2021 2nd Asia Symposium on Signal Processing (ASSP)最新文献_第4页

Research on Modeling and Inversion of Human Skin Tissue Based on Multi-band Microwave Radiometer 基于多波段微波辐射计的人体皮肤组织建模与反演研究

2021 2nd Asia Symposium on Signal Processing (ASSP) Pub Date : 2021-11-01 DOI: 10.1109/ASSP54407.2021.00040

Zhenlin Sun, Jie Liu, Kai Zhang, Hao Wang, De-Min Xu, Jiawei Xu

引用次数: 1

Short Text Classification Based on Cross-Connected GRU Kernel Mapping Support Vector Machine 基于交叉连接GRU核映射支持向量机的短文本分类

2021 2nd Asia Symposium on Signal Processing (ASSP) Pub Date : 2021-11-01 DOI: 10.1109/ASSP54407.2021.00038

Qi Wang, Zhaoying Liu, Ting Zhang, Yujian Li

引用次数: 1

Improvement of Feature Extraction Based on HOG 基于HOG的特征提取改进

2021 2nd Asia Symposium on Signal Processing (ASSP) Pub Date : 2021-11-01 DOI: 10.1109/ASSP54407.2021.00017

Zhe-Hao Liu

引用次数: 1

M-Unity Files: Mapua University Project Document Archive M-Unity文件:Mapua大学项目文件档案

2021 2nd Asia Symposium on Signal Processing (ASSP) Pub Date : 2021-11-01 DOI: 10.1109/ASSP54407.2021.00037

J. P. Tomas, Priam Lars E. Awi, George Z. Chua, Pocholo M. Nolasco

引用次数: 0

DetNAS: Design Object Detection Network via One-Shot Neural Architecture Search 通过一次性神经结构搜索设计目标检测网络

2021 2nd Asia Symposium on Signal Processing (ASSP) Pub Date : 2021-11-01 DOI: 10.1109/ASSP54407.2021.00013

Chuntung Zhuang

{"title":"DetNAS: Design Object Detection Network via One-Shot Neural Architecture Search","authors":"Chuntung Zhuang","doi":"10.1109/ASSP54407.2021.00013","DOIUrl":"https://doi.org/10.1109/ASSP54407.2021.00013","url":null,"abstract":"Previous NAS focus on image classification, so directly implementing traditional NAS, like the last research work DetNet, on object detection tasks are ineffective. In general, conventional NAS only searches the backbone network architecture while completely ignore the head network. Unlike image classification tasks, which can directly perform NAS on classification datasets such as ImageNet, object detection tasks require iterative training on classification and detection datasets multiple times. In addition, using traditional NAS methods on object detection tasks is computation-intensive (more than hundreds of GPU hours) because NAS typically has a two-stage workflow. The best sub-Network architecture derived from the super-Network must be retrained or fine-tuned. To resolve the above challenges, we propose DetNAS, a new NAS method targeting object detection tasks. First of all, DetNAS can accelerate the search process of neural network architecture to meet the various demands of object detection tasks. DetNet only searches the backbone network for object detection tasks, while DetNAS can simultaneously search for the backbone and head network during network architecture search. At the same time, inspired by the previous work, DetNAS replaced the single-path evolutionary algorithm in DetNet with progressive search, which further improved the search efficiency of the network structure. Secondly, previous research suggested a series of techniques to improve image classification-based network architecture search efficiency, but their effects on object detection tasks are still unknown. To thoroughly verify the validity of these conclusions, we conducted ablation experiments on multiple datasets and various experimental settings, which provided a valuable basis and reference for subsequent research work. As far as we know, DetNAS is the first One-shot NAS method that can search the backbone and head network simultaneously. We believe our work will open up a new direction to explore the architecture of object detection models.","PeriodicalId":153782,"journal":{"name":"2021 2nd Asia Symposium on Signal Processing (ASSP)","volume":"282 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123115884","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

Realistic Image-to-Image Translation with Enhanced Texture 具有增强纹理的逼真图像到图像的翻译

2021 2nd Asia Symposium on Signal Processing (ASSP) Pub Date : 2021-11-01 DOI: 10.1109/ASSP54407.2021.00010

Guozhen Duan, Yanxiang Gong, Huijie Zhao, W. Ma, Dongxing Song, Zheng Ma, M. Xie

引用次数: 0

Environmental impact assessment method of highway reconstruction project based on the integration of remote sensing image and GIS 基于遥感影像与GIS集成的公路改造项目环境影响评价方法

2021 2nd Asia Symposium on Signal Processing (ASSP) Pub Date : 2021-11-01 DOI: 10.1109/ASSP54407.2021.00039

