检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Chen, Guang Sun, Zhicong Ding, Yulong Yang, Shuang-Hua Department of Mechanical and Energy Engineering Southern University of Science and Technology China Department of Civil and Environmental Engineering Hong Kong Polytechnic University Hong Kong Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet SUSTech Shenzhen518055 China Department of Computer Science and Engineering SUSTech Shenzhen518055 China Department of Computer Science University of Reading United Kingdom

Stealth attacks pose potential risks to cyber-physical systems because they are difficult to detect. Assessing the risk of systems under stealth attacks remains an open challenge, especially in nonlinear systems. To comprehensively quantify these risks, we propose a framework that considers both the reachability of a system and the risk distribution of a scenario. We propose an algorithm to approximate the reachability of a nonlinear system under stealth attacks with a union of standard sets. Meanwhile, we present a method to construct a risk field to formally describe the risk distribution in a given scenario. The intersection relationships of system reachability and risk regions in the risk field indicate that attackers can cause corresponding risks without being detected. Based on this, we introduce a metric to dynamically quantify the risk. Compared to traditional methods, our framework predicts the risk value in an explainable way and provides early warnings for safety control. We demonstrate the effectiveness of our framework through a case study of an automated warehouse. Copyright © 2024, The Authors. All rights reserved.

关键词： Cyber Physical System

来源：评论

学校读者我要写书评

暂无评论

Metalenses phase characterization by multidistance phase retrieval

引用

Light(Science & Applications) 2024年第9期13卷 1898-1907页

作者： Bowen Liu Jialuo Cheng Maoxiong Zhao Jin Yao Xiaoyuan Liu Shaohu Chen Lei Shi Din Ping Tsai Zihan Geng Mu Ku Chen State Key Laboratory of Surface Physics Key Laboratory of Micro-and Nano-Photonic Structures(Ministry of Education)and Department of PhysicsFudan University200433 ShanghaiChina Department of Electrical Engineering City University of Hong KongKowloonHong Kong SARChina State Key Laboratory of Terahertz and Millimeter Waves City University of Hong KongKowloonHong Kong SARChina Institute for Nanoelectronic Devices and Quantum Computing Fudan University200438 ShanghaiChina Collaborative Innovation Center of Advanced Microstructures Nanjing University210093 NanjingJiangsuChina Shanghai Research Center for Quantum Sciences 201315 ShanghaiChina Centre for Biosystems Neuroscienceand NanotechnologyCity University of Hong KongKowloonHong Kong SARChina Institute of Data and Information Tsinghua Shenzhen International Graduate SchoolTsinghua University518071 ShenzhenGuangdongChina

Metalens,characterized by their unique functions and distinctive physical properties,have gained significant attention for their potential *** further optimize the performance of metalens,it is necessary to characterize the phase modulation of the *** this study,we present a multi-distance phase retrieval system based on optical field scanning and discuss its convergence and *** findings indicate that the system is capable of retrieving the phase distribution of the metalens as long as the measurement noise is low and the total length of the scanned light field is sufficiently *** system enables the analysis of focal length and aberration by utilizing the computed phase *** extend our investigation to measure the phase distribution of the metalens operating in the near-infrared(NIR)spectrum and identify the impact of defects in the sample on the ***,we conduct a comparative analysis of the phase distribution of the metalens in air and ethanol and observe the variations in the phase modulation of the metalens in different working *** system provides a straightforward method for the phase characterization of metalens,aiding in optimizing the metalens design and functionality.

关键词： phase system distribution

来源：评论

学校读者我要写书评

暂无评论

Predicting Readmission Following Hospital Treatment for Patients with Alcohol Related Diagnoses in an Australian Regional Health District 18

Predicting Readmission Following Hospital Treatment for Pati...

引用

18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for digital Innovation, MEDINFO 2021

作者： Zhang, Jingxiang Qian, Siyu Su, Guoxin Deng, Chao Yu, Ping Centre for Digital Transformation School of Computing and Information Technology Faculty of Engineering and Information Sciences University of Wollongong WollongongNSW2522 Australia Drug and Alcohol Service Drug Illawarra Shoalhaven Local Health District Australia School of Computing and Information Technology Faculty of Engineering and Information Sciences University of Wollongong Australia School of Medicine and Molecular Horizons University of Wollongong Wollongong Australia Illawarra Health and Medical Research Institute Wollongong Australia

ISBN: (纸本)9781643682648

This study aims to investigate the prediction of hospital readmission of alcohol use disorder patients within 28 days of discharge and compare the performance of six machine learning methods i.e., random forest (RF), logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM. © 2022 International Medical Informatics Association (IMIA) and IOS Press.

