检索结果-内蒙古大学图书馆

International Conference on Management of Large-Scale System Development (MLSD)

作者： Dmitry Kononov Meran Furugyan Russian State University for the Humanities Institute of Control Sciences of the Russian Academy of Sciences Moscow Russia Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia

The scheduling problem is considered for the case when requests for the execution of job complexes with known characteristics receive at given times. Interrupts and switching from one executive mechanism to another are allowed. The composition of all complexes and the characteristics of jobs become known only at the time of each request. It is required to determine whether there is an admissible schedule for the total set of jobs and build it in case of a positive answer. We have developed a polynomial algorithm for solving the problem. The algorithm is based on constructing a network flow model and searching for the maximum flow. The problem of constructing an admissible schedule for jobs with non-fixed durations is studied. We have developed a method for preliminary (before the experiment in real-time) calculation of schedules, the computational complexity of which is significantly less than the computational complexity of a complete enumeration of all possible duration vectors.

关键词：

来源：评论

学校读者我要写书评

暂无评论

QoI-Aware Configuration Adaptation and Heterogeneous Resource Allocation for Edge Video Analytics

引用

IEEE Internet of Things Journal 2025年第12期12卷 19215-19230页

作者： He, Zixuan Sun, Yanjing Zhang, Beibei Dong, Kaiwen Wang, Bowen Xu, Hongli China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China China University of Mining and Technology IoT Perception Mine Research Center Xuzhou221116 China School of Information and Control Engineering China University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China University of Science and Technology of China Suzhou Institute for Advanced Research Jiangsu Suzhou215123 China

Edge video analytics enables agile responses of machine-centric applications by streaming videos from end devices to edge servers (ESs) for resource-intensive Deep Neural Network (DNN) inference. Quality of Inference (QoI), reflected by end-to-end analytics delay and inference accuracy, is crucial for the high-quality real-time decision-making. Due to the limited ES resources and dynamic network conditions, video configuration adaptation and fine-grained resource allocation are necessary to enhance the QoI of video streams, which involves a delicate balance between delay and accuracy. In this paper, we propose an edge-enabled multi-video analytics framework, in which performing DNN inference relies on heterogeneous resources, including CPU, GPU, and memory. Considering the different impacts of heterogeneous resources on QoI and double-queue backlogs, we formulate the joint video configuration adaptation, CPU-GPU resource allocation, and batch size selection (JCCGB) problem based on semi-Markov Decision Process to maximize the long-term average QoI. Then, Lyapunov optimization is applied to transform the original problem into one that minimizes the Lyapunov drift-plus-penalty upper bound, thereby ensuring the stability of both the transmission and computation queues. To tackle the potential multimodality of the optimal configuration adaptation and resource allocation policy, we propose the diffusion deep reinforcement learning based JCCGB (DD-JCCGB) scheme. This approach effectively enhances the policy performance and accelerates the training process. Experiments driven by real-world network traces demonstrate that the DD-JCCGB algorithm improves the long-term average QoI and outperforms baseline schemes. © 2025 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Clustering-Based State Grouping Reinforcement Learning for Switching Decision of Autonomous Vehicles

Hierarchical Clustering-Based State Grouping Reinforcement L...

引用

2023 IEEE International Conference on Unmanned Systems, ICUS 2023

作者： Ouyang, Wenjie Jiao, Yiwen Liu, Yang Li, Yang Hu, Manjiang Qin, Hongmao College of Mechanical and Vehicle Engineering Hunan University Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electrical Mechanical and Computer Science Delft University of Technology Delft Netherlands College of Mechanical and Vehicle Engineering Hunan University State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Changsha China Institute of Hunan University Wuxi Intelligent Control Research Wuxi China

ISBN: (纸本)9798350316308

Reinforcement learning (RL) has gained wide attention, but its implementation in autonomous vehicles is still limited by insufficient sample efficiency and heavy training costs. The training efficiency of RL agents is influenced by the dimension of the state space, which can be partitioned to reduce the complexity of sampling and computation. This study proposes a hierarchical clustering-based state grouping reinforcement learning (HCSG-RL) method for the switching decision of autonomous vehicles. First, we partition the base state space into groups and generate a hierarchical tree of state space groups. Then, we train multiple sub-agents for each node in the hierarchical tree. Finally, we add these trained-well sub-model into master policy. This method allows us to fully explore all state spaces and improve the training efficiency of individual agents, which handles the 'long-tail' issue and the curse of dimensionality issue. We conduct experiments in a simulation environment and results show that the proposed method has 16-72% reward improvement compared to the tree model in different road length. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Determination of Hydrological Model Parameters by Newton Method 12th

Determination of Hydrological Model Parameters by Newton M...

