检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Zhang, Junpeng Ju, Yue Mu, Biqiang Zhong, Renxin Chen, Tianshi School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China

Spatial-temporal Gaussian process regression is a popular method for spatial-temporal data modeling. Its state-of-art implementation is based on the state-space model realization of the spatial-temporal Gaussian process and its corresponding Kalman filter and smoother, and has computational complexity O(NM3), where N and M are the number of time instants and spatial input locations, respectively, and thus can only be applied to data with large N but relatively small M. In this paper, our primary goal is to show that by exploring the Kronecker structure of the state-space model realization of the spatial-temporal Gaussian process, it is possible to further reduce the computational complexity to O(M3 + NM2) and thus the proposed implementation can be applied to data with large N and moderately large M. The proposed implementation is illustrated over applications in weather data prediction and spatially-distributed system identification. Our secondary goal is to design a kernel for both the Colorado precipitation data and the GHCN temperature data, such that while having more efficient implementation, better prediction performance can also be achieved than the state-of-art result. Copyright © 2022, The Authors. All rights reserved.

关键词： Kalman filters

来源：评论

学校读者我要写书评

暂无评论

Collection: Wound Healing Assay dataset (WHAD) and Cell Adhesion and Motility Assay dataset (CAMAD)

IEEE Data Descriptions

引用

IEEE data Descriptions 2024年 1卷 95-102页

作者： Leonardo O. Iheme Sevgi Onal Yusuf S. Erdem Mahmut Ucar Ozden Yalcin-Ozuysal Devrim Pesen-Okvur Behcet U. Toreyin Devrim Unay Epam Systems Istanbul Türkiye Department of Biotechnology and Bioengineering Izmir Institute of Technology Graduate School of Science and Engineering Izmir Türkiye Center for Advanced Biomaterials for Health Care Istituto Italiano di Tecnologia Napoli Italy Department of Electrical and Electronics Engineering Faculty of Engineering Izmir Democracy University Izmir Türkiye Department of Molecular Biology and Genetics Faculty of Science Izmir Institute of Technology Izmir Türkiye Department of Artificial Intelligence and Data Engineering Istanbul Technical University Informatics Institute Istanbul Türkiye

This article introduces two novel datasets for cell motility and wound healing research: the wound healing assay dataset (WHAD) and the cell adhesion and motility assay dataset (CAMAD). WHAD comprises time-lapse phase-contrast images of wound healing assays using genetically modified MCF10A and MCF7 cells, while CAMAD includes MDA-MB-231 and RAW 264.7 cells cultured on various substrates. These datasets offer diverse experimental conditions, comprehensive annotations, and high-quality imaging data, addressing gaps in existing resources. The collection methods, experimental designs, and annotation processes are detailed, emphasizing the reliability and utility of our datasets. WHAD and CAMAD enable researchers to develop robust algorithms for cell tracking, segmentation, and behavior analysis in phase-contrast microscopy images. The applications of these datasets span cancer research, image processing technique development, and automated analysis of wound healing and cell motility assays. By providing these resources, we aim to accelerate progress in biomedical image analysis and cell biology research. IEEE SOCIETY/COUNCIL engineering in Medicine and Biology Society (EMBS) data TYPE/LOCATION Images; Izmir, Türkiye data DOI/PID 10.21227/f77j-j355

关键词： Wounds Microscopy Substrates Image segmentation Phase change materials Annotations Breast cancer Biomedical imaging Adhesives Pipelines

来源：评论

学校读者我要写书评

暂无评论

Dynamic Localisation of Spatial-Temporal Graph Neural Network

arXiv

引用

arXiv 2025年

作者： Duan, Wenying Guo, Shujun Zhou, Zimu Huang, Wei Rao, Hong He, Xiaoxi Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction Nanchang University Nanchang China School of Mathematics and Computer Sciences Nanchang University Nanchang China School of Data Science City University of Hong Kong Hong Kong School of Software Nanchang University Nanchang China Faculty of Science and Technology University of Macau China

