检索结果-内蒙古大学图书馆

2nd IEEE International Conference on Information Technology, big data and Artificial Intelligence, ICIBA 2021

作者： Zuo, Xianyu Li, Heng Zhou, Liming Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781665428767

In the era of rapid development of remote sensing technology, in order to improve the production efficiency of remote sensing data processing flow and further optimize the editing of remote sensing processes in the visual interface. In this paper we have analyzed the characteristics and attributes of remote sensing processes and algorithms. We made the production of processes and algorithms into scripts, and realizes to create remote sensing processes by inputting scripting language in the visual interface. A remote sensing language based on XML is designed to read and store key information. This language will use XML to drive the production of remote sensing process. With the language we design, a remote sensing process customization system has been implemented. It can improve the editing and production efficiency of remote sensing data processing flow. © 2021 IEEE.

关键词： XML

来源：评论

学校读者我要写书评

暂无评论

Azimuth signal reconstruction for hrws-sar from uniform resampling 2

Azimuth signal reconstruction for hrws-sar from uniform resa...

引用

2nd International Conference on Artificial Intelligence, Information Processing and Cloud Computing, AIIPCC 2021

作者： Zhang, Hanqing Wu, Lin Li, Ning Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China College of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9783800756162

Signal reconstruction is a key step in the azimuth multichannel high-resolution wide-swath synthetic aperture radar (HRWS-SAR) imaging algorithms. Krieger’s digital beam-forming (DBF) algorithm is currently one of the commonly used algorithms for processing HRWS-SAR data. However, this algorithm is only suitable for uniform space-time sampling and moderately non-uniform sampling. In the case of highly non-uniform sampling, the imaging performance of Krieger’s DBF algorithm will be seriously deteriorated. In this paper, a uniform resampling algorithm is presented to solve above problem. The proposed algorithm adopts modern sampling theory to reduce the approximate error. This algorithm consists of two key steps: 1. The given signal samples are projected onto a selected intermediate subspace, spanned by integer translations of a compactly supported kernel function. This process generates a sparse set of equations, which can be effectively solved by using a sparse equation solvers. 2. The results are projected onto the subspace where the sampled signal is known to reside. The orthogonal matching pursuit (OMP) algorithm is used to realize the second projection and reconstruction the signal effectively. Simulation results show that the newly proposed algorithm has a significant improvement in imaging performance than Krieger’s DBF algorithm. © 2021 AIIPCC 2021 - 2nd International Conference on Artificial Intelligence, Information Processing and Cloud Computing. All rights reserved.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Classic4Children: Adapting Chinese Literary Classics for Children with Large Language Model

arXiv

引用

arXiv 2025年

作者： Chen, Jiali Hei, Xusen Xue, Yuqi Wu, Zihan Xie, Jiayuan Cai, Yi Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China School of Software Engineering South China University of Technology China The Hong Kong Polytechnic University Hong Kong

Chinese literary classics hold significant cultural and educational value, offering deep insights into morality, history, and human nature. These works often include classical Chinese and complex narratives, making them difficult for children to read. To bridge this gap, we introduce a child-friendly literary adaptation (CLA) task to adapt the Chinese literary classic into engaging and accessible text for children. However, recent large language models (LLMs) overlook children’s reading preferences (i.e., vivid character portrayals, concise narrative structures, and appropriate readability), which poses challenges in CLA. In this paper, we propose a method called InstructChild, which augments the LLM with these preferences for adaptation. Specifically, we first obtain the characters’ personalities and narrative structure as additional information for fine-grained instruction tuning. Then, we devise a readability metric as the reward to align the LLM with the children’s reading level. Finally, a lookahead decoding strategy is applied to improve the readability of the generated text during inference. To support the evaluation of CLA task, we construct the Classic4Children dataset, which comprises both the original and child-friendly versions of the Four Great Classical Novels of Chinese literature. Experimental results show that our InstructChild significantly improves automatic and human evaluation performance. Copyright © 2025, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Automated Mapping of Global 30-m Tidal Flats Using Time-Series Landsat Imagery:Algorithm and Products

Journal of Remote Sensing

引用

Journal of Remote Sensing 2023年第1期3卷 362-377页

作者： Xiao Zhang Liangyun Liu Jinqing Wang Tingting Zhao Wendi Liu Xidong Chen International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China College of Geomatics Xi’an University of Science and TechnologyXi’an 710054China North China University of Water Resources and Electric Power Zhengzhou 450046China

