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Att kunna mäta vår materialanvändning är centralt i omställningen till ett resurseffektivare samhälle och en cirkulär ekonomi. Inom EU används Domestic Material Consumption (DMC) som indikator för materialanvändning. Den beräknas genom att addera de material som ett land utvinner, plus de material som importeras, minus de material som exporteras. Det finns kritik mot DMC då den endast tar hänsyn till vikten på import- och exportprodukter då de korsar landsgränsen. DMC inkluderar inte de uppströms material som gått åt för att producera en produkt, men som inte syns i slutprodukten (den så kallade materialryggsäcken).   Den ökade globaliseringen har för många länder lett till en förflyttning av produktion utomlands, och för att beräkna dessa länders totala materialanvändning krävs det att hänsyn tas till importerade och exporterade produkters materialryggsäck. Raw Material Consumption (RMC) är en indikator som tar hänsyn till materialryggsäcken, men den har idag ingen standardiserad beräkningsmetod. Både RMC och DMC används inom Agenda 2030 för att följa upp FN:s hållbarhetsmål 12 ”Hållbar konsumtion och produktion”, samt hållbarhetsmål 8 ”Anständiga arbetsvillkor och ekonomisk tillväxt”. Endast DMC används idag inom EU.   Syftet med projektet var att beräkna Sveriges materialanvändning med hjälp av indikatorn Raw Material Consumption (RMC), samt att identifiera styrkor och svagheter hos RMC. Eurostats RME-verktyg användes för att beräkna RMC. Enligt RMC ökade Sveriges totala materialanvändning från 198 miljoner ton råmaterialekvivalenter (RME) år 2008, till 221 miljoner ton RME år 2015, motsvarande 21,4 ton RME per capita 2008 till 22,6 ton RME per capita 2015. Resultaten jämfördes med resultat för Sveriges DMC som Statistiska Centralbyrån tagit fram. RMC och DMC gav liknande resultat för Sveriges totala materialanvändning. Detta tros bero på att materialryggsäcken för den svenska importen är ungefär lika stor som för exporten och att den största materialkategorin, icke-metalliska mineraler, utgör en liten del av vår handelsbalans och därför inte påverkas i någon större utsträckning när importerade och exporterade produkters materialryggsäck inkluderas. På materialkategorinivå är dock skillnaderna mellan RMC och DMC större.   Resultat från Eurostats RME-verktyg som tagits fram i denna studie, har jämförts med resultat som OECD och UNEP tagit fram för Sveriges RMC. De har använt en annan beräkningsmetod än den RME-verktyget tillämpar. Skillnaden i total RMC för de olika beräkningsmetoderna är mellan 11 % och 22 %. På materialkategorinivå är skillnaderna större, över 50 % för fossila bränslen exempelvis. Liknande resultat har påvisats i en studie som jämförde Österrikes RMC för år 2007 med olika beräkningsmetoder.   Både DMC och RMC kan användas som indikatorer för resurseffektivitet och cirkulär ekonomi, men vid jämförelse mellan länder är RMC teoretiskt en mer lämplig indikator. Detta eftersom många länder idag har flyttat stora delar av sin produktion utomlands, och DMC därför riskerar att ge en skev bild över resurseffektivitet och frikoppling, när materialryggsäcken inte inkluderas. / Being able to measure the amount of materials used in society, is central in the transition to a resource-efficient and circular economy. Within the EU, Domestic Material Consumption (DMC) is currently used as indicator for material use. It is calculated by adding the materials that a country extracts, plus the materials imported, minus the materials being exported. There is criticism of DMC, as a measure, since it only considers the weight of imported and exported goods when they cross the country border. It does not consider the upstream materials needed to produce a product, which are not represented in the final product (the so-called material backpack). Globalisation has led to a geographical disconnection in production and consumption, and to consider net-importing countries’ total material consumption, it is necessary to include traded product’s material backpack. Raw Material Consumption (RMC) considers the material backpack, but is currently without a standardised calculation method. Both RMC and DMC are used in Agenda 2030 to follow up the UN Sustainability Development Goal 12 "Sustainable Consumption and Production", as well as Goal 8 "Decent Work Conditions and Economic Growth". Only DMC is used today in the EU.   