Yingjie Tian

Institute of Modern Physics

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Yong Shi

University of Chinese Academy of Sciences

Zhiquan Qi

Chinese Academy of Sciences

Jingjing Tang

Southwestern University of Finance and Economics

Dalian Liu

Beijing Union University

Gang Kou

Southwestern University of Finance and Economics

Jianping Li

Tianjin University

Yi Peng

University of Electronic Science and Technology of China

Saiji Fu

Beijing University of Posts and Telecommunications

Z. Jiao

University of Science and Technology of China

M. Y. Dong

Institute of High Energy Physics

Cooperative Institutions

Institute of High Energy Physics

1,600

University of Chinese Academy of Sciences

939

Chinese Academy of Sciences

679

COMSATS University Islamabad

526

Henan University

519

Inner Mongolia University

497

University of Science and Technology of China

451

China Center of Advanced Science and Technology

397

Central China Normal University

305

Ruhr University Bochum

293

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Forecasting Model of Mass Incidents in China - An Explorative Research Based on Suppport Vector Machine

International Conference on Business Intelligence and Financial Engineering (2009)

Jiashu Zhou Erping Wang Yiwen Chen Xuanna Wu Yujie Ma

[Purpose] Mass incidents have emerged as a serious social problem concerning national security in China. So, it is necessary to construct a forecasting model to predict such public events. In this paper, support vector machines are applied to the model. [Method] Based on the social surveys conducted in 119 counties of Shanxi, Gansu and Hubei provinces, 3 multi-class classification problems were proposed, and then 3 multi-class support vector classification forecasting models were constructed. [Results] Preliminary experiments have proved that our method, compared with multiple cumulative logistic regression, should be more effective and accurate(enter method as well as the stepwise one). [Conclusion] It can be concluded from the results that irrationally behavioral intentions can be predicted more accurate than those rational ones. When the collective attitudes are applied to the forecast of the collective behavioral intentions, SVM method was approved to be the most effective approach. This paper represents an originally explorative research.

Public security

10.1109/bife.2009.44

Cite

Citations (1)

Artificial cheerleading in IEO: Marketing campaign or pump and dump scheme

Information Processing & Management (2023)

Yingjie Tian Yuhao Xie

Sentiment Analysis

10.1016/j.ipm.2023.103537

Cite

Citations (0)

Exosomes secreted from bone marrow mesenchymal stem cells suppress cardiomyocyte hypertrophy through Hippo-YAP pathway in heart failure

Figshare (2023)

Yu Ren Yun Wu Wenshuai He Yingjie Tian Xingsheng Zhao

Abstract Mesenchymal stem cells-derived exosomes (MSCs-exosomes) reportedly possess cardioprotective effects. This study investigated the therapeutic potential and mechanisms of MSCs-exosomes on heart failure (HF). H9c2 cells were used to establish a cardiomyocyte hypertrophy model by angiotensin II (Ang II) treatment. Isolated MSCs-exosomes were identified by transmission electron microscope and CD63 detection. Apoptosis rate was measured by terminal deoxynucleotidyl transferase (TdT) dUTP Nick-End Labeling (TUNEL) assay. Levels of inflammatory factors [interleukin (IL)-1β, IL-4, IL-6, and tumor necrosis factor (TNF)-α] and brain natriuretic peptide (BNP) were determined by ELISA. Expression of apoptosis-related proteins [Bax, B-cell lymphoma-2 (Bcl-2), and caspase 3] and Hippo-Yes-associated protein (YAP) pathway-related proteins [YAP, phosphor (p)-YAP, and tafazzin (TAZ)] was detected by western blotting. Cardiomyocyte hypertrophy of H9c2 cells induced by Ang II was ameliorated by MSCs-exosomes treatment. MSCs-exosomes downregulated Bax and caspase 3 levels and upregulated Bcl-2 level in Ang II-induced H9c2 cells. MSCs-exosomes also reduced the levels of BNP, IL-1β, IL-4, IL-6, and TNF-α in Ang II-induced H9c2 cells. Meanwhile, p-YAP was downregulated and TAZ was upregulated after MSCs-exosomes administration. In conclusion, MSCs-exosomes alleviate the apoptosis and inflammatory response of cardiomyocyte via deactivating Hippo-YAP pathway in HF.

Hippo signaling pathway

10.6084/m9.figshare.22268609

Cite

Citations (0)

Explicit Unsupervised Feature Selection Based on Structured Graph and Locally Linear Embedding

Jianyu Miao Jingjing Zhao Tiejun Yang Chao Fan Yingjie Tian

Numerous redundant and irrelevant features, and outliers are usually contained in high-dimensional data whose presence, if ignored, can bring detrimental effects on the performance of data processing tasks. Feature selection as a trending topic in a great deal of domains has been greatly concerned, which can effectively deal with noise and outlier. In this work, we propose a novel unsupervised method that unifies the explicit feature selective matrix learning and structured graph learning into a framework. An explicit selection matrix only consisting of 0 and 1 are designed, which can be integrated into locally linear embedding, and can be effectively and efficiently solved with closed-form solution. Furthermore, we take into account the local consistency between the samples so that the low dimensional manifold structures of samples can be preserved to a better degree. Instead of using the pre-defined similarity matrix to construct graph, we make use of the way of learning to obtain the adaptive graph. Besides, for the purpose of exploiting the cluster structure of data, we impose a rank constraint on the graph. Notably, both explicit selection matrix and intrinsic structure are learned in low-dimensional feature subspace, and thus the proposed methodology is more straightforward. We present an effective and efficient algorithm for the resulting problem based on alternative optimization strategy, and establish its convergence and computational complexity analysis results. Extensive experiments verify that our proposed approach can significantly outperform advanced methods.

