Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence最新文献

Semi-supervised Cell Classification Based on Deep Learning 基于深度学习的半监督细胞分类

Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence Pub Date : 2022-03-11 DOI: 10.1145/3522749.3523086

Zhao Dong, Zhao Chen

{"title":"Semi-supervised Cell Classification Based on Deep Learning","authors":"Zhao Dong, Zhao Chen","doi":"10.1145/3522749.3523086","DOIUrl":"https://doi.org/10.1145/3522749.3523086","url":null,"abstract":"Pathological examination is an important diagnostic means for cancer, including clinical cytological examination and histopathological examination. In pathological examination, it is often necessary to judge the type of cells. According to identifying the cells type or the number of different cell, doctors can determine whether to have cancer or the stage of cancer. However, pathological images contain a large number of different types of cells, which often need to be labeled with the professional knowledge of pathologists. In order to reduce the burden of pathologists, there are more and more methods to use computer aided cell classification. In recent years, with the rise of deep learning, it has become common to apply it to the segmentation and classification of pathological images. And the semi-supervised learning method can make good use of the image information of a large number of unlabeled samples to improve the performance of the model. But the existing methods of semi-supervised cell classification are not simple enough, and the sample selection mechanism cannot make full use of the characteristics of semi-supervised learning. Therefore, we propose a semi-supervised cell classification framework based on reliable sample selection mechanism, which can flexibly train different classifiers according to different data sets. The framework makes full use of semi-supervised learning, which makes the classification accuracy of model improved steadily.","PeriodicalId":361473,"journal":{"name":"Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126513023","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

Change point detection of time series based on relevance vector machine and Bayesian framework with application to steel manufacturing 基于相关向量机和贝叶斯框架的时间序列变化点检测及其在钢铁制造中的应用

Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence Pub Date : 2022-03-11 DOI: 10.1145/3522749.3523068

Yujie Zhou, Xuefei Du, Fei He

{"title":"Change point detection of time series based on relevance vector machine and Bayesian framework with application to steel manufacturing","authors":"Yujie Zhou, Xuefei Du, Fei He","doi":"10.1145/3522749.3523068","DOIUrl":"https://doi.org/10.1145/3522749.3523068","url":null,"abstract":"Abstract. The change point detection of time series is an urgent issue in the continuous casting quality control. A novel method based on Relevance vector machine (RVM) in the Bayesian framework is proposed for change points detection. First, the posterior distribution of run length is introduced into the change point detection framework. Second, RVM is improved to calculate the predicted distribution of the observation data, which is embedded in the detection framework to achieve the posterior distribution. The posterior probability of the maximum run length is calculated to describe the severity of the data change. Then, the reprocessing is proposed to modify redundant change points in local time. Eventually, traditional Bayesian and Singular Spectrum Transforms are used for comparison, and the effectiveness and superiority of the RVM-Bayesian are illustrated by the continuous casting process. The results show that RVM-Bayesian method can not only accurately detect the change points in the time series, but also characterize the severity of the change points.","PeriodicalId":361473,"journal":{"name":"Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134595399","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

Application of Improved Genetic Algorithm in Aircraft Industry Process Simulation 改进遗传算法在飞机工业过程仿真中的应用

Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence Pub Date : 2022-03-11 DOI: 10.1145/3522749.3523067

Yu-Ning Wang, Hailian Yin, Tian-jiao Zhang, Mingang Yin

引用次数: 0

Analysis and evaluation of regression model for centrifugal chiller 离心式冷水机组回归模型的分析与评价

Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence Pub Date : 2022-03-11 DOI: 10.1145/3522749.3523082

H. Cai, J. Lv

引用次数: 0

Information Bottleneck based Representation Learning for Multimodal Sentiment Analysis 基于信息瓶颈的多模态情感分析表示学习

Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence Pub Date : 2022-03-11 DOI: 10.1145/3522749.3523069

Tonghui Zhang, Haiying Zhang, Shuke Xiang, Tong Wu

{"title":"Information Bottleneck based Representation Learning for Multimodal Sentiment Analysis","authors":"Tonghui Zhang, Haiying Zhang, Shuke Xiang, Tong Wu","doi":"10.1145/3522749.3523069","DOIUrl":"https://doi.org/10.1145/3522749.3523069","url":null,"abstract":"Recently, Multimodal Sentiment Analysis (MSA) has become a hot research topic of cross modal research in artificial intelligence domain. For this task, the research focuses on extract comprehensive information which dispersed in different modalities. In existing research works, some paid attention to the ingenious fusion method inspired by the consideration of intra-modality and inter-modality reaction, while others devoted to remove task-irrelevant information to refine single modal representation by imposing constraints. However, both of these are limited to the lack of effective control over information in the learning of multimodal representation. It may loss task-relevant information or introduce extra noise. In order to address the afore-mentioned issue, we propose a framework named Multimodal Information Bottleneck (MMIB) in this paper. By imposing mutual information constraints between different modal pairs (text-visual, acoustic-visual, text-acoustic) to control the maximization of mutual information between different modalities and minimization of mutual information inside single modalities, the task-irrelevant information in a single modal can be removed efficiency while kept the related ones, so that the multimodal representation is improved greatly. By the experiments on two widely used public datasets, it demonstrates that our proposed method outperforms existing methods (like MAG-BERT, Self-MM) in binary-classification and achieves a comparable performance in other evaluation metrics.","PeriodicalId":361473,"journal":{"name":"Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114523207","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

