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International Conference on Artificial Intelligence and Pattern Recognition最新文献

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Chinese named entity recognition in power domain based on Bi-LSTM-CRF 基于Bi-LSTM-CRF的功率域中文命名实体识别
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357283
Zhenqiang Zhao, Zhenyu Chen, Jinbo Liu, Yunhao Huang, Xingyu Gao, Fangchun Di, Lixin Li, Xiaohui Ji
{"title":"Chinese named entity recognition in power domain based on Bi-LSTM-CRF","authors":"Zhenqiang Zhao, Zhenyu Chen, Jinbo Liu, Yunhao Huang, Xingyu Gao, Fangchun Di, Lixin Li, Xiaohui Ji","doi":"10.1145/3357254.3357283","DOIUrl":"https://doi.org/10.1145/3357254.3357283","url":null,"abstract":"Efficient recognition of proprietary entities is an important basic work for text data mining and intelligent application in power domain. Traditional power domain Named Entity Recognition (NER) methods rely on feature engineering seriously, which unable to learn power entity features automatically. In order to learn entity features automatically and extract power domain named entities efficiently, a model based on Bidirectional Long Short-Term Memory Neural Networks (Bi-LSTM) and Conditional Random Field (CRF) was proposed in this paper. Word representations were fed into the neural networks as an additional feature and Skip-gram embeddings were trained on power domain corpus. Experimental results showed the precision rate reaches higher than 88.25% and the recalling rate reaches higher than 88.04%, which confirm the method based on Bi-LSTM and CRF is effective for named entity recognition in the power domain.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"72 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":"125892043","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
Application research of several LSTM variants in power quality time series data prediction 几种LSTM变量在电能质量时间序列数据预测中的应用研究
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357269
Chen Zhang, Jun Fang
{"title":"Application research of several LSTM variants in power quality time series data prediction","authors":"Chen Zhang, Jun Fang","doi":"10.1145/3357254.3357269","DOIUrl":"https://doi.org/10.1145/3357254.3357269","url":null,"abstract":"Predicting power quality by monitoring data is one of the main topics in power quality research of power grids. With the development of smart grids, power quality monitoring data, as a key indicator for analyzing and regulating the stable transmission of power grids, is exponentially explosive. In recent years, the performance of deep learning methods in large-scale data fitting has been better and better than that of traditional methods. In this paper, based on the strict time series dependence of power quality data, combined with the Long Short-Term Memory neural network (LSTM) of deep learning algorithm, the prediction performance of several LSTM variants (Stacked LSTM, Bi-LSTM, Encoder-Decoder LSTM) on power quality time series data is researched and analyzed. Different LSTM variants are used for training and modeling. The performance comparison and analysis are carried out on the power quality data collected by a State Grid company. From the verification results, the variants have higher prediction accuracy compared with the standard LSTM network variant structure.","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":"130310547","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
Combing colour detection and neural networks for gland detection 结合颜色检测和神经网络进行腺体检测
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357280
Jie Shu, Jiang Lei, Q. Gao, Qian Zhang
{"title":"Combing colour detection and neural networks for gland detection","authors":"Jie Shu, Jiang Lei, Q. Gao, Qian Zhang","doi":"10.1145/3357254.3357280","DOIUrl":"https://doi.org/10.1145/3357254.3357280","url":null,"abstract":"Glands are objects of interest which can be used for quantitatively analysis of histology images. Detecting glands from H&E staining histological images based on neural networks, may suffer stain variation problem. In this paper, we present a new method which combines a statistical colour detection model and a neural network to cover this problem. Colours shown at glands boundaries are pre-detected and enhanced in a pre-processing step. Then a neural network model based on Faster R-CNN is learned from these colour pixels to detect glands. This method has been tested on a Colon Histology Images Challenge Contest (GlaS) held at MICCAI 2015. The experimental results have shown the proposed method is superior to either Faster R-CNN or U-net without colour detection pre-processing. In addition, this proposed method can achieve F1-score rank 8 in detecting benign glands and rank 5 in detecting malignant glands.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"35 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":"130253011","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
Mobile network risk user recognition based on ensemble learning 基于集成学习的移动网络风险用户识别
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357265
Kaili Wu, Xueqi Xu, Zhouxiang Wang
