2020 12th International Conference on Advanced Computational Intelligence (ICACI)最新文献

De-speckling Convolutional Neural Network and Classification Method for SAR Images SAR图像去斑点卷积神经网络及分类方法

2020 12th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2020-08-01 DOI: 10.1109/ICACI49185.2020.9177503

Yapei Zhao, Qingzeng Song, Xuechun Wang, Yijie Zhang, Guanghao Jin

{"title":"De-speckling Convolutional Neural Network and Classification Method for SAR Images","authors":"Yapei Zhao, Qingzeng Song, Xuechun Wang, Yijie Zhang, Guanghao Jin","doi":"10.1109/ICACI49185.2020.9177503","DOIUrl":"https://doi.org/10.1109/ICACI49185.2020.9177503","url":null,"abstract":"In real-world applications, different problems can adopt different models. Most of the existing denoising methods use the framework of deep learning, and the most commonly used denoised algorithm evaluation indicators, such as PSNR, MSE, etc., all without exception, require pictures’ ground truth which is needed as a reference. However, there are few real and noise-free pictures in the field of image denoising, only the noise reduction map can be compared with the noise map, which seems to be less persuasive. Therefore, this paper proposes a new criterion for judging the denoising model. The most important thing is that this method does not require noiseless images compared to PSNR when testing. Moreover, we improved the denoising model and verified the reliability of the criterion. At the same time, we conduct statistics on the recognition rate of different types of targets, and analyze the trend of misjudgment. In this paper, the synthetic aperture radar (SAR) image dataset is used as an experimental sample, and different noise parameters are used to obtain denoising data sets with different noise levels. Then we use different denoising models such as DN-CNN to process the data set. Finally, the CNN classification model is used for screening comparison. In this paper, the experimental results show that it is feasible to use classification to judge denoising, so based on this feasibility, this paper modified the denoising network and used classification to judge. The results show that the denoising effect is better and the classification accuracy is higher, that is, the denoising and classification are a complementary relationship.","PeriodicalId":137804,"journal":{"name":"2020 12th International Conference on Advanced Computational Intelligence (ICACI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125137517","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

A 3D Grid Mapping System Based on Depth Prediction from a Monocular Camera 基于单目相机深度预测的三维网格映射系统

2020 12th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2020-08-01 DOI: 10.1109/ICACI49185.2020.9177824

Peifeng Yan, Yuqing Lan, Shaowu Yang

{"title":"A 3D Grid Mapping System Based on Depth Prediction from a Monocular Camera","authors":"Peifeng Yan, Yuqing Lan, Shaowu Yang","doi":"10.1109/ICACI49185.2020.9177824","DOIUrl":"https://doi.org/10.1109/ICACI49185.2020.9177824","url":null,"abstract":"In complex unknown 3D environments, an accurate 3D volumetric representation of the environment is important for an intelligent robot. Simultaneous Localization and Mapping (SLAM) is considered as a fundamental direction in this area. RGB-D information is very important in traditional SLAM methods. The depth information obtained by sensors like some RGB-D cameras has limits in precision and accuracy. High-precision sensors like lasers and radars are often very expensive. Efficient algorithms should be adopted into those traditional SLAM system. They can not only improve the system efficiency in poorly-equipped conditions but also reduce the resources consumption of robots. To tackle the trade-off between performance and cost, this paper proposes a system producing a 3D grid map that can be used for navigation with a monocular camera and IMU of small size. Our system uses a deep neural network to predict the depth information of a monocular image and utilize the dynamic frame hopping strategy to make a smoother prediction result. Furthermore, we complete a 3D grid map directly used for navigation. The whole grid mapping process occupies little computation and storage resource at the same time. We adopt the octree structure and a keyframe method in the process of the 3D grid mapping to reduce resource consumption. Experiments in a real-world environment show that our approach achieves good results in depth prediction and can well update the 3D grid map for navigation.","PeriodicalId":137804,"journal":{"name":"2020 12th International Conference on Advanced Computational Intelligence (ICACI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125802358","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

Sparse Nonnegative Matrix Factorization Based on a Hyperbolic Tangent Approximation of L0-Norm and Neurodynamic Optimization 基于l0 -范数双曲正切逼近和神经动力学优化的稀疏非负矩阵分解

2020 12th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2020-08-01 DOI: 10.1109/ICACI49185.2020.9177819

Xinqi Li, Jun Wang, S. Kwong

引用次数: 5

The Intelligent Decision-making based on Multisource Heterogeneous Data Fusion in Manufacturing 基于多源异构数据融合的制造业智能决策研究

2020 12th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2020-08-01 DOI: 10.1109/ICACI49185.2020.9177661

Jie Yu, S. Gu, Jiwei Wang, Zhi-Yong Jia, Yunpeng Zhao

引用次数: 0

Knowledge Link Inference of Graph Structure Based on Holographic Model 基于全息模型的图结构知识链接推理

2020 12th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2020-08-01 DOI: 10.1109/ICACI49185.2020.9177812

Yufei Zhao, Liu Chen, Guangping Zeng, Chunguang Zhang

引用次数: 0

An Airfoil Mesh Quality Criterion using Deep Neural Networks 基于深度神经网络的翼型网格质量判据

2020 12th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2020-08-01 DOI: 10.1109/ICACI49185.2020.9177713

Xinhai Chen, Jie Liu, Chunye Gong, Yufei Pang, Bo Chen

引用次数: 5

Application of New Method in Location of Logistics Centers 新方法在物流中心选址中的应用

2020 12th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2020-08-01 DOI: 10.1109/ICACI49185.2020.9177670

Shihui Dong, Hongye Xiong, Zhiping Wang, Xu Li

引用次数: 0

A Task Scheduling Problem in Mobile Robot Fulfillment Systems 移动机器人配送系统中的任务调度问题

2020 12th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2020-08-01 DOI: 10.1109/ICACI49185.2020.9177514

Wei Yuan, Hui Sun

引用次数: 3

Fusion Network Combined With Bidirectional LSTM Network and Multiscale CNN for Remaining Useful Life Estimation 结合双向LSTM网络和多尺度CNN的剩余寿命估计融合网络

2020 12th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2020-08-01 DOI: 10.1109/icaci49185.2020.9177774

Yijie Jiang, Yi Lyu, Yonghua Wang, Pin Wan

引用次数: 3

The Basic Algorithm for Zero-One Unconstrained Quadratic Programming Problem with k-diagonal Matrix 具有k-对角矩阵的0 - 1无约束二次规划问题的基本算法

2020 12th International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2020-08-01 DOI: 10.1109/ICACI49185.2020.9177717

Shenshen Gu, Xinyi Chen

引用次数: 2