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Low-Pressure Electrochemical Synthesis of Complex High-Pressure Superconducting Superhydrides

Physical Review Letters 2022年第18期128卷 186001-186001页

作者： Pin-Wen Guan Ying Sun Russell J. Hemley Hanyu Liu Yanming Ma Venkatasubramanian Viswanathan Department of Mechanical Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA International Center of Computational Method and Software and State Key Laboratory for Superhard Materials College of Physics Jilin University Changchun 130012 China Departments of Physics Chemistry and Earth and Environmental Sciences University of Illinois Chicago Chicago Illinois 60607 USA International Center of Future Science Jilin University Changchun 130012 China Department of Physics Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA

There is great current interest in multicomponent superhydrides due to their unique quantum properties under pressure. A remarkable example is the ternary superhydride Li2MgH16 computationally identified to have an unprecedented high superconducting critical temperature Tc of ∼470 K at 250 GPa. However, the very high synthesis pressures required remains a significant hurdle for detailed study and potential applications. In this Letter, we evaluate the feasibility of synthesizing ternary Li-Mg superhydrides by the recently proposed pressure-potential (P2) method that uniquely combines electrochemistry and applied pressure to control synthesis and stability. The results indicate that it is possible to synthesize Li-Mg superhydrides at modest pressures by applying suitable electrode potentials. Using pressure alone, no Li-Mg ternary hydrides are predicted to be thermodynamically stable, but in the presence of electrode potentials, both Li2MgH16 and Li4MgH24 can be stabilized at modest pressures. Three polymorphs are predicted as ground states of Li2MgH16 below 300 GPa, with transitions at 33 and 160 GPa. The highest pressure phase is superconducting, while the two at lower pressures are not. Our findings point out the potentially important role of the P2 method in controlling phase stability of complex multicomponent superhydrides.

关键词： Electrochemistry Pressure effects Superconductivity Hydrogenated materials Density functional theory

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Data-Driven Switching Optimal Control Strategy for Aircraft Engine Acceleration Dynamics

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The International Journal of intelligent Control and Systems 2024年第4期29卷 154-163页

作者： Zelin Li Yan Shi Shuoshuo Liu Tao Sun Xudong Zhao Lijuan Liu Key Laboratory of Intelligent Control and Optimization for Industrial Equipment(Dalian University of Technology) Ministry of EducationDalian 116024China School of Control Science and Engineering Dalian University of TechnologyDalian 116024China Department of Automation Tsinghua UniversityBeijing 100084China School of Software Dalian Jiaotong UniversityDalian 116028China

The transient-state acceleration control of aircraft engines has consistently been a focal point in the field of aircraft engine control *** address the controller design problem of transient-state acceleration in aircraft engines based on switched linear systems,a data-driven optimization control algorithm is ***,the optimization control problem for the transient-state acceleration process of aircraft engines is mathematically ***,the Bellman optimal criterion in optimal control is utilized to derive the invariance of a value function at switching instants,providing an initial value for the optimization of the next *** optimization problem is then transformed into solving a Lyapunov equation using the principles of dynamic programming,and an iterative adaptive dynamic programming(ADP)algorithm is introduced to obtain optimal feedback control gains,which makes the solution of Lyapunov equation approach the optimal solution ***,simulation validation is conducted on a switched linear system for the transient-state acceleration of a specific aircraft *** effectiveness and superiority of the proposed algorithm are verified by comparing to traditional proportion integral differential(PID)control algorithms and active disturbance rejection control(ADRC).

关键词： Aircraft engine transient-state switched linear system data-driven approach adaptive dynamic programming optimal control

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Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement

arXiv

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arXiv 2020年

作者： Liu, Risheng Ma, Long Zhang, Jiaao Fan, Xin Luo, Zhongxuan International School of Information Science & Engineering Dalian University of Technology China School of Software Technology Dalian University of Technology China Key Laboratory for Ubiquitous Network Service Software of Liaoning Province

