Dissertation/ Thesis
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Análise comparativa de algoritmos de clusterização para reconhecimento de objetos em sistemas de radar automotivo ; Comparative analysis of clustering algorithms for object recognition in automotive radar systems

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Análise comparativa de algoritmos de clusterização para reconhecimento de objetos em sistemas de radar automotivo ; Comparative analysis of clustering algorithms for object recognition in automotive radar systems
Συγγραφείς: Ramos, Daniel Carvalho de
Συνεισφορές: Santos, Max Mauro Dias, Gonçalves, Cristhiane, Andrade, Mauren Louise Squario Coelho de
Στοιχεία εκδότη: Universidade Tecnológica Federal do Paraná
Ponta Grossa
Brasil
Departamento Acadêmico de Engenharia de Elétrica
Engenharia Elétrica
UTFPR
Έτος έκδοσης: 2022
Συλλογή: Universidade Tecnológica Federal do Paraná (UTFPR): Repositório Institucional (RIUT)
Θεματικοί όροι: Algorítmos, Cluster (Sistema de computador), Radar, Simulação (Computadores), Algorithms, Cluster analysis - Computer programs, Computer simulation, CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
Περιγραφή: The great difficulty of the automotive industry is to create a safe and reliable navigation system for the autonomous vehicle, it is something that involves many steps, among them, the fusion of sensors and vehicular communication networks for the recognition of objects, an alternative to this challenge is the use of clustering algorithms in automotive radar systems. Clustering algorithm can be defined as a Machine Learning technique that involves grouping data points, and it works as follows, given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. In theory, data points that are in the same group should have similar properties and/or features, while data points in different groups should have highly different properties and/or features. Clustering is an unsupervised learning method and is a common technique for analyzing statistical data used in many fields. There are several methods that have been developed for the application of clustering, among them we have ten main methods, Affinity Propagation, Agglomerative Clustering, BIRCH, DBSCAN, KMeans, MiniBatch KMeans, Mean Shift, OPTICS, Spectral Clustering, Mixture of Gaussians. In this work, we will present a comparative analysis of the clustering algorithms, to verify which one has the highest efficiency to be used in an automotive radar system for object recognition, a point of extreme importance, for the construction of autonomous vehicles. ; A grande dificuldade do ramo automotivo é criar um sistema de navegação seguro e confiável para o veículo autônomo, é algo que envolve muitas etapas entre elas, a fusão de sensores e redes de comunicação veicular para o reconhecimento de objetos, uma alternativa para esse desafio é a utilização de algoritmos de clusterização em sistemas de radares automotivos. O algoritmo de clusterização pode ser definido como uma técnica de Machine Learning que envolve o agrupamento de pontos de dados, e funciona da seguinte maneira, dado um conjunto de pontos de dados, podemos ...
Τύπος εγγράφου: bachelor thesis
Περιγραφή αρχείου: application/pdf
Γλώσσα: Portuguese
Relation: http://repositorio.utfpr.edu.br/jspui/handle/1/30503
Διαθεσιμότητα: http://repositorio.utfpr.edu.br/jspui/handle/1/30503
Rights: openAccess ; http://creativecommons.org/licenses/by-nc-nd/4.0/
Αριθμός Καταχώρησης: edsbas.BFBAFDB0
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  Data: Análise comparativa de algoritmos de clusterização para reconhecimento de objetos em sistemas de radar automotivo ; Comparative analysis of clustering algorithms for object recognition in automotive radar systems
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  Data: Santos, Max Mauro Dias<br />Gonçalves, Cristhiane<br />Andrade, Mauren Louise Squario Coelho de
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  Data: Universidade Tecnológica Federal do Paraná<br />Ponta Grossa<br />Brasil<br />Departamento Acadêmico de Engenharia de Elétrica<br />Engenharia Elétrica<br />UTFPR
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  Data: The great difficulty of the automotive industry is to create a safe and reliable navigation system for the autonomous vehicle, it is something that involves many steps, among them, the fusion of sensors and vehicular communication networks for the recognition of objects, an alternative to this challenge is the use of clustering algorithms in automotive radar systems. Clustering algorithm can be defined as a Machine Learning technique that involves grouping data points, and it works as follows, given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. In theory, data points that are in the same group should have similar properties and/or features, while data points in different groups should have highly different properties and/or features. Clustering is an unsupervised learning method and is a common technique for analyzing statistical data used in many fields. There are several methods that have been developed for the application of clustering, among them we have ten main methods, Affinity Propagation, Agglomerative Clustering, BIRCH, DBSCAN, KMeans, MiniBatch KMeans, Mean Shift, OPTICS, Spectral Clustering, Mixture of Gaussians. In this work, we will present a comparative analysis of the clustering algorithms, to verify which one has the highest efficiency to be used in an automotive radar system for object recognition, a point of extreme importance, for the construction of autonomous vehicles. ; A grande dificuldade do ramo automotivo é criar um sistema de navegação seguro e confiável para o veículo autônomo, é algo que envolve muitas etapas entre elas, a fusão de sensores e redes de comunicação veicular para o reconhecimento de objetos, uma alternativa para esse desafio é a utilização de algoritmos de clusterização em sistemas de radares automotivos. O algoritmo de clusterização pode ser definido como uma técnica de Machine Learning que envolve o agrupamento de pontos de dados, e funciona da seguinte maneira, dado um conjunto de pontos de dados, podemos ...
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RecordInfo BibRecord:
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    Languages:
      – Text: Portuguese
    Subjects:
      – SubjectFull: Algorítmos
        Type: general
      – SubjectFull: Cluster (Sistema de computador)
        Type: general
      – SubjectFull: Radar
        Type: general
      – SubjectFull: Simulação (Computadores)
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Cluster analysis - Computer programs
        Type: general
      – SubjectFull: Computer simulation
        Type: general
      – SubjectFull: CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
        Type: general
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      – TitleFull: Análise comparativa de algoritmos de clusterização para reconhecimento de objetos em sistemas de radar automotivo ; Comparative analysis of clustering algorithms for object recognition in automotive radar systems
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              Y: 2022
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