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An Introduction to the Integrated Climate Model of the center for monsoon system research and Its Simulated Influence of El Ni?no on East Asian–Western North Pacific Climate

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Advances in Atmospheric Sciences 2014年第5期31卷 1136-1146页

作者： HUANG Ping WANG Pengfei HU Kaiming HUANG Gang ZHANG Zhihua LIU Yong YAN Bangliang center for monsoon system research Institute of Atmospheric Physics Chinese Academy of Science State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Science Key Laboratory of Regional Climate-Environment research for Temperate East Asia Institute of Atmospheric Physics Chinese Academy of Science National Marine Environmental Forecasting center

This study introduces a new global climate model--the Integrated Climate Model （ICM）--developed for the seasonal prediction of East Asian-western North Pacific （EA-WNP） climate by the center for monsoon system research at the Institute of Atmospheric Physics （CMSR, IAP）, Chinese Academy of Sciences. ICM integrates ECHAM5 and NEMO2.3 as its atmospheric and oceanic components, respectively, using OASIS3 as the coupler. The simulation skill of ICM is evaluated here, including the simulated climatology, interannual variation, and the influence of E1 Nifio as one of the most important factors on EA-WNP climate. ICM successfully reproduces the distribution of sea surface temperature （SST） and precipitation without climate shift, the seasonal cycle of equatorial Pacific SST, and the precipitation and circulation of East Asian summer monsoon. The most prominent biases of ICM are the excessive cold tongue and unrealistic westward phase propagation of equatorial Pacific SST. The main interannual variation of the tropical Pacific SST and EA-WNP climate E1 Nifio and the East Asia-Pacific Pattern--are also well simulated in ICM, with realistic spatial pattern and period. The simulated E1 Nifio has significant impact on EA-WNP climate, as in other models. The assessment shows ICM should be a reliable model for the seasonal prediction of EA-WNP climate.

关键词： Integrated Climate Model (ICM) global climate model E1 Nifio East Asian climate

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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Science China(Information Sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering research center for Big Data Technology and system Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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Penalty Function-Based Distributed Primal-Dual Algorithm for Nonconvex Optimization Problem

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 394-402页

作者： Xiasheng Shi Changyin Sun IEEE the Engineering research center of Autonomous Unmanned system Technology Ministry of Education Anhui University the School of Artificial Intelligence Anhui University Anhui Provincial Engineering research center for Unmanned system and Intelligent Technology Anhui University the Anhui Key Laboratory Industrial Energy-Saving and Safety Anhui University

This paper addresses the distributed nonconvex optimization problem, where both the global cost function and local inequality constraint function are nonconvex. To tackle this issue, the p-power transformation and penalty function techniques are introduced to reframe the nonconvex optimization problem. This ensures that the Hessian matrix of the augmented Lagrangian function becomes local positive definite by choosing appropriate control parameters. A multi-timescale primal-dual method is then devised based on the Karush-Kuhn-Tucker(KKT) point of the reformulated nonconvex problem to attain convergence. The Lyapunov theory guarantees the model's stability in the presence of an undirected and connected communication network. Finally, two nonconvex optimization problems are presented to demonstrate the efficacy of the previously developed method.

关键词： Constrained optimization Karush-Kuhn-Tucker (KKT) point nonconvex p-power transformation

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Fault Diagnosis for Railway Point Machines Using VMD Multi-Scale Permutation Entropy and ReliefF Based on Vibration Signals

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Chinese Journal of Electronics 2025年第1期34卷 204-211页

作者： Yongkui Sun Yuan Cao Peng Li Shuai Su National Engineering research center of Rail Transportation Operation and Control system Beijing Jiaotong University School of Automation and Intelligence Beijing Jiaotong University Frontiers Science center for Smart High-speed Railway system Beijing Jiaotong University

The railway point machine plays an important part in railway systems. It is closely related to the safe operation of trains. Considering the advantages of vibration signals on anti-interference, this paper develops a novel vibration signal-based diagnosis approach for railway point machines. First, variational mode decomposition(VMD)is adopted for data preprocessing, which is verified more effective than empirical mode decomposition. Next, multiscale permutation entropy is extracted to characterize the fault features from multiple scales. Then ReliefF is utilized for feature selection, which can greatly decrease the feature dimension and improve the diagnosis accuracy. By experiment comparisons, the proposed approach performs best on diagnosis for railway point machines. The diagnosis accuracies on reverse-normal and normal-reverse processes are respectively 100% and 98.29%.

