Baseline Data from Servo Motors in a Robotic Arm for Autonomous Machine Fault Diagnosis

Fault diagnosis can prolong the life of machines if potential sources of failure are discovered and corrected before they occur. Supervised machine learning, or the use of training data to enable machines to discover these faults on their own, makes failure prevention much easier. The focus of this thesis is to investigate the feasibility of creating datasets of various faults at both the component and system level for a servomotor and a compatible robotic arm, such that this data can be used in machine learning algorithms for fault diagnosis. The faults induced at the component level in different servomotors include: low lubrication, no lubrication, two gears chipped, and four gears chipped. Each fault was also examined at 180, 135, 90, and 45-degree swings of the servo arm. Component level data was obtained using an Arduino microcontroller and a feedback wire in each servomotor to obtain the actual position of the servo arm, which allowed for the calculation of the difference in actual and theoretical position and the speed of the servo arm at the various faults. System level data was obtained using OptiTrack’s motion tracking software, Motive, to track the position of two reflective markers on the hand of the robotic arm. At the component level, the low lubrication and no lubrication faults did not exhibit a large difference from the normal servomotor, whereas the servomotors with the gears chipped exhibited significant differences when compared to the normal servomotor. When evaluating the difference in position and speed of the servo arm at larger degree sweeps it was more evident that failure occurred, as opposed to the data at smaller degree sweeps. At the system level, the error was not as visible in the data as there wasn’t much distinction between the speeds of the robotic arm’s hand when the servomotors with faults were placed in it. The results of this work indicate that servomotors can be used to create fault behavior datasets at the component and system level that are usable for machine learning

Engaging the Digitally Engaged Student: Comparing Technology-Mediated Communication Use and Effects on Student Learning

The role of communication technologies in the learning process is both a dynamic and complex issue. Yet, we know surprisingly little about how the use of specific communication technologies may influence classroom performance, key learning outcomes, and other measures of course satisfaction. The research reported here attempts to add to our knowledge about the role of communication in the technology enhanced classroom (TEC) education and in technology-enhanced online (TEO) education through a direct comparison of two courses. Our findings indicate additional support for “The No Significant Difference Phenomenon.” Furthermore, we found that prior experiences lead students to gravitate towards their preferred learning environments, and that basic website elements are required in any learning environment to enhance student outcomes. Finally, we found that when used appropriately, the benefits of communication technology use in education outweigh many of the drawbacks

Implicit learning of recursive context-free grammars

Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured the distinction between left- and right-branching structures, as well as between centre- and tail-embedding, both distinctions found in natural languages. People acquired unconscious knowledge of relations between grammatical classes even for dependencies over long distances, in ways that went beyond learning simpler relations (e.g. n-grams) between individual words. The structural distinctions drawn from linguistics also proved important as performance was greater for tail-embedding than centre-embedding structures. The results suggest the plausibility of implicit learning of complex context-free structures, which model some features of natural languages. They support the relevance of artificial grammar learning for probing mechanisms of language learning and challenge existing theories and computational models of implicit learning

Persistence and change::issues for LIS educators in the first decade of the twenty-first century’

Considers the five ?persistent issues? relating to LIS education discussed in an article by Denis Grogan in 1983, to see whether they still apply in 2000, together with two additional issues that have emerged since that date. Although the issue of professional education for librarians in an academic institution has largely been resolved, there is a continuing debate on campus-based and distance learning modes of delivery the place of competency based, National Vocational Qualifications, delivered at the workplace. The issue of control by professional bodies has moved from being a matter of debate to one of continuing dialogue. The debate over the relative importance of theory versus practice, still rages, whilst the importance of fieldwork is still regarded as central to an LIS education. The nature and content of the ?core curriculum? appear to be in a constant state of flux. The two new issues identified were: The nature and extent of the information disciplines (the relationship between librarianship, information management and knowledge managent); and. The changing pattern of higher education in the UK and character of the student body (increase in numbers, modularization, and quality assessment).authorsversionPeer reviewe

Building capacity in climate change policy analysis and negotiation: methods and technologies

Capacity building is often cited as the reason “we cannot just pour money into developing countries” and why so many development projects fail because their design does not address local conditions. It is therefore a key technical and political concept in international development. Some of the poorest countries in the world are also some of the most vulnerable to the impacts of climate change. Their vulnerability is in part due to a lack of capacity to plan and anticipate the effects of climate change on crops, water resources, urban electricity demand etc. What capacities do these countries lack to deal with climate change? How will they cope? What steps can they take to reduce their vulnerability? This innovative and high-profile research project was part of a larger project (called C3D) and conducted with non-governmental organisations in Senegal, South Africa and Sri Lanka. The research involved several participatory workshops and a questionnaire to all three research centres

Are words easier to learn from infant- than adult-directed speech? A quantitative corpus-based investigation

We investigate whether infant-directed speech (IDS) could facilitate word form learning when compared to adult-directed speech (ADS). To study this, we examine the distribution of word forms at two levels, acoustic and phonological, using a large database of spontaneous speech in Japanese. At the acoustic level we show that, as has been documented before for phonemes, the realizations of words are more variable and less discriminable in IDS than in ADS. At the phonological level, we find an effect in the opposite direction: the IDS lexicon contains more distinctive words (such as onomatopoeias) than the ADS counterpart. Combining the acoustic and phonological metrics together in a global discriminability score reveals that the bigger separation of lexical categories in the phonological space does not compensate for the opposite effect observed at the acoustic level. As a result, IDS word forms are still globally less discriminable than ADS word forms, even though the effect is numerically small. We discuss the implication of these findings for the view that the functional role of IDS is to improve language learnability.Comment: Draf

The Effects of Motivation, Technology and Satisfaction on Student Achievement in Face-to-Face and Online College Algebra Classes

Demand for online learning has increased in recent years due to the convenience of class delivery. However, some students appear to have difficulties with online education resulting in lack of completion. The study utilized a quantitative approach with archival data and survey design. The factors of demographics, motivation, technology, and satisfaction were compared for face-to-face and online students. MANCOVA tests were performed to analyze the data while controlling age and gender to uncover significant differences between the two groups. The sample and population for this study were predominantly Hispanic students. Motivation and Technology were non-significant, but satisfaction was proven to be significant. In face-to-face courses, females were more satisfied than males. While in online courses, males were more satisfied than females. There was an interaction effect between the methods of instruction and the grade levels of A, B, C, D, and F/W on the dependent variables; Motivation, Technology, and Satisfaction