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43rd Chinese Control Conference, CCC 2024

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

ISBN: (纸本)9789887581581

In DC microgrids, current sharing and voltage regulation are two main objectives for real-time control, while the operation loss reduction is typically achieved by steady-state optimization. However, the ratio of current sharing is generally determined by the result of the centralized optimization in the classical hierarchical control framework of DC microgrids. Such an issue brings insufficient capability in respond to the changes of photovoltaic power and stored energy in the solar-powered energy systems. To handle this, this work proposes a distributed control scheme for single-bus DC microgrids. Under the proposed controller, the objectives of current sharing, bus voltage regulation and the total operation loss (converter loss and line loss) reduction can be guaranteed simultaneously. Moreover, the constraints on line currents and bus voltage are tackled as well. Finally, simulations verify the performance of the proposed control scheme. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Microgrids

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Multi-Time-Scale Variational Mode Decomposition-Based Robust Fault Diagnosis of Railway Point Machines Under Multiple Noises

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Chinese Journal of Electronics 2024年第3期33卷 814-822页

作者： Junqi LIU Tao WEN Guo XIE Yuan CAO Clive Roberts Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology The School of Electronic and Information Engineering Beijing Jiaotong University Birmingham Centre for Railway Research and Education University of Birmingham

The fault diagnosis of railway point machines(RPMs) has attracted the attention of engineers and *** have studies considered diverse noises along the *** fulfill this aspect,a multi-time-scale variational mode decomposition(MTSVMD) is proposed in this paper to realize the accurate and robust fault diagnosis of RPMs under multiple *** decomposes condition monitoring signals after coarse-grained processing in varying *** this manner,the information contained in the signal components at multiple time scales can construct a more abundant feature space than at a single *** the experimental validation,a random position,random type,random number,and random length(4R) noise-adding algorithm helps to verify the robustness of the *** adequate experimental results demoristrate the superiority of the proposed MTSVMD-based fault diagnosis.

关键词： Railway point machines Fault diagnosis Multi-time-scale Variational mode decomposition

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An IT2FNN with a Novel Hierarchical Learning Algorithm for Nonlinear system Modeling

An IT2FNN with a Novel Hierarchical Learning Algorithm for N...

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2023 China Automation Congress, CAC 2023

作者： Sun, Zijian Meng, Xi Qiao, Junfei Beijing University of Technology Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (纸本)9798350303759

Interval type-2 fuzzy neural networks (IT2FNNs) have been widely used for modeling in industrial processes, and the efficient parameter learning methods are crucial for obtaining accurate models. However, the problem of low computational efficiency and poor convergence performance still exists in existing learning methods. In this study, a novel hierarchical learning algorithm is proposed for constructing IT2FNN with better learning ability and generalization performance. Firstly, the improved recursive least square (RLS) method with an adaptive forgetting factor is designed to learn the consequent parameters. Next, the advanced stochastic gradient descent (SGD) method is adopted to learn the antecedent parameters. Finally, the proposed method is applied to real industrial processes and compared with various state-of-the-art IT2FNNs. Simulation results demonstrate the superiority of the proposed method. © 2023 IEEE.

关键词： Learning algorithms

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Production and evaluation of high-throughput reaction data from an automated chemical synthesis platform

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Science China Chemistry 2025年

作者： Leiyun Zhong Yiming Xu Xinghai Li Peihao Cheng Shengyang Tao School of Chemistry State Key Laboratory of Fine Chemicals Frontier Science Center for Smart Materials Dalian Key Laboratory of Intelligent Chemistry Dalian University of Technology

Data quality directly influences artificial intelligence(AI) systems' performance and practical application effects. Highthroughput screening(HTS) can significantly shorten the time to find optimal reaction conditions and provide consistent data support for data-driven scientific research. This study established an automated chemical synthesis platform(Auto CSP), which integrates substance dispensing, transfer, reaction, and analysis functions and a control program that coordinates these *** platform screened 432 organic reactions related to synthesizing the anticancer drug Sonidegib, demonstrating results comparable to manual operation. The random forest algorithm identified anomalies with 98.3% accuracy. More importantly,machine learning algorithms demonstrated that the data obtained from the automated platform offers significant advantages in ensuring experimental consistency and affirmed the platform's capability to generate high-quality data that meets the needs of AI analysis. This paper also addressed challenges of the automated synthesis system, like unstable wireless communication and visual recognition, offering insights for future developments in smarter, more efficient, and stable chemical synthesis systems.

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Rotational Cherenkov-Excited Luminescence Scanned Tomography Reconstruction with Symmetry Vision Mamba 17

Rotational Cherenkov-Excited Luminescence Scanned Tomography...

