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Enhancing Plasticity in BCC Mg-Li-Al Alloys Through controlled Precipitation at Grain Boundaries

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SSRN

SSRN 2024年

作者： Ji, Fan Xin, Tongzheng Zhao, Yuhong Yang, Wenkui Bai, Guoning Tang, Song Guo, Enyu Zhou, Mengran Shi, Qingyu Cui, Luqing Chen, Long-Qing He, Binbin Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China Instrumental Analysis Center Dalian University of Technology Dalian116024 China School of Materials Science and Engineering Dalian University of Technology Dalian116024 China Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing100083 China School of Materials Science and Engineering Collaborative Innovation Center of Ministry of Education Shanxi Province for High-performance Al/Mg Alloy Materials North University of China Taiyuan030051 China Institute of Materials Intelligent Technology Liaoning Academy of Materials Shenyang110004 China Herbert Gleiter Institute of Nanoscience School of Materials Science and Engineering Nanjing University of Science and Technology Jiangsu210094 China State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment Department of Mechanical Engineering Tsinghua University Beijing100084 China Key Laboratory for Advanced Materials Processing Technology Ministry of Education Beijing100084 China The National Key Laboratory of Aerospace Power System and Plasma Technology Xi'an Jiaotong University Xi’an710049 China School of Mechanical Engineering Xi'an Jiaotong University Xi’an710049 China Department of Materials Science and Engineering The Pennsylvania State University University ParkPA16802 United States

This study investigates the improvement of plasticity in body-centered cubic magnesium (Mg)-lithium (Li)-aluminum (Al) alloys, crucial for lightweight structural applications. The ternary Mg-Li-Al alloys exhibits high strength but low ductility. Precipitates at grain boundaries in these alloys, linked to reduced plasticity, are examined for their crystal structure and composition. Advanced microscopic techniques reveal the transformation of precipitates and the development of specific structures at grain boundaries. Thermodynamics of element diffusion at grain boundaries are explored through first-principles calculations, and a phase-field simulation models precipitate evolution. Molecular dynamics simulations elucidate nanoscale mechanisms governing the transition from brittle to ductile fracture modes during artificial aging. The D03-Mg3Al at grain boundaries is a brittle phase, and through a 170°C aging treatment, it induces the precipitation of lamellar α-Mg phase with D03-Mg3Al as nucleation sites. The occupancy energy of Al atoms at Li sites in α-Mg is found to be lower than that in D03-Mg3Al, leading to the dissolution of D03-Mg3Al. The α-Mg, characterized by a stronger metallic nature, exhibits a better-matched modulus with the matrix and enhanced dislocation mobility. The precipitation of α-Mg plays a pivotal role in significantly improving the ductility of the alloy. This work contributes to the understanding of the complex interplay between alloy composition, grain boundary precipitates, and plasticity, as well as brings insights to guide interfacial control in the development of advanced Mg-Li-Al alloys for structural applications. © 2024, The Authors. All rights reserved.

关键词： Grain boundaries

Interdependence between individual and social learning shapes cooperation with asymmetric exploration

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arXiv 2025年

作者： Hou, Zhihao She, Zhikun Liang, Quanyi Su, Qi Li, Daqing School of Mathematical Sciences Beihang University Beijing100191 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Reliability and Systems Engineering Beihang University Beijing100083 China

Cooperation on social networks is crucial for understanding human survival and development. Although network structure has been found to significantly influence cooperation, some human experiments indicate that it cannot fully explain the evolutionary patterns of cooperation. While evidence suggests that this gap arises from human exploration, our understanding of its impact mechanisms and characteristics, such as asymmetry, remains limited. Here, we seek to formalize human exploration as an individual learning process involving trial and reflection, and integrate social learning to examine how their interdependence shapes cooperation. We find that individual learning can alter the imitative tendency of social learning, while its cooperative tendency in turn relies on social learning and players' decision preferences. By analyzing a series of key experiments, we show that the above coupled dynamics can explain human behavior in game interactions. Furthermore, we find that individual learning can promote cooperation when its probability is negatively correlated with payoffs, a mechanism rooted in the psychological tendency to avoid trial-and-error when individuals are satisfied with their current payoffs. These results link long-unexplained asymmetric exploration with the cooperation-promoting ability of social networks, helping to bridge the gap between theoretical research and reality. © 2025, CC BY.

