Environmental Decision Support: a Distributed Artificial Intelligence Approach

Decision making in any environmental domain is a complex and demanding activity, justifying the development of dedicated decision support systems. Every decision is confronted with a large variety and amount of constraints to satisfy as well as contradictory interests that must be sensibly accommodated. The first stage of a project evaluation is its submission to the relevant group of public (and private) agencies. The individual role of each agency is to verify, within its domain of competence, the fulfilment of the set of applicable regulations. The scope of the involved agencies is wide and ranges from evaluation abilities on the technical or economical domains to evaluation competences on the environmental or social areas. The second project evaluation stage involves the gathering of the recommendations of the individual agencies and their justified merge to produce the final conclusion. The incorporation and accommodation of the consulted agencies opinions is of extreme importance: opinions may not only differ, but can be interdependent, complementary, irreconcilable or, simply, independent. The definition of adequate methodologies to sensibly merge, whenever possible, the existing perspectives while preserving the overall legality of the system, will lead to the making of sound justified decisions. The proposed Environmental Decision Support System models the project evaluation activity and aims to assist developers in the selection of adequate locations for their projects, guaranteeing their compliance with the applicable regulations.

Aquisição por Visão Artificial e Medição Automática de Micro Dureza Vickers

O ensaio de dureza, e mais concretamente o ensaio de micro dureza Vickers, é no universo dos ensaios mecânicos um dos mais utilizados quer seja na indústria, no ensino ou na investigação e desenvolvimento de produto no âmbito das ciências dos materiais. Na grande maioria dos casos, a utilização deste ensaio tem como principal aplicação a caracterização ou controlo da qualidade de fabrico de materiais metálicos. Sendo um ensaio de relativa simplicidade de execução, rapidez e com resultados comparáveis e relacionáveis a outras grandezas físicas das propriedades dos materiais. Contudo, e tratando-se de um método de ensaio cuja intervenção humana é importante, na medição da indentação gerada por penetração mecânica através de um sistema ótico, não deixa de exibir algumas debilidades que daí advêm, como sendo o treino dos técnicos e respetivas acuidades visuais, fenómenos de fadiga visual que afetam os resultados ao longo de um turno de trabalho; ora estes fenómenos afetam a repetibilidade e reprodutibilidade dos resultados obtidos no ensaio. O CINFU possui um micro durómetro Vickers, cuja realização dos ensaios depende de um técnico treinado para a execução do mesmo, apresentando todas as debilidades já mencionadas e que o tornou elegível para o estudo e aplicação de uma solução alternativa. Assim, esta dissertação apresenta o desenvolvimento de uma solução alternativa ao método ótico convencional na medição de micro dureza Vickers. Utilizando programação em LabVIEW da National Instruments, juntamente com as ferramentas de visão computacional (NI Vision), o programa começa por solicitar ao técnico a seleção da câmara para aquisição da imagem digital acoplada ao micro durómetro, seleção do método de ensaio (Força de ensaio); posteriormente o programa efetua o tratamento da imagem (aplicação de filtros para eliminação do ruído de fundo da imagem original), segue-se, por indicação do operador, a zona de interesse (ROI) e por sua vez são identificadas automaticamente os vértices da calote e respetivas distâncias das diagonais geradas concluindo, após aceitação das mesmas, com o respetivo cálculo de micro dureza resultante. Para validação dos resultados foram utilizados blocos-padrão de dureza certificada (CRM), cujos resultados foram satisfatórios, tendo-se obtido um elevado nível de exatidão nas medições efetuadas. Por fim, desenvolveu-se uma folha de cálculo em Excel com a determinação da incerteza associada às medições de micro dureza Vickers. Foram então comparados os resultados nas duas metodologias possíveis, pelo método ótico convencional e pela utilização das ferramentas de visão computacional, tendo-se obtido bons resultados com a solução proposta.

Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

Combining artificial intelligence techniques for the training of power system control centre operators

Control Centre operators are essential to assure a good performance of Power Systems. Operators’ actions are critical in dealing with incidents, especially severe faults, like blackouts. In this paper we present an Intelligent Tutoring approach for training Portuguese Control Centre operators in incident analysis and diagnosis, and service restoration of Power Systems, offering context awareness and an easy integration in the working environment.

