Browsing by Subject "Information fusion"

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    Estimation of 3D electron density in the Ionosphere by using fusion of GPS satellite-receiver network measurements and IRI-Plas model
    (IEEE, 2013) Tuna, Tuna; Arıkan, Orhan; Arikan F.; Gulyaeva, T.
    GPS systems can give a good approximation of the Slant Total Electron Content in a cylindrical path between the GPS satellite and the receiver. International Reference Ionosphere extended to Plasmasphere (IRI-Plas) model can also give an estimation of the vertical electron density profile in the ionosphere for any given location and time, in the altitude range from about 50 km to 20000 km. This information can be utilized to obtain total electron content between any given receiver and satellite locations based on the IRI-Plas model. This paper explains how the fusion of measurements obtained from a GPS satellite-receiver network can be utilized together with the IRI-Plas model in order to obtain a robust 3D electron density model of the ionosphere. © 2013 ISIF ( Intl Society of Information Fusi.
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    Investigation on the reliability of earthquake prediction based on ionospheric electron content variation
    (ISIF, 2013-07) Akyol, Ali Alp; Arıkan, Orhan; Arıkan F.; Deviren, M. N.
    Due to lack of statistical reliability analysis of earthquake precursors, earthquake prediction from ionospheric parameters is considered to be controversial. In this study, reliability of earthquake prediction is investigated using dense TEC data estimated from the Turkish National Permanent GPS Network (TNPGN- Active). © 2013 ISIF ( Intl Society of Information Fusi.
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    Land cover classification with multi-sensor fusion of partly missing data
    (American Society for Photogrammetry and Remote Sensing, 2009-05) Aksoy, S.; Koperski, K.; Tusk, C.; Marchisio, G.
    We describe a system that uses decision tree-based tools for seamless acquisition of knowledge for classification of remotely sensed imagery. We concentrate on three important problems in this process: information fusion, model understandability, and handling of missing data. Importance of multi-sensor information fusion and the use of decision tree classifiers for such problems have been well-studied in the literature. However, these studies have been limited to the cases where all data sources have a full coverage for the scene under consideration. Our contribution in this paper is to show how decision tree classifiers can be learned with alternative (surrogate) decision nodes and result in models that are capable of dealing with missing data during both training and classification to handle cases where one or more measurements do not exist for some locations. We present detailed performance evaluation regarding the effectiveness of these classifiers for information fusion and feature selection, and study three different methods for handling missing data in comparative experiments. The results show that surrogate decisions incorporated into decision tree classifiers provide powerful models for fusing information from different data layers while being robust to missing data. © 2009 American Society for Photogrammetry and Remote Sensing.
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    Multimodal assessment of apparent personality using feature attention and error consistency constraint
    (Elsevier BV, 2021-06) Aslan, Süleyman; Güdükbay, Uğur; Dibeklioğlu, Hamdi
    Personality computing and affective computing, where the recognition of personality traits is essential, have gained increasing interest and attention in many research areas recently. We propose a novel approach to recognize the Big Five personality traits of people from videos. To this end, we use four different modalities, namely, ambient appearance (scene), facial appearance, voice, and transcribed speech. Through a specialized subnetwork for each of these modalities, our model learns reliable modality-specific representations and fuse them using an attention mechanism that re-weights each dimension of these representations to obtain an optimal combination of multimodal information. A novel loss function is employed to enforce the proposed model to give an equivalent importance for each of the personality traits to be estimated through a consistency constraint that keeps the trait-specific errors as close as possible. To further enhance the reliability of our model, we employ (pre-trained) state-of-the-art architectures (i.e., ResNet, VGGish, ELMo) as the backbones of the modality-specific subnetworks, which are complemented by multilayered Long Short-Term Memory networks to capture temporal dynamics. To minimize the computational complexity of multimodal optimization, we use two-stage modeling, where the modality-specific subnetworks are first trained individually, and the whole network is then fine-tuned to jointly model multimodal data. On the large scale ChaLearn First Impressions V2 challenge dataset, we evaluate the reliability of our model as well as investigating the informativeness of the considered modalities. Experimental results show the effectiveness of the proposed attention mechanism and the error consistency constraint. While the best performance is obtained using facial information among individual modalities, with the use of all four modalities, our model achieves a mean accuracy of 91.8%, improving the state of the art in automatic personality analysis.