Дисертації з теми "Semantic SLAM"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-19 дисертацій для дослідження на тему "Semantic SLAM".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте дисертації для різних дисциплін та оформлюйте правильно вашу бібліографію.
Salas-Moreno, Renato F. "Dense semantic SLAM." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/24524.
Повний текст джерелаBaxter, David P. Nav E. (David Paul)Massachusetts Institute of Technology. "Toward robust active semantic SLAM via Max-Mixtures." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127041.
Повний текст джерелаThesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 75-78).
In a step towards the level of autonomy seen in humans, this work attempts to emulate a high level and low level approach to world representation and short term adaptation. Specifically, this work demonstrates an implementation of robotic perception that transforms stereo camera and LIDAR sensor data into a sparse map of semantic objects and a locally consistent flexible occupancy grid. This provides a topological representation for grouping objects into higher level classes and a geometric map for traditional planning. Additionally, a reactive dynamic window obstacle avoidance system is shown to quickly plan short term trajectories that avoid both static and dynamic objects while progressing towards a goal. To combine computational efficiency with the robust advantages of multimodal inference, this work uses Semantic Max Mixture factors to approximate multimodal belief in a manner compatible to nonlinear least squares solvers. Experimental results are presented using a RACECAR mobile robot operating in several hallways of MIT, using AprilTags as surrogates for objects in the Semantic Max Mixtures Algorithm. Future work will seek to further integrate the components to create a closed-loop active semantic navigation and mapping algorithm.
by David P. Baxter.
Nav. E.
S.M.
Nav.E. Massachusetts Institute of Technology, Department of Mechanical Engineering
S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering
Ghorpade, Vijaya Kumar. "3D Semantic SLAM of Indoor Environment with Single Depth Sensor." Thesis, Université Clermont Auvergne (2017-2020), 2017. http://www.theses.fr/2017CLFAC085/document.
Повний текст джерелаIntelligent autonomous actions in an ordinary environment by a mobile robot require maps. A map holds the spatial information about the environment and gives the 3D geometry of the surrounding of the robot to not only avoid collision with complex obstacles, but also selflocalization and for task planning. However, in the future, service and personal robots will prevail and need arises for the robot to interact with the environment in addition to localize and navigate. This interaction demands the next generation robots to understand, interpret its environment and perform tasks in human-centric form. A simple map of the environment is far from being sufficient for the robots to co-exist and assist humans in the future. Human beings effortlessly make map and interact with environment, and it is trivial task for them. However, for robots these frivolous tasks are complex conundrums. Layering the semantic information on regular geometric maps is the leap that helps an ordinary mobile robot to be a more intelligent autonomous system. A semantic map augments a general map with the information about entities, i.e., objects, functionalities, or events, that are located in the space. The inclusion of semantics in the map enhances the robot’s spatial knowledge representation and improves its performance in managing complex tasks and human interaction. Many approaches have been proposed to address the semantic SLAM problem with laser scanners and RGB-D time-of-flight sensors, but it is still in its nascent phase. In this thesis, an endeavour to solve semantic SLAM using one of the time-of-flight sensors which gives only depth information is proposed. Time-of-flight cameras have dramatically changed the field of range imaging, and surpassed the traditional scanners in terms of rapid acquisition of data, simplicity and price. And it is believed that these depth sensors will be ubiquitous in future robotic applications. In this thesis, an endeavour to solve semantic SLAM using one of the time-of-flight sensors which gives only depth information is proposed. Starting with a brief motivation in the first chapter for semantic stance in normal maps, the state-of-the-art methods are discussed in the second chapter. Before using the camera for data acquisition, the noise characteristics of it has been studied meticulously, and properly calibrated. The novel noise filtering algorithm developed in the process, helps to get clean data for better scan matching and SLAM. The quality of the SLAM process is evaluated using a context-based similarity score metric, which has been specifically designed for the type of acquisition parameters and the data which have been used. Abstracting semantic layer on the reconstructed point cloud from SLAM has been done in two stages. In large-scale higher-level semantic interpretation, the prominent surfaces in the indoor environment are extracted and recognized, they include surfaces like walls, door, ceiling, clutter. However, in indoor single scene object-level semantic interpretation, a single 2.5D scene from the camera is parsed and the objects, surfaces are recognized. The object recognition is achieved using a novel shape signature based on probability distribution of 3D keypoints that are most stable and repeatable. The classification of prominent surfaces and single scene semantic interpretation is done using supervised machine learning and deep learning systems. To this end, the object dataset and SLAM data are also made publicly available for academic research
Zingoni, Jacopo. "Semantic Enrichment of Scientific Documents with Semantic Lenses – Developing methodologies, tools and prototypes for their concrete use." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4476/.
