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536 results on '"Yang, Chunjie"'

1. Approximated Orthogonal Projection Unit: Stabilizing Regression Network Training Using Natural Gradient

Author

Wang, Shaoqi, Yang, Chunjie, and Lou, Siwei

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

Neural networks (NN) are extensively studied in cutting-edge soft sensor models due to their feature extraction and function approximation capabilities. Current research into network-based methods primarily focuses on models' offline accuracy. Notably, in industrial soft sensor context, online optimizing stability and interpretability are prioritized, followed by accuracy. This requires a clearer understanding of network's training process. To bridge this gap, we propose a novel NN named the Approximated Orthogonal Projection Unit (AOPU) which has solid mathematical basis and presents superior training stability. AOPU truncates the gradient backpropagation at dual parameters, optimizes the trackable parameters updates, and enhances the robustness of training. We further prove that AOPU attains minimum variance estimation (MVE) in NN, wherein the truncated gradient approximates the natural gradient (NG). Empirical results on two chemical process datasets clearly show that AOPU outperforms other models in achieving stable convergence, marking a significant advancement in soft sensor field.

Published
2024

2. Bright solitons in a spin-orbit-coupled dipolar Bose-Einstein condensate trapped within a double-lattice

Author

Wang, Qi, Qin, Jieli, Zhao, Junjie, Qin, Lu, Zhang, Yingying, Zhou, Lu, Feng, Xuejing, Yang, Chunjie, Zhu, Zunlue, Liu, Wuming, and Zhao, Xingdong

Subjects
Condensed Matter - Quantum Gases
Abstract

By effectively controlling the dipole-dipole interaction, we investigate the characteristics of the ground state of bright solitons in a spin-orbit coupled dipolar Bose-Einstein condensate. The dipolar atoms are trapped within a double-lattice which consists of a linear and a nonlinear lattice. We derive the motion equations of the different spin components, taking the controlling mechanisms of the diolpe-dipole interaction into account. An analytical expression of dipole-dipole interaction is derived. By adjusting the dipole polarization angle, the dipole interaction can be adjusted from attraction to repulsion. On this basis, we study the generation and manipulation of the bright solitons using both the analytical variational method and numerical imaginary time evolution. The stability of the bright solitons is also analyzed and we map out the stability phase diagram. By adjusting the long-range dipole-dipole interaction, one can achieve manipulation of bright solitons in all aspects, including the existence, width, nodes, and stability. Considering the complexity of our system, our results will have enormous potential applications in quantum simulation of complex systems.

Published
2023

3. Itinerant ferromagnetism entrenched by the anisotropy of spin-orbit coupling in a dipolar Fermi gas

Author

Feng, Xue-Jing, Li, Jin-Xin, Qin, Lu, Zhang, Ying-Ying, Xia, ShiQiang, Zhou, Lu, Yang, ChunJie, Zhu, ZunLue, Liu, Wu-Ming, and Zhao, Xing-Dong

Subjects
Condensed Matter - Quantum Gases
Abstract

We investigate the the itinerant ferromagnetism in a dipolar Fermi atomic system with the anisotropic spin-orbit coupling (SOC),which is traditionally explored with isotropic contact interaction.We first study the ferromagnetism transition boundaries and the properties of the ground states through the density and spin-flip distribution in momentum space, and we find that both the anisotropy and the magnitude of the SOC play an important role in this process. We propose a helpful scheme and a quantum control method which can be applied to conquering the difficulties of previous experimental observation of itinerant ferromagnetism. Our further study reveals that exotic Fermi surfaces and an abnormal phase region can exist in this system by controlling the anisotropy of SOC, which can provide constructive suggestions for the research and the application of a dipolar Fermi gas. Furthermore, we also calculate the ferromagnetism transition temperature and novel distributions in momentum space at finite temperature beyond the ground states from the perspective of experiment., Comment: 8 pages,7 figures

Published
2023

4. STAD: Self-Training with Ambiguous Data for Low-Resource Relation Extraction

Author

Yu, Junjie, Wang, Xing, Zhao, Jiangjiang, Yang, Chunjie, and Chen, Wenliang

Subjects
Computer Science - Computation and Language
Abstract

We present a simple yet effective self-training approach, named as STAD, for low-resource relation extraction. The approach first classifies the auto-annotated instances into two groups: confident instances and uncertain instances, according to the probabilities predicted by a teacher model. In contrast to most previous studies, which mainly only use the confident instances for self-training, we make use of the uncertain instances. To this end, we propose a method to identify ambiguous but useful instances from the uncertain instances and then divide the relations into candidate-label set and negative-label set for each ambiguous instance. Next, we propose a set-negative training method on the negative-label sets for the ambiguous instances and a positive training method for the confident instances. Finally, a joint-training method is proposed to build the final relation extraction system on all data. Experimental results on two widely used datasets SemEval2010 Task-8 and Re-TACRED with low-resource settings demonstrate that this new self-training approach indeed achieves significant and consistent improvements when comparing to several competitive self-training systems. Code is publicly available at https://github.com/jjyunlp/STAD, Comment: Accepted by COLING'22

