Combate à fraude em jogos de casino, assistida por computador

No decorrer dos últimos anos tem-se verificado um acréscimo do número de sistemas de videovigilância presentes nos mais diversos ambientes, sendo que estes se encontram cada vez mais sofisticados. Os casinos são um exemplo bastante popular da utilização destes sistemas sofisticados, sendo que vários casinos, hoje em dia, utilizam câmeras para controlo automático das suas operações de jogo. No entanto, atualmente existem vários tipos de jogos em que o controlo automático ainda não se encontra disponível, sendo um destes, o jogo Banca Francesa. A presente dissertação tem como objetivo propor um conjunto de algoritmos idealizados para um sistema de controlo e gestão do jogo de casino Banca Francesa através do auxílio de componentes pertencentes à área da computação visual, tendo em conta os contributos mais relevantes e existentes na área, elaborados por investigadores e entidades relacionadas. No decorrer desta dissertação são apresentados quatro módulos distintos, os quais têm como objetivo auxiliar os casinos a prevenir o acontecimento de fraudes durante o decorrer das suas operações, assim como auxiliar na recolha automática de resultados de jogo. Os quatro módulos apresentados são os seguintes: Dice Sample Generator – Módulo proposto para criação de casos de teste em grande escala; Dice Sample Analyzer – Módulo proposto para a deteção de resultados de jogo; Dice Calibration – Módulo proposto para calibração automática do sistema; Motion Detection – Módulo proposto para a deteção de fraude no jogo. Por fim, para cada um dos módulos, é apresentado um conjunto de testes e análises de modo a verificar se é possível provar o conceito para cada uma das propostas apresentadas.

Load Control Timescales Simulation in a Multi-Agent Smart Grid Platform

Environmental concerns and the shortage in the fossil fuel reserves have been potentiating the growth and globalization of distributed generation. Another resource that has been increasing its importance is the demand response, which is used to change consumers’ consumption profile, helping to reduce peak demand. Aiming to support small players’ participation in demand response events, the Curtailment Service Provider emerged. This player works as an aggregator for demand response events. The control of small and medium players which act in smart grid and micro grid environments is enhanced with a multi-agent system with artificial intelligence techniques – the MASGriP (Multi-Agent Smart Grid Platform). Using strategic behaviours in each player, this system simulates the profile of real players by using software agents. This paper shows the importance of modeling these behaviours for studying this type of scenarios. A case study with three examples shows the differences between each player and the best behaviour in order to achieve the higher profit in each situation.

O Processo de gestão de risco nas organizações

Dissertação apresentada ao Instituto Superior de Contabilidade e Administração do Porto para obtenção do Grau de Mestre em Auditoria Orientada por Mestre Carlos Manuel Antunes Mendes

ANN-Based LMP forecasting in a distribution network with large penetration of DG

In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.

Intelligent supervisory control system for home devices using a cyber physical approach

Although we have many electric devices at home, there are just few systems to evaluate, monitor and control them. Sometimes users go out and leave their electric devices turned on what can cause energy wasting and dangerous situations. Therefore most of the users may want to know the using states of their electrical appliances through their mobile devices in a pervasive way. In this paper, we propose an Intelligent Supervisory Control System to evaluate, monitor and control the use of electric devices in home, from outside. Because of the transferring data to evaluate, monitor and control user's location and state of home (ex. nobody at home) may be opened to attacks leading to dangerous situations. In our model we include a location privacy module and encryption module to provide security to user location and data. Intelligent Supervising Control System gives to the user the ability to manage electricity loads by means of a multi-agent system involving evaluation, monitoring, control and energy resource agents.

Adaptable control for electrical generation at irregural wind speeds

The main aims of this work are the development and the validation of one generic algorithm to provide the optimal control of small power wind generators. That means up to 40 kW and blades with fixed pitch angle. This algorithm allows the development of controllers to fetch the wind generators at the desired operational point in variable operating conditions. The problems posed by the variable wind intensity are solved using the proposed algorithm. This is done with no explicit measure of the wind velocity, and so no special equipment or anemometer is required to compute or measure the wind velocity.

Genetic algorithm methodology applied to intelligent house control

In recent years the use of several new resources in power systems, such as distributed generation, demand response and more recently electric vehicles, has significantly increased. Power systems aim at lowering operational costs, requiring an adequate energy resources management. In this context, load consumption management plays an important role, being necessary to use optimization strategies to adjust the consumption to the supply profile. These optimization strategies can be integrated in demand response programs. The control of the energy consumption of an intelligent house has the objective of optimizing the load consumption. This paper presents a genetic algorithm approach to manage the consumption of a residential house making use of a SCADA system developed by the authors. Consumption management is done reducing or curtailing loads to keep the power consumption in, or below, a specified energy consumption limit. This limit is determined according to the consumer strategy and taking into account the renewable based micro generation, energy price, supplier solicitations, and consumers’ preferences. The proposed approach is compared with a mixed integer non-linear approach.

