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Visual Object Tracking via Cascaded RPN Fusion and Coordinate Attention

Computer Modeling in Engineering & Sciences 2022年第9期132卷 909-927页

作者： Jianming Zhang Kai Wang Yaoqi He Lidan Kuang School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China

Recently,Siamese-based trackers have achieved excellent performance in object ***,the high speed and deformation of objects in the movement process make tracking ***,we have incorporated cascaded region-proposal-network(RPN)fusion and coordinate attention into Siamese *** proposed network framework consists of three parts:a feature-extraction sub-network,coordinate attention block,and cascaded RPN *** exploit the coordinate attention block,which can embed location information into channel attention,to establish long-term spatial location dependence while maintaining channel ***,the features of different layers are enhanced by the coordinate attention *** then send these features separately into the cascaded RPN for classification and *** to the two classification and regression results,the final position of the target is *** verify the effectiveness of the proposed method,we conducted comprehensive experiments on the OTB100,VOT2016,UAV123,and GOT-10k *** with other state-of-the-art trackers,the proposed tracker achieved good performance and can run at real-time speed.

关键词： Object tracking deep learning coordinate attention cascaded RPN

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The Heart Rate Monitoring Based On Adaptive Cancellation Method

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

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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

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RSSD: Object Detection via Attention Regions in SSD Detector 2

RSSD: Object Detection via Attention Regions in SSD Detector

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2nd International Conference on Safety Produce Informatization, IICSPI 2019

作者： Zhou, Shuren Qiu, Jia Changsha University of Science and Technology Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha410114 China

ISBN: (纸本)9781728145662

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. © 2019 IEEE.

关键词： Object detection

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Texture synthesis and design based on elements distribution creation 1

Texture synthesis and design based on elements distribution ...

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1st EAI International Conference on Computer Science and Engineering, COMPSE 2016

作者： Gui, Yan Liu, Yang Li, Feng Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha Hunan410114 China

ISBN: (纸本)9781631901362

The texture synthesis and design starts with an initial elements arrangement and expands it outward by using local and global growth, to obtain the new larger distribution of texture elements. There are two types of synthesized distributions, and their diversity consists in changing the layout of texture elements and decreasing or increasing the number of texture elements according to user's creation. Furthermore, we apply a set of deformation operations to locally change the shapes of texture elements when placing the extracted texture elements into the synthesized distribution, in order to guarantee structure consistency in final synthesized textures. Experimental results show that our method creates a large variety of textures from a given texture sample.

关键词： Computer graphics

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Parameters Compressing in Deep Learning

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Computers, Materials & Continua 2020年第1期62卷 321-336页

作者： Shiming He Zhuozhou Li Yangning Tang Zhuofan Liao Feng Li Se-Jung Lim School of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on TransportationChangsha University of Science and TechnologyChangsha410114China Liberal Arts&Convergence Studies Honam UniversityGwangju62399Korea

With the popularity of deep learning tools in image decomposition and natural language processing,how to support and store a large number of parameters required by deep learning algorithms has become an urgent problem to be *** parameters are huge and can be as many as *** present,a feasible direction is to use the sparse representation technique to compress the parameter matrix to achieve the purpose of reducing parameters and reducing the storage *** methods include matrix decomposition and tensor *** let vector take advance of the compressing performance of matrix decomposition and tensor decomposition,we use reshaping and unfolding to let vector be the input and output of Tensor-Factorized Neural *** analyze how reshaping can get the best compress *** to the relationship between the shape of tensor and the number of parameters,we get a lower bound of the number of *** take some data sets to verify the lower bound.

关键词： Deep neural network parameters compressing matrix decomposition tensor decomposition

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DRLO: Optimizing edge server placement in dynamic MEC scenarios using deep reinforcement learning

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Computer Networks 2025年 268卷

作者： Yingya Guo Cen Chen College of Computer and Data Science Fuzhou University PR China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University PR China Engineering Research Center of Big Data Intelligence Ministry of Education Fujian PR China

As an emerging computing paradigm, Mobile Edge Computing (MEC) significantly enhances user experience and alleviates network congestion by strategically deploying edge servers in close proximity to mobile users. However, the effectiveness of MEC hinges on the precise placement of these edge servers, a critical factor in determining the Quality of Experience (QoE) for mobile users. While existing studies predominantly focus on optimizing edge server placement in static scenarios, they often fall short when faced with user mobility, resulting in a degradation of QoE. To address this challenge, we propose an adaptive edge server placement approach that leverages Deep Reinforcement Learning (DRL) to select the base stations for placing edge servers in a dynamic MEC environment. Our objective is to minimize access delay by optimizing edge server placement for adapting to dynamic environment. To tackle the vast action space associated with edge server placement, we introduce a novel activation function in the actor neural network for efficient exploration. Furthermore, to enhance the adaptability of the derived edge server placement strategy, we meticulously design a new reward function, which takes into account the minimization of total access delay within dynamic MEC scenarios. Finally, to validate the effectiveness of our proposed method, extensive experiments were conducted using the Shanghai Telecom dataset. The results demonstrate that our approach outperforms baseline methods in minimizing access delay for users in dynamic MEC scenarios.

