Embedding instruments & modules into an IEEE1451-FPGA-Based Weblab infrastructure

Adopting standard-based weblab infrastructures can be an added value for spreading their influence and acceptance in education. This paper suggests a solution based on the IEEE1451.0 Std. and FPGA technology for creating reconfigurable weblab infrastructures using Instruments and Modules (I&Ms) described through standard Hardware Description Language (HDL) files. It describes a methodology for creating and binding I&Ms into an IEEE1451-module embedded in a FPGA-based board able to be remotely controlled/accessed using IEEE1451-HTTP commands. At the end, an example of a step-motor controller module bond to that IEEE1451-module is described.

A random key based genetic algorithm for the resource constrained project scheduling problem

This paper presents a genetic algorithm for the Resource Constrained Project Scheduling Problem (RCPSP). The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities of the activities are defined by the genetic algorithm. The heuristic generates parameterized active schedules. The approach was tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

Development of fractional order capacitors based on electrolyte processes

In recent years, significant research in the field of electrochemistry was developed. The performance of electrical devices, depending on the processes of the electrolytes, was described and the physical origin of each parameter was established. However, the influence of the irregularity of the electrodes was not a subject of study and only recently this problem became relevant in the viewpoint of fractional calculus. This paper describes an electrolytic process in the perspective of fractional order capacitors. In this line of thought, are developed several experiments for measuring the electrical impedance of the devices. The results are analyzed through the frequency response, revealing capacitances of fractional order that can constitute an alternative to the classical integer order elements. Fractional order electric circuits are used to model and study the performance of the electrolyte processes.

A self-healing real-time system based on run-time self-reconfiguration

The new generations of SRAM-based FPGA (field programmable gate array) devices are the preferred choice for the implementation of reconfigurable computing platforms intended to accelerate processing in real-time systems. However, FPGA's vulnerability to hard and soft errors is a major weakness to robust configurable system design. In this paper, a novel built-in self-healing (BISH) methodology, based on run-time self-reconfiguration, is proposed. A soft microprocessor core implemented in the FPGA is responsible for the management and execution of all the BISH procedures. Fault detection and diagnosis is followed by repairing actions, taking advantage of the dynamic reconfiguration features offered by new FPGA families. Meanwhile, modular redundancy assures that the system still works correctly

Schem@Doc: a web-based XML schema visualizer

XML Schema is one of the most used specifications for defining types of XML documents. It provides an extensive set of primitive data types, ways to extend and reuse definitions and an XML syntax that simplifies automatic manipulation. However, many features that make XML Schema Definitions (XSD) so interesting also make them rather cumbersome to read. Several tools to visualize and browse schema definitions have been proposed to cope with this issue. The novel approach proposed in this paper is to base XSD visualization and navigation on the XML document itself, using solely the web browser, without requiring a pre-processing step or an intermediate representation. We present the design and implementation of a web-based XML Schema browser called schem@Doc that operates over the XSD file itself. With this approach, XSD visualization is synchronized with the source file and always reflects its current state. This tool fits well in the schema development process and is easy to integrate in web repositories containing large numbers of XSD files.

Design of an extensible metadata editor based on RDF

The content of a Learning Object is frequently characterized by metadata from several standards, such as LOM, SCORM and QTI. Specialized domains require new application profiles that further complicate the task of editing the metadata of learning object since their data models are not supported by existing authoring tools. To cope with this problem we designed a metadata editor supporting multiple metadata languages, each with its own data model. It is assumed that the supported languages have an XML binding and we use RDF to create a common metadata representation, independent from the syntax of each metadata languages. The combined data model supported by the editor is defined as an ontology. Thus, the process of extending the editor to support a new metadata language is twofold: firstly, the conversion from the XML binding of the metadata language to RDF and vice-versa; secondly, the extension of the ontology to cover the new metadata model. In this paper we describe the general architecture of the editor, we explain how a typical metadata language for learning objects is represented as an ontology, and how this formalization captures all the data required to generate the graphical user interface of the editor.