The aim of this project was to calculate Sweden’s material consumption, using the indicator Raw Material Consumption (RMC), as well as identifying strengths and weaknesses of RMC. For calculating RMC, Eurostat’s RME-tool has been used. According to RMC, Sweden's total material usage increased from 2008 when it amounted to 198 million tonnes of raw material equivalents (RME), to 221 million tonnes RME in 2015. There has also been an increase per capita: 21,4 RME per capita in 2008 to 22,6 RME per capita in 2015. These results have been compared with the results for Sweden's DMC, calculated by SCB. RMC and DMC gave similar results for Sweden's total material consumption. This is believed to be due to the similar size of the material backpack of imports and exports. Another reason is believed to be due to Sweden’s largest material category, non-metallic minerals, is a small part of our trade balance, and therefore is not affected when the material backpack is included. At the material category level, however, the differences between RMC and DMC are greater.   Results from Eurostat's RME tool, calculated in this study, have been compared with results presented by the OECD and UNEP. They have used a different calculation method for Sweden’s RMC than the RME tool applies. The difference in total RMC for different calculation methods is between 11 % and 22 %. At material category level, the differences are greater, more than 50 % for fossil fuels, for example. Similar results have been presented in a study over Austria's RMC for the year 2007, using different calculation methods.   Both DMC and RMC can be used as policy-support for resource efficiency, but RMC is theoretically a more suitable indicator for comparison of countries. This is since many countries today have moved a significant share of their production abroad, and DMC therefore risks displaying a false perception of resource efficiency and decoupling, when the material backpack is not included.

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This thesis has been motivated by the growing needs for multi-degree of freedom (M-DOF) electromagnetic actuators capable of smooth and accurate multi-dimensional driving motions. Because high coercive rare-earth permanent-magnets (PMs) are widely available at low cost, their uses for developing compact, energy-efficient M-DOF actuators have been widely researched. To facilitate design analysis and optimization, this thesis research seeks to develop a general method based on distributed source models to characterize M-DOF PM-based actuators and optimize their designs to achieve high torque-to-weight performance with compact structures To achieve the above stated objective, a new method that is referred to here as distributed multi-level current (DMC) utilizes geometrically defined point sources has been developed to model electromagnetic components and phenomena, which include PMs, electromagnets (EMs), iron paths and induced eddy current. Unlike existing numerical methods (such as FEM, FDM, or MLM) which solve for the magnetic fields from Maxwell’s equations and boundary conditions, the DMC-based method develops closed-form solutions to the magnetic field and force problems on the basis of electromagnetic point currents in a multi-level structure while allowing trade-off between computational speed and accuracy. Since the multi-level currents can be directly defined at the geometrically decomposed volumes and surfaces of the components (such as electric conductors and magnetic materials) that make up of the electromagnetic system, the DMC model has been effectively incorporated in topology optimization to maximize the torque-to-weight ratio of an electromechanical actuator. To demonstrate the above advantages, the DMC optimization has been employed to optimize the several designs ranging from conventional single-axis actuators, 2-DOF linear-rotary motors to 3-DOF spherical motors. The DMC modeling method has been experimentally validated and compared against published data. While the DMC model offers an efficient means for the design analysis and optimization of electromechanical systems with improved computational accuracy and speed, it can be extended to a broad spectrum of emerging and creative applications involving electromagnetic systems.

