2021 International Conference on Computer Engineering and Application (ICCEA)最新文献_第3页

An Overview of Face Deep Forgery 人脸深度伪造概述

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00078

Li Xinwei, Guo Jinlin, Chen Junnan

引用次数: 0

Relation Path Modeling with Entity Types for Knowledge Graph Completion 基于实体类型的知识图谱关系路径建模

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00049

Jimin Wang, Li Zhang, Bin Han

引用次数: 0

Two-level CFAR Algorithm for Target Detection in MmWave Radar 毫米波雷达目标检测的两级CFAR算法

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00055

Chu Xu, Fenggui Wang, Yanbo Zhang, Li Xu, M. Ai, Guang Yan

引用次数: 3

An Adaptive Kalman-Correlation Based Siamese Network Tracker for Visual Object Tracking 一种基于自适应卡尔曼相关的暹罗网络跟踪器用于视觉目标跟踪

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00094

Ke Liang, Xiaoying Liao, Guangming Liang

{"title":"An Adaptive Kalman-Correlation Based Siamese Network Tracker for Visual Object Tracking","authors":"Ke Liang, Xiaoying Liao, Guangming Liang","doi":"10.1109/ICCEA53728.2021.00094","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00094","url":null,"abstract":"Object tracking is important in a variety of applications from surveillance to robotic vision and traffic monitoring. Because of its importance, there has recently been a lot of research and developments in this field. Meanwhile, since the deep convolutional neural networks has shown its impressive potential, Siamese networks have also drawn increasing attention. However, the trackers may fail when there are rapid motions, occlusions, and similar objects in the video. To address the limitation and improve the robustness, this paper takes advantages of both the Kalman filter and the correlation filter, and further develop an adaptive Kalman-Correlation based Siamese network (AKC-SiamTracker). AKC-SiamTracker can automatically make different adjustment strategies to adjust the detected position of the original tracker based on the adaptive influence coefficient decider. The fully connected Siamese network (SiamFC) and Siamese region proposal network (SiamRPN) are selected as the baseline models. Evaluation of our method is carried out on OTB dataset. The promising results have shown better performance and robustness compared to the baselines and other state-of-the-art models. To the best of our knowledge, our work is the first time to propose the adaptive Kalman-Correlation based Siamese tracker.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130046844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Root of Trust for Embedded Devices based on On-Chip Memory 基于片上存储器的嵌入式设备信任根研究

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00104

Shijun Zhao, Jiangnan Lin, Wei Li, Bing Qi

{"title":"Research on Root of Trust for Embedded Devices based on On-Chip Memory","authors":"Shijun Zhao, Jiangnan Lin, Wei Li, Bing Qi","doi":"10.1109/ICCEA53728.2021.00104","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00104","url":null,"abstract":"Aiming at the problem that embedded devices generally lack hardware trust root and cannot use trusted computing technology to guarantee their operating environment, this paper proposes a method of using on-chip storage to provide trust root and trusted computing services for embedded devices. The physical unclonable function of on-chip memory is used to implement basic security mechanisms such as key storage and random numbers, and then builds a trusted computing environment based on these security mechanisms. The root of trust includes trusted computing primitives such as data sealing and unsealing. The root of trust can provide basic trusted computing services for embedded devices, so that it ensures the security and controllability of the whole embedded device running environment. In this paper, a prototype system is implemented on a hardware embedded device. The test results of prototype system show that the trusted computing root of trust construction method proposed only adds a small amount of basic code lines to the system, and its performance can meet the requirements of embedded applications.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121916124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Data Mining Algorithm for Wireless Sensor Networks Based on Subspace Heterogeneous Fusion Matching 基于子空间异构融合匹配的无线传感器网络数据挖掘算法

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00026

Lizhu Ye, Weirong Xiu, Donghua Zheng

{"title":"Data Mining Algorithm for Wireless Sensor Networks Based on Subspace Heterogeneous Fusion Matching","authors":"Lizhu Ye, Weirong Xiu, Donghua Zheng","doi":"10.1109/ICCEA53728.2021.00026","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00026","url":null,"abstract":"In order to improve the data mining ability of wireless sensor network communication link, a data mining method of multi-dimensional node combination wireless sensor network communication link based on subspace heterogeneous fusion matching is proposed. The fuzzy information detection model of multi-dimensional node combined wireless sensor network big data is constructed, and the statistical analysis of multi-dimensional node combined wireless sensor network big data is carried out by combining linear balanced scheduling analysis method. According to the feature extraction results of multi-dimensional node combined wireless sensor network big data, the data fusion of multi-dimensional node combined wireless sensor network communication link is carried out by using linear balanced scheduling method, and the feature clustering model of wireless sensor network data is established by combining subspace heterogeneous fusion method. The simulation results show that this method has higher accuracy and better feature resolution, and improves the heterogeneous stability of the multi-dimensional node combination wireless sensor network.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122352506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ShuffleNet2MC: A method of light weight fault diagnosis ShuffleNet2MC:一种轻量级故障诊断方法

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00060

Xia Li, Jinhua Li, Zhihan Lv

{"title":"ShuffleNet2MC: A method of light weight fault diagnosis","authors":"Xia Li, Jinhua Li, Zhihan Lv","doi":"10.1109/ICCEA53728.2021.00060","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00060","url":null,"abstract":"Bearing fault diagnosis plays an important role in the field of modern industry. Although convolution neural network achieves good results, large amount of parameters costs a lot of calculation, which brings challenges to the deployment of fault diagnosis tasks in low computational power equipments. To solve the problems, an novel CNN model ShuffleNet2MC based on improved Shufflenetv2 network is proposed. Firstly, Depthwise convolution and Channel Shuffle are used to reduce the computational cost while ensuring the accuracy of diagnosis computation; Secondly, mixed convolution is used to extract the features of different resolutions through multi-scale and multi-channel method, which improves the accuracy of the model; Finally, K-means quantization is applied to the model, which greatly reduces the GFLOPS of the model and further improves the performance of the model while ensuring that the accuracy is basically unchanged. A large number of experiments on the bearing fault dataset of Western Reserve University show that: The times of floating point operation and classification accuracy of ShufflenetV2 are 0.001GFLOPS and 97.9% respectively in the task of fault diagnosis. Compared with other models, it not only reduces the model parameters and compresses the model, but also gets better classification accuracy.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126640648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Multiple EffNet/ResNet Architectures for Melanoma Classification 黑色素瘤分类的多重EffNet/ResNet架构

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00061

Jiaqi Xue, Chentian Ma, Li Li, Xuan Wen

引用次数: 1

Design and Implementation of Arbitration Switch Control Module Based on ARM for Unmanned Helicopter 基于ARM的无人直升机仲裁开关控制模块的设计与实现

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00082

Mengnan Li

引用次数: 0

Non-Rigid Point Set Registration Based on Global Prior and Local Structural Constraint 基于全局先验和局部结构约束的非刚性点集配准

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00074

Xin Chang, Shun Fang, Shiqian Wu

引用次数: 0