Ce Shan

State Key Laboratory of Oral Diseases

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i-10 index

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Document Type

Co-Authors

Zhonghan Li

University of California, Riverside

Yike Yin

Sichuan University

Lulu Guo

Tongji University

Yanglei Zhou

China University of Geosciences (Beijing)

Hong Chen

Tongji University

Shengye Hao

China University of Geosciences (Beijing)

Pengcheng Liu

China Pharmaceutical University

Xinyu Qiu

China University of Geosciences (Beijing)

Fei‐Yue Wang

Chinese Academy of Sciences

Jinwu Gao

Jilin University

Cooperative Institutions

Sichuan University

State Key Laboratory of Oral Diseases

Jilin University

State Key Laboratory of Biotherapy

China University of Geosciences (Beijing)

Ministry of Education of the People's Republic of China

State Key Laboratory of Automotive Simulation and Control

Sun Yat-sen University

Jilin Medical University

Tongji University

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Remaining Useful Life Prediction of Lithium-Ion Battery Using Improved Ant Lion Optimization F-Distribution Particle Fillter

SSRN Electronic Journal (2022)

Yan Ma Ce Shan Jiajun Zhu Yunfeng Hu

Accurate prediction of remaining useful prediction is critical to keep safety and reliability of lithium-ion batteries. A novel RUL prediction method based on the improved ant lion optimization F-distribution particle filter (IALO-FPF) is proposed in this paper. IALO is embedded in the PF to generate the state probability density function to alleviate particle degeneracy. Considering that ant lion optimization method is easy to fall into the local optimum and has poor global search ability, the improved roulette strategy and chaotic logic strategy are introduced. Meanwhile, due to the inconsistency of the battery aging models in different time intervals, F kernel is used to dynamically calculate weights of particles based on the reference value of capacity and the estimation value of the ahead prior capacity to estimate the time-varying parameter of aging models of the battery. The proposed method has been verified based on battery datasets from the laboratory and CALCE.

Particle (ecology)

10.2139/ssrn.4147559

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Citations (1)

Multiple health indicators fusion-based health prognostic for lithium-ion battery using transfer learning and hybrid deep learning method

Reliability Engineering & System Safety (2022)

Yan Ma Ce Shan Jinwu Gao Hong Chen

Transfer of learning

10.1016/j.ress.2022.108818

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Citations (64)

Expression of Concern for: A Vectorized Representation Model for Trajectory Prediction of Intelligent Vehicles in Challenging Scenarios

IEEE Transactions on Intelligent Vehicles (2024)

Lulu Guo Ce Shan Tengfei Shi Xuan Li Fei‐Yue Wang

Representation

Expression (computer science)

10.1109/tiv.2024.3502537

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Citations (0)

Temporal Attention Mechanism Based Indirect Battery Capacity Prediction Combined with Health Feature Extraction

Electronics (2023)

F.Y. Chu Ce Shan Lulu Guo

The burgeoning utilization of lithium-ion batteries within electric vehicles and renewable energy storage systems has catapulted the capacity prediction of such batteries to a pivotal research frontier in the energy storage domain. Precise capacity prognostication is instrumental not merely in safeguarding battery operation but also in prolonging its operational lifespan. The indirect battery capacity prediction model presented in this study is based on a time-attention mechanism and aims to reveal hidden patterns in battery data and improve the accuracy of battery capacity prediction, thereby facilitating the development of a robust time series prediction model. Initially, pivotal health indicators are distilled from an extensive corpus of battery data. Subsequently, this study proposes an indirect battery capacity prediction model intertwined with health feature extraction, hinged on the time-attention mechanism. The efficacy of the proposed model is assayed through a spectrum of assessment metrics and juxtaposed against other well-entrenched deep learning models. The model’s efficacy is validated across various battery datasets, with the Test Mean Absolute Error (MAE) and Test Root Mean Squared Error (RMSE) values consistently falling below 0.74% and 1.63%, respectively, showcasing the model’s commendable predictive prowess and reliability in the lithium-ion battery capacity prediction arena.