Tidal flats are an important part of coastal ecosystems and play an important role in shoreline protection and biodiversity *** many efforts have been made in tidal flat mapping,an accurate global tidal flat product covering all coasts globally is still lacking and urgently *** this study,a novel method is proposed for the automated mapping of global tidal flats at 30 m(GTF30)in 2020 based on the Google Earth Engine,which is also the first global tidal flat dataset covering the high latitudes(>60°N).Specifically,we first propose a new spectral index named the LTideI index through a sensitivity analysis,which is robust and can accurately capture low-tide ***,globally distributed training samples are automatically generated by combining multisource datasets and the spatiotemporal refinement ***,the global coasts are divided into 5885°×5°geographical tiles,and the local adaptive classification strategy is used to map tidal flats in each 5°×5°region by using multisourced training features and the derived globally distributed training *** statistical results show that the total global area of tidal flats is about 140,922.5 km2,with more than 75% distributed on 3 continents in the Northern Hemisphere,especially in Asia(approximately 43.1% of the total).Finally,the GTF30 tidal flat dataset is quantitatively assessed using 13,994 samples,yielding a good overall accuracy of 90.34%.Meanwhile,the intercomparisons with several existing tidal flat datasets indicate that the GTF30 products can greatly improve the mapping accuracy of tidal ***,the novel method can support the automated mapping of tidal flats,and the GTF30 dataset can provide scientific guidance and data support for protecting coastal ecosystems and supporting coastal economic and social *** GTF30 tidal flat dataset in 2020 is freely accessible via https://***/10.5281/zenodo.7936721.

关键词： tidal coastal Tidal

来源：评论

学校读者我要写书评

暂无评论

Attribute Reduction Method Based on Improved Granular Ball Neighborhood Rough Set 7

Attribute Reduction Method Based on Improved Granular Ball N...

引用

7th International Conference on Cloud Computing and big data Analytics, ICCCBDA 2022

作者： Xia, Han Chen, Zizhong Wu, Yanmin Qi, Jinli Chongqing Key Laboratory of Computer Science and Technology Chongqing University of Posts and Telecommunications ChongQing China ChongQing College of Electronic Engineering Department of Artificial Intelligence and Big Data ChongQing China ChongQing College of Electronic Engineering Department of General Education and International Studies ChongQing China

ISBN: (纸本)9781665487115

Feature reduction is an important aspect of big data analytics today, and neighborhood rough set is a classic attribute reduction method. The traditional neighborhood rough set finds out the radius suitable for problem solving by specifying the radius of the neighborhood or using a grid search method. There are a lot of overlapping calculations in the neighborhood calculation, and the calculation complexity is high. The granular ball neighbor-hood rough set can adaptively generate different neighborhood radius, and obtains better algorithm efficiency and learning effect than the traditional neighborhood rough set. However, due to the overlap problem in the process of granular ball generation, the granular ball neighborhood rough set still exists the overlap calculation of neighborhoods. For this reason, this paper solves the problem of granular ball overlap by detecting heterogeneous ball overlap and splitting, and thus designs an improved granular ball neighbor-hood rough set attribute reduction method. Experiments show that the improved granular ball neighborhood rough set has higher classification accuracy on the public benchmark dataset than the classic neighborhood rough set and the standard granular ball neighborhood rough set. © 2022 IEEE.

关键词： Cloud computing Costs Search methods Rough sets big data Benchmark testing Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

MetaDb:a database for metabolites and their regulation in plants with an emphasis on medicinal plants

引用

Molecular Horticulture 2024年第1期4卷 376-379页

作者： Qingqing Gao Jiajin Zhang Juntao Cao Chunfan Xiang Chengxiao Yuan Xia Li Juan Wang Pinhan Zhou Lesong Li Jia Liu Hongchun Xie Ruolan Li Guilin Huang Chaohui Li Guanghui Zhang Shengchao Yang Yan Zhao Key Laboratory of Medicinal Plant Biology of Yunnan Province National&Local Joint Engineering Research Center on Germplasms Innovation&Utilization of Chinese Medicinal Materials in Southwest ChinaYunnan Agricultural UniversityKunming 650201China Yunnan Characteristic Plant Extraction Laboratory Kunming 650106YunnanChina College of Agricultural Honghe Vocational and Technical CollegeHonghe 661199China College of Big Data Yunnan Agricultural UniversityKunming 650201China College of Agronomy&Biotechnology Yunnan Agricultural UniversityKunming 650201China

Natural product biosynthesis in medicinal plants has always been a research focus in ***,phenolics,and alkaloids,all of which have medicinal value for humans,are the three most prominent natural products found in medicinal plants(Cravens et al.2019;Li et al.2023;Zhao et al.2023).Many medicinal plants produce natural products that have special biological functions,such as regulating plant growth and resisting stress.