La mémoire de travail et le contrôle cognitif sont des construits proches ; on suppose généralement qu'une forte capacité en mémoire de travail est associée à un contrôle cognitif efficace. Cette hypothèse a des implications importantes pour la cognition humaine et apporte une explication élégante à la corrélation fréquemment reportée entre mémoire de travail et intelligence fluide. En revanche, les difficultés d'opérationnalisation et de mesure du contrôle cognitif rendent l'hypothèse difficile à tester. Un modèle récent du contrôle cognitif, le modèle à Deux Mécanismes de Contrôle (DMC), offre une solution à ce problème : ce modèle propose l'existence de deux mécanismes de contrôle cognitif distincts et permet de les opérationnaliser de façon efficace. La littérature prédit que l'un de ces deux mécanismes, le contrôle proactif, devrait être lié à la mémoire de travail. L'objectif de cette thèse était de tester l'existence d'une relation entre les différences inter-individuelles en mémoire de travail et la tendance à mettre en place un mécanisme de contrôle proactif. Cette relation a été testée sous quatre axes de travail : 1) en utilisant de nouveaux paradigmes expérimentaux pour mesurer la tendance à utiliser le contrôle proactif, 2) grâce à des tâches classiques de contrôle cognitif choisies pour leur sensibilité au contrôle proactif, 3) à travers une approche par imagerie cérébrale incluant électro-encéphalographie et imagerie par résonance magnétique fonctionnelle, et 4) en tant que facteur explicatif de la relation entre mémoire de travail et intelligence fluide. Dans l'ensemble, nos résultats n'ont pas permis de soutenir l'idée que la capacité en mémoire de travail est directement liée à la tendance à utiliser un mécanisme de contrôle proactif ; les données suggèrent plutôt un avantage général en faveur des participants à forte capacité en mémoire de travail dans toutes les situations. / The constructs of working memory and cognitive control are conceptually close; a high working memory capacity is hypothesized to be associated with an efficient cognitive control. This hypothetical association has large implications for human cognition and provides an elegant explanation for the frequently reported relationship between working memory capacity and fluid intelligence. However, the difficulty in operationalizing and measuring cognitive control makes this hypothesis hard to test. One model of cognitive control, the Dual Mechanisms of Control (DMC) framework, constitutes a possible solution to this problem: the model proposes two distinct mechanisms of cognitive control which can be efficiently operationalized and studied. There is reason to believe that one of these two mechanisms, proactive control, is specifically related to working memory capacity. The objective of the present research work was to assess the relationship between individual differences in working memory capacity and the tendency to use proactive control. This relationship was tested in four steps: 1) by using innovative measures of the tendency to use proactive control, based on newly developed paradigms, 2) with classic cognitive control tasks sensitive to proactive control, 3) with a neuroimaging approach using electro-encephalography and functional magnetic resonance imaging, and 4) by testing whether the use of proactive control explains the relationship between working memory and fluid intelligence. Overall, our results did not support the idea that working memory capacity is uniquely related to the tendency to use proactive control; the data were more consistent with a general advantage of participants with a high working memory capacity in all situations.

The aim of the thesis named Decision criteria for the allocation of conferences and congresses in Prague is to analyse all the factors, which influence the choice of a particular congress or conference destination. The priority is to evaluate the congress tourism potential of the city of Prague and to suggest options for the innovation in order to increase the popularity of the destination. The thesis analyses the characteristics of the whole decision process of allocation a congress or a conference. Moreover, the activity of congress or conference organizers and destination organizations is described as well. The thesis is divided into four chapters. In the first and the second one, the substantial concepts of congress tourism, its organization and subjects are defined and characterized. The second target of this thesis is focused on the analysis of the decision criteria for the choice of the congress destination, the potential of Prague including SWOT analysis and the consequent summarization of impacts and possible innovations for Prague. The given interviews and materials from professionals in this field are the main sources for this thesis. In the end of the thesis, the overall situation in the congress tourism in Prague is evaluated and some suggestions for innovations for congresses or conference organization are proposed.