{"title":"Mobile network risk user recognition based on ensemble learning","authors":"Kaili Wu, Xueqi Xu, Zhouxiang Wang","doi":"10.1145/3357254.3357265","DOIUrl":"https://doi.org/10.1145/3357254.3357265","url":null,"abstract":"With the rapid development of mobile network, there are also some hidden dangers of network security. In order to study the problem of risk user identification in mobile network, based on user desensitization data provided by China Unicom, this paper establishes a risk user identification model using four ensemble learning methods: random forest, Adaboost, Xgboost and Lightgbm, and compares the effect of the model with the accuracy, AUC and F1 scores as evaluation indicators. The results show that the ensemble learning method can effectively identify mobile network risk users. Adaboost, Xgboost and Lightgbm based on boosting algorithm have higher accuracy and stronger generalization ability than random forest based on bagging algorithm. Therefore, mobile network companies can prevent the risk of mobile network by establishing relevant ensemble learning model, and then provide a healthy mobile network environment for the public.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"PP 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":"126535711","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
Feature fusion and recognition of potato disease images based on improved fractional differential mask and FPCA 基于改进分数阶差分掩模和FPCA的马铃薯病害图像特征融合与识别
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357279
Yudie Zhong, Wen Yang, Qiang-feng Zhou, Chaobang Gao
{"title":"Feature fusion and recognition of potato disease images based on improved fractional differential mask and FPCA","authors":"Yudie Zhong, Wen Yang, Qiang-feng Zhou, Chaobang Gao","doi":"10.1145/3357254.3357279","DOIUrl":"https://doi.org/10.1145/3357254.3357279","url":null,"abstract":"For the problem of difficult location and recognition of potato diseases, we propose a method for potato leaves feature fusion and disease recognition which based on the improved fractional differential mask and fractional principal component analysis (FPCA). Firstly, the method preprocess potato leaf images by using improved fractional differential mask, and segment disease affected areas by adaptive threshold method. Secondly, fuse features from affected areas like color, shape and texture by fractional principal component analysis (FPCA). Finally, recognize potato disease images by support vector machine (SVM). We conducted recognition experiments on potato leaf images from those are affected by early blight or late blight, the results show that improved fractional differential mask and FPCA can effectively improve the recognition rate of potato disease images. Therefore, this paper use improved fractional differential mask, FPCA and SVM to recognize potato disease images, the recognition accuracy reached 98%.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"87 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":"132804024","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
FCD detection and display based on combination of cortical thickness and PET image 基于皮质厚度与PET图像结合的FCD检测与显示
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3358602
Dongyue Si, Cuixia Feng, Maoyu Tian, Hulin Zhao, Junhai Wen
{"title":"FCD detection and display based on combination of cortical thickness and PET image","authors":"Dongyue Si, Cuixia Feng, Maoyu Tian, Hulin Zhao, Junhai Wen","doi":"10.1145/3357254.3358602","DOIUrl":"https://doi.org/10.1145/3357254.3358602","url":null,"abstract":"Focal cortical dysplasia (FCD) is one of the causes of drug refractory epilepsy. In clinical treatment, surgical excision is often used to remove the FCD lesion area. Therefore, it is very important to detect and locate the lesion area before operation. The Study showed an increase in the cortical thickness of FCD, and it is hypometabolic in the lesions with a lower value in PET images. In this work, this paper uses a voxel based Laplace method to calculate the cortical thickness, and combine the thickness image calculated with PET image to get a thickness-PET map in which the lesion area can be visualized more easily. It also developed a graphic user interface (GUI) to help doctors processing and displaying easier.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"28 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":"133286997","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
Computing the semantic similarity between documents by the copula-based econometric models 利用基于copula的计量经济模型计算文档间的语义相似度
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357277
Jih-Jeng Huang
{"title":"Computing the semantic similarity between documents by the copula-based econometric models","authors":"Jih-Jeng Huang","doi":"10.1145/3357254.3357277","DOIUrl":"https://doi.org/10.1145/3357254.3357277","url":null,"abstract":"Semantic similarity is important information with which decision-makers can cluster, classify, or compare documents in text mining. Statistical and topological methods are two major ways to determine semantic similarity. However, conventional methods ignore the time factor when calculating the similarity between documents. It should be highlighted that narrative emotions play a critical role in comparing documents. In this paper, copula-based econometric models, including ARMA and GARCH families, are used to calculate the narrative semantic similarity between documents.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"68 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":"132458264","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 effective identification method of video ships and vehicles 一种有效的视频船舶和车辆识别方法
