30 research outputs found

    On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters

    Get PDF
    This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p

    Optimization of AI models as the Main Component in Prospective Edge Intelligence Applications

    Get PDF
    Artificial Intelligence (AI) is a successful paradigm with application in many fields; however, there can be some challenging scenarios like the deployment of AI models in remote locations or with limited connectivity, possibly needing to perform inference closer to where data is collected. A potential solution is the study of ways to optimize AI models, for deployment of intelligent algorithms closer to the edge. This thesis focuses on applications of AI that need to have characteristics that make them suitable for implementation on portable devices (e.g., aeroponics container, drone, mobile robot); thus, the development and implementation of custom models, and their optimization (i.e., reduction in size and inference time) is of upmost importance and the main goal of this dissertation. For this task, a number of options have been explored, including developing techniques to select relevant features from the samples that the model analyzes, and pruning and quantization. Therefore, this thesis proposes a scheme for the development, implementation, and optimization of custom AI models used mainly in agriculture, so that they have the desired characteristics that are needed for their deployment in edge devices. This main goal is fulfilled by implementing a number of sequential steps that include the validation of the hypothesis that there is at least an AI model capable of generating useful predictions for the applications being studied, the exploration and implementation of an approach for their optimization, and their final implementation in hardware of limited resources. The main contributions of this thesis are on the general workflow for optimization of custom models, as well as in the proposed scheme for feature selection based on model interpretability approaches. This carries most of the novelty of the thesis, since we have not found previous implementations of these ideas, at least in the field under study. This optimization is mainly based on a feature selection approach, but finally complemented with pruning and quantization. The implementation of some of these models on an edge-like device, demonstrates the feasibility of this approach. Finally, although this thesis tries to be a self-contained work, encompassing all the aspects required to go from AI model design to implementation on an edge device, there are some aspects that could be further studied, analyzed, and improved. Furthermore, it is almost impossible to keep the pace with all the new developments in the fields of AI, edge computing, hardware and software tools, etc. which opens the field for new discussions and proposals. This work tries to fill some gaps and to propose some ideas that hopefully will be useful for future researchers in the development of new technologies and solutions

    Optimization and Allocation in Some Decision Problems with Several Agents or with Stochastic Elements

    Get PDF
    Programa Oficial de Doutoramento en Estatística e Investigación Operativa. 5017V01[Abstract] This dissertation addresses sorne decision problems that arise in project management, cooperative game theory and vehicle route optimization. We start with the problem of allocating the delay costs of a project. In a stochastic context in which we assume that activity durations are random variables, we propose and study an allocation rule based on the Shapley value. In addition, we present an R package that allows a comprehensive control of the project, including the new rule. We propose and characterize new egalitarian solutions in the context of cooperative games with a coalitional structure. Also, using a necessary player property we introduce a new value for cooperative games, which we later extend and characterize within the framework of cooperative games with a coalitional structure. Finally, we present a two-step algorithm for solving multi-compartment vehicle route problems with stochastic demands. This algorithm obtains an initial solution through a constructive heuristic and then uses a tabu search to improve the solution. Using real data, we evaluate the performance of the algorithm.[Resumo] Nesta memoria abórdanse diversos problemas de decisión que xorden na xestión de proxectos, na teoría de xogos cooperativos e na optimización de rutas de vehículos. Empezamos estudando o problema da repartición dos custos de demora nun proxecto. Nun contexto estocástico no que supoñemos que as duracións das actividades son variables aleatorias, propoñemos e estudamos unha regra de repartición baseada no valor de Shapley. Ademais, presentamos un paquete de R que permite un control integral do proxecto, incluíndo a nova regra de repartición. A continuación, propoñemos e caracterizamos axiomaticamente novas solucións igualitarias no contexto dos xogos cooperativos cunha estrutura coalicional. E introducimos un novo valor, utilizando unha propiedade de xogadores necesarios, para xogos cooperativos, que posteriormente estendemos e caracterizamos dentro do marco dos xogos cooperativos cunha estrutura coalicional. Por último, presentamos un algoritmo en dous pasos para resolver problemas de rutas de vehículos con multi-compartimentos e demandas estocásticas. Este algoritmo obtén unha solución inicial mediante unha heurística construtiva e, a continuación, utiliza unha búsqueda tabú para mellorar a solución. Utilizando datos reais, levamos a cabo unha análise do comportamento do algoritmo.[Resumen] En esta memoria se abordan diversos problemas de decisión que surgen en la gestión de proyectos, en la teoría de juegos cooperativos y en la optimización de rutas de vehículos. Empezamos estudiando el problema del reparto de los costes de demora en un proyecto. En un contexto estocástico en el que suponemos que las duraciones de las actividades son variables aleatorias, proponemos y estudiamos una regla de reparto basada en el valor de Shapley. Además, presentamos un paquete de R que permite un control integral del proyecto, incluyendo la nueva regla de reparto. A continuación, proponemos y caracterizamos axiomáticamente nuevas soluciones igualitarias en el contexto de los juegos cooperativos con una estructura coalicional. E introducimos un nuevo valor, utilizando una propiedad de jugadores necesarios, para juegos cooperativos, que posteriormente extendemos y caracterizamos dentro del marco de los juegos cooperativos con una estructura coalicional. Por último, presentamos un algoritmo en dos pasos para resolver problemas de rutas de vehículos con multi-compartimentos y demandas estocásticas. Este algoritmo obtiene una solución inicial mediante una heurística constructiva y, a continuación, utiliza una búsqueda tabú para mejorar la solución. Utilizando datos reales, llevamos a cabo un análisis del comportamiento del algoritmo

