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Microstructural evolution and its influence on mechanical and corrosion behaviors in a high-Al/Zn containing duplex Mg-Li alloy after friction stir processing

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Journal of Materials Science & Technology 2024年第17期184卷 245-255页

作者： Yixing Zhu Mengran Zhou Yingxin Geng Shun Zhang Tongzheng Xin Gaoqiang Chen Yifan Zhou Xiaoyu Zhou Ruizhi Wu Qingyu Shi State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment Department of Mechanical EngineeringTsinghua UniversityBeijing 100084China Key Laboratory for Advanced Materials Processing Technology Ministry of EducationBeijing 100084China Key Laboratory of Superlight Materials&Surface Technology(Ministry of Education) Harbin Engineering UniversityHarbin 150001China Key Laboratory of Solidification Control and Digital Preparation Technology(Liaoning Province) School of Materials Science and EngineeringDalian University of TechnologyDalian 116024China Beijing North Vehicle Group Corporation Beijing 100072China

Ultralight Mg-Li alloys offer promising applications across various ***-Li alloys enriched with Al and Zn hold theoretical potential for achieving excellent mechanical strength and corrosion ***,the structural and performance characteristics of such Mg-Li alloys,particularly after thermo-mechanical processing,remain inadequately explored and *** study investigated the mi-crostructural evolution of a Mg-9Li-5Al-4Zn alloy after friction stir processing and its consequent effects on the mechanical and corrosion *** grain size of the alloy was effectively refined and sta-bilized during friction stir processing at various heat *** yield strength of the alloy increased by 86.4%after friction stir processing under the highest heat input condition,which was attributed to fine grain strengthening,solid solution strengthening and dispersion ***,the alloy ex-perienced a slight decrease in elongation after the friction stir *** alloy subjected to friction stir processing with the highest heat input exhibited a minimal corrosion current density of 6.10×10^(−6) A/cm^(2),which was only 25%of the base *** enhanced anti-corrosion properties can be attributed to the dispersion and distribution of precipitated particles induced by friction stir processing,which hin-dered the micro-galvanic corrosion and promoted the generation of a compact surface film,leading to minimal and uniform *** investigation can be significant for understanding the metallurgical mechanisms and performance evolution of Mg-Li alloys during thermomechanical processes.

关键词： Mg-Li-Al-Zn alloy Friction stir processing Microstructure evolution Thermal-mechanical stability Corrosion behavior

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Replaying Styles for Continual Semantic Segmentation Across Domains 7th

Replaying Styles for Continual Semantic Segmentation Across...

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Deng, Yao Xiang, Xiang Key Lab of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783031476365

In the context of Domain Incremental Learning for Semantic Segmentation, catastrophic forgetting is a significant issue when a model learns new geographical domains. While replay-based approaches have been commonly used to mitigate this problem by allowing the model to review past knowledge, they require additional storage space for old data, which may not be feasible in real-world applications. To address this limitation, we propose a style replay method that leverages the characteristics of low-level representations in CNN to require only one style feature for each domain, leading to a significant reduction in storage overhead. By fusing the style features of past domains with the semantic features of current data, our method enables style transfer for new domain data, thereby improving the model’s generalization ability to the domain. Through extensive experimental evaluations on various autonomous driving datasets, we demonstrate the efficacy of our proposed method in addressing the challenges of continual semantic segmentation under both label and domain shift, outperforming the previous state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Semantic Segmentation

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Deep Learning-based Serialized Model and Feature Extraction Algorithm for Solid Oxide Fuel Cell State Prediction 43

Deep Learning-based Serialized Model and Feature Extraction ...

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43rd Chinese control Conference, CCC 2024

作者： Wu, Jiajian Zhu, Kanglin Wu, Han Xiang, Guohui Li, Xi Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Hubei Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen China

ISBN: (纸本)9789887581581

Deep learning-based methods have shown their wide application prospects in the field of solid oxide fuel cell(SOFC) prediction. However, the irrationality of the prediction object and the lack of prediction accuracy have hindered the progress of the prediction methods for SOFC systems. Therefore, two algorithms are proposed in this study, a feature selection algorithm based on maximum information coefficient and Bayesian information criterion for identifying key variables that are sensitive to changes in the SOFC system, and a prediction model based on a genetic algorithm optimized for sequence to sequence architecture. The M/BIC algorithm reduces the number of features by 62.5% (from 40 to 15), which contributes to the acceleration of the algorithm training and the subsequent sensor layout optimization. The prediction model incorporates a two-dimensional convolutional neural network and a multi-head attention mechanism to capture more information in the input sequence, an encoder-decoder architecture and a Gated recurrent unit to better predict performance trends. Tests with real experimental data show significant changes in feature dimensionality reduction and prediction accuracy. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Dimensionality reduction

