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Dominique Meizel – 1st expert on this subject based on the ideXlab platform

  • Linear observers for vehicle sideslip angle : experimental validation
    2004 IEEE International Symposium on Industrial Electronics, 2004
    Co-Authors: Joanny Stéphant, Ali Charara, Dominique Meizel

    Abstract:

    The lateral behavior of a vehicle is defined by the transversal forces between the road and the wheels. The sideslip angle is the principal variable for the transversal forces. This paper compares three observers (virtual sensors) of vehicle sideslip angle. Modeling, model simplification and observability analysis are performed. The different observers use sensors available on actual standard car: the yaw rate and the lateral acceleration. This study validate observers on simulated data from a recognized simulator Callas and on experimental data acquired on the Heudiasyc laboratory car. An optical speed sensor “Correvit” is used to acquire the sideslip angle. This paper shows that observers are more accurate than simple model and that the use of both sensors give the better estimation of sideslip angle.

  • experimental validation of vehicle sideslip angle observers
    IEEE Intelligent Vehicles Symposium, 2004
    Co-Authors: Joanny Stéphant, Dominique Meizel

    Abstract:

    This paper compares four observers (virtual sensors) of vehicle sideslip angle. The first is linear and uses a linear vehicle model. The remaining observers use an extended nonlinear model. The three nonlinear observers are: extended Luenberger observer, extended Kalman filter and sliding mode observer. Modeling, model simplification and observers are described. The different observers use sensors available on actual standard car: the yaw rate and the vehicle speed. This study validates observers on simulated data from a recognized simulator Callas and on experimental data acquired on the Heudiasyc laboratory car. An optical speed sensor “Correvit” is used to acquire the sideslip angle.

Ivana Išgum – 2nd expert on this subject based on the ideXlab platform

  • automatic segmentation of mr brain images with a convolutional neural network
    IEEE Transactions on Medical Imaging, 2016
    Co-Authors: Pim Moeskops, Max A. Viergever, Adrienne M. Mendrik, Linda S. De Vries, Manon J.n.l. Benders, Ivana Išgum

    Abstract:

    Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal $ {\rm T}_{2}$ -weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial $ {\rm T}_{2}$ -weighted images of preterm infants acquired at 40 weeks PMA, axial $ {\rm T}_{1}$ -weighted images of ageing adults acquired at an average age of 70 years, and $ {\rm T}_{1}$ -weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86, and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol.

  • Automatic Segmentation of MR Brain Images with a Convolutional Neural Network
    IEEE Transactions on Medical Imaging, 2016
    Co-Authors: Pim Moeskops, Max A. Viergever, Adrienne M. Mendrik, Linda S. De Vries, Manon J.n.l. Benders, Ivana Išgum

    Abstract:

    —Automatic segmentation in MR brain images is im-portant for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the auto-matic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the clas-sification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal -weighted images of preterm in-fants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial -weighted images of preterm infants acquired at 40 weeks PMA, axial -weighted images of ageing adults acquired at an average age of 70 years, and -weighted images of young adults acquired at an average age of 23 years. The method ob-tained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86, and 0.91. The results demonstrate that the method obtains accu-rate segmentations in all five sets, and hence demonstrates its ro-bustness to differences in age and acquisition protocol. Index Terms—Adult brain, automatic image segmentation, con-volutional neural networks, deep learning, MRI, preterm neonatal brain.

Koji Tochinai – 3rd expert on this subject based on the ideXlab platform

  • machine translation using recursive chain link type learning based on translation examples
    Systems and Computers in Japan, 2004
    Co-Authors: Hiroshi Echizenya, Yoshio Momouchi, Kenji Araki, Koji Tochinai

    Abstract:

    Rule-based machine translation analyzes source-language sentences using large-scale linguistic knowledge that is given by the developer beforehand. However, it is difficult to give complete linguistic knowledge to the system ex ante because natural language has various linguistic phenomena. Therefore, we worked to develop learning-based machine translation. In learning-based machine translation, a system acquires translation rules automatically from translation examples that are pairs of source and target language sentences. However, existing learning-based machine translation presents the problem that it requires a large number of similar translation examples. Consequently, it cannot acquire enough useful translation rules from sparse translation examples. This paper proposes a method of machine translation using Recursive Chain-Link-type Learning, which can acquire many useful translation rules from sparse translation examples. Our system, based on this method, efficiently acquires translation rules from each translation example without requiring two similar translation examples. Translation rules are acquired by extracting corresponding parts between source and target language sentences in translation examples. Our system determines those corresponding parts using previously acquired translation rules. Therefore, the system engenders a chain reaction in acquisition of new translation rules. Evaluation experiments using our system demonstrated an effective translation rate of 61.1p. Moreover, the effective translation rate was 85.0p when sufficient learning data were given to our system. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(2): 1–15, 2004; Published online in Wiley InterScience (). DOI 10.1002sscj.10511

  • study of practical effectiveness for machine translation using recursive chain link type learning
    International Conference on Computational Linguistics, 2002
    Co-Authors: Hiroshi Echizenya, Yoshio Momouchi, Koji Tochinai

    Abstract:

    A number of machine translation systems based on the learning algorithms are presented. These methods acquire translation rules from pairs of similar sentences in a bilingual text corpora. This means that it is difficult for the systems to acquire the translation rules from sparse data. As a result, these methods require large amounts of training data in order to acquire high-quality translation rules. To overcome this problem, we propose a method of machine translation using a Recursive Chain-link-type Learning. In our new method, the system can acquire many new high-quality translation rules from sparse translation examples based on already acquired translation rules. Therefore, acquisition of new translation rules results in the generation of more new translation rules. Such a process of acquisition of translation rules is like a linked chain. From the results of evaluation experiments, we confirmed the effectiveness of Recursive Chain-link-type Learning.