In today’s turbulent business environment, an increasing number of businesses and organizations are finding themselves confronted with a crushing pressure to constantly reinvent themselves. Given such a business environment, coupled with high stakes and fierce competitiveness, it is quite unsurprising that most change efforts are reported to fail. Change communication, or rather the lack thereof, has been pointed out as one of the most important reasons. In recent years, many scholars have started to differentiate between participatory and programmatic change communication, elicited by a reconceptualization of change as continuous and emergent rather than episodic and planned. Participatory and programmatic are two diametrically different ways to approach change communication, and thus, they each require different tools, techniques and strategies. Several scholars have called attention to the shortage of tools, techniques and strategies suitable for participatory change communication in particular, however, empirical research remain scarce. At the same time, the data revolution is right upon us. Businesses that fail to harness the true potential of their data see themselves outcompeted by those who do. Even though data since long has been prophesied to transform management altogether, it is only in recent years that its application within the various subfields of management research has received serious attention by academia. However, the application of data in change management in general, and change communication in particular, has largely been left unaddressed.  In this thesis, we are exploring the role of data in the context of change communication through a case study at Skanska Rental, one of Sweden’s largest construction equipment rental companies. We delimit our study to only treat one of the most important aspects of data-driven communication; namely, data visualization. Our findings indicate that data visualization can facilitate change communication by encouraging it to be participatory. In particular, we find that data visualization has the merit of enthusing its audience, aligning the perception of the current state of affairs, reinforcing a data-driven culture, and facilitating interpersonal communication. We also call attention to three important considerations; (1) the democratization of data requires transparency trade-offs, (2) data visualization cannot replace interpersonal communication, but at most facilitate it, and (3) communication, although one of the most important, is not the sole precursor of successful change. We conclude our thesis by addressing the practical and theoretical implications of our conclusions, and lastly, by suggesting directions for future research in data-driven change communication. / I dagens turbulenta omvärld utsätts företag och organisationer för ett ständigt förändringstryck som inte visar några tecken på att avta. Givet ett sådant klimat, där mycket står på spel och där konkurrensen är förkrossande, är det inte förvånande att de flesta förändringsinitiativ misslyckas. Förändringskommunikation, eller snarare bristen därav, har ofta pekats ut som en av de viktigaste orsakerna till detta. På senare tid har många forskare börjat göra skillnad mellan två olika typer av förändringskommunikation: deltagande respektive programmatisk. Att betrakta förändringskommunikation genom denna dikotomi har föranletts av en ny syn på förändring i stort; snarare än att se förändring som episodisk och planerad har många forskare istället konceptualiserat förändring som kontinuerlig och framväxande. Olika tekniker, verktyg och strategier lämpar sig olika väl för de två olika typerna av förändringskommunikation. Många forskare har varnat för en bristande förståelse för i synnerhet de tekniker, verktyg och strategier som lämpar sig för den deltagande typen av förändringskommunikation. Under de senare åren har det förändringstryck många företag och organisationer står inför snarast ökat i styrka på grund av den datarevolution vi befinner oss mitt upp i. Trots att det sedan länge har förutspåtts att data har potentialen att vända spelplanen för företagsledning upp och ner, så är det bara på senare år som ämnet har åtnjutit företagsledningsforskningens fulla uppmärksamhet. Däremot finns det fortfarande ett underskott på akademisk forskning som adresserar hur data kan användas inom förändringsledning i allmänhet och förändringskommunikation i synnerhet. I denna masteruppsats utforskar vi den roll som data kan spela inom förändringskommunikation. Vårt empiriska material inhämtar vi genom en case-studie hos Skanska Rental, ett av Sveriges största uthyrningsföretag inom byggbranschen. Vi avgränsar vår studie genom att endast behandla en utav de viktigaste aspekterna av data-driven kommunikation, nämligen datavisualisering. Våra resultat indikerar att datavisualisering kan underlätta förändringskommunikation genom att göra den deltagande. I synnerhet finner vi att datavisualisering har potential att entusiasmera dess publik, linjera bilden av nuläget, förstärka en data-driven organisationskultur och fungera som en utgångspunkt för mellanmänsklig kommunikation. Vi identifierar också tre viktiga reservationer mot denna slutsats; (1) demokratisering av data fordrar ställningstagande gällande transparens, (2) datavisualisering kan inte ersätta mellanmänsklig kommunikation, och (3) kommunikation, om än viktig, är inte den enda förutsättningen för framgångsrik förändring. Vi avslutar vår uppsats med att adressera de praktiska och teoretiska implikationer som vår slutsats resulterar i, samt föreslår inriktningen för framtida forskning inom data-driven förändringskommunikation.

