This paper develops an intelligent model validation method based on error correcting output coding support vector machine (ECOC SVM). The similarity analysis between simulation time series from computerized model and observed time series from real-world system is formulated as a multi-class classification problem. The ECOC framework, built on the basis of the error correcting principles of communication theory, decomposes the multi-class classification task as multiple binary classification problems. The SVM is used as the base classifier and a set of similarity measure methods is applied to extract the input features. Compared to conventional methods, the proposed validation method based on ECOC SVM incorporates multiple similarity measures to a comprehensive similarity measure and can learn to predict the credibility level from training samples. The application result reveals that the classification accuracy achieved 82%, which means the proposed method is promising for the similarity analysis of large datasets.
{"title":"An Intelligent Model Validation Method Based on ECOC SVM","authors":"Yuchen Zhou, K. Fang, Mingpei Yang, P. Ma","doi":"10.1145/3177457.3177487","DOIUrl":"https://doi.org/10.1145/3177457.3177487","url":null,"abstract":"This paper develops an intelligent model validation method based on error correcting output coding support vector machine (ECOC SVM). The similarity analysis between simulation time series from computerized model and observed time series from real-world system is formulated as a multi-class classification problem. The ECOC framework, built on the basis of the error correcting principles of communication theory, decomposes the multi-class classification task as multiple binary classification problems. The SVM is used as the base classifier and a set of similarity measure methods is applied to extract the input features. Compared to conventional methods, the proposed validation method based on ECOC SVM incorporates multiple similarity measures to a comprehensive similarity measure and can learn to predict the credibility level from training samples. The application result reveals that the classification accuracy achieved 82%, which means the proposed method is promising for the similarity analysis of large datasets.","PeriodicalId":297531,"journal":{"name":"Proceedings of the 10th International Conference on Computer Modeling and Simulation","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129064027","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}
R. Martínek, R. Kahankova, J. Nedoma, M. Fajkus, Kristýna Cholevová
Fetal electrocardiography is one of the most promising methods of Electronic fetal monitoring, which helps physicians to assess the fetal well-being diagnose the hypoxic states. This paper focuses on introducing Wavelet Transform as an effective tool to suppress the most frequent types of fetal electrocardiogram interferences, such as powerline or myopotential interference. We also suggest optimal type of the wavelet and threshold for this purpose.
{"title":"Fetal ECG Preprocessing Using Wavelet Transform","authors":"R. Martínek, R. Kahankova, J. Nedoma, M. Fajkus, Kristýna Cholevová","doi":"10.1145/3177457.3177503","DOIUrl":"https://doi.org/10.1145/3177457.3177503","url":null,"abstract":"Fetal electrocardiography is one of the most promising methods of Electronic fetal monitoring, which helps physicians to assess the fetal well-being diagnose the hypoxic states. This paper focuses on introducing Wavelet Transform as an effective tool to suppress the most frequent types of fetal electrocardiogram interferences, such as powerline or myopotential interference. We also suggest optimal type of the wavelet and threshold for this purpose.","PeriodicalId":297531,"journal":{"name":"Proceedings of the 10th International Conference on Computer Modeling and Simulation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123344534","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}
This paper proposes the reduction of the convergence time on a Convolutional Neural Network (CNN) method for traffic speed prediction, without reducing the performance of speed prediction method. The proposed method contains two procedures: The first one is to convert the traffic network data to images; in this case the speed variable will be transformed. The second step of the procedure presents a modification of the CNN method for speed prediction in which a separable convolution is used to reduce the number of parameters. This separable convolution helps to reducing the convergence time of speed predictions for large-scale transportation network. The proposal is evaluated with real data from the Caltrans Performance Measurement System (PeMS), obtained through sensors. The results show that Separable Convolutional Neural Network (SCNN) reduces convergence time of CNN method without losing the performance of the predictions of traffic speed in a large-scale transportation network.
{"title":"Using a Separable Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction","authors":"Arnold Loaiza, J. Herrera, Luis Mantilla","doi":"10.1145/3177457.3177464","DOIUrl":"https://doi.org/10.1145/3177457.3177464","url":null,"abstract":"This paper proposes the reduction of the convergence time on a Convolutional Neural Network (CNN) method for traffic speed prediction, without reducing the performance of speed prediction method. The proposed method contains two procedures: The first one is to convert the traffic network data to images; in this case the speed variable will be transformed. The second step of the procedure presents a modification of the CNN method for speed prediction in which a separable convolution is used to reduce the number of parameters. This separable convolution helps to reducing the convergence time of speed predictions for large-scale transportation network. The proposal is evaluated with real data from the Caltrans Performance Measurement System (PeMS), obtained through sensors. The results show that Separable Convolutional Neural Network (SCNN) reduces convergence time of CNN method without losing the performance of the predictions of traffic speed in a large-scale transportation network.","PeriodicalId":297531,"journal":{"name":"Proceedings of the 10th International Conference on Computer Modeling and Simulation","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124129877","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}
Intersection is an important component of the urban transport network, in where traffic congestion usually takes place. One of the key to solve urban transport problems is to organize the traffic in the intersection reasonably and effectively. This paper does research on a specific single intersection, using the video traffic data collection technology, considering signal cycle and phase time which are decided by a real-time traffic flow. The paper developed a self-adaptive timing model on the single target constraint to reduce intersection delay. The model is carried out through fuzzy-genetic algorithm. Matlab simulation analysis and a series of comparison show that the methods of optimization models and genetic algorithm are effective and feasible.
