Traditional manual detection method of crop pests is a quite tedious work with low efficiency, which brings great inconvenience to the control and removal of crop pests at early stage. In recently years, computer vision becomes a critical and promising technique for pest detection. However, limited to the shape and size of the pest and other issues, the perforance of these methods are not so effective and accurate. In order to improve the detection accuracy, we propose a discriminative method for pest detection on leaves based on low-rank representation and sparsity. By utilizing the lowrank characteristics of natural images, the sparsity of the noise image and the prior knowledge of color information of the crop pest images, our method decomposes the original image into low-rank image and sparse noise image, which contains all pests on the leaf. After that, the crop pests with leaf can be separate from the background and counted effectively. The experimental results show that our method can detect pests on leaf conveniently. This is of great significance for future pest judgment and management.
{"title":"A Discriminative Pest Detection Method Based on Low-Rank Representation","authors":"Yang Wang, Yong Zhang, Yunhui Shi, Baocai Yin","doi":"10.1109/ICDH.2018.00024","DOIUrl":"https://doi.org/10.1109/ICDH.2018.00024","url":null,"abstract":"Traditional manual detection method of crop pests is a quite tedious work with low efficiency, which brings great inconvenience to the control and removal of crop pests at early stage. In recently years, computer vision becomes a critical and promising technique for pest detection. However, limited to the shape and size of the pest and other issues, the perforance of these methods are not so effective and accurate. In order to improve the detection accuracy, we propose a discriminative method for pest detection on leaves based on low-rank representation and sparsity. By utilizing the lowrank characteristics of natural images, the sparsity of the noise image and the prior knowledge of color information of the crop pest images, our method decomposes the original image into low-rank image and sparse noise image, which contains all pests on the leaf. After that, the crop pests with leaf can be separate from the background and counted effectively. The experimental results show that our method can detect pests on leaf conveniently. This is of great significance for future pest judgment and management.","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115569676","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}
Ruyue Wang, Hanhui Li, Rushi Lan, S. Luo, Xiaonan Luo
In this paper, we propose to tackle the problem of Alzheimer's Disease (AD) classification by a novel Hierarchical Ensemble Learning (HEL) framework. Given an MRI image of a subject, our method will divide it into multiple slices, and generate the classification result in a coarse-to-fine way: First, for each slice, multiple pre-trained deep neural networks are adopted to extract features, and classiflers trained with each type of these features are used to generate the coarse predictions; Second, we employ ensemble learning on the coarse results to generate a refined result for each slice; At last, the given subject is classified based on the refined results aggregated from all slices. Using pre-trained networks for feature extraction can reduce the computational costs of training significantly, and the ensemble of multiple features and predicted results from slices can increase the classification accuracy effectively. Hence, our method can achieve the balance between efficiency and effectiveness. Experimental results show that the HEL framework can obtain notable performance gains with respect to various features and classifiers.
{"title":"Hierarchical Ensemble Learning for Alzheimer's Disease Classification","authors":"Ruyue Wang, Hanhui Li, Rushi Lan, S. Luo, Xiaonan Luo","doi":"10.1109/ICDH.2018.00047","DOIUrl":"https://doi.org/10.1109/ICDH.2018.00047","url":null,"abstract":"In this paper, we propose to tackle the problem of Alzheimer's Disease (AD) classification by a novel Hierarchical Ensemble Learning (HEL) framework. Given an MRI image of a subject, our method will divide it into multiple slices, and generate the classification result in a coarse-to-fine way: First, for each slice, multiple pre-trained deep neural networks are adopted to extract features, and classiflers trained with each type of these features are used to generate the coarse predictions; Second, we employ ensemble learning on the coarse results to generate a refined result for each slice; At last, the given subject is classified based on the refined results aggregated from all slices. Using pre-trained networks for feature extraction can reduce the computational costs of training significantly, and the ensemble of multiple features and predicted results from slices can increase the classification accuracy effectively. Hence, our method can achieve the balance between efficiency and effectiveness. Experimental results show that the HEL framework can obtain notable performance gains with respect to various features and classifiers.","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116833193","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}
With the development of the Beidou satellite navigation system, the monitoring of ionospheric scintillation combined with GPS and Beidou system has become a trend. In this paper, the design and implementation of the upper computer software for ionospheric scintillation monitoring are introduced, and the related algorithms such as ionospheric amplitude scintillation monitoring, ionospheric phase scintillation monitoring and ionospheric TEC monitoring are discussed and analyzed. The ionospheric scintillation monitoring system developed in this paper can calculate the ionospheric amplitude scintillation index and ionospheric phase scintillation index of the L1/L2 frequency point of GPS satellite and the B1/B2 frequency signal of Beidou satellite in real time. It can also calculate the ionospheric parameters such as TEC, σ_TEC, ROT and ROTI of each satellite, and can store the observed data and make the judgement and analysis of ionospheric scintillation events. Finally, the functional test results of ionospheric scintillation monitoring system are given, and discussed and analyzed.
