检索结果-内蒙古大学图书馆

Exponential Synchronization of Delayed Stochastic Complex Dynamical Networks via Hybrid Impulsive Control

IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 785-787页

作者： Yao Cui Pei Cheng Xiaohua Ge the School of Mathematic Science Anhui UniversityHefei 230601China IEEE the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia

Dear Editor,This letter addresses the synchronization problem of a class of delayed stochastic complex dynamical networks consisting of multiple drive and response *** aim is to achieve mean square exponential synchronization for the drive-response nodes despite the simultaneous presence of time delays and stochastic noises in node dynamics.

关键词： dynamics stochastic letter

来源：评论

学校读者我要写书评

暂无评论

The 1st ChatGPT4PCG Competition

引用

IEEE Transactions on Games 2024年 1-17页

作者： Abdullah, Febri Taveekitworachai, Pittawat Dewantoro, Mury F. Thawonmas, Ruck Togelius, Julian Renz, Jochen Graduate School of Information Science and Engineering Ritsumeikan University Kusatsu Japan College of Information Science and Engineering Ritsumeikan University Kusatsu Japan School of Computing The Australian National University Canberra Australia

This study summarizes the first ChatGPT4PCG competition held at the 2023 IEEE Conference on Games. The goal of the competition is to explore emergent abilities of publicly available LLMs in performing complex tasks related to procedural content generation, specifically physics-based level generation for Angry Bird-like games. Participants are tasked with submitting their prompts for ChatGPT to generate Angry Birds-like game structures that resemble English uppercase characters. A structure is a collection of stacked game objects comprising a part of an entire Angry Birds-like level. A prompt is an input for large language models (LLMs) including ChatGPT. Two evaluation metrics, i.e., stability and similarity, are used to evaluate the submitted prompts. Stability measures the sturdiness of a structure to withstand in-game gravity, while similarity measures a structure's resemblance to the target character. With such evaluation, participants are challenged not only to produce character-like but also stable structures by utilizing prompt engineering techniques. Finally, the competition's results are discussed to provide valuable insights for future studies and competitions. IEEE

关键词： Birds

来源：评论

学校读者我要写书评

暂无评论

Towards High-Performance Graph Processing: From a Hardware/Software Co-Design Perspective

引用

Journal of Computer science & Technology 2024年第2期39卷 245-266页

作者：廖小飞赵文举金海姚鹏程黄禹王庆刚赵进郑龙张宇邵志远 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph *** domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software *** article presents a graph processing ecosystem from hardware to *** start by introducing a series of hardware accelerators as the foundation of this ***,the codesigned parallel graph systems and their distributed techniques are presented to support graph ***,we introduce our efforts on novel graph applications and hardware *** results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

关键词： graph processing hardware accelerator software system high performance ecosystem

来源：评论

学校读者我要写书评

暂无评论

A Transfer Function Design for Medical Volume Data Using a Knowledge Database Based on Deep Image and Primitive Intensity Profile Features Retrieval

引用

Journal of Computer science & Technology 2024年第2期39卷 320-335页

作者： Younhyun Jung Jim Kong 盛斌 Jinman Kim School of Computing Gachon UniversitySeongnam 13120Korea School of Computer Science The University of SydneySydney 2006Australia Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Direct volume rendering(DVR)is a technique that emphasizes structures of interest(SOIs)within a volume visually,while simultaneously depicting adjacent regional information,e.g.,the spatial location of a structure concerning its *** DVR,transfer function(TF)plays a key role by enabling accurate identification of SOIs interactively as well as ensuring appropriate visibility of *** generation typically involves non-intuitive trial-and-error optimization of rendering parameters,which is time-consuming and *** at mitigating this manual process have led to approaches that make use of a knowledge database consisting of pre-designed TFs by domain *** these approaches,a user navigates the knowledge database to find the most suitable pre-designed TF for their input volume to visualize the *** these approaches potentially reduce the workload to generate the TFs,they,however,require manual TF navigation of the knowledge database,as well as the likely fine tuning of the selected TF to suit the *** this work,we propose a TF design approach,CBR-TF,where we introduce a new content-based retrieval(CBR)method to automatically navigate the knowledge *** of pre-designed TFs,our knowledge database contains volumes with SOI *** an input volume,our CBR-TF approach retrieves relevant volumes(with SOI labels)from the knowledge database;the retrieved labels are then used to generate and optimize TFs of the *** approach largely reduces manual TF navigation and fine *** our CBR-TF approach,we introduce a novel volumetric image feature which includes both a local primitive intensity profile along the SOIs and regional spatial semantics available from the co-planar images to the *** the regional spatial semantics,we adopt a convolutional neural network to obtain high-level image feature *** the intensity profile,we extend the dynamic time warping technique to address subtle alignment

