Junchi Wang

Tianjin University of Technology

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Jianyong Si

Chinese Academy of Medical Sciences & Peking Union Medical College

Xiaojin Li

Kunming University of Science and Technology

Huaxiang Wang

Taihe Hospital

Li Cao

Beijing Tian Tan Hospital

Guangzhi Li

Jiangxi Agricultural University

Yaqin Zhao

Gansu University of Traditional Chinese Medicine

Liangang Shen

Chinese Academy of Medical Sciences & Peking Union Medical College

Na Lv

Institute of Microbiology

Huijuan Wang

Central University of Finance and Economics

Jun Zhu

Beijing Foreign Studies University

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Intelligent Health (United Kingdom)

251

Chinese Academy of Medical Sciences & Peking Union Medical College

Chinese Academy of Sciences

Shanghai Jiao Tong University

Sichuan University

University of Chinese Academy of Sciences

Peking Union Medical College Hospital

Capital Medical University

Tsinghua University

Zhejiang University

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Sesquiterpene coumarins from seeds of Ferula sinkiangensis

Fitoterapia (2015)

Guangzhi Li Xiaojin Li Li Cao Lijing Zhang Liangang Shen

HeLa

Cancer cell lines

10.1016/j.fitote.2015.03.022

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Citations (55)

Research on Emotional Cognition of Game Players Based on Functional Brain Network

2022 IEEE International Conference on Mechatronics and Automation (ICMA) (2024)

Junchi Wang Yu Song Xuewen Zhao Zhongli Bai

10.1109/icma61710.2024.10633194

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Citations (0)

Chemical constituents from the fruiting bodies of Cryptoporus volvatus

Archives of Pharmacal Research (2016)

Junchi Wang Guangzhi Li Na Lv Li Gao Cao Li

Ergosterol

Derivative (finance)

Pseudouridine

10.1007/s12272-016-0754-4

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Citations (12)

An unusual sesquiterpene coumarin from the seeds of Ferula sinkiangensis

Journal of Asian Natural Products Research (2016)

Guang-Zhi Li Junchi Wang Xiaojin Li Li Cao Li Gao

A sesquiterpene coumarin, sinkiangenorin E, consisting of a novel bicyclo[4.3.1]decane-type sesquiterpene system, was isolated from the seeds of Ferula sinkiangensis. The structure of sinkiangenorin E including the relative stereochemistry and the absolute configuration was elucidated on the basis of spectroscopic data. The new compound showed cytotoxic activity against AGS cells (IC50, 12.7 μM) and inhibiting effect against influenza A H1N1 (IC50, 4.0 μM), which provided important clues for the study on the bioactivities of this type of sesquiterpene coumarins.

Decane

IC50

10.1080/10286020.2016.1168813

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Citations (26)

Noise Signal Classification for Oil Condition Monitoring Using Neural Network

Information engineering (2015)

Jian-Da Wu Ching-Fu Huang Junchi Wang Shih-Cheng Huang

This paper describes a motor oil condition monitoring system that uses wavelet transform and neural network techniques.Experiments were conducted using motor oils with different mileage in the gear mechanism. The gear noise signals were acquired using a data acquisition system and feature extraction using discrete wavelet transform. The discrete wavelet transform extracts the features for use as the input signals for a neural network. The motor oil condition classification is determined using an artificial neural network.According to the laboratory obtained experimental results the average recognition rate is about 90%.Therefore, the condition monitoring system can be achieved using Wavelet Transform and Neural Networks Techniques.

SIGNAL (programming language)

10.14355/ie.2015.03.006

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LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning

arXiv (Cornell University) (2024)

Junchi Wang Lei Ke

Understanding human instructions to identify the target objects is vital for perception systems. In recent years, the advancements of Large Language Models (LLMs) have introduced new possibilities for image segmentation. In this work, we delve into reasoning segmentation, a novel task that enables segmentation system to reason and interpret implicit user intention via large language model reasoning and then segment the corresponding target. Our work on reasoning segmentation contributes on both the methodological design and dataset labeling. For the model, we propose a new framework named LLM-Seg. LLM-Seg effectively connects the current foundational Segmentation Anything Model and the LLM by mask proposals selection. For the dataset, we propose an automatic data generation pipeline and construct a new reasoning segmentation dataset named LLM-Seg40K. Experiments demonstrate that our LLM-Seg exhibits competitive performance compared with existing methods. Furthermore, our proposed pipeline can efficiently produce high-quality reasoning segmentation datasets. The LLM-Seg40K dataset, developed through this pipeline, serves as a new benchmark for training and evaluating various reasoning segmentation approaches. Our code, models and dataset are at https://github.com/wangjunchi/LLMSeg.

Benchmark (surveying)

Market Segmentation

10.48550/arxiv.2404.08767

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CFBC: A network for EEG emotion recognition by selecting the information of crucial frequency bands

IEEE Sensors Journal (2024)

Mu Zhu Zhongli Bai Qingzhou Wu Junchi Wang Wenhui Xu

10.1109/jsen.2024.3440340

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Few-shot named entity recognition framework for forestry science metadata extraction