Peipei Qi, Qing An, Fenggang Liu

{"title":"Environmental impact assessment method of highway reconstruction project based on the integration of remote sensing image and GIS","authors":"Peipei Qi, Qing An, Fenggang Liu","doi":"10.1109/ASSP54407.2021.00039","DOIUrl":"https://doi.org/10.1109/ASSP54407.2021.00039","url":null,"abstract":"Taking a reconstructed highway in Inner Mongolia passing through Baotou and Bayannur districts as the research object, using the integration technology of remote sensing image and GIS, this paper studies the main ecological factors such as vegetation, landform, wind force and soil in Baotou and Bayannur districts, and analyzes the sensitivity of surface erosion, ecological stability The change intensity of ecological environment and the interference of human activities. The results show that when the evaluation technology is the same, the zoning scale is different, and the evaluation results are different. If the scale range is within 2km, the scale evaluation results are more desirable. In 2014, the instability indexes of Baotou and Bayannur regions within 25km were 76.61 and 2.74 respectively, and those within 2km were 75.75 and 4.39 respectively. Through practical demonstration, using remote sensing and GIS technology, this paper evaluates and analyzes the ecological environmental impact of highway construction in Baotou and Bayannur, Inner Mongolia, and provides a new ecological environmental impact assessment method of highway construction projects, which provides a reference for the environmental assessment of highway construction in the future.","PeriodicalId":153782,"journal":{"name":"2021 2nd Asia Symposium on Signal Processing (ASSP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124725693","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

Fine Classification Method of Product Image Based on Multi-Level Convolutional Neural Networks 基于多层卷积神经网络的产品图像精细分类方法

2021 2nd Asia Symposium on Signal Processing (ASSP) Pub Date : 2021-11-01 DOI: 10.1109/ASSP54407.2021.00025

Xuesong Jin, Xin Du, Xiaowei Han, Huadong Sun, Jing Li

引用次数: 0

Fault detection method based on residual network and Faster R-CNN 基于残差网络和Faster R-CNN的故障检测方法

2021 2nd Asia Symposium on Signal Processing (ASSP) Pub Date : 2021-11-01 DOI: 10.1109/ASSP54407.2021.00024

Jing Li, Lei Chen, Ting Zhang, Xueqiang Lv, S. Huo

{"title":"Fault detection method based on residual network and Faster R-CNN","authors":"Jing Li, Lei Chen, Ting Zhang, Xueqiang Lv, S. Huo","doi":"10.1109/ASSP54407.2021.00024","DOIUrl":"https://doi.org/10.1109/ASSP54407.2021.00024","url":null,"abstract":"To improve the fault detection accuracy, a method based on residual network and Faster R-CNN is proposed. First, input the image into the ResNet-50 feature extraction network to obtain the corresponding feature map, and then use the RPN structure to generate a candidate frame, and project the candidate frame generated by the RPN onto the feature map to obtain the corresponding feature matrix. Finally, each feature matrix is scaled to a fixed-size feature map through the ROI pooling layer, and then the feature map is flattened through a series of fully connected layers to obtain the prediction result. ResNet50 solves the problem of network degradation and over-fitting caused by the deepening of network layers when extracting features from faults. Faster R-CNN implements end-to-end training, combining the advantages of ResNet50 and Faster-RCNN, and has accurate positioning efficiency. In the aspect of data enhancement, it is further optimized to enhance the generalization ability of the network, optimize the detection results of the network, and effectively improve the accuracy of the verification, and the feasibility of the method is verified through actual seismic data.","PeriodicalId":153782,"journal":{"name":"2021 2nd Asia Symposium on Signal Processing (ASSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132089815","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 Water Level Prediction on CEEMDAN-GRU Model under the IMFs Recombination imf复合条件下CEEMDAN-GRU模型水位预测研究

2021 2nd Asia Symposium on Signal Processing (ASSP) Pub Date : 2021-11-01 DOI: 10.1109/ASSP54407.2021.00020

Sun Tao, W. Yibin, Chen Wei, Liang Xuechun

{"title":"Research on Water Level Prediction on CEEMDAN-GRU Model under the IMFs Recombination","authors":"Sun Tao, W. Yibin, Chen Wei, Liang Xuechun","doi":"10.1109/ASSP54407.2021.00020","DOIUrl":"https://doi.org/10.1109/ASSP54407.2021.00020","url":null,"abstract":"This research proposes a model bases on the median filter- complete ensemble empirical mode decomposition with adaptive noise and gated recurrent unit network model (CEEMDAN-GRU) under IMFs recombination to predict the water level. We firstly use the median filtering method for data preprocessing and apply CEEMDAN method to decompose the historical water level sequence. Then we obtain 6 IMFs (Intrinsic mode function), recombine IMF1-IMF4 into high-frequency IMFs(H-F) and take IMF5-IMF6 as low-frequency IMFs by t-test method. This research also proposes a method of recombining the IMFs in the high-frequency IMFs to optimize the H-F. On this basis, the optimized H-F, low-frequency IMFs and residual are respectively predicted by the GRU neural network model, and finally the prediction results of the three IMFs are superimposed in equal proportions. Taking the monitoring Hong Ze Lake as an example, the performance of the optimized CEEMDAN-GRU model in the test set is increased by 2.06% and the RMSE increased by 13.86% MAE increased by 9.11 %, and MAPE increased by 11.98% compared with the unoptimized full-IMFs prediction model. Meanwhile, we compared the optimized CEEMDAN-GRU with CEEMDAN-LSTM and LSTM for further investigation. The results show that: the optimized CEEMDAN-GRU has stronger predictive performance compared to the other two models.","PeriodicalId":153782,"journal":{"name":"2021 2nd Asia Symposium on Signal Processing (ASSP)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133609659","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