检索结果-内蒙古大学图书馆

Efficient Anti-Glare Ceramic Decals Defect Detection by Incorporating Homomorphic Filtering

Computer Systems science & Engineering 2021年第3期36卷 551-564页

作者： Xin Chen Ying Zhang Lang Lin Junxiang Wang Jiangqun Ni Southeast Digital Economic Development Institute Quzhou324000China School of Mechanical and Electronic Engineering Jingdezhen Ceramic InstituteJingdezhen333400China School of Data and Computer Science Sun Yat-Sen UniversityCanton510000China

Nowadays the computer vision technique has widely found applicationsin industrial manufacturing process to improve their efficiency. However, it ishardly applied in the field of daily ceramic detection due to the following twokey reasons: (1) Low detection accuracy as a result of ceramic glare, and (2) Lackof efficient detection algorithms. To tackle these problems, a homomorphic filtering based anti-glare ceramic decals defect detection technique is proposed in thispaper. Considering that smooth ceramic surface usually causes glare effects andleads to low detection results, in our approach, the ceramic samples are takenin low light environment and their luminance and details restored by a homomorphic filtering based image enhancement technique. With relatively high quality preprocessed images, an effective ceramic decal defect detection algorithm isthen designed to rapidly locate those out-of-bounds defects and further estimatetheir size. The experimental results show that the proposed scheme could achieveits desired performance.

关键词： Ceramic glaring homomorphic filtering ceramic decal border extraction out-of-bounds detection

来源：评论

学校读者我要写书评

暂无评论

vGameReviews: An R package for harnessing video game reviews for scientific research

引用

SoftwareX 2023年 23卷

作者： Fox, Nathan Berkel, Derek Van Verge, Ramiro Serrano Lindquist, Mark School for Environment and Sustainability University of Michigan MI United States Michigan Institute for Data Science University of Michigan MI United States

Researchers can harness feedback on video games in the form of online reviews and apply these datasets to investigate a wide range of disciplines including urban and landscape planning and science education. However, accessing online datasets requires advanced coding knowledge. Here, we introduce the vGameReviews R package which has been designed to allow users to access video game reviews easily. We also developed a shiny application, which provides users a graphical user interface option for searching for and saving these valuable datasets. The vGameReviews package increases the accessibility of Steam store data to a wide range of researchers. © 2023 The Author(s)

关键词： Graphical user interfaces

来源：评论

学校读者我要写书评

暂无评论

GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability

arXiv

引用

arXiv 2024年

作者： Luo, Zihan Song, Xiran Huang, Hong Lian, Jianxun Zhang, Chenhao Jiang, Jinqi Xie, Xing Huazhong University of Science and Technology Wuhan China Microsoft Research Asia Beijing China The National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

Evaluating and enhancing the general capabilities of large language models (LLMs) has been an important research topic. Graph is a common data structure in the real world, and understanding graph data is a crucial part for advancing general intelligence. To evaluate and enhance the graph understanding abilities of LLMs, in this paper, we propose a benchmark named GraphInstruct, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed reasoning steps. Based on GraphInstruct, we further construct GraphLM through efficient instruction-tuning, which shows prominent graph understanding capability. In order to enhance the LLM with graph reasoning capability as well, we propose a step mask training strategy, and construct a model named GraphLM+. As one of the pioneering efforts to enhance the graph understanding and reasoning abilities of LLMs, extensive experiments have demonstrated the superiority of GraphLM and GraphLM+ over other LLMs. We look forward to more researchers exploring the potential of LLMs in the graph data mining domain through GraphInstruct. Our code for generating GraphInstruct is released publicly at: https://***/CGCL-codes/GraphInstruct. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

An Efficient Decomposition Algorithm for Large-Scale Network Slicing 24

An Efficient Decomposition Algorithm for Large-Scale Network...

引用

24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Chen, Wei-Kun Liu, Ya-Feng Zhang, Rui-Jin Dai, Yu-Hong Luo, Zhi-Quan Beijing Institute of Technology School of Mathematics and Statistics/Beijing Key Laboratory on Mcaaci Beijing China Chinese Academy of Sciences Lsec Icmsec Academy of Mathematics and Systems Science Beijing China Shenzhen Research Institute of Big Data Chinese University of HongKong Shenzhen China

ISBN: (纸本)9781665496261

In this paper, we consider the network slicing (NS) problem which attempts to map multiple customized virtual network requests to a common shared network infrastructure and allocate network resources to meet diverse service requirements. We propose an efficient decomposition algorithm for solving this NP-hard problem. The proposed algorithm decomposes the large-scale hard NS problem into two relatively easy function placement (FP) and traffic routing (TR) subproblems and iteratively solves them enabling information feedback between each other, which makes it particularly suitable to solve large-scale problems. Specifically, the FP subproblem is to place service functions into cloud nodes in the network, and solving it can return a function placement strategy based on which the TR subproblem is defined;and the TR subproblem is to find paths connecting two nodes hosting two adjacent functions in the network, and solving it can either verify that the solution of the FP subproblem is an optimal solution of the original problem, or return a valid inequality to the FP subproblem that cuts off the current infeasible solution. The proposed algorithm is guaranteed to find the global solution of the NS problem. We demonstrate the effectiveness and efficiency of the proposed algorithm via numerical experiments. © 2023 IEEE.

