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1,066 results on '"Wang Yu-jie"'

1. Effect of oxygen flow on compactness of micro-arc oxidation coatings on LY12 Al alloy

Author

WANG Yu-jie, ZHANG Peng, WANG Xuan, DU Yun-hui, WANG Sheng-lin, ZHANG Wei-yi, and LU Hong-mei

Subjects
aluminum alloy, micro-arc oxidation, oxygen flow, compact coating, corrosion resistance, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

Compact and uniform coatings were fabricated on LY12 aluminum alloy surface by adjusting process parameters and oxygen flow after micro-arc oxidation (MAO) treatment. The thickness, morphology, phase compositions and corrosion resistance were examined by SEM, XRD and electrochemical test system. The mechanism of MAO with oxygen was discussed, and the effect of oxygen flow on the coating compactness was analyzed. The results indicate that with the increase of time, voltage and concentration of electrolyte, the coating thickness changes regularly. The effect of oxygen on sintering can promote the growth of compact coatings, and the thickness of compact coatings first increases and then decreases with O2 rising. Under the conditions of 105g/L KF, 85g/L KOH, 12g/L NaAlO2, 110V for the voltage, 15min for treatment time and 0.010L/s for oxygen flow, a 30μm-thick compact coating can be formed on LY12 aluminum alloy. The corrosion potential of the coating increases to -0.11V, and the current density reduces to 2.1×10-6A/cm2, which is reduced by two orders of magnitude compared with the matrix and the corrosion resistance of the LY12 aluminum alloy significantly improves.

Published
2019
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3. QuantTune: Optimizing Model Quantization with Adaptive Outlier-Driven Fine Tuning

Author

Chen, Jiun-Man, Chao, Yu-Hsuan, Wang, Yu-Jie, Shieh, Ming-Der, Hsu, Chih-Chung, and Lin, Wei-Fen

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia
Abstract

Transformer-based models have gained widespread popularity in both the computer vision (CV) and natural language processing (NLP) fields. However, significant challenges arise during post-training linear quantization, leading to noticeable reductions in inference accuracy. Our study focuses on uncovering the underlying causes of these accuracy drops and proposing a quantization-friendly fine-tuning method, \textbf{QuantTune}. Firstly, our analysis revealed that, on average, 65\% of quantization errors result from the precision loss incurred by the dynamic range amplification effect of outliers across the target Transformer-based models. Secondly, \textbf{QuantTune} adjusts weights based on the deviation of outlier activations and effectively constrains the dynamic ranges of the problematic activations. As a result, it successfully mitigates the negative impact of outliers on the inference accuracy of quantized models. Lastly, \textbf{QuantTune} can be seamlessly integrated into the back-propagation pass in the fine-tuning process without requiring extra complexity in inference software and hardware design. Our approach showcases significant improvements in post-training quantization across a range of Transformer-based models, including ViT, Bert-base, and OPT. QuantTune reduces accuracy drops by 12.09\% at 8-bit quantization and 33.8\% at 7-bit compared to top calibration methods, outperforming state-of-the-art solutions by over 18.84\% across ViT models.

Published
2024

31. The activities and mechanisms of intestinal microbiota metabolites of TCM herbal ingredients could be illustrated by a strategy integrating spectrum-effects, network pharmacology, metabolomics and molecular docking analysis: Platycodin D as an example

Author

Zhong, Yuan-han, Liang, Jian, Qin, Qian, Wang, Yu-jie, Peng, Yi-ming, Zhang, Ting, Liu, Fang-yuan, Zhang, Xin-yu, He, Jun-wei, Zhang, Shou-wen, Zhong, Guo-yue, Huang, Hui-lian, and Zeng, Jin-xiang

Published
2023
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38. Using a Simplified Quantum Counter to Implement Quantum Circuits Based on Grover's Algorithm to Tackle the Exact Cover Problem.

