检索结果-内蒙古大学图书馆

14th International Conference on Information science and technology, ICIST 2024

作者： Yang, Man Wang, Yibo Wang, Yadi Guo, Xiaoding Mu, Huiyu Che, Hangjun Institute of Data and Knowledge Engineering School of Computer and Information Engineering Henan University Kaifeng475004 China Computer Science and Technology School of International Business Henan University Zhengzhou450018 China College of Electronic and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9798350353334

In recent years, multi-label feature selection has been widely used in fields such as bioinformatics, information retrieval, and multimedia annotation. Most of the previous multi-label feature selection methods are directly transformed from the traditional single-label feature selection methods, or they cannot make full use of label information. As a result, the selected feature subset involves features that are redundant or irrelevant to label information. Moreover, most algorithms do not use discretization when processing continuous data sets, so they cannot effectively eliminate the interference of abnormal data. In order to solve these problems, based on the GRRO (Global Relevance and Redundancy Optimization) algorithm, the JS divergence is used to better measure the overall similarity between two distributions and introduces data discretization preprocessing operations for continuous data sets. The experimental results after experimental verification on ten typical high-dimensional biological data sets show that the GRRO-JS algorithm is superior to the traditional multi-label feature selection method and the GRRO algorithm in terms of accuracy and efficiency and has high practical value. © 2024 IEEE.

关键词： data accuracy

来源：评论

学校读者我要写书评

暂无评论

Robust Adaptive Beamforming via Worst-Case SINR Maximization With Nonconvex Uncertainty Sets

Robust Adaptive Beamforming via Worst-Case SINR Maximization...

引用

作者： Huang, Yongwei Fu, Hao Vorobyov, Sergiy A. Luo, Zhi-Quan Guangdong University of Technology University Town School of Information Engineering Guangdong Guangzhou510006 China Huawei Technologies Co. Ltd Jiangsu Nanjing518129 China Espoo02150 Finland The Chinese University of Hong Kong School of Science and Engineering Shenzhen518172 Hong Kong Shenzhen Research Institute of Big Data Guangdong518172 China

This paper considers a formulation of the robust adaptive beamforming (RAB) problem based on worst-case signal-to-interference-plus-noise ratio (SINR) maximization with a nonconvex uncertainty set for the steering vectors. The uncertainty set consists of a similarity constraint and a (nonconvex) double-sided ball constraint. The worst-case SINR maximization problem is turned into a quadratic matrix inequality (QMI) problem using the strong duality of semidefinite programming. Then a linear matrix inequality (LMI) relaxation for the QMI problem is proposed, with an additional valid linear constraint. Necessary and sufficient conditions for the tightened LMI relaxation problem to have a rank-one solution are established. When the tightened LMI relaxation problem still has a high-rank solution, the LMI relaxation problem is further restricted to become a bilinear matrix inequality (BLMI) problem. We then propose an iterative algorithm to solve the BLMI problem that finds an optimal/suboptimal solution for the original RAB problem by solving the BLMI formulations. To validate our results, simulation examples are presented to demonstrate the improved array output SINR of the proposed robust beamformer. © 1991-2012 IEEE.

关键词： Linear matrix inequalities

来源：评论

学校读者我要写书评

暂无评论

Regularized Estimation of High-Dimensional Matrix-Variate Autoregressive Models

arXiv

引用

arXiv 2024年

作者： Jiang, Hangjin Shen, Baining Li, Yuzhou Gao, Zhaoxing Center for Data Science Zhejiang University China School of Mathematical Sciences University of Electronic Science and Technology of China China

Matrix-variate time series data are increasingly popular in economics, statistics, and environmental studies, among other fields. This paper develops regularized estimation methods for analyzing high-dimensional matrix-variate time series using bilinear matrix-variate autoregressive models. The bilinear autoregressive structure is widely used for matrix-variate time series, as it reduces model complexity while capturing interactions between rows and columns. However, when dealing with large dimensions, the commonly used iterated least-squares method results in numerous estimated parameters, making interpretation difficult. To address this, we propose two regularized estimation methods to further reduce model dimensionality. The first assumes banded autoregressive coefficient matrices, where each data point interacts only with nearby points. A two-step estimation method is used: first, traditional iterated least-squares is applied for initial estimates, followed by a banded iterated least-squares approach. A Bayesian Information Criterion (BIC) is introduced to estimate the bandwidth of the coefficient matrices. The second method assumes sparse autoregressive matrices, applying the LASSO technique for regularization. We derive asymptotic properties for both methods as the dimensions diverge and the sample size T → ∞. Simulations and real data examples demonstrate the effectiveness of our methods, comparing their forecasting performance against common autoregressive models in the literature. © 2024, CC BY.

