No description available.

Anthropogenic changes to forests, agriculture and hydrology are being driven by a need to provide water, food and shelter to more than six billion people. Unfortunately, these changes have a major impact on hydrology, biodiversity, climate, socio-economic stability and food security. The most pervasive form of land-cover change in South Africa is human settlement expansion. In many cases, new human settlements and settlement expansion are informal and occur in areas that are typically covered by natural vegetation. Settlements are infrequently mapped on an ad-hoc basis in South Africa which makes information on when and where new settlements form very difficult. Determining where and when new informal settlements occur is beneficial from not only an ecological but also a social development standpoint. The objective of this thesis is to make use of coarse resolution satellite data to infer the location of new settlement developments in an automated manner by making use of machine learning methods. The specific sensor that is considered in this thesis is the MODIS sensor on-board the Terra and Aqua satellites. By using samples taken at regular intervals (8 days), a hyper-temporal time-series is constructed and consequently used to detect new human settlement formations in South Africa. Two change detection methods are proposed in this thesis to achieve the goal of automated new settlement development detection using this high-temporal coarse resolution satellite time-series data. / Thesis (PhD(Eng))--University of Pretoria, 2012. / Electrical, Electronic and Computer Engineering / unrestricted

The primary purpose of this study was to use some of the action methods of playback theatre to facilitate the acquisition of knowledge through the experience of building community. The impact of action methods on group dynamics and the relationship among methods, individual perceptions, and the acquisition of knowledge were analyzed. The researcher suggested that playback theatre action methods provided a climate in which groups can improve the quality of their interactions. The Hill Interaction Matrix (HIM) formed the basis for the study's analysis of interactions. Since the researcher concluded there were significantly more interactions coded in the "power quadrant" after training, the researcher assumed that playback theatre action methods are a catalyst for keeping the focus on persons in the group, encouraging risk-taking behaviors, and producing constructive feedback between members. Based on session summaries, individual interviews, and an analysis of the Group Environment Scale (GES), the training group became more cohesive, became more expressive, promoted independence, encouraged self-discovery, and adapted in innovative ways. The experience of an interconnected community created a space where positive growth could occur. The researcher concluded that the process of community building is intricately connected with a person's ability to make meaning out of experiences. Participants in the study noted several processes by which they acquired new knowledge: (a) knowledge through internal processes, (b) knowledge through modeling, (c) knowledge through experiences, (d) knowledge through acknowledgment and application. Acknowledging and applying knowledge were behaviors identified as risk-taking, communication and active listening, acceptance of diverse cultures and opinions, and building community relations. The study suggested further research in the effects of these methods compared to other learning methods, the effects of these methods on other types of groups, the effects of the leader's relationship to the group, and the long-term effects on group dynamics.

Thesis (PhD (Process Engineering))--University of Stellenbosch, 2007. / Dissertation presented for the degree of Doctor of Philosophy in Engineering at the University of Stellenbosch. / ENGLISH ABSTRACT: The development of advanced methods of process monitoring, diagnosis, and control has been identified as a major 21st century challenge in control systems research and application. This is particularly the case for chemical and metallurgical operations owing to the lack of expressive fundamental models as well as the nonlinear nature of most process systems, which makes established linearization methods unsuitable. As a result, efforts have been directed in the search of alternative approaches that do not require fundamental or analytical models. Data-based methods provide a very promising alternative in this regard, given the huge volumes of data being collected in modern process