检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Li, Xiang Lan, Long Lahza, Husam Yang, Shaowu Wang, Shuihua Yang, Wenjing Liu, Hengzhu Zhang, Yudong College of Computer Science and Technology National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Biological Sciences Xi’an Jiaotong-Liverpool University Jiangsu Suzhou215123 China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Jiangsu Nanjing210096 China Department of Mathematical Sciences University of Liverpool LiverpoolL69 3BX United Kingdom

(Aim) Dragon Boat Racing, a popular aquatic folklore team sport, is traditionally held during the Dragon Boat Festival. Inspired by this event, we propose a novel human-based meta-heuristic algorithm called dragon boat optimization (DBO) in this paper. (Method) It models the unique behaviors of each crew member on the dragon boat during the race by introducing social psychology mechanisms (social loafing, social incentive). Throughout this process, the focus is on the interaction and collaboration among the crew members, as well as their decision-making in different situations. During each iteration, DBO implements different state updating strategies. By modelling the crew's behavior and adjusting the state updating strategies, DBO is able to maintain high-performance efficiency. (Results) We have tested the DBO algorithm with 29 mathematical optimization problems and 2 structural design problems. (Conclusion) The experimental results demonstrate that DBO is competitive with state-of-the-art meta-heuristic algorithms as well as conventional methods. © 2023, CC BY.

关键词： Structural optimization

来源：评论

学校读者我要写书评

暂无评论

Modeling Neural Networks Training Process with Markov Decision Process

Modeling Neural Networks Training Process with Markov Decisi...

引用

Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Yantao Bai Wanwei Liu Xinjun Mao Zhen Liang Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing Institute for Quantum Information &#x0026 National University of Defense Technology Changsha China

ISBN: (纸本)9781665421874

With the development of computer technology, statistics-based machine learning method has made great break-throughs, and also improved the development of artificial intelligence. Nevertheless, as a very influential model, neural networks are still treated as “black boxes”. The results of neural networks are extremely sensitive to the training samples, which lead to great challenges to the controllability of the algorithm. With the wide application of machine learning, demand for interpretability and controllability of neural networks algorithms is increasing. As a result, various scholars have tried to explain and verify neural networks algorithms based on formal methods in recent years. In this paper, a method (called MNNTP) is presented to model the training process of neural networks by using a Markov decision process (MDP). Through MNNTP, the neural networks are abstracted into the form of MDP, which makes notable contributions for verifying some mathematical properties of the neural networks.

关键词： Training Machine learning algorithms Computational modeling Neural networks Machine learning Markov processes Controllability

来源：评论

学校读者我要写书评

暂无评论

Measurement-Based Non-Stationary Markov Tapped Delay Line Channel Model for 5G-Railways

引用

IEEE Antennas and Wireless Propagation Letters 2025年

作者： Zhang, Xuejian He, Ruisi Yang, Mi Ding, Jianwen Chen, Ruifeng Gao, Shuaiqi Qi, Ziyi Zhang, Zhengyu Ai, Bo Zhong, Zhangdui Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-speed Railway System Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Henan High Speed Railway Operation and Maintenance Engineering Research Center Zhengzhou451460 China China Academy of Railway Sciences Corporation Ltd. Institute of Computing Technology Beijing100081 China

5 G for Railways (5G-R) is globally recognized as a promising next-generation railway communication system designed to meet increasing demands. Channel modeling serves as foundation for communication system design, with tapped delay line (TDL) models widely utilized in system simulations due to their simplicity and practicality and serves as a crucial component of various standards like 3GPP. However, existing TDL models applicable to 5G-R systems are limited. Most fail to capture non-stationarity, a critical characteristic of railway communications, while others are unsuitable for the specific frequency bands and bandwidths of 5G-R. In this paper, a channel measurement campaign for 5G-R dedicated network is carried out, resulting in a measurement-based 5-tap TDL model utilizing a first-order two-state Markov chain to represent channel non-stationarity. key model parameters, including number of taps, statistical distribution of amplitude, phase and Doppler shift, and state transition probability matrix, are extracted. The correlation between tap amplitudes is also obtained. Finally, the accuracy of model is validated through comparisons with measurement data and 3GPP model. These findings are expected to offer valuable insights for design and link-level simulation of 5G-R systems. © 2025 IEEE.

