Browsing by Author "Lin, Yen-Yu"

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Metal substituted pyrochlore phase LixLa2-xCe1.8Ru0.2O7- (x=0.0-0.6) as an effective catalyst for oxidative and auto-thermal steam reforming of ethanol
(2019-03-21)
Hsieh, Ho-Chen
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Chang, Yuan-Chia
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Tsai, Ping-Wen
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Lin, Yen-Yu
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Chuang, Yu-Chun
Pyrochlore phase LixLa2-xCe1.8Ru0.2O7- (x = 0.0-0.6) [LLCRO] substituted by Li and Ru in A and B sites was synthesized and studied for performance in oxidative steam and auto-thermal reforming of ethanol (OSRE, ATR). The samples were characterized by powder X-ray diffraction (PXRD), X-ray absorption spectroscopy (XAS), and X-ray photoelectron spectroscopy (XPS) to examine the effect of substituted Li+ and Run+ on OSRE performance. The results indicated that the substituted Li ions modified the oxidation states of Ru and Ce in LLCRO, which led to oxygen vacancies/mobility and played an important role in the catalytic performance of OSRE. Using LLCRO as the catalyst and supported by Al2O3, La2Zr2O7 (LZO) was investigated for its hydrogen production performance. The catalytic stability of the LLCRO (x = 0.6)/LZO catalysts was tested at 350 degrees C under oxidative steam conditions (OSRE) and showed a stable ethanol conversion and hydrogen production (X-EtOH = 98(3)%; S-H2 = 95(3)%). Under autothermal conditions (ATR), the LLCRO (x = 0.6) catalyst supported by LZO exhibited a stable catalytic performance with an average ethanol conversion and hydrogen selectivity of 90(3)% and 71(4)%, respectively. Carbon deposition was significantly suppressed during the OSRE and ATR processes on LLCRO (x = 0.6) catalysts supported by LZO, whereas, a significant carbon deposition was observed for LLCRO (x = 0.6)/Al2O3 as the catalyst. These results indicate that LLCRO (x = 0.6) supported by LZO is an effective catalyst for hydrogen production. The enhanced activity was attributed to the effectively suppressed carbon deposition and the sintering of highly dispersed Ru ions in the LLCRO catalyst, which may be due to the strong binding between the catalyst and LZO.
Object-detection method multi-class Bhattacharyya Boost algorithm used therein
(2006-11-02)
Liu, Tyng-Luh
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Lin, Yen-Yu
The present invention discloses an object-detection method and a multi-class Bhattacharyya Boost algorithm used therein, wherein firstly, integral images are calculated from an image data in order to speed up the extraction of the characteristics of the objects; then, multiple rectangles of different sizes are scanned at different locations of the image data, and the multi-class Bhattacharyya Boost algorithm is used to detect multi-class objects. In the present invention, the detection framework can use only one single boosted cascade to determine the status and position of the object inside the image data. The simultaneous multi-class detection of the present invention can effectively overcome the detection difficulties resulting from the diversification of object appearances under different conditions. Therefore, the present invention can detect multiple objects simultaneously and can also detect one object having multiple classes of appearances, and further, the detection speed and the system robustness of the present invention are superior to those of the conventional technologies.
VOSTR: Video Object Segmentation via Transferable Representations
(1970-01-01)
Chen, Yi-Wen
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Tsai, Yi-Hsuan
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Lin, Yen-Yu
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Yang, Ming-Hsuan
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交大名義發表
In order to learn video object segmentation models, conventional methods require a large amount of pixel-wise ground truth annotations. However, collecting such supervised data is time-consuming and labor-intensive. In this paper, we exploit existing annotations in source images and transfer such visual information to segment videos with unseen object categories. Without using any annotations in the target video, we propose a method to jointly mine useful segments and learn feature representations that better adapt to the target frames. The entire process is decomposed into three tasks: (1) refining the responses with fully-connected CRFs, (2) solving a submodular function for selecting object-like segments, and (3) learning a CNN model with a transferable module for adapting seen categories in the source domain to the unseen target video. We present an iterative update scheme between three tasks to self-learn the final solution for object segmentation. Experimental results on numerous benchmark datasets demonstrate that the proposed method performs favorably against the state-of-the-art algorithms.
Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior
(2019-01-01)
Hsu, Cheng-Chun
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Hsu, Kuang-Jui
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Tsai, Chung-Chi
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Lin, Yen-Yu
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Chuang, Yung-Yu
This paper presents a weakly supervised instance segmentation method that consumes training data with tight bounding box annotations. The major difficulty lies in the uncertain figure-ground separation within each bounding box since there is no supervisory signal about it. We address the difficulty by formulating the problem as a multiple instance learning (MIL) task, and generate positive and negative bags based on the sweeping lines of each bounding box. The proposed deep model integrates MIL into a fully supervised instance segmentation network, and can be derived by the objective consisting of two terms, i.e., the unary term and the pairwise term. The former estimates the foreground and background areas of each bounding box while the latter maintains the unity of the estimated object masks. The experimental results show that our method performs favorably against existing weakly supervised methods and even surpasses some fully supervised methods for instance segmentation on the PASCAL VOC dataset. The code is available at https://github.com/chengchunhsu/WSIS_BBTP.