Nonlinear data driven techniques for process monitoring

The goal of this research is to develop process monitoring technology capable of taking advantage of the large stores of data accumulating in modern chemical plants. There is demand for new techniques for the monitoring of non-linear topology and behavior, and this research presents a topological preservation method for process monitoring using Self Organizing Maps (SOM). The novel architecture presented adapts SOM to a full spectrum of process monitoring tasks including fault detection, fault identification, fault diagnosis, and soft sensing. The key innovation of the new technique is its use of multiple SOM (MSOM) in the data modeling process as well as the use of a Gaussian Mixture Model (GMM) to model the probability density function of classes of data. For comparison, a linear process monitoring technique based on Principal Component Analysis (PCA) is also used to demonstrate the improvements SOM offers. Data for the computational experiments was generated using a simulation of the Tennessee Eastman process (TEP) created in Simulink by (Ricker 1996). Previous studies focus on step changes from normal operations, but this work adds operating regimes with time dependent dynamics not previously considered with a SOM. Results show that MSOM improves upon both linear PCA as well as the standard SOM technique using one map for fault diagnosis, and also shows a superior ability to isolate which variables in the data are responsible for the faulty condition. With respect to soft sensing, SOM and MSOM modeled the compositions equally well, showing that no information was lost in dividing the map representation of process data. Future research will attempt to validate the technique on a real chemical process

Data exploration process based on the self-organizing map

With the advances in computer technology, the amount of data that is obtained from various sources and stored in electronic media is growing at exponential rates. Data mining is a research area which answers to the challange of analysing this data in order to find useful information contained therein. The Self-Organizing Map (SOM) is one of the methods used in data mining. It quantizes the training data into a representative set of prototype vectors and maps them on a low-dimensional grid. The SOM is a prominent tool in the initial exploratory phase in data mining. The thesis consists of an introduction and ten publications. In the publications, the validity of SOM-based data exploration methods has been investigated and various enhancements to them have been proposed. In the introduction, these methods are presented as parts of the data mining process, and they are compared with other data exploration methods with similar aims. The work makes two primary contributions. Firstly, it has been shown that the SOM provides a versatile platform on top of which various data exploration methods can be efficiently constructed. New methods and measures for visualization of data, clustering, cluster characterization, and quantization have been proposed. The SOM algorithm and the proposed methods and measures have been implemented as a set of Matlab routines in the SOM Toolbox software library. Secondly, a framework for SOM-based data exploration of table-format data - both single tables and hierarchically organized tables - has been constructed. The framework divides exploratory data analysis into several sub-tasks, most notably the analysis of samples and the analysis of variables. The analysis methods are applied autonomously and their results are provided in a report describing the most important properties of the data manifold. In such a framework, the attention of the data miner can be directed more towards the actual data exploration task, rather than on the application of the analysis methods. Because of the highly iterative nature of the data exploration, the automation of routine analysis tasks can reduce the time needed by the data exploration process considerably.reviewe