引用

12th International Conference on Optimization and Applications, OPTIMA 2021

作者： Zasukhin, Sergey Zasukhina, Elena Moscow Institute of Physics and Technology Dolgoprudny Russia Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783030927103

The problem of determining parameters of a hydrological model is studied. The model describes vertical water transfer in unsaturated soil and includes some parameters that characterize hydrophysical properties of soil. The problem is formulated as a variational problem with differential constraints in the form of the Richards equation describing a process of water transfer in soil. The objective functional is proportional to the distance squared between measured and modeled values of the soil moisture. Numerical solution of the discretized problem is carried out using Newton method. Gradient and second order derivatives of the objective function are calculated using formulas obtained by fast automatic differentiation techniques. © 2021, Springer Nature Switzerland AG.

关键词： Newton-Raphson method

来源：评论

学校读者我要写书评

暂无评论

Assessing the spatiotemporal malaria transmission intensity with heterogeneous risk factors:A modeling study in Cambodia

引用

Infectious Disease Modelling 2023年第1期8卷 253-269页

作者： Mutong Liu Yang Liu Ly Po Shang Xia Rekol Huy Xiao-Nong Zhou Jiming Liu Department of Computer Science Hong Kong Baptist UniversityHong Kong Special administrative region of China HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control China National Center for Parasitology Entomology&Malaria Control(CNM)Ministry of HealthPhnom PenhCambodia National Institute of Parasitic Diseases Chinese Center for Disease Control and PreventionShanghaiChina Key Laboratory of Parasite and Vector Biology National Health and Commission of the People's Republic of ChinaShanghaiChina WHO Collaborating Center for Tropical Diseases ShanghaiChina

Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity,which needs to incorporate spatiotemporally varying risk *** this study,we conduct a systematic investigation to characterize malaria transmission intensity by taking a spatiotemporal network perspective,where nodes capture the local transmission intensities resulting from dominant vector species,the population density,and land cover,and edges describe the cross-region human mobility *** inferred network enables us to accurately assess the transmission intensity over time and space from available empirical *** study focuses on malaria-severe districts in *** malaria transmission intensities determined using our transmission network reveal both qualitatively and quantitatively their seasonal and geographical characteristics:the risks increase in the rainy season and decrease in the dry season;remote and sparsely populated areas generally show higher transmission intensities than other *** findings suggest that:the human mobility(e.g.,in planting/harvest seasons),environment(e.g.,temperature),and contact risk(coexistences of human and vector occurrence)contribute to malaria transmission in spatiotemporally varying degrees;quantitative relationships between these influential factors and the resulting malaria transmission risk can inform evidence-based tailor-made responses at the right locations and times.

关键词： Malaria Transmission intensity assessment Spatiotemporal network Computational approach Heterogeneous risk factors

来源：评论

学校读者我要写书评

暂无评论

A distributed algorithm with network-independent step-size and event-triggered mechanism for economic dispatch problem

A distributed algorithm with network-independent step-size a...

引用

作者： Chen, Baitong Yang, Jianhua Lu, Wei Pedrycz, Witold Sun, Changhai School of Control Science and Engineering Dalian University of Technology Dalian China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Institute of Systems Engineering Macau University of Science and Technology SARTaipa 999078 China Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey School of Electrical Engineering Dalian University of Technology Dalian China

The economic dispatch problem (EDP) poses a significant challenge in energy management for modern power systems, particularly as these systems undergo expansion. This growth escalates the demand for communication resources and increases the risk of communication failures. To address this challenge, we propose a distributed algorithm with network-independent step sizes and an event-triggered mechanism, which reduces communication requirements and enhances adaptability. Unlike traditional methods, our algorithm uses network-independent step sizes derived from each agent's local cost functions, thus eliminating the need for detailed network topology knowledge. The theoretical derivation identifies a range of step size values that depend solely on the objective function's strong convexity and the gradient's Lipschitz continuity. Furthermore, the proposed algorithm is shown to achieve a linear convergence rate, assuming the event triggering threshold criteria are met for linear convergence. Numerical experiments further validate the effectiveness and advantages of our proposed distributed algorithm by demonstrating its ability to maintain good convergence characteristics while reducing communication frequency. © 2024 John Wiley & Sons Ltd.

关键词： Cost functions

来源：评论

学校读者我要写书评

暂无评论

CorPN: Corner Prediction Network for Visual Tracking

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Li, Qun Zhang, Haijun Yang, Kai Dai, Wei Ma, Jianghong Liu, Yuping the Department of Computer Science Harbin Institute of Technology Shenzhen Xili University Town Shenzhen518055 China the School of Computer Science and Artificial Intelligence WuHan Textile University Wuhan430200 China the School of Information and Control Engineering Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou221116 China the School of Microelectronics Fudan University Shanghai200433 China

Object tracking is a well-established task in the field of computer vision. Despite numerous efforts over the last few decades, object tracking remains a formidable challenge, largely due to the complexity of the scenes encountered in video sequences. In line with the powerful capabilities of Transformers for object feature extraction and fusion, as well as drawing inspiration from CornerNet, this paper introduces a new Transformer-based anchor-free method designed for object tracking called CorPN (i.e., Corner Prediction Network). Specifically, a Transformer-based backbone is proposed to extract and fuse deep features in a Siamese network architecture. Furthermore, our methodology aims to emulate the intuitive cognitive process by which humans identify the bounding-box corners of objects. CorPN turns out to be a more effective and robust tracking method because it jointly leverages multi-level feature aggregation, corner prediction, and probabilistic prediction incorporated in the tracking head. In extensive evaluations on public datasets, our method achieves a state-of-the-art performance. In particular, our approach achieves an area-under-the-curve (AUC) of 85.2%, a precision score (P) of 84.5%, and a normalized precision score (Pnorm) of 89.6%, outperforming extant state-of-the-art trackers on these metrics. © 2025 IEEE. All rights reserved,

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Bin Packing Scheduling Algorithm with Energy Constraints in Cloud Computing 17

Bin Packing Scheduling Algorithm with Energy Constraints in ...

