Principles of Navy budget : self-instructional learning unit.

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Math without stress – an open online learning project

MOOC (as an acronym for Massive Open Online Courses) are a quite new model for the delivery of online learning to students. As “Massive” and “Online”, these courses are proposed to be accessible to many more learners than would be possible through conventional teaching. As “Open” they are (frequently) free of charge and participation is not limited by the geographical situation of the learners, creating new learning opportunities in Higher Education Institutions (HEI). In this paper we describe a recently started project “Matemática 100 STRESS” (Math Without STRESS) integrated in the e-IPP project | e-Learning Unit of Porto’s Polytechnic Institute (IPP) which has created its own MOOC platform and launched its first course – Probabilities and Combinatorics – in early June/2014. In this MOOC development were involved several lecturers from four of the seven IPP schools.

Math without stress: an open online learning project

MOOC (as an acronym for Massive Open Online Courses) are a quite new model for the delivery of online learning to students. As “Massive” and “Online”, these courses are proposed to be accessible to many more learners than would be possible through conventional teaching. As “Open” they are (frequently) free of charge and participation is not limited by the geographical situation of the learners, creating new learning opportunities in Higher Education Institutions (HEI). In this paper we describe a recently started project “Matemática 100 STRESS” (Math Without STRESS) integrated in the e-IPP project | e-Learning Unit of Porto’s Polytechnic Institute (IPP) which has created its own MOOC platform and launched its first course – Probabilities and Combinatorics – in early June/2014. In this MOOC development were involved several lecturers from four of the seven IPP schools.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Matemática 100 stress: um projeto aberto no IPP

A Era Tecnológica em que nos vemos inseridos, cujos avanços acontecem a uma velocidade vertiginosa exige, por parte das Instituições de Ensino Superior (IES) uma atitude proactiva no sentido de utilização dos muitos recursos disponíveis. Por outro lado, os elementos próprios da sociedade da informação – flexibilidade, formação ao longo da vida, acessibilidade à informação, mobilidade, entre muito outros – atuam como fortes impulsionadores externos para que as IES procurem e analisem novas modalidades formativas. Perante a mobilidade crescente, que se tem revelado massiva, a aprendizagem tende a ser cada vez mais individualizada, visual e prática. A conjugação de várias formas/tipologias de transmissão de conhecimento, de métodos didáticos e mesmo de ambientes e situações de aprendizagem induzem uma melhor adaptação do estudante, que poderá procurar aqueles que melhor vão ao encontro das suas expetativas, isto é, favorecem um processo de ensino-aprendizagem eficiente na perspetiva da forma de aprender de cada um. A definição de políticas estratégicas relacionadas com novas modalidades de ensino/formação tem sido uma preocupação constante na nossa instituição, nomeadamente no domínio do ensino à distância, seja ele e-Learning, b-Learning ou, mais recentemente, “open-Learning”, onde se inserem os MOOC – Massive Open Online Courses (não esquecendo a vertente m-Learning), de acordo com as várias tendências europeias (OECD, 2007) (Comissão Europeia, 2014) e com os objetivos da “Europa 2020”. Neste sentido surge o Projeto Matemática 100 STRESS, integrado no projeto e-IPP | Unidade de e-Learning do Politécnico do Porto que criou a sua plataforma MOOC, abrindo em junho de 2014 o seu primeiro curso – Probabilidades e Combinatória. Pretendemos dar a conhecer este Projeto, e em particular este curso, que envolveu vários docentes de diferentes unidades orgânicas do IPP.

Ensino, aprendizagem e avaliação no Ensino Superior: Perspetivas e práticas

Tese de Doutoramento em Ciências da Educação - Especialidade de Desenvolvimento Curricular

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In the case of such a very special building project, the crucial stake for sustainable development is the fact that space systems are extreme cases of environmental constraints. In- deed, they constitute an interesting model as an analogy can be made between Martian utmost conditions and some of the possible extreme one's that Earth might soon face. The didactic ob- jective of the project is to use the context of a building on Mars to teach an approach which raises the students awareness to design and plan all steps of a building in a sustainable way, i.e. build, with the available resources, living spaces that satisfy human needs and leave as intact as possible the external environment. The paper presents the approach and the feedback of this student project, more specifically ENAC Learning Unit", which involved 17 students from envi- ronmental, civil engineering and architecture sections from EPFL. All the same, it involved pro- fessors from all three domains, as well as aerospace and Mars specialists, which gave seminars during the course of the semester. The students were separated in groups, and the project con- sisted of two phases: 1) analysis of the context and resources, 2) project design and critic. Both organisational, technical and pedagogical aspects of the experience are presented. The outcome was very positive, with students experiencing for their first time multidisciplinary work and the iterative process of design under multiple constraints.

Análisis didáctico fenomenológico como instrumento para la planificación currícular por parte de los docentes de matemáticas de la educación básica primaria

La planificación curricular (PC) constituye una de las actividades y competencias más importantes de los docentes en los distintos niveles de la educación escolar en general. Por esta razón en el trabajo de maestría que presentamos nos proponemos reflexionar con los participantes sobre los aportes que puede hacer el Análisis Didáctico Matemático (ADM) en general, y el Análisis Didáctico Fenomenológico (ADF) en particular, al desarrollo de los procesos de PC y de formación profesional relativa a la PC por parte de los docentes de matemáticas de EBP. Para esto nos enmarcamos en la propuesta teórica de los organizadores del currículo (Rico, 1998; Castro, 2001; Rico y Segovia, 2001; Bedoya, 2002) y sobre el ADF (Freudenthal, 1983; Puig, 1997). Desde el punto de vista metodológico se trabajó mediante estrategias de investigación y sistematización de experiencias educativas, que articulan en el diseño procesos de investigación acción y estudio de casos. Se llevaron a cabo talleres de formación docente en los que se propuso la planificación de una unidad didáctica (UD) sobre el CME (Conocimiento Matemático Escolar) de estadística descriptiva para grado quinto, a fin de analizarlas a la luz de las nociones conceptuales y concepciones de los maestros sobre el proceso de PC.

