[pt] A deterioração do desempenho da turbina a gás é resultado de vários tipos de falhas, como acúmulo de sujeira, erosão e corrosão, que afetam os componentes no caminho do gás, sendo os principais o compressor, o combustor e a turbina. No presente trabalho é avaliado o desempenho de Redes Neurais Artificiais (RNA) no emprego de diagnóstico de falha de turbinas a gás. Todas as redes projetadas são do tipo MLP (multi-layer perceptron) com algoritmo de retropropagação (backpropagation). Para cada função de diagnóstico, várias arquiteturas foram testadas, modificando parâmetros de rede como o número de camadas escondidas e o número de neurônios em cada uma destas camadas. As RNAs para diagnóstico de falhas foram aplicadas ao modelo termodinâmico de uma turbina a gás industrial. Este modelo foi responsável pela criação de dados da usina saudável e também degradada, utilizados para o treinamento e validação das redes. Com os resultados obtidos do treinamento das redes é possível mostrar que as mesmas são capazes de detectar, isolar e quantificar falhas de componentes de turbinas a gás de forma satisfatória. / [en] The gas turbine performance deterioration is a result of several types of faults such as fouling, erosion and corrosion, which affects the components throughout the gas path. As the most significant of these components we can enumerate the compressor, the combustion chamber and the turbine itself. In this work the performance of different types of Artificial Neural Networks (ANN) are evaluated in the diagnosis of this kind of fault. Every neural network designed in this work is MLP (multi-layer perceptron) with back propagation algorithm. For each diagnosis function several architectures were tested, varying network parameters as the numbers of hidden layers and the number of neurons in each layer. The ANNs for fault diagnosis were applied in an industrial gas turbine thermodynamic model. This model was also used for healthy and degraded turbine data generation, which were used for ANNs training and validation. With the ANNs training results we can conclude that these networks are capable of detecting, isolating and quantifying gas turbine components faults in a satisfactory way.

The parallel nature of process execution on chip multiprocessors (CMPs) has considerably boosted levels of application performance in the past decade. Generally, a certain number of computing resources are shared among the several cores of a CMP, such as shared last-level caches, shared-buses, and shared-memory. This ensures architectural simplicity while also boosting performance for multi-threaded applications. However, a consequence of sharing computing resources is that concurrently executing applications may suffer performance degradation if their collective resource requirements exceed the total amount of resources available. If resource allocation is not carefully considered, the potential performance gain from having multiple cores may be outweighed by the losses due to contention among processes for shared resources. Furthermore, CMPs with inbuilt dynamic voltage-frequency scaling (DVFS) may try to compensate for the performance loss by scaling to a higher frequency. For performance degradation due to shared-resource contention, this does not necessarily improve performance but guarantees a significant penalty on power consumption due to the quadratic relation of electrical power and voltage (P ∝ V^{2}*f).

Several models for predicting future customer profitability early into customer life-cycles in the property and casualty business are constructed and studied. The objective is to model risk at a customer level with input data available early into a private consumer’s lifespan. Two retained models, one using Generalized Linear Model another using a multilayer perceptron, a special form of Artificial Neural Network are evaluated using actual data. Numerical results show that differentiation on estimated future risk is most effective for customers with highest claim frequencies.

This thesis examines how convolutional neural networks can applied to the problem of hand detection and hand pose estimation. Two families of convolutional neural networks are trained, aimed at performing the task of classification or regression. The networks are trained on specialized data generated from publicly available datasets. The algorithms used to generate the specialized data are also disclosed. The main focus has been to investigate the different structural properties of convolutional neural networks, not building optimized hand detection, or hand pose estimation, systems. Experiments revealed, that classifier networks featuring a relatively high number of convolutions offers the highest performance on external validation data. Additionally, shallow classifier networks featuring a relatively low number of convolutions, yields a high classification accuracy on training and testing data, but a very low accuracy on the validation set. This effect uncovers one of the fundamental difficulties in building a hand detection system: The asymmetric classification problem. In further investigation, it is also remarked, that relatively shallow classifier networks probably becomes color sensitive. Furthermore, regressor networks featuring multiscale inputs typically yielded the lowest error, when tasked with computing key-point locations directly from data. It is also revealed, that color data implicitly contain more information, making it easier to compute key-point locations, especially in the image space. However, to be able to derive the color invariant features, deeper regressor networks are required. / I detta examensarbete undersöks hur faltande neuronnät kan användas för detektering av, samt skattning av pose hos, händer. Två familjer av neuronnät tränas, med syftet att utföra klassificering eller regression. Neuronnäten tränas med specialiserad data genererad ur publikt tillgängliga dataset. Algoritmerna för att generera den specialiserade datan presenteras även i sin helhet. Huvudsyftet med arbetet, har varit att undersöka neuronnätens strukturella egenskaper, samt relatera dessa till prestanda, och inte bygga ett färdigt system för handdetektering eller skattning av handpose. Experimenten visade, att neuronnät för klassificering med ett relativt stor antal faltningar ger högst prestanda på valideringsdata. Vidare, så verkar neuronnät för klassificering med relativt litet antal faltningar ge en god prestanda på träning- och testdata, men mycket dålig prestand på valideringsdata. Detta sambandet avslöjar en fundamental svårighet med att träna ett neuronnät för klassificering av händer, nämligen det kraftigt asymmetriska klassificeringsproblemet. I vidare undersökningar visar det sig också, att neuronnät för klassificering med ett relativt litet antal faltningar troligtvis enbart blir färgkänsliga. Experimenten visade också, att neuronnät för regression som använde sig av data i flera skalor gav lägst fel när de skulle beräkna positioner av handmarkörer direkt ur data. Slutligen framkom det, att färgdata, i konstrast till djupdata, implicit innehåller mer information, vilket gör det relativt sett lättare att beräkna markörer, framför allt i det tvådimensionella bildrummet. Dock, för att kunna få fram den implicita informationen, så krävs relativt djupa neuronnät.

