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507 results on '"Wu, Xiaochen"'

1. Targeting RBM39 through indisulam induced mis-splicing of mRNA to exert anti-cancer effects in T-cell acute lymphoblastic leukemia

Author

Ji, Tongting, Yang, Yang, Yu, Juanjuan, Yin, Hongli, Chu, Xinran, Yang, Pengju, Xu, Ling, Wang, Xiaodong, Hu, Shaoyan, Li, Yizhen, Wu, Xiaochen, Liu, Wengyuan, Zhou, Bi, Wang, Wenjuan, Zhang, Shuqi, Cheng, Wei, Chen, Yanling, Shi, Lei, Li, Zhiheng, Zhuo, Ran, Zhang, Yongping, Tao, Yanfang, Wu, Di, Li, Xiaolu, Zhang, Zimu, Fan, Jun-jie, Pan, Jian, and Lu, Jun

Published
2024
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13. Investigating the Impact of Metacognition on Working Memory and Procedural Learning Mechanisms

Author

Wu, Xiaochen, Haile, Theodros, and Prat, Chantel

Subjects
cognitive science
Abstract

This study examined the influence of metacognition on declarative and reinforcement learning (RL) mechanisms. We collected data from 218 undergraduates using a within-subjects metacognitive manipulation of a stimulus-response (S-R) learning task created by Collins (2018). Contributions of declarative and RL mechanisms are assessed by differences in learning rate for blocks of 3 items versus 6 items, and by the rate of forgetting with an incidental post-test. If metacognition differentially affects declarative and RL, we expect a three-way interaction between the task phase (learning/post-test), block type (long/short), and metacognition (before/during). Our results showed significant main effects of phase (F(1,217) =143.18, p=9.18e-32), length (F(1,217)=541.11, p=2.06e-104) and metacognition (F(1,217) = 19.78, p = 9.22e-06), with better performance during the learning phase, short blocks, and metacognitive manipulation. A significant phase by metacognition interaction (F(1,217) =8.11, 4.45e-03) suggested that metacognition monitoring improved test performance while having little effect on learning performance.

Published
2021

34. A research study of lightweight state perception algorithm based on improved YOLOv5s‐Tiny for fully mechanized top‐coal caving mining.

Author

Shan, PengFei, Yang, Tong, Wu, XiaoChen, and Sun, HaoQiang

Subjects
CAVING, COAL mining, CAVES, LONGWALL mining, ALGORITHMS, DEEP learning, COAL
Abstract

Real‐time monitoring of the coal caving process in fully mechanized mining is crucial for achieving intelligent and efficient top‐coal caving. While the coal gangue identification method, employing vision and deep learning, has advanced in the realm of intelligent monitoring, it exhibits a dependency on high‐performance hardware. This reliance poses challenges for deploying identification equipment on mobile terminals, hindering the widespread application of this method. To address the issues above, the paper presents a lightweight algorithm, utilizing You Only Look Once version 5s (YOLOv5s), utilizing YOLOv5s for the real‐time perception of the top‐coal caving state in fully mechanized caving mining. We replace the backbone network of YOLOv5s with the ShuffleNetv2 structure that is more suitable for lightweight deployment, and add the Simple Attention Mechanism attention mechanism to the network structure to enhance the model's receptive field and feature expression ability, and reduce the impact of falling debris on the detection results. A dynamic experimental platform for top‐coal caving in fully mechanized caving mining for thick coal seams is set up, and preprocessing operations such as brightness, sharpening, and denoising are performed on the image data sets collected by high‐speed industrial cameras. Research results show that compared with the traditional YOLOv5s, the improved model's P, mAP, F1 score, and other indicators have increased by 3.4%, 2.1%, and 1.1%, respectively, the model size is 70% of the original, and the detection frames per second value has increased by 48.1%. The lightweight algorithm stabilizes the accuracy of coal gangue identification dramatically in real time. It dramatically reduces the computing pressure on the mobile terminal, providing basic theory and practice for real‐time monitoring of fully mechanized coal caving mining. [ABSTRACT FROM AUTHOR]

Published
2024
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