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357274
Yongmei Zhang, Mengmeng Liu, Zhirong Du
{"title":"An effective identification method of video ships and vehicles","authors":"Yongmei Zhang, Mengmeng Liu, Zhirong Du","doi":"10.1145/3357254.3357274","DOIUrl":"https://doi.org/10.1145/3357254.3357274","url":null,"abstract":"In usual videos, there are problems such as small amplitude motion interference and sudden change of illumination in the background. It is difficult for ship and vehicle recognition. This paper takes full advantage of classical mixture Gauss background model in the context of small-scale motion. The inter frame difference method is not sensitive to illumination changes. So this paper proposes an identification method combining mixture of Gauss background model and three-frame difference method, and achieves potential region acquisition. The connected components are analyzed and merged to obtain the minimum outer rectangles, and the geometric characteristics of the targets are extracted from the rectangles. This paper uses the LS_SVM classifier to classify each potential region and outputs whether there are targets and the location of the targets. The experiment results show the average accuracy of the target recognition method in this paper is significantly improved.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"27 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":"122367065","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 convolutional neural network based method for low-illumination image enhancement 基于卷积神经网络的低照度图像增强方法
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357255
Huan Huang, Haijun Tao, Haifeng Wang
{"title":"A convolutional neural network based method for low-illumination image enhancement","authors":"Huan Huang, Haijun Tao, Haifeng Wang","doi":"10.1145/3357254.3357255","DOIUrl":"https://doi.org/10.1145/3357254.3357255","url":null,"abstract":"Nowadays, images can be conveniently captured by various image acquisition devices. Weak lighting conditions and devices with poor filling flash will produce low-illumination images. These degraded images are difficult to identify, and must be processed by some methods through the computer. With the inspiring performance of convolutional neural network (CNN) in image classification, object detection and tracking, some studies have been made to enhance low-illumination images by using CNN in recent years. In this paper, based on the existing researches of CNN based low-illumination image enhancement, an improved Unet model is proposed to enhance low-illumination images. At the same time, this paper introduces two new loss functions: Peak signal-to-noise ratio (PSNR) loss and multi-scale Structural similarity (MS-SSIM) loss, and use a mixture of these two loss functions as loss function in our model. Our method can effectively balance the brightness of the processed image, accurately restore the color, so that the enhanced image have a better perception. Results demonstrate that the proposed method outperforms other enhancement methods.","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":"127438092","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: 9
An inadvertent modulation signal processing method based on modified LCD 一种基于改进液晶显示器的无意调制信号处理方法
International Conference on Artificial Intelligence and Pattern Recognition Pub Date : 2019-08-16 DOI: 10.1145/3357254.3357261
Jian Sun, Chengwei Wang, Guangxun Lu, Yaqing Du, Haitao Bai
{"title":"An inadvertent modulation signal processing method based on modified LCD","authors":"Jian Sun, Chengwei Wang, Guangxun Lu, Yaqing Du, Haitao Bai","doi":"10.1145/3357254.3357261","DOIUrl":"https://doi.org/10.1145/3357254.3357261","url":null,"abstract":"Inadvertent modulation feature is the most valuable evidence for the identification. In order to better obtain the inadvertent modulation information, a signal processing method based on modified LCD is presented in this paper. First of all, the inadvertent modulation signal model is established. On the basement of the signal decomposition by LCD, Intrinsic Scale Components (ISCs) are selected for reducing unnecessary information. The remained ISCs are fused according to mutual information. Simulation results shows that the method is feasible and effective to process the inadvertent modulation signal.","PeriodicalId":361892,"journal":{"name":"International Conference on Artificial Intelligence and Pattern Recognition","volume":"130 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":"126905503","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
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