Pneumatic muscle actuator is difficult to model and has strong nonlinear and time-varying properties. In this paper, to control a pneumatic muscle actuator a fuzzy adaptive internal model control algorithm (FAIMC) is proposed by combining internal model control and fuzzy control. The FAIMC controller includes a fuzzy inverse internal model controller and a filter. Both the fuzzy model and the inverse model of the process are obtained by T-S fuzzy model identification, and the filter parameters are adjusted online by fuzzy logic. Through the matlab simulation and the experimental platform of the pneumatic muscle actuator, the results show that the FAIMC algorithm can effectively control the pneumatic muscle actuator.
{"title":"Fuzzy Adaptive Internal Model Control for a Pneumatic Muscle Actuator","authors":"Xiong Zhang, Jiwei Hu, Zemin Liu","doi":"10.1145/3318299.3318360","DOIUrl":"https://doi.org/10.1145/3318299.3318360","url":null,"abstract":"Pneumatic muscle actuator is difficult to model and has strong nonlinear and time-varying properties. In this paper, to control a pneumatic muscle actuator a fuzzy adaptive internal model control algorithm (FAIMC) is proposed by combining internal model control and fuzzy control. The FAIMC controller includes a fuzzy inverse internal model controller and a filter. Both the fuzzy model and the inverse model of the process are obtained by T-S fuzzy model identification, and the filter parameters are adjusted online by fuzzy logic. Through the matlab simulation and the experimental platform of the pneumatic muscle actuator, the results show that the FAIMC algorithm can effectively control the pneumatic muscle actuator.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122780502","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}
Text deduplication is an important operation for text document analysis applications. Given a set of text documents, we often need to remove the text documents whose similarity values are not less than the specified threshold. However, if the set of similar text documents to be removed is too large, the remaining set of text documents may be not enough for text analysis. In this paper, we consider the problem on how to balance the removed set and the remaining set of text documents. We try to reduce the duplication information as much as possible with the minimum number of text documents to be removed. We propose a greedy algorithm for our problem based on the concept of similarity graph which can represent the similar relationship for a set of text documents. We also consider the incremental algorithm for the dynamic settings. The experimental results based on the real news document datasets show the efficiency of the proposed algorithms.
{"title":"Text Deduplication with Minimum Loss Ratio","authors":"Youming Ge, Jiefeng Wu, Genan Dai, Yubao Liu","doi":"10.1145/3318299.3318369","DOIUrl":"https://doi.org/10.1145/3318299.3318369","url":null,"abstract":"Text deduplication is an important operation for text document analysis applications. Given a set of text documents, we often need to remove the text documents whose similarity values are not less than the specified threshold. However, if the set of similar text documents to be removed is too large, the remaining set of text documents may be not enough for text analysis. In this paper, we consider the problem on how to balance the removed set and the remaining set of text documents. We try to reduce the duplication information as much as possible with the minimum number of text documents to be removed. We propose a greedy algorithm for our problem based on the concept of similarity graph which can represent the similar relationship for a set of text documents. We also consider the incremental algorithm for the dynamic settings. The experimental results based on the real news document datasets show the efficiency of the proposed algorithms.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122888837","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}
Relation extraction in text data is considered as an important task in the field of natural language processing. So far, distant supervision is widely adopted in relation extraction to get labeled data. However, such a method is often lack of semantic information, and thus may bring wrong labelling problem. In this paper, a moderately deep convolutional neural network (CNN) is proposed to tackle the difficulty in relation extraction. The proposed CNN integrates low-level features of text sentences with high-level ones. The proposed CNN-based model has been evaluated on the NYT freebase larger dataset and the results show that our model is superior to the popular models such as CNN+ATT, PCNN+ATT, and ResCNN-9.