关键词： Hospitals

来源：评论

学校读者我要写书评

暂无评论

Panicle-Cloud:An Open and Al-Powered Cloud computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice

引用

Plant Phenomics 2023年第4期5卷 905-916页

作者： Zixuan Teng Jiawei Chen Jian Wang Shuixiu Wu Riqing Chen Yaohai Lin Liyan Shen Robert Jackson Ji Zhou Changcai Yang Digital Fujian Research Institute of Big Data for Agriculture and Forestry College of Computer and Information SciencesFujian Agriculture and Forestry UniversityFuzhou 350002China State Key Laboratory of Crop Genetics&Germplasm Enhancement academy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjing 210095China Ningxia Academy of Agriculture and Forestry Sciences Yinchuan 750002China College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry UniversityFuzhou 350002China Cambridge Crop Research National Institute of Agricultural Botany(NIAB)Cambridge CB3 OLEUK Key Laboratory of Smart Agriculture and Forestry(Fujian Agriculture and Forestry University) Fujian Province UniversityFuzhou 350002China Center for Agroforestry Mega Data Science School of Future TechnologyFujian Agriculture and Forestry UniversityFuzhou 350002China

Rice(Oryza sativa)is an essential stable food for many rice consumption nations in the world and,thus,the importance to improve its yield production under global climate *** evaluate different rice varieties'yield performance,key yield-related traits such as panicle number per unit area(PNpM^(2))are key indicators,which have attracted much attention by many plant research ***,it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM^(2)trait due to complex field conditions,a large variation of rice cultivars,and their panicle morphological ***,we present Panicle-Cloud,an open and artificial intelligence(AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected *** facilitate the development of Al-powered detection models,we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists;then,we integrated several state-of-the-art deep learning models(including a preferred model called Panicle-AI)into the Panicle-Cloud platform,so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial *** trialed the Al models with images collected at different attitudes and growth stages,through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were ***,we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM^(2)trait from hundreds of rice *** correlation analysis between computational analysis and manual scoring,we found that the platform could quantify the PNpM^(2)trait reliably,based on which yield production was classified with high ***,we trust that our work demonstrates a valuable advance in phenotyping the PNpM^(2)trait in rice,which provides a useful toolkit to enable rice br

关键词： breeding cultivar hundreds

来源：评论

学校读者我要写书评

暂无评论

A Survey on LLM-as-a-Judge

arXiv

引用

arXiv 2024年

作者： Gu, Jiawei Jiang, Xuhui Shi, Zhichao Tan, Hexiang Zhai, Xuehao Xu, Chengjin Li, Wei Shen, Yinghan Ma, Shengjie Liu, Honghao Wang, Yuanzhuo Guo, Jian IDEA Research International Digital Economy Academy China Institute of Computing Technology Chinese Academy of Sciences China Department of Civil and Environmental Engineering Imperial College London United Kingdom Gaoling School of Artificial Intelligence Renmin University of China China

Accurate and consistent evaluation is crucial for decision-making across numerous fields, yet it remains a challenging task due to inherent subjectivity, variability, and scale. Large Language Models (LLMs) have achieved remarkable success across diverse domains, leading to the emergence of "LLM-as-a-Judge," where LLMs are employed as evaluators for complex tasks. With their ability to process diverse data types and provide scalable, cost-effective, and consistent assessments, LLMs present a compelling alternative to traditional expert-driven evaluations. However, ensuring the reliability of LLM-as-a-Judge systems remains a significant challenge that requires careful design and standardization. This paper provides a comprehensive survey of LLM-as-a-Judge, addressing the core question: How can reliable LLM-as-a-Judge systems be built? We explore strategies to enhance reliability, including improving consistency, mitigating biases, and adapting to diverse assessment scenarios. Additionally, we propose methodologies for evaluating the reliability of LLM-as-a-Judge systems, supported by a novel benchmark designed for this purpose. To advance the development and real-world deployment of LLM-as-a-Judge systems, we also discussed practical applications, challenges, and future directions. This survey serves as a foundational reference for researchers and practitioners in this rapidly evolving field. The associated resources can be accessed at https://***/IDEA-FinAI/LLM-as-a-Judge. © 2024, CC0.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation 15th

RobNODDI: Robust NODDI Parameter Estimation with Adaptive S...

引用

15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Multiple Images Steganography of JPEG Images Based on Optimal Payload Distribution

引用

Computer Modeling in engineering & Sciences 2020年第10期125卷 417-436页

作者： Yang Pei Xiangyang Luo Yi Zhang Liyan Zhu Zhong Yuan Network Security Research Institute Zhengzhou UniversityZhengzhou450000China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000China

Multiple images steganography refers to hiding secret messages in multiple natural images to minimize the leakage of secret messages during ***,the main multiple images steganography algorithms mainly distribute the payloads as sparsely as possible inmultiple cover images to improve the detection error rate of stego *** order to enable the payloads to be accurately and efficiently distributed in each cover image,this paper proposes a multiple images steganography for JPEG images based on optimal payload ***,the algorithm uses the principle of dynamic programming to redistribute the payloads of the cover images to reduce the time required in the process of payloads ***,by reducing the difference between the features of the cover images and the stego images to increase the detection error rate of the stego ***,this paper uses a data decomposition mechanism based on Vandermonde *** if part of the data is lost during the transmission of the secret messages,as long as the data loss rate is less than the data redundancy rate,the original secret messages can be *** results show that the method proposed in this paper improves the efficiency of payloads distribution compared with existing multiple images *** the same time,the algorithm can achieve the optimal payload distribution of multiple images steganography to improve the anti-statistical detection performance of stego images.