Studies in probabilistic methods for scene analysis

In this thesis, probabilistic methods are applied to a number of problems in computer vision. The goal is to provide means for a vision based system that is able to analyze and recognize scenes and objects in camera images and to use that information for autonomous navigation and machine learning. New methods are developed for different functions that are needed in such a system, including segmentation of images, model-based recognition of objects, robot navigation and model complexity control. The approach is based on generative probability models, and Bayesian statistical inference is used to match these models with image data. Stochastic sampling methods are applied to obtain numerical results. The self-organizing map is a neural network algorithm that has many applications in computer vision. In this thesis, the algorithm is analyzed in a probabilistic framework. A probability density model is derived and new model selection techniques are proposed, which enable complexity control for the self-organizing map. The analysis of images is discussed from the point of view of segmentation and object recognition. Segmentation aims at dividing the image into parts of different appearance, while object recognition is meant to identify objects that fulfill given criteria. These are different goals, but they complement each other. When the recognition of all objects in an image is not possible, segmentation can provide an explanation to the rest of the image. For object recognition, different two and three dimensional object models are considered and Bayesian matching techniques are applied to them. Efficient techniques for image segmentation are proposed and results are presented.Tässä väitöskirjassa sovelletaan todennäköisyyslaskennan menetelmiä eräisiin tietokonenäköongelmiin. Työn tarkoituksena on tuottaa keinoja näköön perustuvaan järjestelmään, joka voi analysoida ja tunnistaa näkymiä ja kohteita kamerakuvista ja käyttää näin saatua informaatiota itsenäiseen navigointiin ja koneoppimiseen. Työssä kehitetään uusia menetelmiä järjestelmän tarvitsemiin toimintoihin kuten kuvien segmentointiin, mallipohjaiseen kohteiden tunnistukseen, robottinavigointiin ja mallien kompleksisuuden hallintaan. Työssä käytettävä lähestymistapa perustuu generatiivisiin todennäköisyysmalleihin, ja mallit sovitetaan kuvadataan bayesiläistä tilastollista päättelyä soveltaen. Numeeristen tulosten saamiseksi käytetään stokastisia poimintamenetelmiä. Itsejärjestyvä kartta on neuroverkkoalgoritmi, jolla on useita tietokonenäköalan sovelluksia. Tässä työssä algoritmia analysoidaan todennäköisyyspohjaisesti. Algoritmin tuottamalle mallille johdetaan todennäköisyysjakaumamalli ja sille esitetään uusia mallinvalintamenetelmiä, jotka mahdollistavat itsejärjestyvän kartan kompleksisuuden hallinnan. Kuvien analysointia käsitellään sekä segmentoinnin että kohteiden tunnistuksen näkökulmasta. Segmentoinnissa kuva jaetaan erilaisilta näyttäviin osiin. Kohteiden tunnistus perustuu niiden ennalta tunnettuihin ominaisuuksiin. Tavoitteet ovat siten varsin erilaisia, mutta ne täydentävät toisiaan. Silloin kun vain osa kuvassa olevista kohteista pystytään tunnistamaan, segmentoinnilla voidaan saada kuvan muille osille selitys. Väitöskirjassa esitetään laskennallisesti tehokkaita menetelmiä kuvien segmentointiin. Kohteiden tunnistusta kaksi- ja kolmiulotteisten mallien avulla tarkastellaan bayesiläisiä menetelmiä käyttäen.reviewe

Process Monitoring and Data Mining with Chemical Process Historical Databases

Modern chemical plants have distributed control systems (DCS) that handle normal operations and quality control. However, the DCS cannot compensate for fault events such as fouling or equipment failures. When faults occur, human operators must rapidly assess the situation, determine causes, and take corrective action, a challenging task further complicated by the sheer number of sensors. This information overload as well as measurement noise can hide information critical to diagnosing and fixing faults. Process monitoring algorithms can highlight key trends in data and detect faults faster, reducing or even preventing the damage that faults can cause. This research improves tools for process monitoring on different chemical processes. Previously successful monitoring methods based on statistics can fail on non-linear processes and processes with multiple operating states. To address these challenges, we develop a process monitoring technique based on multiple self-organizing maps (MSOM) and apply it in industrial case studies including a simulated plant and a batch reactor. We also use standard SOM to detect a novel event in a separation tower and produce contribution plots which help isolate the causes of the event. Another key challenge to any engineer designing a process monitoring system is that implementing most algorithms requires data organized into “normal” and “faulty”; however, data from faulty operations can be difficult to locate in databases storing months or years of operations. To assist in identifying faulty data, we apply data mining algorithms from computer science and compare how they cluster chemical process data from normal and faulty conditions. We identify several techniques which successfully duplicated normal and faulty labels from expert knowledge and introduce a process data mining software tool to make analysis simpler for practitioners. The research in this dissertation enhances chemical process monitoring tasks. MSOM-based process monitoring improves upon standard process monitoring algorithms in fault identification and diagnosis tasks. The data mining research reduces a crucial barrier to the implementation of monitoring algorithms. The enhanced monitoring introduced can help engineers develop effective and scalable process monitoring systems to improve plant safety and reduce losses from fault events