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Surrogate-Assisted Multiobjective Gene Selection for Cell Classification From Large-Scale Single-Cell RNA Sequencing Data

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IEEE Transactions on Evolutionary Computation 2025年第3期29卷 601-615页

作者： Lin, Jianqing He, Cheng Jiang, Hanjing Huang, Yabing Jin, Yaochu Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Wuhan430074 China Renmin Hospital of Wuhan University Department of Pathology Wuhan430060 China Westlake University Trustworth and General Artificial Intelligence Laboratory School of Engineering Hangzhou310030 China

Accurate cell classification is crucial but expensive for large-scale single-cell RNA sequencing (scRNA-seq) analysis. Gene selection (GS) emerges as a pivotal technique in identifying gene subsets of scRNA-seq for classification accuracy improvement and gene scale reduction. Nevertheless, the rising scale of scRNA-seq data presents challenges to existing GS methods regarding performance and computational time. Thus, we propose a surrogate-assisted evolutionary algorithm for multiobjective GS to address these deficiencies. An innovative two-phase initialization method is proposed to select sparse solutions to provide preliminary insights into gene contributions. Then, a binary competitive swarm optimizer is proposed for effective global search, where a local search method is embedded to eliminate irrelevant genes for efficiency consideration. Additionally, a surrogate model is adopted to forecast classification accuracy efficiently and substitutes part of the computationally expensive classification process. Experiments are conducted on eight large-scale scRNA-seq datasets with more than 20 000 genes. The effectiveness of the proposed GS method for scRNA-seq cell classification compared with eight state-of-the-art methods is validated. Gene expression analysis results of selected genes further validated the significance of the genes selected by the proposed method in the classification of scRNA-seq data. © 1997-2012 IEEE.

关键词： Gene expression

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Microgrid energy management strategy considering microgrid economy and rSOC stability: A model-based study 43

Microgrid energy management strategy considering microgrid e...

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43rd Chinese control Conference, CCC 2024

作者： Huang, Haizhou Xia, Zhiping Shan, Xinkai Wang, Jie Li, Xi Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China

ISBN: (纸本)9789887581581

Microgrids possess the ability to use renewable energy efficiently and play an increasingly significant role in environmental protection and sustainable development. Meanwhile, reversible solid oxide fuel cell (rSOC) is currently a new type of energy conversion and storage. Combining with microgrid systems is a great way to benefit from its high capacity, high efficiency, and cleanliness. However, rSOC's complex system of coupled heat, electricity, and gas presents a significant challenge in finding energy management strategies for microgrid applications containing rSOC. In this study, for a microgrid system combining rSOC, battery energy storage system (BESS), solar photovoltaic (PV), and wind turbine generator (WTG), we propose a model-based energy management strategy that simultaneously takes into account the economy of the microgrid system and the stability of the rSOC system. First of all, the microgrid is modeled with great detail, including the wind turbine, solar panels, battery storage system, and rSOC. In addition, the microgrid energy management problem is framed as a mathematical programming problem, with the attention focusing on the costs of BESS aging and operational energy loss of rSOC. In particular, this study also proposes a penalty strategy to reduce the power fluctuation amplitude of the rSOC system and reduce the mode switching frequency of the rSOC system. Using the solver, solving for each device's target power in a microgrid. The actual power operating curve is output by tracking the target power under slope constraints using the Simulink model. We then conduct a case study using data from Wuhan, China. Our research shows that compared with the control strategy based on rule control, the control strategy proposed in this study can reduce the power variation amplitude of the rSOC system by 79%, reduce the mode switching frequency by 85%, reduce the system operating cost by 4.5% and increase the energy self-sufficiency rate by 9%. © 2024 Techn

关键词： Wind turbines

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An Optimal control-Based Distributed Reinforcement Learning Framework for A Class of Non-Convex Objective Functionals of the Multi-Agent Network

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2081-2093页

作者： Zhe Chen Ning Li Department of Automation Shanghai Jiao Tong UniversityShanghai 200240 Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240 Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China Department of Automation Tsinghua UniversityBeijing 100084China IEEE

This paper studies a novel distributed optimization problem that aims to minimize the sum of the non-convex objective functionals of the multi-agent network under privacy protection, which means that the local objective of each agent is unknown to others. The above problem involves complexity simultaneously in the time and space aspects. Yet existing works about distributed optimization mainly consider privacy protection in the space aspect where the decision variable is a vector with finite dimensions. In contrast, when the time aspect is considered in this paper, the decision variable is a continuous function concerning time. Hence, the minimization of the overall functional belongs to the calculus of variations. Traditional works usually aim to seek the optimal decision function. Due to privacy protection and non-convexity, the Euler-Lagrange equation of the proposed problem is a complicated partial differential ***, we seek the optimal decision derivative function rather than the decision function. This manner can be regarded as seeking the control input for an optimal control problem, for which we propose a centralized reinforcement learning(RL) framework. In the space aspect, we further present a distributed reinforcement learning framework to deal with the impact of privacy protection. Finally, rigorous theoretical analysis and simulation validate the effectiveness of our framework.