Spatial-temporal data, fundamental to many intelligent applications, reveals dependencies indicating causal links between present measurements at specific locations and historical data at the same or other locations. Within this context, adaptive spatial-temporal graph neural networks (ASTGNNs) have emerged as valuable tools for modelling these dependencies, especially through a data-driven approach rather than pre-defined spatial graphs. While this approach offers higher accuracy, it presents increased computational demands. Addressing this challenge, this paper delves into the concept of localisation within ASTGNNs, introducing an innovative perspective that spatial dependencies should be dynamically evolving over time. We introduce DynAGS, a localised ASTGNN framework aimed at maximising efficiency and accuracy in distributed deployment. This framework integrates dynamic localisation, time-evolving spatial graphs, and personalised localisation, all orchestrated around the Dynamic Graph Generator, a light-weighted central module leveraging cross attention. The central module can integrate historical information in a node-independent manner to enhance the feature representation of nodes at the current moment. This improved feature representation is then used to generate a dynamic sparse graph without the need for costly data exchanges, and it supports personalised localisation. Performance assessments across two core ASTGNN architectures and nine real-world datasets from various applications reveal that DynAGS outshines current benchmarks, underscoring that the dynamic modelling of spatial dependencies can drastically improve model expressibility, flexibility, and system efficiency, especially in distributed settings. © 2025, CC BY-NC-ND.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

引用

Procedia computer science 2023年 220卷 339-347页

作者： Leila Ismail Muhammad Danish Waseem Clouds and Distributed Computing and Systems (CLOUDS) Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia. Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE. National Water and Energy Center United Arab Emirates University UAE.

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.

关键词： Computer Vision Deep Learning Image Processing Machine Learning Pain Detection Patient-Centric Smart healthcare eHealth

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of preprocessing methods and machine learning models for geographical origin identification in an imbalanced Panax notoginseng dataset with near-infrared spectroscopy

Comparative analysis of preprocessing methods and machine le...

引用

2024 International Conference on Artificial Intelligence and Machine Learning Research, CAIMLR 2024

作者： Cheng, Xuefeng Buhari, Abudhahir Zhu, Hongmei Cheng, Changyin Liu, Juan Faculty of Engineering Science & Technology Infrastructure University Kuala Lumpur Kajang Malaysia School of Big Data and Information Industry Chongqing City Management College Chongqing China School of Artificial Intelligence Chongqing University of Education Chongqing China Chongqing University Chongqing China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing College University of Chinese Academy of Sciences Chongqing China

ISBN: (纸本)9781510691797

Imbalanced sample distributions pose challenges in accurately identifying the geographical origin of Panax notoginseng (P. Notoginseng), impacting machine learning model performance. This study investigates the efficacy of various data preprocessing methods and machine learning models on geographical origin classification accuracy using near-infrared spectroscopy (NIRS) in imbalanced datasets. Results demonstrate that specific combinations of preprocessing methods and models can significantly improve classification accuracy, bridging the gap between imbalanced and balanced datasets. These findings highlight the importance of tailored approaches to mitigate model and evaluation bias, imbalanced sample weights, and decision boundary shifts in P. Notoginseng datasets. Moreover, the insights gleaned from this study have implications for similar analyses in other traditional Chinese medicinal materials. © 2025 SPIE.

关键词： data Preprocessing Geographical Origin Identification Machine Learning Near-Infrared Spectroscopy Panax Notoginseng

来源：评论

学校读者我要写书评

暂无评论

Thermal and mechanical properties of a high-density polyethylene (HDPE) composite reinforced with wood flour

Thermal and mechanical properties of a high-density polyethy...