关键词： Vibrations Fault diagnosis Accuracy Data preprocessing Switches Feature extraction Rail transportation Entropy Maintenance Monitoring

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Risk factors, prevention and screening of colorectal cancer: A rising problem

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World Journal of Gastroenterology 2025年第5期31卷 117-123页

作者： Vladislav V Tsukanov Alexander V Vasyutin Julia L Tonkikh Clinical Department of The Digestive system Pathology of Adults and Children Federal Research Center“Krasnoyarsk Science Center”of the Siberian Branch of the Russian Academy of SciencesScientific Research Institute of Medical Problems of the NorthKrasnoyarsk 660022Russia

Colorectal cancer(CRC)is the third most commonly diagnosed cancer and the second leading cause of cancer death *** leading risk factors for CRC include male gender,age over 50,family history,obesity,tobacco smoking,alco-hol consumption,and unhealthy *** screening methods vary considerably between countries and depend on incidence,economic resources and healthcare *** aspects of screening include adherence,which can vary signi-ficantly across ethnic and socioeconomic *** concepts of CRC screening include pre-stratification of patients by identifying risk factors and then using fecal immunochemical test or guaiac-based fecal occult blood test and/or colono-scopy or radiologic imaging *** capabilities for CRC scree-ning are rapidly evolving and include stool DNA test,liquid biopsy,virtual colo-nography,and the use of artificial intelligence.A CRC prevention strategy should be comprehensive and include active patient education along with targeted imple-mentation of screening.

关键词： Colorectal cancer Screening Prevention Risk factors Colonoscopy Fecal immunochemical test Fecal occult blood test Stool DNA test Diet Lynch syndrome

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Exponential Stability of Impulsive system via Saturated Sliding Mode Control

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 469-471页

作者： Miaomiao Yu Xiaodi Li the Shandong Provincial Engineering research center of system Control and Intelligent Technology Shandong Normal University School of Mathematics and Statistics Shandong Normal University IEEE

Dear Editor,This letter presents a class of saturated sliding mode control (SMC)strategy for linear systems subject to impulsive disturbance and input saturation. To ensure the feasibility of proposed SMC under saturation, a relationship is established among attraction domain, saturation structure and control gain.

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Substrate bias effects in p-channel GaN-on-Si transistors

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Science China(Information Sciences) 2025年第1期68卷 404-405页

作者： Mengyao ZHAO Jie MA Lanlan YANG Chuanqi PAN Denggui WANG Jianjun ZHOU Sheng LI Jiaxing WEI Long ZHANG Siyang LIU Weifeng SUN National ASIC system Engineering research center School of Integrated Circuits Southeast University National center of Technology Innovation for Electronic Design Automation Southeast University National Key Laboratory of Solid-State Microwave Devices and Circuits

The drain current （ID） of the n-channel GaN field-effecttransistor （n-FET） is influenced by the substrate-to-source bias voltage (VBS)（substrate bias effect）[1].Actually,nonzero VBSis also likely to occur in p-FET in real application,because the source electrode of p-FET is usually connected to high *** study reports the dependency between IDand VBSin p-FET for the first time,and analyzes the underlying mechanisms from the perspective of vertical electric field （EF） distribution.

