To improve the test precision and inefficient of the mechanical self-locking button of automotive air conditioner control panel, high-precision automated test system was developed with virtual instrument technology. Design programs and hardware components of this system were introduced. A stable amplifying and filtering circuit was designed. Software of the test system was developed with Lab VIEW. The system realized the functions that high-speed data acquisition, serial communication between IPC and MCU, and database management. It has been running in the automotive air conditioner panel development and testing of a large automotive air conditioner manufacturer. The application shows that this system runs stably and accurately.
{"title":"Design of Mechanical Self-Locking Button Test System Based on Virtual Instrument","authors":"Shaopeng Tian, D. Xiong","doi":"10.1109/ISCID.2013.169","DOIUrl":"https://doi.org/10.1109/ISCID.2013.169","url":null,"abstract":"To improve the test precision and inefficient of the mechanical self-locking button of automotive air conditioner control panel, high-precision automated test system was developed with virtual instrument technology. Design programs and hardware components of this system were introduced. A stable amplifying and filtering circuit was designed. Software of the test system was developed with Lab VIEW. The system realized the functions that high-speed data acquisition, serial communication between IPC and MCU, and database management. It has been running in the automotive air conditioner panel development and testing of a large automotive air conditioner manufacturer. The application shows that this system runs stably and accurately.","PeriodicalId":297027,"journal":{"name":"2013 Sixth International Symposium on Computational Intelligence and Design","volume":"301 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123041538","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}
Load balancing technology can solve the network congestion problems of modern network which is caused by uneven distribution of traffic. As the network link load balancing is an NP-complete problem, it is difficult to use traditional method to deal with, introducing the idea of genetic algorithm. Using genetic algorithm, the characteristics of efficient and parallel can help to find the global optimal solution quickly. Article on the basis of traditional genetic algorithm, this paper puts forward a network link load balancing strategy based on improved genetic algorithm. Experiments show that it can find the answer to the problem better.
{"title":"A Study of Link Load Balancing Based on Improved Genetic Algorithm","authors":"Li Zhao, Yu-min Dong, Chen-yang Huang","doi":"10.1109/ISCID.2013.183","DOIUrl":"https://doi.org/10.1109/ISCID.2013.183","url":null,"abstract":"Load balancing technology can solve the network congestion problems of modern network which is caused by uneven distribution of traffic. As the network link load balancing is an NP-complete problem, it is difficult to use traditional method to deal with, introducing the idea of genetic algorithm. Using genetic algorithm, the characteristics of efficient and parallel can help to find the global optimal solution quickly. Article on the basis of traditional genetic algorithm, this paper puts forward a network link load balancing strategy based on improved genetic algorithm. Experiments show that it can find the answer to the problem better.","PeriodicalId":297027,"journal":{"name":"2013 Sixth International Symposium on Computational Intelligence and Design","volume":"291 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122717966","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}
The network traffic prediction model is the foundation of network performance analysis and designing. Aiming at limitation of the conventional network traffic time series prediction model and the problem that BP algorithms easily plunge into local solution, an optimization algorithm-PSO-QI which combine particle swarm optimization (PSO) and the quantum principle is proposed, and can alleviate the premature convergence validly. Then, the parameters of BP neural network were optimized and the time series of network traffic data was modeled and forecasted based on BP neural network and PSO-QI. Experiments showed that PSOQI-BP neural network has better precision and adaptability compared with the traditional neural network.