{"title":"A Moderately Deep Convolutional Neural Network for Relation Extraction","authors":"Xinyang Bing, Liu Shen, Liying Zheng","doi":"10.1145/3318299.3318326","DOIUrl":"https://doi.org/10.1145/3318299.3318326","url":null,"abstract":"Relation extraction in text data is considered as an important task in the field of natural language processing. So far, distant supervision is widely adopted in relation extraction to get labeled data. However, such a method is often lack of semantic information, and thus may bring wrong labelling problem. In this paper, a moderately deep convolutional neural network (CNN) is proposed to tackle the difficulty in relation extraction. The proposed CNN integrates low-level features of text sentences with high-level ones. The proposed CNN-based model has been evaluated on the NYT freebase larger dataset and the results show that our model is superior to the popular models such as CNN+ATT, PCNN+ATT, and ResCNN-9.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117067286","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}
Jean-Paul Ainam, Ke Qin, Guisong Liu, Guangchun Luo
In this paper, we present an attention mechanism scheme to improve the person re-identification task. Inspired by biology, we propose Self Attention Grid (SAG) to discover the most informative parts from a high-resolution image using its internal representation. In particular, given an input image, the proposed model is fed with two copies of the same image and consists of two branches. The upper branch processes the high-resolution image and learns high dimensional feature representation while the lower branch processes the low-resolution image and learns a filtering attention grid. We apply a max filter operation to non-overlapping sub-regions on the high feature representation before element-wise multiplied with the output of the second branch. The feature maps of the second branch are subsequently weighted to reflect the importance of each patch of the grid using a softmax operation. Our attention module helps the network to learn the most discriminative visual features of multiple image regions and is specifically optimized to attend feature representation at different levels. Extensive experiments on three large-scale datasets show that our self-attention mechanism significantly improves the baseline model and outperforms various state-of-art models by a large margin.
{"title":"Deep Residual Network with Self Attention Improves Person Re-Identification Accuracy","authors":"Jean-Paul Ainam, Ke Qin, Guisong Liu, Guangchun Luo","doi":"10.1145/3318299.3318324","DOIUrl":"https://doi.org/10.1145/3318299.3318324","url":null,"abstract":"In this paper, we present an attention mechanism scheme to improve the person re-identification task. Inspired by biology, we propose Self Attention Grid (SAG) to discover the most informative parts from a high-resolution image using its internal representation. In particular, given an input image, the proposed model is fed with two copies of the same image and consists of two branches. The upper branch processes the high-resolution image and learns high dimensional feature representation while the lower branch processes the low-resolution image and learns a filtering attention grid. We apply a max filter operation to non-overlapping sub-regions on the high feature representation before element-wise multiplied with the output of the second branch. The feature maps of the second branch are subsequently weighted to reflect the importance of each patch of the grid using a softmax operation. Our attention module helps the network to learn the most discriminative visual features of multiple image regions and is specifically optimized to attend feature representation at different levels. Extensive experiments on three large-scale datasets show that our self-attention mechanism significantly improves the baseline model and outperforms various state-of-art models by a large margin.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"16 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127559437","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 improvement of people's living standards, people's consumption of meat is getting higher and higher, and pork has become the core of Chinese meat production and consumption structure. Among pig farmers, retail investors account for more than half, their risk resistance capacity is weak, and they are vulnerable to price shocks. The price of live pigs showed significant seasonal changes, and violent fluctuations not only affected the interests of various links in the pig industry chain and the welfare of consumers, but also affected the development of the entire Chinese pig industry. Effective hog price forecast which is conducive to social stability and unity can not only ensure the income of farmers, but also ensure relationship between supply and demand. The article synthesizes the main indicators related to pork prices in the Chinese pork market, applying DBN (Dynamic Bayesian network) method and the SVM (support vector machine) method, the BP neural network method, these Machine Learning methods, and compare with traditional methods of the ARIMA method, to establish a predictive model of pork prices. The experiment was conducted in R and Bayes Server using 2001-2016 price data from the National Bureau of Statistics. The price is forecasted and analysed, the prediction effects of the four models are compared in this paper. The results show that the accuracy of predicting the pork price based on DBN model is better than other methods, RMSE=1.200822, MAPE=1.137312, TIC=0.0351875, all belong to a minimum.
