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Your search keyword '"Yu, Liang"' showing total 129 results
129 results on '"Yu, Liang"'

1. OPM-MEG Measuring Phase Synchronization on Source Time Series: Application in Rhythmic Median Nerve Stimulation

Author

Yu-Yu Ma, Yang Gao, Huan-Qi Wu, Xiao-Yu Liang, Yong Li, Hao Lu, Chang-Zeng Liu, and Xiao-Lin Ning

Subjects
OPM-MEG, source time series, inter-trial phase synchronization, inter-reginal phase synchronization, functional connectivity, rhythmic stimulation, Medical technology, R855-855.5, Therapeutics. Pharmacology, RM1-950
Abstract

The magnetoencephalogram (MEG) based on array optically pumped magnetometers (OPMs) has the potential of replacing conventional cryogenic superconducting quantum interference device. Phase synchronization is a common method for measuring brain oscillations and functional connectivity. Verifying the feasibility and fidelity of OPM-MEG in measuring phase synchronization will help its widespread application in the study of aforementioned neural mechanisms. The analysis method on source-level time series can weaken the influence of instantaneous field spread effect. In this paper, the OPM-MEG was used for measuring the evoked responses of 20Hz rhythmic and arrhythmic median nerve stimulation, and the inter-trial phase synchronization (ITPS) and inter-reginal phase synchronization (IRPS) of primary somatosensory cortex (SI) and secondary somatosensory cortex (SII) were analysed. The results find that under rhythmic condition, the evoked responses of SI and SII show continuous oscillations and the effect of resetting phase. The values of ITPS and IRPS significantly increase at the stimulation frequency of 20Hz and its harmonic of 40Hz, whereas the arrhythmic stimulation does not exhibit this phenomenon. Moreover, in the initial stage of stimulation, the ITPS and IRPS values are significantly higher at Mu rhythm in the rhythmic condition compared to arrhythmic. In conclusion, the results demonstrate the ability of OPM-MEG in measuring phase pattern and functional connectivity on source-level, and may also prove beneficial for the study on the mechanism of rhythmic stimulation therapy for rehabilitation.

Published
2024
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2. The Influence of Non-Sinusoidal Inductance on Saliency-Based Position Estimation for Permanent Magnet Machine

Author

Kuo-Yuan Hung, Po-Huan Chou, Nai-Wen Liu, Yu-Liang Hsu, and Shih-Chin Yang

Subjects
Sensorless machine drive, saliency-based position estimation, high-frequency voltage injection, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

This paper analyzes the influence of non-sinusoidal inductance distortion on the saliency-based position estimation for the permanent magnet (PM) machines. Because of the high-power-density design of PM machines, the flux saturation usually causes the inductance self-saturation, inductance cross-saturation, and most importantly, secondary inductance harmonics. This paper fully investigates the saturation effect on saliency-based drives. Analytical model for the position estimation with saturated inductance is developed to understand the stability of saliency-based drive. It is confirmed that the saturation effect deviates the estimated flux position from actual rotor position. These estimation errors are critical for the saliency-based drive especially under load. This paper defines the feasible estimation region for PM machines with saturated and non-sinusoidal inductances. Considering the position estimation errors due to the saturated and non-sinusoidal inductance, the typical maximum torque per amp (MTPA) current trajectory based on the encoder-based control system should be modified especially for the IPM machines at full load. For the machines where the saturation condition is unknown, $q$ -axis current without negative $d$ -axis current is suggested to maintain control stability while the torque reduction is resultant. By contrast, the modified current trajectory could also be designed once the saturation reflected position estimation errors could be obtained. Finite element analysis (FEA) with inverter co-simulation is used to investigate the inductance distortion at various loads. A 6 kW IPM machine prototype with highly non-sinusoidal inductance is tested for the experimental verification.

Published
2022
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8. Encoding Syntactic Information into Transformers for Aspect-Based Sentiment Triplet Extraction.

Author

Yuan, Li, Wang, Jin, Yu, Liang-Chih, and Zhang, Xuejie

Abstract

Aspect-based sentiment triplet extraction (ASTE) aims to extract triplets consisting of aspect terms and their associated opinion terms and sentiment polarities from sentences, a relatively new and challenging subtask of aspect-based sentiment analysis (ABSA). Previous studies have used either pipeline models or unified tagging schema models. These models ignore the syntactic relationships between the aspect and its corresponding opinion words, which leads them to mistakenly focus on syntactically unrelated words. One feasible option is to use a graph convolution network (GCN) to exploit syntactic information by propagating the representation from the opinion words to the aspect. However, such a method considers all syntactic dependencies to be of the same type and thus may still incorrectly associate unrelated words to the target aspect through the iterations of graph convolutional propagation. Herein, a syntax-aware transformer (SA-Transformer) is proposed to extend the GCN strategy by fully exploiting the dependency types of edges to block inappropriate propagation. The proposed approach can obtain different representations and weights even for edges with the same dependency type according to their adjacent dependency type of edges. Instead of using a GCN layer, we used an L-layer SA transformer to encode syntactic information in the word-pair representation to improve performance. Experimental results on four benchmark datasets show that the proposed model outperforms various previous models for ASTE. [ABSTRACT FROM AUTHOR]