operations as well as advances in both theoretical and practical aspects of extracting information from observations. In this thesis, the use of kernel-based learning methods in fault detection and diagnosis of complex processes is considered. Kernel-based machine learning methods are a robust family of algorithms founded on insights from statistical learning theory. Instead of estimating a decision function on the basis of minimizing the training error as other learning algorithms, kernel methods use a criterion called large margin maximization to estimate a linear learning rule on data embedded in a suitable feature space. The embedding is implicitly defined by the choice of a kernel function and corresponds to inducing a nonlinear learning rule in the original measurement space. Large margin maximization corresponds to developing an algorithm with theoretical guarantees on how well it will perform on unseen data. In the first contribution, the characterization of time series data from process plants is investigated. Whereas complex processes are difficult to model from first principles, they can be identified using historic process time series data and a suitable model structure. However, prior to fitting such a model, it is important to establish whether the time series data justify the selected model structure. Singular spectrum analysis (SSA) has been used for time series identification. A nonlinear extension of SSA is proposed for classification of time series. Using benchmark systems, the proposed extension is shown to perform better than linear SSA. Moreover, the method is shown to be useful for filtering noise in time series data and, therefore, has potential applications in other tasks such as data rectification and gross error detection. Multivariate statistical process monitoring methods are well-established techniques for efficient information extraction from multivariate data. Such information is usually compact and amenable to graphical representation in two or three dimensional plots. For process monitoring purposes control limits are also plotted on these charts. These control limits are usually based on a hypothesized analytical distribution, typically the Gaussian normal distribution. A robust approach for estimating con dence bounds using the reference data is proposed. The method is based on one-class classification methods. The usefulness of using data to define a confidence bound in reducing fault detection errors is illustrated using plant data. The use of both linear and nonlinear supervised feature extraction is also investigated. The advantages of supervised feature extraction using kernel methods are highlighted via illustrative case studies. A general strategy for fault detection and diagnosis is proposed that integrates feature extraction methods, fault identification, and different methods to estimate confidence bounds. For kernel-based approaches, the general framework allows for interpretation of the results in the input space instead of the feature space. An important step in process monitoring is identifying a variable responsible for a fault. Although all faults that can occur at any plant cannot be known beforehand, it is possible to use knowledge of previous faults or simulations to anticipate their recurrence. A framework for fault diagnosis using one-class support vector machine (SVM) classification is proposed. Compared to other previously studied techniques, the one-class SVM approach is shown to have generally better robustness and performance characteristics. Most methods for process monitoring make little use of data collected under normal operating conditions, whereas most quality issues in process plants are known to occur when the process is in-control . In the final contribution, a methodology for continuous optimization of process performance is proposed that combines support vector learning with decision trees. The methodology is based on continuous search for quality improvements by challenging the normal operating condition regions established via statistical control. Simulated and plant data are used to illustrate the approach. / AFRIKAANSE OPSOMMING: Die ontwikkeling van gevorderde metodes van prosesmonitering, diagnose en -beheer is geïdentifiseer as 'n groot 21ste eeuse uitdaging in die navorsing en toepassing van beheerstelsels. Dit is veral die geval in die chemiese en metallurgiese bedryf, a.g.v. die gebrek aan fundamentele modelle, sowel as die nielineêre aard van meeste prosesstelsels, wat gevestigde benaderings tot linearisasie ongeskik maak. Die gevolg is dat pogings aangewend word om te soek na