关键词： Multiple images steganography payloads distribution dynamic programming messages recovery

来源：评论

学校读者我要写书评

暂无评论

Comparison of Machine Learning Models for Injury Prediction in Athletes

Comparison of Machine Learning Models for Injury Prediction ...

引用

Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： Suganiya Murugan S. Pradeep Kumar Kalaiarasi G B. Saritha B. Rubini Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu Department of EEE Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu Department of ACSE Vignan foundation for Science technology and Research Andhrapradesh India Department of Biomedical Engineering Erode Sengunthar Engineering College Thudupathi Erode Tamil Nadu India Department of EEE Vels Institute of Science Technology and Advanced Studies Chennai Tamilnadu

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Running is a favoured exercise for millions, but it carries a risk of musculoskeletal injuries, particularly to the lower extremities. Identifying and predicting potential injuries is crucial for prevention. This study explores the use of machine learning techniques to predict injuries among runners, leveraging data on demographics, running habits, health conditions, and previous injuries. Using models like Random Forest, K-Nearest Neighbours, Decision Trees, and Logistic Regression, the study examines the relevance of various attributes and assesses the predictive power of these models. Remarkably, our model achieved an impressive accuracy of over 99%, setting a new benchmark for predictive precision in injury prevention. Visualizations provide insight into attribute interactions and their influence on injury risk, facilitating the development of targeted prevention strategies.

关键词： Training Measurement Logistic regression Prevention and mitigation Data visualization Machine learning Predictive models Injuries Random forests Wearable sensors

来源：评论

学校读者我要写书评

暂无评论

Stress Severity Detection in College Students Using Emotional Pulse Signals and Deep Learning

引用

IEEE Transactions on Affective computing 2025年

作者： Li, Mi Li, Junzhe Chen, Yanbo Hu, Bin School of Information Science and Technology Beijing University of Technology Beijing International Collaboration Base on Brain Informatics and Wisdom Services Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education China Engineering Research Center of Digital Community Ministry of Education Beijing China Beijing University of Technology School of Information Science and Technology Beijing China Beijing Institute of Technology Institute of Engineering Medicine Beijing China Lanzhou University Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou China

College students face increasing stress from difficulties with studies, employment, and social interactions, which, if left unaddressed, may lead to depression and physical illnesses. Currently, the detection of stress severity relies on self-assessment scales, while machine learning or deep learning-based approaches primarily focus on classification. This study proposes an approach using pulse signals containing emotional cues and deep learning to automatically detect the severity of stress in college students. Firstly, pulse signals of 177 college students were collected using photoplethysmography (PPG) during they watched five virtual reality (VR) emotional scenes, including calm, sadness, happiness, fear, and tension. Pulse rate variability (PRV) and discrete PPG (dPPG) were extracted from these signals as input for detecting stress severity. Then, the proposed stress detection framework, 1DCNN-BiLSTM + Cross-Attention + XGBoost, was employed to detect stress severity, incorporating an emotional Cross-Attention mechanism. The impact of induced emotions on stress severity detection performance was examined. The results indicated that stress severity detection in emotional scenes outperformed in calm. Furthermore, the detection performance that integrates multiple emotions surpassed single emotions. The fusion of PRV and dPPG signals yielded the best detection performance. This study provides an end-to-end automated approach for detecting stress severity in college students. © 2010-2012 IEEE.

关键词： Photoplethysmography

来源：评论

学校读者我要写书评

暂无评论

Voxelization Algorithm Based on Octree Spatial Averaging

Voxelization Algorithm Based on Octree Spatial Averaging

引用

Information and Communication Technology (ICTech), International Conference of

作者： HongChao Lv Jun Wang HongLiang Wang XingYu Zhou Shenyang University of Chemical Technology Shandong China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

This paper proposes a high-precision and efficient voxelization algorithm for the development of voxelization in the preprocessing stage of industrial simulation. The main idea is to establish an octree data structure to evenly divide the three-dimensional mesh model space into voxel sets. By using the separation axis method and the improved scan line method, the position relationship between voxels and triangles can be directly determined by skipping the calculation of intersection between voxels and triangles. The innovation of this article lies in the use of the separation axis algorithm and the improved scanning line method for voxelization, and the simplification of the algorithm's operations through the octree data structure, thereby reducing the time and spatial complexity of the algorithm.

关键词： Technological innovation Solid modeling Octrees Data models Information and communication technology Complexity theory

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：