Повний текст джерелаRogers, John Gilbert. "Life-long mapping of objects and places in domestic environments." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47736.
Повний текст джерелаTrevor, Alexander J. B. "Semantic mapping for service robots: building and using maps for mobile manipulators in semi-structured environments." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53583.
Повний текст джерелаSalehi, Achkan. "Localisation précise d'un véhicule par couplage vision/capteurs embarqués/systèmes d'informations géographiques." Thesis, Université Clermont Auvergne (2017-2020), 2018. http://www.theses.fr/2018CLFAC064/document.
Повний текст джерелаThe fusion between sensors and databases whose errors are independant is the most re-liable and therefore most widespread solution to the localization problem. Current autonomousand semi-autonomous vehicles, as well as augmented reality applications targeting industrialcontexts exploit large sensor and database graphs that are difficult and expensive to synchro-nize and calibrate. Thus, the democratization of these technologies requires the exploration ofthe possiblity of exploiting low-cost and easily accessible sensors and databases. These infor-mation sources are naturally tainted by higher uncertainty levels, and many obstacles to theireffective and efficient practical usage persist. Moreover, the recent but dazzling successes ofdeep neural networks in various tasks seem to indicate that they could be a viable and low-costalternative to some components of current SLAM systems.In this thesis, we focused on large-scale localization of a vehicle in a georeferenced co-ordinate frame from a low-cost system, which is based on the fusion between a monocularvideo stream, 3d non-textured but georeferenced building models, terrain elevation models anddata either from a low-cost GPS or from vehicle odometry. Our work targets the resolutionof two problems. The first one is related to the fusion via barrier term optimization of VS-LAM and positioning measurements provided by a low-cost GPS. This method is, to the bestof our knowledge, the most robust against GPS uncertainties, but it is more demanding in termsof computational resources. We propose an algorithmic optimization of that approach basedon the definition of a novel barrier term. The second problem is the data association problembetween the primitives that represent the geometry of the scene (e.g. 3d points) and the 3d buil-ding models. Previous works in that area use simple geometric criteria and are therefore verysensitive to occlusions in urban environments. We exploit deep convolutional neural networksin order to identify and associate elements from the map that correspond to 3d building mo-del façades. Although our contributions are for the most part independant from the underlyingSLAM system, we based our experiments on constrained key-frame based bundle adjustment.The solutions that we propose are evaluated on synthetic sequences as well as on real urbandatasets. These experiments show important performance gains for VSLAM/GPS fusion, andconsiderable improvements in the robustness of building constraints to occlusions
Drouilly, Romain. "Cartographie hybride métrique topologique et sémantique pour la navigation dans de grands environnements." Thesis, Nice, 2015. http://www.theses.fr/2015NICE4037/document.
Повний текст джерелаUtonomous navigation is one of the most challenging tasks for mobile robots. It requires the ability to localize itself or a target and to find the best path linking both positions avoiding obstacles. Towards this goal, robots build a map of the environment that models its geometry or topology. However building such a map in large scale environments is challenging due to the large amount of data to manage and localization could become intractable. Additionally, an ever changing environment leads to fast obsolescence of the map that becomes useless. As shown in this thesis, introducing semantics in those maps dramatically improves navigation performances of robots in realistic environments. Scene parsing allows to build extremely compact semantic models of the scene that are used for fast relocalization using a graph-matching approach. They are powerful tools to understand scene and they are used to extend the map beyond perceptual limits of the robot through reasoning. Statistical analysis of those models is used to build an embryo of common sens which allows to detect labeling errors and to update the map using algorithms designed to maintain a stable model of the world despite occlusions due to dynamic objects. Finally semantics is used to select the best route to a target position according to high level criteria instead of metrical constraints, allowing intelligent navigation
Fakhfakh, Inès. "Semantic based cloud broker architecture optimizing users satisfaction." Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0008/document.