Author

Jiang, Jehn-Ruey and Wang, Yu-Jie

Subjects
*QUANTUM gates, *CIRCUIT complexity, *QUBITS, *ALGORITHMS, *COUNTING
Abstract

In this paper, we use a simplified quantum counter to implement Grover's algorithm-based quantum circuits to tackle the NP-hard exact cover problem (ECP). The ECP seeks a subcollection of sets such that every element is covered by exactly one set. Leveraging Grover's algorithm, our quantum circuits achieve a quadratic speedup, querying the oracle O( N ) times, compared to O(N) for classical methods, where N = 2 n is the total number of unstructured input instances and n is the number of input (quantum) bits. For the whole quantum circuit, the simplified quantum counter saves (4 m b − 4 m) ⌊ π / 4 N / M ⌋ quantum gates and reduces the quantum circuit depth by (2 m b) ⌊ π / 4 N / M ⌋ compared to Heidari et al.'s design, where b = ⌊ log n ⌋ + 1 is the number of counting qubits used in a counter. Experimental results obtained using IBM Qiskit packages confirm the effectiveness of our quantum circuits. [ABSTRACT FROM AUTHOR]

Published
2025
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46. Clinical practice guideline for transurethral plasmakinetic resection of prostate for benign prostatic hyperplasia (2021 Edition)

Author

Zeng, Xian-Tao, Jin, Ying-Hui, Liu, Tong-Zu, Chen, Fang-Ming, Ding, De-Gang, Fu, Meng, Gu, Xin-Quan, Han, Bang-Min, Huang, Xing, Hou, Zhi, Hu, Wan-Li, Kang, Xin-Li, Li, Gong-Hui, Li, Jian-Xing, Li, Pei-Jun, Liang, Chao-Zhao, Liu, Xiu-Heng, Liu, Zhi-Yu, Liu, Chun-Xiao, Liu, Jiu-Min, Luo, Guang-Heng, Luo, Yi, Qin, Wei-Jun, Qiu, Jian-Hong, Qiu, Jian-Xin, Shang, Xue-Jun, Shi, Ben-Kang, Sun, Fa, Tian, Guo-Xiang, Tian, Ye, Wang, Feng, Wang, Feng, Wang, Yin-Huai, Wang, Yu-Jie, Wang, Zhi-Ping, Wang, Zhong, Wei, Qiang, Xiao, Min-Hui, Xu, Wan-Hai, Yi, Fa-Xian, Zhu, Chao-Yang, Zhuang, Qian-Yuan, Zhou, Li-Qun, Zou, Xiao-Feng, Xing, Nian-Zeng, He, Da-Lin, and Wang, Xing-Huan

Published
2022
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49. The Assessment of Airline Service Performance with Dependent Evaluation Criteria by Generalized QFD and SAW Under Interval-Valued Fuzzy Environment

Author

Wang, Yu-Jie, Liu, Li-Jen, Han, Tzeu-Chen, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Liu, Yong, editor, Wang, Lipo, editor, Zhao, Liang, editor, and Yu, Zhengtao, editor

Published
2020
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50. Copper(I) and cobalt(II) frameworks with a tetraphenylethene–imidazole ligand for the electroreduction of CO2 to CH4.

Author

Wang, Fang-Fang, Wang, Yu-Jie, Qiu, Zhao-Feng, Zhang, Kai-Yang, Zhao, Yue, and Sun, Wei-Yin

Subjects
*CHARGE transfer kinetics, *COPPER, *STRUCTURAL frames, *LIGANDS (Chemistry), *ELECTROLYTIC reduction
Abstract

Targeted development of crystalline materials for the CO2 reduction reaction (CO2RR) is currently a hot topic. Copper is a common electrocatalyst for the reduction of CO2 to CH4. In this study, two new MOFs [Cu(TIPE)0.5](ClO4) (Cu-MOF) and [Co(TIPE)(TFA)2]·2DMF (Co-MOF) (TIPE = 1,1,2,2-tetrakis(4-(imidazole-1-yl)phenyl)ethene, TFA = trifluoroacetate and DMF = N,N-dimethylformamide) were synthesized. Crystallographic analysis shows that Cu-MOF and Co-MOF are different 2D networks. Interestingly, Cu-MOF can serve as an efficient electrocatalyst for CO2 conversion to CH4, while the Co-MOF is not suitable for the electrocatalytic CO2RR due to its low stability. In 1 M KOH electrolyte, Cu-MOF exhibits high performance for the electrocatalytic reduction of CO2 to CH4 with a faradaic efficiency (FE) of 41.53% at a potential of −1.28 V vs. RHE. The high performance and stability may be caused by the framework structure leading to a large electrochemically active surface area and fast charge transfer kinetics. This work offers an approach to design and construct CO2 electroreduction catalysts and viable solutions to energy and environmental issues caused by excessive CO2 emission. [ABSTRACT FROM AUTHOR]

Published
2024
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