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MetaCoorNet: an improved generated residual network for grasping pose estimation

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Science China(Information Sciences) 2025年第03期68卷 242-257页

作者： Hejia GAO Chuanfeng HE Junjie ZHAO Changyin SUN School of Artificial Intelligence Anhui University Engineering research center of Autonomous Unmanned system Technology Ministry of Education Anhui Provincial Key Laboratory of Security Artificial Intelligence Anhui University School of Automation Southeast University

Robotic grasping presents significant challenges due to variations in object properties, environmental complexities,and the demand for real-time operation. This study proposes the MetaCoorNet(MCN), which is a novel deep learning architecture specifically designed to address these challenges in robotic grasping pose estimation. By combining spatial and channel operators, the MetaCoor block is utilized to extract features efficiently. This architecture enhances feature selectivity by embedding location information into channel attention using a positional embedding technique within the coordinate attention mechanism. Consequently, the proposed MCN can focus on pertinent grasp-related regions. Furthermore, convolutional fusion blocks seamlessly integrate spatial and channel features, resulting in enhanced feature resolution and representation *** innovative design enables the proposed MCN to achieve state-of-the-art performance on the Cornell and Jacquard datasets,attaining accuracies of 98% and 91.2%, respectively. The effectiveness and robustness of MCN are further validated through real-world experiments conducted using a seven-degree-of-freedom Kinova manipulator.

关键词： generative ResNet meta light block coordinate attention feature resolution robot grasping

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Fractal autoencoder with redundancy regularization for unsupervised feature selection

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Science China(Information Sciences) 2025年第2期68卷 89-102页

作者： Meiting SUN Fangyu LI Honggui HAN School of Information Science and Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent system Engineering research center of Digital Community Ministry of Education Beijing Institute of Artificial Intelligence

Feature selection is a crucial step in data preprocessing because feature selection reduces the dimensionality of data by eliminating irrelevant and redundant features. Since manual labeling is expensive, unsupervised feature selection has received increasing attention in recent years. However, existing unsupervised feature selection methods tend to prioritize selecting highly correlated features over exploring feature diversity. Thus, a regularized fractal autoencoder(RFAE) method is proposed to select informative features in an unsupervised way. Specifically, the fractal autoencoder network extends autoencoders to construct a correspondence neural network and a selection neural network. The correspondence neural network exploits interfeature correlations and the selection neural network selects the informative features. A redundancy regularization strategy consists of a redundancy elimination regularization term based on the dependency between features and a sparse regularization term based on the group lasso. The redundancy regularization strategy eliminates feature subset redundancy and enhances network generalization ability. Extensive experimental results on six publicly available datasets show that the proposed RFAE outperforms the compared methods regarding clustering accuracy and classification accuracy. Moreover, the proposed RFAE achieves acceptable computation efficiency.

关键词： unsupervised feature selection fractal autoencoder correspondence neural network selection neural network redundancy regularization strategy

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Spiking Reinforcement Learning Enhanced by Bioinspired Event Source of Multi-dendrite Spiking Neuron and Dynamic Thresholds

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 618-629页

作者： Xingyue Liang Qiaoyun Wu Yun Zhou Chunyu Tan Hongfu Yin Changyin Sun the School of Artificial Intelligence Anhui University Engineering research center of Autonomous Unmanned system Technology Ministry of Education IEEE the Institute of Artificial Intelligence Hefei Comprehensive National Science Center the School of Automation Southeast University the Engineering research center of Autonomous Unmanned system Technology Ministry of Education

Deep reinforcement learning(DRL) achieves success through the representational capabilities of deep neural networks(DNNs). Compared to DNNs, spiking neural networks(SNNs),known for their binary spike information processing, exhibit more biological characteristics. However, the challenge of using SNNs to simulate more biologically characteristic neuronal dynamics to optimize decision-making tasks remains, directly related to the information integration and transmission in *** by the advanced computational power of dendrites in biological neurons, we propose a multi-dendrite spiking neuron(MDSN) model based on Multi-compartment spiking neurons(MCN), expanding dendrite types from two to multiple and deriving the analytical solution of somatic membrane *** apply the MDSN to deep distributional reinforcement learning to enhance its performance in executing complex decisionmaking tasks. The proposed model can effectively and adaptively integrate and transmit meaningful information from different sources. Our model uses a bioinspired event-enhanced dendrite structure to emphasize features. Meanwhile, by utilizing dynamic membrane potential thresholds, it adaptively maintains the homeostasis of MDSN. Extensive experiments on Atari games show that the proposed model outperforms some state-of-the-art spiking distributional RL models by a significant margin.

关键词： Deep reinforcement learning multi-compartment spiking neurons spiking neural network

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