{"title":"A Network Traffic Prediction Model Based on Quantum Inspired PSO and Neural Network","authors":"Kun Zhang, L. Liang, Ying Huang","doi":"10.1109/ISCID.2013.168","DOIUrl":"https://doi.org/10.1109/ISCID.2013.168","url":null,"abstract":"The network traffic prediction model is the foundation of network performance analysis and designing. Aiming at limitation of the conventional network traffic time series prediction model and the problem that BP algorithms easily plunge into local solution, an optimization algorithm-PSO-QI which combine particle swarm optimization (PSO) and the quantum principle is proposed, and can alleviate the premature convergence validly. Then, the parameters of BP neural network were optimized and the time series of network traffic data was modeled and forecasted based on BP neural network and PSO-QI. Experiments showed that PSOQI-BP neural network has better precision and adaptability compared with the traditional neural network.","PeriodicalId":297027,"journal":{"name":"2013 Sixth International Symposium on Computational Intelligence and Design","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123872846","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}
At present many hospitals have to deal with the patient's care and nursing for Acute Hypotensive Episodes (AHE) in the Intensive Care Unit (ICU). But the prediction of clinical AHE largely depends on the doctor's experience. It is meaningful for clinical care if we can use appropriate methods to predict the AHE in advance and automatically. In this paper, we propose a method to predict the AHE that uses the particle swarm optimization (PSO) algorithm to optimize the initial cluster centers of K-means which extracts the features of patient's mean arterial blood pressure (MAP). Then these features extracted from K-means coupled with the average of a sequence of MAP signal are classified with the support vector machine (SVM). We classified 2863 records, and the best accuracy achieved for the prediction based on the method proposed in this work was 81.2% (sensitivity=83.2% and specificity=80.4%).
{"title":"A Method for Prediction of Acute Hypotensive Episodes in ICU via PSO and K-Means","authors":"Hao-jun Sun, Shukun Sun, Yunxia Wu, Meijuan Yan, Chengdian Zhang","doi":"10.1109/ISCID.2013.32","DOIUrl":"https://doi.org/10.1109/ISCID.2013.32","url":null,"abstract":"At present many hospitals have to deal with the patient's care and nursing for Acute Hypotensive Episodes (AHE) in the Intensive Care Unit (ICU). But the prediction of clinical AHE largely depends on the doctor's experience. It is meaningful for clinical care if we can use appropriate methods to predict the AHE in advance and automatically. In this paper, we propose a method to predict the AHE that uses the particle swarm optimization (PSO) algorithm to optimize the initial cluster centers of K-means which extracts the features of patient's mean arterial blood pressure (MAP). Then these features extracted from K-means coupled with the average of a sequence of MAP signal are classified with the support vector machine (SVM). We classified 2863 records, and the best accuracy achieved for the prediction based on the method proposed in this work was 81.2% (sensitivity=83.2% and specificity=80.4%).","PeriodicalId":297027,"journal":{"name":"2013 Sixth International Symposium on Computational Intelligence and Design","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124227638","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 view of the current situation of the industry, our country energy resources are relatively scarce and cement enterprises have been as high energy consumption industry, this paper presented electric power management information system, to realize the energy saving of cement enterprises, the scheme can provide data support for the enterprises with accurate management, can be found in time metering device failures and anomalies to improve equipment management level, can realized remote meter reading function to improve staff reading accuracy and work efficiency, can be calculated per unit energy consumption of electricity. This was through the establishment of a rotary kiln system energy expert system, to analyze the power consumption for many reasons, to give the control member of energy-saving advice, finally, hope that through the control of rotary kiln, the system of rotary kiln can achieve energy saving.
{"title":"The Power Consumption Analysis of Cement Rotary Kiln Production Line System","authors":"Yuan Zhu-gang, Chenghao Pu, W. Xiaohong","doi":"10.1109/ISCID.2013.10","DOIUrl":"https://doi.org/10.1109/ISCID.2013.10","url":null,"abstract":"In view of the current situation of the industry, our country energy resources are relatively scarce and cement enterprises have been as high energy consumption industry, this paper presented electric power management information system, to realize the energy saving of cement enterprises, the scheme can provide data support for the enterprises with accurate management, can be found in time metering device failures and anomalies to improve equipment management level, can realized remote meter reading function to improve staff reading accuracy and work efficiency, can be calculated per unit energy consumption of electricity. This was through the establishment of a rotary kiln system energy expert system, to analyze the power consumption for many reasons, to give the control member of energy-saving advice, finally, hope that through the control of rotary kiln, the system of rotary kiln can achieve energy saving.","PeriodicalId":297027,"journal":{"name":"2013 Sixth International Symposium on Computational Intelligence and Design","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126180727","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}
The grey prediction model is a kind of data prediction method in data mining area. The construction of the grey prediction model is introduced in the paper first. Then in order to obtain higher precision of the prediction model, the way on how to select adjust parameters to adjust the original model is introduced. Finally the prediction procedure is described in a figure and it is explained in details. In case study, fifty traffic indexes come from transport bureau of Hunan province are predicted. It shows the effect and efficiency of the grey prediction model and its adjust parameters.