Published
2022

17. Toxicity Assessment of Five Pesticides Using Vibrio qinghaiensis sp. Q67

Author

PENG Yue, WEN Chunlu, YANG Chunjie, JIANG Shengqi, DING Wu

Subjects
v. qinghaiensis sp. q67, joint toxicity, pesticides, acute toxicity, rapid test, Food processing and manufacture, TP368-456
Abstract

Vibrio qinghaiensis sp. Q67 was used as an indicator strain to study the toxic effects of five common pesticides, pirimicarb (PIR), carbaryl (CAR), glyphosate (GLY), dimethoate (DIM), and acephate (ACE). The toxicities were evaluated by toxic unit and concentration addition model. The results showed that the toxicity of the pesticides decreased in the following order: PIR > GLY > CAR > DIM > ACE. Four of the 10 binary mixtures of these five pesticides showed antagonistic toxic effects, while the remaining six showed additive or synergistic toxic effects. Among the seven five-component mixtures designed by uniform design ray, four showed synergistic and then antagonistic effects, while the remaining three showed a consistent antagonistic effect. The toxicity of the five-component mixtures was strongly positively correlated with the concentration of CAR (r = 0.947). Based on this result, a linear model was established to predict the toxicity of pesticide mixtures. This method has the advantages of low cost, rapidity, simplicity and convenience, which can be used as a potential preliminary screening method for rapid detection of pesticide residues in agricultural products and foods.

Published
2023
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21. Dual Antiplatelet Therapies and Causes in Minor Stroke or Transient Ischemic Attack: A Prespecified Analysis in the CHANCE-2 Trial

Author

Xie, Xuewei, Jing, Jing, Meng, Xia, Claiborne Johnston, S., Bath, Philip M., Li, Zixiao, Zhao, Xingquan, Liu, Liping, Wang, Yilong, Xu, Qin, Wang, Anxin, Jiang, Yong, Li, Hao, Wang, Yongjun, Wang, Yongjun, Johnston, S. Claiborne, Xu, Anding, Bath, Philip M. W., Dong, Qiang, Wang, Yongjun, Li, Hao, Meng, Xia, Jing, Jing, Xie, Xuewei, Lin, Jinxi, Simon, Tabassome, Danchin, Nicolas, Zhi, Helen, Peng, Bin, Fan, Dongsheng, Wang, David, Youn, Teddy, Adams, Harold P., Jr., Meng, Xia, Xie, Xuewei, Jing, Jing, Lin, Jinxi, Niu, Siying, Li, Hao, Pan, Yuesong, Wang, Anxin, Jiang, Yong, Jin, Aoming, Wu, Haibo, Shi, Wei, Wu, Haibo, Li, Jianhua, Wang, Yanxia, Wei, Yan, Liu, Haichao, Zhao, Jianhua, Han, Xinshen, Sheng, Baoying, Yang, Xiaofan, Jing, Ping, Chen, Jiechun, Huang, Panbing, Chen, Guofang, Feng, Yefang, Wu, Lie, Zhu, Runxiu, Liu, Peiru, Chang, Liguo, Yang, Xiafeng, Wang, Xinqiang, Zhao, Jinguo, Zhang, Wei, Cui, Zhigang, Wu, Xuemei, Huang, Hua, Liu, Qiuwu, Zhao, Xingquan, Cheng, Dongli, Zhu, Yulan, Wang, Lihua, Li, Guozhong, Zhang, Xiaoxuan, Xue, Wenjun, Chen, Guohua, Li, Haipeng, Deng, Ke, Liu, Jingyao, Liu, Yansong, Wei, Xiu’e, Wang, Dunjing, Feng, Juan, Wang, Baojun, Chen, Yongming, Wu, Bing, Chai, Xiangting, Shi, Fudong, Cai, Xueli, Yang, Hongqin, Zhang, Yundong, Wang, Xia, Wang, Yumei, Shi, Zhimin, Chen, Shengli, Chen, Yangmei, Zhou, Lichun, Hong, Weimin, Yin, Lufeng, Ren, Lijie, Ma, Jianhua, Lei, Yongqian, Wu, Minghua, Zeng, Guoyong, Wu, Lingfeng, Cao, Mingha, Guo, Cunju, Man, Honghao, Bai, Yu, Wu, Aimei, Huang, Xuerong, Zhang, Chunhua, Jiang, Xiaoling, Shao, Peining, Luo, Dexin, Cai, Zhiyou, Jia, Yi, Ma, Aijun, Bai, Ying, Gong, Xuhai, Liu, Jirong, Zhang, Meng, Xu, Anding, Tan, Hong, An, Yi, Wang, Zhimin, Zhao, Weili, Wu, Xiangbin, Gao, Wenwei, Gao, Yueqiang, Li, Xinyi, Wu, Jin, Wang, Feng, Du, Ganqin, Wu, Kaiyi, Wan, Hebin, Chu, Lan, Hong, Daojun, Cao, Hui, Xu, Bing, Liu, Jie, Zhang, Hao, Yang, Jie, Zhang, Guiru, Chen, Hong, Zhang, Jianling, Li, Xuemei, Zou, Yu’an, Wang, Yan, Wei, Xia, Wang, Jianlin, Chen, Weifeng, Liu, Chunfeng, Sui, Changbai, Li, Hongyan, Zhong, Hua, Wu, Aisheng, Fan, Jinqi, Ji, Qiuhong, Lin, Jinshen, Shi, Xueying, Kang, Guangming, Xie, Haiyang, Ma, Dan, Wang, Hongliang, Chen, Wei, Shi, Peng, Shangguan, Wen, Gu, Jun, Feng, Xiaopeng, Zhan, Qunling, Han, Xianwen, Cao, Hongyuan, Wei, Liang, Lu, Weifeng, Zheng, Chunling, Yang, Chunjie, Zhao, Min, Wu, Heng, Dong, Chunhua, He, Yongli, Yao, Shufang, Zhu, Xiangyang, Zhao, Zhong, Ye, Min, Cai, Guodong, Peng, Guangyao, Li, Hongfei, Zhang, Lujun, Zhang, Shoucheng, Zhou, Wei, Liu, Hong, Fan, Xuewen, Tan, Wei, Zhu, Renxiang, He, Tianhui, Wang, Qizhang, Zhang, Lifang, Yang, Jinsuo, Liu, Yuqing, Su, Shaohua, He, Li, Yu, Yadong, Wei, Zhang, Zhankui, Lin, Rong Qiu, Xue, Bin Qi, Ying, Sheng, Lei, Zheng Wu, Shi, Di Wu, Song, Liang Wu, Sheng, Qin, Ding, Xiao, Bo, Wang, Kai, Ting Zhang, Xi, and Jing Pang, Jin