关键词： Time series analysis

来源：评论

学校读者我要写书评

暂无评论

RoDyn-SLAM: Robust Dynamic Dense RGB-D SLAM with Neural Radiance Fields

arXiv

引用

arXiv 2024年

作者： Jiang, Haochen Xu, Yueming Li, Kejie Feng, Jianfeng Zhang, Li School of Data Science Fudan University China The School of Data Science Fudan University Shanghai200433 China The Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China ByteDance Seattle United States

Leveraging neural implicit representation to conduct dense RGB-D SLAM has been studied in recent years. However, this approach relies on a static environment assumption and does not work robustly within a dynamic environment due to the inconsistent observation of geometry and photometry. To address the challenges presented in dynamic environments, we propose a novel dynamic SLAM framework with neural radiance field. Specifically, we introduce a motion mask generation method to filter out the invalid sampled rays. This design effectively fuses the optical flow mask and semantic mask to enhance the precision of motion mask. To further improve the accuracy of pose estimation, we have designed a divide-and-conquer pose optimization algorithm that distinguishes between keyframes and non-keyframes. The proposed edge warp loss can effectively enhance the geometry constraints between adjacent frames. Extensive experiments are conducted on the two challenging datasets, and the results show that RoDyn-SLAM achieves state-of-the-art performance among recent neural RGB-D methods in both accuracy and robustness. Our implementation of the Rodyn-SLAM will be open-sourced to benefit the community. © 2024, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Provably Efficient and Fast Technique for Determining the Size of a Brain Tumor in T1 MRI Images 6

Provably Efficient and Fast Technique for Determining the Si...

引用

6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Ali, Ali Hasan Muslim Jasim, Hend Ameen Abduljabbar, Zaid Nyangaresi, Vincent Omollo Umran, Samir M. Ma, Junchao Honi, Dhafer G. College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Jaramogi Oginga Odinga University of Science and Technology Department of Computer Scienec and Engineering Bondo40601 Kenya Iraqi Cement State Company Ministry of Industry and Minerals Baghdad10011 Iraq College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China

ISBN: (纸本)9798350344349

This work proposes an efficient a nd effective technique for determining the size of neoplasms in the brain using image processing. The cerebral hemispheres make up the largest part of the human brain, and abnormal growth of cells within them can lead to the development of neoplasms, or brain tumors. Many tumors are believed to occur for unknown reasons, which highlights the importance of annual check-ups to detect any early signs of a tumor. Image processing is a crucial component of these annual check-ups, as it can identify any changes that may have occurred in the brain since the previous check-up. This work proposes a new method that can segment the tumor through the skull and estimate its size using MRI images. The proposed method involves three steps: first, extracting t he b rain from the skull;second, applying thresholding to identify abnormal cells and segment the neoplasm;and finally, estimating the size of the neoplasm in a fast manner. To validate the results, a comparison with other techniques such as K-means and C-means is performed. Overall, this proposed method provides a promising approach to detecting and measuring neoplasms in the brain, which could ultimately improve the diagnosis and treatment of these potentially life-Threatening conditions. © 2024 IEEE.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

A Novel science and technology Resource Recommendation Service based on Knowledege Graph and Collaborative Filtering

A Novel Science and Technology Resource Recommendation Servi...

引用

2022 CCF International Conference on Service science, CCF ICSS 2022

作者： Zhao, Xinyu Liu, Chen Zhang, Shuo You, Xin North China University of Technology School of Information Science and Technology Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

To address the problems of large volume of science and technology information, low information value density, and matrix sparsity of recommendation algorithms, we propose STIR-KG, a science and technology information recommendation method integrating knowledge graph, and build a science and technology information recommendation service. The main contributions are: (1) Establishing a new material knowledge graph, which has been open-sourced in GitHub (2) Combining collaborative filtering methods with knowledge graphs to solve the cold-start and matrix sparsity problems. (3) Propose the representation learning method TransAR, which enhances the representation capability compared with traditional methods, and uses the Mahalanobis distance metric score function to reduce the influence of irrelevant dimensions on the similarity calculation. (4) Based on the STIR-KG method, we use the streaming computing framework Flink to build a recommendation service for scientific and technical information, which captures user interest migration in real time and makes the recommendation results more time-efficient. And according to the experimental verification, STIR-KG has significantly improved the accuracy and recall rate compared with other algorithms. © 2022 IEEE.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

FusionANNS: An Efficient CPU/GPU Cooperative Processing Architecture for Billion-scale Approximate Nearest Neighbor Search

arXiv

引用

arXiv 2024年

作者： Tian, Bing Liu, Haikun Tang, Yuhang Xiao, Shihai Duan, Zhuohui Liao, Xiaofei Zhang, Xuecang Zhu, Junhua Zhang, Yu National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd China

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services None of modern ANNS systems can address these issues simultaneously. We present FusionANNS, a high-throughput, low-latency cost-efficient, and high-accuracy ANNS system for billion scale datasets using SSDs and only one entry-level GPU The key idea of FusionANNS lies in CPU/GPU collabo rative filtering and re-ranking mechanisms, which signifi cantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We imple ment FusionANNS and compare it with the state-of-the-art SSD-based ANNS system–SPANN and GPU-accelerated in memory ANNS system–RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN;2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Nearest neighbor search

