Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

Capacity building in complex environments: seeking meaningful methodology for social change

This dissertation explores ways in which “capacity-building” might contribute to processes of social change in complex environments. This exploration emerged as part of a personal journey as a capacity-building practitioner to help make sense out of my prior work experience. In my experience, I learned first-hand how many of the “capacity” challenges that my colleagues and I were trying to address in different organizations were complex, “messy” and uncertain. At the same time, many of the capacity-building tools and methodological processes I commonly used assumed a world that was predictable, neat and controllable. These assumptions led to many occasions in which capacity-building processes and methods did not make sense in specific situations, or did not generate expected significant changes. I saw my PhD as a way of addressing many unanswered questions and developing capacity-building methodology that would be relevant to the complex realities in which I worked. At the Institute of Development Studies (IDS), I became much more aware not only of the complexity of my prior capacity-building work in development, but also of its apolitical nature. I was well aware of the contested nature of social change, both from my prior studies and my previous life and work experiences. However, after nine years working as a capacity-building process designer and facilitator for a large American Non-governmental Organization (NGO), I had come to use methodology without considering whether it might even be compatible with concepts of social change. I mostly assumed methodology to be neutral and apolitical, but did not see this as a problem. In my PhD process, I was fortunate to see first-hand how methodology that practitioners assume to be apolitical actually lacks a theory capable of explaining change, and thereby may reproduce the status quo. This is a strong political position indeed. My research starts from the assumption that the way people and organizations change in relation to economic, social and environmental concerns is complex and contested. Complex, in that multiple actors and factors—many of them unknowable—combine to affect how social change actually emerges in real life. Contested, in that power relations enable and constrain the fields of possibility for positive change for all people, and thereby generate winners and losers in the process. Indeed, the contested nature of social change is one of its primary sources of complexity. Methodologically, I conducted two action-research processes over 18 months; one with a progressive organization that supports social movements in Perú, and the other with a private environmental conservation organization in Ecuador. I used an emergent, learning-based action-research (AR) approach strongly influenced by systemic theories, with a particular focus on Peter Checkland’s Soft Systems Thinking (SST). Different methodological principles emerged in each organizational AR process, providing important insights into how capacity-building can support social (and socio-environmental) change processes in complex environments. Whereas SST and AR prominently informed my methodology, Ralph Stacey, Patricia Shaw, and Douglas Griffin’s “Complex Responsive Processes” (CRP) was the main theory I used to connect methodological capacity-building intervention to complexity theory. CRP is a theory that explains how complex adaptive systems (CAS) emergently self-organize from local, communicative interaction. Drawing on these different sources and based on my empirical data, my dissertation explores the following themes: – How organizational learning and change occur through the shifting interacting dynamics of conversations and other forms of communicative interaction, and how organizational capacity emerges in these shifting dynamics. – How capacity-building methodology can help surface—via communicative interaction—the complexity of social change that organizations face. Particularly: o How methodology that engages multiple ways of knowing is helpful in accessing doorways to diverse thought, feelings, and identity, and how this diversity plays a key role in influencing the patterns of communicative interaction that emerge. o How the intentional contrasting of multiple, diverse perspectives, and worldviews (i.e.—SST focus) charges conversations with meaning and is capable of shifting patterns and generating learning in communicative interaction. o How two ostensibly oppositional forms of methodology—methodological redundancy and unstructured reflection—enable and constrain how patterns of communicative interaction emerge and support learning, when diversity is also present. – How all communicative interaction enacts power relationships that generate dynamics of inclusion and exclusion, and how these dynamics affect the patterns of communicative interaction—i.e. learning and change—that emerge. These methodological findings lead to some interesting implications for how CB is conceived and practiced. If capacity as learning emerges in complex environments via shifts communicative interaction, then a core purpose of CB becomes strengthening the ability of organizational participants—“within” an organization and in relation to key “system” stakeholders—to actively relate and interact with each other in organic (i.e. uncontrived) ways. This active relating is situational and as such implies looking for opportunities to “add” systemic methodological support to real-life situations and experiences. My research has contributed new knowledge by helping explain how systemic capacity-building methodology can support processes of social change in complex environments. Systems thinking is often used anecdotally in capacity-building, without making explicit connections between theory and practice. Complexity theory, when referenced at all in capacity-building literature, is limited to claims about the need to act differently in a complex world. My research has made the following important contributions: 1) Provides empirical cases that connect systemic capacity-building methodology to Complex Responsive Processes theory in a plausible manner, and thus, make these connections more explicit. 2) Develops plausible connections between concepts of extended epistemologies (as a source of diversity) and complexity theory 3) Demonstrates the relative importance of critical reflection alongside the use of more-structured methods to generate organizational capacity 4) Offers—as a conversation starter—an alternative interactive communication understanding of capacity development, which asks critical questions of much dominant CD theory and practice. I believe that the findings and learning from this research can help generate critical, non-linear approaches to capacity-building methodology that serve the needs of complex, contested social change in a more meaningful manner