Low-light image enhancement plays very important roles in low-level vision field. Recent works have built a large variety of deep learning models to address this task. However, these approaches mostly rely on significant architecture engineering and suffer from high computational burden. In this paper, we propose a new method, named Retinex-inspired Unrolling with Architecture Search (RUAS), to construct lightweight yet effective enhancement network for low-light images in real-world scenario. Specifically, building upon Retinex rule, RUAS first establishes models to characterize the intrinsic underexposed structure of low-light images and unroll their optimization processes to construct our holistic propagation structure. Then by designing a cooperative reference-free learning strategy to discover low-light prior architectures from a compact search space, RUAS is able to obtain a top-performing image enhancement network, which is with fast speed and requires few computational resources. Extensive experiments verify the superiority of our RUAS framework against recently proposed state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Image enhancement

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HeSQLNet: A Heterogeneous graph neural Network for SQL-to-Text generation

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Information and software Technology 2025年

作者： Junsan Zhang Ao Lu Junxiao Han Yang Zhu Yudie Yan Juncai Guo Yao Wan Qingdao Institute of Software College of Computer Science and Technology Shandong Key Laboratory of Intelligent Oil and Gas Industrial Software China University of Petroleum (East China) Qingdao Shandong China School of Computer & Computing Science Hangzhou City University Hangzhou Zhejiang China School of Computer Science Wuhan University Wuhan Hubei China College of Computer Science and Technology Huazhong University of Science and Technology Wuhan Hubei China

Context: Understanding the semantics of SQL queries is crucial for maintaining code and reusing functionalities in database access and management. However, SQL queries often remain challenging to comprehend, even for expert users. In this work, we address this challenge by focusing on SQL-to-Text, a task that translates SQL queries into corresponding natural language questions. Existing approaches predominantly encode SQL queries using their Abstract Syntax Tree (AST) representation and then decode this structure into textual explanations. However, these methods often treat the AST as a homogeneous graph, overlooking the diverse relationships between its nodes, such as parent–child and sibling relationships. Objective: To address this issue, this paper introduces HeSQLNet: a Heterogeneous Graph Neural Network for SQL-to-Text Generation. Methods: Specifically, we first propose a Heterogeneous Feature Graph (HFG), which augments the AST with six distinct edge types to better capture the heterogeneous relationships inherent in SQL queries. We further develop a heterogeneous graph neural network with attention, leveraging a two-stage aggregation process to effectively extract and encode these heterogeneous features within the HFG. The enriched HFG representation is then incorporated into an encoder–decoder framework, called HeSQLNet, to generate natural language descriptions of SQL queries. To assess the ability of SQL-to-Text models to handle complex queries and demonstrate compositional generalization, we introduce SpiderComGen, a new compositional generalization dataset derived from the Spider dataset. Results: We conduct extensive experiments on both the widely-used and our proposed datasets. The experimental results reveal that HeSQLNet significantly outperforms existing state-of-the-art approaches in both effectiveness and generalization capability. Additionally, compared to the recent large language models, human evaluations and case studies show that HeSQLNet de

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A real-time one-time pad DNA-chaos image encryption algorithm based on multiple keys

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Optics & Laser Technology 2021年 143卷

作者： Shihua Zhou Key laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China

Image encryption is an important necessity when sharing data over public networks, and many image encryption algorithms have been proposed for the task. However, most of these algorithms are complex and slow. We propose a real-time one-time pad DNA-chaos image encryption algorithm using multiple keys. The proposed encryption algorithm uses a greatly simplified mechanism and a large, unpredictable sequence to encrypt the image. Our algorithm has three main advantages. First, its reliance on the original image for four of the keys makes it sensitive to changes in the original image. Small changes in the image produce large changes in the keys, making the algorithm resistant to differential attacks. Second, we integrate the natural DNA sequence into the process of generating the key, and the algorithm uses the massive DNA sequences stored in the DNA gene bank to solve the problem that it is difficult to store one-time pad. The keys have the property of one-time pad, and the key space is considerablely large. Finally, our algorithm encrypts quickly enough to make it suitable for real-time applications. On the premise of ensuring that the results of this paper are not worse than those of the comparison literature in other performance indicators, in terms of encryption time, even if we use a computing environment that is worse than all the comparison literature computer configurations, the algorithm of this paper can still save at least 14%–86%. As the picture size becomes larger, the advantages of the algorithm in this paper are more obvious.

关键词： Image encryption Multiple keys DNA coding Chaos 00-01 99-00

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Sparse random compressive sensing based data aggregation in wireless sensor networks

Sparse random compressive sensing based data aggregation in ...