{"title":"Application of Machine Learning Methods in Pork Price Forecast","authors":"Zaixin Ma, Zhongmin Chen, Taotao Chen, Mingwei Du","doi":"10.1145/3318299.3318364","DOIUrl":"https://doi.org/10.1145/3318299.3318364","url":null,"abstract":"With the improvement of people's living standards, people's consumption of meat is getting higher and higher, and pork has become the core of Chinese meat production and consumption structure. Among pig farmers, retail investors account for more than half, their risk resistance capacity is weak, and they are vulnerable to price shocks. The price of live pigs showed significant seasonal changes, and violent fluctuations not only affected the interests of various links in the pig industry chain and the welfare of consumers, but also affected the development of the entire Chinese pig industry. Effective hog price forecast which is conducive to social stability and unity can not only ensure the income of farmers, but also ensure relationship between supply and demand. The article synthesizes the main indicators related to pork prices in the Chinese pork market, applying DBN (Dynamic Bayesian network) method and the SVM (support vector machine) method, the BP neural network method, these Machine Learning methods, and compare with traditional methods of the ARIMA method, to establish a predictive model of pork prices. The experiment was conducted in R and Bayes Server using 2001-2016 price data from the National Bureau of Statistics. The price is forecasted and analysed, the prediction effects of the four models are compared in this paper. The results show that the accuracy of predicting the pork price based on DBN model is better than other methods, RMSE=1.200822, MAPE=1.137312, TIC=0.0351875, all belong to a minimum.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"327 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124301196","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}
Zhihao He, Tian Jin, Amlan Basu, J. Soraghan, G. D. Caterina, L. Petropoulakis
In this paper, we describe a new image pre-processing method, which can show features or important information clearly. Deep learning methods have grown rapidly in the last ten years and have better performance than the traditional machine learning methods in many domains. Deep learning shows its powerful ability particular in difficult multi-classes classification challenges. Video Facial expression recognition is one of the most popular classification topics and will become essential in robotics and auto-motion fields. The new system presented is a combination of new video pre-processing and Convolutional Neural Network (CNN). The new pre-processing method is proposed because we believe individual emotions are dynamic, which means the change of the face is the key feature. RAVDESS is the video set used, to train and test the neural network. From RAVDESS dataset the video songs without audio are taken for focusing on video frames differences. The chosen video set has six different classes of emotions. Each video presents a sentence in a melodious way. Based on the chosen video set, the new system with a new pre-processing method has been designed and trained. Later, the classification result of the new method has been compared with others in which the same dataset for video emotion recognition was used.
