检索结果-内蒙古大学图书馆

第40届中国控制会议

作者： Shiyu Zhou Xiaobao Wei Xiwang Dong Yongzhao Hua Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang University Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education of ChinaChongqing University Institute of Artificial Intelligence Beihang University

Group formation-tracking problem for heterogeneous multi-agent systems（HMASs） with both switching networks and communication delays is investigated in this paper. In order to achieve different tasks, the agents are classified into various groups. The followers are allowed to realize the formation and track the trajectory of the leader in each group. Firstly, by utilizing the consensus control, an observer is proposed to estimate the state of the leader. Then, an observer-based control protocol is put forward to solve the group formation-tracking problem with both communication delays and switching networks. Moreover, an algorithm to determine the gain feedbacks is demonstrated, in the meantime, the observer’s error systems as well as the group formation-tracking error systems are proved to be convergent. Finally, an example in the simulation prat is presented to verify the theoretical results.

关键词： Group formation-tracking problem general linear multi-agent systems time-varying delays switching topologies

来源：评论

学校读者我要写书评

暂无评论

QDL: Quantitative Dynamic Latency Model in Permissioned Blockchain System

QDL: Quantitative Dynamic Latency Model in Permissioned Bloc...

引用

International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Jiacheng Wei Qingmei Wang Zheng Wang School of Artificial Intelligence Beihang University Beijing Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing National Center for Materials Service Safety University of Science and Technology Beijing Beijing Institute of Cyber Science and Technology Shanghai Jiao Tong University Shanghai Key Laboratory of Integrated Administration Technologies for Information Security

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Latency, as a critical attribute influencing scalability and consistency in blockchain systems, has attracted significant research attention. This paper addresses the issue of latency evaluation by presenting a novel framework. We thoroughly analyze the factors affecting latency in blockchain system, including concurrency, consistency, and contention. To accurately model the correlation between latency and the quantity of nodes in permissioned blockchain setup, we introduce the Latency Regression Loss (LRL) function. By utilizing Newton’s interpolation and least squares methods, we determine the hyperparameters. Subsequently, we devise the framework for the Quantitative Dynamic Latency (QDL) evaluation model, which accounts for both the growth rate and magnitude of latency. Utilizing the parameters derived from the LRL function facilitated the calculation of latency growth rate, allowing for a comprehensive evaluation of latency. Finally, we validated the accuracy of the LRL function through extensive experiments and simulated the adaptability of the QDL model in latency assessment.

关键词： Concurrent computing Adaptation models Interpolation Distributed processing Protocols Scalability Evaluation models Mathematical models Blockchains Least mean squares methods

来源：评论

学校读者我要写书评

暂无评论

The 3D McMaster Algorithm for Traffic Congestion Detection

The 3D McMaster Algorithm for Traffic Congestion Detection

引用

第32届中国控制与决策会议

作者： Tingrang Liu Min Zhao College of Automation Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University

Traffic congestion detection is a key issue in the research of intelligent transportation system（ITS）.Mc Master Algorithm,one of the most classical single-station congestion detection algorithms,has been widely used in ***,it doesn’t consider the effect of speed on volume and occupancy,and requires manual calibration of parameters,which results in a lower detection *** solve these problems,a three-dimensional McMaster algorithm is proposed in this *** on the analysis of the spatial distribution characteristics of congestion and non-congestion data,3 D McMaster algorithm extend McMaster algorithm from 2 D parameter space to 3 D parameter space for *** position of the lower boundary surface of uncongested data（LSUD） in 3 D parameter space is obtained by statistical method,and then the surface is fitted by a Bayesian regularization back propagation neural network to improve the detection *** proposed algorithm is validated with the real data from Shuijie expressway in *** experimental results show that 3 D McMaster algorithm has higher detection rate and lower false alarm rate.

关键词： Traffic Congestion Detection Intelligent Transportation System 3D McMaster Algorithm Bayesian Regularization Back Propagation Neural Network

来源：评论

学校读者我要写书评

暂无评论

Two-Stage Attention-Based Model for Code Search with Textual and Structural Features

Two-Stage Attention-Based Model for Code Search with Textual...