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作者： Yin, Li Liu, Cuiye Guo, Songtao Yang, Yuanyuan Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing College of Electronic and Information Engineering Southwest University Chongqing400715 China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Electrical and Computer Engineering Stony Brook University Stony BrookNY11794 United States

In wireless sensor networks (WSNs), the volume of data is increasing at an unpredictable rate, which inevitably leads to high spatial-temporal correlation. To eliminate data redundancy, some researchers have proposed many data aggregation methods. However, a few of aggregation approaches can handle energy consumption and latency simultaneously. Therefore, in this paper, we propose an efficient algorithm, called Delay-Minimum Energy-Balanced (DMEB) data aggregation, which benefits from the superiority of the sparse random measurement matrix and minimum delay algorithm. Owing to the sparsity characteristics of the measurement matrix, only the nodes whose corresponding elements in the matrix are non-zero take part in the measurement. Each measurement can form an aggregation tree with minimum delay. After a sink node receives all the measurements, original readings can be recovered precisely. In addition, we adopt a novel scheduling method to avoid information interference. Experiment results demonstrate that, under recovering the original data accurately, the proposed data aggregation algorithm can not only shorten delay in data collection process but also reduce communication cost and prolong network lifetime during data transmission process. © 2018 John Wiley & Sons, Ltd.

关键词： Compressed sensing

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Geometrically nonlinear isogeometric topology optimization via extended finite element method

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Computers & Structures 2025年 316卷

作者： Jianli Liu Haobo Zhang Qiang Min Peng Li Jianzhong Yang Zhaohui Xia State Key Laboratory of Intelligent Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China National Key Laboratory of Digital and Agile Aircraft Design Chengdu Aircraft Design & Research Institute AVIC Chengdu 610091 China Shanghai Radio Equipment Research Institute Shanghai 200090 China School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Dalian University of Technology Dalian 116024 China

Geometric nonlinearity can accurately simulate and predict the large deformation behavior of structures, which is crucial for ensuring the safety and reliability of engineering designs. To address the limitations of existing linear isogeometric topology optimization methods in accurately describing the nonlinear behavior of boundary elements, this paper proposes a geometric nonlinear isogeometric topology optimization method based on the extended finite element method. Firstly, to accurately capture the nonlinear behavior of boundary elements, Gaussian integration points and weights of boundary elements are regenerated during the optimization process using the extended finite element method. Secondly, an isogeometric topology optimization model is developed based on the principle of minimum complementary work and the bi-directional evolutionary structural optimization method. A geometric nonlinear isogeometric analysis in the total Lagrangian formulation is proposed, and the incremental form of the Newton-Raphson method is employed to solve the nonlinear equilibrium equations, improving the accuracy and stability of structural response analysis. Lastly, the effectiveness and necessity of isogeometric topology optimization considering geometric nonlinearity are demonstrated through 2D and 3D examples. The results show that structures experience significant deformation under full load conditions, leading to the failure of linearly optimized structures, while geometrically nonlinear optimized structures exhibit superior performance.

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Semi-automated Construction of Air Pollution Domain Ontology 1

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9th International Conference on Frontier Computing, FC 2019

作者： Liu, Bo Zhang, Jiahui Li, Jianqiang Qu, Guangzhi Li, Yong Lang, Jianlei School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China Computer Science and Engineering Department Oakland University RochesterMI48309 United States Key Laboratory of Beijing on Regional Air Pollution Control College of Environmental and Energy Engineering Beijing University of Technology Beijing100124 China

ISBN: (数字)9789811532504

ISBN: (纸本)9789811532498

Air pollution has become a substantial environmental issue in China, which has seriously affected human health. To clarify the relationship among air pollutants, pollution sources, influencing factors, evaluation indicators and harms, it is essential to build a domain ontology for air pollution. Domain ontologies have been gradually accepted as a method to indicate the relationship between terms. However, there is not a complete ontology in air pollution domain, and building domain ontologies manually is time-consuming and inconvenient. In this paper, a semi-automatic approach is presented to build an ontology in air pollution domain. This paper proposes a method of entity relationship joint extraction based on attention mechanism, which is combined with core concept mining method to extract knowledge, and then concepts, relationships, and relevant instances are organized in hierarchy. Finally, the ontology model is constructed semi-automatically and semantic inference is also carried out. The research shows that this knowledge extraction method can avoid the error accumulation caused by entity recognition and relationship extraction and deepen the inner connection between them, and a large amount of effective knowledge in the field of air pollution can be extracted. In addition, the ontology constructed based on this method can also visually analyze the relationship between various classes and concepts in the field of air pollution, deduce the pollutant propagation path, and provide practical experience for the semi-automatic ontology construction in other fields and data support for further research. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Ontology