{"title":"Human Emotion Recognition in Video Using Subtraction Pre-Processing","authors":"Zhihao He, Tian Jin, Amlan Basu, J. Soraghan, G. D. Caterina, L. Petropoulakis","doi":"10.1145/3318299.3318321","DOIUrl":"https://doi.org/10.1145/3318299.3318321","url":null,"abstract":"In this paper, we describe a new image pre-processing method, which can show features or important information clearly. Deep learning methods have grown rapidly in the last ten years and have better performance than the traditional machine learning methods in many domains. Deep learning shows its powerful ability particular in difficult multi-classes classification challenges. Video Facial expression recognition is one of the most popular classification topics and will become essential in robotics and auto-motion fields. The new system presented is a combination of new video pre-processing and Convolutional Neural Network (CNN). The new pre-processing method is proposed because we believe individual emotions are dynamic, which means the change of the face is the key feature. RAVDESS is the video set used, to train and test the neural network. From RAVDESS dataset the video songs without audio are taken for focusing on video frames differences. The chosen video set has six different classes of emotions. Each video presents a sentence in a melodious way. Based on the chosen video set, the new system with a new pre-processing method has been designed and trained. Later, the classification result of the new method has been compared with others in which the same dataset for video emotion recognition was used.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126293858","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}
Muhammad Asad, Usman Qamar, Aimal Khan, Rahmat Ullah Safdar
Background: Diabetes Mellitus is one of the most common diseases, which is rapidly increasing worldwide. Early detection of Blood Glucose Level not only helps in better management of Diabetes Mellitus but also decreases the cost of treatment. In the recent past, numerous researches have been carried out to monitor blood glucose level which suggests the quantity of insulin i.e. artificial pancreas. Method: In this paper, we summarize and analyze the past work of continuous blood glucose monitoring and automatic insulin suggestion, in a systematic way. Particularly, 24 journal studies from 2015 to 2018 are identified and analyzed. The paper provided a dynamic study of insulin-glucose regulators by identifying some research questions and answering from the literature. Moreover, it provides brief of the methodology of each study and how it contributes towards this field. It also underlines the advantages of the methods used in past and how they lack in determining other aspects for achieving a completely autonomous, adaptive and individualized model. Results: A comprehensive investigation of the selected studies leads to identify four major areas i.e. Machine learning techniques (8 studies), MPC (6 studies), PID (2 studies), mixed (6) and others (2 studies).Conclusion: This study is helpful in opening a gateway for new researchers to have an overview of the past work on continuous glucose monitoring and insulin suggestion. It identifies the challenges in this particular domain in order to lay the foundation for future research. The survey discovers the most popular techniques used for blood glucose monitoring and insulin suggestion, exogenous or intravenous (Subcutaneous) or artificial pancreas. For future work, the nonlinear autoregressive neural network based model predictive controller is suggested.
{"title":"A Systematic Literature Review of Continuous Blood Glucose Monitoring and Suggesting the Quantity of Insulin or Artificial Pancreas (AP) for Diabetic Type 1 Patients","authors":"Muhammad Asad, Usman Qamar, Aimal Khan, Rahmat Ullah Safdar","doi":"10.1145/3318299.3318352","DOIUrl":"https://doi.org/10.1145/3318299.3318352","url":null,"abstract":"Background: Diabetes Mellitus is one of the most common diseases, which is rapidly increasing worldwide. Early detection of Blood Glucose Level not only helps in better management of Diabetes Mellitus but also decreases the cost of treatment. In the recent past, numerous researches have been carried out to monitor blood glucose level which suggests the quantity of insulin i.e. artificial pancreas. Method: In this paper, we summarize and analyze the past work of continuous blood glucose monitoring and automatic insulin suggestion, in a systematic way. Particularly, 24 journal studies from 2015 to 2018 are identified and analyzed. The paper provided a dynamic study of insulin-glucose regulators by identifying some research questions and answering from the literature. Moreover, it provides brief of the methodology of each study and how it contributes towards this field. It also underlines the advantages of the methods used in past and how they lack in determining other aspects for achieving a completely autonomous, adaptive and individualized model. Results: A comprehensive investigation of the selected studies leads to identify four major areas i.e. Machine learning techniques (8 studies), MPC (6 studies), PID (2 studies), mixed (6) and others (2 studies).Conclusion: This study is helpful in opening a gateway for new researchers to have an overview of the past work on continuous glucose monitoring and insulin suggestion. It identifies the challenges in this particular domain in order to lay the foundation for future research. The survey discovers the most popular techniques used for blood glucose monitoring and insulin suggestion, exogenous or intravenous (Subcutaneous) or artificial pancreas. For future work, the nonlinear autoregressive neural network based model predictive controller is suggested.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126328141","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}
Haifeng Li, Yan Li, Xutao Li, Yunming Ye, Xian Li, Pengfei Xie
In this paper, we make a comparative study to examine the performance of different machine learning approaches for the thunderstorm gale identification. To this end, a thunderstorm gale benchmark dataset is constructed, which comprises radar images in Guangdong from 2015 to 2017. The corresponding wind velocities recorded by the automatic meteorological observation stations are utilized to offer the ground-truth. Based on the dataset, we evaluate the performance of Decision Tree Regressor (DT), Linear Regression (LR), Ridge regression, Lasso regression, Random Forest Regressor (RFR), K-nearest Neighbor Regressor (KNNR), Bayesian Ridge Regressor (BR), Adaboost Regressor (AR), Support Vector Regressor (SVR), Gradient Boosting Regressor (GBR), and Convolutional Neural Network (CNN). Ten important features are extracted to apply these approaches, except CNN, which include radar echo intensity, radar reflectivity factor, radar combined reflectivity, vertical integrated liquid, echo tops and their changes with respect to (w.r.t.) time. Experimental results demonstrate the machine learning approaches can effectively identify the thunderstorm gale, and the CNN model performs the best. Finally, a thunderstorm system is developed based on CNN model, which help meteorologists to identify thunderstorm gales in terms of radar images.