alternatiewe benaderings wat nie fundamentele of analitiese modelle benodig nie. Data-gebaseerde metodes voorsien belowende alternatiewe in dié verband, gegewe die enorme volumes data wat in moderne prosesaanlegte geberg word, sowel as die vooruitgang wat gemaak word in beide die teoretiese en praktiese aspekte van die onttrekking van inligting uit waarnemings. In die tesis word die gebruik van kern-gebaseerde metodes vir foutopsporing en -diagnose van komplekse prosesse beskou. Kern-gebaseerde masjienleermetodes is 'n robuuste familie van metodes gefundeer op insigte uit statistiese leerteorie. Instede daarvan om 'n besluitnemingsfunksie te beraam deur passingsfoute op verwysingsdata te minimeer, soos wat gedoen word met ander leermetodes, gebruik kern-metodes 'n kriterium genaamd groot marge maksimering om lineêre reëls te pas op data wat ingebed is in 'n geskikte kenmerkruimte. Die inbedding word implisiet gedefinieer deur die keuse van die kern-funksie en stem ooreen met die indusering van 'n nielineêre reël in die oorspronklike meetruimte. Groot marge-maksimering stem ooreen met die ontwikkeling van algoritmes waarvan die prestasie t.o.v. die passing van nuwe data teoreties gewaarborg is. In die eerste bydrae word die karakterisering van tydreeksdata van prosesaanlegte ondersoek. Alhoewel komplekse prosesse moeilik is om vanaf eerste beginsels te modelleer, kan hulle geïdentifiseer word uit historiese tydreeksdata en geskikte modelstrukture. Voor so 'n model gepas word, is dit belangrik om vas te stel of die tydreeksdata wel die geselekteerde modelstruktuur ondersteun. 'n Nielineêre uitbreiding van singuliere spektrale analise (SSA) is voorgestel vir die klassifikasie van tydreekse. Deur gebruik te maak van geykte stelsels, is aangetoon dat die voorgestelde uitbreiding beter presteer as lineêre SSA. Tewens, daar word ook aangetoon dat die metode nuttig is vir die verwydering van geraas in tydreeksdata en daarom ook potensiële toepassings het in ander take, soos datarektifikasie en die opsporing van sistematiese foute in data. Meerveranderlike statistiese prosesmonitering is goed gevestig vir die doeltreffende onttrekking van inligting uit meerveranderlike data. Sulke inligting is gewoonlik kompak en geskik vir voorstelling in twee- of drie-dimensionele grafieke. Vir die doeleindes van prosesmonitering word beheerlimiete dikwels op sulke grafieke aangestip. Hierdie beheerlimiete word gewoonlik gebaseer op 'n hipotetiese analitiese verspreiding van die data, tipiese gebaseer op 'n Gaussiaanse model. 'n Robuuste benadering vir die beraming van betroubaarheidslimiete gebaseer op verwysingsdata, word in die tesis voorgestel. Die metode is gebaseer op eenklas-klassifikasie en die nut daarvan deur data te gebruik om die betroubaarheidsgrense te beraam ten einde foutopsporing te optimeer, word geïllustreer aan die hand van aanlegdata. Die gebruik van beide lineêre en nielineêre oorsiggedrewe kenmerkonttrekking is vervolgens ondersoek. Die voordele van oorsiggedrewe kenmerkonttrekking deur van kern-metodes gebruik te maak is beklemtoon deur middel van illustratiewe gevallestudies. 'n Algemene strategie vir foutopsporing en -diagnose word voorgestel, wat kenmerkonttrekkingsmetodes, foutidenti kasie en verskillende metodes om betroubaarheidsgrense te beraam saamsnoer. Vir kern-gebaseerde metodes laat die algemene raamwerk toe dat die resultate in die invoerruimte vertolk kan word, in plaas van in die kenmerkruimte. 'n Belangrike stap in prosesmonitering is om veranderlikes te identifiseer wat verantwoordelik is vir foute. Alhoewel alle foute wat by 'n chemiese aanleg kan plaasvind, nie vooraf bekend kan wees nie, is dit moontlik om kennis van vorige foute of simulasies te gebruik om die herhaalde voorkoms van die foute te antisipeer. 'n Raamwerk vir foutdiagnose wat van eenklas-steunvektormasjiene (SVM) gebruik maak is voorgestel. Vergeleke met ander tegnieke wat voorheen bestudeer is, is aangetoon dat die eenklas-SVM benadering oor die algemeen beter robuustheid en prestasiekenmerke het. Meeste metodes vir prosesmonitering maak min gebruik van data wat opgeneem is onder normale bedryfstoestande, alhoewel meeste kwaliteitsprobleme ondervind word waneer die proses onder beheer is. In die laaste bydrae, is 'n metodologie vir die kontinue optimering van prosesprestasie voorgestel, wat steunvektormasjiene en beslissingsbome kombineer. Die metodologie is gebaseer op die kontinue soeke na kwaliteitsverbeteringe deur die normale bedryfstoestandsgrense, soos bepaal deur statistiese beheer, te toets. Gesimuleerde en werklike aanlegdata is gebruik om die benadering te illustreer.