引用

IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Ling Xu Huanhuan Yang Chao Liu Jianhang Shuai Meng Yan Yan Lei Zhou Xu Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China

Searching and reusing existing code from a large scale codebase can largely improve developers' programming efficiency. To support code reuse, early code search models leverage information retrieval (IR) techniques to index a large-scale code corpus and return relevant code according to developers' search query. However, IR-based models fail to capture the semantics in code and query. To tackle this issue, developers applied deep learning (DL) techniques to code search models. However, these models either are too complex to determine an effective method efficiently or learning for semantic correlation between code and query *** bridge the semantic gap between code and query effectively and efficiently, we propose a code search model TabCS (Two-stage Attention-Based model for Code Search) in this study. TabCS extracts code and query information from the code textual features (i.e., method name, API sequence, and tokens), the code structural feature (i.e., abstract syntax tree), and the query feature (i.e., tokens). TabCS performs a two-stage attention net-work structure. The first stage leverages attention mechanisms to extract semantics from code and query considering their semantic gap. The second stage leverages a co-attention mechanism to capture their semantic correlation and learn better code/query representation. We evaluate the performance of TabCS on two existing large-scale datasets with 485k and 542k code snippets, respectively. Experimental results show that TabCS achieves an MRR of 0.57 on Hu et al.'s dataset, outperforming three state-of-the-art models CARLCS-CNN, DeepCS, and UNIF by 18%, 70%, 12%, respectively. Meanwhile, TabCS gains an MRR of 0.54 on Husain et al.'s, outperforming CARLCS-CNN, DeepCS, and UNIF by 32%, 76%, 29%, respectively.

关键词： Deep learning Correlation Semantics Syntactics Programming Feature extraction Information retrieval

来源：评论

学校读者我要写书评

暂无评论

PicT: A SlimWeakly Supervised Vision Transformer for Pavement Distress Classification

arXiv

引用

arXiv 2022年

作者： Tang, Wenhao Huang, Sheng Zhang, Xiaoxian Huangfu, Luwen School of Big Data & Software Engineering Chongqing University Chongqing China School of Big Data & Software Engineering Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Chongqing China Fowler College of Business Center for Human Dynamics in the Mobile Age San Diego State University San DiegoCA United States

Automatic pavement distress classification facilitates improving the efficiency of pavement maintenance and reducing the cost of labor and resources. A recently influential branch of this task divides the pavement image into patches and infers the patch labels for addressing these issues from the perspective of multi-instance learning. However, these methods neglect the correlation between patches and suffer from a low efficiency in the model optimization and inference. As a representative approach of vision Transformer, Swin Transformer is able to address both of these issues. It first provides a succinct and efficient framework for encoding the divided patches as visual tokens, then employs self-attention to model their relations. Built upon Swin Transformer, we present a novel vision Transformer named Pavement Image Classification Transformer (PicT) for pavement distress classification. In order to better exploit the discriminative information of pavement images at the patch level, the Patch Labeling Teacher is proposed to leverage a teacher model to dynamically generate pseudo labels of patches from image labels during each iteration, and guides the model to learn the discriminative features of patches via patch label inference in a weakly supervised manner. The broad classification head of Swin Transformer may dilute the discriminative features of distressed patches in the feature aggregation step due to the small distressed area ratio of the pavement image. To overcome this drawback, we present a Patch Refiner to cluster patches into different groups and only select the highest distress-risk group to yield a slim head for the final image classification. We evaluate our method on a large-scale bituminous pavement distress dataset named CQU-BPDD. Extensive results demonstrate the superiority of our method over baselines and also show that PicT outperforms the second-best performed model by a large margin of +2.4% in P@R on detection task, +3.9% in F 1 on recogni

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Accepted Influence Maximization under Linear Threshold Model on Large-Scale Social Networks

Accepted Influence Maximization under Linear Threshold Model...

引用

IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Xiaojuan Yang Jiaxing Shang Linjiang Zheng Dajiang Liu Shu Fu Baohua Qiang Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education College of Computer Science Chongqing University Chongqing China School of Microelectronics and Communication Engineering Chongqing University Chongqing China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781665416597

The influence maximization (IM) problem, which aims to find $k$ most influential individuals from a social network to maximize the influence spread, has been extensively studied. Existing works all rely on the assumption that influenced individuals will definitely try to propagate the information to their neighbors through social trust. However, in real-world this assumption can be over-simplistic since trust-levels among different individuals usually exhibit high diversity. As a result, an influenced individual may choose not to further propagate the information to his neighbors. Motivated by the observation, in this paper we propose a new accepted influence maximization (AIM) problem where the influenced individuals are further divided into two subgroups, i.e., accepted and active, where only active individuals will continue to propagate the information. We prove this problem is NP-hard and the objective function is submodular, based on which a greedy algorithm is proposed with ( $1-1/e-\epsilon$ ) approximation guarantee. Considering the low computational efficiency of the greedy algorithm, we further propose a scalable path-based algorithm ALDAG. We conduct experiments on real datasets and the results demonstrate the effectiveness and efficiency of our method.

关键词： Greedy algorithms Data privacy Social networking (online) Computational modeling Heuristic algorithms Linear programming Approximation algorithms

来源：评论

学校读者我要写书评

暂无评论

Multi-Scale Vehicle Detection and Tracking Method in Highway Scene

Multi-Scale Vehicle Detection and Tracking Method in Highway...

