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42nd Chinese Control Conference, CCC 2023

作者： Zuo, Zhiqiang Du, Yunhao Wang, Yijing Zhang, Zhicheng Li, Peng School of Electrical and Information Engineering Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin University Tianjin300072 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9789887581543

State-of-charge (SoC) balancing is crucial for improving the efficiency and lifetime of the battery energy storage system in near-space vehicles. In this paper, the SoC balancing control problem is investigated by a coupling battery model with electric-thermal-aging dynamics. Firstly, a system identification experiment is carried out to obtain the internal parameters of the battery. Secondly, a weight optimization index is designed by integrating the balancing speed and battery state-of-health (SoH). Then, a receding horizon control algorithm is proposed to realize multi-unit battery SoC balancing with partial swarm optimization (PSO). Finally, the effectiveness of the proposed strategy is verified by simulation results. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Battery management systems

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scGAT: A Cell-Type Annotation Framework for Single-Cell Transcriptomics Using Graph Attention Network and Meta Learning 1

scGAT: A Cell-Type Annotation Framework for Single-Cell Tran...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Dong, Jiayi Li, Ming Wang, Fei School of Computer Science and Technology Fudan University Shanghai Key Lab of Intelligent Information Processing Shanghai China

ISBN: (纸本)9798350358780

Single-cell RNA sequencing (scRNA-seq) technology has emerged as a valuable tool for classifying cell types across various species, tissues, and environmental conditions, thereby advancing the field of life sciences. Automatic cell annotation plays a crucial role in bioinformatics, addressing the challenges associated with time-consuming and labor-intensive manual cell annotation. Although numerous cell annotation algorithms exist, their performance is often constrained by the quality of reference data and inherent technical noise in biological experiments. To address these challenges, we propose the integration of a heterogeneous graph neural network into our cell annotation model, named scGAT. This integration leverages the intricate relationships between expressed genes and cells while disregarding unexpressed genes. Additionally, we introduce a novel approach that utilizes prototype cells to mitigate technical noise. However, the use of prototype cells may lead to limitations in sample size, prompting us to employ meta-learning techniques to determine the model's initial parameters. To assess the effectiveness of scGAT in cell annotation tasks, we conducted comparative analyses using real datasets against other models. The experimental results clearly demonstrate that scGAT significantly outperforms alternative methods. © 2023 IEEE.

关键词： Bioinformatics

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GROD: Joint Inference of Gene Regulatory Networks and Data Imputation in Single-Cell RNA Sequencing with Temporal Consideration

GROD: Joint Inference of Gene Regulatory Networks and Data I...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Dong, Jiayi Wang, Fei Fudan University Shanghai Key Lab of Intelligent Information Processing School of Computer Science and Technology Shanghai China

ISBN: (纸本)9798350337488

Since single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular dynamics, the construction of gene relationships based on dynamic information has attracted much attention. However, the sparsity and dropout events inherent in scRNA-seq data present challenges for downstream analysis like Gene Regulatory Network (GRN) inference. Existing data imputation methods have yielded good results beneficial for cell clustering, but it does not adequately consider gene expression dynamics. To address this, we introduce GROD, a novel deep learning approach that simultaneously infers GRN and imputes scRNA-seq data from a temporal perspective. GROD consists of three key components: an encoder, a graph learner, and a decoder. Experimental results demonstrate that GROD outperforms existing methods in both GRN inference and data imputation tasks, providing superior accuracy in capturing gene regulatory relationships and accurately imputing missing values. By integrating these two tasks, GROD enables more accurate downstream analysis and facilitates deeper insights into cellular dynamics. © 2023 IEEE.

关键词： cellular dynamics data imputation gene regulatory network scRNA-seq

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Paradigm Shift in Natural Language processing

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Machine Intelligence Research 2022年第3期19卷 169-183页

作者： Tian-Xiang Sun Xiang-Yang Liu Xi-Peng Qiu Xuan-Jing Huang School of Computer Science Fudan UniversityShanghai 200438China Shanghai Key Laboratory of Intelligent Information Processing Fudan UniversityShanghai 200438China

In the era of deep learning, modeling for most natural language processing (NLP) tasks has converged into several mainstream paradigms. For example, we usually adopt the sequence labeling paradigm to solve a bundle of tasks such as POS-tagging, named entity recognition (NER), and chunking, and adopt the classification paradigm to solve tasks like sentiment analysis. With the rapid progress of pre-trained language models, recent years have witnessed a rising trend of paradigm shift, which is solving one NLP task in a new paradigm by reformulating the task. The paradigm shift has achieved great success on many tasks and is becoming a promising way to improve model performance. Moreover, some of these paradigms have shown great potential to unify a large number of NLP tasks, making it possible to build a single model to handle diverse tasks. In this paper, we review such phenomenon of paradigm shifts in recent years, highlighting several paradigms that have the potential to solve different NLP tasks.

