Learning benefits of self-controlled knowledge of results in 10-year-old children

PIBIC-CNPq-Conselho Nacional de Desenvolvimento Cientifico e Technologico

Constant-random practice and the adaptive process in motor learning with varying amounts of constant practice

The adaptive process in motor learning was examined in terms of effects of varying amounts of constant practice performed before random practice. Participants pressed five response keys sequentially, the last one coincident with the lighting of a final visual stimulus provided by a complex coincident timing apparatus. Different visual stimulus speeds were used during the random practice. 33 children (M age=11.6 yr.) were randomly assigned to one of three experimental groups: constant-random, constant-random 33%, and constant-random 66%. The constant-random group practiced constantly until they reached a criterion of performance stabilization three consecutive trials within 50 msec. of error. The other two groups had additional constant practice of 33 and 66%, respectively, of the number of trials needed to achieve the stabilization criterion. All three groups performed 36 trials under random practice; in the adaptation phase, they practiced at a different visual stimulus speed adopted in the stabilization phase. Global performance measures were absolute, constant, and variable errors, and movement pattern was analyzed by relative timing and overall movement time. There was no group difference in relation to global performance measures and overall movement time. However, differences between the groups were observed on movement pattern, since constant-random 66% group changed its relative timing performance in the adaptation phase.

Effect of bandwidth knowledge of results on the learning of a grip force control task

An experiment was conducted to investigate the persistence of the effect of ""bandwidth knowledge of results (KR)"" manipulated during the learning phase of performing a manual force-control task. The experiment consisted of two phases, an acquisition phase with the goal of maintaining 60% maximum force in 30 trials, and a second phase with the objective of maintaining 40% of maximum force in 20 further trials. There were four bandwidths of KR: when performance error exceeded 5, 10, or 15% of the target, and a control group (0% bandwidth). Analysis showed that 5, 10, and 15% bandwidth led to better performance than 0% bandwidth KR at the beginning of the second phase and persisted during the extended trials.

Combined heuristic with fuzzy system to transmission system expansion planning

A heuristic algorithm that employs fuzzy logic is proposed to the power system transmission expansion planning problem. The algorithm is based on the divide to conquer strategy, which is controlled by the fuzzy system. The algorithm provides high quality solutions with the use of fuzzy decision making, which is based on nondeterministic criteria to guide the search. The fuzzy system provides a self-adjusting mechanism that eliminates the manual adjustment of parameters to each system being solved. (C) 2010 Elsevier B.V. All rights reserved.

Multi-objective analysis of impacts of distributed generation placement on the operational characteristics of networks for distribution system planning

Recent advances in energy technology generation and new directions in electricity regulation have made distributed generation (DG) more widespread, with consequent significant impacts on the operational characteristics of distribution networks. For this reason, new methods for identifying such impacts are needed, together with research and development of new tools and resources to maintain and facilitate continued expansion towards DG. This paper presents a study aimed at determining appropriate DG sites for distribution systems. The main considerations which determine DG sites are also presented, together with an account of the advantages gained from correct DG placement. The paper intends to define some quantitative and qualitative parameters evaluated by Digsilent (R), GARP3 (R) and DSA-GD software. A multi-objective approach based on the Bellman-Zadeh algorithm and fuzzy logic is used to determine appropriate DG sites. The study also aims to find acceptable DG locations both for distribution system feeders, as well as for nodes inside a given feeder. (C) 2010 Elsevier Ltd. All rights reserved.

Evaluating an Agile Method for Planning and Controlling Innovative Projects

This article presents an extensive investigation carried out in two technology-based companies of the So Carlos technological pole in Brazil. Based on this multiple case study and literature review, a method, entitled hereafter IVPM2, applying agile project management (APM) principles was developed. After the method implementation, a qualitative evaluation was carried out by a document analysis and questionnaire application. This article shows that the application of this method at the companies under investigation evidenced the benefits of using simple, iterative, visual, and agile techniques to plan and control innovative product projects combined with traditional project management best practices, such as standardization.

Collaborative e-learning and Learning Objects

The Learning Object (OA) is any digital resource that can be reused to support learning with specific functions and objectives. The OA specifications are commonly offered in SCORM model without considering activities in groups. This deficiency was overcome by the solution presented in this paper. This work specified OA for e-learning activities in groups based on SCORM model. This solution allows the creation of dynamic objects which include content and software resources for the collaborative learning processes. That results in a generalization of the OA definition, and in a contribution with e-learning specifications.

Learning Profile Identification Based on the Analysis of the User Context of Interaction

One of the e-learning environment goal is to attend the individual needs of students during the learning process. The adaptation of contents, activities and tools into different visualization or in a variety of content types is an important feature of this environment, bringing to the user the sensation that there are suitable workplaces to his profile in the same system. Nevertheless, it is important the investigation of student behaviour aspects, considering the context where the interaction happens, to achieve an efficient personalization process. The paper goal is to present an approach to identify the student learning profile analyzing the context of interaction. Besides this, the learning profile could be analyzed in different dimensions allows the system to deal with the different focus of the learning.