Pastaruoju metu vis labiau akcentuojamas ne mokymo turinys ar programas, bet mokymo(si) būdai ir metodai, t.y. – kaip mokyti? (Walsh, 2001; Bartkevičienė, 2008; Hargreaves, 2008). Jaunesniame mokykliniame amžiuje vyrauja pažintinis vaiko santykis su aplinka, dėl ko pradinė mokykla yra palankus metas pradėti formuoti asmens mokslinį raštingumą, ugdyti mokslinę kultūrą, pradėti taikyti mokslinio tyrimo metodus, kas neabejotinai plėtoja mokinių pažinimo kompetencijas (Lamanauskas, 2004; Savickaitė, 2005; Vilkonienė, 2005). Atsižvelgiant į tai, tyrimo problema formuluojama klausimu: ar pradinėje mokykloje šiuo metu naudojamų pasaulio pažinimo vadovėlių turinys suteikia palankias galimybes tiriamųjų metodų taikymui ir tuo pačiu – mokinių gamtotyrinės / aplinkotyrinės veiklos aktyvinimui? Lietuvoje nėra atlikta tyrimų, kurių metu būtų aiškintasi ar pasaulio pažinimui skirti vadovėliai skatina tiriamąją mokinių veiklą. Tuo pasireiškia šio tyrimo naujumas. Tyrimo objektas: I-IV klasių pasaulio pažinimo vadovėlių turinys tiriamųjų mokymo(si) metodų taikymo aspektu. Darbo tikslas: įvertinti I-IV klasių pasaulio pažinimo vadovėlių teikiamas galimybes taikyti tiriamuosius mokymo(si) metodus. Darbo uždaviniai: 1) Remiantis moksline ir metodine literatūra, atskleisti tiriamųjų mokymo(si) metodų reikšmę ugdymo turinio struktūroje. 2) Mokslinės ir metodinės literatūros analizės pagrindu išskirti vadovėlių turinio vertinimo kriterijus galimybių taikyti tiriamuosius mokymo(si) metodus aspektu... [toliau žr. visą tekstą] / Recently, more and more emphasis is put on the teaching/learning methods and techniques, i.e. “How to teach?”, rather than on the teaching contents or programs (Walsh, 2001; Bartkevičienė, 2008; Hargreaves, 2008). In the young school age, a cognitive child’s relationship with the environment prevails, therefore a primary school is a favourable time to start developing a personal scientific literacy, fostering a scientific culture, and introducing scientific research methods, which undoubtedly develop pupils cognitive competencies (Lamanauskas, 2004; Savickaitė, 2005; Vilkonienė, 2005). With consideration of the above, the research problem is formulated as a question: whether the content of the world cognition textbooks currently used in the primary school provides a favourable context for application of exploratory methods and, at the same time, for activation of the pupils nature research / environmental research activities? No studies have been carried out in Lithuania to investigate, if the world cognition textbooks promote the pupils research activities. This is the novelty of this research. The object of the research: the content of the world cognition textbooks of the 1st-4th forms in the respect of application of exploratory teaching/learning methods. The aim of the thesis: to assess the possibilities provided by the world cognition textbooks of the 1st-4th forms for application of exploratory teaching/learning methods. The targets of the thesis: 1) Based on the... [to full text]

Learning styles can be defined as the set of features that characterize the way people learn and process information. The aim of this study is to establish the relationship between learning styles and academic performance of students who completed the fifth year of the Pharmacy Program at Universidad de Costa Rica during the years 2011 to 2013. We applied the Honey-Alonso Learning Styles questionnaire. Regarding the overall mean, we obtained that there are no differences in the preference for an active, reflective, theoretical or pragmatic style. Students don’t have a pure learning style, but a combination of two or more of them, which favours the learning process as they have more tools to adapt to the teachers’, the courses’ and the study program’s requirements. / Los estilos de aprendizaje se pueden definir como el conjunto de rasgos que caracterizan la forma en que aprenden y procesan información las personas. El objetivo del presente estudio es establecer la relación entre los estilos de aprendizaje y el rendimiento académico de los estudiantes que cursaron el V año de la carrera de licenciatura en Farmacia de la Universidad de Costa Rica, durante los años 2011 a 2013. Se aplicó el Cuestionario de Honey-Alonso de Estilos de Aprendizaje. Se obtuvo que en relación con el promedio global, no existen diferencias entre preferencias para los estilos activo, reflexivo, teórico o pragmático. Los estudiantes no presentan un estilo de aprendizaje puro, sino una combinación de dos o más de estos, lo cual favorece el proceso de aprendizaje al tener más herramientas de adaptación a los requerimientos de los profesores, los cursos y la carrera.