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In situ resource utilization of lunar soil for highly efficient extraterrestrial fuel and oxygen supply

National science Review 2023年第2期10卷 224-231页

作者： Yuan Zhong Jingxiang Low Qing Zhu Yawen Jiang Xiwen Yu Xinyu Wang Fei Zhang Weiwei Shang Ran Long Yingfang Yao Wei Yao Jun Jiang Yi Luo Weihua Wang Jinlong Yang Zhigang Zou Yujie Xiong Hefei National Research Center for Physical Sciences at the Microscale School of Chemistry and Materials Science National Synchrotron Radiation Laboratory School of Information Science and TechnologyUniversity of Science and Technology of China Eco-Materials and Renewable Energy Research Center (ERERC) Jiangsu Key Laboratory for Nano Technology National Laboratory of Solid State MicrostructuresSchool of PhysicsCollaborative Innovation Center of Advanced MicrostructuresCollege of Engineering and Applied SciencesNanjing University Qian Xuesen Laboratory of Space Technology China Academy of Space Technology

Building up a lunar settlement is the ultimate aim of lunar ***, limited fuel and oxygen supplies restrict human survival on the ***,we demonstrate the in situ resource utilization of lunar soil for extraterrestrial fuel and oxygen production,which may power up our solely natural satellite and supply respiratory ***,the lunar soil is loaded with Cu species and employed for electro catalytic CO2conversion,demonstrating significant production of *** addition,the selected component in lunar soil（***3） loaded with Cu can reach a CH4F aradaic efficiency of 72.05% with a CH4production rate of 0.8 mL/min at 600 mA/***,an O2production rate of 2.3 mL/min can be ***,we demonstrate that our developed process starting from catalyst preparation to electrocatalytic CO2conversion is so accessible that it can be operated in an unmmaned manner via a robotic *** a highly efficient extraterrestrial fuel and oxygen production system is expected to push forward the development of mankind's civilization toward an extraterrestrial settlement.

关键词： Chang’E-5 lunar soil fuel production CO2 conversion extraterrestrial settlement in situ resource utilization

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Grape Leaf Disease Detection and Recognition Method Based on Improved Model RTDETR-DRA 4

Grape Leaf Disease Detection and Recognition Method Based on...

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4th International Conference on Electronic information engineering and Computer technology, EIECT 2024

作者： Chen, Zhiyuan Yang, Yanhua School of Information Science & Engineering Yunnan University Yunnan China School of Information Science & Engineering University Key Laboratory of Internet of Things Technology and Application Yunnan University Yunnan China

ISBN: (纸本)9798331528850

In recent years, the rapid development of artificial intelligence has revolutionized agriculture, particularly in the detection and identification of plant diseases. Grape leaf diseases pose a significant threat to grape production, directly impacting yield and quality. To address the limitations of existing disease detection models, such as high computational cost and inefficient deployment on resource-limited devices, we propose an improved model based on RTDETR, named RTDETR-DRA. This model integrates the DRBNCSPELAN module, leveraging multi-path feature fusion and channel split design to optimize real-time detection accuracy while minimizing computational cost. Experimental results show that RTDETR-DRA outperforms the original RTDETR model, achieving higher accuracy in disease detection with significantly reduced parameters and GFLOPs, making it suitable for lightweight deployment on platforms like mobile devices. Our proposed method contributes to the advancement of efficient and precise plant disease detection technologies. © 2024 IEEE.

关键词： Software design

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MULTI-ROBOT PATH PLANNING AND COOPERATIVE OBSTACLE AVOIDANCE BASED ON MONTE-CARLO TREE SEARCH 10

MULTI-ROBOT PATH PLANNING AND COOPERATIVE OBSTACLE AVOIDANCE...