关键词： biology medicinal alkaloids

来源：评论

学校读者我要写书评

暂无评论

AAV-Assisted Joint Mobile Edge Computing and data Collection via Matching-Enabled Deep Reinforcement Learning

引用

IEEE Internet of Things Journal 2025年第12期12卷 19782-19800页

作者： Boxiong Wang Hui Kang Jiahui Li Geng Sun Zemin Sun Jiacheng Wang Dusit Niyato College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology and the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computing and Data Science Nanyang Technological University Jurong West Singapore

Autonomous aerial vehicle (AAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this article investigates a multi-AAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a nonconvex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the AAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collaboratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the DC volume compared with other benchmark algorithms.

关键词： Internet of Things Optimization Autonomous aerial vehicles Interference Wireless networks Sun data collection Real-time systems Production facilities Performance evaluation

来源：评论

学校读者我要写书评

暂无评论

Exploring Freight Loading Management by Deep Learning: a Case Study in Home Furnishing Industry

引用

Annals of data Science 2022年第2期9卷 213-228页

作者： Deng, Wei Patil, Rajvardhan Liu, Fangyao Daji, Ergu Shi, Yong College of Big Data Statistics Guizhou University of Finance and Economics Guizhou550025 China College of Computer and Information Science Arkansas Tech University RussellvilleAR68182 United States College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Research Center on Fictitious Economy and Data Science The Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Nebraska Furniture Mart OmahaNE68124 United States Division of Science Research and Technology Southwest Minzu University Chengdu610041 China

Long short-term memory (LSTM) networks as state-of-the-art Deep Learning models, have achieved remarkable results in time series forecasting. However, they are less commonly applied to the industry of logistics. This paper presents two novel LSTM networks to predict the freight loading of routing areas, and the design of a smart loading management system is introduced. While most existing works on LSTM utilize the power of its prediction very well, our study shows less accurate and instable results if only LSTM network is applied, that arise from the big variation in the dataset. Instead, two constraints are inspired in this paper. The first constraint adds a time factor node to strengthen the correlation between predicted final loading units and booked loading units as close to delivery date. And the second proposal extends to parallel training by groupwise constraint. Experiments with across 3-year records in a national wide home furnishing retail company show that constraint LSTM networks significantly improve both the accuracy and stability of the prediction. Besides, the design of a smart loading management system will be shown, in which LSTM model plays the core rule to predict loading capacity, meanwhile the rule-based system triggers different events based on loading prediction and truck size. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised Teaching and Learning of Representations on Graphs 23

Self-Supervised Teaching and Learning of Representations on ...

引用

32nd ACM World Wide Web Conference, WWW 2023

作者： Wan, Liangtian Fu, Zhenqiang Sun, Lu Wang, Xianpeng Xu, Gang Yan, Xiaoran Xia, Feng Key Laboratory for Ubiquitous Network Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian China State Key Laboratory of Marine Resource Utilization in South China Sea School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing China Research Center of Big Data Intelligence Research Institute of Artificial Intelligence Zhejiang Lab Hangzhou China School of Computing Technologies Rmit University Melbourne Australia

ISBN: (纸本)9781450394161

Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification. © 2023 ACM.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Credit and guarantee insurance: A consistent pricing approach in supply chain finance 9th

Credit and guarantee insurance: A consistent pricing approac...

引用

9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Zhang, Linzi Xue, Jiayu Shang, Mengyu Qi, Zhiquan School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Credit insurance is a new loan guarantee contract which can effectively stimulate domestic demand and promote consumption. According to different modes of payment, individual credit loan guarantees mainly include credit insurance and guarantee insurance. In this paper, we apply the Complete Information Game to analyze the strategies of the insured subject and the risk subject under different scenarios of personal loan guarantee, and obtain the influencing factors of each decision-making subject in supply chain finance. The conclusion shows that the credit status of individual loan applicants greatly affects the decision-making of supply chain financial entities such as banks. Based on this, this paper puts forward corresponding suggestions for the credit insurance business management. © 2022 The Authors. Published by Elsevier B.V.

关键词： Costs

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：