Exploration of Older Adults’ Travel Behavior and Their Transportation Barriers

Both the number of older adults and their proportion of the population are increasing rapidly in the United States. By 2040, about 20.7% of the U.S. population will be 65 and older (Harrison & Ragland, 2003a). These dramatic changes in the composition of the population will bring new challenges to the provision of transportation services. This is because the travel patterns and needs of older adults are likely to become more complicated. A growing number of people will find it increasingly difficult to meet their transportation needs. As the life expectancy of older adults is likely to continue to increase, a greater number of older people will face mobility issues alone (Alsnih & Hensher, 2003). Researchers widely agree that the aging population in the U.S. relies heavily on cars (as drivers or passengers) because they are convenient, flexible, and allow them to live independently and participate in normal daily activities (Haustein, 2012; Rosenbloom, 2005). However, dispersed land use patterns in the United States, the growing number of older adults living in suburban areas, and the current transportation infrastructure in the country make the use of a car a necessity rather than an option for a large proportion of older adults. However, as they age, their physical and mental health deteriorates, making driving dangerous for them. Therefore, it is of great importance to understand the transportation problems of older adults and provide them with reliable and acceptable alternative modes of transportation to help them meet their transportation needs. The study presented here aims to examine the transportation problems of older adults living in urban and suburban areas, make policy recommendations, and identify effective strategies to help them meet their mobility needs. To this end, the study used a mixed-method approach to identify the factors that influence older adults\u27 travel behavior and the issues they face when walking, biking, and using transit. In-depth, one-on-one surveys were conducted in three counties in southeastern Wisconsin with 178 English-speaking older adults aged 65 and older living independently in institutionalized senior housing (i.e., subsidized housing and retirement communities) and in noninstitutionalized buildings. The first main chapter of the thesis (Chapter 4) examines the factors that influence older adults\u27 mode choice for grocery shopping and aims to predict older adults\u27 travel behavior for going to the grocery store. A quantitative analysis involving statistical and machine learning techniques was conducted with older adults who traveled to the grocery store by car, carpool, walking, or public transit (N=153). The results of the study show that household car ownership and having a valid driver\u27s license are the most important factors influencing travel mode choice by older adults. However, age group (65-74 or 75+) and physical disability were not significant factors influencing older adults\u27 choice of transportation mode for grocery shopping. The second main chapter of this study (Chapter 5) examines the reasons why older adults who hold a valid driver\u27s license intend to renew their license when it expires (yes), or whether they do not intend to do so or are hesitant (no/not sure). Using a mixed-method approach including binomial logit regression and qualitative analysis, 116 older adults were surveyed. Results suggest that being 75 years of age and older, having a physical disability, and having a lower level of education (high school and below) negatively influence older adults\u27 decision to renew their driver\u27s license. Older adults who drive frequently and indicate that they would like to be able to drive to destinations easily are more likely to renew their driver\u27s license after it expires. The third main chapter of this thesis (Chapter 6) aims to examine the barriers and challenges older adults face when using modes of transportation other than the personal automobile, such as walking, bicycling, public transit, and ride-hailing. A qualitative content analysis of the 103 open-ended responses was used to fit the results into an ecological model. The study recommends four main actions to help policymakers and city governments overcome these barriers: (1) implement transportation education and outreach programs, (2) improve accessibility to services and facilities through land use policies, (3) improve transportation infrastructure and services, and (4) help for-profit and nonprofit organizations organize informal groups to walk, bike, or carpool together. This thesis has important implications for policy makers and urban practitioners to meet the transportation needs of older adults. Improving transportation infrastructure and providing older adults with reliable and high-standard non-automobile transportation alternatives, managing future land use dynamics and investing in sustainable land use patterns, and coordinating with organizations to support social networks (such as informal clubs and local groups) that help older adults meet their travel needs are among some of these important implications