引用

作者： Fatima Ezzahrae, Morchid Nacer, Akkouri Latifa, Elfarissi Abdellah, Zamma Mohamed, Idiri Mustapha, Jammoukh Laboratory of Modeling and Simulation of Intelligent Industrial Systems M2S2I Higher Normal School of Technical Education Mohammedia University of Hassan II Casablanca Mohammedia Morocco Technical Center of Plastics and Rubber CTPC Casablanca Morocco Laboratory of Engineering and Materials LIMAT Faculty of Science Ben M'Sik University of Hassan II Casablanca Casablanca Morocco

To keep up with the rate of growth and global demand, it is necessary to manufacture products with high performance and improved durability. Wood-plastic composites (WPCs) are a rapidly growing class of construction materials formed by mixing wood fibers with a polymer matrix at a high temperature. However, significant technical issues remain, such as low thermal resistance, low water resistance, and limited interfacial contact or compatibility between wood fiber and the polymer matrix, which results in poor adhesion of the wood particles, etc. The structure and properties of the matrix/fiber interface can indeed have an impact on the final properties of the composites. To this end, good interfacial adhesion improves stress transmission from the matrix to the fiber, as well as composite performance. Therefore, chemical modification has been the most effective way to chemically bond the matrix to the filler. In this study, the modification is achieved by using maleic anhydride as a coupling agent which promotes the formation of chemical or material bonds between the wood flour (WF) and the polymer matrix. Three WF/high-density polyethylene (HDPE) composite blends with WF contents of 40%, 50%, and 60% were evaluated for density, flexural properties, hardness test, and thermal analysis. Physical tests showed that increasing the wood content of the composite from 40% to 60% increases the density and hardness of the WPC from 1,047 g/cm3 to 1,097 g/cm3 and from 64.5 to 67.7 respectively. On the other hand, increasing the wood flour content to 60% decreases the flexural strength from 37.524 MPa to 27.512 MPa, while the flexural modulus increases from 2176.125 MPa to 3568.284 MPa. In general, the results indicate that the developed WF/HDPE has suitable mechanical and thermal properties for outdoor applications. © 2022

关键词： High density polyethylenes

来源：评论

学校读者我要写书评

暂无评论

DAC-MD: A privacy preserving drone-access control scheme for last mile delivery

引用

Transactions on Emerging Telecommunications Technologies 2024年第3期35卷

作者： Irshad, Azeem Farooq, Muhammad Mahmood, Khalid Mallah, Ghulam Ali Chaudhry, Shehzad Ashraf Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan Department of Mathematics and Statistics College of Arts and Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Science Shah Abdul Latif University Sindh Khairpur Pakistan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul Turkey

The service delivery has lately witnessed strides in the form of paradigm shifts from conventional logistics to drone-oriented supply chain to conserve ecosystem. The use of drones or unmanned aerial vehicles (UAVs) for package deliveries offer enhanced accessibility, flexibility, efficiency, speed, safety, and even few environmental gains. However, the drones can come across various security hazards while communicating with ground installations on open channels including physical capture attack, privacy infringement, and so on. Thus, earnest endeavors are needed to fix those security challenges in applications related to drone communications. To address these problems, an efficient authenticated key agreement technique, drone access control scheme for last mile delivery (DAC-MD) is suggested for user-drone interactions in IoD environment that help the participating entities in developing a mutually agreed session key for securing subsequent communication interactions. The proposed scheme is proved using automated ProVerif tool as well as analyzed by using formal analysis based on RoR random oracle model. The comparative analysis against recently presented known studies reveals that our scheme not only carries comparable properties in terms of security and privacy, but also low computational and communication costs. © 2024 John Wiley & Sons, Ltd.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

Parameter Filter-based Event-triggered Learning

arXiv

引用

arXiv 2022年

作者： Schlor, Sebastian Solowjow, Friedrich Trimpe, Sebastian University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70550 Germany Institute for Data Science in Mechanical Engineering RWTH Aachen University Aachen52068 Germany Intelligent Control Systems Group Max Planck Institute for Intelligent Systems Stuttgart70569 Germany