{"title":"Algorithm of Ionospheric Scintillation Monitoring","authors":"Xiyan Sun, Zheyang Zhang, Yuanfa Ji, Suqing Yan, Wentao Fu, Qidong Chen","doi":"10.1109/ICDH.2018.00053","DOIUrl":"https://doi.org/10.1109/ICDH.2018.00053","url":null,"abstract":"With the development of the Beidou satellite navigation system, the monitoring of ionospheric scintillation combined with GPS and Beidou system has become a trend. In this paper, the design and implementation of the upper computer software for ionospheric scintillation monitoring are introduced, and the related algorithms such as ionospheric amplitude scintillation monitoring, ionospheric phase scintillation monitoring and ionospheric TEC monitoring are discussed and analyzed. The ionospheric scintillation monitoring system developed in this paper can calculate the ionospheric amplitude scintillation index and ionospheric phase scintillation index of the L1/L2 frequency point of GPS satellite and the B1/B2 frequency signal of Beidou satellite in real time. It can also calculate the ionospheric parameters such as TEC, σ_TEC, ROT and ROTI of each satellite, and can store the observed data and make the judgement and analysis of ionospheric scintillation events. Finally, the functional test results of ionospheric scintillation monitoring system are given, and discussed and analyzed.","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126614662","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}
In order to solve the problem of ambiguous acquisition of BOC signals caused by its property of multiple peaks, an unambiguous acquisition algorithm is proposed in this paper. The concept of sub quadratic correlation function and sub QBOC code are first defined in this letter by analyzing the autocorrelation and quadratic correlation of BOC(n, n). Then a new correlation function without multiple peaks can be obtained by a simple combination of sub quadratic correlation, which can be used to unambiguous acquisition for BOC(n, n). The theoretical analysis and simulation prove that the proposed algorithm not only can accomplish unambiguous acquisition but also have more effective de-ambiguity, higher capture sensitivity and higher peak to average power ratio compared to the classical BPSK-like, SPCP and ASPeCT algorithms.
{"title":"A Novel Unambiguous Acquisition Algorithm for BOC(n, n) Signals","authors":"Xiyan Sun, Xiaoqian Chen, Qiang Fu, Suqing Yan, Weimin Zhen","doi":"10.1109/icdh.2018.00054","DOIUrl":"https://doi.org/10.1109/icdh.2018.00054","url":null,"abstract":"In order to solve the problem of ambiguous acquisition of BOC signals caused by its property of multiple peaks, an unambiguous acquisition algorithm is proposed in this paper. The concept of sub quadratic correlation function and sub QBOC code are first defined in this letter by analyzing the autocorrelation and quadratic correlation of BOC(n, n). Then a new correlation function without multiple peaks can be obtained by a simple combination of sub quadratic correlation, which can be used to unambiguous acquisition for BOC(n, n). The theoretical analysis and simulation prove that the proposed algorithm not only can accomplish unambiguous acquisition but also have more effective de-ambiguity, higher capture sensitivity and higher peak to average power ratio compared to the classical BPSK-like, SPCP and ASPeCT algorithms.","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125021998","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}
There emerges an increasing need to improve the accuracy of computer recognition of fundus medical images. Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. In this study, an improved DensenNet method based on Transfer Learning techniques is proposed for fundus medical images. Two experiments for fundus medical image data have been conducted respectively. The first one is to train the DenseNet models from scratch; the second one is fine-tuning operations by transfer learning, in which the DenseNet models pre-trained from natural image dataset to fundus medical images are improved. Experimental Results prove that the proposed method can improve the accuracy of fundus medical image classification, which is valuable for medical diagnosis.
