A Model-Predictive Motion Planner for the IARA Autonomous Car

We present the Model-Predictive Motion Planner (MPMP) of the Intelligent Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses a path planner to compute a path from its current position to the desired destination. Using this path, the current position, a goal in the path and a map, IARA's MPMP is able to compute smooth trajectories from its current position to the goal in less than 50 ms. MPMP computes the poses of these trajectories so that they follow the path closely and, at the same time, are at a safe distance of eventual obstacles. Our experiments have shown that MPMP is able to compute trajectories that precisely follow a path produced by a Human driver (distance of 0.15 m in average) while smoothly driving IARA at speeds of up to 32.4 km/h (9 m/s).Comment: This is a preprint. Accepted by 2017 IEEE International Conference on Robotics and Automation (ICRA

Comparação de Algoritmos de Aprendizado de Máquina para Predição de Pontuação de Crédito

ABSTRACTAccording to the Central Bank of Brazil, the total value of creditoperations in Brazil reached R$4.2 trillion in May 2021. Financialinstitutions must consider the risk of default associated with eachoperation. Credit analysis, which evaluates this risk, can be performedusing machine learning algorithms. These algorithms comparenew loan proposals to historical data to estimate the default probabilitybased on the proposal and proponent characteristics. Theaccuracy of the model is critical to the profitability of institutions,so choosing the right algorithm is crucial. This study comparesthe performance of machine learning algorithms on three publicdatasets in the task of credit risk estimation. The results show that astack of multiple classifiers achieved the highest accuracy at 81.41%,followed by XGBoost at 80.87% and Regressão Logística at 80.48%

Abordagem para Análise de Múltiplas Fontes de Dados de Evasão Escolar

Scholar dropout is a challenge for educational institutions. It is caused by many factors that should be known to be treated. Some analytical tools can help in this intent, especially when the proper data is well organized in a consistent structure and format. This paper presents a technical approach to allow homogeneous analysis for different scholar data sources, applying it to the dropout domain. This proposal makes use of an ontology to provide a better under- standing of the domain semantics, and to act as an interlanguage for the creation of standardized repositories from where dropout data can be analyzed

Comparação de Técnicas para Representação Vetorial de Imagens com Redes Neurais para Aplicações de Recuperação de Produtos do Varejo

ABSTRACTProduct retrieval from images has multiple applications rangingfrom providing information and recommentations for customersin supermarkets to automatic invoice generation in smart stores.However, this task present important challenges such as the largenumber of products, the scarcity of images of items, differencesbetween real and iconic images of the products, and the constantchanges in the portfolio due to the addition or removal of products.Hence, this work investigates ways of generating vector representationsof images using deep neural networks such that theserepresentations can be used for product retrieval even in face ofthese challenges. Experimental analysis evaluated the effect thatnetwork architecture, data augmentation techniques and objectivefunctions used during training have on representation quality. Thebest configuration was achieved by fine-tuning a VGG-16 modelin the task of classifying products using a mix of Randaugmentand Augmix data augmentations and a hierarchical triplet loss as aregularization function. The representations built using this modelled to a top-1 accuracy of 80,38% and top-5 accuracy of 92.62% inthe Grocery Products dataset

Um Sistema baseado em IoT para Monitoramento da Saúde de Idosos e Detecção de Quedas

ABSTRACTThe advancement of the internet to the paradigm of the Internetof Things (IoT) has brought to society new ways of generating,sharing and using information. The evolution of computing capacityand energy savings in IoT equipament combined with bettersoftware can enabled several new applications, among which wecan highlight the monitoring of people’s health through pervasivedevices connected to the body. In view of this, this work proposesan algorithm to detect atypical situations such as falls in the elderlyand other groups that need health care using accelerometerscontained in wearable devices, particularly smartwatches. For theexperimental evaluation of the proposed algorithm, a database thatcontains data from wearable sensors, environmental sensors, andvisual devices was employed. The metrics used in the evaluationwere accuracy, precision, recall and f1-score, with recall being themost relevant metric in the context. Results show that the bestconfiguration of the algorithm is able to identify falls with 96%recall and F1-score of 90%

Predição da Temperatura do Ferro-Gusa em um Alto-Forno utilizando Redes Neurais LSTM

Due to the importance of the steel industry in the national economy and the inherent complexity of operating a blast furnace, it is necessary to study ways to optimize its operation and the consumption of resources, therefore, this work aims to investigate the use of the LSTM (Long Short Term Memory) neural network to perform the prediction of the next temperature of the hot metal being produced. In this way, it is possible to support the work of the blast furnace operators, in order to optimize the consumption of resources to keep the blast furnace operating. With the results obtained from the experiments using the blast furnace operating data as a time series, it is concluded that the use of LSTM is satisfactory and that improvement of these experiments will meet the needs of the steel industry. The best result for LSTM, using 2 layers and 2048 neurons, achieved a Root Mean Square Error of 11.82ºC