Dictionary learning has been widely used in image representation under the framework of sparse theory. However, most of the current dictionary learning strategies can only be used for single-frame ...image separately, which are insufficient from the perspective of incremental information acquisition and global optimization for the sequential or multiframe satellite images. To this end, this article proposes an incremental dictionary learning method for multiframe satellite images representation in the spectral domain. The incremental dictionary learning is formulated analytically in the framework of sparse representation with low-rank constraint, as a frame-by-frame gradual optimization process of global and local dictionaries, and their corresponding sparse coefficients with the sequence. Specifically, the global dictionary representing the common spectral information of the sequential frames is optimized by two adjacent frames gradually. Meanwhile, the local dictionary representing the specific spectral information of each frame is optimized by the newly added frame itself. In addition, an activity ratio for separating the global dictionary from the local dictionaries, an outlier detection method for initializing the local dictionary are also given, and the alternating direction method of multipliers (ADMMs) is employed to implement the above optimization. Comparison results with the related state-of-the-art methods on different datasets demonstrate that, our proposed method achieves the best representation performance in both spatial and spectral domains, and also helps to improve the performance of dictionary-based tasks using sequential satellite images, such as sea surface anomaly detection.
•A novel MFI method based on learning dictionary with double sparsity is proposed.•Dictionary learning technique is employed to design a better fit force dictionary based on the measured response ...data.•The force dictionary is assumed to be expressed sparsely over the base dictionary, and the moving force is sparse over the force dictionary simultaneously.•Sparse K-SVD algorithm is employed to realize the learning process through alternatively updating between double sparse codes.
Moving force identification (MFI) is essential for the bridge safety as it is one of the major loads acting on the bridge deck. MFI techniques based on force dictionary are promising owing to their prominent performance in solving ill-posed problems and calculation efficiency. Since the specific forms of authentic moving forces are complex and unknown, a fixed dictionary normally adopted tends to fail in expressing moving forces sparsely enough. In this study, dictionary learning (DL) is introduced into the field of MFI to design a better fit force dictionary based on the measured response data. A novel MFI method based on learning dictionary with double sparsity is proposed. Firstly, the MFI equation in time domain which describes the relationship between moving force and measured structural responses is established. Then a sparse dictionary model is designed in which the force dictionary is assumed to be expressed sparsely over the base dictionary, and the moving force is sparse over the force dictionary simultaneously. Moreover, the sparse K-singular-value-decomposition (K-SVD) algorithm is employed to realize the learning process through alternatively updating between double sparse codes. Finally, the learned force dictionary and moving forces are estimated through base force dictionary and double sparse codes. Numerical simulations and experimental studies are carried out to investigate the performance of the proposed method, and the results clearly certify its effectiveness and robustness.
Sonic experience Augoyard, Jean François; McCartney, Andra; Torgue, Henry ...
Sonic experience,
2006, 20060405, 2014, 2005-08-31
eBook
In a multidisciplinary work spanning musicology, electro-acoustic composition, architecture, urban studies, communication, phenomenology, social theory, physics, and psychology, Jean-François ...Augoyard, Henry Torgue, and their associates at the Centre for Research on Sonic Space and the Urban Environment (CRESSON) in Grenoble, France, provide an alphabetical sourcebook of eighty sonic/auditory effects. Their accounts of sonic effects such as echo, anticipation, vibrato, and wha-wha integrate information about the objective physical spaces in which sounds occur with cultural contexts and individual auditory experience. Sonic Experience attempts to rehabilitate general acoustic awareness, combining accessible definitions and literary examples with more in-depth technical information for specialists.
Zero-shot learning for visual recognition, which approaches identifying unseen categories through a shared visual-semantic function learned on the seen categories and is expected to well adapt to ...unseen categories, has received considerable research attention most recently. However, the semantic gap between discriminant visual features and their underlying semantics is still the biggest obstacle, because there usually exists domain disparity across the seen and unseen classes. To deal with this challenge, we design two-stage generative adversarial networks to enhance the generalizability of semantic dictionary through low-rank embedding for zero-shot learning. In detail, we formulate a novel framework to simultaneously seek a two-stage generative model and a semantic dictionary to connect visual features with their semantics under a low-rank embedding. Our first-stage generative model is able to augment more semantic features for the unseen classes, which are then used to generate more discriminant visual features in the second stage, to expand the seen visual feature space. Therefore, we will be able to seek a better semantic dictionary to constitute the latent basis for the unseen classes based on the augmented semantic and visual data. Finally, our approach could capture a variety of visual characteristics from seen classes that are "ready-to-use" for new classes. Extensive experiments on four zero-shot benchmarks demonstrate that our proposed algorithm outperforms the state-of-the-art zero-shot algorithms.