Recasting Genre in Tennessee Williams\u27s Apprentice Plays

This dissertation investigates Tennessee Williams’s earliest full-length plays, also known as the apprentice plays—Candles to the Sun, Fugitive Kind, Not About Nightingales, Spring Storm, and Stairs to the Roof—by comparing, contrasting and contextualizing them in relation to Daniel Chandler’s generic criteria of drama; namely, narrative, characterization, setting, topics, iconography, and staging techniques. The present study also draws upon an extensive body of scholarship pertaining to genre theory, Williams’s cultural contemporaries, and the historical and psychological backdrop of Depression-era America. In these early plays, Williams diverged sharply from the dramatic generic conventions of his day, manipulating them in new and unique ways, to create plays that reflect and embody authentic generic innovations. Their immense impact, not only on his own subsequent works but also on other playwrights, is widely acknowledged. While the initial rediscovery of these plays in 1998 led to their widespread appreciation, publication, and/or production, no study to date has analyzed their distinctive generic innovations. This analysis demonstrates how Williams reworks and exploits the contemporary repertoire of dramatic narratives, while situating their generic locales—the coal mine, the prison, the urban gangster milieu, Southern Gothic, and science fiction—within the overarching genres of protest and fantasy. These generic conventions often intertwine through both the major and minor narratives of a single play. Separate chapters introduce each play, discussing its specific formal organization and generic attributes, and noting its relation to contemporary dramatic and cinematic traditions. Williams’s reinterpretation and revision of his personal artistic philosophy is examined in light of formal and stylistic concerns bearing on his ingenious handling of a broad mixture of borrowings and innovations, and the following scrutiny of genres always situates the plays’ unconventionality within the cultural and theatrical context in which Williams was active

The Role of CSR in Risk Management: A Case Study of the Extractives Industry in Australia

So far, little consideration has been given to investigate beneficial impacts between corporate social responsibility (CSR) and risk management. This research investigates the role of CSR in risk management in Australian extractives industries to enhance business value through CSR’s positive aspects. A large contributor to Australia’s economy, this sector was selected because it deploys separate programs for managing risk and CSR. The study confirms CSR plays a critical part, demonstrating links between business success, CSR as social value, and holistic risk management. Organisations should consider optimising integration of CSR and risk management to maximise value and minimise corporate failures

Urban in-between

No abstract

Transformation of graphical models to support knowledge transfer

Menschliche Experten verfügen über die Fähigkeit, ihr Entscheidungsverhalten flexibel auf die jeweilige Situation abzustimmen. Diese Fähigkeit zahlt sich insbesondere dann aus, wenn Entscheidungen unter beschränkten Ressourcen wie Zeitrestriktionen getroffen werden müssen. In solchen Situationen ist es besonders vorteilhaft, die Repräsentation des zugrunde liegenden Wissens anpassen und Entscheidungsmodelle auf unterschiedlichen Abstraktionsebenen verwenden zu können. Weiterhin zeichnen sich menschliche Experten durch die Fähigkeit aus, neben unsicheren Informationen auch unscharfe Wahrnehmungen in die Entscheidungsfindung einzubeziehen. Klassische entscheidungstheoretische Modelle basieren auf dem Konzept der Rationalität, wobei in jeder Situation die nutzenmaximale Entscheidung einer Entscheidungsfunktion zugeordnet wird. Neuere graphbasierte Modelle wie Bayes\u27sche Netze oder Entscheidungsnetze machen entscheidungstheoretische Methoden unter dem Aspekt der Modellbildung interessant. Als Hauptnachteil lässt sich die Komplexität nennen, wobei Inferenz in Entscheidungsnetzen NP-hart ist. Zielsetzung dieser Dissertation ist die Transformation entscheidungstheoretischer Modelle in Fuzzy-Regelbasen als Zielsprache. Fuzzy-Regelbasen lassen sich effizient auswerten, eignen sich zur Approximation nichtlinearer funktionaler Beziehungen und garantieren die Interpretierbarkeit des resultierenden Handlungsmodells. Die Übersetzung eines Entscheidungsmodells in eine Fuzzy-Regelbasis wird durch einen neuen Transformationsprozess unterstützt. Ein Agent kann zunächst ein Bayes\u27sches Netz durch Anwendung eines in dieser Arbeit neu vorgestellten parametrisierten Strukturlernalgorithmus generieren lassen. Anschließend lässt sich durch Anwendung von Präferenzlernverfahren und durch Präzisierung der Wahrscheinlichkeitsinformation ein entscheidungstheoretisches Modell erstellen. Ein Transformationsalgorithmus kompiliert daraus eine Regelbasis, wobei ein Approximationsmaß den erwarteten Nutzenverlust als Gütekriterium berechnet. Anhand eines Beispiels zur Zustandsüberwachung einer Rotationsspindel wird die Praxistauglichkeit des Konzeptes gezeigt.Human experts are able to flexible adjust their decision behaviour with regard to the respective situation. This capability pays in situations under limited resources like time restrictions. It is particularly advantageous to adapt the underlying knowledge representation and to make use of decision models at different levels of abstraction. Furthermore human experts have the ability to include uncertain information and vague perceptions in decision making. Classical decision-theoretic models are based directly on the concept of rationality, whereby the decision behaviour prescribed by the principle of maximum expected utility. For each observation some optimal decision function prescribes an action that maximizes expected utility. Modern graph-based methods like Bayesian networks or influence diagrams make use of modelling. One disadvantage of decision-theoretic methods concerns the issue of complexity. Finding an optimal decision might become very expensive. Inference in decision networks is known to be NP-hard. This dissertation aimed at combining the advantages of decision-theoretic models with rule-based systems by transforming a decision-theoretic model into a fuzzy rule-based system. Fuzzy rule bases are an efficient implementation from a computational point of view, they can approximate non-linear functional dependencies and they are also intelligible. There was a need for establishing a new transformation process to generate rule-based representations from decision models, which provide an efficient implementation architecture and represent knowledge in an explicit, intelligible way. At first, an agent can apply the new parameterized structure learning algorithm to identify the structure of the Bayesian network. The use of learning approaches to determine preferences and the specification of probability information subsequently enables to model decision and utility nodes and to generate a consolidated decision-theoretic model. Hence, a transformation process compiled a rule base by measuring the utility loss as approximation measure. The transformation process concept has been successfully applied to the problem of representing condition monitoring results for a rotation spindle