Published
2023
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31. Multihead-Res-SE Residual Network with Attention for Human Activity Recognition.

Author

Kang, Hongbo, Lv, Tailong, Yang, Chunjie, and Wang, Wenqing

Subjects
DEEP learning, CONVOLUTIONAL neural networks, LEARNING, WEARABLE technology, HUMAN activity recognition, PROBLEM solving
Abstract

Human activity recognition (HAR) typically uses wearable sensors to identify and analyze the time-series data they collect, enabling recognition of specific actions. As such, HAR is increasingly applied in human–computer interaction, healthcare, and other fields, making accurate and efficient recognition of various human activities. In recent years, deep learning methods have been extensively applied in sensor-based HAR, yielding remarkable results. However, complex HAR research, which involves specific human behaviors in varied contexts, still faces several challenges. To solve these problems, we propose a multi-head neural network based on the attention mechanism. This framework contains three convolutional heads, with each head designed using one-dimensional CNN to extract features from sensory data. The model uses a channel attention module (squeeze–excitation module) to enhance the representational capabilities of convolutional neural networks. We conducted experiments on two publicly available benchmark datasets, UCI-HAR and WISDM, to evaluate our model. The results were satisfactory, with overall recognition accuracies of 96.72% and 97.73% on their respective datasets. The experimental results demonstrate the effectiveness of the network structure for the HAR, which ensures a higher level of accuracy. [ABSTRACT FROM AUTHOR]

Published
2024
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32. An Approach of Query Audience's Attention in Virtual Speech.

Author

Kang, Hongbo, Yang, Rui, Song, Ruoyang, Yang, Chunjie, and Wang, Wenqing

Subjects
EYE tracking, EYE contact, RANK correlation (Statistics), EYE movements, REFERENCE values, GAZE
Abstract

Virtual speeches are a very popular way for remote multi-user communication, but it has the disadvantage of the lack of eye contact. This paper proposes the evaluation of an online audience attention based on gaze tracking. Our research only uses webcams to capture the audience's head posture, gaze time, and other features, providing a low-cost method for attention monitoring with reference values across multiple domains. Meantime, we also propose a set of indexes which can be used to evaluate the audience's degree of attention, making up for the fact that the speaker cannot gauge the audience's concentration through eye contact during online speeches. We selected 96 students for a 20 min group simulation session and used Spearman's correlation coefficient to analyze the correlation between our evaluation indicators and concentration. The result showed that each evaluation index has a significant correlation with the degree of attention (p = 0.01), and all the students in the focused group met the thresholds set by each of our evaluation indicators, while the students in the non-focused group failed to reach the standard. During the simulation, eye movement data and EEG signals were measured synchronously for the second group of students. The EEG results of the students were consistent with the systematic evaluation. The performance of the measured EEG signals confirmed the accuracy of the systematic evaluation. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Stage grinding--magnetic separation--flotation combined beneficiation processing technology for a complex refractory iron ore.