Efeito das condições ambientais na produtividade lipídica da Dunaliella tertiolecta

Este trabalho teve como objectivo a optimização das condições de crescimento de biomassa algal tendo em vista a sua utilização como fonte de lípidos para biocombustíveis. Assim, procedeu-se à inoculação de duas estirpes, a Dunaliella tertiolecta (água salgada) e a Tetraselmis subcordiformis (água salobra), seleccionando-se a Dunaliella tertiolecta uma vez que esta apresentou um crescimento mais rápido. Escolhida a estirpe a usar, avaliou-se a influência da composição do meio de cultura da espécie, variando-se a concentração de macronutrientes (Magnésio, Potássio, Azoto, Fósforo) e de micronutrientes (Manganês, Zinco, Ferro, Cobalto) presentes no meio em 10 e 20 vezes, comparativamente à do meio de cultura padrão, o meio Artificial Seawater Medium with Vitamins. Avaliou-se o crescimento algal, a uma temperatura de 25 ºC ± 2 ºC, com uma intensidade de iluminação de 5000 lux (lâmpadas luz dia) e fotoperíodos 12:12 h, controlando possíveis contaminações nas culturas em estudo. Para os ensaios realizados com a Dunaliella tertiolecta, os melhores resultados para a produtividade média e máxima de biomassa, 63,06 mgbiomassa seca/L.dia e 141,79 mgbiomassa seca/L.dia, respectivamente, foram obtidos no ensaio em que se fez variar 10 vezes a concentração de azoto (sob a forma de nitrato). Os resultados mais satisfatórios para o teor lípidico e para a produtividade lipídica máxima, 33,45% e 47,43 mgóleo/L.dia respectivamente, também foram obtidos no ensaio em que se fez variar 10 vezes a concentração de azoto (sob a forma de nitrato), (com extracção dos lípidos usando o método de Bligh e Dyer). Foram testados dois solventes para a extracção de lipídos, o clorofórmio e o hexano, tendose obtido resultados superiores com o clorofórmio, comparativamente aos obtidos quando se usou hexano, com excepção do ensaio em que se aumentou 20 vezes a concentração de fósforo no meio de cultura das microalgas. Verificou-se que, em todos os ensaios foi atingido o estado estacionário sensivelmente na mesma altura, isto é, decorridos cerca de 25 dias após o início do estudo, excepto os ensaios em que se fez variar a concentração de cobalto, para os quais as culturas não se adaptaram às alterações do meio, acabando por morrer passados 15 dias. A adição dos macronutrientes e micronutrientes usados nos ensaios, nas quantidades testadas, não influenciou significativamente a produtividade lipídica, com excepção do azoto e ferro. Conclui-se que o aumento da concentração de azoto para 10x o valor padrão potencia o aumento da produtividade lipídica máxima para mais do dobro (3,6 vezes – Padrão: 13,25 mgóleo/L.dia; 10x N: 47,43 mgóleo/L.dia) e que o aumento da concentração de ferro para 10x o valor padrão potencia o aumento da produtividade lipídica máxima para aproximadamente o dobro (1,9 vezes - Padrão: 14,61 mgóleo/L.dia; 10x Fe: 28,04 mgóleo/L.dia). Nos ensaios realizados com adição de azoto ou ferro, os resultados obtidos para a concentração, teor lípidico e produtividade lipídica máxima, foram sempre superiores aos do padrão correspondente, pelo que se pode concluir que estes ensaios se apresentam como os mais promissores deste estudo, embora o ensaio mais satisfatório tenha sido aquele em que se promoveu a alteração da concentração de azoto para 10 vezes o valor padrão.

Mixed integer non linear programming and artificial neural network based approach to ancillary services dispatch in competitive electricity markets

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.

QoS-based surrogates selection and service proposal formulation in offloading environments

Resource constraints are becoming a problem as many of the wireless mobile devices have increased generality. Our work tries to address this growing demand on resources and performance, by proposing the dynamic selection of neighbor nodes for cooperative service execution. This selection is in uenced by user's quality of service requirements expressed in his request, tailoring provided service to user's speci c needs. In this paper we improve our proposal's formulation algorithm with the ability to trade o time for the quality of the solution. At any given time, a complete solution for service execution exists, and the quality of that solution is expected to improve overtime.