Повний текст джерелаCloud Computing is a dynamic new technology that has huge potentials in enterprises and markets. The dynamicity and the increasing complexity of Cloud architectures involve several management challenges. In this work, we are interested in Service Level Agreement (SLA) management. Actually, there is no standard to express Cloud SLA, so, providers describe their SLAs in different manner and different languages, which leaves the user puzzled about the choice of its Cloud provider. To overcome these problems, we introduce a Cloud Broker Architecture managing the SLA between providers and consumers. It aims to assist users in establishing and negotiating SLA contracts and to help them in finding the best provider that satisfies their service level expectations. Our broker SLA contracts are formalized as OWL ontologies as they allow hiding the heterogeneity in the distributed Cloud environment and enabling interoperability between Cloud actors. Besides, by combining our ontology with our proposed inference rules, we contribute to detect violations in the SLA contract assuring thereby the sustainability of the user satisfaction. Based on the requirements specified in the SLA contract, our Cloud Broker assists users in selecting the right provider using a multi attribute utility theory method. This method is based on utility functions representing the user satisfaction degree. To obtain accurate results, we have modelled both functional and non functional attributes utilities. We have used personalized utilities for each criterion under negotiation so that our cloud broker satisfies the best consumer requirements from functional and non functional point of view
Karlsson, Therése, and Hanna Lawrence. "English as a Second Language for Kenyan Children in Primary School : A Trial of the Spoken Language Assessment Profile – Revised Edition." Thesis, Linköpings universitet, Institutionen för klinisk och experimentell medicin, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119193.
Повний текст джерелаSub-Sahara Afrika är en flerspråkig miljö och det finns en brist på material för tal- och språkbedömningar inom detta område (Hartley & Krämer, 2013). Normerna för ett bedömningsinstrument kan inte användas för både enspråkiga och tvåspråkiga barn, eftersom tvåspråkiga barn kan ha varierande kunskapsnivåer inom språken (Kohnert, 2010). Spoken Language Assessment Profile – Revised edition (SLAP-R) är ett bedömningsmaterial som är avsett att utvärdera engelska som andraspråk i Sub-Sahara Afrika. Syftet med detta instrument är att försöka fylla tomrummet av lämpliga tal- och språkbedömningsmaterial som kan användas av samtliga inblandade vid att starta upp kliniker, skolor eller logopedmottagningar (Hartley & Krämer, 2013). Syftet med föreliggande studie var att undersöka engelska som andraspråk för Kenyanska barn i grundskolan baserat på deras resultat i SLAP-R. Föreliggande studie bestod av 68 deltagare med rapporterad typisk hörsel och språkutveckling som gick i klass ett eller två i en kommunal skola i västra Kenya. Alla deltagarna var mellan sex och nio år, hade ett bantuspråk som förstaspråk och hade exponerats till engelska i mindre än ett år upp till åtta år. De hade även gått i den förskolan som tillhörde deras nuvarande skola. De oberoende variablerna i föreliggande studie var klass, ålder och exponeringstid till engelska. SLAP-R består av sex deltest som testar expressiv och receptiv fonologi, semantik och grammatik. De här delarna utgör de beroende variablerna. Det finns ytterligare en del som kallas för ultimate expressive language skill (UELS) som består av sekvensbilder där deltagaren ska berätta en historia om vad som händer på bilderna. Resultatet indikerade att klass var variabeln som hade störst inverkan på deltagarnas prestationer i engelska som andraspråk. Klass två hade signifikant högre resultat gällande receptiv fonologi, såväl som expressiv och receptiv semantik och grammatik än deltagarna i klass ett. De flesta felsvaren gjordes i deltestet expressiv grammatik och var i huvudsak på grund av andra skäl än svar på kiswahili.
Borking, Ulrika. "Engelskan i skolan : en undersökning av vokabulär i gymnasieskolans textböcker i engelska." Thesis, Södertörn University College, Lärarutbildningen, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-2138.
Повний текст джерелаThis essay reviews vocabulary samples from three different textbooks, which are readers for the basic course in English at an upper secondary school in Sweden. The aim of the study is to determine whether the word samples from the readers’ word lists consist mostly of high- or low frequency words and if the words denote any particular semantic fields. Moreover, the possible use of word frequencies in second language acquisition is also examined. The method used in ascertaining the quality of the words is comparing the word samples to the BNC (the British National Corpus) and analysing how frequently they occur in written and spoken modern English. The results are based on the findings from the analysis made in this study and also compared to current research in the fields of linguistics and language acquisition. The results exhibit both overrepresentation- and absence of words in particular semantic fields. For instance, words from the semantic field concerning ‘food and cooking’ were found to be somewhat predominant. The findings also include support for the use of word frequencies in language acquisition, especially in terms of how words are translated from English into Swedish in the textbooks’ wordlists. The only Swedish synonym given was in some cases item of the least frequent usage in modern English, according to the BNC.