{"title":"An Improved DenseNet Method Based on Transfer Learning for Fundus Medical Images","authors":"Xiaowei Xu, Jiancheng Lin, Ye Tao, Xiaodong Wang","doi":"10.1109/ICDH.2018.00033","DOIUrl":"https://doi.org/10.1109/ICDH.2018.00033","url":null,"abstract":"There emerges an increasing need to improve the accuracy of computer recognition of fundus medical images. Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. In this study, an improved DensenNet method based on Transfer Learning techniques is proposed for fundus medical images. Two experiments for fundus medical image data have been conducted respectively. The first one is to train the DenseNet models from scratch; the second one is fine-tuning operations by transfer learning, in which the DenseNet models pre-trained from natural image dataset to fundus medical images are improved. Experimental Results prove that the proposed method can improve the accuracy of fundus medical image classification, which is valuable for medical diagnosis.","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125068078","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}
{"title":"[Copyright notice]","authors":"","doi":"10.1109/icdh.2018.00003","DOIUrl":"https://doi.org/10.1109/icdh.2018.00003","url":null,"abstract":"","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"37 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124516864","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}
With the development of Chinese financial market, more and more investors paid attention to the stock market. How to analysis stock scientifically is a crutial issue that investors should consider. In order to do stock selection, the financial indicators of listed companies are particularly important. However, in real world the number of high-quality stocks is much smaller than ordinary stocks, that is, the dataset is unbalanced. And company's financial data is often high dimensional and contain many irrelevant features. In this paper, firstly we propose a hybrid BASMOTE algorithm based on the borderline-SMOTE algorithm and ADASYN algorithm. Introduce the ADASYN algorithm's adaptive thought to the borderline-SMOTE algorithm, so as to obtain more effective and reasonable new minority examples. Secondly, a hybrid feature selection method, HPMG, is proposed, which introduces the wrapper thought and ensemble thought into traditional feature selection methods. We use multi-dimensional financial indicators of A-Shares data of Chinese market, the validity of the BASMOTE algorithm and the HPMG are compared respectively with existing over-sampling methods and feature selection methods. It proves that the BASMOTE algorithm and HPMG are better than the existing over-sampling methods and feature selection methods.
{"title":"Modified Machine Learning Model and Stock Classification Research Based on Unbalanced Data","authors":"Marui Du, Zuoquan Zhang, Yuqing Zhang","doi":"10.1109/ICDH.2018.00043","DOIUrl":"https://doi.org/10.1109/ICDH.2018.00043","url":null,"abstract":"With the development of Chinese financial market, more and more investors paid attention to the stock market. How to analysis stock scientifically is a crutial issue that investors should consider. In order to do stock selection, the financial indicators of listed companies are particularly important. However, in real world the number of high-quality stocks is much smaller than ordinary stocks, that is, the dataset is unbalanced. And company's financial data is often high dimensional and contain many irrelevant features. In this paper, firstly we propose a hybrid BASMOTE algorithm based on the borderline-SMOTE algorithm and ADASYN algorithm. Introduce the ADASYN algorithm's adaptive thought to the borderline-SMOTE algorithm, so as to obtain more effective and reasonable new minority examples. Secondly, a hybrid feature selection method, HPMG, is proposed, which introduces the wrapper thought and ensemble thought into traditional feature selection methods. We use multi-dimensional financial indicators of A-Shares data of Chinese market, the validity of the BASMOTE algorithm and the HPMG are compared respectively with existing over-sampling methods and feature selection methods. It proves that the BASMOTE algorithm and HPMG are better than the existing over-sampling methods and feature selection methods.","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122894511","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}
Testing applications with GUI is one of the most tedious tasks in software development. Test automation alleviates this burden by executing scripts that simulate how users interact with GUI. However, in practice efforts spent on developing GUI test automation scripts can be wasteful when the application evolves and modifies GUI components that are referenced by the scripts. In this paper, we propose a new test script repair framework to address this problem. The framework of our method consists of three main modules. Firstly, the script processing module calculates a similarity matrix of each script pair. Secondly, the code analysis module maintains an object repair map which is used to store repair operations. Finally, the script update module applies the map to the scripts under repair. The repair rate of our method keeps increasing with more test script pairs provided and it could be more than 95%. Experiments also show that our method is better than the tool for scrip maintaining within QTP not only in repair rate but also in time consuming.