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10th International Symposium on Test Automation and Instrumentation, ISTAI 2024

作者： Dai, Juan Jing, Yanzhang Su, Zhong Zhu, Cui Beijing Key Laboratory of High Dynamic Navigation Technology Beijing Information Science & Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing China School of Automation Beijing Information Science & Technology University Beijing China School of Information and Communication Engineering Beijing Information Science & Technology University Beijing China

ISBN: (纸本)9781837242672

With the rapid development of artificial intelligence technologies, multi-robot cooperative processing tasks are becoming more and more common in various applications. However, traditional single-robot planning algorithms show obvious limitations in dealing with complex social interactions and dynamic environments in the real world. To address this challenge, this paper applies Monte Carlo Tree Search (MCTS) to multi-robot path planning and proposes a collaborative obstacle avoidance motion planning algorithm based on MCTS. Specifically, the algorithm prunes the Monte Carlo tree by excluding cases that do not meet the robot speed constraints and potential collisions, which significantly improves the computational speed and success rate of the algorithm, making it applicable to the multi-robot path conflict problem. In addition, we design a policy space that conforms to the robot's operation and a cost function that meets the requirements of robot path planning to plan an efficient cooperative path for each robot. Through a series of simulation experiments, we demonstrate the algorithm's path planning and obstacle avoidance performance in a variety of conflict scenarios. The results show that the method is highly cooperative and effective in complex environments. © The Institution of engineering & technology 2024.

关键词： Cost functions

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Optimisation of sparse deep autoencoders for dynamic network embedding

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1361-1376页

作者： Huimei Tang Yutao Zhang Lijia Ma Qiuzhen Lin Liping Huang Jianqiang Li Maoguo Gong College of Computer Science and Software Engineering Shenzhen UniversityShenzhenChina Institute for Infocomm Research Agency for Science Technology and ResearchSingaporeSingapore Key Laboratory of Collaborative Intelligence Systems Ministry of EducationXidian UniversityXi'anChina

Network embedding(NE)tries to learn the potential properties of complex networks represented in a low-dimensional feature ***,the existing deep learningbased NE methods are time-consuming as they need to train a dense architecture for deep neural networks with extensive unknown weight parameters.A sparse deep autoencoder(called SPDNE)for dynamic NE is proposed,aiming to learn the network structures while preserving the node evolution with a low computational *** tries to use an optimal sparse architecture to replace the fully connected architecture in the deep autoencoder while maintaining the performance of these models in the dynamic ***,an adaptive simulated algorithm to find the optimal sparse architecture for the deep autoencoder is *** performance of SPDNE over three dynamical NE models(*** architecture-based deep autoencoder method,DynGEM,and ElvDNE)is evaluated on three well-known benchmark networks and five real-world *** experimental results demonstrate that SPDNE can reduce about 70%of weight parameters of the architecture for the deep autoencoder during the training process while preserving the performance of these dynamical NE *** results also show that SPDNE achieves the highest accuracy on 72 out of 96 edge prediction and network reconstruction tasks compared with the state-of-the-art dynamical NE algorithms.

关键词： deep autoencoder dynamic networks low-dimensional feature space network embedding sparse structure

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Tackling Real-World Complexity: Hierarchical Modeling and Dynamic Prompting for Multimodal Long Document Classification

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IEEE Transactions on Circuits and systems for Video technology 2025年第6期35卷 5776-5790页

作者： Liu, Tengfei Hu, Yongli Li, Mingjie Yi, Junfei Chang, Xiaojun Gao, Junbin Yin, Baocai Beijing University of Technology Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence School of Information Science and Technology Beijing100124 China Stanford University Department of Radiation Oncology StanfordCA94305 United States Hunan University National Engineering Laboratory of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha410082 China ReLER Laboratory Australian Artificial Intelligence Institute Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia University of Sydney Business School The University of Sydney Discipline of Business Analytics CamperdownNSW2006 Australia