ANN based support for distributed energy resources scheduling in smart grids

The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant. Virtual Power Players (VPP) and single players must optimize their energy resource management in order to accomplish their goals. This is relatively easy to larger players, with financial means to have access to adequate decision support tools, to support decision making concerning their optimal resource schedule. However, the smaller players have difficulties in accessing this kind of tools. So, it is required that these smaller players can be offered alternative methods to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), intended to support smaller players’ resource scheduling. The used methodology uses a training set that is built using the energy resource scheduling solutions obtained with a reference optimization methodology, a mixed-integer non-linear programming (MINLP) in this case. The trained network is able to achieve good schedule results requiring modest computational means.

Intelligent behavior in a Cyber-Ambient training system for control center operators

This paper deals with the application of an intelligent tutoring approach to delivery training in diagnosis procedures of a Power System. In particular, the mechanisms implemented by the training tool to support the trainees are detailed. This tool is part of an architecture conceived to integrate Power Systems tools in a Power System Control Centre, based on an Ambient Intelligent paradigm. The present work is integrated in the CITOPSY project which main goal is to achieve a better integration between operators and control room applications, considering the needs of people, customizing requirements and forecasting behaviors.

Using an ambient intelligent architecture for developing an intelligent tutoring system for training operators of power system control centres

This paper describes an architecture conceived to integrate Power Sys-tems tools in a Power System Control Centre, based on an Ambient Intelligent (AmI) paradigm. This architecture is an instantiation of the generic architecture proposed in [1] for developing systems that interact with AmI environments. This architecture has been proposed as a consequence of a methodology for the inclu-sion of Artificial Intelligence in AmI environments (ISyRAmI - Intelligent Sys-tems Research for Ambient Intelligence). The architecture presented in the paper will be able to integrate two applications in the control room of a power system transmission network. The first is SPARSE expert system, used to get diagnosis of incidents and to support power restoration. The second application is an Intelligent Tutoring System (ITS) incorporating two training tools. The first tutoring tool is used to train operators to get the diagnosis of incidents. The second one is another tutoring tool used to train operators to perform restoration procedures.

Training control centers' operators in incident diagnosis and power restoration using intelligent tutoring systems

The activity of Control Center operators is important to guarantee the effective performance of Power Systems. Operators’ actions are crucial to deal with incidents, especially severe faults like blackouts. In this paper, we present an Intelligent Tutoring approach for training Portuguese Control Center operators in tasks like incident analysis and diagnosis, and service restoration of Power Systems. Intelligent Tutoring System (ITS) approach is used in the training of the operators, having into account context awareness and the unobtrusive integration in the working environment. Several Artificial Intelligence techniques were criteriously used and combined together to obtain an effective Intelligent Tutoring environment, namely Multiagent Systems, Neural Networks, Constraint-based Modeling, Intelligent Planning, Knowledge Representation, Expert Systems, User Modeling, and Intelligent User Interfaces.

Cyber-ambient intelligent training of operators in power systems control centres

Cyber-Physical Systems and Ambient Intelligence are two of the most important and emerging paradigms of our days. The introduction of renewable sources gave origin to a completely different dimension of the distribution generation problem. On the other hand, Electricity Markets introduced a different dimension in the complexity, the economic dimension. Our goal is to study how to proceed with the Intelligent Training of Operators in Power Systems Control Centres, considering the new reality of Renewable Sources, Distributed Generation, and Electricity Markets, under the emerging paradigms of Cyber-Physical Systems and Ambient Intelligence. We propose Intelligent Tutoring Systems as the approach to deal with the intelligent training of operators in these new circumstances.

An intelligent tutoring system for operators’ training in power system control centres

The activity of Control Center operators is important to guarantee the effective performance of Power Systems. Operators’ actions are crucial to deal with incidents, especially severe faults, like blackouts. In this paper we present an Intelligent Tutoring approach for training Portuguese Control Centre operators in tasks like incident analysis and diagnosis, and service restoration of Power Systems. Intelligent Tutoring System (ITS) approach is used in the training of the operators, taking into account context awareness and the unobtrusive integration in the working environment.

Optimal intelligent supervisory control system in cyber-physical intelligence

This paper studies Optimal Intelligent Supervisory Control System (OISCS) model for the design of control systems which can work in the presence of cyber-physical elements with privacy protection. The development of such architecture has the possibility of providing new ways of integrated control into systems where large amounts of fast computation are not easily available, either due to limitations on power, physical size or choice of computing elements.

Intelligent training in control centres based on an ambient intelligence paradigm

This article describes a new approach in the Intelligent Training of Operators in Power Systems Control Centres, considering the new reality of Renewable Sources, Distributed Generation, and Electricity Markets, under the emerging paradigms of Cyber-Physical Systems and Ambient Intelligence. We propose Intelligent Tutoring Systems as the approach to deal with the intelligent training of operators in these new circumstances.