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Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses

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npj Computational Materials 2020年第1期6卷 1-9页

作者： Lu, Zhichao Chen, Xin Liu, Xiongjun Lin, Deye Wu, Yuan Zhang, Yibo Wang, Hui Jiang, Suihe Li, Hongxiang Wang, Xianzhen Lu, Zhaoping Beijing Advanced Innovation Center for Materials Genome Engineering State Key Laboratory for Advanced Metals and Materials University of Science and Technology Beijing Beijing100083 China Institute of Applied Physics and Computational Mathematics Beijing100088 China Software Center for High Performance Numerical Simulation Chinese Academy of Engineering Physics Beijing100088 China Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing100083 China

Fe-based metallic glasses (MGs) have been extensively investigated due to their unique properties, especially the outstanding soft-magnetic properties. However, conventional design of soft-magnetic Fe-based MGs is heavily relied on "trial and error" experiments, and thus difficult to balance the saturation flux density (Bs) and thermal stability due to the strong interplay between the glass formation and magnetic interaction. Herein, we report an eXtreme Gradient Boosting (XGBoost) machine-learning (ML) model for developing advanced Fe-based MGs with a decent combination of Bs and thermal stability. While it is an attempt to apply ML for exploring soft-magnetic property and thermal stability, the developed XGBoost model based on the intrinsic elemental properties (i.e., atomic size and electronegativity) can well predict Bs and Tx (the onset crystallization temperature) with an accuracy of 93.0% and 94.3%, respectively. More importantly, we derived the key features that primarily dictate Bs and Tx of Fe-based MGs from the ML model, which enables the revelation of the physical origins underlying the high Bs and thermal stability. As a proof of concept, several Fe-based MGs with high Tx (>800 K) and high Bs (>1.4 T) were successfully developed in terms of the ML model. This work demonstrates that the XGBoost ML approach is interpretable and feasible in the extraction of decisive parameters for properties of Fe-based magnetic MGs, which might allow us to efficiently design high-performance glassy materials. © 2020, The Author(s).

关键词： Thermodynamic stability

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A new Technique in Protein Structure Quantitative Identification

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Procedia Computer Science 2022年 214卷 1546-1553页

作者： Mengjiao Guo Haobin Cao Jing He Tengfei Ji Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne 3122 Australia Department of mechanical engineering University College London London WC1E 6BT United Kingdom Department of Neuroscience University of Oxford Oxford OX39DU United Kingdom State Key Laboratory of Bioactive Substance and Function of Natural Medicines Institute of Materia Medica Peking Union Medical College and Chinese Academy of Medical Sciences Beijing 100050 China

Graph distance is also named approximate graph isomorphism, error-tolerant graph matching, it is a measure of similarity (or dissimilarity) between two graphs. The graph distance function is an elusive question remaining a pressing problem in a wide range of fields. In general, graphs are usually enriched with node and edge attributes, namely, heterogeneous graphs, our focus is encapsulating node and edge identities in a graph. In addition, an adequate representation of the graph-adjacency matrix can capture the connectivity of the graph structure, while an adjacency matrix bears high-order combinatorial nature. Solving these challenges in the effective analysis of these graphs is of utmost importance. In this paper, structural and semantic features are both preserved by graphs, so the analysis of row sum and eigenspectra for vertex and edge adjacency matrix has captured the affinity interactions within graphs locally and globally. Therefore, we estimate the geometrical and semantic dissimilarities/distances between graphs extensively and systemically. Several illustrative chemical compound cases implement in practical scenarios, thus, the theory could be easily understood. Our method defines a quantitative distance (or dissimilarity) measure for comparing graphs, it has a good measure of expressiveness and a suited polynomial computation complexity, which paves the way for graph analysis.

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