关键词： Natural language processing pre-trained language models deep learning sequence-to-sequence paradigm shift

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Enhanced Tube-Based Event-Triggered Stochastic Model Predictive Control With Additive Uncertainties

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 596-605页

作者： Chenxi Gu Xinli Wang Kang Li Xiaohong Yin Shaoyuan Li Lei Wang the School of Control Science and Engineering Shandong University IEEE the School of Electronic and Electrical Engineering University of Leeds the Qingdao University of Science and Technology the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system *** with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of a heating, ventilation, and air conditioning(HVAC) system confirm the efficacy of the proposed control.

关键词： Event-triggered mechanism heating, ventilation, and air conditioning (HVAC) control probabilistic reachable set stochastic model predictive control

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DCSN: A Flexible and Efficient Lightweight Network for Dense Cell Segmentation 1

DCSN: A Flexible and Efficient Lightweight Network for Dense...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Pan, Futai Wang, Fei School of Computer Science and Technology Fudan University Shanghai Key Lab of Intelligent Information Processing Shanghai China

ISBN: (纸本)9798350358780

Cells serve as the fundamental building blocks of living organisms and necessitate the utilization of diverse microscopic imaging techniques for observation. Nevertheless, there exist numerous limitations within phase-contrast microscopy imaging technology, leading to indistinct imaging in a considerable number of acquired cell images. Furthermore, the wide range of cell shapes and sizes, often densely distributed, poses challenges for research in the field of cell image analysis. We propose Dense Cell Segmentation Network (DCSN), an anchor-based single-stage instance segmentation method, to address the challenge of dense detection and instance segmentation in high-density cell images. While methods based on transformers have emerged as prominent approaches in natural image detection and segmentation, they are limited in their applicability to cell image analysis due to the scarcity of cell image datasets. Consequently, we aim to introduce a simple yet effective instance segmentation method to further address the challenge of cell image segmentation. DCSN is a lightweight multi-task model, combining object detection and semantic segmentation to achieve accurate instance segmentation. We validate our approach on LIVECell, a publicly available annotated cell dataset, and demonstrate its efficacy. By combining the advantages of anchor-based methods and single-stage methods, DCSN achieves a favorable balance between speed and performance. In comparison to the anchor-based two-stage model Mask R-CNN, DCSN offers a streamlined process and faster inference speed. Compared to the anchor-free single-stage method, CenterMask, our model demonstrates superior segmentation performance and generalization capabilities. Moreover,DCSN is a general end-to-end model that can be applied to various domains beyond cell segmentation tasks. © 2023 IEEE.

关键词： Object detection

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An approximate algorithm for simulating Fresnel diffraction patterns without Fourier transform 6

An approximate algorithm for simulating Fresnel diffraction ...

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6th International Conference on Optoelectronic Science and Materials, ICOSM 2024

作者： Zhang, Zhiyuan Li, Chongguang Lou, Yuli Liu, Siqi Liang, Xiyuan Song, Qinghe Gui, Jinbin Key Laboratory of Laser Information Processing Technology and Application College of Science Kunming University of Science and Technology Yunnan Kunming650500 China

ISBN: (数字)9781510686823

ISBN: (纸本)9781510686816

In the study of optical systems, accurately calculating Fresnel diffraction patterns is crucial for understanding and designing various applications, from imaging systems to laser technologies. However, existing commonly used calculation methods each have their limitations. This paper proposes an approximate algorithm for calculating Fresnel diffraction to address these shortcomings. The algorithm is based on the oscillatory characteristics of the quadratic phase exponential function in the Fresnel diffraction integral. By convolving this quadratic phase exponential function with another slowly varying function, it is found that the rapidly oscillating parts cancel each other out, having little impact on the final calculation result. Therefore, only the areas that primarily contribute to the diffraction need to be computed. The study shows that this algorithm significantly reduces the computational load while maintaining a certain level of accuracy. Compared to the direct integration method, it shows a notable improvement in calculation speed. It also maintains high accuracy compared to both the direct integration method and the Fast Fourier Transform (FFT) algorithm. Additionally, since it does not involve the frequency domain transformation used in FFT, it avoids under-sampling issues. This algorithm offers a feasible method for calculating diffraction patterns while ensuring a balance between accuracy and computational efficiency. © 2024 SPIE.