Garg, Sourav. "Robust visual place recognition under simultaneous variations in viewpoint and appearance." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/134410/1/Sourav%20Garg%20Thesis.pdf.
Повний текст джерелаHimri, Khadidja. "Automated 3D object recognition in underwater scenarios for manipulation." Doctoral thesis, Universitat de Girona, 2021. http://hdl.handle.net/10803/673811.
Повний текст джерелаA les darreres dècades, el ràpid desenvolupament de vehicles intel·ligents i de les tecnologies d’escaneig 3D han contribuït a augmentar l’interès en les aplicacions basades en processament de núvols de punts 3D, amb aplicacions com la realitat augmentada, la manipulació robòtica, l’evasió d’obstacles, la comprensió d’escenes, la navegació robòtica, el seguiment d’objectes i la tecnologia d’assistència, etc., que requereixen una soluci´o precisa de la posició 3D i l’orientació d’un objecte. Per tant, el reconeixement d’objectes s’està convertint en un tema, on la visió per computador i les tècniques robòtiques esdevenen protagonistes clau. En aquest treball de tesi, l’objectiu principal és desenvolupar un mètode per a la construcció de mapes semàntic mitjançant la integració d’una cadena de processament per al reconeixement d’objectes 3D, amb un sistema de SLAM basat en característiques, amb l’objectiu d’ajudar a les futures intervencions submarines. Per això, el treball proposat en aquesta tesi es divideix en tres eixos principals. El primer té com a objectiu comparar el rendiment de descriptors globals d’última generació, centrant-se en els basats en núvols de punts 3D i destinats a aplicacions de reconeixement d’objectes en temps real. Per a aquest objectiu, s’ha seleccionat un conjunt d’objectes de prova representatius d’aplicacions d’inspecció, manteniment i reparació (IMR), la forma dels quals es coneix a priori. Els seus models CAD s’han utilitzat per a: 1) crear una base de dades amb les vistes sintètiques dels objectes, i 2) simular els núvols de punts que adquiriria, en condicions realistes, un escàner làser incloent soroll sintètic i simulant diferents resolucions. S’han dut a terme experiments tant a partir d’escaneigs virtuals com de dades reals recopilades amb un AUV equipat amb un escàner làser de temps real desenvolupat al nostre centre de recerca. El segon objectiu del nostre treball va consistir en utilitzar aquest escàner làser, muntat a un AUV per detectar, reconèixer i localitzar objectes a l’entorn del robot, per tal de permetre, a un Vehicle Submarí Autònoms d’Intervenció (IAUV), saber quines accions de manipulació podria fer amb cada objecte. Aquest objectiu es va abordar amb el disseny i el desenvolupament d’un mètode de reconeixement d’objectes 3D en núvols de punts incolors (escanejos làser) utilitzant descriptors dels punts 3D. L’algorisme utilitza una base de dades de vistes parcials dels objectes emmagatzemats en forma de núvols de punts. El procés de reconeixement consta de 5 passos: 1) Segmentació de plànols, 2) Detecció de canonades, 3) Segmentació semàntica d’objectes, 4) Reconeixement d’objectes a partir dels descriptors de punts 3D i 5) Estimació bayesiana. Per aplicar l’estimació bayesiana, cal ser capaços de fer un seguiment dels objectes en escanejos successius. Per fer-ho, s’ha proposat l’algorisme Inter-distance Joint-Compatibility Branch and Bound (IJCBB) d’associació de dades basada en les distancies entre objectes dins del núvol de punts. El rendiment del mètode es va avaluar fent servir dades experimentals relatives a la inspecció d’una infraestructura composta de canonades interconnectades per objectes de PVC. L’estructura ´es representativa de les comunament utilitzades per la indústria offshore. Els resultats experimentals mostren que l’estimació bayesiana millora el rendiment del reconeixement en comparació de l’ús ´únic del descriptor. La inclusió d’informació semàntica sobre la connectivitat d’objectes a canonades millora encara més el rendiment del reconeixement. L’objectiu final de la tesi va abordar la integració del sistema de reconeixement d’objectes 3D basat en descriptors amb un sistema de SLAM basat en característiques, per implementar un mapa semàntic que proporciona al robot informació sobre la ubicació i el tipus d’objectes a l’entorn. La utilització de tècniques de SLAM ha millorat la precisió i la fiabilitat de les estimacions de la postura del robot i els objectes. Això és especialment important en escenaris difícils on es produeixen canvis significatius de perspectiva i aparença
Programa de Doctorat en Tecnologia
Canizares, Carlos I. "Second Language Learners’ Performance on Non-Isomorphic Cross-Language Cognates in Translation." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/3061.
