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A survey of robotized seam-tracking techniques was conducted in preparation for a project consisting of using an infrared camera on a robot for on one side collect continuous weld images for NDT inspection and on the other one track the weld joint. The tracking system can be used to discover the position of the weld bead without previous information. The robust system is outlined, along with its merits and disadvantages.

In order to create a machining line layout that supports the principles of lean a systematic approach is needed to ensure that a wide range of factors are taken into consideration. Despite this, many companies today design new layouts mainly considering delivery times of machines and equipment, and available space in the factory. A combined literature and case study has aimed to identify the most important factors in a lean line layout and a supporting structure to apply these in the design or redesign of automated machining lines. Highly automated machining environments mainly distinguish themselves from the more thoroughly studied area of assembly line layouts in two ways. Primarily, automated machining lines separate the operator from the actual processing, making line balancing and productivity less dependent on the workstation design around the operator. Secondly, automated machining lines often involve a higher level of complexity, leading to a more comprehensive work load, requiring longer training times but also offering less repetitive assignments. Automation offers improved productivity, quality and ergonomics, but if the acquisition and allocation of automation is not substantiated by a well-developed strategy, automation risks contradicting lean principles by creating a more complex, rigid layout that places the machines in the center instead of the workers. Factors that are important in the design of the typically less automated assembly lines, such as minimizing the walking distance of the operator and rotating stations to provide meaningful work assignments, must in an automated machining environment give way to factors like visualization, material flow and maintenance. Visualizing a factory helps operators and managers learn and understand the factory better. Problems can be detected and corrected faster and disturbances in production can thus be reduced. A good material flow is straight with no intersecting flows, triggered by downstream demand and reduces unnecessary buffers and WIP that bind up capital and consume space. Finally, since the machines rather than the operators produce, a good maintenance is required to avoid unplanned stops. The value of teamwork and humans in production which are strongly advocated within lean remain important also in automated machining lines but acquires a new content compared to assembly lines. Teamwork in automated machining environments occur within a group of lines rather than in a single line and it is a major factor when it comes to competence development, production planning and worker satisfaction. While teamwork in assembly lines works to balance the production flow within the line, teamwork in automated machining lines has little or no effect on the line balancing. However, joint efforts in setups and in case of machine failures or worker absence help increasing productivity, and potentially smooth the production at the plant in its entirety. The empirical studies showed that there is no standardized way of working with machining line layout design and redesign, and factors considered were often coincidental and dependent on the functions and priorities of the participants at different layout meeting. To ensure that all factors are taken into consideration a supporting tool where the most important factors were divided into ten categories was developed. Layouts are evaluated and rated on one category at a time to support a systematic way of working. Ongoing discussions, adjustments and improvements to better comply with the factors are encouraged.

Large quantities of bulk grain are moved using graincars in Canada and other parts of the world. Automation has not progressed significantly in the grain industry probably because the market is limited for automated systems. A prototype of a robot (“Grain-o-bot”) using machine vision to automatically open and close graincar hopper gates and detect the contents of the graincar was built and studied. The “Grain-o-bot” was a Cartesian robot equipped with two cameras and an opening tool as the end-effector. One camera acted as the eye to determine the sprocket location, and guided the end-effector to the sprocket opening. For most applications, machine vision solutions based on pattern recognition were developed using images acquired in a laboratory setting. Major constraints with these solutions occurred when implementing them in real world applications. So the first step for this automation was to correctly identify the hopper gate sprocket on the grain car. Algorithms were developed to detect and identify the sprocket under proper lighting conditions with 100% accuracy. The performance of the algorithms was also evaluated for the identification of the sprocket on a grain car exposed to different lighting conditions, which are expected to occur in typical grain unloading facilities. Monochrome images of the sprocket from a model system were acquired using different light. Correlation and pattern recognition techniques using a template image combined with shape detection were used for sprocket identification. The images were pre-processed using image processing techniques, prior to template matching. The template image developed from the light source that was similar to the light source used to acquire ii images was more successful in identifying the sprocket than the template image developed using different light sources. A sample of the graincar content was taken by slightly opening and immediately closing the hopper gates. The sample was identified by taking an image using the second camera and performing feature matching. An accuracy of 99% was achieved in identifying Canada Western Red Spring (CWRS) wheat and 100% for identifying barley and canola.