关键词： Exponential functions

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Superatomic catalyst for efficient nitric oxide reduction using Al8O3 superatom-oxide framework

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Science China Chemistry 2025年第5期68卷 2096-2100页

作者： Kaidong Shen Jiuqi Yi Chang Xu Qiquan Luo Xiaojun Wu Jinlong Yang Department of Chemistry Anhui University School of Chemistry and Materials Sciences University of Science and Technology of China Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Innovation Center of Chemistry for Energy Materials (iChEM) University of Science and Technology of China

For cluster catalysts,reducing their size to single atoms gives rise to precise and high-selective catalytic performance,at the cost of losing some ***,the entirety with atom-like electronic shells and fine-tunable properties as clusters,are promising candidates for cluster ***,we predicted a superatom-assembled two-dimension Al8O3superatom-oxide framework（SOF） using first principles calculation,where the Al8core comprises two 8-electron Al4superatoms and further linked by oxygen atoms in a graphene-like lattice,resulting in porous and stable *** Al8O3-SOF serves as an efficient superatomic catalyst for nitric oxide（NO） reduction reaction,where the Al4superatomic unit acts cohesively as the active site throughout the catalytic process and its superatomic P orbital plays an important role in activating NO ***,the catalytic activity of Al8O3-SOF increases when the two central Al atoms of the Al8core are replaced by Ga atoms,reducing the limiting potential to -0.48 V comparable to that of the reported Pt（100）.Our work proposes a novel series of superatomic catalysts and reveals the superatomic behavior in the catalytic process,providing references for the development of efficient heterogeneous catalysts.

关键词： superatomic catalyst cluster-assembling nitric oxide reduction first principles calculation

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Dual-Level Contrast Enhancement Method for Infrared Small Target Detection 2

Dual-Level Contrast Enhancement Method for Infrared Small Ta...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Sun, Haotian Wang, Wenzheng Xu, Yuhan Jin, Qiuyu Zhang, Zipeng Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China Beijing Institute of Technology School of Information and Electronics Beijing China

ISBN: (纸本)9798331515669

In the field of target detection, infrared small target detection(ISTD) is a critical area of research, playing a vital role in reconnaissance, guidance, and early warning systems. Existing spatial Filtering algorithms perform well in clean backgrounds;however, as these filters require manual design based on prior knowledge, they are prone to interference from complex scenes (e.g., clouds, buildings), leading to target omission. Small targets typically exhibit blurry features and weak energy, making direct detection susceptible to interference from high-intensity areas, which may result in missed detections. To tackle these challenges, this paper presents a two-tier contrast improvement method (DLCE). First, a gradient filter group is used to enhance the signal-to-noise ratio (SNR) of the target, and then, built upon the concept of local contrast measurement (LCM), a novel intensity calculation method is developed. The new contrast window design effectively enhances the target's SNR. Compared with existing algorithms, like MPCM (Multi-Scale Patch-based Contrast Measurement) on publicly available datasets, the proposed method more effectively enhances the target's radiative intensity while suppressing background noise. The effectiveness of the proposed algorithm is confirmed by the simulation results. © 2024 IEEE.

关键词： Alarm systems

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Trajectory optimization for passenger and freight trains on a single-track railway line 8

Trajectory optimization for passenger and freight trains on ...

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8th International Conference on Traffic Engineering and Transportation System, ICTETS 2024

作者： Wang, Dandan Deng, Heng Zhan, Jingyuan Zhang, Liguo Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent Systems Beijing100124 China

ISBN: (数字)9781510686298

ISBN: (纸本)9781510686281

This paper focuses on the trajectory optimization problem for both passenger and freight trains traveling in opposite directions on a single-track railway line. Firstly, we formulate a trajectory optimization problem that aims to simultaneously minimize the traction energy consumption of passenger and freight trains, as well as the total travel time of freight trains. Both types of trains are subject to their respective operational constraints, such as safety, punctuality and static speed limits. Secondly, we transform this problem into a multi-phase optimal control problem that incorporates the timetable, track gradients, speed limits and signal positions. The multi-phase optimal control problem is then solved by GPOPS-II software. Our approach enables us to model the multi-tracks at stations, allowing trains to meet and overtake at stations. Finally, the proposed method is applied to case studies of passenger and freight trains in a single-track railway, and the numerical simulation results validate the effectiveness of the proposed method. © 2024 SPIE.

关键词： Travel time

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