关键词： Markov chains

来源：评论

学校读者我要写书评

暂无评论

EAFP-Med: An Efficient Adaptive Feature Processing Module Based on Prompts for Medical Image Detection

arXiv

引用

arXiv 2023年

作者： Li, Xiang Lan, Long Lahza, Husam Yang, Shaowu Wang, Shuihua Yang, Wenjing Liu, Hengzhu Zhang, Yudong College of Computer Science and Technology National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Biological Sciences Xi'an Jiaotong-Liverpool University Jiangsu Suzhou215123 China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Jiangsu Nanjing210096 China Department of Mathematical Sciences University of Liverpool LiverpoolL69 3BX United Kingdom

In the face of rapid advances in medical imaging, cross-domain adaptive medical image detection is challenging due to the differences in lesion representations across various medical imaging technologies. To address this issue, we draw inspiration from large language models to propose EAFP-Med, an efficient adaptive feature processing module based on prompts for medical image detection. EAFP-Med can efficiently extract lesion features of different scales from a diverse range of medical images based on prompts while being flexible and not limited by specific imaging techniques. Furthermore, it serves as a feature preprocessing module that can be connected to any model front-end to enhance the lesion features in input images. Moreover, we propose a novel adaptive disease detection model named EAFP-Med ST, which utilizes the Swin Transformer V2 – Tiny (SwinV2-T) as its backbone and connects it to EAFP-Med. We have compared our method to nine state-of-the-art methods. Experimental results demonstrate that EAFP-Med ST achieves the best performance on all three datasets (chest X-ray images, cranial magnetic resonance imaging images, and skin images). EAFP-Med can efficiently extract lesion features from various medical images based on prompts, enhancing the model's performance. This holds significant potential for improving medical image analysis and diagnosis. © 2023, CC BY.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

high-Ni Low-Co high-Capacity Li-Rich Cathode Materials Via Adjusting Li2mno3 Content and Li/Ni Mixing Structures

SSRN

引用

SSRN 2023年

作者： Wu, Zhen Zou, Kunyang Dai, Xin Zhang, Yu-Han Zhang, Xudong Wang, Hao Hou, Zhufeng Ma, Lijing Liu, Yan Chen, Yuanzhen Guo, Shengwu Liu, Yongning State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong University Xi’an710049 China Qingdao Industrial Energy Storage Research Institute Qingdao Institute of Bioenergy and Bioprocess Technology Chinese Academy of Sciences Qingdao266101 China Center for High Performance Computing Network Information Center Xi’an Jiaotong University Xi’an710049 China Hefei Advanced Computing Center Operation Management Corp Ltd Hefei230088 China State Key Laboratory of Structural Chemistry Fujian Institute of Research the Structure of Matter Chinese Academy of Sciences Fuzhou350002 China International Research Center for Renewable Energy State Key Laboratory of Multiphase Flow in Power Engineering Xi’an Jiaotong University Xi’an710049 China

Li-rich layered oxides (LLOs) are considered as the promising cathode materials for next-generation high energy density lithium-ion batteries (LIBs). However, severe voltage fade and capacity decay hinder their commercial applications. Increasing the Ni content in LLOs effectively improves the stability and discharge voltage, but shows reduced capacities, typically © 2023, The Authors. All rights reserved.

关键词： Lithium compounds

来源：评论

学校读者我要写书评

暂无评论

$\mathbf{A^{3}}$A3-CodGen: A Repository-Level Code Generation Framework for Code Reuse With Local-Aware, Global-Aware, and Third-Party-Library-Aware

引用

IEEE Transactions on Software Engineering 2024年第12期50卷 3369-3384页

作者： Dianshu Liao Shidong Pan Xiaoyu Sun Xiaoxue Ren Qing Huang Zhenchang Xing Huan Jin Qinying Li School of Computing Australian National University Canberra ACT Australia CSIRO’s Data61 Sydney NSW Australia State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Hangzhou China School of Computer Information Engineering Jiangxi Normal University Jiangxi China Jiangxi University of Technology Jiangxi China