Published
2024
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24. NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

Author

Li, Yawei, primary, Zhang, Yulun, additional, Timofte, Radu, additional, Van Gool, Luc, additional, Yu, Lei, additional, Li, Youwei, additional, Li, Xinpeng, additional, Jiang, Ting, additional, Wu, Qi, additional, Han, Mingyan, additional, Lin, Wenjie, additional, Jiang, Chengzhi, additional, Luo, Jinting, additional, Fan, Haoqiang, additional, Liu, Shuaicheng, additional, Wang, Yucong, additional, Cai, Minjie, additional, Li, Mingxi, additional, Zhang, Yuhang, additional, Fan, Xian-Jun, additional, Sheng, Yankai, additional, Mao, Yanyu, additional, Zhang, Nihao, additional, Wang, Qian, additional, Zheng, Mingjun, additional, Sun, Long, additional, Pan, Jinshan, additional, Dong, Jiangxin, additional, Tang, Jinhui, additional, Yang, Zhongbao, additional, Wang, Yan, additional, Pan, Erlin, additional, Cai, Qixuan, additional, Dai, Xinan, additional, Zhussip, Magauiya, additional, Kalyazin, Nikolay, additional, Vyal, Dmitry, additional, Zou, Xueyi, additional, Yan, Youliang, additional, Chung, Heaseo, additional, Zhang, Jin, additional, Yu, Gaocheng, additional, Zhang, Feng, additional, Wang, Hongbin, additional, Liao, Bohao, additional, Du, Zhibo, additional, Wu, Yu-Liang, additional, Shi, Gege, additional, Peng, Long, additional, Wang, Yang, additional, Cao, Yang, additional, Zha, Zhengjun, additional, Huang, Zhi-Kai, additional, Chen, Yi-Chung, additional, Chiang, Yuan-Chun, additional, Yang, Hao-Hsiang, additional, Chen, Wei-Ting, additional, Chang, Hua-En, additional, Chen, I-Hsiang, additional, Hsieh, Chia-Hsuan, additional, Kuo, Sy-Yen, additional, Liu, Xin, additional, Pan, Jiahao, additional, Yu, Hongyuan, additional, Yu, Weichen, additional, Ge, Lin, additional, Dong, Jiahua, additional, Zou, Yajun, additional, Wu, Zhuoyuan, additional, Han, Binnan, additional, Zhang, Xiaolin, additional, Zhang, Heng, additional, Yin, Xuanwu, additional, Zuo, Kunlong, additional, Deng, Weijian, additional, Yuan, Hongjie, additional, Lu, Zengtong, additional, Ouyang, Mingyu, additional, Ma, Wenzhuo, additional, Liu, Nian, additional, Zheng, Hanyou, additional, Zhang, Yuantong, additional, Zhang, Junxi, additional, Chen, Zhenzhong, additional, Gendy, Garas, additional, Sabor, Nabil, additional, Hou, Jingchao, additional, He, Guanghui, additional, Zhu, Yurui, additional, Wang, Xi, additional, Fu, Xueyang, additional, Zha, Zheng-Jun, additional, Yin, Daheng, additional, Liu, Mengyang, additional, Chen, Baijun, additional, Li, Ao, additional, Luo, Lei, additional, Jin, Kangjun, additional, Zhu, Ce, additional, Zhang, Xiaoming, additional, Xie, Chengxing, additional, Li, Linze, additional, Meng, Haiteng, additional, Zhang, Tianlin, additional, Li, Tianrui, additional, Zhao, Xiaole, additional, Zhang, Zhao, additional, Li, Baiang, additional, Zheng, Huan, additional, Zhao, Suiyi, additional, Gao, Yangcheng, additional, Ren, Jiahuan, additional, Hu, Kang, additional, Shi, Jingpeng, additional, Wu, Zhijian, additional, Huang, Dingjiang, additional, Zhu, Jinchen, additional, Li, Hui, additional, Xv, Qianru, additional, Liu, Tianle, additional, Weng, Shizhuang, additional, Wu, Gang, additional, Jiang, Junpeng, additional, Liu, Xianming, additional, Jiang, Junjun, additional, Zhang, Mingjian, additional, Hu, Jing, additional, Wu, Chengxu, additional, Fan, Qinrui, additional, Feng, Chengming, additional, Luo, Ziwei, additional, Hu, Shu, additional, Lyu, Siwei, additional, Wu, Xi, additional, and Wang, Xin, additional

Published
2023
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