Star Vault AB is a video game development company that has developed the video game Mortal Online. The company has stated that they believe that players new to the game repeatedly find themselves being lost in the game. The objective of this study is to evaluate whether or not an Artificial Neural Network can be used to evaluate when a player is lost in the game Mortal Online. This is done using the free open source library Fast Artifical Neural Network Library. People are invited to a data collection event where they play a tweaked version of the game to facilitate data collection. Players specify whether they are lost or not and the data collected is flagged accordingly. The collected data is then prepared with different parameters to be used when training multiple Artificial Neural Networks. When creating an Artificial Neural Network there exists several parameters which have an impact on its performance. Performance is defined as the balance of high prediction accuracy against low false positive rate. These parameters vary depending on the purpose of the Artificial Neural Network. A quantitative approach is followed where these parameters are varied to investigate which values result in the Artificial Neural Network which best identifies when a player is lost. The parameters are grouped into stages where all combinations of parameter values within each stage are evaluated to reduce the amount of Artificial Neural Networks which have to be trained, with the best performing parameters of each stage being used in subsequent stages. The result is a set of values for the parameters that are considered as ideal as possible. These parameter values are then altered one at a time to verify that they are ideal. The results show that a set of parameters exist which can optimize the Artificial Neural Network model to identify when a player is lost, however not with the high performance that was hoped for. It is theorized that the ambiguity of the word "lost" and the complexity of the game are critical to the low performance.

Artificial neural networks are increasing in power, with attendant increases in demand for efficient processing. Performance is limited by clock speed and degree of parallelization available through multi-core processors and GPUs. With a design tailored to a specific network, a field-programmable gate array (FPGA) can be used to minimize latency without the need for geographically distributed computing. However, the task of programming an FPGA is outside the realm of most data scientists. There are tools to program FPGAs from a high level description of a network, but there is no unified interface for programmers across these tools. In this thesis, I present the design and implementation of NeuralSynth, a prototype Python framework which aims to bridge the gap between data scientists and FPGA programming for neural networks. My method relies on creating an extensible Python framework that is used to automate programming and interaction with an FPGA. The implementation includes a digital design for the FPGA that is completed by a Python framework. Programming and interacting with the FPGA does not require leaving the Python environment. The extensible approach allows multiple implementations, resulting in a similar workflow for each implementation. For evaluation, I compare the results of my implementation with a known neural network framework. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

Accurate and timely diagnosis of faults is essential for the reliability and security of power grid operation and maintenance. The emergence of big data has enabled the incorporation of a vast amount of information in order to create custom fault datasets and improve the diagnostic capabilities of existing frameworks. Intelligent systems have been successful in incorporating big data to improve diagnostic performance using computational intelligence and machine learning based on fault datasets. Among these systems are fuzzy inference systems with the ability to tackle the ambiguities and uncertainties of a variety of input data such as climate data. This makes these systems a good choice for extracting knowledge from energy big data. In this thesis, qualitative climate information is used to construct a fault dataset. A fuzzy inference system is designed whose parameters are optimized using a single layer artificial neural network. This fault diagnosis framework maps the relationship between fault variables in the fault dataset and fault types in real-time to improve the accuracy and cost efficiency of the framework. / Graduate

En el presente artículo se investiga la implementación de las Redes Neuronales Artificiales como un tipo de Inteligencia Artificial con la finalidad de reducir los costos de concreto armado. Por esto, se propuso el uso de este tipo de algoritmo con el objetivo de optimizar las secciones de los muros de corte en edificaciones de 6 pisos sin irregularidades. Se configuraron 10 redes neuronales distintas con el fin de elegir la que se adapte mejor a los datos empleados para el entrenamiento. En cada algoritmo se establecieron como variables de entrada el ancho y largo de la edificación; y la distancia entre luz máxima del eje X e Y. Sin embargo, el número de capas ocultas y el de neuronas en cada una de ellas fue distinto. En la etapa de entrenamiento se emplearon 30 casos con dimensiones optimizadas, con esto se obtuvo que la red neuronal predice la longitud total de la placa y su espesor con un error del 10%. / This article investigates the use of Artificial Neural Networks as a type of Artificial Intelligence in order to reduce the costs of reinforced concrete. For this reason, the use of this type of algorithm was proposed with the objective of optimizing the sections of the shear walls in 6-story buildings without irregularities. Ten different neural networks were configured in order to choose the one that best suits the data used for training. In each algorithm, the width and length of the building; and the distance between maximum span of the X and Y axis were established as input variables. However, the number of hidden layers and the number of neurons in each of them was different. In the training stage, 30 cases with optimized dimensions were used, with this it was obtained that the neuronal network predicts the total length of the shear wall and its thickness with an error of 10%. / Trabajo de investigación

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