关键词： data retrieval direct volume rendering ray analysis transfer function

来源：评论

学校读者我要写书评

暂无评论

Code Reviewer Intelligent Prediction in Open Source Industrial Software Project

引用

Computer Modeling in engineering & sciences 2023年第10期137卷 687-704页

作者： Zhifang Liao Bolin Zhang Xuechun Huang Song Yu Yan Zhang School of Computer Science and Engineering Central South UniversityChangsha410083China Department of Computing School of ComputingEngineering and Built EnvironmentGlasgow Caledonian UniversityGlasgowG40BAUK

Currently, open-source software is gradually being integrated into industrial software, while industry protocolsin industrial software are also gradually transferred to open-source community development. Industrial protocolstandardization organizations are confronted with fragmented and numerous code PR (Pull Request) and informalproposals, and differentworkflowswill lead to increased operating costs. The open-source community maintenanceteam needs software that is more intelligent to guide the identification and classification of these issues. To solvethe above problems, this paper proposes a PR review prediction model based on multi-dimensional features. Weextract 43 features of PR and divide them into five dimensions: contributor, reviewer, software project, PR, andsocial network of developers. The model integrates the above five-dimensional features, and a prediction model isbuilt based on a Random Forest Classifier to predict the review results of PR. On the other hand, to improve thequality of rejected PRs, we focus on problems raised in the review process and review comments of similar *** a PR revision recommendation model based on the PR review knowledge graph. Entity information andrelationships between entities are extracted from text and code information of PRs, historical review comments,and related issues. PR revisions will be recommended to code contributors by graph-based similarity *** experimental results illustrate that the above twomodels are effective and robust in PR review result predictionand PR revision recommendation.

关键词： Open source software pull request random forest knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Real Time Road Lane Detection and Vehicle Detection on YOLOv8 with Interactive Deployment 16

Real Time Road Lane Detection and Vehicle Detection on YOLOv...

引用

16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Segu, Girish Sai Pavan Kumar Sivannarayana, Annam Devi Satya Naga Ramesh, S. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies India

ISBN: (纸本)9798331505264

Modern strides in autonomous vehicles and embedded advanced driver assistant part systems(ADAS) have forced the need of an efficient in addition to accurate system intended for clear road lane as in addition to vehicle detection. For road lane detection, this study introduces a new proposal through the combination of YOLOv8 (You Only Look Once), and YOLO for vehicle detection, creating a complete package for road understanding and navigation. To achieve accurate road lane detection, YOLOv8, which is the fastest and most accurate model to date, is used to ensure that it can keep track of these lanes in real-time with consistent accuracy whenever the camera is placed under various scenarios with insufficient lighting and occlusions as well. At the same time it uses YOLO for vehicle detection, which improves the recognition and interpretation of the presence and motion of vehicles in real time. The system uses Streamlit, which is an open-source app framework for Machine Learning and Data science projects to provide an intuitive user experience. Finally, we build an interface to provide results of lane and vehicle detection in realtime manner to allow ease of monitoring and evaluation on the overall system. The combination of YOLOv8 and YOLO with Streamlit provides a powerful, scalable, and deployable solution for practical computer vision applications. Combining well established object detection algorithms with a simplified deployment platform, the proposed system tackles some of the hardest and time consuming areas of autonomous driving. The goal is to make significant contributions to both the safety and efficiency of autonomous vehicles and pave the way for further inevitable advances in ADAS. © 2024 IEEE.

关键词： Advanced driver assistance systems

来源：评论

学校读者我要写书评

暂无评论

HGNN-ETC: Higher-Order Graph Neural Network Based on Chronological Relationships for Encrypted Traffic Classification

引用

Computers, Materials & Continua 2024年第11期81卷 2643-2664页

作者： Rongwei Yu Xiya Guo Peihao Zhang Kaijuan Zhang Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan430072China

Encrypted traffic plays a crucial role in safeguarding network security and user ***,encrypting malicious traffic can lead to numerous security issues,making the effective classification of encrypted traffic *** methods for detecting encrypted traffic face two significant ***,relying solely on the original byte information for classification fails to leverage the rich temporal relationships within network ***,machine learning and convolutional neural network methods lack sufficient network expression capabilities,hindering the full exploration of traffic’s potential *** address these limitations,this study introduces a traffic classification method that utilizes time relationships and a higher-order graph neural network,termed *** approach fully exploits the original byte information and chronological relationships of traffic packets,transforming traffic data into a graph structure to provide the model with more comprehensive context ***-ETC employs an innovative k-dimensional graph neural network to effectively capture the multi-scale structural features of traffic graphs,enabling more accurate *** select the ISCXVPN and the USTC-TK2016 dataset for our *** results show that compared with other state-of-the-art methods,our method can obtain a better classification effect on different datasets,and the accuracy rate is about 97.00%.In addition,by analyzing the impact of varying input specifications on classification performance,we determine the optimal network data truncation strategy and confirm the model’s excellent generalization ability on different datasets.