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Sanjoy Das and Anil Pahwa / Because a majority of day-to-day activities rely on electricity, it plays an important role in daily life. In this digital world, most of the people’s life depends on electricity. Without electricity, the flip of a switch would no longer produce instant light, television or refrigerators would be nonexistent, and hundreds of conveniences often taken for granted would be impossible. Electricity has become a basic necessity, and so any interruption in service due to disturbances in power lines causes a great inconvenience to customers. Customers and utility commissions expect a high level of reliability. Power distribution systems are geographically dispersed and exposure to environment makes them highly vulnerable part of power systems with respect to failures and interruption of service to customers. Following the restructuring and increased competition in the electric utility industry, distribution system reliability has acquired larger significance. Better understanding of causes and consequences of distribution interruptions is helpful in maintaining distribution systems, designing reliable systems, installing protection devices, and environmental issues. Various events, such as equipment failure, animal activity, tree fall, wind, and lightning, can negatively affect power distribution systems. Weather is one of the primary causes affecting distribution system reliability. Unfortunately, as weather-related outages are highly random, predicting their occurrence is an arduous task. To study the impact of weather on overhead distribution system several models, such as linear and exponential regression models, neural network model, and ensemble methods are presented in this dissertation. The models were extended to study the impact of animal activity on outages in overhead distribution system. Outage, lightning, and weather data for four different cities in Kansas of various sizes from 2005 to 2011 were provided by Westar Energy, Topeka, and state climate office at Kansas State University weather services. Models developed are applied to estimate daily outages. Performance tests shows that regression and neural network models are able to estimate outages well but failed to estimate well in lower and upper range of observed values. The introduction of committee machines inspired by the ‘divide & conquer” principle overcomes this problem. Simulation results shows that mixture of experts model is more effective followed by AdaBoost model in estimating daily outages. Similar results on performance of these models were found for animal-caused outages.

Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis is to advance the state of the art in imitation learning by adopting deep learning methods to address two major challenges of learning from demonstrations. Firstly, representing the demonstrations in a manner that is adequate for learning. We propose novel Convolutional Neural Networks (CNN) based methods to automatically extract feature representations from raw visual demonstrations and learn to replicate the demonstrated behaviour. This alleviates the need for task specific feature extraction and provides a general learning process that is adequate for multiple problems. The second challenge is generalizing a policy over unseen situations in the training demonstrations. This is a common problem because demonstrations typically show the best way to perform a task and don't offer any information about recovering from suboptimal actions. Several methods are investigated to improve the agent's generalization ability based on its initial performance. Our contributions in this area are three fold. Firstly, we propose an active data aggregation method that queries the demonstrator in situations of low confidence. Secondly, we investigate combining learning from demonstrations and reinforcement learning. A deep reward shaping method is proposed that learns a potential reward function from demonstrations. Finally, memory architectures in deep neural networks are investigated to provide context to the agent when taking actions. Using recurrent neural networks addresses the dependency between the state-action sequences taken by the agent. The experiments are conducted in simulated environments on 2D and 3D navigation tasks that are learned from raw visual data, as well as a 2D soccer simulator. The proposed methods are compared to state of the art deep reinforcement learning methods. The results show that deep learning architectures can learn suitable representations from raw visual data and effectively map them to atomic actions. The proposed methods for addressing generalization show improvements over using supervised learning and reinforcement learning alone. The results are thoroughly analysed to identify the benefits of each approach and situations in which it is most suitable.

