642 research outputs found
Privacy Protection Performance of De-identified Face Images with and without Background
Li Meng, 'Privacy Protection Performance of De-identified Face Images with and without Background', paper presented at the 39th International Information and Communication Technology (ICT) Convention. Grand Hotel Adriatic Congress Centre and Admiral Hotel, Opatija, Croatia, May 30 - June 3, 2016.This paper presents an approach to blending a de-identified face region with its original background, for the purpose of completing the process of face de-identification. The re-identification risk of the de-identified FERET face images has been evaluated for the k-Diff-furthest face de-identification method, using several face recognition benchmark methods including PCA, LBP, HOG and LPQ. The experimental results show that the k-Diff-furthest face de-identification delivers high privacy protection within the face region while blending the de-identified face region with its original background may significantly increases the re-identification risk, indicating that de-identification must also be applied to image areas beyond the face region
An Improved YOLOv8-Based Method for Small Object Detection in UAV perspective
The target objects in UAV aerial images are usually smaller targets, and the detection of such small targets is an important research area. Although the progress of target detection is constantly improving thanks to the advancement of deep learning technology, the accuracy of small target recognition in images still poses a great challenge. This study proposes an improved YOLOv8 algorithm to improve the performance of the small target object detection algorithm. This method proposes a C2F-DCNv2 module that integrates deformable convolutional network v2 (DCNv2) to replace the C2F module of the original backbone part; in addition, a Dynamic Head (DyHead) with a self-attention mechanism is used on the head) replaced the original detection head. Through training and testing in the VisDrones2019 data set, it is shown that the method proposed in this article reached 37.9% in the mAp50 indicator in the verification data set, and the average detection speed was 33.7 FPS. Compared with the results of the baseline model, the results increased by 3.6%. Experimental results show that the target detection algorithm proposed in this article significantly improves the recognition effect of small targets in UAV aerial images
A Spatial Self-Similarity Based Feature Learning Method for Face Recognition under Varying Poses
High dimentional neural fuzzy controller for nonlinear systems
De nos jours, la théorie de contrôle joue un rôle significatif dans presque tous les domaine de la science et de l'ingénierie. Les contrôleurs linéaires PID sont les applications principales de la théorie de contrôle, et ils se basent sur les systèmes de contrôle simples. Mais beaucoup de vrais systèmes possèdent des caractéristiques non-linéaires. Dans la pratique, il est nécessaire de faire beaucoup de linéarisations. Quand nous employons le contrôleur classique dans un système non-linéaire fortement complexe, les difficultés augmentent exponentiellement. Pour éviter les imperfections, on peut employer des contrôleurs flous. Le contrôleurs flous se basent sur le système de connaisance. Ce sont des outils importants dans le domaine de l'automatique. Ils possèdent beaucoup plus d'avantages que les contrôleurs classiques"PID", mais ils ont besoin d'experts pour concevoir les règles de base. La limite principale des contrôleurs flous est la difficulté d'établir les règles de base. Maintenant, beaucoup de recherches sont consacrées à la fusion des réseaux de neurones et de systèmes flous dans une nouvelle structure (les réseaux de neuro-floue). Cette approche combine les avantages de deux paradigmes puissants dans une capsule simple, et fournit un cadre puissant pour extraire des règles floues des données numériques. Cependant, cette technologie n'est pas parfaite. Il reste quelques difficultés: beaucoup de règles floues sont nécessaires, les algorithmes sont complexes et la fiabilité est basse (Par exemple, pour un même modèle ou fonction, les résultats dépendent des ensembles d'apprentissage). Pour éviter les difficultés, ce mémoire présente une nouvelle méthode, appelée"inférence neuro-floue de haute-dimension". L'idée fondamentale de cette méthode proposé est de considérer chaque donné dans ce système comme point avec la haute dimension. Chaque dimension d'entrée sera traitée en même temps dans les mêmes sous-ensembles de haute dimension. L'algorithme proposé a été examiné sur différentes applications, et les résultats ont été comparés aux données éditées sur trois problèmes de repère. Cet algorithme est simple à employer, et les résultats expérimentaux prouvent que le nombre de faisceaux exigés est inférieur à ceux rapportés dans la littérature. L'exactitude de rendement est bonne dans beaucoup d'applications
Assessment of hospital length of stay and direct costs of type 2 diabetes in Hubei Province, China
Study of the optimization of matching between torsional vibration damper and elastic coupling based on energy method
To reduce the torsional vibration of vehicle power transmission system (VPTS), a torsional vibration model with multiple degrees of freedom (multi-DOFs) of VPTS was established for reasonable parameter selection of the torsional vibration damper (TVD) and elastic coupling (EC). Several schemes combining TVD and EC on the driveshaft were enacted. Based on this, an optimization model was established to minimize the maximum torsional vibration elastic potential energy (TVEPE) of VPTS. In this model, the torsional stiffness of EC and torsional stiffness and torsional damping coefficient of TVD were chosen as the optimization parameters. After calculation, the optimization results of different schemes were compared and analyzed. Results showed the model incorporating TVD and EC to have a better effect on reducing torsional vibration than TVD after reasonable structure selection and parameter optimization
Palaeomagnetic evidence and tectonic origin of clockwise rotations in the Yangtze fold belt, South China Block
International audienceFold axis strikes in the Yangtze fold belt of the South China Block (SCB) undergo significant changes over distances of >1000 km. This large-scale variation provides an ideal opportunity to test the oroclinal-bending hypothesis using palaeomagnetic methods, which we have attempted by drilling the Lower Triassic Daye Formation limestones in western Hubei Province. Thermal demagnetization isolated two components in most samples. A low unblocking temperature component (400 °C , unblocks univectorially towards the origin. The HTC passes the McFadden-fold test with an overall mean tilt-corrected direction of Dec = 255°, Inc =-24° (N= 7, α95= 9°) . Scanning electron microscopy (SEM) observations suggest an early diagenetic, possible (bio)chemical origin for the magnetic extracts dominated by Ti-poor magnetite. Rock magnetic data show no evidence that the HTC has been affected by tectonic or compaction strain. Our data together with previously published results suggest a general clockwise rotation pattern in the Middle Yangtze fold belt, which is probably related to the collision between the North and SCBs. Comparison of palaeomagnetic rotations with fold axis trends in the fold belt suggests that about 30° clockwise rotation occurred in the Middle reaches of the Yangtze River, while a 15° difference in fold axis trends would be due to initial variation within the fold belt. However, since little is known about the timing of the clockwise rotation, whether the Middle Yangtze fold belt is an orocline awaits further studies
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