2021 International Conference on Computer Engineering and Application (ICCEA)最新文献

Design of an EIT-based flexible tactile sensor with center electrodes 基于eit的中心电极柔性触觉传感器设计

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00105

Yucheng He, Xinyan Li, Rui Li

{"title":"Design of an EIT-based flexible tactile sensor with center electrodes","authors":"Yucheng He, Xinyan Li, Rui Li","doi":"10.1109/ICCEA53728.2021.00105","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00105","url":null,"abstract":"For large area robot skin design, the distribution of rigid components and wires in traditional array sensors leads to the decrease of the flexibility and extensibility. The flexible sensors based on non-invasive electrical impedance tomography (EIT) can avoid these shortcomings. However, the number, position and driving pattern of the central electrode will have a great impact on the performance of the sensor. Multi-walled carbon nanotubes (MWCNTs) are used as filler materials to prepare flexible materials. Different numbers of central electrodes are introduced into the traditional 16-electrode flexible sensor. The corresponding driving pattern is designed and the position error is taken as the evaluation index to carry out comparative experiments. The simulation and experimental results show that the 18-electrode flexible sensor with two central electrodes has the best detection performance, and the detection position error can be reduced by 57.6% at the optimal position of the central electrodes at 0.24 from the center of the circle. The optimal design of the central electrode can effectively improve the performance of the flexible sensor, which has a certain guiding significance for the design of large area robot skin.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"13 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":"123911172","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

ERNIE-BiLSTM Based Chinese Text Sentiment Classification Method 基于ERNIE-BiLSTM的中文文本情感分类方法

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00024

Haiyuan Guo, Chengying Chi, Xuegang Zhan

引用次数: 4

CNN-based MRI Brain Tumor Detection Application 基于cnn的MRI脑肿瘤检测应用

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00097

Hongli Chen, Di Chen, Luyao Wang

引用次数: 2

Few-shot Image Classification based on LMRNet 基于LMRNet的少拍图像分类

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00019

Yu Chen, Junjie Liu, Yuanzhuo Li

引用次数: 0

Wireless sensor network data compression sampling technology based on unbalanced data collaborative filtering 基于非平衡数据协同滤波的无线传感器网络数据压缩采样技术

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00046

Donghua Zheng, Weirong Xiu, Lizhu Ye

{"title":"Wireless sensor network data compression sampling technology based on unbalanced data collaborative filtering","authors":"Donghua Zheng, Weirong Xiu, Lizhu Ye","doi":"10.1109/ICCEA53728.2021.00046","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00046","url":null,"abstract":"In order to improve the data acquisition capability of wireless sensor networks, a data compression sampling technology based on unbalanced data collaborative filtering is proposed. Establishing a data compression sampling state feature analysis model, designing a linear kernel function, a probability density feature kernel function and a Gaussian kernel function for wireless sensor network communication transmission data compression sampling, realizing wireless sensor network data compression and feature separation by an unbalanced data collaborative filtering method, constructing a boundary solution vector function for data compression sampling by adopting a support vector machine model, and realizing classification processing after data feature compression by adopting a fuzzy c-means clustering analysis method. Combined with threshold judgment method, the filtering analysis and subspace noise reduction of wireless sensor network data compression are realized, and the unbalanced data collaborative filtering detection model is constructed. According to the data feature detection results, the wireless sensor network data compression sampling is realized. The simulation results show that the feature clustering of wireless sensor network data compression sampling is better and the data detection accuracy is higher.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"1 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":"130168170","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

Using the Solution Space Constraint to Pick the Best Velocity Automatically 利用解空间约束自动选取最佳速度

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00054

Shao-yu Lv, Mu-yuan Jiang, Yuzhuo Chen, Yunsheng Wang

引用次数: 0

Research on Visualization Method of Large-scale User Location Distribution Based on CesiumJS 基于CesiumJS的大规模用户位置分布可视化方法研究

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00033

W. Yuan, S. Jianwei

引用次数: 0

Multi resource inventory automatic coordination model of Supply Chain Based on Artificial Intelligence 基于人工智能的供应链多资源库存自动协调模型