Rotating machinery is widely applied in industrial fields. However, it generally operates under tough working conditions, which leads to the weak fault features and renders fault diagnosis more ...difficult. In this case, an emerging method called sparse representation classification (SRC) is proposed to enhance the fault features and identify the fault status. However, the typical SRC theory fails to consider the locality of the test sample and training sample, and the training set generally contains much redundant information, which may reduce the fault recognition accuracy. Moreover, the time-shift deviation of vibration signal cannot be avoided effectively using a typical SRC model. To overcome the above-mentioned problems, a novel SRC model, i.e., weighted SRC based on dictionary learning (DL-WSRC), is proposed. For the training set, different fault signals are learned based on the improved K-singular value decomposition (K-SVD) algorithm, which can not only adaptively update the whole training set but also reduce redundant information so as to enhance the sample fault features. For the test sample, DL-WSRC selects an accurate time-domain parameter using the K-means clustering algorithm and computes the weighted coefficients according to the parameter distance between the test sample and the training samples. Then, it sparsely represents the test sample by solving a weighted l 0 -norm problem. The goal of weighting is to pay more attention to the locality of the sample so as to improve the recognition accuracy. Finally, according to the results of sparse representation, the fault status can be identified through the correlation analysis, which can effectively solve the time-shift deviation problem. The effectiveness of the proposed method is validated by the experiments of rotating machinery, and the results indicate that the proposed method realizes fault classification with a high accuracy.
In this paper, a multi-resolution dictionary collaborative representation(MRDCR) method for face recognition is proposed. Unlike most of the traditional sparse learning methods, such as sparse ...representation-based classification(SRC) methods and dictionary learning(DL)-based methods, which concentrate only on a single resolution, we consider the fact that the resolutions of real-world face images are variable. We use multiple dictionaries each being related with a resolution to collaboratively represent the test image. Main advantages of this work are summarized as follows. First, we extend the traditional collaborative representation-based classification(CRC) method to the multi-resolution dictionary case, which obtains better recognition accuracy than traditional SRC/CRC methods. Second, comparing with conventional DL methods and recently proposed multi-resolution dictionary learning(MRDL) method, MRDCR still shows superior performance, even in the case of random baboon block occlusion. Third, on the small-scale face databases, our method has achieved better results than some deep learning methods. Last, MRDCR has a closed-form solution, which makes it more efficient than most of the traditional sparse learning methods. The experimental results on five benchmark face databases and a Virus database demonstrate that our proposed MRDCR method outperforms many state-of-the-art dictionary learning and sparse representation methods. The MATLAB code will be available at
https://github.com/masterliuhzen/
.
ТЮРКИЗМЫ В СОВРЕМЕННОМ АНГЛИЙСКОМ ЯЗЫКЕ Валерьевна, Власичева Виктория
Vestnik Volgogradskogo gosudarstvennogo universiteta. Serii͡a︡ 2, I͡A︡zykoznanie.,
11/2010, Letnik:
9, Številka:
2
Journal Article
Recenzirano
Odprti dostop
функционирование тюркизмов в современном английском языке рассматривается на примере слов, обозначающих виды одежды, обуви и материалы для их изготовления. В основу исследования легли данные корпусов ...английского языка XX–XXI веков. Проведенный анализ позволил уточнить словарные дефиниции и выявить случаи орфографической вариантности некоторых тюркизмов.
This is an encyclopedic dictionary of close to 400 important philosophical, literary, and political terms and concepts that defy easy--or any--translation from one language and culture to another. ...Drawn from more than a dozen languages, terms such asDasein(German),pravda(Russian),saudade(Portuguese), andstato(Italian) are thoroughly examined in all their cross-linguistic and cross-cultural complexities. Spanning the classical, medieval, early modern, modern, and contemporary periods, these are terms that influence thinking across the humanities. The entries, written by more than 150 distinguished scholars, describe the origins and meanings of each term, the history and context of its usage, its translations into other languages, and its use in notable texts. The dictionary also includes essays on the special characteristics of particular languages--English, French, German, Greek, Italian, Portuguese, Russian, and Spanish.
Originally published in French, this one-of-a-kind reference work is now available in English for the first time, with new contributions from Judith Butler, Daniel Heller-Roazen, Ben Kafka, Kevin McLaughlin, Kenneth Reinhard, Stella Sandford, Gayatri Chakravorty Spivak, Jane Tylus, Anthony Vidler, Susan Wolfson, Robert J. C. Young, and many more.The result is an invaluable reference for students, scholars, and general readers interested in the multilingual lives of some of our most influential words and ideas.
Covers close to 400 important philosophical, literary, and political terms that defy easy translation between languages and culturesIncludes terms from more than a dozen languagesEntries written by more than 150 distinguished thinkersAvailable in English for the first time, with new contributions by Judith Butler, Daniel Heller-Roazen, Ben Kafka, Kevin McLaughlin, Kenneth Reinhard, Stella Sandford, Gayatri Chakravorty Spivak, Jane Tylus, Anthony Vidler, Susan Wolfson, Robert J. C. Young, and many moreContains extensive cross-references and bibliographiesAn invaluable resource for students and scholars across the humanities
In this letter, we propose two improvements of the MOD and K-SVD dictionary learning algorithms, by modifying the two main parts of these algorithms-the dictionary update and the sparse coding ...stages. Our first contribution is a different dictionary-update stage that aims at finding both the dictionary and the representations while keeping the supports intact. The second contribution suggests to leverage the known representations from the previous sparse-coding in the quest for the updated representations. We demonstrate these two ideas in practice and show how they lead to faster training and better quality outcome.