Повний текст джерелаYu, Chun-Yi, and 余俊毅. "Semantic SLAM for Dynamic Environments Using Cameras." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/krqsms.
Повний текст джерела國立臺灣大學
資訊網路與多媒體研究所
107
Typically, simultaneous localization and mapping (SLAM) algorithms are assumed to be performed in the stationary environments only. However, there are ordinarily moving cars and people in the real world. Such a strict assumption restricts its usability especially on the robots or autonomous vehicles. In this work, we present a semantic SLAM for RGB-D and stereo cameras, which can deal with highly dynamic scenes. The visual SLAM system is built on ORB-SLAM2 and the semantic information is acquired from state-of-the-art object detection with high frame rate. Experiments are conducted on two public benchmarks. Our system is faster than DynaSLAM and keeps the similar localization accuracy. The analysis of the computation time is also presented.
Liao, Szu-Yu, and 廖思羽. "Implementing 3D Semantic Maps by Visual SLAM Integrated with YOLOv3." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5441095%22.&searchmode=basic.
Повний текст джерела國立中興大學
電機工程學系所
107
In recent years, Simultaneous Localization And Mapping (SLAM) becomes an important topic in the research area of unmanned vehicles. The sensors adopted by SLAM are mainly divided into lidar and camera approaches. A camera is cost-efficeint and easy to obtain. Thus it is extensively used in implementing SLAM. Particularly, ORB-SLAM2 is a real-time visual SLAM method based on feature points, supporting high-precision three-dimensional (3D) maps with monocular cameras, stereo cameras, and RGB-D cameras. However, it is unable to assign semantic labels to objects observed in the environment for the unmanned vehicle to learn the class of a detected object in an ORB-SLAM2 3D map. Although it is possible to use a method like semantic SLAM to make up the above deficiency, the bounding box of a region proposal generated by the aforementioned object detection method can vary depending on factors such as the angle or offset of the proposal itself. In this thesis, we construct a 3D semantic map by integrating ORB-SLAM2 with the deep learning object detection tool YOLOv3 (You Only Look Once v3). More specifically, we obtain the class of detected objects with YOLOv3 at the same time ORB-SLAM2 inserts a new keyframe. Then we project these semantic labels to the point cloud clusters to achieve a 3D semantic map. Particularly, we remove duplicate recognition results by averaging the object size, and also calculate the coordinate of the object in the map according to the camera position. Then we output obtained object labels together with their corresponding coordinates. Finally, based on public datasets, we perform relative experiments to demonstrate the correctness of our proposed method.
Li, Kejie. "Object-centric Mapping." Thesis, 2021. https://hdl.handle.net/2440/132889.
Повний текст джерелаThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2021
Croon, Dennis Gerardus. "The outperformance of the semantic learning machine, against commonly used algorithms, for binary and multi-class medical image classification: combined with the usage of feature extraction by several convolutional neural networks." Master's thesis, 2020. http://hdl.handle.net/10362/103901.
Повний текст джерелаExtensive recent research has shown the importance of innovation in medical healthcare, with a focus on Pneumonia. It is vital and lifesaving to predict Pneumonia cases as fast as possible and preferably in advance of the symptoms. An online database source managed to gather Pneumonia-specific image data, with not just the presence of the infection, but also the nature of it, divided in bacterial- and viral infection. The first achievement is extracting valuable information from the X-Ray image datasets. Using several ImageNet pre-trained CNNs, knowledge can be gained from images and transferred to numeric arrays. This, both binary and multi-class classification data, requires a sophisticated prediction algorithm that recognizes X-Ray image patterns. Multiple, recently performed experiments show promising results about the innovative Semantic Learning Machine (SLM) that is essentially a geometric semantic hill climber for feedforward Neural Networks. This SLM is based on a derivation of the Geometric Semantic Genetic Programming (GSGP) mutation operator for real-value semantics. To prove the outperformance of the binary and multi-class SLM in general, a selection of commonly used algorithms is necessary in this research. A comprehensive hyperparameter optimization is performed for commonly used algorithms for those kinds of real-life problems, such as: Random Forest, Support Vector Machine, KNearestNeighbors and Neural Networks. The results of the SLM are promising for the Pneumonia application but could be used for all types of predictions based on images in combination with the CNN feature extractions.
