检索结果-内蒙古大学图书馆

Erratum to: Incremental algorithms for the maximum internal spanning tree problem

Science China Information Sciences 2022年第10期65卷 1-1页

作者： Zhu, Xianbin Li, Wenjun Yang, Yongjie Wang, Jianxin School of Computer Science and Engineering Central South University Changsha China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China Chair of Economic Theory Saarland University Saarbrücken Germany

In the original paper [1], there are two errors on page 5. The rules in the algorithm are not sufficient since we omitted one rule during publication. The following rule should be added to the paper. Rule 3: If P1 = (u, v) and P2 = (x, y) are two maximal leaf-paths both with two vertices in T, where u and x are leaves in T and (v, y) ∈ E(G), we remove (y′, y) and then add (v, y) to T where y′ is the non-leaf neighbor of y in T. The expression of Algorithm 2 is not accurate. It should be modified as follows. Line 3: Exhaustively apply Rules 1–3, and for i ∈ {2, 3} only apply Rule i when none of Rules j where 1 ⩽ j < i is applicable. Line 8: Determine the longest path P among all paths whose endpoints have nonempty neighbors in T \ V(T′).

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Asynchronous Clustering and Mobile data Gathering Schema Based on Timer Mechanism in Wireless Sensor Networks

引用

Computers, Materials & Continua 2019年第3期58卷 711-725页

作者： Jin Wang Yu Gao Wei Liu Wenbing Wu Se-Jung Lim Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer&Communication EngineeringChangsha University of Science&TechnologyChangsha410114China School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China College of Information Engineering Yangzhou UniversityYangzhou225000China Department of Computer Engineering Chonnam National UniversityYeosu596597Korea.

Recently,Wireless sensor networks(WSNs)have become very popular research topics which are applied to many *** provide pervasive computing services and techniques in various potential applications for the Internet of Things(IoT).An Asynchronous Clustering and Mobile data Gathering based on Timer Mechanism(ACMDGTM)algorithm is proposed which would mitigate the problem of“hot spots”among sensors to enhance the lifetime of *** clustering process takes sensors’location and residual energy into consideration to elect suitable cluster ***,one mobile sink node is employed to access cluster heads in accordance with the data overflow time and moving time from cluster heads to *** experimental results display that the presented method can avoid long distance communicate between sensor ***,this algorithm reduces energy consumption effectively and improves package delivery rate.

关键词： Internet of things wireless sensor networks clustering mobile data collection timer.

来源：评论

学校读者我要写书评

暂无评论

RSSD: Object Detection via Attention Regions in SSD Detector

RSSD: Object Detection via Attention Regions in SSD Detector

引用

IEEE International Conference of Safety Produce Informatization (IICSPI)

作者： Shuren Zhou Jia Qiu Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China

ISBN: (数字)9781728145662

ISBN: (纸本)9781728145679

This paper designs a module of attention regions in SSD detector for accurate and efficient object detection (RSSD). Different from previous one-stage detection method like SSD which just simply applied the multi-scale head-features and directly extracted from backbone network, for classification and regression, our method aims to strengthen the characterization of head-features further. The parallel encode-to-decode structure is constructed and a computation method of regional distribution on features (R-Softmax) is proposed. What's more, in order to reduce time-costs, the down-sampling layers are shared with the multi-scale layers from backbone network. Our detector performs better on PASCAL VOC datasets (e.g., 78.4% mAP V.S. SSD 76.4% on VOC 07test) and costs 0.001s per image more than SSD.

关键词： Object detection Training Convolution Feature extraction Detectors Proposals Mathematical model

来源：评论

学校读者我要写书评

暂无评论

Cycle extendability of hamiltonian strongly chordal graphs

arXiv

引用

arXiv 2020年

作者： Rong, Guozhen Li, Wenjun Wang, Jianxin Yang, Yongjie School of Computer Science and Engineering Central South University Changsha China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China Faculty of Human and Business Sciences Saarland University Saarbrücken Germany

In 1990, Hendry conjectured that all Hamiltonian chordal graphs are cycle extendable. After a series of papers confirming the conjecture for a number of graph classes, the conjecture is yet refuted by Lafond and Seamone in 2015. Given that their counterexamples are not strongly chordal graphs and they are all only 2-connected, Lafond and Seamone asked the following two questions: (1) Are Hamiltonian strongly chordal graphs cycle extendable? (2) Is there an integer k such that all k-connected Hamiltonian chordal graphs are cycle extendable? Later, a conjecture stronger than Hendry’s is proposed. In this paper, we resolve all these questions in the negative. On the positive side, we add to the list of cycle extendable graphs two more graph classes, namely, Hamiltonian 4-FAN-free chordal graphs where every induced K5 − e has true twins, and Hamiltonian {4-FAN, A}-free chordal graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Hamiltonians

来源：评论

学校读者我要写书评

暂无评论

Efficient differentiated storage architecture for large-scale flow tables in openflow networks 21

Efficient differentiated storage architecture for large-scal...