关键词： Human engineering

Artificial intelligence-based methods for renewable power system operation

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Nature Reviews Electrical Engineering

Nature Reviews Electrical Engineering 2024年第3期1卷 163-179页

作者： Yuanzheng Li Fei Hu Juntao Duan Yong Zhao Zhigang Zeng Yizhou Ding Shangyang He Guanghui Wen Hua Geng Zhengguang Wu Hoay Beng Gooi Chenghui Zhang Shengwei Mei Key Laboratory of lmage Information Processing and Intelligent Control of Ministry of Education of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China National Key Laboratory of Multispectral Information Intelligent Processing Technology Huazhong University of Science and Technology Wuhan China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan China Department of Systems Science School of Mathematics Southeast University Nanjing China Department of Automation Tsinghua University Beijing China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China School of Electrical and Electronics Engineering Nanyang Technological University Singapore Singapore School of Control Science and Engineering Shandong University Jinan China Department of Electrical Engineering Tsinghua University Beijing China

Carbon neutrality goals are driving the increased use of renewable energy (RE). Large-scale use of RE requires accurate energy generation forecasts; optimized power dispatch, which minimizes costs while satisfying operational constraints; effective system control to ensure a stable power supply; and electricity markets that support bidding and trading decisions associated with RE. However, the uncertainties in RE generation make renewable power systems challenging to operate. For example, the intermittent nature of wind power can make it difficult to balance the supply and demand of electricity in real time; therefore, traditional power sources could be needed to meet the demand, which can increase electricity prices. This Review outlines the potential of artificial intelligence-based methods for supporting renewable power system operation. We discuss the ability of machine learning, deep learning and reinforcement learning methods to facilitate power system forecasts, dispatch, control and markets to support the use of RE. We also emphasize the applicability of these techniques to different operational problems. Finally, we discuss potential trends in renewable power system development and approaches to address the associated operational challenges such as the increasingly distributed nature of RE installations, diversification of energy storage systems and growing market complexity.

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Tigc-Net: Transformer-Improved Graph Convolution Network for Spatio-Temporal Prediction

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SSRN

SSRN 2022年

作者： Chen, Kai Yang, Chunfeng Zhou, Zhengyuan Liu, Yao Ji, Tianjiao Sun, Weiya Chen, Yang School of Cyber Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China The College of Software Engineering Southeast University Nanjing210096 China Laboratory of Image Science and Technology The School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China NHC Key Laboratory of Medical Virology and Viral Diseases National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Beijing China Beijing Institute of Tracking and Communication Technology Beijing100094 China

Modeling spatio-temporal sequences is an important topic yet challenging for existing neural networks. Most of the current spatio-temporal sequence prediction methods usually capture features separately in temporal and spatial dimensions or employ multiple mutually independent local spatio-temporal graphs to represent a spatio-temporal sequence. The first kind of method mentioned above is difficult to mine the complex spatio-temporal correlations, while the other is limited for the accuracy of long-term predictions. To handle these issues, this paper proposes a Transformer-Improved Graph Convolution Network for spatio-temporal prediction. Specifically, the temporal location encoding method is exploited to derive the spatio-temporal characteristics of the sequence utilizing a spatio-temporal feature fusion network. In addition, a spatio-temporal attention network is developed to enhance the spatio-temporal correlation of the sequence, and the dynamic spatial features of sequence are further extracted through the adaptive graph convolution network. A private dataset and a public dataset are employed to demonstrate the performance of the proposed TIGC-Net. The qualitative and quantitative results show that the proposed TIGC-Net can extract dynamic spatiotemporal properties more effectively, enhance the spatio-temporal correlation of sequences and improve the prediction accuracy compared with four stateof-the-art. © 2022, The Authors. All rights reserved.

关键词： Convolution

Spatial non-locality induced non-markovian EIT in a single giant atom

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arXiv 2021年

作者： Zhu, Y.T. Wu, R.B. Xue, S. Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Department of Automation Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China

In recent experiments, electromagnetically induced transparency (EIT) were observed with giant atoms, but nothing unconventional were found from the transmission spectra. In this letter, we show that unconventional EIT does exist in giant atoms, and indicate why it has not been observed so far. Different from these existing works, this letter presents a consistent theory including a real space method and a time delayed master equation for observing unconventional EIT. We discover that this phenomenon is a quantum effect which cannot be correctly described in a semi-classical way as those in recent works. Our theory shows that it can be observed when the time delay between two neighboring coupling points is comparable to the relaxation time of the atom, which is crucial for a future experimental observation. This new phenomenon results from inherent non-locality of the giant atom, which physically forces propagating fields to be standing waves in space and the atom exhibiting retardations in time. Our theory establishes a framework for application of nonlocal systems to quantum information processing. Copyright © 2021, The Authors. All rights reserved.

关键词： Atoms

Channel and Trials Selection for Reducing Covariate Shift in EEG-based Brain-Computer Interfaces

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Channel and Trials Selection for Reducing Covariate Shift in...

IEEE International Conference on Systems, Man, and Cybernetics

作者： He He Dongrui Wu Key Laboratory for Image Processing and Intelligent Control Ministry of Education and the School of Artificial Intelligence and Automation Huazhong University of Science and Technology

ISBN: (纸本)9781728145709

This paper aims at reducing the calibration effort of EEG-based brain-computer interfaces (BCIs). More specifically, in the context of cross-subject classification, we correct covariate shift of EEG data from different subjects, so that a classifier trained on auxiliary subjects can also be applied to a new subject, without any labeled trials from the new subject. Methods: We propose two approaches to enhance the performance of a state-of-the-art Riemannian space transfer learning (TL) algorithm: 1) trials selection, which resamples trials from the auxiliary subjects so that they become more consistent with those of the new subject;and, 2) channel selection, which reduces the number of channels and hence makes the Riemannian space computations more accurate and efficient. Results: We tested the proposed approaches on two motor imagery datasets. The results verified that they can enhance the performance of the state-of-the-art TL algorithm. Conclusion and significance: Our proposed approaches make the state-of-the-art TL algorithm more effective and efficient.