{"title":"Traffic Indexes Prediction Based on Grey Prediction Model","authors":"P. Hui, Wenqi Qu, Junjian Tang, J. Chen","doi":"10.1109/ISCID.2013.68","DOIUrl":"https://doi.org/10.1109/ISCID.2013.68","url":null,"abstract":"The grey prediction model is a kind of data prediction method in data mining area. The construction of the grey prediction model is introduced in the paper first. Then in order to obtain higher precision of the prediction model, the way on how to select adjust parameters to adjust the original model is introduced. Finally the prediction procedure is described in a figure and it is explained in details. In case study, fifty traffic indexes come from transport bureau of Hunan province are predicted. It shows the effect and efficiency of the grey prediction model and its adjust parameters.","PeriodicalId":297027,"journal":{"name":"2013 Sixth International Symposium on Computational Intelligence and Design","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128071304","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}
T. Peng, Delong Zhang, Xiaoming Liu, Shang Wang, Wanli Zuo
Most researches on co-authorship network analyze the author's information globally according to the overall network topology structure, instead of analyzing the author's local network. Therefore, this paper presents a community mining algorithm and divides big co-authorship network into small communities, in which entities' relationship is closer. Then we mine central authors in community by three different centrality standards including closeness centrality, eigenvector centrality and a new proposed measure termed extensity degree centrality. We choose the SIGMOD data as datasets and measure the centrality from different views. And experiments in co-authorship network achieve many interesting results, which indicate our technique is efficient and feasible, and also have reference value for scientific evaluation.
{"title":"Central Author Mining from Co-authorship Network","authors":"T. Peng, Delong Zhang, Xiaoming Liu, Shang Wang, Wanli Zuo","doi":"10.1109/ISCID.2013.64","DOIUrl":"https://doi.org/10.1109/ISCID.2013.64","url":null,"abstract":"Most researches on co-authorship network analyze the author's information globally according to the overall network topology structure, instead of analyzing the author's local network. Therefore, this paper presents a community mining algorithm and divides big co-authorship network into small communities, in which entities' relationship is closer. Then we mine central authors in community by three different centrality standards including closeness centrality, eigenvector centrality and a new proposed measure termed extensity degree centrality. We choose the SIGMOD data as datasets and measure the centrality from different views. And experiments in co-authorship network achieve many interesting results, which indicate our technique is efficient and feasible, and also have reference value for scientific evaluation.","PeriodicalId":297027,"journal":{"name":"2013 Sixth International Symposium on Computational Intelligence and Design","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128839197","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}
HLA has become the mainstream of distributed simulation technology, but serial execution of simulation objects in the existing HLA run-time infrastructure results in huge computation and communication bottlenecks. It has been becoming a focus of attention to accomplish the parallel scheduling of HLA simulation application. This paper proposed a method for parallel scheduling of HLA-based simulation objects. We introduced the base classes named SimObject and Interaction to map object attributes and interaction parameters from FOM files to object instances and interaction instances, and constructed additional three new service components: simulation manager, object manager, interaction manager. This way, simulation objects in a federate or across different federates can be scheduled in parallel, improving the running efficiency of simulation system.