引用

21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Xiong, Bing Wu, Rengeng Liao, Zhuofan Wang, Jin School of Computer AND Communication Engineering Changsha University of Science AND Technology Changsha410114 China Department of Computer and Information Sciences Temple University Philadelphia19122 United States Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science AND Technology Changsha China

ISBN: (纸本)9781728120584

As a novel network paradigm, Software Defined Networking (SDN) decouples control logic functions from data forwarding devices, and introduces a separate control plane to manipulate underlying switches via southbound interfaces like OpenFlow. However, it leads to large-scale flow tables and poses serious challenges on their storage resources and lookup performance in OpenFlow switches. This paper is thus motivated to propose an efficient differentiated storage architecture for large-scale flow tables in OpenFlow networks. Firstly, we investigate into the impact of wildcards in match fields on packet-in-batch property within a flow based on network traffic locality. Then, packet flows are dynamically distinguished into active ones and idle ones in terms of their short-term states. Subsequently, we store the match fields of active flows and idle flows respectively in TCAM and SRAM, and the content fields of both types of flows in DRAM, to effectively relieve the insufficiency of TCAM capacity. Finally, we evaluate the performance of our proposed flow table storage architecture with real network traffic traces by experiments. The experimental results indicate that our proposed storage architecture obviously outperforms the traditional one applying the elephant/mice flow differentiation method in terms of TCAM hit rates and average flow table access time. © 2019 IEEE.

关键词： Software defined networking

来源：评论

学校读者我要写书评

暂无评论

The Heart Rate Monitoring Based On Adaptive Cancellation Method

The Heart Rate Monitoring Based On Adaptive Cancellation Met...

引用

International Conference on Mechatronics and intelligent Robotics, ICMIR 2017

作者： Wei, Wang Ying, Xiong Xin, Wang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha China

ISBN: (纸本)9783319659770

For solving the problem that PPG signals were usually corrupted by motion artifact, a comprehensive approach for heart rate monitoring of real-time wearable devices was proposed in this paper. This method was based on the adaptive noise cancellation technique. Firstly, PPG signals and acceleration signals were pre processed through a band-pass filter. Acceleration signals and red PPG signals would be regarded as a set of noise reference signal. Next, an adaptive filtering algorithm was used to remove the motion artifacts. Finally, the heart rate could be extracted from the denoising PPG signals by using peak to peak value estimation method. The simulation results show that, compared with traditional LMS algorithm and FFT algorithm, the proposed method is more accurate, less error. And the method has the advantages of real-time, high efficiency and simplicity to monitor heart rate in different motion states. © 2018, Springer International Publishing AG.

关键词： Adaptive filters

来源：评论

学校读者我要写书评

暂无评论

MultiAdapt: A Neural Network Adaptation For Pruning Filters Base on Multi-layers Group

引用

Journal of Physics: Conference Series 2021年第1期1873卷

作者： Jie Yang Zhihong Xie Ping Li School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Hunan Changsha 410114 China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha Hunan Changsha 410114 China

Deep convolutional neural networks have been widely used in various AI applications. The most advanced neural networks are becoming deeper and wider, which has caused some large convolutional neural networks to exceed the size limit of the server or application. The pruning algorithm provides a way to reduce the size of the neural network while keeping the accuracy as high as possible. The automatic progressive pruning algorithm is one of the widely used pruning algorithms. The progressive pruning algorithm prunes a certain layer of the network in each iteration to reduce the sparsity while preserving the accuracy as much as possible. In this article, we design a new automatic progressive pruning algorithm named MultiAdapt. MultiAdapt combines the combination method and the greedy algorithm. This multi-layers progressive pruning method greatly increases the search space of the greedy algorithm, making it possible to obtain a better pruning network. We use MultiAdapt to prune large neural networks VGG-16 and ResNet. The experimental results show that the MultiAdapt algorithm is better than other mainstream methods in the balance of neural network model size and accuracy. For image classification tasks on the ImageNet dataset, our method achieved 88.72% and 90.55% TOP-5 accuracy on the 50% sparsity VGG-16 and ResNet, while obtaining nearly 2×reduction in parameters and floating point numbers. The operation is reduced, and the reduction is higher than the recent popular method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CuckooFlow: Achieving Fast Packet Classification for Virtual OpenFlow Switching by Exploiting Network Traffic Locality

CuckooFlow: Achieving Fast Packet Classification for Virtual...

