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Your search keyword '"Kuan, Ta-Wen"' showing total 107 results
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107 results on '"Kuan, Ta-Wen"'

1. Research on Value-Added Evaluation of University Teachers Based on University Functions

Author

Li, Lin, wei, Shi, Kuan, Ta Wen, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Lin, Jerry Chun-Wei, editor, Shieh, Chin-Shiuh, editor, Horng, Mong-Fong, editor, and Chu, Shu-Chuan, editor

Published
2024
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2. Yolo V3 for Market MBFVS Food Materials Detection

Author

Kuan, Ta-Wen, Yu, Xiaodong, Wang, Qi, Wang, Yihan, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Lin, Jerry Chun-Wei, editor, Shieh, Chin-Shiuh, editor, Horng, Mong-Fong, editor, and Chu, Shu-Chuan, editor

Published
2024
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3. Multi-scale Convolution Kernels and Residual Components for Deciduous Leaves Detection

Author

Yu, Xiaodong, Kuan, Ta-Wen, Luo, Shunnian, Lin, Yuh-Chung, Chen, Ying, Wang, Yanze, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Lin, Jerry Chun-Wei, editor, Shieh, Chin-Shiuh, editor, Horng, Mong-Fong, editor, and Chu, Shu-Chuan, editor

Published
2024
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4. Implementation of Campus Pedestrian Detection Using YOLOv5

Author

Lin, Yuh-Chung, Kuan, Ta-Wen, Tseng, Shih-Pang, Lv, Xinhang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Pan, Jeng-Shyang, editor, Pan, Zhigeng, editor, Hu, Pei, editor, and Lin, Jerry Chun-Wei, editor

Published
2024
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5. Research on a Capsule Network Text Classification Method with a Self-Attention Mechanism.

Author

Yu, Xiaodong, Luo, Shun-Nain, Wu, Yujia, Cai, Zhufei, Kuan, Ta-Wen, and Tseng, Shih-Pang

Subjects
CAPSULE neural networks, CONVOLUTIONAL neural networks, NATURAL language processing, FEATURE extraction, CLASSIFICATION, SELF-adaptive software
Abstract

Convolutional neural networks (CNNs) need to replicate feature detectors when modeling spatial information, which reduces their efficiency. The number of replicated feature detectors or labeled training data required for such methods grows exponentially with the dimensionality of the data being used. On the other hand, space-insensitive methods are difficult to encode and express effectively due to the limitation of their rich text structures. In response to the above problems, this paper proposes a capsule network (self-attention capsule network, or SA-CapsNet) with a self-attention mechanism for text classification tasks, wherein the capsule network itself, given the feature with the symmetry hint on two ends, acts as both encoder and decoder. In order to learn long-distance dependent features in sentences and encode text information more efficiently, SA-CapsNet maps the self-attention module to the feature extraction layer of the capsule network, thereby increasing its feature extraction ability and overcoming the limitations of convolutional neural networks. In addition, in this study, in order to improve the accuracy of the model, the capsule was improved by reducing its dimension and an intermediate layer was added, enabling the model to obtain more expressive instantiation features in a given sentence. Finally, experiments were carried out on three general datasets of different sizes, namely the IMDB, MPQA, and MR datasets. The accuracy of the model on these three datasets was 84.72%, 80.31%, and 75.38%, respectively. Furthermore, compared with the benchmark algorithm, the model's performance on these datasets was promising, with an increase in accuracy of 1.08%, 0.39%, and 1.43%, respectively. This study focused on reducing the parameters of the model for various applications, such as edge and mobile applications. The experimental results show that the accuracy is still not apparently decreased by the reduced parameters. The experimental results therefore verify the effective performance of the proposed SA-CapsNet model. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Dynamic Fixed-Point Arithmetic Design of Embedded SVM-Based Speaker Identification System

Author

Wang, Jhing-Fa, Kuan, Ta-Wen, Wang, Jia-Ching, Sun, Ta-Wei, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Zhang, Liqing, editor, Lu, Bao-Liang, editor, and Kwok, James, editor

Published
2010
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18. Ubiquitous and Robust Text-Independent Speaker Recognition for Home Automation Digital Life

Author

Wang, Jhing-Fa, Kuan, Ta-Wen, Wang, Jia-chang, Gu, Gaung-Hui, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sandnes, Frode Eika, editor, Zhang, Yan, editor, Rong, Chunming, editor, Yang, Laurence T., editor, and Ma, Jianhua, editor

Published
2008
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26. A humem system for online multimedia feedback

Author

Kuan Ta-Wen, Ming-Hsuan Lu, Bo-Hao Su, and Jhing-Fa Wang

Subjects
Multimedia, Computer science, business.industry, Mean opinion score, media_common.quotation_subject, Usability, Semantics, computer.software_genre, Facial recognition system, Sadness, Eigenface, Happiness, General knowledge, business, computer, media_common
Abstract

In this paper, a Humanized multimedia system with emotional cognition for home-care applications, naming HUMEM system, is proposed to accompany and interact with user for happiness improvement. Three main functionalities are built and coordinated in the proposed work. Firstly, HUMEM gives the capability to recognize user identity by facial recognition based on haar-like features and PCA method, to yield the eigenface for facial expert. Secondly, the emotional semantics recognition is to recognize four kinds of user's verbal emotion (i.e. happiness, anger, sadness, and neutral) through CKIP dictionary and Point-Wise Mutual Information (PMI) method. Particularly, above two audio-visual recognizers are useful for the third user-oriented multimedia feedback system, in which ten more online multimedia services that can meet user daily life and pastime request by embedded google ASR, for example, user can listen or watch the favorite music, video, on-line TV news, weather report, taking picture, general knowledge information retrieval etc. In the experiment, the objective analysis for user recognition and emotion recognition is applied, where the outside-test accuracies for user recognition and emotion recognition can reach 96% and 70%, respectively. In system usability, the mean opinion score (MOS) is applied for subjective evaluation, the result of MOS for the entire HUMEM system is able to acquire 4.4, which initially reveals that the proposed system is acceptable.

Published
2016

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