comparison regional prices forecasting, Taipei is more suitable for detecting the prices trend.
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Hubáček, Lukáš. "LinkedIn jako fenomén na trhu práce." Master's thesis, 2016. http://www.nusl.cz/ntk/nusl-266283.
Full textAbstract:
The diploma thesis LinkedIn as a phenomenon in the labour market focuses on the social network called LinkedIn and its impacts in the area of recruitment and selection of technical staff on the Czech labour market. The aim of the thesis is to point to the existence of the phenomenon LinkedIn. The thesis is divided into four chapters. The first one is dedicated to social networks, their definition, history, importance, classification and users. The second one is centered on LinkedIn as a specific example of a social network focusing on its history and present and overlapping into the recruitment and selection of staff. Also mentioned is the setting and appearance of the account on LinkedIn and how to reach workers on LinkedIn. The methodological part describes the chosen method of research. The analytical part tries to summarize and interpret the acuired data.
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Bubeníčková, Veronika. "Sítě zájmových skupin - zájmové skupiny, jejich pozice a vztahy v legislativním procesu EU." Master's thesis, 2014. http://www.nusl.cz/ntk/nusl-330494.
Full textAbstract:
This diploma thesis examines networking of interest groups. It presents potentional entrance points for influencing legislative processes in the EU. The thesis focuses mainly on European Commission's public consultations. It tries to answer the question whether the interest groups cooperate strategically or whether they try to reach their goals on their own. This thesis is grounded in the framework of the network theory and uses the method of graphic modeling. For the research we have selected three areas of European Commission's consultations. The participating interest groups were chosen based on their characteristics and the consultations they took part in. We have found out that among the interest groups gathered around European Commission there is no close collaboration taking place. Furthermore, the interest groups act rather spontaneously as they are generally only taking part in individual consultations. Only 12.3% of them participated in more than one consultation and they were mainly registered organizations.
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Hsu, Pei-chu, and 徐培哲. "Using regression analysis , Kalman filter , and neural networks to the prediction of tendering price on roadway construction." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/10126315070326085395.
Full textAbstract:
碩士逢甲大學
土木工程所
95
Although domestic public infrastructure budget is notified during tendering (internet notice in accordance with Article 27 of Procurement Act), bottom price used to select vendor is setup by the proprietor. In addition, due to fierce market competitions, most vendors make deductions based on estimated price during tendering to increase the likelihood of winning a tender. Based on existing public engineering tender procedures in this country, most infrastructure project vendors are selected using the lowest bid mechanism. However, this type of selection method is likely to result to inappropriate tender strategies. Excessive bidding result to vicious price cut among bidders. It will in turn affect the quality of tender. Therefore, this study has constructed more precise engineering bidding price prediction model based on related mathematical statistics basics to assist vendors increase the likelihood of winning bid and optimize resource distribution and application. This study adopts engineering tenders of road infrastructure sponsor in 2005 as study subjects. Public invitation for tender in the amount of NT$50million or lower and NT$1million or higher (not exceeding threshold amount value) serves as the engineering tender sample. Based on engineering tender related data such as bottom price, price of award, budget price, contract period (calendar day), and bid bond, lowest price of award and proprietor ceiling price prediction model is constructed using Regression Analysis, Kalman Filter, and Artificial Neural Networks. In consideration of regional influential factors, tender data source is divided as to region into northern region, central region, southern region, eastern region, and integrated. Results of comparative analysis conducted show that the error range for these three prediction methods is between 10.63％ and 24.29％. Meanwhile, the respective prediction methods do not show absolute systematic advantage or disadvantage. Moreover, prediction on bottom price is more feasible. As for prediction for price of award, since more influential factors are involved and there are more unpredictable decision-making behaviors during tendering, it is impossible to construct a set of more reasonable quantified mathematical prediction model.
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Lin, Chun-hui, and 林春暉. "Application of Gray Relational Analysis Method and Neural Networks on The Real Estate Investment Trust Price Forecasting." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/92796657772033188704.
Full textAbstract:
碩士義守大學
工業管理學系
102
In recent years, Real Estate Investment Trust (REIT) has become a popular investment around the world. REIT has the advantages of inflation-resistance as well as real estate and high return on stock market. Researchers of Taiwan institute Economic Research expect that the situation of investment in real estate will become worse in the second half of 2013 and the Burst of Real Estate Bubbles could happen around 2015. Therefore, by considering how to reduce the risk of investing real estate and anticipating return rate correctly, those techniques not only can help the investors choose their investment but also keep them from the effect of the Burst of Real Estate Bubbles. This research combines the factors of acroeconomics and the performance of investing real estate in the past focuses on real estate in Taiwan by using Grey Relational Analysis and Artificial Neural Network. Therefore, the main purpose of prediction is to estimate the oncoming events or situations in advance and provide the best information to management level to detect those uncertain conditions and help reduce the risks during decision-making process. After that, apply the result on production output assessment to increase the accuracy of forecast and provide the consultation to related industries for production forecasting.
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Chen, Yi-En, and 陳以恩. "Trend Analysis of House Pricing using Neural Network with Adaptive Differential Evolution - A Case Study of Taiwanese Real Price Registration." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/fmkvfs.
Full textAbstract:
碩士國立交通大學
資訊管理研究所
104
Real estate market has its own method for pricing depends on each property’s location、type and characteristics etc. It makes the difficulty for predict the trend of real estate market. In recent years, the Taiwan Government started to promote the Real Price Registration System to record all data of real estate transaction publicly. This RPR system is used to intensify the price transparency of real estate market. With higher transparency, the prediction model could be made more effective and reliable. In this research, we would like to build up a prediction model of house pricing which is made by RPR transaction data. We focus on the properties which are domestic estate in Taipei City and were traded from August 2014 to February 2015. Using the features, for example, address、type of building、floor made a K-Means clustering model to recognize different groups of buyers and sub-market. The prediction model is made for each sub-market by using modified neural network and time series methods.