关键词： Brain-Computer Interfaces experimentation Trials Computer interfaces Thallium Shift Classifiers biological control 2-channel

RGB-based combustion state classification and stability analysis of SOFC afterburner: effects of fuel utilization and steam-to-carbon ratio

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Fuel 2025年 402卷

作者： Jingjing Wang Shuyu Zhang Jingang Lai Jiashu Jin Chun Zou Jung-Sik Kim Zhonghua Deng Yuanwu Xu Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Wuhan 430074 China State Key Laboratory of Coal Combustion Huazhong University of Science and Technology Wuhan 430074 China Sino-French Engineer School/School of General Engineering Beihang University Beijing 100191 China Wuhan Huamao Automation Co. Ltd. Wuhan 430205 China School of Artificial Intelligence and Automation Wuhan University of Science and Technology Wuhan 430081 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518055 China

Solid oxide fuel cell (SOFC) systems efficiently convert chemical energy into electricity but produce high-temperature exhaust gases containing unreacted fuels. The afterburner combusts these residual fuels to enhance heat recovery and system efficiency, with its performance influenced by stack fuel composition variations. This study investigates how fuel utilization rate ( U f ) and steam-to-carbon ratio (S/C) affect afterburner combustion characteristics in a 1 kW methane-reformed SOFC system. Results showed maximum afterburner temperature decreased from 981.3 K to 835 K as U f increased to 0.6, while S/C minimally impacted temperature distribution. A dimensionless stability index was developed to quantitatively assess combustion performance. An image-based analysis approach extracted five key parameters describing flame characteristics under varying conditions. These parameters supported the development of a three-tier hierarchical classification framework to identify weak, stable, and unstable combustion states, achieving an overall recognition accuracy of 74.5 %. These findings provide insights into SOFC operating parameters, residual stack fuel compositions, and afterburner stability relationships. The proposed assessment methodology offers guidance for optimizing thermal management and improving energy recovery efficiency in SOFC systems.

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Deep reinforcement learning with a stage incentive mechanism of dense reward for robotic trajectory planning

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arXiv 2020年

作者： Peng, Gang Yang, Jin Khyam, Mohammad Omar Key Laboratory of Image Processing and Intelligent Control Ministry of EducationB School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Automation South East University Nanjing China School of Engineering and Technology Central Queensland University Melbourne Australia

To improve the efficiency of deep reinforcement learning (DRL)-based methods for robot manipulator trajectory planning in random working environments, we present three dense reward functions. These rewards differ from the traditional sparse reward. First, a posture reward function is proposed to speed up the learning process with a more reasonable trajectory by modeling the distance and direction constraints, which can reduce the blindness of exploration. Second, a stride reward function is proposed to improve the stability of the learning process by modeling the distance and movement distance of joint constraints. Finally, in order to further improve learning efficiency, we are inspired by the cognitive process of human behavior and propose a stage incentive mechanism, including a hard stage incentive reward function and a soft stage incentive reward function. Extensive experiments show that the soft stage incentive reward function is able to improve the convergence rate by up to 46.9% with the state-of-the-art DRL methods. The percentage increase in the convergence mean reward was 4.4-15.5% and the percentage decreases with respect to standard deviation were 21.9-63.2%. In the evaluation experiments, the success rate of trajectory planning for a robot manipulator reached 99.6%. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Improving adiabatic quantum factorization via chopped random-basis optimization

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arXiv 2025年

作者： Yang, Tianlai Xiong, Mo Xue, Ming Li, Xinwei Li, Jinbin College of Physics Nanjing University of Aeronautics and Astronautics Nanjing211106 China State Key Laboratory of Mechanics and Control for Aerospace Structures Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education Institute for Frontier Science Nanjing University of Aeronautics and Astronautics Nanjing210016 China Key Laboratory of Aerospace Information Materials and Physics MIIT Nanjing211106 China Beijing Academy of Quantum Information Sciences Xibeiwang East Road Beijing100193 China

Integer factorization remains a significant challenge for classical computers and is fundamental to the security of RSA encryption. Adiabatic quantum algorithms present a promising solution, yet their practical implementation is limited by the short coherence times of current NISQ devices and quantum simulators. In this work, we apply the chopped random-basis (CRAB) optimization technique to enhance adiabatic quantum factorization algorithms. We demonstrate the effectiveness of CRAB by applying it to factor the integers ranging from 21 to 2479, achieving significantly improved fidelity of the target state when the evolution time exceeds the quantum speed limit. Notably, this performance improvement shows resilience in the presence of dephasing noise, highlighting CRAB’s practical utility in noisy quantum systems. Our findings suggest that CRAB optimization can serve as a powerful tool for advancing adiabatic quantum algorithms, with broader implications for quantum information processing tasks. © 2025, CC BY-NC-SA.

关键词： Qubits