{"title":"Signal Timing Simulation of Single Intersection based on Fuzzy-Genetic Algorithm","authors":"Q. Luo, Yufei Hou, Zhuoqun Wang","doi":"10.1145/3177457.3177496","DOIUrl":"https://doi.org/10.1145/3177457.3177496","url":null,"abstract":"Intersection is an important component of the urban transport network, in where traffic congestion usually takes place. One of the key to solve urban transport problems is to organize the traffic in the intersection reasonably and effectively. This paper does research on a specific single intersection, using the video traffic data collection technology, considering signal cycle and phase time which are decided by a real-time traffic flow. The paper developed a self-adaptive timing model on the single target constraint to reduce intersection delay. The model is carried out through fuzzy-genetic algorithm. Matlab simulation analysis and a series of comparison show that the methods of optimization models and genetic algorithm are effective and feasible.","PeriodicalId":297531,"journal":{"name":"Proceedings of the 10th International Conference on Computer Modeling and Simulation","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126318430","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 this paper, we present a novel task scheduling algorithm, called rTuner. The key objective of the rTuner is to enhance the reduce task execution time in heterogeneous environments. Because, the reduce task is a very expensive process. The reduce tasks comprise of three phases, unlike to the map task, namely, copy phase, shuffle phase, and reduce phase. Therefore, the rescheduling a straggler reduce task can negatively affect the performance, if the scheduling algorithms does not analyze the underlying situation. The rTuner analyzes the reduce tasks' straggling reason, and tunes the reduce task. If a reduce task becomes straggler, then rTuner reschedules it in a suitable node depending on the situation. Our benchmark result shows that enhancement of reduce tasks boosts up the CPU elapsed time significantly. Moreover, we show the efficacy of the rTuner by extensive experiment in low-cost commodity hardware. The rTuner is able to improve the total job execution time of MapReduce significantly, either a heterogeneous environment or homogeneous environment. The rTuner is capable of reducing the execution time by 86.86 seconds and 100.67 seconds on an average over the Longest Approximate Time to End (LATE) in homogeneous and heterogeneous environment respectively. In addition, the rTuner is also able to improve the execution time by 142.44 seconds and 132.52 seconds over LATE in homogeneous and heterogeneous environment at the best situation respectively.
{"title":"rTuner: A Performance Enhancement of MapReduce Job","authors":"Ripon Patgiri, Rajdeep Das","doi":"10.1145/3177457.3191710","DOIUrl":"https://doi.org/10.1145/3177457.3191710","url":null,"abstract":"In this paper, we present a novel task scheduling algorithm, called rTuner. The key objective of the rTuner is to enhance the reduce task execution time in heterogeneous environments. Because, the reduce task is a very expensive process. The reduce tasks comprise of three phases, unlike to the map task, namely, copy phase, shuffle phase, and reduce phase. Therefore, the rescheduling a straggler reduce task can negatively affect the performance, if the scheduling algorithms does not analyze the underlying situation. The rTuner analyzes the reduce tasks' straggling reason, and tunes the reduce task. If a reduce task becomes straggler, then rTuner reschedules it in a suitable node depending on the situation. Our benchmark result shows that enhancement of reduce tasks boosts up the CPU elapsed time significantly. Moreover, we show the efficacy of the rTuner by extensive experiment in low-cost commodity hardware. The rTuner is able to improve the total job execution time of MapReduce significantly, either a heterogeneous environment or homogeneous environment. The rTuner is capable of reducing the execution time by 86.86 seconds and 100.67 seconds on an average over the Longest Approximate Time to End (LATE) in homogeneous and heterogeneous environment respectively. In addition, the rTuner is also able to improve the execution time by 142.44 seconds and 132.52 seconds over LATE in homogeneous and heterogeneous environment at the best situation respectively.","PeriodicalId":297531,"journal":{"name":"Proceedings of the 10th International Conference on Computer Modeling and Simulation","volume":"132 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113940017","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}
Takeshi Tsuchiya, Hiroo Hirose, T. Miyosawa, Tetsuyasu Yamada, Hiroaki Sawano, K. Koyanagi
This paper is discussed and proposed the weighting manner of MPTCP (Multi-Path TCP) subflow adapted to network conditions, and it improves network throughput efficiency. In our proposal, subflows are controlled expansion and suppression of congestion window size according to state of subflow under the environment which communications among subflows does not affect each other. From the results of simulation, it shows improvement of average throughput on application layer, and increase of packet arrival rate between sessions.
{"title":"Improving Network Throughput on Application by Weighting Subflows of Muti-Path TCP Adapted to Conditions","authors":"Takeshi Tsuchiya, Hiroo Hirose, T. Miyosawa, Tetsuyasu Yamada, Hiroaki Sawano, K. Koyanagi","doi":"10.1145/3177457.3177460","DOIUrl":"https://doi.org/10.1145/3177457.3177460","url":null,"abstract":"This paper is discussed and proposed the weighting manner of MPTCP (Multi-Path TCP) subflow adapted to network conditions, and it improves network throughput efficiency. In our proposal, subflows are controlled expansion and suppression of congestion window size according to state of subflow under the environment which communications among subflows does not affect each other. From the results of simulation, it shows improvement of average throughput on application layer, and increase of packet arrival rate between sessions.","PeriodicalId":297531,"journal":{"name":"Proceedings of the 10th International Conference on Computer Modeling and Simulation","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131774745","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":"Proceedings of the 10th International Conference on Computer Modeling and Simulation","authors":"","doi":"10.1145/3177457","DOIUrl":"https://doi.org/10.1145/3177457","url":null,"abstract":"","PeriodicalId":297531,"journal":{"name":"Proceedings of the 10th International Conference on Computer Modeling and Simulation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128716068","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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