10.3390/electronics12244951

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Citations (1)

A Chemically Defined Culture for Tooth Reconstitution

Advanced Science (2024)

Ziwei Zhang Hong Hu Zhiheng Xu Ce Shan Hanyi Chen

Abstract It is known for decades that dental epithelium and mesenchyme can reconstitute and regenerate a functional tooth. However, the mechanism of tooth reconstitution remains largely unknown due to the lack of an efficient in vitro model. Here, a chemically defined culture system is established that supports tooth reconstitution, further development with normal anatomy, and prompt response to chemical interference in key developmental signaling pathways, termed as toothoids. By using such a system, it is discovered that, during reconstitution, instead of resetting the developmental clock, dental cells reorganized and restarted from the respective developmental stage where they are originally isolated. Moreover, co‐stimulation of Activin A and Hedgehog/Smoothened agonist (SAG) sustained the initial induction of tooth fate from the first branchial arch, which would be otherwise quickly lost in culture. Furthermore, activation of Bone Morphogenetic Protein (BMP) signaling triggered efficient enamel formation in the late‐stage toothoids, without affecting the normal development of ameloblasts. Together, these data highlight the toothoid culture as a powerful tool to dissect the molecular mechanisms of tooth reconstitution and regeneration.

Mesenchyme

Smoothened

Organ culture

10.1002/advs.202404345

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MiR-27b regulates podocyte survival through targeting adenosine receptor 2B in podocytes from non-human primate

Cell Death and Disease (2018)

Zuokang Zheng Hong Hu Yanrong Tong Zhixia Hu Shiyu Cao

Abstract MicroRNAs are a group of small non-coding RNAs that play key roles in almost every aspect of mammalian cell. In kidney, microRNAs are required for maintaining normal function of renal cells, disruption of which contributes to pathogenesis of renal diseases. In this study, we investigated the potential role of miRNAs as key regulators of podocyte survival by using a primary cell culture model from non-human primates (NHPs). Through microRNA profile comparison in glomeruli from mouse, rat and NHP, miR-27b was found to be among a list of glomeruli-enriched miRNA conserved across species. In NHP primary podocyte culture, significant downregulation of miR-27b was observed during treatment of puromycin aminonucleoside (PAN), a classic nephrotoxin. Overexpression of miR-27b enhanced PAN-induced apoptosis and cytoskeleton destruction in podocytes while its inhibition had a protective effect. Target identification analysis identified Adora2b as a potential direct target of miR-27b. Ectopic expression of miR-27b suppressed both Adora2b mRNA and protein expression, whereas inhibition of miR-27b increased the transcript and protein expression levels of Adora2B. Dual luciferase assay further confirmed Adora2b as a direct target of miR-27b. Furthermore, knockdown of Adora2b by siRNAs enhanced PAN-induced apoptosis, similar to the phenotypes we had observed with miR-27b overexpression. In addition, stimulating the adenosine signaling by an Adora2b agonist, NECA, improved podocyte survival upon PAN treatment. Taken together, our data identified a novel role of miR-27b-adora2b axis in primary podocyte survival upon injury and suggested a critical role of adenosine signaling pathway in podocyte protection.

10.1038/s41419-018-1178-5

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Citations (11)

Correction: Chu et al. Temporal Attention Mechanism Based Indirect Battery Capacity Prediction Combined with Health Feature Extraction. Electronics 2023, 12, 4951

Electronics (2024)

F.Y. Chu Ce Shan Lulu Guo

There was an error in the original publication [...]

Battery capacity

10.3390/electronics13122323

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Citations (0)

Health Prognostics for Lithium-ion Battery Based on Hybrid Data-driven Method

Ma Yan Ce Shan Yunfeng Hu Hong Chen

Accurate State of health prediction of lithium-ion battery provides a guarantee for the safe driving of electric vehicles. However, battery aging is a long-term complex process accompanied by capacity self- regeneration phenomenon, which also brings challenges to accurately prediction of SOH. Therefore, a novel SOH prediction method based on mode decomposition and hybrid machine learning is proposed in this paper. The original capacity data is decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to obtain the trend item and detail item, which indicates the long-term degradation trend and self-regeneration effect. After that, long short-term memory(LSTM) and support vector regression (SVR) are employed for prediction of trend item and detail item respectively. The final SOH prediction is obtained by accumulating the prediction results of the trend item and the detail item. The effectiveness of the proposed method is verified by 2 different datasets. The prediction error of the proposed method is under 2%, which is less than the compared methods. The prediction results of different dataset show good accuracy, which indicates that the proposed method has high robustness, good accuracy, and applicability.

Prognostics

Robustness

Battery capacity

10.1109/spies55999.2022.10082516

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A new dynamic capillary-number-recovery evaluation method for tight gas reservoirs

Journal of Petroleum Science and Engineering (2021)

Fulei Zhao Pengcheng Liu Shengye Hao Xinyu Qiu Ce Shan

Tight gas

Slippage

Percolation (cognitive psychology)