Use of web-based formative assessment to support student learning in metabolism/nutrition unit

In 2002, an integrated basic science course was introduced into the Bachelor of Dental Sciences programme at the University of Queensland, Australia. Learning activities for the Metabolism and Nutrition unit within this integrated course included lectures, problem-based learning tutorials, computer-based self-directed learning exercises and practicals. To support student learning and assist students to develop the skills necessary to become lifelong learners, an extensive bank of formative assessment questions was set up using the commercially available package, WebCT®. Questions included short-answer, multiple-choice and extended matching questions. As significant staff time was involved in setting up the question database, the extent to which students used the formative assessment and their perceptions of its usefulness to their learning were evaluated to determine whether formative assessment should be extended to other units within the course. More than 90% of the class completed formative assessment tasks associated with learning activities scheduled in the first two weeks of the block, but this declined to less than 50% by the fourth and final week of the block. Patterns of usage of the formative assessment were also compared in students who scored in the top 10% for all assessment for the semester with those who scored in the lowest 10%. High-performing students accessed the Web-based formative assessment about twice as often as those who scored in the lowest band. However, marks for the formative assessment tests did not differ significantly between the two groups. In a questionnaire that was administered at the completion of the block, students rated the formative assessment highly, with 80% regarding it as being helpful for their learning. In conclusion, although substantial staff time was required to set up the question database, this appeared to be justified by the positive responses of the students.

Reinforcement Learning Solution for Unit Commitment Problem Considering Minimum Start Up and Shut Down Times

Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems

Reinforcement Learning solution for Unit Commitment Problem through pursuit method

Unit commitment is an optimization task in electric power generation control sector. It involves scheduling the ON/OFF status of the generating units to meet the load demand with minimum generation cost satisfying the different constraints existing in the system. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision task and Reinforcement Learning solution is formulated through one efficient exploration strategy: Pursuit method. The correctness and efficiency of the developed solutions are verified for standard test systems

The automatic layout model in the sequencing of construction activities of multi-objective diaphragm wall unit by using self-learning neural network

Foundation construction process has been an important key point in a successful construction engineering. The frequency of using diaphragm wall construction method among many deep excavation construction methods in Taiwan is the highest in the world. The traditional view of managing diaphragm wall unit in the sequencing of construction activities is to establish each phase of the sequencing of construction activities by heuristics. However, it conflicts final phase of engineering construction with unit construction and effects planning construction time. In order to avoid this kind of situation, we use management of science in the study of diaphragm wall unit construction to formulate multi-objective combinational optimization problem. Because the characteristic (belong to NP-Complete problem) of problem mathematic model is multi-objective and combining explosive, it is advised that using the 2-type Self-Learning Neural Network (SLNN) to solve the N=12, 24, 36 of diaphragm wall unit in the sequencing of construction activities program problem. In order to compare the liability of the results, this study will use random researching method in comparison with the SLNN. It is found that the testing result of SLNN is superior to random researching method in whether solution-quality or Solving-efficiency.

The exact sample complexity of PAC-learning problems with unit VC dimension

The Vapnik-Chervonenkis (VC) dimension is a combinatorial measure of a certain class of machine learning problems, which may be used to obtain upper and lower bounds on the number of training examples needed to learn to prescribed levels of accuracy. Most of the known bounds apply to the Probably Approximately Correct (PAC) framework, which is the framework within which we work in this paper. For a learning problem with some known VC dimension, much is known about the order of growth of the sample-size requirement of the problem, as a function of the PAC parameters. The exact value of sample-size requirement is however less well-known, and depends heavily on the particular learning algorithm being used. This is a major obstacle to the practical application of the VC dimension. Hence it is important to know exactly how the sample-size requirement depends on VC dimension, and with that in mind, we describe a general algorithm for learning problems having VC dimension 1. Its sample-size requirement is minimal (as a function of the PAC parameters), and turns out to be the same for all non-trivial learning problems having VC dimension 1. While the method used cannot be naively generalised to higher VC dimension, it suggests that optimal algorithm-dependent bounds may improve substantially on current upper bounds.

Learning to live with Parkinson’s disease in the family unit:an interpretative phenomenological analysis of well-being

We investigated family members’ lived experience of Parkinson’s disease (PD) aiming to investigate opportunities for well-being. A lifeworld-led approach to healthcare was adopted. Interpretative phenomenological analysis was used to explore in-depth interviews with people living with PD and their partners. The analysis generated four themes: It’s more than just an illness revealed the existential challenge of diagnosis; Like a bird with a broken wing emphasizing the need to adapt to increasing immobility through embodied agency; Being together with PD exploring the kinship within couples and belonging experienced through support groups; and Carpe diem! illuminated the significance of time and fractured future orientation created by diagnosis. Findings were interpreted using an existential-phenomenological theory of well-being. We highlighted how partners shared the impact of PD in their own ontological challenges. Further research with different types of families and in different situations is required to identify services required to facilitate the process of learning to live with PD. Care and support for the family unit needs to provide emotional support to manage threats to identity and agency alongside problem-solving for bodily changes. Adopting a lifeworld-led healthcare approach would increase opportunities for well-being within the PD illness journey.