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International Conference on Artificial Intelligence and Pattern Recognition最新文献

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Availability assessment of avionics display system based on fuzzy fault dependent matrix 基于模糊故障相关矩阵的航电显示系统可用性评估
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357289
Haiyong Dong, Zhengjun Zhai, Yanhong Lu, Qingfan Gu, Guoqing Wang, Miao Wang
{"title":"Availability assessment of avionics display system based on fuzzy fault dependent matrix","authors":"Haiyong Dong, Zhengjun Zhai, Yanhong Lu, Qingfan Gu, Guoqing Wang, Miao Wang","doi":"10.1145/3357254.3357289","DOIUrl":"https://doi.org/10.1145/3357254.3357289","url":null,"abstract":"The unstable voltage from the power supply modular in the avionics display system may cause errors or loss of subsequent calculation modules. Such fault causal relationship is uncertain and cannot be described by traditional safety analysis methods. This paper extends the safety method based on fuzzy fault dependent matrix, expresses the fuzzy rate as a triple, and recommends using the worst estimated probability value as the availability estimate to ensure that the safety design can meet the requirement. This extended method is also used to analyze the availability of the displaying airspeed function of the avionics system, and the safety design of the model is verified.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121806662","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
Label generation system based on generative adversarial network for medical image 基于生成对抗网络的医学图像标签生成系统
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357256
Jiyun Li, Yongliang Hong
{"title":"Label generation system based on generative adversarial network for medical image","authors":"Jiyun Li, Yongliang Hong","doi":"10.1145/3357254.3357256","DOIUrl":"https://doi.org/10.1145/3357254.3357256","url":null,"abstract":"In recent years, the generation model has made great progress in the task of less label sample data. Aiming at the heavy task, high cost, time-consuming and laborious problems of medical image labeling, this paper proposes an image label generation model based on generative adversarial network (GAN). The generator consists of a convolution network and a long-term and short-term memory network. It generates a text description for the input image. At the same time, the discriminator consists of a convolution network, calculates the difference between the generated description and the real description, and transfers the gradient to complete the confrontation training. In this paper, the model is trained on the INbreast dataset, and the experiment show that the model achieves good results in the generation of medical image data labels.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"23 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120821534","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
Experimental research on emotion recognition based on brain-computer interface and brain waves 基于脑机接口和脑电波的情绪识别实验研究
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357272
Jingru Zhang, Qunyong Yuan, N. Xiao
{"title":"Experimental research on emotion recognition based on brain-computer interface and brain waves","authors":"Jingru Zhang, Qunyong Yuan, N. Xiao","doi":"10.1145/3357254.3357272","DOIUrl":"https://doi.org/10.1145/3357254.3357272","url":null,"abstract":"Emotion recognition and classification are important research contents in the field of emotional computing. The current research focuses on the visual field and the speech field, but the accuracy of the emotion recognition and the classification which can be achieved so far is low, which is not enough for commercial applications. At present, due to the rapid progress in research on the brain waves and the brain-computer interfaces, and the great application value in the fields of the medicine and the military, this paper uses the brain electrode caps to collect the brain waves of the human brains under the seven different emotional states. The brain-computer interface transmits the brain wave patterns and the data to the computer, observes the brain waves in the OpenBCI_GUI graphical interface and records the changes in real time. After obtaining the brainwave data under the different emotional states, this paper uses the three statistical methods, such as the AdaBoosting algorithm, to perform the emotional classification on the recorded brainwave data. The experimental results show that the classification effect is good.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121106647","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: 3
A lock-free algorithm of tree-based reduction for large scale clustering on GPGPU 基于GPGPU的大规模聚类的无锁树约简算法
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357271
R. Ando
