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427 results on '"Cao, Jingyu"'

1. XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

Author

Liu, Wenzhang, Cai, Wenzhe, Jiang, Kun, Cheng, Guangran, Wang, Yuanda, Wang, Jiawei, Cao, Jingyu, Xu, Lele, Mu, Chaoxu, and Sun, Changyin

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Digital Libraries
Abstract

In this paper, we present XuanCe, a comprehensive and unified deep reinforcement learning (DRL) library designed to be compatible with PyTorch, TensorFlow, and MindSpore. XuanCe offers a wide range of functionalities, including over 40 classical DRL and multi-agent DRL algorithms, with the flexibility to easily incorporate new algorithms and environments. It is a versatile DRL library that supports CPU, GPU, and Ascend, and can be executed on various operating systems such as Ubuntu, Windows, MacOS, and EulerOS. Extensive benchmarks conducted on popular environments including MuJoCo, Atari, and StarCraftII multi-agent challenge demonstrate the library's impressive performance. XuanCe is open-source and can be accessed at https://github.com/agi-brain/xuance.git., Comment: 16 pages, 4 figures, 32 conferences

Published
2023

16. Spectral self-adaptive absorber/emitter for harvesting energy from the sun and outer space

Author

Ao, Xianze, Li, Bowen, Zhao, Bin, Hu, Mingke, Ren, Hui, Yang, Honglun, Liu, Jie, Cao, Jingyu, Feng, Junsheng, Yang, Yuanjun, Qi, Zeming, Li, Liangbin, Pei, Gang, and Zou, Chongwen

Subjects
Condensed Matter - Materials Science, Physics - Optics, 74K35
Abstract

The sun (~6000 K) and outer space (~3 K) are the original heat source and sink for human beings on Earth. The energy applications of absorbing solar irradiation and harvesting the coldness of outer space for energy utilization have attracted considerable interest from researchers. However, combining these two functions in a static device for continuous energy harvesting is unachievable due to the intrinsic infrared spectral conflict. In this study, we developed spectral self-adaptive absorber/emitter (SSA/E) for daytime photothermal and nighttime radiative sky cooling modes depending on the phase transition of the vanadium dioxide coated layer. A 24-hour day-night test showed that the fabricated SSA/E has continuous energy harvesting ability and improved overall energy utilization performance, thus showing remarkable potential in future energy applications., Comment: 15 pages, 4 figures

Published
2020
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18. A Case of Primary Hepatic Neuroendocrine Tumor with Skin Lump, Rash and Bone Pain as First Symptoms

Author

ZANG Bo, CAO Jingyu, WANG Jun, WANG Yan, WANG Liankun, WANG Changyao, and LIU Bin

Subjects
skin lump, non-bacterial osteomyelitis, focal liver lesion, neuroendocrine tumor, Medicine
Abstract

Neuroendocrine tumors(NETs) commonly occur in organs such as the gastrointestinal tract and pancreas, with the liver being the most common site of metastasis. Primary hepatic neuroendocrine tumors(PHNETs) originating from the liver are exceedingly rare. Herein, we report a case of PHNETs in a patient who presented initially with skin nodules, rash, and bone pain, followed by hyperglycemia and hypoproteinemia. Imaging studies revealed hepatic lesions, and the patient was ultimately diagnosed with PHNETs using 18F-OCT combined with 18F-FDG PET/CT and histopathology. The case not only presented a diagnostic challenge, but also exhibited rapid progression involving multiple systems. The experience gained in the diagnosis and treatment of this case may improve clinical awareness of NETs.

Published
2023
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35. Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM.

Author

Bui, Thioanh, Liu, Juncheng, Cao, Jingyu, Wei, Geng, and Zeng, Qian

Subjects
CONVOLUTIONAL neural networks, ASPECT ratio (Images), PHYSIOLOGICAL models, HOME environment, OLDER people
Abstract

This work was conducted mainly to provide a healthy and safe monitoring system for the elderly living in the home environment. In this paper, two different target fall detection schemes are proposed based on whether the target is visible or not. When the target is visible, a vision-based fall detection algorithm is proposed, where an image of the target captured by a camera is transmitted to the improved You Only Look Once version 5s (YOLOv5s) model for posture detection. In contrast, when the target is invisible, a WiFi-based fall detection algorithm is proposed, where channel state information (CSI) signals are used to estimate the target's posture with an improved long short-term memory (LSTM) model. In the improved YOLOv5s model, adaptive picture scaling technology named Letterbox is used to maintain consistency in the aspect ratio of images in the dataset, and the weighted bidirectional feature pyramid (BiFPN) and the attention mechanisms of squeeze-and-excitation (SE) and coordinate attention (CA) modules are added to the Backbone network and Neck network, respectively. In the improved LSTM model, the Hampel filter is used to eliminate the noise from CSI signals and the convolutional neural network (CNN) model is combined with the LSTM to process the image made from CSI signals, and thus the object of the improved LSTM model at a point in time is the analysis of the amplitude of 90 CSI signals. The final monitoring result of the health status of the target is the result of combining the fall detection of the improved YOLOv5s and LSTM models with the physiological information of the target. Experimental results show the following: (1) the detection precision, recall rate, and average precision of the improved YOLOv5s model are increased by 7.2%, 9%, and 7.6%, respectively, compared with the original model, and there is almost no missed detection of the target; (2) the detection accuracy of the improved LSTM model is improved by 15.61%, 29.36%, and 52.39% compared with the original LSTM, CNN, and neural network (NN) models, respectively, while the convergence speed is improved by 90% compared with the original LSTM model; and (3) the proposed algorithm can meet the requirements of accurate, real-time, and stable applications of health monitoring. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Molecular simulation study on the adsorption and storage behavior of CO2 in different matrix components of shale.

Author

Wu, Xudi, Liu, Wei, Fu, Shuoran, Ma, Jian, Cao, Jingyu, Wang, Xiongfei, Niu, Baolun, and Lai, Xiaopeng

Subjects
MOLECULAR dynamics, SHALE, WATER temperature, TEMPERATURE effect, HIGH temperatures
Abstract

This study investigates the adsorption and storage behaviour of CO2 in the different matrix components of shale based on a molecular simulation method. First, based on the Grand Canonical Monte Carlo (GCMC) method, we successfully established five pore models three fluid models to study the effects of temperature, pressure, pore diameter, water content, and CH4 on the adsorption of CO2. Then, based on the actual data from the shale of the Longmaxi Formation in Sichuan Basin, China, the storage capacity of CO2 in the shale is determined by calculating the density distribution of CO2 and the proportion of excess adsorbed gas. The results demonstrate that both high temperatures and water content are unfavourable for the adsorption of CO2 in the shale pores. The adsorption of CO2 increases with rising pressure while it decreases with increasing pore size. The magnitude and stability of CO2 adsorption in the pores of various matrix components is as follows: organic matter > montmorillonite > illite > kaolinite > quartz. Compared to the scenarios neglecting the adsorption of CO2, the storage capacity of CO2 decreases by 7.87%. The main finding of this study is expected to provide theoretical guidelines for CO2 adsorption storage in shale. [ABSTRACT FROM AUTHOR]

Published
2024
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