关键词： Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Near-optimal algorithms for making the gradient small in stochastic minimax optimization

引用

The Journal of Machine Learning Research 2024年第1期25卷 18945-18988页

作者： Lesi Chen Luo Luo Institute for Interdisciplinary Information Sciences Tsinghua University Beijing China School of Data Science Fudan University Shanghai China and Shanghai Key Laboratory for Contemporary Applied Mathematics Shanghai China

We study the problem of finding a near-stationary point for smooth minimax optimization. The recently proposed extra anchored gradient (EAG) methods achieve the optimal convergence rate for the convex-concave minimax problem in the deterministic setting. However, the direct extension of EAG to stochastic optimization is not efficient. In this paper, we design a novel stochastic algorithm called Recursive Anchored IteratioN (RAIN). We show that the RAIN achieves near-optimal stochastic first-order oracle (SFO) complexity for stochastic minimax optimization in both convex-concave and strongly-convex-stronglyconcave cases. In addition, we extend the idea of RAIN to solve structured nonconvex-nonconcave minimax problem and it also achieves near-optimal SFO complexity.

关键词： stochastic minimax optimization ε-stationary point recursive anchoring

来源：评论

学校读者我要写书评

暂无评论

Flexible parametrization of graph-theoretical features from individual-specific networks for prediction

arXiv

引用

arXiv 2023年

作者： Gregorich, Mariella Simpson, Sean L. Heinze, Georg Medical University of Vienna Center for Medical Data Science Institute of Clinical Biometrics Vienna Austria Department of Biostatistics and Data Science Wake Forest University School of Medicine Winston-SalemNC United States

Statistical techniques are needed to analyse data structures with complex dependencies such that clinically useful information can be extracted. Individual-specific networks, which capture dependencies in complex biological systems, are often summarized by graph-theoretical features. These features, which lend themselves to outcome modelling, can be subject to high variability due to arbitrary decisions in network inference and noise. Correlation-based adjacency matrices often need to be sparsified before meaningful graph-theoretical features can be extracted, requiring the data analysts to determine an optimal threshold.. To address this issue, we propose to incorporate a flexible weighting function over the full range of possible thresholds to capture the variability of graph-theoretical features over the threshold domain. The potential of this approach, which extends concepts from functional data analysis to a graph-theoretical setting, is explored in a plasmode simulation study using real functional magnetic resonance imaging (fMRI) data from the Autism Brain Imaging data Exchange (ABIDE) Preprocessed initiative. The simulations show that our modelling approach yields accurate estimates of the functional form of the weight function, improves inference efficiency, and achieves a comparable or reduced root mean square prediction error compared to competitor modelling approaches. This assertion holds true in settings where both complex functional forms underlie the outcome-generating process and a universal threshold value is employed. We demonstrate the practical utility of our approach by using resting-state fMRI data to predict biological age in children. Our study establishes the flexible modelling approach as a statistically principled, serious competitor to ad-hoc methods with superior performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

IoT-Driven Image Recognition for Microplastic Analysis in Water Systems using Convolutional Neural Networks 2

IoT-Driven Image Recognition for Microplastic Analysis in Wa...

引用

2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Hasan, M D Ashfaqul Balasubadra, K. Vadivel, G. Arunfred, N. Ishwarya, M.V. Murugan, S. Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India R.M.D. Engineering College Kavaraipettai Department of Information Technology Tamil Nadu Chennai India University of Technology and Applied Science Department of Information Technology Dhofar Salalah Oman Srm Institute of Science and Technology Kattankulathur Faculty of Management Studies Tamil Nadu Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Microplastic pollution in water systems is growing, requiring novel detection and analysis methods. This research presents an Internet of Things (IoT)-driven image identification system using Convolutional Neural Networks (CNNs) to detect and quantify microplastics in water samples. The suggested method is more scalable and responsive due to IoT real-time data capture and remote monitoring of water infrastructure. An innovative CNN architecture for image processing allows the system to accurately identify micro plastics. The CNN model is trained and validated using a large dataset of micro plastic-containing water samples. The trained model can recognize various sizes, shapes, and colors of micro plastics, making it responsive to different environmental situations. The IoT architecture also allows image recognition modules in dispersed sensor nodes to cover water systems. Extensive studies prove the system can analyze vast amounts of image data quickly and reliably. Edge computing also minimizes latency and improves micro plastic analysis system responsiveness. The suggested IoT-driven image recognition method for continuous micro plastic pollution monitoring and evaluation in water systems seems promising. Scalability, realtime capabilities, and accuracy make it useful for environmental monitoring agencies and academics trying to reduce microplastics' influence on aquatic ecosystems. This system advances IoT applications in environmental and pollution management. © 2024 IEEE.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

