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Relevant bibliographies by topics / DDAE

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Journal articles

Academic literature on the topic 'DDAE'

Author: Grafiati

Published: 20 April 2024

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Journal articles on the topic "DDAE"

1

Lai, Po-Ting, Wei-Liang Lu, Ting-Rung Kuo, et al. "Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study." JMIR Medical Informatics 7, no. 4 (2019): e14502. http://dx.doi.org/10.2196/14502.

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Background Research on disease-disease association (DDA), like comorbidity and complication, provides important insights into disease treatment and drug discovery, and a large body of the literature has been published in the field. However, using current search tools, it is not easy for researchers to retrieve information on the latest DDA findings. First, comorbidity and complication keywords pull up large numbers of PubMed studies. Second, disease is not highlighted in search results. Finally, DDA is not identified, as currently no disease-disease association extraction (DDAE) dataset or too

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Unger, Benjamin. "Discontinuity Propagation in Delay Differential-Algebraic Equations." Electronic Journal of Linear Algebra 34 (February 21, 2018): 582–601. http://dx.doi.org/10.13001/1081-3810.3759.

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The propagation of primary discontinuities in initial value problems for linear delay differential-algebraic equations (DDAEs) is discussed. Based on the (quasi-) Weierstra{\ss} form for regular matrix pencils, a complete characterization of the different propagation types is given and algebraic criteria in terms of the matrices are developed. The analysis, which is based on the method of steps, takes into account all possible inhomogeneities and history functions and thus serves as a worst-case scenario. Moreover, it reveals possible hidden delays in the DDAE and allows to study exponential s

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3

Azmat, A., I. Ali, W. Ariyanti, M. G. L. Putra, and T. Nadeem. "Environmental Noise Reduction based on Deep Denoising Autoencoder." Engineering, Technology & Applied Science Research 12, no. 6 (2022): 9532–35. http://dx.doi.org/10.48084/etasr.5239.

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Speech enhancement plays an important role in Automatic Speech Recognition (ASR) even though this task remains challenging in real-world scenarios of human-level performance. To cope with this challenge, an explicit denoising framework called Deep Denoising Autoencoder (DDAE) is introduced in this paper. The parameters of DDAE encoder and decoder are optimized based on the backpropagation criterion, where all denoising autoencoders are stacked up instead of recurrent connections. For better speech estimation in real and noisy environments, we include matched and mismatched noisy and clean pair

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4

Saad, Omar M., and Yangkang Chen. "Deep denoising autoencoder for seismic random noise attenuation." GEOPHYSICS 85, no. 4 (2020): V367—V376. http://dx.doi.org/10.1190/geo2019-0468.1.

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Attenuation of seismic random noise is considered an important processing step to enhance the signal-to-noise ratio of seismic data. A new approach is proposed to attenuate random noise based on a deep-denoising autoencoder (DDAE). In this approach, the time-series seismic data are used as an input for the DDAE. The DDAE encodes the input seismic data to multiple levels of abstraction, and then it decodes those levels to reconstruct the seismic signal without noise. The DDAE is pretrained in a supervised way using synthetic data; following this, the pretrained model is used to denoise the fiel

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Lazim, Raghad Yaseen, Zhu Yun, and Xiaojun Wu. "Improving Speech Quality for Hearing Aid Applications Based on Wiener Filter and Composite of Deep Denoising Autoencoders." Signals 1, no. 2 (2020): 138–56. http://dx.doi.org/10.3390/signals1020008.