LLM-based code generation tools are essential to help developers in the software development process. Existing tools often disconnect with the working context, i.e., the code repository, causing the generated code to be not similar to human developers. In this paper, we propose a novel code generation framework, dubbed $A^{3}$ -CodGen, to harness information within the code repository to generate code with fewer potential logical errors, code redundancy, and library-induced compatibility issues. We identify three types of representative information for the code repository: local-aware information from the current code file, global-aware information from other code files, and third-party-library information. Results demonstrate that by adopting the $A^{3}$ -CodGen framework, we successfully extract, fuse, and feed code repository information into the LLM, generating more accurate, efficient, and highly reusable code. The effectiveness of our framework is further underscored by generating code with a higher reuse rate, compared to human developers. This research contributes significantly to the field of code generation, providing developers with a more powerful tool to address the evolving demands in software development in practice.

关键词： Codes Libraries Software development management Data mining Benchmark testing Transformers Software engineering Context modeling Chatbots Australia

来源：评论

学校读者我要写书评

暂无评论

A Technical Design of Inland Intelligent 64 TEU Container Ship for Beijing-Hangzhou Canal

A Technical Design of Inland Intelligent 64 TEU Container Sh...

引用

International Conference on Transportation Information and Safety (ICTIS)

作者： Wang Xu-Ming Song Xue-Min Liu Wei-Qin Bi Xueqing Dai Hong-Liang National Engineering Research Center for Water Transportation Safety Wuhan University of Technology Wuhan China Yangtze River Delta Shipping Research Institute (JIANGSU) CO. LTD Nanjing China Key Laboratory of High Performance Ship Technology Wuhan University of Technology Ministry of Education Wuhan China Zhejiang Scientific Research Institution of Transport Hangzhou China

With the rise and breakthrough of big data, cloud computing, artificial intelligence and other technologies, the shipping industry is transitioning from the era of automation and information to the era of intelligence. Intelligent shipping is a new modern shipping business form formed by the deep integration of modern information, artificial intelligence and other high-tech and shipping elements. In this paper, firstly the stat of art for inland intelligent container ship is introduced, the overview of the inland intelligent 64 TEU container ship is presented. Secondly, the design of 64 TEU container ship is applied in the consideration of optimization. For the integrated design scheme of the ship, the power system, propulsion device, power system layout, power distribution system, power system monitoring, engine room layout optimization, energy conversion, ship-engine and oar matching and propulsion efficiency are integrated into the overall design. Finally, by systematically studying such problems as rapid construction and real-time service of holographic navigation scene map in inland water network area, design and development of shipboard and shore-based information infrastructure of ship-shore collaboration, intelligent cross-domain computing of ship-shore collaboration, etc., the enhanced navigation and navigation support technology of inland intelligent container ship are performed. In the conclusion, the guidance of the design for intelligent 64 TEU container ship is proposed.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Decoupled Doubly Contrastive Learning for Cross Domain Facial Action Unit Detection

arXiv

引用

arXiv 2025年

作者： Li, Yong Liu, Menglin Cui, Zhen Ding, Yi Zong, Yuan Zheng, Wenming Shan, Shiguang Guan, Cuntai School of Computer Science and Engineering The Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing210096 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Artificial Intelligence Beijing Normal University Beijing100875 China Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Key Laboratory of Child Development and Learning Science of Ministry of Education School of Biological Science and Medical Engineering Nanjing210096 China