ErasmusApp: A Location-Based Collaborative System for Erasmus Students

This paper reports on the design and development of an Android-based context-aware system to support Erasmus students during their mobility in Porto. It enables: (i) guest users to create, rate and store personal points of interest (POI) in a private, local on board database; and (ii) authenticated users to upload and share POI as well as get and rate recommended POI from the shared central database. The system is a distributed client / server application. The server interacts with a central database that maintains the user profiles and the shared POI organized by category and rating. The Android GUI application works both as a standalone application and as a client module. In standalone mode, guest users have access to generic info, a map-based interface and a local database to store and retrieve personal POI. Upon successful authentication, users can, additionally, share POI as well as get and rate recommendations sorted by category, rating and distance-to-user.

Reason Maintenance Systems: Tools for Foundations-based Belief Revision

Dynamic and distributed environments are hard to model since they suffer from unexpected changes, incomplete knowledge, and conflicting perspectives and, thus, call for appropriate knowledge representation and reasoning (KRR) systems. Such KRR systems must handle sets of dynamic beliefs, be sensitive to communicated and perceived changes in the environment and, consequently, may have to drop current beliefs in face of new findings or disregard any new data that conflicts with stronger convictions held by the system. Not only do they need to represent and reason with beliefs, but also they must perform belief revision to maintain the overall consistency of the knowledge base. One way of developing such systems is to use reason maintenance systems (RMS). In this paper we provide an overview of the most representative types of RMS, which are also known as truth maintenance systems (TMS), which are computational instances of the foundations-based theory of belief revision. An RMS module works together with a problem solver. The latter feeds the RMS with assumptions (core beliefs) and conclusions (derived beliefs), which are accompanied by their respective foundations. The role of the RMS module is to store the beliefs, associate with each belief (core or derived belief) the corresponding set of supporting foundations and maintain the consistency of the overall reasoning by keeping, for each represented belief, the current supporting justifications. Two major approaches are used to reason maintenance: single-and multiple-context reasoning systems. Although in the single-context systems, each belief is associated to the beliefs that directly generated it—the justification-based TMS (JTMS) or the logic-based TMS (LTMS), in the multiple context counterparts, each belief is associated with the minimal set of assumptions from which it can be inferred—the assumption-based TMS (ATMS) or the multiple belief reasoner (MBR).

Tech-based Innovations in pedagogical practices: the case of ISCAP

TICEduca. III Congresso Internacional TIC e Educação. 14 a 16 Novembro, Lisboa

Media Brokerage: Agent-Based SLA Negotiation

Media content personalisation is a major challenge involving viewers as well as media content producer and distributor businesses. The goal is to provide viewers with media items aligned with their interests. Producers and distributors engage in item negotiations to establish the corresponding service level agreements (SLA). In order to address automated partner lookup and item SLA negotiation, this paper proposes the MultiMedia Brokerage (MMB) platform, which is a multiagent system that negotiates SLA regarding media items on behalf of media content producer and distributor businesses. The MMB platform is structured in four service layers: interface, agreement management, business modelling and market. In this context, there are: (i) brokerage SLA (bSLA), which are established between individual businesses and the platform regarding the provision of brokerage services; and (ii) item SLA (iSLA), which are established between producer and distributor businesses about the provision of media items. In particular, this paper describes the negotiation, establishment and enforcement of bSLA and iSLA, which occurs at the agreement and negotiation layers, respectively. The platform adopts a pay-per-use business model where the bSLA define the general conditions that apply to the related iSLA. To illustrate this process, we present a case study describing the negotiation of a bSLA instance and several related iSLA instances. The latter correspond to the negotiation of the Electronic Program Guide (EPG) for a specific end viewer.

Short-term Load Forecasting Based on Load Profiling

Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.

Multi-Agent Based Smart Grid Management and Simulation: Situation Awareness and Learning in a Test Bed with Simulated and Real Installations and Players

The rising usage of distributed energy resources has been creating several problems in power systems operation. Virtual Power Players arise as a solution for the management of such resources. Additionally, approaching the main network as a series of subsystems gives birth to the concepts of smart grid and micro grid. Simulation, particularly based on multi-agent technology is suitable to model all these new and evolving concepts. MASGriP (Multi-Agent Smart Grid simulation Platform) is a system that was developed to allow deep studies of the mentioned concepts. This paper focuses on a laboratorial test bed which represents a house managed by a MASGriP player. This player is able to control a real installation, responding to requests sent by the system operators and reacting to observed events depending on the context.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Strategic Bidding in Electricity Markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.