Model-based algorithms are deeply rooted in modern control and systems theory. However, they usually come with a critical assumption – access to an accurate model of the system. In practice, models are far from perfect. Even precisely tuned estimates of unknown parameters will deteriorate over time. Therefore, it is essential to detect the change to avoid suboptimal or even dangerous behavior of a control system. We propose to combine statistical tests with dedicated parameter filters that track unknown system parameters from state data. These filters yield point estimates of the unknown parameters and, further, an inherent notion of uncertainty. When the point estimate leaves the confidence region, we trigger active learning experiments. We update models only after enforcing a sufficiently small uncertainty in the filter. Thus, models are only updated when necessary and statistically significant while ensuring guaranteed improvement, which we call event-triggered learning. We validate the proposed method in numerical simulations of a DC motor in combination with model predictive control. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

4D-CS: Exploiting Cluster Prior for 4D Spatio-Temporal LiDAR Semantic Segmentation

arXiv

引用

arXiv 2025年

作者： Zhong, Jiexi Li, Zhiheng Cui, Yubo Fang, Zheng The Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China The National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 China

Semantic segmentation of LiDAR points has significant value for autonomous driving and mobile robot systems. Most approaches explore spatio-temporal information of multi-scan to identify the semantic classes and motion states for each point. However, these methods often overlook the segmentation consistency in space and time, which may result in point clouds within the same object being predicted as different categories. To handle this issue, our core idea is to generate cluster labels across multiple frames that can reflect the complete spatial structure and temporal information of objects. These labels serve as explicit guidance for our dual-branch network, 4D-CS, which integrates point-based and cluster-based branches to enable more consistent segmentation. Specifically, in the point-based branch, we leverage historical knowledge to enrich the current feature through temporal fusion on multiple views. In the cluster-based branch, we propose a new strategy to produce cluster labels of foreground objects and apply them to gather point-wise information to derive cluster features. We then merge neighboring clusters across multiple scans to restore missing features due to occlusion. Finally, in the point-cluster fusion stage, we adaptively fuse the information from the two branches to optimize segmentation results. Extensive experiments confirm the effectiveness of the proposed method, and we achieve state-of-the-art results on the multi-scan semantic and moving object segmentation on SemanticKITTI and nuScenes datasets. The code will be available at https://***/NEU-REAL/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

A New Transmission Cause and Effect Analysis (TCEA) Approach to Risk Management for Non-Healthcare Context: A Case Study on COVID-19

A New Transmission Cause and Effect Analysis (TCEA) Approach...

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Yi Wen Kerk Kai Meng Tay Chian Haur Jong Chee Shee Chai Chee Peng Lim Faculty of Information Science & Technology Universiti Kebangsaan Malaysia Bangi Malaysia Faculty of Engineering Universiti Kebangsaan Malaysia Kota Samarahan Malaysia School of Engineering & Technology Universty of Technology Sarawak Sibu Malaysia Faculty of Medicine & Health Sciences Universiti Malaysia Sarawak Kota Samarahan Malaysia Institute for Intelligent Systems Research and Innovation Deakin University Geelong VIC Australia

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Leveraging the concept of Failure Mode and Effect Analysis (FMEA), we propose a simple and systematic approach, namely Transmission Cause and Effect Analysis (TCEA), to achieve a defined goal of reducing transmission risk through effective preventive and control actions in real-world environments. Specifically, the transmission risk of an infectious disease (e.g., COVID-19) is perceived as a combination of the presence of a transmission agent (e.g., SARS-CoV-2 virus or its variants) and the requisite factors that lead to infection of humans and the associated aftermath of infection. TCEA adopts a causal map to represent all possible transmission risks via a brainstorming process. Next, appropriate preventive and control actions associated with each transmission risk are identified. Similar to FMEA, a Risk Priority Number model with Severity, Occurrence, and Detection ratings is adopted for analysis, prioritization, and decision-making. To demonstrate the usefulness of TCEA, a real-world case study on COVID-19 is conducted. The empirical results indicate that TCEA provide a simple, systematic and easy-to-implement approach to effectively analyze and manage transmission risks of COVID-19 in non-healthcare workplaces.

关键词： COVID-19 Systematics Infectious diseases Heuristic algorithms Employment Decision making Probabilistic logic Fuzzy set theory Risk management Particle swarm optimization

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：