2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)最新文献_第3页

Distributed High-Dimension Matrix Operation Optimization on Spark 基于Spark的分布式高维矩阵运算优化

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778546

Qi She, Jingwei Zhang, Ya Zhou, Qing Yang, Mingfei Qin

引用次数: 0

A Design and Implementation of an Intelligent Care System for the Elderly Based on Internet of Things 基于物联网的智能养老系统的设计与实现

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778502

Xiaochun Lei, Jianan Chen, Guangcai Li, Feilong Chen, Wu Longshen

引用次数: 2

Multitask Classification of Breast Cancer Pathological Images Using SE-DenseNet 基于SE-DenseNet的乳腺癌病理图像多任务分类

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778592

Guanglu Ye, Jun Ruan, Chenchen Wu, Jingfan Zhou, Simin He, Jianlian Wang, Zhikui Zhu, Junqiu Yue, Yanggeling Zhang

{"title":"Multitask Classification of Breast Cancer Pathological Images Using SE-DenseNet","authors":"Guanglu Ye, Jun Ruan, Chenchen Wu, Jingfan Zhou, Simin He, Jianlian Wang, Zhikui Zhu, Junqiu Yue, Yanggeling Zhang","doi":"10.1109/ICACI.2019.8778592","DOIUrl":"https://doi.org/10.1109/ICACI.2019.8778592","url":null,"abstract":"Breast cancer has always been the main killer of women. The constantly development of Convolutional Neural Network (CNN) greatly improved the possibility of early diagnostics of breast cancer owing to its high efficiency and accuracy. In this paper, we apply the architecture of Densely Connected Convolutional Network (DenseNet), and then assimilate into Squeeze-and-Excitation Network (SeNet) to perform multitask classification on Camelyon16 which is a set of images of hematoxylin and eosin (H&E) stained breast histology microscopy. Whole-slide images (WSIs) are generally stored in a multi-resolution pyramid, our dataset contains patches of Camelyon16 under ×5, ×20, ×40 three magnifications. Our multitask is to identify the magnification of the patch and distinguish whether the extracted patch belongs to metastatic tumor area of WSIs at the same time by link two classifiers at the end of the same network. Whether on multitask or a single subtask, our network has showed excellent performance, SE-DenseNet-40 has even achieved an accuracy of 92.92% on CIFAR-10.","PeriodicalId":213368,"journal":{"name":"2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)","volume":"38 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":"131039250","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: 8

optimization Design of Submodule Cascade Sequence in Biomedical Circuits 生物医学电路中子模块级联序列的优化设计

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778460

Caijun Gan, Y. Zhong, Zimin Wang, Huadeng Wang, Linfa Lu, Huayi Liu, Xiaonan Luo

引用次数: 0

A New AI Open Problem: WUGU Chess 一个新的AI开放问题:五谷棋

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778618

Chunxiao Ren, Yuxiao Wu

引用次数: 0

Zhuang National Costume Images Generation Method Based On Deep Convolutional Generative Adversarial Network 基于深度卷积生成对抗网络的壮族服饰图像生成方法

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778595

Zhenrong Deng, Bai Shanjin, Ma Fuxin, Wenming Huang, Xiaonan Luo

引用次数: 2

Indoor Position Algorithm Based on the Fusion of Wifi and Image 基于Wifi与图像融合的室内定位算法

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778542

Zhongshuai Wang, Pheng Sokliep, Chengpei Xu, Jiayu Huang, Linfa Lu, Zhuo Shi

{"title":"Indoor Position Algorithm Based on the Fusion of Wifi and Image","authors":"Zhongshuai Wang, Pheng Sokliep, Chengpei Xu, Jiayu Huang, Linfa Lu, Zhuo Shi","doi":"10.1109/ICACI.2019.8778542","DOIUrl":"https://doi.org/10.1109/ICACI.2019.8778542","url":null,"abstract":"In this paper, an image and WiFi multi-mode fusion localization method is proposed. WiFi fingerprint 10-calization is one of the most commonly used methods at present, but WiFi fingerprint localization has the phenomena of multipath and non-line-of-sight, signal fluctuation, scattering and so on, which leads to the unstable positioning effect. In addition, the image uses feature matching to determine the user’s location, which is less affected by the environment, more stable and lower cost. Taking advantage of the advantages of the two algorithms, a new fusion location estimation algorithm is proposed. In the offline phase, WiFi fingerprint and image are collected, fingerprint database is established, and the collected images are split and matched using AlexNet to determine the overall range. In the online stage, fingerprint location matching is carried out in the area through the fusion WiFi. After experiments, the results show that our method can effectively reduce the fluctuation of WiFi fingerprint positioning and improve the positioning accuracy; it can be widely used to provide indoor positioning services for people.","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":"121386382","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: 5

Large-scale Multi-label Image Retrieval Using Residual Network with Hash Layer 基于哈希层残差网络的大规模多标签图像检索

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778549

Baohua Qiang, Peiyao Wang, Shui-ping Guo, Zhi Xu, Wu Xie, Jinlong Chen, Xianjun Chen

{"title":"Large-scale Multi-label Image Retrieval Using Residual Network with Hash Layer","authors":"Baohua Qiang, Peiyao Wang, Shui-ping Guo, Zhi Xu, Wu Xie, Jinlong Chen, Xianjun Chen","doi":"10.1109/ICACI.2019.8778549","DOIUrl":"https://doi.org/10.1109/ICACI.2019.8778549","url":null,"abstract":"In recent years, increasing deep hashing methods have been applied in large-scale multi-label image retrieval. However, in the existing deep network models, the extracted low-level features cannot effectively integrate the multi-level semantic information and the similarity ranking information of pairwise multi-label images into one hash coding learning scheme. Therefore, we cannot obtain an efficient and accurate index method. Motivated by this, in this paper, we proposed a novel approach adopting the cosine distance of pairwise multi-label images semantic vector to quantify existing multi-level similarity in a multi-label image. Meanwhile, we utilized the residual network to learn the final representation of multi-label images features. Finally, we constructed a deep hashing framework to extract features and generate binary codes simultaneously. On the one hand, the improved model uses a deeper network and more complex network structures to enhance the ability of low-level features extraction. On the other hand, the improved model was trained by a fine-tuning strategy, which can accelerate the convergence speed. Extensive experiments on two popular multi-label datasets demonstrate that the improved model outperforms the reference models regarding accuracy. The mean average precision is improved by 1.0432 and 1.1114 times on two datasets, respectively.","PeriodicalId":213368,"journal":{"name":"2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI)","volume":"2010 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":"127342920","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

Transformer Fault On-line Diagnosis System 变压器故障在线诊断系统

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778543

Lin Yan, Wei Wang, Ying Zhang

引用次数: 0

Unit Commitment Problem Using An Efficient PSO Based Algorithm 基于高效粒子群算法的机组承诺问题

2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI) Pub Date : 2019-06-01 DOI: 10.1109/ICACI.2019.8778557

Yu Zhai, Nankun Mu, X. Liao, Junqing Le, Tingwen Huang

引用次数: 3