    Aplicação de interpretabilidade para melhorar o desempenho de um classificador LSTM para eventos de sistema de potência

    Get PDF
    Orientador: Daniel DottaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Atualmente, uma grande quantidade de dados é coletada pelos WAMS (Wide Area Measurement Systems). Portanto, existe uma clara necessidade de métodos de aprendizagem de máquina (ML - Machine Learning), capazes de extrair informações relevantes e confiáveis dos dados de sincrofasores. Entre as abordagens de ML, os modelos de Rede Neural Profunda (DNN - Deep Neural Network) têm a vantagem de aprender diretamente com os dados, tornando essas abordagens não dependentes das técnicas de extração de atributos. No entanto, esses modelos profundos produzem classificadores caixa-preta (black-box) que podem suscitar preocupações quando aplicados a ambientes de alto risco (infraestrutura crítica), como o sistema elétrico de potência (EPS-Electric Power Systems). Neste trabalho, a aplicação de um método orientado a dados (data-driven) explicável é realizada a fim de inspecionar o desempenho do classificador DNN para identificação de eventos usando medições de sincrofasores. O classificador DNN é uma LSTM (Long-Short Term Memory) que tem demostrado bom desempenho na extração de características dinâmicas. A principal vantagem dessa abordagem é o uso de uma inspeção baseada em interpretabilidade denominada SHAP (SHapley Additive exPlanation), que é baseada na teoria dos jogos cooperativos (valores Shapley), que fornece os meios para avaliar as previsões da LSTM, destacando as partes das séries temporais de entrada que mais contribuíram para a identificação dos eventos e detecção de possíveis vieses. Além disso, usando a inspeção SHAP juntamente com o conhecimento de domínio (domain knowledge) sobre o problema, o desempenho e a coerência do classificador LSTM são aprimorados ao escolher o classificador que não apenas possui a maior acurácia de identificação (IAR - Identification Accuracy Rate), mas também é coerente com o conhecimento de domínio do problema, minimizando possíveis vieses detectados. O uso dessa abordagem interpretável é útil porque: i) explica como o classificador LSTM está tomando suas decisões; ii) ajuda o designer a melhorar o treinamento do classificador; iii) certifica que o classificador resultante tem um desempenho consistente e coerente de acordo com o conhecimento do domínio; iv) quando o usuário entende que o classificador está tomando decisões coerentes, reduz claramente as preocupações da aplicação dos métodos DNN em uma infraestrutura crítica. O método proposto é avaliado usando registros reais de eventos sincrofasores do Sistema Interligado Nacional (SIN)Abstract: Nowadays, vast amounts of data are collected by Wide Area Measurement Systems (WAMS). Therefore, there is an obvious necessity for Machine Learning (ML) methods, as useful knowledge to extract relevant and reliable information from this synchrophasor data. Among the ML approaches, the Deep Neural Network (DNN) models provide an important opportunity to advance direct learning from the data, making these approaches independent from feature extraction techniques. However, these deep models produce black-box classifiers that can be matter of concern when applying to high-risk environment (critical infrastructure) such as the EPS (Electric Power Systems). In this work, the application of an explainable data-driven method is carried out in order to inspect the performance of DNN classifier for event identification using synchrophasor measurements. The DNN classifier is a Long-Short Term Memory (LSTM) with positive performance in the extraction of dynamic features. The principal benefit of this approach is the use of an interpretability inspection named SHAP (SHapley Additive exPlanation) values, which are based on cooperative game theory (Shapley values). These SHAP values provide the means to evaluate the predictions of the LSTM, highlight the parts of the input time-series with the most contribution to the identification of the events, and detect possible bias. Moreover, by employing the SHAP inspection along with domain knowledge of the problem, the performance and coherence of the LSTM classifier will be improved by choosing the classifier that not only has highest Identification Accuracy Rate (IAR) but is also coherent with domain knowledge of the problem, minimizing detected bias. The application of this interpretable approach is desirable because: i) it explains how the LSTM classifier is making its decisions; ii) it helps the designer to improve the training of the classifier; iii) it certifies that the resulting classifier has a consistent and coherent performance according to domain knowledge of the problem; iv) it clearly reduces the concerns of the application of DNN methods in a critical infrastructure, in the cases that the user understands that the classifier is taking coherent decisions. The proposed method has been evaluated using real synchrophasor event records from the Brazilian Interconnected Power System (BIPS)MestradoEnergia ElétricaMestre em Engenharia Elétrica2017/25425-5FAPES

    Design of large polyphase filters in the Quadratic Residue Number System

    Full text link

    Temperature aware power optimization for multicore floating-point units

    Full text link

    Pertanika Journal of Science & Technology

    Get PDF

    Pertanika Journal of Science & Technology

    Get PDF

    Air Force Institute of Technology Research Report 2020

    Get PDF
    This Research Report presents the FY20 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document
    corecore