Author

LIU Guokai, ZHENG Yongxing, YANG Chunjie, JIAN Sheng, XIE Xiaochen, YU Yong, and WANG Heng

Subjects
IRON ores, MAGNETIC separation, MAGNETITE, SULFIDE minerals, MAGNETIC flux density, SURFACE active agents
Abstract

Taking a complex and difficult-to-process iron ore from western Yunnan as the research object, X-ray fluorescence spectroscopy (XRF), chemical multi-element analysis, X-ray diffraction (XRD) and other methods were used to analyze the ore. The results show that the most valuable metal element in the ore is iron, which is mainly present in the form of magnetite. The sulfide minerals primarily include magnetite, pyrite and sphalerite, and the gangue minerals mainly include serpentine and talc. Based on the ore properties of the mine, a combined beneficiation process involving stage grinding and separation, magnetic separation, and flotation desulfurization was adopted to process the iron ore. By studying the effect of process parameters such as magnetic field strength, grinding fineness, types of activators, collectors and foaming agents on the quality improvement of iron ore by magnetic separation and flotation desulfurization, the optimal process parameters were obtained and closed-circuit tests were carried out according to the obtained best process conditions. Finally, a sulfur concentrate with an S grade of 21.90% and an S recovery rate of 68.52%, as well as an iron concentrate with a TFe grade of 61.56%, a TFe recovery rate of 67. 41%, and an S content of 0.67%, were obtained. The results could provide support for the efficient utilization of the iron ore resources. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Siamese Neural Network and Multimodal Data Fusion Approach for Small-Sample Learning in Industrial Soft Sensor Modeling

Author

Zhao, Yuchen, Liu, Zhe, Yan, Feng, Yang, Chunjie, Cao, Yang, Bao, Yaoyao, Mao, Jiayun, Lou, Siwei, and Xiao, Hang

Abstract

In industrial scenarios, soft sensor modeling often faces the challenge of underfitting due to limited available data. Therefore, in the field of industrial soft sensing, it is crucial to develop solutions for regression problems based on small-sample learning. One potentially feasible approach is to pair the data used for modeling to generate a larger set of data pairs, and then utilize Siamese neural networks (SNNs) to extract additional information from the paired data, thereby addressing the underfitting issues. Moreover, the integration of data from multiple sources can also help alleviate the problem of underfitting. Based on these ideas, this article proposes an SNN-based multimodal data fusion framework (SNN-MMDFF) for tackling the challenges of small-sample learning in industrial soft sensor modeling. The SNN-MDDFF employs end-to-end multimodal feature extraction (MMFE) networks to extract multimodal features from the paired multimodal data sequences, which are then employed to predict the soft sensing target. A three-stage training strategy, which incorporates hierarchical metric learning and component-by-component training approaches, is implemented to train this framework. The SNN-MMDFF is validated in a case study on cement clinker free calcium oxide (f-CaO) content soft sensing, which demonstrates superior performance and robust modeling stability.

Published
2024
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41. The Design of Intelligent Energy Consumption Acquisition System Based on Narrowband Internet of Things

Author

Wang, Wenqing, Zhang, Li, Yang, Chunjie, 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, Krömer, Pavel, editor, Zhang, Hong, editor, Liang, Yongquan, editor, and Pan, Jeng-Shyang, editor

Published
2019
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42. Design of Self-balancing Vehicle System

Author

Wang, Wenqing, Yan, Yuan, Zhang, Ruyue, Zhang, Li, Kang, Hongbo, Yang, Chunjie, 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, Krömer, Pavel, editor, Zhang, Hong, editor, Liang, Yongquan, editor, and Pan, Jeng-Shyang, editor

Published
2019
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43. Intelligent Water Environment Monitoring System

Author

Wang, Wenqing, Zhang, Ruyue, Yang, Chunjie, Kang, Hongbo, Zhang, Li, Yan, Yuan, 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, Krömer, Pavel, editor, Zhang, Hong, editor, Liang, Yongquan, editor, and Pan, Jeng-Shyang, editor

Published
2019
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44. An Analysis and Research of Network Log Based on Hadoop

Author

Wang, Wenqing, Niu, Xiaolong, Yang, Chunjie, Kang, Hongbo, Chen, Zhentong, Wang, Yuchen, 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, Krömer, Pavel, editor, Zhang, Hong, editor, Liang, Yongquan, editor, and Pan, Jeng-Shyang, editor

Published
2019
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