关键词： Distributed optimization multi-agent optimal control reinforcement learning(RL)

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RBP-Former: Joint Prediction of RNA-protein Binding Sites on Full-length RNA Transcripts for Multiple RBPs

RBP-Former: Joint Prediction of RNA-protein Binding Sites on...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Yichong Liu, Xiaojian Cheng, Fan Pan, Xiaoyong Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions. © 2024 IEEE.

关键词： Binding sites

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Dual-type Hyperspectral Microscopic Imaging System based on Fourier Transform Imaging Spectrometer 15

Dual-type Hyperspectral Microscopic Imaging System based on ...

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15th International Conference on Information Optics and Photonics, CIOP 2024

作者： Wei, Zhanqi Wu, Kaijie Qin, Yuheng Cheng, Hao Gu, Chaochen Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (数字)9781510686175

ISBN: (纸本)9781510686168

This article proposes a hyperspectral microscopic imaging system based on Fourier transform interferometer to broaden the spectral range and obtain more types of spectral information under large field of view. Through stage scanning and light sources switching, transmission and fluorescence spectral images can be obtained successively. The system was validated using fibre tissues imaging. The spectral range covers 300-800 nm, and the spectral resolution is approximately 5 nm. It can be effectively applied to pathology and cytogenetics, obtaining morphological and spectral information to study the structure of the object and observe the similarities and differences of components in different positions. © 2024 SPIE.

关键词： Spectral resolution

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NeFF: Semantic Information Learning In 3D Space Using Neural Feature Field

NeFF: Semantic Information Learning In 3D Space Using Neural...

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2024 China Automation Congress, CAC 2024

作者： Yang, Yimu Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368604

Inferring a 3D space with partial observation requires prior knowledge from previous experience, and deep learning offers an intuitive solution. Prior knowledge can be categorized into two extremes Global and Local. Global prior uses input image as a clue to retrieve seen intact 3D objects yielding 3D consistent inference with fewer details. In contrast, Local prior takes advantage of closeby views’ overlapping areas and propagates input image cues to their vicinity via geometry transformation directly, producing realistic inferences around the reference views yet inconsistent inferences away from the reference views. We propose Neural feature field, an implicit 3D neural representation that combines the strength of the Global and Local prior. We represent each 3D scene with a latent embedding, then we perform a global scene-level prior to local view-specific feature prior with the neural feature field and volume rendering technique. Our model learns general scene properties across a category. Given a single input image at test time, the scene latent code is acquired via a two-round optimization process. Experiments on the SRN benchmark show that NeFF generalizes well to unseen objects within the same object category and is capable of producing 3D consistent inferences with sharp details from a single observation. © 2024 IEEE.

关键词： 3D reconstruction

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Uncertainty-Aware Reactive control for Signal Temporal Logic with Unknown Obstacles 14

Uncertainty-Aware Reactive Control for Signal Temporal Logic...

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14th Asian control Conference, ASCC 2024

作者： Chen, Chen Li, Shaoyuan Yin, Xiang Shanghai Jiao Tong University Department of Automation Shanghai200240 China The Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (纸本)9789887581598

In this paper, we investigate the challenge of con-trol synthesis for Signal Temporal Logic (STL) specifications within workspaces harboring unknown obstacles. We consider a sensing-based control setting, where robots are equipped with sensors with a limited sensing range to acquire obstacle locations with prior information. The objective is to synthesize a control strategy that maximize the robustness degree of the STL tasks while simultaneously minimizing the probability of collisions with obstacles during its operation. To address this, we present a receding horizon reactive control framework, tackling the problem by solving an uncertainty-aware optimal planning problem at each time instant. A key aspect of our approach is the avoidance of explicit computation of collision probability. Instead, we transform the uncertainty-aware synthesis problem into a non-probabilistic optimization problem, and the results of which still provide probabilistic guarantees. Simulation results are provided to illustrate the effectiveness of our approach. © 2024 Asian control Association.

关键词： Temporal logic

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