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00017

Limei Wu, Heng Yue, Honghong Hu

{"title":"Multi resource inventory automatic coordination model of Supply Chain Based on Artificial Intelligence","authors":"Limei Wu, Heng Yue, Honghong Hu","doi":"10.1109/ICCEA53728.2021.00017","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00017","url":null,"abstract":"Aiming at the problem of measuring the degree of supply chain resource coordination, based on the synergetics theory, an artificial intelligence based supply chain multi resource inventory automatic coordination model is constructed. By constructing five index systems of supply chain logistics, capital flow, information flow, market flow and management flow of artificial intelligence, this paper makes an empirical study on the application of the model. The results show that the measurement model of the degree of supply chain resource coordination can reflect the degree and trend of the coordinated development of the supply chain system, and the specific reasons for the low degree of supply chain coordination can be found through the analysis, which is conducive to the targeted and fundamental improvement of enterprises. The supply chain multi resource inventory automatic coordination model based on artificial intelligence not only makes up for the lack of qualitative research in the existing coordination degree model, but also has great significance for guiding the actual operation of the supply chain.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"36 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":"132263986","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

Meta-learning Based Breast Abnormality Classification on Screening Mammograms 基于元学习的乳腺异常分类筛查

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00038

Yu Wang, Mingjie Song, Xinyu Tian

{"title":"Meta-learning Based Breast Abnormality Classification on Screening Mammograms","authors":"Yu Wang, Mingjie Song, Xinyu Tian","doi":"10.1109/ICCEA53728.2021.00038","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00038","url":null,"abstract":"General breast cancer detection contains two steps, the breast abnormality classification, and the diagnostic classification. The determination of the abnormality contributes further to the following steps, and computational technologies can aid in the process. A lot of machine learning methods have been applied to automate the detection. However, most of them focus on the diagnostic classification and the breast abnormality classification only attracts little attention. The insufficient size of public mammogram datasets also limits the performance of many machine learning algorithms. Considering the importance of breast abnormality classification and the shortage of public large-scale medical datasets, we proposed a meta-learning-based breast abnormality classification method. Our model referred to the latest work of meta-learning-based image classifier and modified it. Specifically, we applied the idea of meta-learning to retrain a pretrained embedding neural network in order to adapt its feature extraction ability to the CBIS-DDSM dataset [1]. The dataset contains two types of abnormal breast mammograms, mass and calcification, and each type is made of two categories of medical images, full mammograms, and ROI [2]. The application of the data augmentation techniques and the idea of meta-learning helped to deal with the insufficient training sample problem and showed a final accuracy of 76%, which beat the 71% accuracy reached by a neural network baseline model.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"18 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":"132441273","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: 3

Robust Attack with Adaptive Compress Adversarial Perturbations 自适应压缩对抗摄动的鲁棒攻击

2021 International Conference on Computer Engineering and Application (ICCEA) Pub Date : 2021-06-01 DOI: 10.1109/ICCEA53728.2021.00071

Jinping Su, L. Jing

{"title":"Robust Attack with Adaptive Compress Adversarial Perturbations","authors":"Jinping Su, L. Jing","doi":"10.1109/ICCEA53728.2021.00071","DOIUrl":"https://doi.org/10.1109/ICCEA53728.2021.00071","url":null,"abstract":"Adversarial examples expose the vulnerability of deep neural networks that perform well in various fields. However, adversarial perturbations crafted by the existing attack methods are often aimed at the whole image. They are usually random, and the human eye can even easily perceive some of them. This paper proposes an adaptive method to compress the adversarial perturbation. Under the premise of ensuring the success of attacks, generating perturbations as small as possible to change the decision of classifiers. First, the authors find the minimum point of loss function by the optimization method, to expand the spanning space of adversarial examples. Calculating and selecting the smaller perturbation between this point and the original input. Then, in order to retain the useful perturbation and remove redundancy, the authors look for important regions in the input data that determine the network predict results, and construct an importance mask for the smaller perturbation of the previous stage. Extensive experiments on the ImageNet dataset and multiple network classifiers show that our method is effective. Compared with advanced attack methods, the $mathbf{L}_{2}$ distance of adversarial perturbation obtained by our method is smaller and more practical, and the generated adversarial examples have strong transferability.","PeriodicalId":325790,"journal":{"name":"2021 International Conference on Computer Engineering and Application (ICCEA)","volume":"15 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":"115695852","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