Change detection and diagnosis are important activities in many domains, such as mechanical, aerospace, biomedical and seismic engineering. Process monitoring systems based on vibration measuring and processing use va...
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ISBN:
(纸本)9783319625218
Change detection and diagnosis are important activities in many domains, such as mechanical, aerospace, biomedical and seismic engineering. Process monitoring systems based on vibration measuring and processing use various transforms, for instance time-frequency transforms. Wigner-Ville distribution is used as an example of time-frequency transform, but it generates many interference terms. Some basic properties of the input signal, concerning the number of components, the average time and frequency, the bandwidth and time duration are estimated by computing statistical moments and the Renyi entropy. the paper also presents some results of the evaluation of an automatic system to detect and remove artifacts from time-frequency images. the point is to build a database with blocks, whose size depend on application, with and without interferences (bad blocks), and - by cross-correlation -to detect these blocks in the processed image. In the present state of development, the system is working for some particular structures, concerning the number and the parameters of the analyzed signal components. Adding knowledge about the analyzed signal, the performances can be improved. the results are encouraging and suggest optimization of the proposed method.
Numerous approaches based on metrics, token sequence pattern-matching, abstract syntax tree (AST) or program dependency graph (PDG) analysis have already been proposed to highlight similarities in source code: in this...
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Numerous approaches based on metrics, token sequence pattern-matching, abstract syntax tree (AST) or program dependency graph (PDG) analysis have already been proposed to highlight similarities in source code: in this paper we present a simple and scalable architecture based on AST fingerprinting. thanks to a study of several hashing strategies reducing false-positive collisions, we propose a framework that efficiently indexes AST representations in a database, that quickly detects exact (w.r.t source code abstraction) clone clusters and that easily retrieves their corresponding ASTs. Our aim is to allow further processing of neighboring exact matches in order to identify the larger approximate matches, dealing withthe common modification patterns seen in the intra-project copy-pastes and in the plagiarism cases.
In this paper we analyze some shape-based image retrieval methods which use different types of geometric and algebraic moments and Fourier descriptors. Moments have been widely used in patternrecognition applications...
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In this paper we analyze some shape-based image retrieval methods which use different types of geometric and algebraic moments and Fourier descriptors. Moments have been widely used in patternrecognition applications to describe the geometrical characteristics of different objects. they provide fundamental geometric properties (e.g. area, centroid, moment of inertia, etc.). We consider various description techniques: Hu, Flusser and Taubin invariants, Legendre and Zernike moments, Generic Fourier Descriptors (GFD). the set of absolute orthogonal (i.e. rotation) moment invariants defined by Hu can be used for scale, position, and rotation invariant pattern identification. Flusser' s complete set of invariants appears as a particular case, with invariance only to rotation. the Taubin's affine moment invariants introduce the concept of covariant matrix. Legendre moments are based on orthogonal Legendre polynomials and are not invariant under image rotation. Zernike moments consist of a set of complex polynomials that form a complete orthogonal set over the interior of the unit circle. GFDs are derived by applying a modified polar Fourier transform on shape image. We have applied the retrieval methods on a collection of images chosen from MPEG7 database. the image retrieval performance of each method is described by the precision-recall graph. In the paper we propose a novel approach that combines the described techniques after a coarse partitioning of the image dataset by their morphological features. the proposed approach provides much better performance than each method described above.
patternrecognition is one of the most important tasks in aerospace image processing. Various methods based on convolutional neural networks attain state-of-the-art accuracy; however, their effectiveness on exact imag...
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ISBN:
(数字)9781728166360
ISBN:
(纸本)9781728166377
patternrecognition is one of the most important tasks in aerospace image processing. Various methods based on convolutional neural networks attain state-of-the-art accuracy; however, their effectiveness on exact images is influenced by the chosen architecture and its training *** work present methods based on convolutional neural networks for patternrecognition on the aerospace images. A possibility for objects segmentation into ten classes is demonstrated on example of the multispectral images from the World View 3 satellite. Four networks with different architectures were built, trained and optimized parametrically based on the auto-encoder neural networks. Segmentation results has been analyzed by means of three parameters: training Jacard Index, testing Jacard Index and weight numbers. the positive impact of the properly selected shearing augmentation on extension of a small marked dataset is discussed. the influence of the nonequilibrium classes on the segmentation accuracy and how to account this feature during training of deep neural networks is pointing out.
In spatial object detection tasks, due to the far observation distance, objects often exist in the form of point objects. Due to the extremely small size of the object, the number of detectable objects is very limited...
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ISBN:
(数字)9798350350890
ISBN:
(纸本)9798350350906
In spatial object detection tasks, due to the far observation distance, objects often exist in the form of point objects. Due to the extremely small size of the object, the number of detectable objects is very limited, which can easily lead to missed detection and false positives in object detection. To solve these issues, we proposes a spatial object detection algorithm based on the improved YOLOv8. this algorithm enhances the network's feature extraction ability by adding GAM Attention to the Neck part of the YOLOv8 object detection algorithm, reduces information reduction, and magnifies the global interactive representations to improve the performance of deep neural networks and reduce the interference of background noise in small object detection tasks in complex environments. It introduces 3D permutation and multilayer perceptron to achieve channel attention and a convolutional spatial attention submodule to better capture the detail information of small objects. the experimental results show that the improved YOLOv8 algorithm has improved the $\boldsymbol{mAP_{50}}$ by 1.3%, and $\boldsymbol{mAP_{50-95}}$ by 1.9% on the ISS dataset. In conclusion, the improved YOLOv8 can enhance the performance of small object detection.
A major challenge in stationary care in hospitals is the limited amount of time for each patient due to a large overhead being created by manual documentation efforts. Studies show that it is common for caregivers to ...
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ISBN:
(纸本)9781450396240
A major challenge in stationary care in hospitals is the limited amount of time for each patient due to a large overhead being created by manual documentation efforts. Studies show that it is common for caregivers to spend more than one hour per day for documentation *** this paper a novel concept for reducing the manual documentation effort by leveraging methods of human activity recognition is introduced and a corresponding dataset is published. the dataset captures different care activities like repositioning, sitting up, transfer and patient mobilization using body worn sensors in a realistic setting with multiple patients and *** evaluation of the data, two experimental setups are presented: an unsegmented case, where the duration of the care activity is unknown and a segmented case, where the beginning and the end of the activity is known beforehand. First experiments show the feasibility of recognizing care activities using different types of Neural Networks.
the Silicon Strip Tracker (SST) is the intermediate part of the CMS Central Tracker System. SST is based on microstrip silicon devices and in combination with pixel detectors and the Microstrip Gas Chambers aims at pe...
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the Silicon Strip Tracker (SST) is the intermediate part of the CMS Central Tracker System. SST is based on microstrip silicon devices and in combination with pixel detectors and the Microstrip Gas Chambers aims at performing patternrecognition, track reconstruction and momentum measurements for all tracks with p(T) greater than or equal to 2 GeV/c originating from high luminosity interactions at root s = 14 TeV at LHC. We aim at exploiting the advantages and the physics potential of the precise tracking performance provided by the microstrip silicon detectors' on a large scale apparatus and in a much more difficult environment than ever. In this paper we describe the actual SST layout and the readout system. (C) 1999 Elsevier Science B.V. All rights reserved.
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