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Titlebook: Industrial Crystallization; Process Simulation A Narayan S. Tavare Book 1995 Springer Science+Business Media New York 1995 crystal.crystall

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发表于 2025-3-21 19:38:09 | 显示全部楼层 |阅读模式
书目名称Industrial Crystallization
副标题Process Simulation A
编辑Narayan S. Tavare
视频video
丛书名称The Plenum Chemical Engineering Series
图书封面Titlebook: Industrial Crystallization; Process Simulation A Narayan S. Tavare Book 1995 Springer Science+Business Media New York 1995 crystal.crystall
描述Incorporating all recent developments and applications ofcrystallization technology, this volume offers a clear account of thefield‘s underlying principles, reviews of past and current research,and provides guidelines for equipment and process design. The booktakes a balanced functional approach in its critical survey ofresearch literature, and includes several problems based on realpractical situations that illustrate theoretical development. Severalnew concepts and techniques used in process simulation andidentification analysis are featured.
出版日期Book 1995
关键词crystal; crystallization; design; development; distribution; growth; identification; kinetics; mixing; proces
版次1
doihttps://doi.org/10.1007/978-1-4899-0233-7
isbn_softcover978-1-4899-0235-1
isbn_ebook978-1-4899-0233-7Series ISSN 1566-7944
issn_series 1566-7944
copyrightSpringer Science+Business Media New York 1995
The information of publication is updating

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Narayan S. Tavarecrease the quality of real-world low-resolution images. We have applied the proposed pipeline for the problem of face super-resolution where we report large improvement over baselines and prior work although the proposed method is potentially applicable to other object categories.
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Narayan S. Tavarecessing of RGB-D data with . includes noise and temporal flickering removal, hole filling and resampling. As a substitute of the observed scene, our . can additionally be applied to compression and scene reconstruction. We present experiments performed with our framework in indoor scenes of differen
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Narayan S. Tavare joint locations to estimate a sequence of 3D poses. We designed a sequence-to-sequence network composed of layer-normalized LSTM units with shortcut connections connecting the input to the output on the decoder side and imposed temporal smoothness constraint during training. We found that the knowl
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Narayan S. Tavareize the elements of the feature pool to a uniform size and aggregate their contextual information to generate each level of the final FP. The experimental results confirmed that PFPNet increases the performance of the latest version of the single-shot multi-box detector (SSD) by mAP of 6.4% AP and e
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Narayan S. Tavare TPN with bi-directional training on pairs of frames. We apply the switchable TPN to three tasks: colorizing a gray-scale video based on a few colored key-frames, generating an HDR video from a low dynamic range (LDR) video and a few HDR frames, and propagating a segmentation mask from the first fra
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Narayan S. Tavareproposed to predict the labels of unlabeled image sequences. With the newly estimated labeled sequences, the unified anchor embedding framework enables the feature learning process to be further facilitated. Extensive experimental results on the large-scale dataset show that the proposed method outp
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