{"title":"A Comparative Study on Machine Learning Approaches to Thunderstorm Gale Identification","authors":"Haifeng Li, Yan Li, Xutao Li, Yunming Ye, Xian Li, Pengfei Xie","doi":"10.1145/3318299.3318317","DOIUrl":"https://doi.org/10.1145/3318299.3318317","url":null,"abstract":"In this paper, we make a comparative study to examine the performance of different machine learning approaches for the thunderstorm gale identification. To this end, a thunderstorm gale benchmark dataset is constructed, which comprises radar images in Guangdong from 2015 to 2017. The corresponding wind velocities recorded by the automatic meteorological observation stations are utilized to offer the ground-truth. Based on the dataset, we evaluate the performance of Decision Tree Regressor (DT), Linear Regression (LR), Ridge regression, Lasso regression, Random Forest Regressor (RFR), K-nearest Neighbor Regressor (KNNR), Bayesian Ridge Regressor (BR), Adaboost Regressor (AR), Support Vector Regressor (SVR), Gradient Boosting Regressor (GBR), and Convolutional Neural Network (CNN). Ten important features are extracted to apply these approaches, except CNN, which include radar echo intensity, radar reflectivity factor, radar combined reflectivity, vertical integrated liquid, echo tops and their changes with respect to (w.r.t.) time. Experimental results demonstrate the machine learning approaches can effectively identify the thunderstorm gale, and the CNN model performs the best. Finally, a thunderstorm system is developed based on CNN model, which help meteorologists to identify thunderstorm gales in terms of radar images.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127158003","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}
Similarity comparison between two biological sequences is one of the main problems in computational biology research. A powerful statistical method D2 which depends on the joint k-tuples content in the two sequences, has been applied to the alignment-free sequences comparison. Two mutually independent random sequences under the null model have been produced, which is composed by AT-rich (P a =P t =0.33, P c =P g =0.17) distribution, and based on the null model, we got two foreground sequences with Bernoulli variables by a pattern transfer model. For the foreground sequences, by comparing local sequences pairs and then summing over all the local sequences pairs of certain length, and the local alignment-free of two sequences has been tested by statistics D2, D2star, D2shepp, then from the power of the three statistics, we can find the optimal parameters. The simulation results show that D2star is better than D2shepp, and D2 is relatively weak. We also analyze the power value distribution under different parameters, including Bernoulli variable g and tuple sizek and type I Error. At the same time by comparing the proposed local with globalalignment-freeabout D2star, and D2shepp under the same parameters, it showed that the power of local alignment-free based on D2star tends to 1 quickly with the increase of the length of the sequence, faster and more accurate than the global alignment.
