2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)最新文献_第4页

Large-scale Partially Separable Function optimization Using Cooperative Coevolution and Competition Strategies 基于合作协同进化和竞争策略的大规模部分可分离函数优化

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778451

Yu Zhu, Li Zhang, Rushi Lan, Xiaonan Luo

引用次数: 2

A spectrum sensing algorithm based on correlation coefficient and K-means 一种基于相关系数和k均值的频谱感知算法

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778589

Yi Li, Yonghua Wang, Pin Wan, Shunchao Zhang, Yongwei Zhang, Tianyu Zhao

引用次数: 1

Ocean Data Anomaly Detection Algorithm Based on Improved k-medoids 基于改进k-介质的海洋数据异常检测算法

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778515

Hua Jiang, Yao Wu, Kuilin Lyu, Huijiao Wang

引用次数: 8

A Combination Model Based on EEMD-PE and Echo State Network for Chaotic Time Series Prediction 基于EEMD-PE和回波状态网络的混沌时间序列预测组合模型

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778487

Xinghan Xu, Weijie Ren

{"title":"A Combination Model Based on EEMD-PE and Echo State Network for Chaotic Time Series Prediction","authors":"Xinghan Xu, Weijie Ren","doi":"10.1109/ICACI.2019.8778487","DOIUrl":"https://doi.org/10.1109/ICACI.2019.8778487","url":null,"abstract":"Prediction of chaotic time series has broad application prospects and becomes a research hotspot. Since chaotic time series is strongly non-stationary and nonlinear, it’s difficult to predict based on any single model. Therefore, the empirical mode decomposition (EMD)-based combination model becomes an important means of prediction. To reduce the scale of prediction models of conventional combination method, this paper proposes a high-efficiency combination model using ensemble EMD (EEMD), permutation entropy (PE) and echo state network(ESN). EEMD decomposes the original time series into a group of intrinsic mode functions (IMFs), and the number of IMFs is consistent with the number of predictors. On account of the complexity of the chaotic time series, there is a large demand of predictors. Through complexity analysis by PE, we combine some IMFs whose complexities are similar, and predict the combined signals instead of the initial ones based on ESNs. Finally, these obtained estimates are assembled as the ultimate prediction results. In the experiment, we use real-world dataset to examine the proposed model. The experimental results confirm that our combination approach outperforms existing single models, and efficiently reduces the scale of prediction models comparing to the EEMD-ESN.","PeriodicalId":213368,"journal":{"name":"2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114323344","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

A dynamical model for generating synthetic Ballistocardiogram signals 生成综合ballo心图信号的动力学模型

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778508

Zimin Wang, Zhiyu Gan, Zhenbing Liu, Linfa Lu, Xiaonan Luo

引用次数: 0

The Intrusion Detection Model based on Parallel Multi - Artificial Bee Colony and Support Vector Machine 基于并行多人工蜂群和支持向量机的入侵检测模型

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778482

Long Li, Shaowei Zhang, Yongchao Zhang, Liang Chang, T. Gu

引用次数: 3

Image Segmentation Based on Deep Learning Features 基于深度学习特征的图像分割

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778464

D. Liao, Hu Lu, Xingpei Xu, Quansheng Gao

{"title":"Image Segmentation Based on Deep Learning Features","authors":"D. Liao, Hu Lu, Xingpei Xu, Quansheng Gao","doi":"10.1109/ICACI.2019.8778464","DOIUrl":"https://doi.org/10.1109/ICACI.2019.8778464","url":null,"abstract":"Image segmentation is an important technique in image analysis. Existing methods in image segmentation rely on an artificial neural network to extract the feature of the image. In this study, we propose an image segmentation method based on deep learning features and community detection. We propose the use of a pre-trained convolution neural network (CNN) to extract the deep learning features of the image. The deep CNN is trained on ImageNet dataset and transferred to image segmentations for constructing potentials of super-pixels. We first convert the original image from the pixel level to the region level based on Simple Linear Iterative Clustering super-pixels and then aim at each superpixel region to extract the deep learning features. We combine the color features and deep learning features of the superpixel region. The weights of deep learning features and color features are subsequently adjusted using a random walk algorithm to construct a new similarity matrix. We conduct community detection based on a similarity matrix. To automatically identify the number of image segmentation, we use modularity Q in order to determine the optimal number of associations. To illustrate the effectiveness of our proposed method, we evaluate the BSDS300 dataset and compare the technique with several other wellknown image segmentation methods. The segmentation experiments conducted on different images show that our proposed image segmentation algorithm outperforms other methods.","PeriodicalId":213368,"journal":{"name":"2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128358219","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: 6

Combined DTCWT-SVD-Based Video Watermarking Algorithm Using Finite State Machine 基于有限状态机的dtwt - svd组合视频水印算法

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778517

Chen Guangxi, Chen Ze, Wang Daoshun, Li Shundong, Huang Yong, Zhang Baoying

{"title":"Combined DTCWT-SVD-Based Video Watermarking Algorithm Using Finite State Machine","authors":"Chen Guangxi, Chen Ze, Wang Daoshun, Li Shundong, Huang Yong, Zhang Baoying","doi":"10.1109/ICACI.2019.8778517","DOIUrl":"https://doi.org/10.1109/ICACI.2019.8778517","url":null,"abstract":"The video carrying the watermark will undergo various attacks such as transcoding, noise, and temporal synchronization during network propagation (upload/ download), and the existing algorithm doesn’t involve analyzing and testing the combined attacks encountered by the video during network propagation. In order to correctly extract the water-mark in the video after network propagation to protect the rights of copyright holders, this paper proposes a video watermarking algorithm based on the finite state machine in DTCWT-SVD domain. Firstly, the carrier video is divided into several groups of the frame and the watermark image is scanned into several segments by Zig-Zag. Then, the watermark sequence collection is generated by the finite state machine, and each watermark sequence is embedded into the corresponding video frame by modifying the singular value of the DTCWT low-frequency coefficient matrix. Finally, the watermark is extracted according to the predefined relationship of singular values. The experimental results show that the proposed algorithm can effectively resist transcoding, noise, temporal synchronization or other attacks. It is also can accurately extract watermark information in the video after network propagation, which can effectively resist the combined attacks encountered by video in the process of network propagation.","PeriodicalId":213368,"journal":{"name":"2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127238638","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: 4

Rain-Density Squeeze-and-Excitation Residual Network for Single Image Rain-removal 单幅图像去雨的雨密度压缩激励残差网络

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778583

Xipu Hu, Wenhao Wang, Cheng Pang, Rushi Lan, Xiaonan Luo

引用次数: 2

Intelligent Prediction of Vulnerability Severity Level Based on Text Mining and XGBboost 基于文本挖掘和XGBboost的漏洞严重程度智能预测

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778469

Peichao Wang, Yun Zhou, Baodan Sun, Weiming Zhang

引用次数: 9