Feature (linguistics)

Graph Embedding

10.2139/ssrn.4658542

Cite

Citations (0)

Credit card churn forecasting by logistic regression and decision tree

Expert Systems with Applications (2011)

Guangli Nie Wei Rowe Lingling Zhang Yingjie Tian Yong Shi

Variables

Tree (set theory)

10.1016/j.eswa.2011.06.028

Cite

Citations (204)

Mining residential power consumption behaviour based on association rules

Fan Li Yingjie Tian Yi Wu Yingying Zhao Yun Su

In recent years, with the clean energy consumption, decentralized supply and market-oriented trading, the composition of power system has become increasingly complex. As one of the most important consumer groups, the mining of residents' power consumption behavior is of great value to strengthen demand side management, improve energy efficiency and promote the development of smart grid. Therefore, this paper studies the pattern recognition and associated factors of power consumption behavior based on unsupervised clustering and Apriori. We use the hourly load curve of Shanghai residents from 2016 to 2018 to carry out the experiment. According to the results of the survey, we distinguish the single household characteristics and combined household characteristics, analyze their relationship with these typical power consumption modes, and eliminate the impact of unbalanced distribution of categories. The experimental results show that socio-economic factors, environmental cognitive factors and housing factors will affect Chinese residents' power consumption behavior to varying degrees. The association rules of combined household characteristics formed in different seasons are also quite different.

Consumption

Association (psychology)

Affect

10.1117/12.2672873

Cite

Citations (0)

Ramp loss nonparallel support vector machine for pattern classification

Knowledge-Based Systems (2015)

Dalian Liu Yong Shi Yingjie Tian

Hyperplane

Hinge loss

Convexity

Binary classification

Relevance vector machine

10.1016/j.knosys.2015.05.008

Cite

Citations (49)

The new interpretation of support vector machines on statistical learning theory

Science China Mathematics (2010)

Chunhua Zhang Yingjie Tian Nai-Yang Deng

Structural risk minimization

Statistical learning theory

Empirical risk minimization

Minification

Relevance vector machine

10.1007/s11425-010-0018-6

Cite

Citations (52)

Anomaly classification based on self-supervised learning and its application

Journal of Radiation Research and Applied Sciences (2024)

Yongsheng Han Zhiquan Qi Yingjie Tian

Anomaly classification remains a challenging task in computer vision applications across diverse practical fields such as industrial detection and security check. The purpose of this study is to develop a new anomaly classification method based on self-supervised learning (Self-ACM), which is expected to enhance anomaly classification accuracy significantly. The feature maps of images were abstracted using VGG16 model pre-trained on ImageNet, which were then fed into the innovative model to capture the normality distribution within the dataset domain. Then, a self-supervised adversarial anomaly classification learning framework was proposed to facilitate the acquisition of a higher-level semantic representations for improved anomaly detection. Thirdly, we collected and constructed a novel terahertz (THZ) dataset, which serves as a pioneering resource for benchmarking anomaly classification tasks in the field. Through a series of rigorous experiments, our findings unequivocally demonstrate the following key insights: Firstly, harnessing feature maps as input data yielded a significant enhancement in anomaly detection performance, underscoring the effectiveness of this approach. Secondly, the integration of self-supervision enriched the dataset with invaluable information, empowering both the discriminator and generator to acquire superior feature representations. The culmination of these advancements is our novel method achieving unparalleled state-of-the-art performance across multiple benchmark datasets. This breakthrough underscores the transformative impact of our approach on anomaly detection methodologies, solidifying its position as a pioneering solution in the field. The Self-ACM strategy not only advances anomaly detection methodologies but also offers a remarkable contribution to dataset creation, setting a new standard for anomaly classification research.

Anomaly (physics)

10.1016/j.jrras.2024.100918

Cite

Citations (0)

Research on the Effectiveness Evaluation and Investment Decision of Distribution Network Assets Based on Multi-dimensional Lean Management System

2022 China International Conference on Electricity Distribution (CICED) (2022)

Xiaocun Wang Yihua Guo Lu Liu Yingjie Tian

With the deepening reform of electric power, the company is facing the challenge of continuous steady operation, to actively adapt to the new situation of reform of electric power, promote the development of the company and the grid quality, based on the power big data information, combined with the financial value of the asset information, using big data analysis technology, the main research point is the assets of the power grid station area, first of all, based on multi-dimensional lean management system reform, revise the calculation logic of power sales revenue, power purchase cost and power distribution area expenses in distribution area, automatically generate revenue, cost and profit statements, and integrate them with related business indicators to form comprehensive statements of industry and finance integration. Then, based on the characteristics of assets, the requirements of power reform investment, and the methods of cost supervision, the evaluation index system of asset effectiveness is revised, the efficiency and benefit of asset operation are tracked and analyzed, the effectiveness of assets is controlled in real time, and the calculation of the capital and cost investment requirements of assets, providing effective support for the company's business decisions.

10.1109/ciced56215.2022.9929223

Cite

Citations (0)