台灣位於環太平洋地震帶上，地形為山地居多，且地質脆弱，加上位於西太平洋副熱帶地區，使得山區常受到颱風的侵擾而發生崩塌，導致土石流和洪水等災害發生，進而影響人民的生命和財產安全。因此，如何有效地建置崩塌地區域資料庫，成為國土保育與災害防治的重要課題。以往利用遙測與航測技術於崩塌地萃取的研究中，大多是於幾何糾正後衛星影像或是航測正射影像上分析崩塌地，但產製正射影像或是糾正衛星影像時，都需要花費較多的時間，對於講求時效性的救災行動而言頗為不利。本研究之目的為發展一套不需使用正射影像萃取崩塌地的方法，以物件導向影像分類法，於DMC（Digital Mapping Camera）航測原始影像上直接萃取崩塌地資訊。首先採取多重解析影像分割的技術，將航測影像依像元光譜和形狀同質性分割成不同區塊（物件），接著利用影像光譜統計值搭配區域成長法，偵測影像中的雲覆蓋地區並過濾。其次，根據光譜亮度統計特徵值，將影像區分成陰暗地區、正常地區以及較亮地區之三種土地覆蓋類型，使用線性相關糾正法（Linear-correlation Correction）將陰暗地區光譜亮度值轉換至正常地區，並利用物件的特徵值，如光譜、面積、形狀以及相關性依序萃取此三種土地覆蓋類型內的崩塌地。最後，使用光線追蹤法 （Ray-tracing），將崩塌地區塊從影像坐標轉換至地圖坐標，使其可以套疊地形資料如坡度、坡向，並進行空間分析以提升崩塌地的判釋精度。研究結果顯示，崩塌地萃取之使用者精度和生產者精度，均有82%以上，並且整個實驗可大量批次處理影像，及快速建立崩塌地資料庫，本研究之方法和崩塌地資料庫將有助於國土保育與崩塌地的災害防治。 / Being located in a subtropical and seismic zone of the West Pacific, the geology is fragile and topography is mountainous in Taiwan. Landslides, floods and other disasters induced by typhoons occur frequently, and it cause the life-threatening and property loss of human beings in Taiwan. Therefore, how to establish landslides data effectively become an important issue of land conservation and disasters management. In recently years, most of the researchers used aerial ortho-images or satellite georeferencing images to detect landslides sites. However, it spent a lot of time generating aerial ortho-images and rectifying satellite images, and it also reduced the efficiency of landslides analysis. Thus, this study developed an object-oriented classification method, which can be directly applied in raw image data, to detect landslides sites. Firstly, this study used multi-resolution image segmentation technique to segment images acquired by Z/I DMC（Digital Mapping Camera） into individual regions （objects） according to the homogeneity of spectral and shape features, and then removed cloud areas by using brightness features depended on the spectral information of images. Secondly, the study divided the entire image into three areas, which are darker area, normal area and lighter area, according to brightness value. Next, Linear-correlation correction （LCC） method was used in this study to transform darker area to normal area so that it can easily detect the landslides sites in darker area, and the object features, such as spectral, area, shape and space correlation indices, were used to extract landslide sites in images. Finally, in order to enhance the accuracy of landslide, the initial landslides were converted from image coordinate system to map coordinate system by ray-tracing method, so the initial landslides data can be further extracted by using topographic data, including slope and aspect data. The results of this study showed that the user and producer accuracies of detecting landslides can reach up to 82%. Moreover, the entire experiments process of this study can batch analyze automatically and establish landslides database quickly. It is expected that the method and landslides data of this study may have contribution to land conservation and disasters management.

This Master of Applied Science thesis proposes a new design of low power, power factor corrected (PFC), power supplies. By lifting the hold up time restriction for devices that have a battery built in, the energy storage elements of the converter can be reduced, permitting a small and inexpensive power converter to be built. In addition, a new control technique for controlling cascaded converters is presented, named duty mode control (DMC). Its advantages are shown through simulations. The system was proven using a prototype developed in the laboratory designed for a universal ac input voltage (85 - 265VRMS at 50 - 60Hz) and a 40W output at 12V. It consisted of two interleaved phases sensed and digitally controlled on the isolated side of the converter. The prototype was able to achieve a power factor of greater than 0.98 for all operating conditions, and input harmonic current distortion well below any set of standards.

This Master of Applied Science thesis proposes a new design of low power, power factor corrected (PFC), power supplies. By lifting the hold up time restriction for devices that have a battery built in, the energy storage elements of the converter can be reduced, permitting a small and inexpensive power converter to be built. In addition, a new control technique for controlling cascaded converters is presented, named duty mode control (DMC). Its advantages are shown through simulations. The system was proven using a prototype developed in the laboratory designed for a universal ac input voltage (85 - 265VRMS at 50 - 60Hz) and a 40W output at 12V. It consisted of two interleaved phases sensed and digitally controlled on the isolated side of the converter. The prototype was able to achieve a power factor of greater than 0.98 for all operating conditions, and input harmonic current distortion well below any set of standards.