Synopsis: Privacy Meets Performance: Enhancing Distributed Simulation-based Federated Multi-agent Learning with Privacy-preserving Surrogate Model✱ 25

Synopsis: Privacy Meets Performance: Enhancing Distributed S...

引用

39th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

作者： Bo Zhang Wen Jun Tan Wentong Cai Allan Zhang College of Computing and Data Science Nanyang Technological University Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Nanyang Technological University Singapore Singapore Nanyang Technological University Singapore Singapore and Singapore Institute of Manufacturing Technology Singapore Singapore

来源：评论

学校读者我要写书评

暂无评论

Medical Image Segmentation Based on 3d Pdc with Swin Transformer

SSRN

引用

SSRN 2025年

作者： Fan, Lin Ding, Xiaojia Wang, Zhongmin Wang, Hai Zhang, Rong School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China The School of Information Science and Technology Northwest University Shaanxi Xi’an710121 China

3D medical image segmentation is vital for disease diagnosis and effective treatment strategies. Despite the advancements in Convolutional Neural Networks (CNN), their fixed receptive fields constrain global context modeling, leading to suboptimal performance, particularly with complex shapes and multi-scale variations. Transformer enhances the global modeling capability through self-attention mechanism. However, challenges persist in capturing fine-grained local features and extracting edge features. To overcome these issues, In this study, a dual-path encoder is proposed, which consists of a 3D PDC and a Swin Transformer and is combined with a CNN for the decoder. The 3D PDC module extracts local features by calculating the differences between neighboring pixels. To improve the ability of capturing edge structures, three 3D PDC variants (3D CPDC, 3D APDC, and 3D RPDC) are proposed to optimize the processing ability of complex edges, multi-directional edges, and multi-scale structures, respectively. After performance evaluation, the selected CARV combinations (3D CPDC, 3D APDC, 3D RPDC, and 3D ordinary convolution) achieve a compromise between high segmentation accuracy and low computational cost. The Swin Transformer encoder captures global context using a hierarchical shift-window mechanism, while the CNN decoder fuses features progressively to produce precise pixel-level segmentation. The proposed method exceeds the performance of existing state-of-the-art techniques, as shown by experiments conducted on the BTCV, FLARE21, and AMOS22 datasets. © 2025, The Authors. All rights reserved.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Ballistic response of armour plates using Generative Adversarial Networks

引用

Defence Technology（防务技术） 2022年第9期18卷 1513-1522页

作者： S.Thompson F.Teixeira-Dias M.Paulino A.Hamilton Institute for Infrastructure and Environment(IIE) School of EngineeringThe University of Edinburgh Alexander Graham Bell BuildingThe King's BuildingsEdinburghEH93FGUnited Kingdom School of Engineering Faculty of ScienceEngineering and Built EnvironmentDeakin University Waurn PondsVictoria3220Australia Data Science and AI EngineeringGroup Chief Information O CeLloyds Banking Group69 Morrison StEdinburghEH38BWUnited Kingdom

It is important to understand how ballistic materials respond to impact from projectiles such that informed decisions can be made in the design process of protective armour systems. Ballistic testing is a standards-based process where materials are tested to determine whether they meet protection, safety and performance criteria. For the V50ballistic test, projectiles are fired at different velocities to determine a key design parameter known as the ballistic limit velocity(BLV), the velocity above which projectiles perforate the target. These tests, however, are destructive by nature and as such there can be considerable associated costs, especially when studying complex armour materials and systems. This study proposes a unique solution to the problem using a recent class of machine learning system known as the Generative Adversarial Network(GAN). The GAN can be used to generate new ballistic samples as opposed to performing additional destructive experiments. A GAN network architecture is tested and trained on three different ballistic data sets, and their performance is compared. The trained networks were able to successfully produce ballistic curves with an overall RMSE of between 10 and 20 % and predicted the V50BLV in each case with an error of less than 5 %. The results demonstrate that it is possible to train generative networks on a limited number of ballistic samples and use the trained network to generate many new samples representative of the data that it was trained on. The paper spotlights the benefits that generative networks can bring to ballistic applications and provides an alternative to expensive testing during the early stages of the design process.

关键词： Machine learning Generative Adversarial Networks GAN Terminal ballistics Armour systems

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：