No description available.

The purpose of this master's project was to write a resource book that demonstrates how teachers can use data-driven learning methods to teach vocabulary. First, a brief overview of corpus linguistics, data-driven learning, and the corpus used in this book (COCA) is given. Then, the book presents different aspects of vocabulary learning in the context of a corpus. Topics included are frequency knowledge, part of speech knowledge, morphological knowledge, synonym knowledge, collocational knowledge, and register knowledge with a chapter on each topic. For each aspect of vocabulary learning there is a section that introduces the topic to the teacher, followed by instructions on performing topic related searches in the corpus. Each chapter also includes examples and ideas for application to the vocabulary classroom. Additional chapters provide information on individual language learning, and an evaluation of the project. The goal of this project was to provide teachers with specific knowledge of vocabulary and corpus-linguistics to be able to teach less-frequently addressed aspects of vocabulary instruction and to encourage more use of corpora in the language classroom. It is hoped that after reading this book, teachers will be able to improve their vocabulary teaching and ability to use the Corpus of Contemporary American English and DDL methods in the ESL/EFL classroom. The evaluation of this project will consist of teacher reviews of the book after reading. Specifically, the questionnaire addresses readers' feelings of increased knowledge and understanding of these areas and desire to use them in the classroom.

No description available.

No description available.

The prevalence of batch and batch-like operations, in conjunction with the continued resurgence of artificial intelligence techniques for clustering and classification applications, has increasingly motivated the exploration of the applicability of deep learning for modeling and feedback control of batch and batch-like processes. To this end, the present study seeks to evaluate the viability of artificial intelligence in general, and neural networks in particular, toward process modeling and control via a case study. Nonlinear autoregressive with exogeneous input (NARX) networks are evaluated in comparison with subspace models within the framework of model-based control. A batch polymethyl methacrylate (PMMA) polymerization process is chosen as a simulation test-bed. Subspace-based state-space models and NARX networks identified for the process are first compared for their predictive power. The identified models are then implemented in model predictive control (MPC) to compare the control performance for both modeling approaches. The comparative analysis reveals that the state-space models performed better than NARX networks in predictive power and control performance. Moreover, the NARX networks were found to be less versatile than state-space models in adapting to new process operation. The results of the study indicate that further research is needed before neural networks may become readily applicable for the feedback control of batch processes. / Thesis / Master of Applied Science (MASc)