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In hearing aid devices, speech enhancement techniques are a critical component to enable users with hearing loss to attain improved speech quality under noisy conditions. Recently, the deep denoising autoencoder (DDAE) was adopted successfully for recovering the desired speech from noisy observations. However, a single DDAE cannot extract contextual information sufficiently due to the poor generalization in an unknown signal-to-noise ratio (SNR), the local minima, and the fact that the enhanced output shows some residual noise and some level of discontinuity. In this paper, we propose a hybrid

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6

Driessen, W. L., R. A. G. de Graaff, F. J. Parlevliet, J. Reedijk, and R. M. de Vos. "Transition metal compounds of the tridentate pyrazole substituted amine ligand bis(2-(3,5-dimethyl-1-pyrazolyl)ethyl)ethylamine (ddae). X-ray structures of [Co(ddae)(NO3)2], [Cu(ddae)(NO3)(H2O)](NO3) and [Cu(ddae)(Cl)2]·C2H5OH." Inorganica Chimica Acta 216, no. 1-2 (1994): 43–49. http://dx.doi.org/10.1016/0020-1693(93)03707-h.

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7

Chen, Honghao, Xiangwen Kong, Xiangyu Zhang, Xin Zhao, and Kaiqi Huang. "DDAE: Towards Deep Dynamic Vision BERT Pretraining." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 2 (2024): 1037–45. http://dx.doi.org/10.1609/aaai.v38i2.27864.

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Recently, masked image modeling (MIM) has demonstrated promising prospects in self-supervised representation learning. However, existing MIM frameworks recover all masked patches equivalently, ignoring that the reconstruction difficulty of different patches can vary sharply due to their diverse distance from visible patches. In this paper, we propose a novel deep dynamic supervision to enable MIM methods to dynamically reconstruct patches with different degrees of difficulty at different pretraining phases and depths of the model. Our deep dynamic supervision helps to provide more locality ind

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8

Sulkhana, Sayyidah Irma, Fathul Niam, and Ahmad Saifudin. "Pengembangan Media DAKOTAR (Dakon Matematika Pintar) Materi Satuan Panjang dan Satuan Berat untuk Meningkatkan Keaktifan dan Kemampuan Berhitung Siswa Kelas IV di SDN Gogodeso 01." Patria Eduacational Journal (PEJ) 2, no. 2 (2022): 7–12. http://dx.doi.org/10.28926/pej.v2i2.376.

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Abstract: Learning media is an intermediary tool to convey messages or learning materials used dby dteachers/educators dto dstudents dso dthat dthey dcan dattract dstudents' dattention, dthoughts dand dinterests dto dparticipate din dthe dlearning dprocess. dBased don dinterviews dthat dresearchers dconducted dwith dfourth dgrade dteachers dat dSDN dGogodeso dd01, ddthey ddfound ddproblems, ddnamely dd(1) ddlack ddof ddvariety ddof ddlearning ddmedia dd(2) ddteachers ddstill dduse ddthe ddlecture ddmethod ddin ddthe ddlearning ddprocess. ddThe ddpurpose ddof ddthis dddevelopment ddresearch ddi

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Deshmukh, Venkatesh. "Approximate Stability Analysis and Computation of Solutions of Nonlinear Delay Differential Algebraic Equations with Time Periodic Coefficients." Journal of Vibration and Control 16, no. 7-8 (2010): 1235–60. http://dx.doi.org/10.1177/1077546309341137.

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Approximate stability analysis of nonlinear delay differential algebraic equations (DDAEs) with periodic coefficients is proposed with a geometric interpretation of evolution of the linearized system. Firstly, a numerical algorithm based on direct integration by expansion in terms of Chebyshev polynomials is derived for linear analysis. The proposed algorithm is shown to have deeper connections with and be computationally less cumbersome than the solution of the underlying semi-explicit system via a similarity transformation. The stability of time periodic DDAE systems is characterized by the

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Wei, Chengyan, and Shenxiang Wang. "Research on Improving the Executive Ability of University Administrators Based on Deep Learning." Computational and Mathematical Methods in Medicine 2022 (June 9, 2022): 1–10. http://dx.doi.org/10.1155/2022/6354801.

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Over the years, experts have focused their research on ways to increase the executive capacity of university administrators. This is because only by improving the quality of execution of college and university administrative personnel can they actively execute various policies and measures, fully exploit their subjective initiative, and ensure the educational reform of colleges and universities. Increasing the executive capacity of administrative staff can help colleges and universities manage more effectively. Therefore, in the development process of higher education institutions, it is neces

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