{"title":"A lock-free algorithm of tree-based reduction for large scale clustering on GPGPU","authors":"R. Ando","doi":"10.1145/3357254.3357271","DOIUrl":"https://doi.org/10.1145/3357254.3357271","url":null,"abstract":"Recently, the art of concurrency and parallelism has been advanced rapidly. However, conventional techniques still suffer of the drawback of lock contention. To name a few, atomic instruction has relatively low scalability as the number of iterations are increasing. This causes a serious slowdown when programmer cope with large-scale data mining processing such as clustering billions of data with numerous iterations. This paper proposes a Lock-free technique of tree-based reduction for large scale clustering on GPGPU. Proposal method is divided into two steps: fine reduction and coarse reduction. In the first reduction step, the clustering program obtain K * N intermediate array where K is the number of clusters and N is the number of blocks. In the following step, new mean value is calculated over N blocks. By doing this, the clustering program can evade using atomic instruction which causes lock contention in coping with large scale clusters. In experiment, the performance of native GPU kernel with atomic instruction, Thrust template libraries and proposal method is compared and evaluated.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126460529","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
An improved baseline for person re-identification 改进的人员再识别基线
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357270
Yu Liu, Youdong Ding
{"title":"An improved baseline for person re-identification","authors":"Yu Liu, Youdong Ding","doi":"10.1145/3357254.3357270","DOIUrl":"https://doi.org/10.1145/3357254.3357270","url":null,"abstract":"Person re-identification(Re-ID) using deep learning has made great progress in the past few years, but there is one problem that many state-of-the-art Re-ID methods all use a complex network most of which use the structure of multi-branch and multi-loss function. At present, the database used for Person re-identification is relatively small. This complex network structure may bring a problem that although current methods may perform well in the small databases, but there may be some problems of overfitting problem, once applied in the bigger dataset or real scene these complex methods may perform not well. So this paper mainly proposes a new powerful baseline network. This end-to-end network only uses a global feature and does not use multi-branch structure, but achieves state-of-the-art level. The key point is that this network has good improvement potential to adapt to larger datasets and even practical application scenarios.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116654796","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
Information interaction based two-stream neural networks for fatigue detection 基于信息交互的双流神经网络疲劳检测
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357257
Rui Huang, Yan Wang
{"title":"Information interaction based two-stream neural networks for fatigue detection","authors":"Rui Huang, Yan Wang","doi":"10.1145/3357254.3357257","DOIUrl":"https://doi.org/10.1145/3357254.3357257","url":null,"abstract":"Fatigue driving is the primary cause of traffic accidents. Because fatigue detection has the characteristics of high real-time requirements and low complexity, shallow convolutional neural networks are often chosen as the deep learning framework for common fatigue detection methods. However, the capability of feature learning and abstraction of shallow network is limited by the depth of the network. In this paper, multiple information interaction modules based on cross fully connected layer or bidirectional recurrent neural network are added to dual-stream neural networks for fatigue detection without changing the network depth. Experimental results demonstrate the proposed information interaction modules greatly improve the accuracy of shallow network in fatigue detection while the additional time loss can be ignored. Furthermore, these modules can be regarded as a way of information fusion and extended to behavior recognition and other deep learning tasks using multi-stream networks to alleviate the inconsistent distribution and representation of different streams.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132561311","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
An abnormal behavior detection method of video crowds and vehicles based on deep learning 基于深度学习的视频人群和车辆异常行为检测方法
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357273
Jianzhe Ma, Yulong Xu, Yongmei Zhang, Yan Jiang
{"title":"An abnormal behavior detection method of video crowds and vehicles based on deep learning","authors":"Jianzhe Ma, Yulong Xu, Yongmei Zhang, Yan Jiang","doi":"10.1145/3357254.3357273","DOIUrl":"https://doi.org/10.1145/3357254.3357273","url":null,"abstract":"Video monitoring-based exception behavior detection of crowds and vehicles has become a hot research hotspot in image processing, machine vision and other related fields. In view of the difficulty of detecting abnormal targets in complex structured outdoor scenes, an anomaly detection method combining optical flow method and convolutional neural network (CNN) is proposed in this paper, the method can be used to detect and warn abnormal targets in complex structured scenes. Extract video motion characteristics by Lucas-Kanade (LK) optical flow, normalize the extracted optical flow through a simple scaling method, detect and alert the anomalies of video crowds and vehicles adopting CNN, evaluate the abnormal behavior detection method using the accuracy and time. The experiment results show the method can detect the abnormal behaviors of crowds and vehicles in complex scenes in time and effectively.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125150502","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: 3