Aircraft engines are one of the most heavily instrumented parts of an aircraft, and the data from various types of instrumentation across these engines are continuously monitored both offline and online for potential anomalies. Vibration monitoring in aircraft engines is traditionally performed using an order tracking methodology. Currently, there are no representative and efficient physics-based models with the adequate fidelity to perform vibration predictions in aircraft engines, given various parametric dependencies existing among different attributes such as temperature, pressure, and external conditions. This gap in research is primarily attributed to the limited understanding of mutual interactions of different variables and the nonlinear nature of engine vibrations. The objective of the current study is three-fold: (i) to present a preliminary investigation of tracked order vibrations in aircraft engines and statistically analyze them in the context of their operating environment, (ii) to develop data-driven modeling methodology to approximate a dynamical system from input-output data, and (iii) to leverage these data-driven modeling methodologies to develop highly accurate models for tracked order vibration in a turbo-fan engine valid over a wide range of operating conditions. Off-the-shelf data-driven modeling techniques, such as machine learning methods (eg., regression, neural networks), have several drawbacks including lack of interpretability and limited scope, when applying them to a complex multiscale multi-physical dynamical system. Moreover, for dynamical systems with external forcing, the identified model should not only be suitable for a specific forcing function, but should also generally approximate the input-output behavior of the data source. The author proposes a novel methodology known as Wavelet-based Dynamic Mode Decomposition (WDMD). The methodology entails using wavelets in conjunction with input-output dynamic mode decomposition (ioDMD). Similar to time-delay embedded DMD (Delay-DMD), WDMD builds on the ioDMD framework without the restrictive assumption of full state measurements. The author demonstrates the present methodology's applicability by modeling the input-output response of an Euler-Bernoulli finite element beam model, followed by an experimental investigation. As a first step towards modeling the tracked order vibration amplitudes of turbofan engines, the interdependencies and cross-correlation structure between various thermo-mechanical variables and tracked order vibration are analyzed. The order amplitudes are further contextualized in terms of their operating regime, and exploratory data analyses are performed to quantify the variability within each operating condition (OC). The understanding of complex correlation structures is leveraged and subsequently utilized to model tracked order vibrations. Switching linear dynamical system (SLDS) models are developed using individual data-driven models constructed using WDMD, and its performance in approximating the dynamics of the $1^{st}$ order amplitudes are compared with the state-of-the-art time-delay embedded dynamic mode decomposition (Delay-DMD) and Lasso regression. A parametric approach is proposed to improve the model further by leveraging previously developed WDMD and Delay-DMD methods and a parametric interpolation scheme. In particular, a recently developed pole-residue interpolation scheme is adopted to interpolate between several linear, data-driven reduced-order models (ROMs), constructed using WDMD and Delay-DMD surrogates, at known parameter samples. The parametric modeling approach is demonstrated by modeling the transverse vibration of an axially loaded finite element (FE) beam, where the axial loading is the parameter. Finally, a parametric modeling strategy for tracked order amplitudes is presented by constructing locally valid ROMs at different parametric samples corresponding to each pass-off test. The performance of the parametric-ROM is quantified and compared with the previous frameworks. This work was supported by the Rolls-Royce Fellowship, sponsored by the College of Engineering, Virginia Tech. / Doctor of Philosophy / Vibrations in commercial aircraft engines are of utmost importance as they directly translate to aviation health and safety, and hence are continuously monitored both online and offline for potential abnormalities. Notably, this is of increased interest with the abundance of air transportation in today's world. However, there is limited understanding of the complex higher vibration in aircraft engines. Vibration engineers often face ambiguity when interpreting higher vibrations. This can often lead to a lengthy investigative process resulting in longer downtime and increased testbed occupancy, ultimately leading to revenue loss. It is often hypothesized that prior engine running conditions such as shutdown/cooling time between one engine run to another engine run affect the vibration profile. Nonetheless, there exists a gap in understanding tying together various historical operational conditions, temperature, pressures, and current operational conditions with the expected vibration in the engine. This study aims to fill some of these gaps in our understanding by proposing a data-driven strategy to model the vibrations in commercial aircraft engines. Subsequently, this data-driven model can serve as a baseline model to compare the observed vibrations with the model predicted vibration and supplement physics-based models. The data for the present study is generated by operating a commercial turbofan engine in a testbed. With the advent of machine learning and data fusion, various data-driven techniques exist to model dynamical systems. However, the complexity of the turbofan engine vibrations calls for developing new techniques applicable towards modeling the vibration characteristics of a turbofan engine. Specifically, this dissertation details the development of a novel methodology called Wavelet-based Dynamic Mode Decomposition (WDMD) and applies the technique to model input-output characteristics of various dynamical systems ranging from a numerical finite element (FE) beam to an experimental free-free beam to shaft vibrations in a turbofan engine. The study finally presents an improved modeling framework by incorporating the existing techniques with parametric dependencies. This enables the existing method to consider slight differences existing from one engine run to another, such as the history of the engine, the shutdown time, and the outside environmental parameters.