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

Accelerating autonomous learning by using heuristic selection of actions

This paper investigates how to make improved action selection for online policy learning in robotic scenarios using reinforcement learning (RL) algorithms. Since finding control policies using any RL algorithm can be very time consuming, we propose to combine RL algorithms with heuristic functions for selecting promising actions during the learning process. With this aim, we investigate the use of heuristics for increasing the rate of convergence of RL algorithms and contribute with a new learning algorithm, Heuristically Accelerated Q-learning (HAQL), which incorporates heuristics for action selection to the Q-Learning algorithm. Experimental results on robot navigation show that the use of even very simple heuristic functions results in significant performance enhancement of the learning rate.

Planning and operation of large-scale water distribution systems with preemptive priorities

This paper describes the development of an optimization model for the management and operation of a large-scale, multireservoir water supply distribution system with preemptive priorities. The model considers multiobjectives and hedging rules. During periods of drought, when water supply is insufficient to meet the planned demand, appropriate rationing factors are applied to reduce water supply. In this paper, a water distribution system is formulated as a network and solved by the GAMS modeling system for mathematical programming and optimization. A user-friendly interface is developed to facilitate the manipulation of data and to generate graphs and tables for decision makers. The optimization model and its interface form a decision support system (DSS), which can be used to configure a water distribution system to facilitate capacity expansion and reliability studies. Several examples are presented to demonstrate the utility and versatility of the developed DSS under different supply and demand scenarios, including applications to one of the largest water supply systems in the world, the Sao Paulo Metropolitan Area Water Supply Distribution System in Brazil.

Learning about knowledge management for improving environmental impact assessment in a government agency: The Western Australian experience

How does knowledge management (KM) by a government agency responsible for environmental impact assessment (EIA) potentially contribute to better environmental assessment and management practice? Staff members at government agencies in charge of the EIA process are knowledge workers who perform judgement-oriented tasks highly reliant on individual expertise, but also grounded on the agency`s knowledge accumulated over the years. Part of an agency`s knowledge can be codified and stored in an organizational memory, but is subject to decay or loss if not properly managed. The EIA agency operating in Western Australia was used as a case study. Its KM initiatives were reviewed, knowledge repositories were identified and staff surveyed to gauge the utilisation and effectiveness of such repositories in enabling them to perform EIA tasks. Key elements of KM are the preparation of substantive guidance and spatial information management. It was found that treatment of cumulative impacts on the environment is very limited and information derived from project follow-up is not properly captured and stored, thus not used to create new knowledge and to improve practice and effectiveness. Other opportunities for improving organizational learning include the use of after-action reviews. The learning about knowledge management in EIA practice gained from Western Australian experience should be of value to agencies worldwide seeking to understand where best to direct their resources for their own knowledge repositories and environmental management practice. (C) 2011 Elsevier Ltd. All rights reserved.

Tiering strategic environmental assessment and project environmental impact assessment in highway planning in Sao Paulo, Brazil

Constructing highways in dense urban areas is always a challenge. In Sao Paulo Metropolitan Region, heavy truck traffic contributes to clog streets and expressways alike. As part of the traffic neither originates nor head to the region, a peripheral highway has been proposed to reduce traffic problems. This project called Rodoanel, is an expressway approximately 175 km long. The fact that the projected south and north sections would cross catchments that supply most of the metropolis water demand was strongly disputed and made the environmental permitting process particularly difficult. The agency in charge commissioned a strategic environmental assessment (SEA) of a revamped project, and called it the Rodoanel Programme. However, the SEA report failed to satisfactorily take account of significant strategic issues. Among these, the highway potential effect of inducing urban sprawl over water protection zones is the most critical issue, as it emerged later as a hurdle to project licensing. Conclusion is that, particularly where no agreed-upon framework for SEA exists, when vertical tiering with downstream project EIA is sought, then a careful scoping of strategic issues is more than necessary. If an agreement on `what is strategic` is not reached and not recognized by influential stakeholders, then the unsettled conflicts will be transferred to project EIA. In such a context, SEA will have added another loop to the usually long road to project approval. (c) 2008 Elsevier Inc. All rights reserved.

Simulated annealing with adaptive neighborhood: A case study in off-line robot path planning

Simulated annealing (SA) is an optimization technique that can process cost functions with degrees of nonlinearities, discontinuities and stochasticity. It can process arbitrary boundary conditions and constraints imposed on these cost functions. The SA technique is applied to the problem of robot path planning. Three situations are considered here: the path is represented as a polyline; as a Bezier curve; and as a spline interpolated curve. In the proposed SA algorithm, the sensitivity of each continuous parameter is evaluated at each iteration increasing the number of accepted solutions. The sensitivity of each parameter is associated to its probability distribution in the definition of the next candidate. (C) 2010 Elsevier Ltd. All rights reserved.

On Building a Memory Evolutive System for Application to Learning and Cognition Modeling

We address here aspects of the implementation of a memory evolutive system (MES), based on the model proposed by A. Ehresmann and J. Vanbremeersch (2007), by means of a simulated network of spiking neurons with time dependent plasticity. We point out the advantages and challenges of applying category theory for the representation of cognition, by using the MES architecture. Then we discuss the issues concerning the minimum requirements that an artificial neural network (ANN) should fulfill in order that it would be capable of expressing the categories and mappings between them, underlying the MES. We conclude that a pulsed ANN based on Izhikevich`s formal neuron with STDP (spike time-dependent plasticity) has sufficient dynamical properties to achieve these requirements, provided it can cope with the topological requirements. Finally, we present some perspectives of future research concerning the proposed ANN topology.