Despite the impressive performance of current vision-based facial action unit (AU) detection approaches, they are heavily susceptible to the variations across different domains and the cross-domain AU detection methods are under-explored. In response to this challenge, we propose a decoupled doubly contrastive adaptation (D2CA) approach to learn a purified AU representation that is semantically aligned for the source and target domains. Specifically, we decompose latent representations into AU-relevant and AU-irrelevant components, with the objective of exclusively facilitating adaptation within the AU-relevant subspace. To achieve the feature decoupling, D2CA is trained to disentangle AU and domain factors by assessing the quality of synthesized faces in cross-domain scenarios when either AU or domain attributes are modified. To further strengthen feature decoupling, particularly in scenarios with limited AU data diversity, D2CA employs a doubly contrastive learning mechanism comprising image and feature-level contrastive learning to ensure the quality of synthesized faces and mitigate feature ambiguities. This new framework leads to an automatically learned, dedicated separation of AU-relevant and domain-relevant factors, and it enables intuitive, scale-specific control of the cross-domain facial image synthesis. Extensive experiments demonstrate the efficacy of D2CA in successfully decoupling AU and domain factors, yielding visually pleasing cross-domain synthesized facial images. Meanwhile, D2CA consistently outperforms state-of-the-art cross-domain AU detection approaches, achieving an average F1 score improvement of 6%-14% across various cross-domain scenarios. © 2025, CC BY-NC-SA.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Anomalous strain effect on the thermal conductivity of low-buckled two-dimensional silicene

引用

National Science Review 2021年第9期8卷 23-31页

作者： Bin Ding Xiaoyan Li Wuxing Zhou Gang Zhang Huajian Gao Institute of High Performance Computing A*STAR Centre for Advanced Mechanics and Materials Applied Mechanics Laboratory Department of Engineering Mechanics Tsinghua University School of Materials Science and Engineering & Hunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and Conversion Hunan University of Science and Technology School of Mechanical and Aerospace Engineering College of Engineering Nanyang Technological University

The thermal conductivity of two-dimensional materials, such as graphene, typically decreases when tensile strain is applied, which softens their phonon modes. Here, we report an anomalous strain effect on the thermal conductivity of monolayer silicene, a representative low-buckled two-dimensional (LB-2D)material. Reax FF-based molecular dynamics simulations are performed to show that biaxially stretched monolayer silicene exhibits a remarkable increase in thermal conductivity, by as much as 10 times the freestanding value, with increasing applied strain in the range of [0, 0.1], which is attributed to increased contributions from long-wavelength phonons. A further increase in strain in the range of [0.11, 0.18] results in a plateau of the thermal conductivity in an oscillatory manner, governed by a unique dynamic bonding behavior under extreme loading. This anomalous effect reveals new physical insights into the thermal properties of LB-2D materials and may provide some guidelines for designing heat management and energy conversion devices based on such materials.

关键词： low-buckled two-dimensional materials strain effect thermal conductivity bonding dynamics

来源：评论

学校读者我要写书评

暂无评论

Unveiling the unexpected sinking and embedding dynamics of surface supported Mo/S clusters on 2D MoS2with active machine learning

Smart Molecules

引用

Smart Molecules 2024年

作者： Luneng Zhao Yanhan Ren Xiaoran Shi Hongsheng Liu Zhigen Yu Junfeng Gao Jijun Zhao State Key Laboratory of Structural Analysis for Industrial Equipment&School of Physics Dalian University of Technology Dalian China Institute of High Performance Computing (IHPC) Agency for Science Technology and Research(A*STAR) Singapore Singapore

Surface-supported clusters forming by aggregation of excessive adatoms could be the main defects of 2D materials after chemical vapor deposition. They will significantly impact the electronic/magnetic properties. Moreover, surface supported atoms are also widely explored for high active and selecting catalysts. Severe deformation, even dipping into the surface, of these clusters can be expected because of the very active edge of clusters and strong interaction between supported clusters and surfaces. However, most models of these clusters are supposed to simply float on the top of the surface because ab initio simulations cannot afford the complex reconstructions. Here, we develop an accurate graph neural network machine learning potential (MLP) from ab initio data by active learning architecture through fine-tuning pre-trained models, and then employ the MLP into Monte Carlo to explore the structural evolutions of Mo and S clusters (1–8 atoms) on perfect and various defective MoS 2 monolayers. Interestingly, Mo clusters can always sink and embed themselves into MoS 2 layers. In contrast, S clusters float on perfect surfaces. On the defective surface, a few S atoms will fill the vacancy and rest S clusters float on the top. Such significant structural reconstructions should be carefully taken into account.

关键词： active learning machine learning potential Monte Carlo surface-supported clusters

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：