两个生物序列的相似性比较是计算生物学研究中的主要问题之一。将一种依赖于两个序列中联合k元组含量的强大统计方法D2应用于无比对序列的比较。在零模型下产生了两个相互独立的随机序列，该序列由AT-rich (P a =P t =0.33, P c =P g =0.17)分布组成，并在零模型的基础上，通过模式转移模型得到了两个具有伯努利变量的前景序列。对于前景序列，通过比较局部序列对，然后对所有一定长度的局部序列对进行求和，并通过统计量D2, D2star, D2shepp对两个序列的局部不对齐进行检验，然后从三种统计量的幂函数中找到最优参数。仿真结果表明，D2star优于D2shepp，而D2相对较弱。我们还分析了不同参数下的功率值分布，包括伯努利变量g和元组大小和I型误差。同时，在相同参数下，通过对D2star局部对齐与D2shepp全局对齐进行比较，结果表明，随着序列长度的增加，基于D2star的局部对齐功率迅速趋于1，比全局对齐更快、更准确。
{"title":"The power study about three statistics of alignment-free comparison based on AT-RICH model","authors":"Meiliu Xue, Binhe Rui, Dongliu Bo, Xiangzang, Xiazhang Yu, Yaoliang Wen","doi":"10.1109/ICMLC.2015.7340613","DOIUrl":"https://doi.org/10.1109/ICMLC.2015.7340613","url":null,"abstract":"Similarity comparison between two biological sequences is one of the main problems in computational biology research. A powerful statistical method D2 which depends on the joint k-tuples content in the two sequences, has been applied to the alignment-free sequences comparison. Two mutually independent random sequences under the null model have been produced, which is composed by AT-rich (P a =P t =0.33, P c =P g =0.17) distribution, and based on the null model, we got two foreground sequences with Bernoulli variables by a pattern transfer model. For the foreground sequences, by comparing local sequences pairs and then summing over all the local sequences pairs of certain length, and the local alignment-free of two sequences has been tested by statistics D2, D2star, D2shepp, then from the power of the three statistics, we can find the optimal parameters. The simulation results show that D2star is better than D2shepp, and D2 is relatively weak. We also analyze the power value distribution under different parameters, including Bernoulli variable g and tuple sizek and type I Error. At the same time by comparing the proposed local with globalalignment-freeabout D2star, and D2shepp under the same parameters, it showed that the power of local alignment-free based on D2star tends to 1 quickly with the increase of the length of the sequence, faster and more accurate than the global alignment.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132978619","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}
Pub Date : 2015-07-12DOI: 10.1109/ICMLC.2015.7340638
Na Liu, J. Qiu, Li-Jun Zhang
This paper studies the robust H ∞ control for uncertain switched nonlinear singular systems using the linear matrix inequality (LMI) approach. We investigates the uncertain problems in both the time and the state function. The state function satisfies the Lipschitz condition. The LMI optimization approach and the Lyapunov function are used to solve the H ∞ control problem. The first step finds a suitable switching law guaranteeing that the switched system meets the desired control objective. Then, a state feedback controller is constructed by using the LMI approach. Finally, a simulation example is performed to exhibit the effectiveness of the proposed theory.
{"title":"Robust H∞ control for uncertain switched nonlinear singular systems using LMI approach","authors":"Na Liu, J. Qiu, Li-Jun Zhang","doi":"10.1109/ICMLC.2015.7340638","DOIUrl":"https://doi.org/10.1109/ICMLC.2015.7340638","url":null,"abstract":"This paper studies the robust H ∞ control for uncertain switched nonlinear singular systems using the linear matrix inequality (LMI) approach. We investigates the uncertain problems in both the time and the state function. The state function satisfies the Lipschitz condition. The LMI optimization approach and the Lyapunov function are used to solve the H ∞ control problem. The first step finds a suitable switching law guaranteeing that the switched system meets the desired control objective. Then, a state feedback controller is constructed by using the LMI approach. Finally, a simulation example is performed to exhibit the effectiveness of the proposed theory.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133264224","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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