A Mathematical Framework on Machine Learning: Theory and Application

The dissertation addresses the research topics of machine learning outlined below. We developed the theory about traditional first-order algorithms from convex opti- mization and provide new insights in nonconvex objective functions from machine learning. Based on the theory analysis, we designed and developed new algorithms to overcome the difficulty of nonconvex objective and to accelerate the speed to obtain the desired result. In this thesis, we answer the two questions: (1) How to design a step size for gradient descent with random initialization? (2) Can we accelerate the current convex optimization algorithms and improve them into nonconvex objective? For application, we apply the optimization algorithms in sparse subspace clustering. A new algorithm, CoCoSSC, is proposed to improve the current sample complexity under the condition of the existence of noise and missing entries. Gradient-based optimization methods have been increasingly modeled and inter- preted by ordinary differential equations (ODEs). Existing ODEs in the literature are, however, inadequate to distinguish between two fundamentally different meth- ods, Nesterov’s acceleration gradient method for strongly convex functions (NAG-SC) and Polyak’s heavy-ball method. In this paper, we derive high-resolution ODEs as more accurate surrogates for the two methods in addition to Nesterov’s acceleration gradient method for general convex functions (NAG-C), respectively. These novel ODEs can be integrated into a general framework that allows for a fine-grained anal- ysis of the discrete optimization algorithms through translating properties of the amenable ODEs into those of their discrete counterparts. As a first application of this framework, we identify the effect of a term referred to as gradient correction in NAG-SC but not in the heavy-ball method, shedding deep insight into why the for- mer achieves acceleration while the latter does not. Moreover, in this high-resolution ODE framework, NAG-C is shown to boost the squared gradient norm minimization at the inverse cubic rate, which is the sharpest known rate concerning NAG-C itself. Finally, by modifying the high-resolution ODE of NAG-C, we obtain a family of new optimization methods that are shown to maintain the accelerated convergence rates as NAG-C for minimizing convex functions

The effects of two secondary science teacher education program structures on teachers\u27 habits of mind and action

This study investigated the effects of the Iowa State University Secondary Science Teacher Education Program (ISU SSTEP) on the educational goals and habits of mind exhibited by its graduates. Ten teachers from ISU SSTEP participated in the study---five from the former program featuring one semester of science teaching methods, five from the current program featuring three semesters of science teaching methods (four for the graduate certification consortium). A naturalistic inquiry research approach included the following methods used with each teacher: three classroom observations, classroom artifact analysis, teacher questionnaires and semi-structured interviews, and questionnaires for students about perceived emphasis of educational goals.;Evidence exists that graduates from the current ISU SSTEP format exhibited a closer match to the educational goals promoted, modeled, and advocated by the science teaching methods faculty. Graduates from the current ISU SSTEP also exhibited a closer match to the habits of mind---understanding, action, reflection, action plan for improvement---promoted and modeled by the program. This study has implications for other secondary science teacher education programs, particularly increasing the number of science teaching methods courses; teaching meaningful content of both concepts and skills through a research-based framework; modeling the appropriate teacher behaviors, strategies, habits, and goal promotion by methods instructors; and addressing issues of institutional constraints experienced by future teachers

The Change in Business Culture Caused by the Introduction of Anglo-American Business Concepts in Post-Soviet Russia : Linguistic Evidence of Paradigm Shift in Business

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