[EN] Statistical Process Control (SPC) and the Automatic Process Control (APC) are two control philosophies have evolved independently until recently. The overall objective of both APC and SPC is to optimize the performance of processes, reducing the variability of the resulting characteristics around the desired values. The fundamentals of the two disciplines arise from the idea that the whole process has operation variations. These variations may affect, to a greater or lesser extent, the final product quality and the process productivity. The two methodologies conceptualize processes and control in different ways, they originated in different industrial sectors and have evolved independently, until the interest to integrate them in the control of industrial processes was deduced. It was warned that they could be complementary rather than conflicting methodologies as they were understood until then. The possibility of combining the advantages of both, integrating them into a new control paradigm was explored. First, the problem of identifying and estimating a process model is considered. Controlled variables in this model are the main feature of product quality and a productivity variable. The latter is innovative since the productivity variables are measured, but they are not considered as controlled variables. For this, two methods of multivariate time series are used, the Box-Jenkins multiple transfer function in the parsimonious way and the impulse response function obtained by Partial Least Squares regression (Time Series-Partial Least Squares, TS-PLS). These two methods were compared taking into account different aspects such as the simplicity of the modeling process in the stages of identification, estimation and model validation, as well as the utility of graphical tools that provide both methodologies, the goodness of fit obtained, and the simplicity of the mathematical structure of the model. The DMC (Dynamic Matrix Control) controller, an automatic control algorithm belonging to the family of MPC (Model Predictive Control) controllers, is derived from the estimated Box-Jenkins multiple transfer function that has been selected as the most suitable for this kind of processes. An optimal tuning method to maximize the controller performance, applying experimental design 2k-p, is presented. Finally, an integrated control system MESPC (Multivariate Engineering Statistical Process Control) whose monitoring component has been implemented applying latent structures based multivariate statistical process control methods (Lsb-MSPC), has been developed. The monitoring module is designed to act as both process and DMC controller supervisor. To do this, we estimate a NOC-PCA model (Normal Operation Conditions Principal Component Analysis), which has as variables both process-related and quality-related variables, all derived from the automatic control system. From this model, and DModX graphics have been derived. We assessed the performance of MESPC system, subjecting it to simulated potential failures or special causes of variability. / [ES] El Control Estadístico de Procesos (Statistical Process Control, SPC) y el Control Automático de Procesos (Automatic Process Control, APC) son dos filosofías de control se han desarrollado hasta recientemente de forma independiente. El objetivo general tanto del SPC como del APC, es optimizar el funcionamiento de los procesos, reduciendo la variabilidad de las características resultantes en torno a los valores deseados. El fundamento de ambas disciplinas, parte de la idea de que todo proceso presenta variaciones en su funcionamiento. Estas variaciones pueden afectar en mayor o en menor medida a la calidad final del producto y a la productividad del proceso. Las dos metodologías conceptualizan los procesos y su control de diferentes formas, se originaron en diferentes sectores industriales y han evolucionado de forma independiente, hasta que se dedujo el interés de integrarlas en el control de los procesos industriales, ya que se advirtió que podían ser complementarias, antes que contrapuestas, como se entendían hasta entonces y se exploró la posibilidad de aunar las ventajas de ambas, integrándolas en un nuevo paradigma de control. Esta tesis se centra en el estudio de la integración de procedimientos multivariantes para la regulación óptima y la monitorización estadística de procesos, con el propósito de contribuir a la mejora de la calidad y de la productividad de los procesos. La metodología propuesta se ha aplicado con fines ilustrativos a un proceso MIMO de producción en continuo de Polietileno de Alta Densidad (PEAD).En primer lugar, se considera el problema de la identificación y posterior estimación de un modelo del proceso. Las variables controladas en este modelo han sido la principal característica de calidad del producto y una variable de productividad, esto último es innovador puesto que las variables de productividad se miden, pero no se consideran variables controladas. Para ello, se emplean dos metodologías de series temporales multivariantes, la obtención de la función de transferencia múltiple en forma parsimoniosa de Box-Jenkins y la obtención de la función de respuesta a impulsos mediante los modelos de regresión por mínimos cuadrados parciales (Time Series-Partial Least Squares, TS-PLS). Estas dos metodologías se han comparado teniendo en cuenta distintos aspectos como son la simplicidad del proceso de modelado en las etapas de identificación, estimación y validación del modelo, así como la utilidad de las herramientas gráficas que