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17th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Zhang, Jingyue Zhang, Hu Hu, Ting Li, Zhe Sun, Zhonghua Jia, Kebin Feng, Jinchao Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

Rotational Cherenkov-Excited Luminescence Scanned Tomography (RCELST) is an emerging optical imaging technology that visualizes the distribution of luminescent quantum yield within a treated subject. This technology involves collecting luminescence signals resulting from the excitation of luminescence probes by Cherenkov emissions induced by the rotational scanning of MV X-rays. These signals are then mapped into a sinogram for reconstructing the distribution of luminescent quantum yield by neural networks. Vision Transformers (ViTs), an effective deep learning algorithm known for capturing long-distance dependencies, have been applied to medical image reconstruction tasks. However, the large scale of medical images, combined with the quadratic complexity of ViTs, leads to erratic and time-consuming reconstruction performance. Therefore, a more efficient algorithm is essential for reducing reconstruction time while maintaining accuracy. In this study, we propose the Symmetry Vision Mamba (S-VM) to address this challenge, reducing computational time while maintaining high reconstruction accuracy. The S-VM builds on the Vision Mamba, which leverages state Space Models (SSMs) to extract global information from 2D sinogram signals. With linear computational complexity, S-VM significantly accelerates the learning process compared to the Transformer algorithms. Additionally, S-VM utilizes a symmetrical encoder architecture, incorporating convolutional stems to extract local features and enable multi-scale feature fusion by sharing parameters between the two encoder branches. Training on 10,000 sinogram signals, the S-VM algorithm achieves a peak signal-to-noise ratio (PSNR) of up to 38.67 dB and a structural similarity index measure (SSIM) of 0.97. Remarkably, these results are achieved in a floating point operations (FLOPs) of 4.7G. © 2025 SPIE.

关键词： Photons

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Impact of the Yellow River capture on the paleoenvironmental changes of Hongze Lake, China

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International Journal of Sediment Research 2023年第4期38卷 503-515页

作者： Hongwu Tang Ming Gao Saiyu Yuan Huiming Zhang Yang Xiao Fuxin Zhang Kun Zhang State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources Hohai University Institute of Water Science and Technology Hohai University

The Yellow River(YR) capture of the Huai River had a profound effect on the landscape evolution,ecology, and social development in China, disrupting the evolution of some vital shallow floodplain *** of the absence of lacustrine sediment provenance analysis, previous studies were inadequate in distinguishing the influence of YR capture on paleoenvironmental changes in these lakes, especially on landscape evolution, redox conditions, lake level changes, and organic productivity. The current study aims to use X-ray fluorescence-core scanning(XRF-CS) and multivariate statistics on a 9.8 m sediment core from Hongze Lake(HZL), China, to analyze the impact of the YR capture on the lake’s paleoenvironmental changes and the sediment provenance simultaneously. High-resolution images, magnetic susceptibility, and XRF-based multi-element data were obtained using a GEOTEK Multi-Sensor Core Logger. The calcium(Ca) content indicates the presence of YR sediment in HZL, given the high Ca content in YR sediment and the identification of mainly exogenous sources of Ca in HZL. The high values of Ca indicate that 48.7% of the sediment in the center of HZL is related to the YR’s incursion. Periods of higher Ca content coincide with periods of higher oxygenation levels of the lake(as indicated by the ratio of manganese to titanium) and lower organic content in the lake sediment(as indicated by the ratio of incoherent to coherent scatter). These analyses reveal that the YR’s capture of HZL significantly increased the lake’s oxygen level. Furthermore, the decrease in the organic content of sediment tends to be the result of the accelerated deposition caused by the YR. The decrease in Ca content coincides with an increase in lake levels(as indicated by magnetic susceptibility). Thus, it can be inferred that the embankment on the eastern side of HZL, built to increase lake storage, has reduced the incursion frequency of the YR.

关键词： Yellow River capture Paleoenvironmental reconstruction XRF-CS Hongze Lake

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A merged machine learning model for seasonal climate prediction in China

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Science China Earth Sciences 2025年第5期68卷 1469-1484页

作者： Danwei QIAN Yanyan HUANG Huijun WANG State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science and Technology Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) Nansen-Zhu International Research Centre Institute of Atmospheric Physics Chinese Academy of Sciences

Improving the current level of skill in seasonal climate prediction is urgent for achieving sustainable socioeconomic development, and this is especially true in China where meteorological disasters are experienced frequently. In this study, based upon big climate data and traditional statistical prediction experiences, a merged machine learning model(Y-model) was developed to address this, as well as to further explore unknown potential predictors. In Y-model, empirical orthogonal function analysis was firstly applied to reduce the data dimensionality of the target predictand(temperature and precipitation in the four seasons over China). Image recognition techniques were used to automatically identify possible predictors from the big climate data. These predictors, associated with significant circulation anomalies, were recombined into a large ensemble according to different threshold settings for five factors determining the statistical forecast skill. Facebook Prophet was chosen to conduct the independent hindcasts for each season's climate at a lead time of two months. During 2011–2022, the seasonal climate in China was skillfully predicted by Y-model, with an averaged pattern correlation coefficient skill of 0.60 for temperature and 0.24 for precipitation, outperforming CFSv2. Potential predictor analysis for recent extreme events suggested that prior signals from the Indian Ocean and the stratosphere were important for determining the super Mei-yu in 2020, while the prior sea surface temperature over the western Pacific and the soil temperature over West Asia may have contributed to the extreme high temperatures in 2022. Our study provides new insights for seasonal climate prediction in China.