{"title":"Study for Parallel Scheduling of HLA-Based Simulation Objects","authors":"L. Meng, Liu Bu-quan, Zuo Xiao-liang","doi":"10.1109/ISCID.2013.146","DOIUrl":"https://doi.org/10.1109/ISCID.2013.146","url":null,"abstract":"HLA has become the mainstream of distributed simulation technology, but serial execution of simulation objects in the existing HLA run-time infrastructure results in huge computation and communication bottlenecks. It has been becoming a focus of attention to accomplish the parallel scheduling of HLA simulation application. This paper proposed a method for parallel scheduling of HLA-based simulation objects. We introduced the base classes named SimObject and Interaction to map object attributes and interaction parameters from FOM files to object instances and interaction instances, and constructed additional three new service components: simulation manager, object manager, interaction manager. This way, simulation objects in a federate or across different federates can be scheduled in parallel, improving the running efficiency of simulation system.","PeriodicalId":297027,"journal":{"name":"2013 Sixth International Symposium on Computational Intelligence and Design","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127634322","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 a novel classification method named KF-SVM (Kernel Fisher, Support Vector Machine), which is used for the EEG (Electroencephalography) classification of two classes of imagery data in BCIs (brain-computer interfaces). This method combines the kernel fisher and SVM. Its detailed process is as follows: First, the CSP (Common Spatial Patterns) is used to obtain features, and then the within-class scatter is calculated based on these features. The scatter is added into the RBF (Radical Basis Function) kernel function to construct a new kernel function. The obtained new kernel is integrated into the support vector machine to get a new classification model. The KF-SVM may overcome the following defects of the SVM: 1) the SVM maximizes the classification margin without considering within-class scatter. 2) The classification surface of the SVM between two types of EEG data only depends on boundary samples and misclassified samples. To evaluate effectiveness of the proposed KF-SVM method, the data from the 2008 international BCI competition and experiments of our laboratory are processed. The experimental result shows that the proposed KF-SVM classification algorithm can well classify EEG data and improve the correct rate of EEG recognition in BCIs.
{"title":"A New Classification Method Based on KF-SVM in Brain Computer Interfaces","authors":"Yang Banghua, Han Zhijun, Wang Qian, He Liangfei","doi":"10.1109/ISCID.2013.55","DOIUrl":"https://doi.org/10.1109/ISCID.2013.55","url":null,"abstract":"This paper proposes a novel classification method named KF-SVM (Kernel Fisher, Support Vector Machine), which is used for the EEG (Electroencephalography) classification of two classes of imagery data in BCIs (brain-computer interfaces). This method combines the kernel fisher and SVM. Its detailed process is as follows: First, the CSP (Common Spatial Patterns) is used to obtain features, and then the within-class scatter is calculated based on these features. The scatter is added into the RBF (Radical Basis Function) kernel function to construct a new kernel function. The obtained new kernel is integrated into the support vector machine to get a new classification model. The KF-SVM may overcome the following defects of the SVM: 1) the SVM maximizes the classification margin without considering within-class scatter. 2) The classification surface of the SVM between two types of EEG data only depends on boundary samples and misclassified samples. To evaluate effectiveness of the proposed KF-SVM method, the data from the 2008 international BCI competition and experiments of our laboratory are processed. The experimental result shows that the proposed KF-SVM classification algorithm can well classify EEG data and improve the correct rate of EEG recognition in BCIs.","PeriodicalId":297027,"journal":{"name":"2013 Sixth International Symposium on Computational Intelligence and Design","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125448961","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}
Peng Zhang, Ronggang Han, Rui Jin, K. Yu, Jun Liu, H. Bao, Yu Zhang, J. Che
Simulation technology provides a powerful tool for the IGBT module design. The IGBT chip model and the interconnect parasitics are the most important factors for the package design of IGBT module. By the aid of the simulation software, the IGBT chip model is fulfilled and the package parasitics is extracted. So the whole simulation deign platform of IGBT package achieves.
{"title":"Research in the Simulation Model of IGBT Package","authors":"Peng Zhang, Ronggang Han, Rui Jin, K. Yu, Jun Liu, H. Bao, Yu Zhang, J. Che","doi":"10.1109/ISCID.2013.111","DOIUrl":"https://doi.org/10.1109/ISCID.2013.111","url":null,"abstract":"Simulation technology provides a powerful tool for the IGBT module design. The IGBT chip model and the interconnect parasitics are the most important factors for the package design of IGBT module. By the aid of the simulation software, the IGBT chip model is fulfilled and the package parasitics is extracted. So the whole simulation deign platform of IGBT package achieves.","PeriodicalId":297027,"journal":{"name":"2013 Sixth International Symposium on Computational Intelligence and Design","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125504763","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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