<p> We propose a data-driven estimation algorithm in survival mixture model. The objective of this study is to provide an alternative fitting procedure to the conventional EM algorithm. The EM algorithm is the classical ML fitting of the parametric mixture model. If the initial values for the EM algorithm are not properly chosen, the maximizers might be local or divergent. Traditionally, initial values are given manually according to experience or a gridpoint search. This is a heavy burden for a high-dimensional data sets. Also, specifying the ranges of parameters for a grid-point search is difficult. To avoid the specification of initial values, we employ the random partition. Then, improvement of fitting is adjusted according to model specification. This process is repeated a large number of times, so it is computer intensive. The large repetitions makes the solution more likely to be the global maximizer, and it is driven purely by the data. We conduct a simulation study for three cases of two-component Log-Normal, two-component Weibull, and two-component Log-Normal and Wei bull, in order to illustrate the effectiveness of the proposed algorithm. Finally, we apply our algorithm to a breast cancer study data which follows a cure model. The program is written in R. It calls existing R functions, so it is flexible to use in regression situations where model formula must be specified. </p> / Thesis / Master of Science (MSc)

Due to the rapid and prosperous development of information technology, data centers are widely used in every aspect of social life, such as industry, economy or even our daily life. This work considers the idea of developing a data-driven model based model predictive control (MPC) to regulate temperature for a class of single-rack data centers (DCs). An auto-regressive exogenous (ARX) model is identified for our DC system using partial least square (PLS) to predict the behavior of multi-inputs-single-output (MISO) thermal system. Then an MPC controller is designed to control the temperature inside IT rack based on the identified ARX model. Moreover, fuzzy c-means (FCM) is employed to cluster the measured data set. Based on the clustered data sets, PLS is adopted to identify multiple locally linear ARX models which will be combined by appropriate weights in order to capture the nonlinear behavior of the highly-nonlinear thermal system inside the IT rack. The effectiveness of the proposed method is illustrated through experiments on our single-rack DC and it is also compared with proportional-integral (PI) control. / Thesis / Master of Applied Science (MASc)

We propose solution strategies for hard Mixed Integer Programming (MIP) problems, with a focus on distributed parallel MIP optimization. Although our proposals are inspired by the Less-than-truckload (LTL) freight routing problem, they are more generally applicable to hard MIPs from other domains. We start by developing an Integer Programming model for the Less-than-truckload (LTL) freight routing problem, and present a novel heuristic for solving the model in a reasonable amount of time on large LTL networks. Next, we identify some adaptations to MIP branching strategies that are useful for achieving improved scaling upon distribution when the LTL routing problem (or other hard MIPs) are solved using parallel MIP optimization. Recognizing that our model represents a pseudo-Boolean optimization problem (PBO), we leverage solution techniques used by PBO solvers to develop a CPLEX based look-ahead solver for LTL routing and other PBO problems. Our focus once again is on achieving improved scaling upon distribution. We also analyze a technique for implementing subtree parallelism during distributed MIP optimization. We believe that our proposals represent a significant step towards solving big-data driven optimization problems (such as the LTL routing problem) in a more efficient manner. / Thesis / Doctor of Philosophy (PhD) / Less-than-truckload (LTL) freight transportation is a vital part of Canada's economy, with revenues running into billions of dollars and a cascading impact on many other industries. LTL operators often have to deal with large volumes of shipments, unexpected changes in traffic conditions, and uncertainty in demand patterns. In an industry that already has low profit margins, it is therefore vitally important to make good routing decisions without expending a lot of time. The optimization of such LTL freight networks often results in complex big-data driven optimization problems. In addition to the challenge of finding optimal solutions for these problems, analysts often have to deal with the complexities of big-data driven inputs. In this thesis we develop several solution strategies for solving the LTL freight routing problem including an exact model, novel heuristics, and techniques for solving the problem efficiently on a cluster of computers. Although the techniques we develop are inspired by LTL routing, they are more generally applicable for solving big-data driven optimization problems from other domains. Experiments conducted over the years in consultation with industry experts indicate that our proposals can significantly improve solution quality and reduce time to solution. Furthermore, our proposals open up interesting avenues for future research.