An efficient scene text detection neural network 一个高效的场景文本检测神经网络

Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence Pub Date : 2022-03-11 DOI: 10.1145/3522749.3523074

Yifan Su

{"title":"An efficient scene text detection neural network","authors":"Yifan Su","doi":"10.1145/3522749.3523074","DOIUrl":"https://doi.org/10.1145/3522749.3523074","url":null,"abstract":"Abstract: We introduce a new type of text detection neural network, which can accurately locate the position of the text in a variety of complex environments and give the best rectangle containing them. It is composed of three parts, the first part is the backbone composed of residual network, which is responsible for refining the feature map. the second part is the sequence module composed of transformer, which processes the feature map as a linear behavioral unit, so as to deeply mine the context of characters in the image, and the last part is the multi-scale detection module, which is based on different sizes of feature maps The best target box is detected as the result. The residual backbone ensures that there will be no gradient explosion in the process of back propagation.as information between grid cells in the same line is consistent, the transformer module pay more attention to the text line. The detection module uses multiple anchors in the vertical direction at the same time, which achieves good results in speed and accuracy. Based on the data set icdar2015, which is commonly used in the field of text detection, we do experiments and achieve a f score of 0.7.","PeriodicalId":361473,"journal":{"name":"Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127737418","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 waste image classification using convolutional neural networks and ensemble learning 基于卷积神经网络和集成学习的垃圾图像分类

Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence Pub Date : 2022-03-11 DOI: 10.1145/3522749.3523079

Jianzhou Xiao

引用次数: 0

Adaptive Fixed-Time Constraint Control for Human-Robot Interaction with Uncertainties using Neural Networks 基于神经网络的不确定性人机交互自适应固定时间约束控制

Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence Pub Date : 2022-03-11 DOI: 10.1145/3522749.3522750

Jing Lin

引用次数: 0

Research on glaucoma classification of college students based on deep convolutional neural network 基于深度卷积神经网络的大学生青光眼分类研究

Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence Pub Date : 2022-03-11 DOI: 10.1145/3522749.3523072

Meng Li, Lei Qi, Fuchun Zhang, Baiyang Wang

{"title":"Research on glaucoma classification of college students based on deep convolutional neural network","authors":"Meng Li, Lei Qi, Fuchun Zhang, Baiyang Wang","doi":"10.1145/3522749.3523072","DOIUrl":"https://doi.org/10.1145/3522749.3523072","url":null,"abstract":"With the advancement of deep learning technology, using deep convolutional neural network to figure out image classification has always been a research hotspot. At present, the incidence rate of high myopia is increasing. High myopia can cause pathological changes of the eyeground, which can cause various eye diseases. Glaucoma is one of the diseases that seriously threaten the vision of college students. Glaucoma caused by myopia seriously threatens the vision of patients. However, because the process of diagnosing glaucoma needs to be manually realized by doctors and is very time-consuming, it is great necessity for us to realize fast diagnosis of glaucoma. Convolutional neural network has self-learning ability and can improve the diagnosis speed of glaucoma. In order to figure out this issue, this paper proposes a classification network based on deep convolutional neural network to promote the feature extraction ability of network, and realize the accurate diagnosis of glaucoma. Experiments show that our method has achieved good accuracy in the classification of glaucoma.","PeriodicalId":361473,"journal":{"name":"Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126762067","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

Subjective Prediction of Questions in Q & A System based on the Open Domain of Daily Life 基于日常生活开放域的问答系统问题的主观预测

Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence Pub Date : 2022-03-11 DOI: 10.1145/3522749.3523085

Wenzhe Wang, Yong Yue, Xiaohui Zhu

{"title":"Subjective Prediction of Questions in Q & A System based on the Open Domain of Daily Life","authors":"Wenzhe Wang, Yong Yue, Xiaohui Zhu","doi":"10.1145/3522749.3523085","DOIUrl":"https://doi.org/10.1145/3522749.3523085","url":null,"abstract":"People and computers have different understandings of questions, and people have different needs for answers. For some questions, people may not need objective answers, but developmental opinions. This paper analyzes long and difficult questions in an open domain question answering system and provides effective information to the system with subjective predictions. It uses pseudo-label technology and the blending of multiple pre-trained language models to improve the understanding of long and difficult text question sentences. In addition, by designing a variety of subjective labels, the model's prediction of the subjectivity and objectivity of questions can provide effective information for the question-and-answer system. Since there are currently no standard definitions or standards for subjective labels and long and difficult text question sentences, we have conducted a subjective analysis of long text questions based on 30 question sentence subjective labels and long text question longer than 512 characters, using Spearman's relative coefficient as the evaluation standard for model prediction. This work is the first to implement subjective prediction of long and difficult text in the open domain area by designing 30 subjective labels.","PeriodicalId":361473,"journal":{"name":"Proceedings of the 6th International Conference on Control Engineering and Artificial Intelligence","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116852402","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