Corporate entrepreneurship has gained renewed interested in research since global markets are evolving and industries become more and more competitive. Information is transferred across the globe rapidly so that products and processes can be copied quickly. In order to be competitive, companies need to enhance creativity, their technological knowledge and market know-how. This high competitiveness leads to a dilemma where innovation is a key to survive whilst the size and administration may signify barriers to replicate entrepreneurial behavior through the entire business.   Considering the relevance of corporate entrepreneurship in the rapidly changing market of the 21st century, our purpose was to develop a deeper understanding of how corporate entrepreneurship can be used by companies. We then looked deeper into the subject of organizational transformation and decided to do a case study. The aim of the research was to make a theoretical contribution by examining the subject in the context of a medium-sized enterprise in a specific environment where corporate entrepreneurship is vital. Therefore, we chose a medium-sized German company that operates in the increasingly complex and competitive process automation industry. The Gefeba Elektro GmbH was found to be an interesting case for a case study for several reasons. The company was situated in a highly competitive market, in the heart of the industrial ‘Rurhgebiet’, with numerous competitors. However, and despite the lack of resources faced by this SME, Gefeba is an important actor in the automation industry.   Although researchers have examined various factors that promote corporate entrepreneurship, the literature has focused on defining factors in isolation without linking them to architectural factors, especially when it comes to SMEs. These factors are defined in our study as leadership, culture, structure and strategy. Acting within the extremes of small businesses and large corporations, we focus our study on a single medium-sized company that enables us to reach different levels of the organization and grasp a holistic understanding of this specific organization in relation to its use of CE. In accordance to this, the main research question is: How does Gefeba Elektro GmbH use corporate entrepreneurship in the automation sector industry?   The study was conducted using a qualitative research method. One of the major findings is that the Gefeba Elektro GmbH is using a balance between the organizational antecedents of common values and flexibility to build a mechanism that aligns the organizational architecture towards the development of corporate entrepreneurship. Another aspect is the fact that every architectural factor is used for the development of CE, even if some architectural factors such as leadership and culture seem to have more importance in this development. Thereby, the findings about organizational antecedents and architectural factors are relevant for the managerial implications in others SMEs facing the same context as Gefeba Elektro GmbH, which are willing to implement corporate entrepreneurship without knowing exactly how to do it. Indeed, the lack of resources of an SME could however allow developing organizational transformation through a sensitive equilibrium between the common values and beliefs for the control and the flexibility for the innovation. Moreover, another point highlighted in our findings is the crucial role of the individual in the implementation and development of corporate entrepreneurship.

As automation becomes increasing ubiquitous, it is important to know how differences in introducing automated systems will affect human-automation interactions. There are two main ways of introducing expected reliability of an automated system to users: explicitly telling operators what to expect or giving operators experience using the system. This study systematically investigated the effect of expectation format initially and over time on: 1) perceptions of reliability and system usage, and 2) human responses to automation (e.g., compliance, reliance, and overall dependence). Initially, there was an effect of expected level for explicit statement groups, whereas there was no effect of expected level for initial exposure groups. Over time, explicit statement groups had more stable perceptions of system reliability than the initial exposure groups. In general, perceived reliability did not converge to actual system reliability (75%) by the end of the study. Additionally, perceived reliability had a weak, but positive relationship with actual system use, whereas perceptions of system use (e.g., perceived dependence) had a strong, but negative relationship with actual system use. Outside of initial effects seen with perceived reliability, there were few initial differences between expectation formats. Almost all groups tended to initially comply more than rely, with the exception of the initial exposure – lower-than group. Over time, level of expectation for initial exposure groups influenced reliance. There were no differences between expectation groups on compliance and dependence over time. In general, dependence and compliance increased or stayed the same as time using the system increased. This pattern was also seen with reliance, with the exception of the initial exposure - higher-than group decreasing reliance over time. Results from this study have implications for both theory and practice. The research findings both support and augment the existing conceptual model of automation. A better understanding of the differential effects of expectation format and introduced level of expectations can lead to introductions of automated systems that are best suited to the system’s goals, ultimately improving system performance.

Predictive control has attracted much attention from both industry and academia alike due to its intuitive time domain formulation and since it easily affords adaption. The time domain formulation enables the user to build in prior knowledge of the operating constraints and thus the process can be controlled more efficiently, and the adaptive mechanism provides tighter control for systems whose behaviour changes with time. This thesis presents a fusion of technologies for dealing with the more practical aspects of obtaining suitable models for predictive control, especially in the adaptive sense. An accurate model of the process to be controlled is vital to the success of a predictive control scheme, and most the of work to date has assumed that this model is of fixed order, a restriction which can lead to poor controller performance associated with under/overparameterisation of the estimated model. To overcome this restriction a strategy which estimates both the parameters and the order of a linear model of the time-varying plant online is suggested. This Multiple Model Least-Squares technique is based on the recent work of Niu and co-workers who have ingeniously extended Bierman's method of UD updating so that, with only a small change to the existing UD update code, a wealth of additional information can be obtained directly from the U and D matrices including estimates of all the lower order models and their loss functions. The algorithm is derived using Clarke's Lagrange multiplier approach leading to a neater derivation and possibly a more direct understanding of Niu's Augmented UD Identification algorithm. An efficient and robust forgetting mechanism is then developed by analysing the properties of the continuous-time differential equations corresponding to existing parameter tracking methods. The resulting Multiple Model Recursive Least-Squares estimator is also ported to the δ-domain in order to obtain models for predictive controllers that employ fast sampling. The MMRLS estimator is then used in an adaptive multiple model based predictive controller for a coupled tanks system to compare performance with the fixed model order case.

Modern IT development infrastructure has a large number of components that must be monitored, for instance servers and network components. Various system-metrics (build time, CPU utilization, queries time etc.) are gathered to monitor system performance. In practice, it is extremely difficult for a system administrator to observe a correlation between several systemmetrics and predict a target system-metric based on highly correlated system-metrics without machine learning support. The experiments were performed on development logs at Ericsson. There were many system-metrics available in the system. Our goal is use machine learning techniques to find correlation between buildtime and other system-metrics and predict its trends in the future.