{"title":"A Black-Box Based Script Repair Method for GUI Regression Test","authors":"Weina Jiang, Xiaozhe Li, Xinming Wang","doi":"10.1109/ICDH.2018.00035","DOIUrl":"https://doi.org/10.1109/ICDH.2018.00035","url":null,"abstract":"Testing applications with GUI is one of the most tedious tasks in software development. Test automation alleviates this burden by executing scripts that simulate how users interact with GUI. However, in practice efforts spent on developing GUI test automation scripts can be wasteful when the application evolves and modifies GUI components that are referenced by the scripts. In this paper, we propose a new test script repair framework to address this problem. The framework of our method consists of three main modules. Firstly, the script processing module calculates a similarity matrix of each script pair. Secondly, the code analysis module maintains an object repair map which is used to store repair operations. Finally, the script update module applies the map to the scripts under repair. The repair rate of our method keeps increasing with more test script pairs provided and it could be more than 95%. Experiments also show that our method is better than the tool for scrip maintaining within QTP not only in repair rate but also in time consuming.","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130860356","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}
We used HDP-based models to model the progression of a basketball game. As known to all, the hidden Markov model can be used for analyzing sequences of the game's content. By introducing Hierarchical Dirichlet Processes on feature extraction and HMMs, we can tackle down the challenges of unknown numbers of mixtures in both models by resorting to nonparametric approach. We employ variational inference for model calculation and cluster the extracted rounds of a basketball match in the form of HMM parameters to forecast the overcome. The proposed scheme is then verified by comparing with other commonly used forecasting approaches: logit regression of the outcome, Naive Bayes method, and Neural Networks. We found that HDP-based models are appropriate for modeling a basketball match and produces more accurate predictions.
{"title":"Simulating a Basketball Game with HDP-Based Models and Forecasting the Outcome","authors":"Xin Du, Weihong Cai","doi":"10.1109/ICDH.2018.00042","DOIUrl":"https://doi.org/10.1109/ICDH.2018.00042","url":null,"abstract":"We used HDP-based models to model the progression of a basketball game. As known to all, the hidden Markov model can be used for analyzing sequences of the game's content. By introducing Hierarchical Dirichlet Processes on feature extraction and HMMs, we can tackle down the challenges of unknown numbers of mixtures in both models by resorting to nonparametric approach. We employ variational inference for model calculation and cluster the extracted rounds of a basketball match in the form of HMM parameters to forecast the overcome. The proposed scheme is then verified by comparing with other commonly used forecasting approaches: logit regression of the outcome, Naive Bayes method, and Neural Networks. We found that HDP-based models are appropriate for modeling a basketball match and produces more accurate predictions.","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128503082","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}
{"title":"[Title page i]","authors":"","doi":"10.1109/icdh.2018.00001","DOIUrl":"https://doi.org/10.1109/icdh.2018.00001","url":null,"abstract":"","PeriodicalId":117854,"journal":{"name":"2018 7th International Conference on Digital Home (ICDH)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126623839","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}
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