关键词： Encrypted network traffic graph neural network traffic classification deep learning

来源：评论

学校读者我要写书评

暂无评论

Exploring Ensemble Architectures for Lung X-Ray Multi-Class Image Classification using CNN-LSTM 3

Exploring Ensemble Architectures for Lung X-Ray Multi-Class ...

引用

3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Nair, Rekha R Singh, Tripty Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwavidyapeetham Bengaluru India

Early diagnose of lung diseases is important as it aids In the treatment of the diseases and increment of the patient's quality of life. Chest X-rays are a widely used and cost-effective diagnostic tool for examining the lungs and detecting abnormalities. However, interpreting chest X-ray images can be challenging, even for experienced radiologists, due to the complexity of lung anatomies and the subtle variations in disease manifestations. Thus, the ensemble-based approach to Lung X-Ray Multi-Class image classification has been proposed and used Convolutional Neural Network and Long Short-term Memory (CNN-LSTM). The proposed model is compared with Several models such as ensemble of DenseNet and InceptionV3, Vgg16 and Mobilenet V2 and deep bidirectional LSTM(DBLSTM). The proposed model applied for multiclass classification on lung disorders such as TB, normal, pneumothorax, pneumonia, COVID-19, and cardiomegaly. The proposed method provides a testing F1-Score of 94%, Recall of 99%, Precision of 95% and accuracy of 89.31% compared to other ensemble models. The proposed model can be utilized for medicos as a second opinion for diagnosis and treatment planning. © 2025 The Authors. Published by Elsevier B.V.

关键词： Pulmonary diseases

来源：评论

学校读者我要写书评

暂无评论

Smart Nutrient Deficiency Prediction System for Groundnut Leaf

引用

Intelligent Automation & Soft computing 2023年第5期36卷 1845-1862页

作者： Janani Malaisamy JebakumarRethnaraj Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengalpattuTamil Nadu603203India

Prediction of the nutrient deﬁciency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative *** fertiliser in optimum amounts will protect the environment’s condition and human health *** identiﬁcation also prevents the disease’s occurrence in groundnut crops.A convo-lutional neural network is a computer vision algorithm that can be replaced in the place of human experts and laboratory methods to predict groundnut crop nitro-gen nutrient deﬁciency through image *** chlorophyll and nitrogen are proportionate to one another,the Smart Nutrient Deﬁciency Prediction System(SNDP)is proposed to detect and categorise the chlorophyll concentration range via which nitrogen concentration can be *** model’sﬁrst part is to per-form preprocessing using Groundnut Leaf Image Preprocessing(GLIP).Then,in the second part,feature extraction using a convolution process with Non-negative ReLU(CNNR)is done,and then,in the third part,the extracted features areﬂat-tened and given to the dense layer(DL)***,the Maximum Margin clas-siﬁer(MMC)is deployed and takes the input from DL for the classiﬁcation process toﬁnd *** dataset used in this work has no visible symptoms of a deﬁciency with three categories:low level(LL),beginning stage of low level(BSLL),and appropriate level(AL).This model could help to predict nitrogen deﬁciency before perceivable *** performance of the implemented model is analysed and compared with ImageNet pre-trained *** result shows that the CNNR-MMC model obtained the highest training and validation accuracy of 99%and 95%,respectively,compared to existing pre-trained models.

关键词： CNN chlorophyll groundnut crop deep learning nitrogen deﬁciency

来源：评论

学校读者我要写书评

暂无评论

Depth Map Prediction of Occluded Objects Using Structure Tensor with Gain Regularization

引用

Intelligent Automation & Soft computing 2023年第7期37卷 1145-1161页

作者： H.Shalma P.Selvaraj Department of Computing Technologies Faculty of Engineering and TechnologySchool of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203Tamil NaduIndia

The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images.A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map.3D modeling and view synthesis algorithms could effectively handle the obtained disparity *** work uses the consistency check method to find an accurate depth map for identifying occluded *** prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for *** improved depth map quality within a reasonable process-ing time outperforms the other existing depth map prediction *** experimental results have shown that the proposed depth map predictioncould identify the inter-object boundaryeven with the presence ofocclusion with less detection error and *** observed that the Middlebury stereo dataset has very few images with occluded objects,which made the attainment of gain *** this gain,we have created our dataset with occlu-sion using the structured lighting *** proposed regularization term as an optimization process in the graph cut algorithm handles occlusion for different smoothing *** experimented results demonstrated that our dataset had outperformed the Tsukuba dataset regarding the percentage of occluded pixels.

关键词： Depth maps occlusion detection reconstruction refined disparity map

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：