proporcionan ambas metodologías, la bondad de ajuste obtenida, y la simplicidad de la estructura matemática del modelo. A partir del modelo de función de transferencia múltiple estimado, elegido como el más adecuado para este tipo de procesos, se desarrolla el controlador DMC (Dynamic Matrix Control), un algoritmo de control automático que pertenece a la familia del Control Predictivo basado en Modelos (Model Predictive Control, MPC). Se presenta un método de sintonizado óptimo del controlador que permita maximizar su rendimiento, aplicando diseño de experimentos 2k-p.Finalmente, se ha desarrollado un sistema de control integrado MESPC (Multivariate Engineering Statistical Process Control), cuya componente de monitorización se ha implementado aplicando métodos de control estadístico multivariante de procesos basados en técnicas de proyección en estructuras latentes. Este módulo de monitorización se ha diseñado para que actúe como supervisor tanto del proceso como del controlador DMC. Para ello, se ha estimado un modelo NOC-PCA (Normal Operation Conditions Principal Component Analysis), en el que han intervenido tanto variables relacionadas con el proceso como con la calidad, todas derivadas de la componente del control automático. A partir de este modelo se han derivado los gráficos y DModX. Se ha evaluado el funcionamiento del sistema MESPC, sometiéndolo a fallos potenciales o causas especiales de variabiliabilidad. / [CAT] El Control Estadístic de Processos (Statistical Process Control, SPC) i del Control Automàtic de Processos (Automatic Process Control, APC) son dues filosofies de control s'han desenvolupat fins a recentment de forma independent. L'objectiu general tant del SPC com del APC, és optimitzar el funcionament dels processos, reduint la variabilitat de les característiques resultants entorn dels valors desitjats. El fonament d'ambdues disciplines, part de la idea que tot procés presenta variacions en el seu funcionament. Aquestes variacions poden afectar en major o en menor mesura a la qualitat final del producte i a la productivitat del procés. Les dues metodologies conceptualitzen els processos i el seu control de diferents formes, es van originar en diferents sectors industrials i han evolucionat de forma independent, fins que es va deduir l'interès d'integrar-les en el control dels processos industrials, ja que es va advertir que podien ser complementàries, abans que contraposades, com s'entenien fins llavors i es va explorar la possibilitat de conjuminar els avantatges d'ambdues, integrant-les en un nou paradigma de control. Aquesta tesi se centra en l'estudi de la integració de procediments multivariants per a la regulació òptima i el monitoratge estadístic de processos amb el propòsit de contribuir a la millora de la qualitat i de la productivitat dels processos. La metodologia proposada s'ha aplicat amb finalitats il·lustratives a un procés MIMO de producció en continu de Polietilè d'Alta Densitat (PEAD). En primer lloc, es considera el problema de la identificació i posterior estimació d'un model del procés. Les variables controlades en aquest model han sigut la principal característica de qualitat del producte i una variable de productivitat, açò últim és innovador ja que les variables de productivitat es mesuren, però no es consideren variables controlades. Per a açò, s'utilitzen dues metodologies de sèries temporals multivariants, l'obtenció de la funció de transferència múltiple en forma parsimòniosa de Box-Jenkins i l'obtenció de la funció de resposta a impulsos mitjançant els models de regressió per mínims quadrats parcials (Times Series-Partial Least Squares, TS-PLS). Aquestes dues metodologies s'han comparat tenint en compte diferents aspectes com són la simplicitat del procés de modelatge en les etapes d'identificació, estimació i validació del model, així com la utilitat de les eines gràfiques que proporcionen ambdues metodologies, la bondat d'ajust obtinguda, i la simplicitat de l'estructura matemàtica del model. A partir del model de funció de transferència múltiple estimat, triat com el més adequat per a aquest tipus de processos, es desenvolupa el controlador DMC (Dynamic Matrix Control), un algorisme de control automàtic que pertany a la família del Control Predictiu basat en Models (Model Predictive Control, MPC). Es presenta un mètode de sintonitzat òptim del controlador que permeta maximitzar el seu rendiment, aplicant disseny d'experiments 2k-p. Finalment, s'ha desenvolupat un sistema de control integrat MESPC (Multivariate Engineering Statistical Process Control). Per a implementar la component de monitoratge d'aquest sistema integrat s'han usat mètodes de control estadístic multivariants de processos basats en tècniques de projecció en estructures latents (Latent structures based-Multivariate Statistical Process Control). Aquest mòdul de monitoratge s'ha dissenyat perquè actue com a supervisor tant del procés com del controlador DMC. Per a açò, s'ha estimat un model NOC-PCA (Normal Operation Conditions Principal Component Analysis), en el qual han intervingut variables relacionades tant amb el procés, com amb la qualitat, totes derivades de la component del control automàtic. A partir d'aquest model s'han derivat els gràfics i DModX. S'ha avaluat el funcionament del sistema MESPC, sotmetent-lo a fallades potencials o causes especials de / Barceló Cerdá, S. (2016). Estudio de la integración de procedimientos multivariantes para la regulación óptima y monitorización estadística de procesos [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/63442 / TESIS