引用

17th IEEE International Conference on Intelligent computer Communication and Processing, ICCP 2021

作者： Tudosoiu, Marius-Florinel Pop, Florin University Politehnica of Bucharest Faculty of Automatic Control and Computer Science Romania National Institute for Research and Development in Informatics - ICI Bucharest Romania

ISBN: (纸本)9781665409766

Cloud computing systems are the backbone of our technology needs in everyday life and are one of the major electric energy consumers globally. Any improvement that can be added to the energy efficiency of these vast systems constitutes a big gain worldwide in our never-ending battle with climate change and pollution. This paper proposes a new algorithm for task scheduling in Cloud systems based on the bin packing algorithm using a greedy implementation in conjunction with an optimization algorithm for resource task execution. By executing tasks in as much time as possible without impacting customer experience too much due to latency and by using resources at close to 100% in order to use the minimum number of servers we can achieve on average a 3.79% increase in energy efficiency. On top of that, our algorithm is robust to extreme variations of incoming task deadline distributions. © 2021 IEEE.

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

LiSD: An Efficient Multi-Task Learning Framework For Lidar Segmentation and Detection

LiSD: An Efficient Multi-Task Learning Framework For Lidar S...

引用

IEEE International Conference on Image Processing

作者： Jiahua Xu Si Zuo Chenfeng Wei Wei Zhou Wuxi Intelligent Control Research Institute Hunan University China College of Mechanical and Vehicle Engineering Hunan University China School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

With the rapid proliferation of autonomous driving, there has been a heightened focus on the research of lidar-based 3D semantic segmentation and object detection methodologies, aiming to ensure the safety of traffic participants. In recent decades, learning-based approaches have emerged, demonstrating remarkable performance gains in comparison to conventional algorithms. However, the segmentation and detection tasks have traditionally been examined in isolation to achieve the best precision. To this end, we propose an efficient multi-task learning framework named LiSD which can address both segmentation and detection tasks, aiming to optimize the overall performance. Our proposed LiSD is a voxel-based encoder-decoder framework that contains a hierarchical feature collaboration module and a holistic information aggregation module. Different integration methods are adopted to keep sparsity in segmentation while densifying features for query initialization in detection. Besides, cross-task information is utilized in an instance-aware refinement module to obtain more accurate predictions. Experimental results on the nuScenes dataset and Waymo Open Dataset demonstrate the effectiveness of our proposed model. It is worth noting that LiSD achieves the state-of-the-art performance of $83.3 \% \mathrm{mIoU}$ on the nuScenes segmentation benchmark for lidar-only methods.

关键词： Laser radar Three-dimensional displays Semantic segmentation Collaboration Object detection Performance gain Multitasking

来源：评论

学校读者我要写书评

暂无评论

LISD: AN EFFICIENT MULTI-TASK LEARNING FRAMEWORK FOR LIDAR SEGMENTATION AND DETECTION

arXiv

引用

arXiv 2024年

作者： Xu, Jiahua Zuo, Si Wei, Chenfeng Zhou, Wei Wuxi Intelligent Control Research Institute Hunan University China College of Mechanical and Vehicle Engineering Hunan University China School of Computer Science and Informatics Cardiff University United Kingdom

With the rapid proliferation of autonomous driving, there has been a heightened focus on the research of lidar-based 3D semantic segmentation and object detection methodologies, aiming to ensure the safety of traffic participants. In recent decades, learning-based approaches have emerged, demonstrating remarkable performance gains in comparison to conventional algorithms. However, the segmentation and detection tasks have traditionally been examined in isolation to achieve the best precision. To this end, we propose an efficient multi-task learning framework named LiSD which can address both segmentation and detection tasks, aiming to optimize the overall performance. Our proposed LiSD is a voxel-based encoder-decoder framework that contains a hierarchical feature collaboration module and a holistic information aggregation module. Different integration methods are adopted to keep sparsity in segmentation while densifying features for query initialization in detection. Besides, cross-task information is utilized in an instance-aware refinement module to obtain more accurate predictions. Experimental results on the nuScenes dataset and Waymo Open Dataset demonstrate the effectiveness of our proposed model. It is worth noting that LiSD achieves the state-of-the-art performance of 83.3% mIoU on the nuScenes segmentation benchmark for lidar-only methods. © 2024, CC BY.

关键词： Object detection

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：