引用

IEEE International Conference on big data and Cloud Computing (BdCloud)

作者： Bing Xiong Zhixiong Hu Yao Luo Jin Wang School of Computer & Communication Engineering Changsha University of Science & Technology Changsha China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science & Technology Changsha China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

The growing trend of network virtualization leads to a widespread deployment of virtual switches in virtualized environments. However, virtual switching is confronted with great performance challenges regarding packet classification especially in OpenFlow-based software defined networks. This paper takes an insight into network traffic locality from the perspective of exact flows and wildcard flows, and propose a fast packet classification scheme CuckooFlow. In particular, we devise an active exact flow cache with cuckoo hashing to predict the respective flow entry for each arrived packet. Subsequently, we formulate its cache hit rate based on packet-in-batch arrivals, and average search length of the packet classification scheme. Finally, our proposed CuckooFlow scheme is evaluated with real network traffic traces by experiments. The experimental results indicate that our proposed scheme outperforms traditional ones Open vSwitch and CuckooDistributor in terms of cache hit rates and average search length, and contributes to promote virtual OpenFlow switching performance.

关键词： Telecommunication traffic Switches Acceleration Classification algorithms Cloud computing Virtualization Decision trees

来源：评论

学校读者我要写书评

暂无评论

HSCS:a hybrid shared cache scheduling scheme for multiprogrammed workloads

引用

Frontiers of Computer Science 2018年第6期12卷 1090-1104页

作者： Jingyu ZHANG Chentao WU Dingyu YANG Yuanyi CHEN Xiaodong MENG Liting XU Minyi GUO Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha 410004China School of Electronics and Information Shanghai Dianji UniversityShanghai 200240China

The traditional dynamic random-access memory (DRAM) storage medium can be integrated on chips via modern emerging 3D-stacking technology to architect a DRAM shared cache in multicore systems. Compared with static random-access memory (SRAM), DRAM is larger but slower. In the existing research, a lot of work has been devoted to improving the workload performance using SRAM and stacked DRAM together in shared cache systems, ranging from SRAM structure improvement to optimizing cache tags and data access. However, little attention has been paid to designing a shared cache scheduling scheme for multiprogrammed workloads with different memory footprints in multicore systems. Motivated by this, we propose a hybrid shared cache scheduling scheme that allows a multicore system to utilize SRAM and 3D-stacked DRAM efficiently, thus achieving better workload performance. This scheduling scheme employs (1) a cache monitor, which is used to collect cache statistics; (2) a cache evaluator, which is used to evaluate the cache information during the process of programs being executed; and (3) a cache switcher, which is used to self-adaptively choose SRAM or DRAM shared cache modules. A cache data migration policy is naturally developed to guarantee that the scheduling scheme works correctly. Extensive experiments are conducted to evaluate the workload performance of our proposed scheme. The experimental results showed that our method can improve the multiprogrammed workload performance by up to 25% compared with state-of-the-art methods (including conventional and DRAM cache systems).

关键词： multicore system shared cache workload performance

来源：评论

学校读者我要写书评

暂无评论

Improved VGG Model for Road Traffic Sign Recognition

引用

Computers, Materials & Continua 2018年第10期57卷 11-24页

作者： Shuren Zhou Wenlong Liang Junguo Li Jeong-Uk Kim Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science&TechnologyChangsha410114China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Department of Energy Grid Sangmyung UniversitySeoul110743Korea

Road traffic sign recognition is an important task in intelligent transportation *** neural networks(CNNs)have achieved a breakthrough in computer vision tasks and made great success in traffic sign *** this paper,it presents a road traffic sign recognition algorithm based on a convolutional neural *** natural scenes,traffic signs are disturbed by factors such as illumination,occlusion,missing and deformation,and the accuracy of recognition decreases,this paper proposes a model called Improved VGG(IVGG)inspired by VGG *** IVGG model includes 9 layers,compared with the original VGG model,it is added max-pooling operation and dropout operation after multiple convolutional layers,to catch the main features and save the training *** paper proposes the method which adds dropout and Batch Normalization(BN)operations after each fully-connected layer,to further accelerate the model convergence,and then it can get better classification *** uses the German Traffic Sign Recognition Benchmark(GTSRB)dataset in the *** IVGG model enhances the recognition rate of traffic signs and robustness by using the data augmentation and transfer learning,and the spent time is also reduced greatly.

关键词： intelligent transportation traffic sign deep learning GTSRB data augmentation

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：