Through the development and implementation of modified learning contexts, the current study encouraged undergraduate teacher education students to modify their approaches to learning by reducing their reliance on surface approaches and progressively adopting deeper approaches. This outcome was considered desirable because students who employed deep approaches would exit the course having achieved higher quality learning than those who relied primarily on surface approaches. It was expected that higher quality learning in a preservice teacher education program would also translate into greater self-confidence in the management of teaching tasks, leading to improvements in students� teaching self-efficacy beliefs. Altered learning contexts were developed through the application of action research methodology involving core members of the teaching team. Learning activities were designed with a focus on co-operative small-group problem-based learning, which included multiple subtasks requiring variable outcome presentation modes. Linked individual reflection was encouraged by personal learning journals and learning portfolios. Students also provided critical analyses of their own learning during the completion of tasks, from both individual and group perspectives. Assessment methods included lecturer, peer and self-assessment, depending on the nature of the learning task. Often these were integrated, so that subtasks within larger ones were assessed using combinations of methods. Learning approach theorists (Biggs, 1993a, 1999; Entwistle, 1986, 1998; Prosser & Trigwell, 1999; Ramsden, 1992, 1997) contend that learning outcomes are directly related to the learning approaches used in their development. They further contend that the approach adopted is largely a result of students� intent, which in turn, is influenced by their perception of the learning context. The present study therefore aimed to develop an integrated and pervasive course-based learning context, constructively aligned (after: Biggs, 1993a, 1996), achievable within the normal constraints of a university program, that would influence students� adoption of deep learning approaches. The cognitive processes students used in response to the altered contexts were interpreted in accordance with self-regulatory internal logic (after: Bandura, 1986, 1991b; Zimmerman, 1989, 1998b). Longitudinal quasi-experimental methods with repeated measures on non-equivalent dependent variables were applied to three cohorts of students. Cohort 1 represented the contrast group who followed a traditional program. Cohort 2 was the main treatment group to whom the modified program was presented. Cohort 3 represented a comparison group that was also presented with the modified program over a shorter period. Student data on learning approach, teaching efficacy and academic attributions were gathered from repeated administrations of the Study Process Questionnaire (Biggs, 1987b), Teacher Efficacy Scale (Gibson & Dembo, 1984) and Multidimensional-Multiattributional Causality Scale (Lefcourt, 1991). In addition, reflective journals, field observations and transcripts of interviews undertaken at the beginning and conclusion of the course, were used to clarify students� approaches to learning and their responses to program modifications. Analyses of learning approaches adopted by Cohorts 1 and 2 revealed that they both began their course predominantly using surface approaches. While students in Cohort 1 completed the course with approximately equal reliance on deep and surface approaches, students in Cohort 2 reported a predominant use of deep approaches on course completion. The relative impact of the modified learning context on students with differing approaches to learning in this cohort were further explained through qualitative data and cluster analyses. The partial replication of the study with Cohort 3, across the first three semesters of their program, produced similar effects to those obtained with Cohort 2. The analyses conducted with teaching efficacy data indicated a similar pattern of development for all cohorts. Little change in either personal or general dimensions was noted in the first half of the program, followed by strong growth in both, in the latter half. While a relationship between learning approach usage and teaching efficacy was not apparent in Cohort 1, developmental path and mediation analyses indicated that the use of deep learning approaches considerably influenced the development of personal teaching efficacy in Cohort 2. The current research suggests that value lies in the construction of learning environments, in teacher education, that enhance students� adoption of deep learning approaches. The nature of the task is complex, multifaceted and context specific, most likely requiring the development of unique solutions in each environment. Nevertheless, this research demonstrates that such solutions can be developed and applied within the prevailing constraints of pre-existing course structures.

<p><strong>Purpose:</strong> my aim was to explore ways that teachers can support students with dyslexia and what/ which tools some teachers / special education teachers use to facilitate students.</p><p><strong>Method:</strong> I used a quantitative method by interviewing some regular teachers and special education teachers</p><p><strong>Results:</strong> In my study, I learned how some teachers can support students by reading loud to them; a main task for the teachers could be to create the love of reading for the students. That task was reinforced by students' self-image.</p>