Nuevos métodos para análisis visual de mapas auto-organizativo

El mapa auto-organizativo (MAO) es un tipo de red neuronal artificial competitiva y no-supervisada. Ha sido utilizado tradicionalmente en tareas de ingeniería como herramienta de clasificación automática (clustering) y especialmente en tareas relacionadas con el análisis exploratorio de datos y la minería de datos, ya que su propósito principal es la visualización de relaciones no-lineales de datos multidimensionales. Sin embargo, a pesar de la importancia de la tarea de visualización, las técnicas gráficas para analizar MAO no son abundantes en la literatura. Esta tesis presenta varias técnicas nuevas que complementan, mejoran y facilitan el anáfisis visual de MAO de Kohonen, tanto desde el punto de vista del análisis exploratorio de datos, como desde el punto de vista de comprender el proceso de adaptación del MAO a una distribución de datos. La motivación para desarrollar técnicas de visualización nuevas surge por los siguientes motivos: IÍL relativa carencia de métodos destinados a la importante tarea de visualización, la necesidad de analizar MAO con diferentes métodos, la necesidad de mejorar varios métodos descritos en la literatura y la posibilidad de innovar desarrollando nuevas estrategias de visualización. De esta manera, se ha hecho hincapié en desarrollar técnicas generalmente no utilizadas con anterioridad en un intento por superar limitaciones de varios métodos descritos en la literatura. El primer nuevo método denominado "método de semejanza de triángulos" consiste en una estrategia de interpolación geométrica donde los patrones de una distribución de entrada son proyectados a un espacio de observación continuo. Está basado en la preservación de la semejanza geométrica entre varios triángulos formados por un patron y dos vectores de referencia del MAO en el espacio de los datos, y por un punto candidato y las dos correspondientes neuronas en el espacio de observación. El método encuentra la proyección minimizando una función de coste que mide distancias o errores entre varios triángulos. El método supera notablemente a otras estrategias de interpolación descritas en la literatura. Puede proyectar todos los datos de manera no-lineal, resulta adecuado cuando el tamaño del MAO es pequeño, es robusto y puede describir adecuadamente ciertos tipos de distribuciones difíciles de visualizar con la mayoría de métodos de visualización. Varios métodos de visualización de MAO generan imágenes monocromáticas las cuales son analizadas individualmente y aportan información específica sobre los datos. Se propone una estrategia para facilitar la labor del analista a la hora de combinar la información de varios métodos mediante una simple superposición de imágenes basada en un modelo aditivo de colores. Las imágenes son definidas con colores diferentes y combinadas mediante una simple suma de sus componentes de color. Las imágenes resultantes son más completas y robustas, especialmente cuando las imágenes a combinar aportan el mismo tipo de información. El estudio llevado a cabo se centra principalmente en la combinación de matrices de distancias con histogramas de datos. Una alternativa a las matrices de distancias, que generan imágenes monocromáticas y son los métodos más populares para visualizar la estructura de clusters de los datos, consiste en emplear estrategias que ilustren los diferentes clusters mediante colores diferentes. Una de estas estrategias consiste en utilizar modelos de contracción de neuronas. Se presenta un eficiente método de contracción, el "algoritmo de agrupación de neuronas", cuya estructura y filosofía es similar a la del algoritmo de entrenamiento de los MAO, donde los conceptos han sido invertidos para actualizar las posiciones de las neuronas en un mapa continuo en vez de los propios vectores de referencia del MAO. De esta manera, las neuronas son atraídas en el mapa en función de la distancia entre sus vectores de referencia en el espacio de los datos. Su principal ventaja es su bajo coste computacional que lo habilita para analizar MAO de tamaño elevado. Finalmente, el trabajo propone una técnica alternativa basada en la visualización explícita en el mapa o espacio de observación de grafos que unen neuronas cuyos vectores de referencia se hallan próximos en el espacio de los datos, como son el árbol generador mínimo o el "grafo Hebbiano" creado con el principio de aprendizaje Hebbiano competitivo. Las imágenes resultantes ayudan a analizar la dimensión intrínseca de los datos en cada zona del mapa y aportan una medida visual e intuitiva de la preservación de la topología del MAO

Big Data in Organizations and the Role of Human Resource Management

Big data are changing the way we work. This book conveys a theoretical understanding of big data and the related interactions on a socio-technological level as well as on the organizational level. Big data challenge the human resource department to take a new role. An organization’s new competitive advantage is its employees augmented by big data

The association of formal and informal institutions with total entrepreneurial activity: a neo-institutional theory approach

The thesis aims to identify the antecedents of total entrepreneurial activity (TEA) across OECD nations as this is key to national economic progress. Successive studies have associated TEA with informal institutions such as power distance (PD), individualism-collectivism (IND) and uncertainty avoidance (UA) but policymakers can do nothing about these in the short term. Two contributions are claimed. First, the thesis considers relatively new informal institutional dimensions (long-term orientation (LTO) and indulgence versus restraint (IVR)). However, these informal institutions (dimensions of national culture) cannot be modified by governments within a country but may influence the design of formal institutions (FIs). Thus, the second contribution arises from a consideration of two FIs (property rights (PRs) and access to finance (ATF)) as moderators of the main associations between informal institutions and TEA. These institutions may be modified by governments, so it is important to understand them, theoretically and empirically. PRs are analysed as they allow an organised market system to function, providing certainty for entrepreneurs engaging in TEA. Similarly, ATF may be needed for entrepreneurs to sustain or grow their ventures. A lack of ATF encountered by entrepreneurs is commonly viewed as the largest constraint to the creation and development of ventures. The results for this thesis are mixed, Model 1 looks at direct associations between national culture and TEA, and half of the hypotheses are supported. Model 2 (which studies the moderation by PRs and ATF of the relationships between informal institutions and TEA) has five out of the six hypotheses accepted for the PR-moderated hypotheses. This demonstrates that PRs have generally been found to have a positive moderating association with TEA, but ATF surprisingly generates an overall negative moderating association on informal institutions’ associations with TEA. OECD nations may therefore wish to encourage more effective PRs to further exploit the potential of TEA. In relation to ATF, FIs may need to tailor specific financial packages (or assistance) for specific industries which may have different gestation periods and tangible assets. Only one hypothesis appears to be accepted for ATF moderations. This may be explained by the quality of the institutional environment where a higher-quality institutional environment may have less impact as a moderator on TEA. In lower-quality institutional environments, FIs may have much more explanatory power. Overall, the models utilised in the thesis have resulted in a more nuanced study of the institutions that influence TEA