Semi-supervised entity recognition of Chinese government document 中国政府文件的半监督实体识别
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357288
Dagang Chen, Zeyuan Li, Zesong Li, Kunnan Liu, Yajun Song, Peng Wang
{"title":"Semi-supervised entity recognition of Chinese government document","authors":"Dagang Chen, Zeyuan Li, Zesong Li, Kunnan Liu, Yajun Song, Peng Wang","doi":"10.1145/3357254.3357288","DOIUrl":"https://doi.org/10.1145/3357254.3357288","url":null,"abstract":"There is a large amount of entity information in government documents. Identifying the entity information in government documents is the core foundation of intelligent document processing tasks, such as word segmentation, semantic analysis and knowledge graph construction. To recognize entity, traditional Machine Learning algorithm has the advantage of relatively small tagging corpus requirement. However, this feature also means that this algorithm can hardly capture the implicit semantic information in sentences, which leads to the low accuracy of document entity recognition. Also, this method requires tremendous manual work of feature designing. In contrast, Deep Learning algorithm needs a large tagging corpus. But it gives the algorithm ability to automatically acquire semantic feature information between context. So, the accuracy performance of entity recognition is greatly improved. Combining respective advantages of these above methods, this paper proposes a semi-supervised Deep Learning algorithm framework, which first implement the Conditional Random Field (CRF) and pseudo-labeling to expand the corpus, and then utilize the Dilated Convolution Neural Network (CNN) with Bi-directional Long Short-Term Memory (BiLSTM) plus CRF for extracting entities in official documents. The experimental results show that, compared with other methods, the accuracy, recall rate and F1 value of entity recognition are improved by 5.02%, 5.85% and 5.44% respectively. The proposed method can effectively extract entity information in a document.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131232640","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: 3
Internal node bagging: a layer-wise ensemble training method 内部节点套袋:一种分层集成训练方法
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357268
Jinhong Li, Shun Yi
{"title":"Internal node bagging: a layer-wise ensemble training method","authors":"Jinhong Li, Shun Yi","doi":"10.1145/3357254.3357268","DOIUrl":"https://doi.org/10.1145/3357254.3357268","url":null,"abstract":"When training neural networks, regularization methods are needed to avoid model overfitting. Dropout is a widely used regularization method, but its working principle is inconclusive and it does not work well for small models. This paper introduced a novel view to understand how dropout works as a layer-wise ensemble training method, that each feature in hidden layers is learned by multiple nodes, and next layer integrates the outputs of these nodes. Basing on the novel understanding of dropout, we proposed a new neural network training algorithm named internal node bagging, which explicitly forces a group of nodes to learn the same feature during training phase and combines these nodes into one node during testing phase. This means that more parameters can be used during training phase to improve the fitting ability of models while keeping model remains small during testing phase. After experimenting on three datasets, it is found that this algorithm can significantly improve the test performance of small models.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115023525","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
Product marketing prediction based on XGboost and LightGBM algorithm 基于XGboost和LightGBM算法的产品营销预测
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357290
Yunxin Liang, Jiyu Wu, Wei Wang, Yujun Cao, B. Zhong, Zhenkun Chen, Zhenzhang Li
{"title":"Product marketing prediction based on XGboost and LightGBM algorithm","authors":"Yunxin Liang, Jiyu Wu, Wei Wang, Yujun Cao, B. Zhong, Zhenkun Chen, Zhenzhang Li","doi":"10.1145/3357254.3357290","DOIUrl":"https://doi.org/10.1145/3357254.3357290","url":null,"abstract":"The XGboost and LightGBM algorithm performs predictive analysis of sales volume in the product sales data set. The principle of XGboost and LightGBM algorithm is studied, the predicted objects and conditions are fully analyzed, and the algorithm parameters and data set characteristics are compared. The results show that n_estimators have a small effect on the prediction of model XGboost, while gamma has a large effect on the prediction of model XGboost. Learning_rate has a small impact on LightGBM prediction, while n_estimators have a large impact on LightGBM prediction. Finally, the optimal parameters were obtained, and the sales volume from January to October 2015 was predicted based on the optimal parameters, and RMSE values of the two algorithms were obtained. Statistical analysis shows that there is no significant difference between the two algorithms in the optimal prediction results after adjusting their own parameters.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"189 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133490140","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: 22
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