Supplier's selection model based on an empirical study

The problem of selecting suppliers/partners is a crucial and important part in the process of decision making for companies that intend to perform competitively in their area of activity. The selection of supplier/partner is a time and resource-consuming task that involves data collection and a careful analysis of the factors that can positively or negatively influence the choice. Nevertheless it is a critical process that affects significantly the operational performance of each company. In this work, there were identified five broad selection criteria: Quality, Financial, Synergies, Cost, and Production System. Within these criteria, it was also included five sub-criteria. After the identification criteria, a survey was elaborated and companies were contacted in order to understand which factors have more weight in their decisions to choose the partners. Interpreted the results and processed the data, it was adopted a model of linear weighting to reflect the importance of each factor. The model has a hierarchical structure and can be applied with the Analytic Hierarchy Process (AHP) method or Value Analysis. The goal of the paper it's to supply a selection reference model that can represent an orientation/pattern for a decision making on the suppliers/partners selection process

Analysis of the domain of applicability of an algorithm for a resources system selection problem for Distributed/Agile/Virtual Enterprises integration

The process of resources systems selection takes an important part in Distributed/Agile/Virtual Enterprises (D/A/V Es) integration. However, the resources systems selection is still a difficult matter to solve in a D/A/VE, as it is pointed out in this paper. Globally, we can say that the selection problem has been equated from different aspects, originating different kinds of models/algorithms to solve it. In order to assist the development of a web prototype tool (broker tool), intelligent and flexible, that integrates all the selection model activities and tools, and with the capacity to adequate to each D/A/V E project or instance (this is the major goal of our final project), we intend in this paper to show: a formulation of a kind of resources selection problem and the limitations of the algorithms proposed to solve it. We formulate a particular case of the problem as an integer programming, which is solved using simplex and branch and bound algorithms, and identify their performance limitations (in terms of processing time) based on simulation results. These limitations depend on the number of processing tasks and on the number of pre-selected resources per processing tasks, defining the domain of applicability of the algorithms for the problem studied. The limitations detected open the necessity of the application of other kind of algorithms (approximate solution algorithms) outside the domain of applicability founded for the algorithms simulated. However, for a broker tool it is very important the knowledge of algorithms limitations, in order to, based on problem features, develop and select the most suitable algorithm that guarantees a good performance.

Crossing genetic and swarm intelligence algorithms to generate logic circuits

Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. On the other hand, Particle swarm optimization (PSO) is a population based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as GAs. The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. PSO is attractive because there are few parameters to adjust. This paper presents hybridization between a GA algorithm and a PSO algorithm (crossing the two algorithms). The resulting algorithm is applied to the synthesis of combinational logic circuits. With this combination is possible to take advantage of the best features of each particular algorithm.

Solving Conflicting Beliefs with a Distributed Belief Revision Approach

The ability to solve conflicting beliefs is crucial for multi- agent systems where the information is dynamic, incomplete and dis- tributed over a group of autonomous agents. The proposed distributed belief revision approach consists of a distributed truth maintenance sy- stem and a set of autonomous belief revision methodologies. The agents have partial views and, frequently, hold disparate beliefs which are au- tomatically detected by system’s reason maintenance mechanism. The nature of these conflicts is dynamic and requires adequate methodolo- gies for conflict resolution. The two types of conflicting beliefs addressed in this paper are Context Dependent and Context Independent Conflicts which result, in the first case, from the assignment, by different agents, of opposite belief statuses to the same belief, and, in the latter case, from holding contradictory distinct beliefs. The belief revision methodology for solving Context Independent Con- flicts is, basically, a selection process based on the assessment of the cre- dibility of the opposing belief statuses. The belief revision methodology for solving Context Dependent Conflicts is, essentially, a search process for a consensual alternative based on a “next best” relaxation strategy.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

Finding a school technological partner: a multicriteria method for school information system producer selection

The choice of an information systems is a critical factor of success in an organization's performance, since, by involving multiple decision-makers, with often conflicting objectives, several alternatives with aggressive marketing, makes it particularly complex by the scope of a consensus. The main objective of this work is to make the analysis and selection of a information system to support the school management, pedagogical and administrative components, using a multicriteria decision aid system – MMASSITI – Multicriteria Method- ology to Support the Selection of Information Systems/Information Technologies – integrates a multicriteria model that seeks to provide a systematic approach in the process of choice of Information Systems, able to produce sustained recommendations concerning the decision scope. Its application to a case study has identi- fied the relevant factors in the selection process of school educational and management information system and get a solution that allows the decision maker’ to compare the quality of the various alternatives.

Group decision making for selection of an information system in a business context

The main objective of this work is to report on the development of a multi-criteria methodology to support the assessment and selection of an Information System (IS) framework in a business context. The objective is to select a technological partner that provides the engine to be the basis for the development of a customized application for shrinkage reduction on the supply chains management. Furthermore, the proposed methodology di ers from most of the ones previously proposed in the sense that 1) it provides the decision makers with a set of pre-defined criteria along with their description and suggestions on how to measure them and 2)it uses a continuous scale with two reference levels and thus no normalization of the valuations is required. The methodology here proposed is has been designed to be easy to understand and use, without a specific support of a decision making analyst.