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An improved branching algorithm for the proper interval edge deletion problem

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Frontiers of Computer Science 2022年第2期16卷 178-180页

作者： Wenjun LI Xiaojing TANG Yongjie YANG School of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on TransportationChangsha University of Science and TechnologyChangsha 410015China School of Computer Science and Engineering Central South UniversityChangsha 410083China

1 Introduction For a graph class G,the G-EDGE DELETION problem is to determine whether a given graph can be transformed into a graph in G by deleting at most k *** G-EDGE DELETION problem for a large body of graph cla... 详细信息

关键词： branching problem transformed

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LogADCH: Hyperspheric-Based Log Message Anomaly Detection And Classification

LogADCH: Hyperspheric-Based Log Message Anomaly Detection An...

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2023 International Conference on Frontiers of Robotics and Software Engineering, FRSE 2023

作者： He, Shiming Xiao, Jinpan Chen, Bowen Lei, Ying Changsha University of Science & Technology School of Computer and Communication Engineering The Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha China

ISBN: (纸本)9798350301113

Web services have been integrated with all walks of life in society. Abnormalities in the network and services seriously affect user experience and company revenue. The system log records various information of the system, and the information-rich log messages usually show different anomalies. It is particularly important to accurately and timely identify the anomalies of the log messages and classify the anomalies. In addition, most of the current existing work only detects anomalies and does not classify them, and a few existing works require anomaly labels to classify anomalies. Based on these potential challenges, we propose an unsupervised hypersphere-based log message anomaly detection and classification method, LogADCH. We use the hypersphere loss function to let the BERT model learn compact representations of log messages and perform anomaly detection. Finally, K-Means clustering is performed on the semantic feature vectors detected as anomalies to form clusters of different types of anomalies. Compared with other works, LogADCH further classifies the abnormal types after detecting the abnormal logs, helping the operation and maintenance personnel to quickly locate the fault. We compare our method with other methods on two public datasets, and the experimental results show that LogADCH has superior performance in anomaly detection as well as anomaly classification. © 2023 IEEE.

关键词： Classification (of information)

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Fast Multivariate Time Series Anomaly Detection Based on Matrix Completion

Fast Multivariate Time Series Anomaly Detection Based on Mat...

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2023 International Conference on Frontiers of Robotics and Software Engineering, FRSE 2023

作者： He, Shiming Yi, Tongzhijian Yang, Bo Guo, Chenxi Changsha University of Science & Technology School of Computer and Communication Engineering The Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha China

ISBN: (纸本)9798350301113

Increasingly complex systems contain large numbers of devices that generate great number of multivariate time series that are monitored and recorded. For anomaly detection of these complex time series, deep learning technology is often used to build a learning model for anomaly detection. The method based on deep learning usually takes a long time to train data in practice, and the online service system is deployed or changed very frequently, which makes the online service system need to frequently spend a lot of time updating the anomaly detection model. To overcome this challenge, this paper proposes a fast multivariate time series anomaly detection (FMAD) approach based on matrix completion. After sampling the primitive low-rank matrix, FMAD uses matrix completion to reconstruct the original low-rank matrix to detect anomalies in multivariate time series. We conducted related experimental analysis and discussion. The experimental results on three real data sets show that FMAD has improved the problem of too long model training time and improved the detection efficiency. © 2023 IEEE.

关键词： Anomaly detection

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On the parameterized complexity of minimum/maximum degree vertex deletion on several special graphs

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Frontiers of Computer Science 2023年第4期17卷 97-107页

作者： Jia LI Wenjun LI Yongjie YANG Xueying YANG School of Information Engineering Hunan Industry PolytechnicChangsha 410036China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha 410015China Chair of Economic Theory Saarland UniversitySaarbrücken 66123Germany

In the minimum degree vertex deletion problem,we are given a graph,a distinguished vertex in the graph,and an integer κ,and the question is whether we can delete at most κ vertices from the graph so that the distinguished vertex has the unique minimum *** maximum degree vertex deletion problem is defined analogously but here we want the distinguished vertex to have the unique maximum *** is known that both problems areΨ-hard and fixed-parameter intractable with respect to some natural *** this paper,we study the(parameterized)complexity of these two problems restricted to split graphs,p-degenerate graphs,and planar *** study provides a comprehensive complexity landscape of the two problems restricted to these special graphs.

关键词： minimum degree maximum degree vertex deletion split graphs planar graphs parameterized complexity

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