关键词： Machine learning Seasonal climate prediction China’s climate Image recognition techniques Large ensemble

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Fabrication of Superhydrophobic–Hydrophilic Patterned Cu@Ag Composite SERS Substrate via Femtosecond Laser

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Nanomanufacturing and Metrology 2024年第1期7卷 1-14页

作者： Yuheng Zhang Zongwei Xu Kun Zhang Ying Song Bing Dong Jianshi Wang Mengzhi Yan Qingqing Sun State Key Laboratory of Precision Measuring Technology and Instruments Laboratory of Micro/Nano Manufacturing TechnologyTianjin UniversityTianjin 300072China Shandong Agricultural Equipment Intelligent Engineering Laboratory Shandong Provincial Key Laboratory of Horticultural Machinery and EquipmentCollege of Mechanical and Electronic EngineeringShandong Agricultural UniversityTai’an 827101China

Ultralow concentration molecular detection is critical in various fields,e.g.,food safety,environmental monitoring,and dis-ease *** sensitive surface-enhanced Raman scattering(SERS)based on ultra-wettable surfaces has attracted attention due to its unique ability to detect trace ***,the complexity and cost associated with the preparation of traditional SERS substrates restrict their practical ***,an efficient SERS substrate preparation with high sensitivity,a simplified process,and controllable cost is *** this study,a superhydrophobic–hydrophilic patterned Cu@Ag composite SERS substrate was fabricated using femtosecond laser processing technology combined with silver plating and surface modification *** inducing periodic stripe structures through femtosecond laser processing,the developed substrate achieves uniform distribution *** the surface wettability difference,the object to be measured can be confined in the hydrophilic region and the edge of the hydrophilic region,where the analyte is enriched by the coffee ring effect,can be quickly located by surface morphology difference of micro-nanostructures;thus,greatly improving detec-tion *** fabricated SERS substrate can detect Rhodamine 6G(R6G)at an extraordinarily low concentration of 10^(−15)mol/L,corresponding to an enhancement factor of 1.53×10^(8).This substrate has an ultralow detection limit,incurs low processing costs and is simple to prepare;thus,the substrate has significant application potential in the trace analysis field.

关键词： Femtosecond laser Surface-enhanced Raman scattering Coffee ring effect Superhydrophobic–hydrophilic surface

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Knowledge-Guided Evolutionary Optimization for Large-Scale Air Defense Resource Allocation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6267-6279页

作者： Li, Wenhua Wang, Rui Heng, Yong Zhang, Tao Wang, Ling National University of Defense Technology College of Systems Engineering Changsha410073 China Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha410073 China Xiangjiang Laboratory Changsha410073 China Beijing Institute of Electronic System Engineering Beijing100854 China Tsinghua University Department of Automation Beijing100084 China

In the high-stakes domain of air defense, resource allocation is a challenging optimization problem that involves many constraints and requires fast solutions. Previous work mainly addressed the single-intercept sensor-weapon-target assignment problem, which assigns missiles and radars to targets without optimizing the launch time. However, this problem can become large-scale and difficult to solve when saturation attack tactics are employed. Therefore, we propose a more realistic model that allows multiple missiles to intercept the same target and considers the launch time as a decision variable. We also incorporate radar relay guidance to maximize the interception probability and minimize the missile cost. To solve this model efficiently, we present a novel evolutionary algorithm with linear population size reduction (LPSR) and knowledge-guided strategies, called LKEA. Specifically, we design a heuristic method to quickly find the optimal launch time for each missile and a knowledge-guided strategy to use the radar channel collision information to guide the evolution. Experimental results show that LKEA is competitive in solving small/medium/large air defense resource allocation problems (ADRAPs). © 2020 IEEE.

关键词： Combinatorial optimization

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Integrating GAT and LSTM for Robotic Arms Motion Planning 43

Integrating GAT and LSTM for Robotic Arms Motion Planning

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43rd Chinese Control Conference, CCC 2024

作者： Gao, Zehua Zhang, Junnan Liu, Zhaoyang Jiang, Zewu Wang, Ke Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin300072 China

ISBN: (纸本)9789887581581

In the field of robotic arm control, efficient motion planning, especially in environments with multiple arms, poses significant challenges, including longer planning times and extensive collision detection in complex scenes. We use a learning based approach to solve this problem. This paper proposes a motion planning algorithm that integrates graph attention network (GAT) and Long Short-Term Memory (LSTM) network for the problem of motion planning of two robotic arms. This method uses imitation learning to train the control strategy of the robotic arm, constructs graph nodes through sampling, and encodes the trajectory of the robotic arm using LSTM. The experimental results show that compared with advanced planning algorithms in dynamic environments, our algorithm can maintain similar success rates while reducing collision detection and accelerating online planning speed. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Robotic arms

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