Uma extensa pesquisa recente mostrou a importância da inovação na assistência médica, com foco na pneumonia. É vital e salva-vidas prever os casos de pneumonia o mais rápido possível e, de preferência, antes dos sintomas. Uma fonte on-line conseguiu coletar dados de imagem específicos da pneumonia, identificando não apenas a presença da infecção, mas também seu tipo, bacteriana ou viral. A primeira conquista é extrair informações valiosas dos conjuntos de dados de imagem de raios-X. Usando várias CNNs pré-treinadas da ImageNet, é possível obter conhecimento das imagens e transferi-las para matrizes numéricas. Esses dados de classificação binários e multi-classe requerem um sofisticado algoritmo de predição que reconhece os padrões de imagem de raios-X. Vários experimentos realizados recentemente mostram resultados promissores sobre a inovadora Semantic Learning Machine (SLM), que é essencialmente um hill climber semântico geométrico para feedforward neural network. Esse SLM é baseado em uma derivação do operador de mutação da Geometric Semantic Genetic Programming (GSGP) para valor-reais semânticos. Para provar o desempenho superior do SLM binário e multi-classe em geral, é necessária uma seleção de algoritmos mais comuns na pesquisa. Uma otimização abrangente dos hiperparâmetros é realizada para algoritmos comumente utilizados para esses tipos de problemas na vida real, como Random Forest, Support Vector Machine,K-Nearest Neighbors and Neural Networks. Os resultados do SLM são promissores para o aplicativo pneumonia, mas podem ser usados para todos os tipos de previsões baseadas em imagens em combinação com as extrações de recursos da CNN.
(10725957), Daniela Marinho Ribeiro. "THIRD LANGUAGE ACQUISITION: A STUDY OF UNSTRESSED VOWEL REDUCTION." Thesis, 2021.
Знайти повний текст джерелаA great deal of the research on cross-linguistic phonetic influence demonstrates that a speaker’s knowledge of their first language (L1) significantly affects their ability to perceive and produce sounds in any other language. While current studies show that cross-linguistic transfer occurs at the L3 level, some research suggests that properties of both L1 and L2 are present in the production of L3 (Ionin, Montrul & Santos, 2011). Many studies have addressed perception, production and factors that influence foreign speech in Second Language Acquisition (SLA) (Watkins, Rauber & Baptista, 2009). As the number of multilingual individuals rises, so does the need for studies that investigate not only SLA but also that of additional languages (i.e., Third Language Acquisition). This dissertation examines how cross-linguistic influence (CLI) occurs among English, Spanish, and Brazilian Portuguese (BP), examining instances of vowel reduction, an aspect of phonological production. English and BP are assumed as vowel reducing languages, whereas Spanish displays negligible vowel reduction in comparison. The vowel productions in L3 BP of two multilingual groups, L1English-L2Spanish-L3BP (ESP) and L1 Spanish-L2 English-BP (SEP) were investigated in two tasks: a paragraph reading task (PRT) and a carrier phrase task (CPT). The study sought to determine whether i) a native speaker of a vowel reducing L1 and a non-vowel reducing L2 displays more or less vowel reduction in a vowel reducing L3 than a native speaker of a non-vowel reducing L1 and vowel reducing L2 and ii) how length of exposure to an L3 affects phonological production. Three fixed effects were considered: duration ratio, intensity ratio and height (F1). The goal was to ascertain whether the Typological Primacy Model (TPM) (Rothman 2011, 2015) or the L2 Status Factor Model (Bardel & Falk 2007, 2012; Hammarberg, 2001) would be a better predictor for how vowel reduction would occur in the L3. Results for duration ratio and vowel height showed no significant difference between groups ESP and SEP. Results for intensity ratio suggest L2 Status as a better predictor, as group SEP displayed more phonological transfer than the ESP group. A hybrid approach to L3 acquisition models is proposed.