引用

第32届中国控制与决策会议

作者： Tingming Zhang Min Zhao College of Automation Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University

Based on tracking-by-detection strategy,this paper proposes a detection and tracking method for multi-scale vehicle *** method consists of object detection and trajectory *** at object detection,an improved SNIPER sampling strategy was proposed,which combined with Faster R-CNN deep learning model to achieve variable scale vehicle *** the trajectory correlation,motion features,apparent features and trajectory features are matched to achieve the matching between the detection target and the vehicle *** experimental results show that the method in this paper has made significant improvements over previous methods,both in accuracy and speed,and can detect and track multi-scale vehicle targets in highway scenes in real time.

关键词： Highway Scene Vehicle Detection Multi-target Tracking Trajectory Matching

来源：评论

学校读者我要写书评

暂无评论

Battlefield Situation Deduction and Maneuver Decision Using Deep Q-Learning

Battlefield Situation Deduction and Maneuver Decision Using ...

引用

第40届中国控制会议

作者： Minghui Shi Xiwang Dong Liang Han Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education of ChinaChongqing University School of Sino-French Engineer Beihang University

As the pace and complexity of modern warfare accelerates, it is of great significance to apply intelligent technology to defense decision-making. Due to the high dynamics and randomness of the aircraft, traditional methods are difficult to solve the optimal control strategy. The characteristics of reinforcement learning match the difficulty of the problem. In this paper, the situation of hypersonic aircraft is deduced, and the deep reinforcement learning method is used to make autonomous penetration decisions in the reentry phase. The model of aircraft and environment is established, and the maneuvering decision-making model is established based on deep Q-learning and its optimization algorithm. Through a large number of simulation training,this method can effectively give the real-time decision output of the agent and make a good prediction of the situation. It has the ability of short-range accurate operation and long-term planning and prediction. This method can improve the probability of successful penetration, and can be used as the decision-making basis of glider penetration.

关键词： Hypersonic aircraft situational deduction maneuver decision-making reinforcement learning deep Q-learning

来源：评论

学校读者我要写书评

暂无评论

Multi-sensor Measurement Information Fusion for Maneuvering Target Recognition and Tracking

Multi-sensor Measurement Information Fusion for Maneuvering ...

引用

第40届中国控制会议

作者： Zheng Zhang Liang Han Xiwang Dong Qingdong Li Zhang Ren School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University School of Sino-French Engineer Beihang University Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education of ChinaChongqing University

This paper proposes multi-sensor information fusion algorithm for maneuvering target recognition and tracking based on mobile sensor networks（MSNs）. In order to improve target recognition accuracy, the modified evidence combination rule is designed to obtain the identity information of target based on the Dempster-Shafer（DS） algorithm. The target recognition process is designed by two stages, namely, sensing stage and fusion stage. During the first stage, each sensor node in the MSNs can obtain the feature information of the target, which makes up the measurement information. In the information fusion stage,each sensor combine the information exchanged from neighboring nodes with local measurement information to obtain the highprecise identity information of the maneuvering target based on the improved DS algorithm. Then, the identity information can be used to obtain the accurate state estimation of the maneuvering target. For the purpose of obtaining the accurate state estimation of target, the target tracking process is designed by local filtering stage and fusion stage. Finally, a numerical simulation is designed to illustrate the effectiveness of the algorithm.

关键词： Information Fusion Target Recognition and Tracking Mobile Sensor Network

来源：评论

学校读者我要写书评

暂无评论

Improving Code Summarization Through Automated Quality Assurance

Improving Code Summarization Through Automated Quality Assur...

引用

International Symposium on Software Reliability Engineering (ISSRE)

作者： Yuxing Hu Meng Yan Zhongxin Liu Qiuyuan Chen Bei Wang Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China Pengcheng Laboratory Shenzhen China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781665425889

The code summarization task aims to generate brief descriptions of source code automatically. It is beneficial for developers to understand source code. However, almost all of current code summarization approaches may generate low-quality (BLEU4<40) summaries, which will mislead developers. Previous work has shown that it is possible to conduct quality assurance for document generation (QA4DG) and improve the practicability of document generation approaches. Code summarization can also be regarded as a document generation task. This work aims to investigate whether QA4DG approaches can be leveraged to improve code summarization. Specifically, we first investigate whether existing QA4DG approaches can be plugged in code summarization approaches. We find that an automated quality assurance framework for commit message generation named QACom performs best. In-spired by the idea behind QAcom, we propose an ensemble code summarization approach called Ensum. Precisely, given a code snippet, Ensum first uses current code summarization approaches to generate candidate summaries. Then, Ensum predicts the quality of each candidate summary using a collaborative filtering-based component and a retrieval-based component and selects the best candidate summary as the output. Experimental results on two public datasets show that Ensum outperforms three state-of-the-art single approaches and one ensemble approach for code summarization in terms of BLEU-4, METEOR, and ROUGE-L.

关键词： Codes Quality assurance Filtering Collaboration Software reliability Task analysis

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：