来源：评论

学校读者我要写书评

暂无评论

The historical evolution and research trends of life cycle assessment

引用

Green Carbon 2024年第4期2卷 425-437页

作者： Min Liu Guangyan Zhu Yajun Tian Extended Energy Big Data and Strategy Research Center Qingdao Institute of Bioenergy and Bioprocess TechnologyChinese Academy of SciencesQingdao 266101China Shandong Energy Institute Qingdao 266101China Qingdao New Energy Shandong Laboratory Qingdao 266101China School of Chemical Engineering University of Chinese Academy of SciencesBeijing 100049China

Life cycle assessment(LCA)is a widely used tool for environmental decision-making;however,it still has theoretical and practical *** a comprehensive review of traditional LCA development and case studies,this study examines the overall trajectory of the evolution of the LCA methodological *** specifically addresses perspectives on typical LCA methods,dynamic LCA methods,expanding LCA into multidimensional assessment,simplifying the methodological framework,and integrating with other ***,it delves into improvements and optimizations of the methodological framework alongside their distinct *** on insights from current analyses and the evolutionary path of the LCA methodological framework,this study outlines future research directions for *** aims to serve as a reference for scholars in this field,thereby fostering further methodological enhancements and broadening the scope of LCA applications.

关键词： Life cycle assessment Methodological framework Research progress Multi-dimensional assessment

来源：评论

学校读者我要写书评

暂无评论

Multi-agent Reinforcement Learning for a Special Formation Problem 5th

Multi-agent Reinforcement Learning for a Special Formation P...

引用

5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021

作者： Qu, Changsheng Ke, Liangjun Xuan, Shuzhe Wang, Zhigang State Key Laboratory for Manufacturing Systems Engineering School of Automation Science and Engineering Xi’an Jiaotong University 710049 Xi’an China CETC Key Laboratory of Data Link Technology Xi’an China

ISBN: (纸本)9789811939976

For a long time, the formation control problem has been one of the core problems in the field of multi-agent collaboration. It’s goal is to make multiple agents form a formation in the tasks and move to a designated target point. In this paper, the reinforcement learning method is used to deal with a special formation problem, and the mean field theory is applied to Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to make it effective in large-scale multi-agent formation problems. The algorithm is compared with MADDPG and Deep Deterministic Policy Gradient (DDPG) algorithm. In the simulation experiment, a team of UAVs are initialized at random positions in the two-dimensional space. The goal is to make the UAVs form an equilateral triangle with the shortest total displacement. We model the formation problem. In our algorithm, a reward function is designed based on the goal of making equilateral triangle formation and requirement of the shortest total displacement;Moreover, we define the equilateral triangle criterion and use it to evaluate the formation effect of multi-agent reinforcement learning. The results show that, compared with DDPG and MADDPG algorithms, MADDPG algorithm using the mean field method has obvious advantages over MADDPG and DDPG in terms of convergence speed and success rate in the formation problem. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mean field theory

来源：评论

学校读者我要写书评

暂无评论

AMRUNet: An Attention-Guided MultiResUNet for Continuous Noninvasive Blood Pressure Estimation

AMRUNet: An Attention-Guided MultiResUNet for Continuous Non...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Ruijie Zhao Yu Lu Leya Li Huilin Ge Xianghua Fu College of Big Data and Internet Shenzhen Technology University Shenzhen China College of Applied Science Shenzhen University Shenzhen China College of Automation Jiangsu University Science and Technology Zhenjiang China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Cardiovascular diseases (CVDs) are the leading cause of global morbidity and mortality, necessitating the precise and continuous monitoring of blood pressure for proactive management. Our study presents the AMRUNet: a novel network designed exclusively for PPG-only, noninvasive, cuff-less blood pressure estimation. The network innovates upon the U-Net architecture, integrating a MultiRes Block for detailed multi-scale feature fusion and a residual block to mitigate the issue of vanishing gradients. An attention mechanism is further employed to selectively enhance salient features within the PPG signal. Our PPG-only AMRUNet demonstrates exceptional performance in translating PPG data into accurate ABP waveforms, achieving mean absolute errors (MAE) that comply with the standards of both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). Our method demonstrates highest MAE for both systolic blood pressure (SBP) and diastolic blood pressure (DBP), achieving a 2.85 MAE for SBP and 1.79 MAE for SBP among competing models. The model’s proficiency in precisely estimating systolic and diastolic blood pressure, along with its ability to reconstruct continuous ABP waveforms, contributes to reliable and trustworthy medical decision-making systems. The code for AMRUNet can be accessible at https://***/ijcnn2024/AMRUNet.

关键词： Accuracy Estimation Predictive models Blood pressure data models Robustness Biomedical monitoring

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：