With the rapid growth of internet content, multimodal long document data has become increasingly prominent, drawing significant attention from researchers. However, most existing methods primarily focus on scenarios where all modalities are present, often overlooking more challenging and realistic cases involving missing image modality. To address this limitation, we propose a robust multimodal long document classification (MLDC) framework that integrates hierarchical modeling and dynamic prompting to handle complex multimodal long document data. Our approach begins by leveraging hierarchical modeling combined with an Adaptive Correlation Multimodal Transformer (ACMT) to effectively capture relationships between text and images at both section and sentence levels. We also introduce a Dynamic Prompt Generation (DPG) module at both levels to enhance the model’s robustness in handling missing image data. By evaluating sample uncertainty, the DPG module dynamically adjusts both the number of prompts and the prompts themselves, allowing the model to better adapt to the varying needs of different samples. Finally, a Hierarchical Heterogeneous Graph (HHG) is introduced to enhance feature interactions across levels, further improving the coherence and accuracy of the model. Extensive experiments on four multi-modal long document datasets demonstrate that our model shows superior performance compared to existing state-of-the-art MLDC classification methods in various conditions. © 1991-2012 IEEE.

关键词： Adaptation models Transformers Data models Correlation Circuits and systems Electronic mail Robustness Uncertainty Training Complexity theory

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Sparsity-based efficient simulation of cluster targets electromagnetic scattering

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Journal of systems engineering and Electronics 2023年第2期34卷 299-306页

作者： TIAN Yuguang LIU Yixin CHEN Xuan CHEN Penghui WANG Jun CHEN Junwen Communication University of China Beijing 100024China School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Science and Technology on Electromagnetic Scattering Laboratory Beijing 100854China

An efficient and real-time simulation method is proposed for the dynamic electromagnetic characteristics of cluster targets to meet the requirements of engineering practical ***,the coordinate transformation method is used to establish a geometric model of the observation scene,which is described by the azimuth angles and elevation angles of the radar in the target reference frame and the attitude angles of the target in the radar reference ***,an approach for dynamic electromagnetic scattering simulation is ***,a fast-computing method based on sparsity in the time domain,space domain,and frequency domain is *** method analyzes the sparsity-based dynamic scattering characteristic of the typical cluster *** error between the sparsity-based method and the benchmark is small,proving the effectiveness of the proposed method.

关键词： geometric model of the observation scene dynamic electromagnetic scattering simulation sparsity-based method

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Specification of Complex Analytics Workflows: A Formal Language Model of Decision Options 20th

Specification of Complex Analytics Workflows: A Formal Lang...

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20th International Conference on the Economics of Grids, Clouds, systems, and Services, GECON 2024

作者： Miri, Pouriya Kochovski, Petar Tuler de Oliveira, Marcela Stankovski, Vlado Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Department of Engineering Systems Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031812255

The specification of experiments expressed as Complex Analytics Workflows is a complex task that involves many decision-making steps with various degrees of complexity. The use of the context, the expert knowledge, and the potential for its sharing and reuse in the context of experiment specification have not been addressed sufficiently until now. Moreover, to make such knowledge instrumental, it should be coupled with specific probabilistic measures, such as particular assurances, ranking, and verification of various options. The paper aims to present a novel semantic model for probabilistic reasoning in any experimentation context coupled with a functional system for knowledge generation, reuse, and sharing. The result of our work can be used within existing experimentation engines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov processes

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Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait

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PeerJ Computer science 2025年 11卷 1-42页

作者： Naz, Anam Khan, Hikmat Ullah Alsahfi, Tariq Alhajlah, Mousa Alshemaimri, Bader Daud, Ali Department of Information Technology University of Sargodha Punjab Sargodha Pakistan Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Applied of Computer Science College King Saud University Riyadh Saudi Arabia Software Engineering Department College of Computing and Information Sciences King Saud University Riyadh Saudi Arabia Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates

Understanding human personality traits is significant as it helps in decision making related to consumers’ behavior, career counselling, team building and top candidates’ selection for recruitment. Among various traits, openness is essential as it shows both diverse aspects of sensitive nature or intuitive nature. The individuals having a sensing nature tends to be more practical and prefer to focus on concrete information whereas the users having intuitive trait type is characterized by a focus on abstract ideas, creative thinking and future-oriented perspectives. In this research work, we aim to explore diverse natural language processing (NLP) based features and apply state of the art deep learning algorithms for openness trait prediction. Using standard Myers-Briggs Type Indicator (MBTI) dataset, we propose the use of the latest deep features of sentence embeddings which captures contextual semantics of the content to be used with deep learning models. For comparison, we explore textual features of Frequency-Inverse Document (TF-IDF) and parts of speech (POS) tagging with machine learning models and deep features of word2vec and global vectors for word representation (GloVe) with deep learning models. The comprehensive empirical analysis reveals that TF-IDF used with gradient boosting achieves high accuracy of 90% whereas, the deep feature of sentence embeddings when used and with deep model bidirectional long short-term memory (Bi-LSTM) achieves 90.5% accuracy. The best results have been achieved using the latest Transformer-based DistilBERT, which achieves the highest accuracy of 92% outperforming the existing studies in relevant literature. Subjects Human-Computer Interaction, Artificial Intelligence, Data Mining and Machine Learning, Natural Language and Speech © 2025 xxxx

关键词： Deep learning

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Dynamic Self-Triggered Control for Nonzero-Sum Games of Unknown Nonlinear Constrained systems via Generalized Fuzzy Hyperbolic Models

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IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3506-3518页

作者： Liu, Fan Su, Hanguang Zhang, Huaguang Luo, Biao Wang, Jiawei Northeastern University School of Information Science and Engineering Shenyang110004 China Northeastern University School of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China Central South University School of Automation Changsha410083 China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China

In this article, a novel adaptive dynamic programming algorithm is devised to handle the multiplayer nonzero-sum (NZS) games of completely unknown nonlinear systems, subject to state and input constraints under the dynamic self-triggered mechanism. Initially, in order to eliminate the demand for system dynamics, a generalized fuzzy hyperbolic model based identifier is established, which only relies on input-output data. Then, the equivalent transformation of the reconstructed system is implemented by virtue of barrier functions. With the aid of the nonquadratic utility function, the Hamilton-Jacobi equation of the NZS game is derived. After that, an adaptive critic scheme with experience replay is employed to acquire the Nash equilibrium solution. Furthermore, a novel dynamic self-triggered rule is proposed with the dead-zone operation, which not only significantly reduces source consumption but also overcomes the implementation difficulty of monitoring hardware in the event-triggered mechanism. Moreover, the stability of the system and the uniform ultimate boundedness of the critic weights are guaranteed. Ultimately, two simulation examples are given to validate the feasibility of the developed method. © 2025 IEEE.

关键词： Adaptive algorithms

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Distributed Deep Reinforcement Learning-based Approach for Fast Preventive Control Considering Transient Stability Constraints

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CSEE Journal of Power and Energy systems 2023年第1期9卷 197-208页

作者： Hongtai Zeng Yanzhen Zhou Qinglai Guo Zhongmin Cai Hongbin Sun State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Automation Science and Technology Xi’an Jiaotong UniversityXi’an 710000China

Preventive transient stability control is an effective measure for the power system to withstand high-probability severe *** is mathematically an optimal power flow problem with transient stability *** to the constraints involved for differential algebraic equations of transient stability,it is difficult and time-consuming to solve this *** address these issues,this paper presents a novel deep reinforcement learning(DRL)framework for preventive transient stability control of power systems.A distributed deep deterministic policy gradient is utilized to train a DRL agent that can learn its control policy through massive interactions with a grid *** properly trained,the DRL agent can instantaneously provide effective strategies to adjust the system to a safe operating position with a near-optimal operational *** effectiveness of the proposed method is verified through numerical experiments conducted on a New England 39-bus system and NPCC 140-bus system.

关键词： Deep reinforcement learning preventive Control transient stability

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