2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim)最新文献_第2页

System Based Modelling Approach for Biomechanical Models in the Field of Prosthetics 基于系统的假肢领域生物力学模型建模方法

2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim) Pub Date : 2017-04-05 DOI: 10.1109/UKSim.2017.24

R. Leskovar, A. Körner, F. Breitenecker

引用次数: 0

A Comparison between ECG Beat Classifiers Using Multiclass SVM and SIMCA with Time Domain PCA Feature Reduction 多类支持向量机与时域PCA特征约简SIMCA心电心跳分类器的比较

2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim) Pub Date : 2017-04-04 DOI: 10.1109/UKSim.2017.16

N. Jannah, S. Hadjiloucas

{"title":"A Comparison between ECG Beat Classifiers Using Multiclass SVM and SIMCA with Time Domain PCA Feature Reduction","authors":"N. Jannah, S. Hadjiloucas","doi":"10.1109/UKSim.2017.16","DOIUrl":"https://doi.org/10.1109/UKSim.2017.16","url":null,"abstract":"Detection and treatment of arrhythmias has become one of the main goals in cardiac care diagnosis provided by general practitioners. Electrocardiogram (ECG) analysis is one of the most commonly used tools to test and diagnose heart problems. Classification of ECG heartbeats enables the identification of specific arrhythmia or other heart conditions. This paper presents and contrasts the results from two effective ECG arrhythmia classification schemes. The first scheme consists of a principal component analysis (PCA) step for feature reduction at the input vector to the classifier, combined with soft independent modelling of class analogy (SIMCA). The second method uses a multi-class support vector machine (MSVM) classifier to differentiate between four different types of arrhythmia from ECG beats. The four types of beats include Normal (N), Premature Ventricular Contraction (PVC), and Atrial premature contraction (APC) and Right Bundle Branch Block Beat (RBBB). The time domain features were obtained from the St Petersburg INCART 12-lead Arrhythmia Database (incartdb). Between 10 and 30 Principal Components (PCs) were selected for reconstructing individual ECG beats and create the input vector to the classifier. The average classification accuracy of the proposed scheme is 76.83% and 98.33% using MSVM and SIMCA classifier respectively. The SIMCA classification algorithm provided better performance than the MSVM classifiers.","PeriodicalId":309250,"journal":{"name":"2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130069408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

A System for Handwritten and Printed Text Classification 手写和印刷文本分类系统

2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim) Pub Date : 2017-04-01 DOI: 10.1109/UKSim.2017.37

B. Garlapati, S. Chalamala

引用次数: 20

Comparison of Seven Numerical Methods for Estimating Weibull Parameters for Wind Energy Applications 风能应用中威布尔参数估计的七种数值方法的比较

2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim) Pub Date : 2017-04-01 DOI: 10.1109/UKSim.2017.31

A. Teyabeen, F. Akkari, A. Jwaid

{"title":"Comparison of Seven Numerical Methods for Estimating Weibull Parameters for Wind Energy Applications","authors":"A. Teyabeen, F. Akkari, A. Jwaid","doi":"10.1109/UKSim.2017.31","DOIUrl":"https://doi.org/10.1109/UKSim.2017.31","url":null,"abstract":"Weibull distribution is widely used for the purpose of wind energy estimation. Their parameters should be precisely estimated since the wind energy is affected by it. The aim of this paper is to compare seven numerical methods to find out which is the most efficient for determining the parameters of Weibull distribution based on wind speed data, collected in Zuwara, Libya during 2007 at three hub heights of 10 m, 30 m, and 50 m above ground, by recording the wind speed every 10 minutes. The selected methods used in this study included graphical method, standard deviation method, empirical method of Justus, empirical method of Lysen, energy pattern factor method, maximum likelihood method, and modified maximum likelihood method. The performance of the seven numerical methods is evaluated by using different statistical criteria including mean absolute percentage error, mean absolute bias error, root mean square error, and correlation coefficient. The presented results indicated if Weibull distribution matches well with observed wind speed data, the empirical methods of Justus and Lysen present favorable efficiency; but if not, maximum likelihood gives the best performance followed by empirical methods of Justus and Lysen. The graphical method shows weak performance.","PeriodicalId":309250,"journal":{"name":"2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130983349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Power Curve Modelling for Wind Turbines 风力发电机功率曲线建模

2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim) Pub Date : 2017-04-01 DOI: 10.1109/UKSim.2017.30

A. Teyabeen, F. Akkari, A. Jwaid

{"title":"Power Curve Modelling for Wind Turbines","authors":"A. Teyabeen, F. Akkari, A. Jwaid","doi":"10.1109/UKSim.2017.30","DOIUrl":"https://doi.org/10.1109/UKSim.2017.30","url":null,"abstract":"The wind turbine power curve WTPC describes the relationship between wind speed and turbine power output. Power curve, provided by the manufacturer is one of the most important tools used to estimate turbine power output and capacity factor. Hence, an accurate WTPC model is essential for predicting wind energy potential. This paper presents a comparative study of various models for mathematical modelling of WTPC based on manufacturer power curve data gathered from 32 wind turbines ranging from 330 to 7580 kW. The selected models are validated by comparing the capacity factor obtained using the models based on Gamma probability density function with the capacity factor estimated using manufacturer power curves based on measured wind speed data. The selected models are also validated by comparing the instantaneous power obtained using the models with manufacturer power curve data. The accuracy of the models is evaluated using statistical criteria such as Normalized Root Mean Square Error (NRMSE), relative error (RE), and correlation coefficient ( ). The adopted model allows predicting the behavior of wind turbine generated under different wind speeds. Results of the analysis presented in this paper show that the power-coefficient based model presents favorable efficiency followed by general model, since they have lower values of RE in estimation of capacity factor, whereas the polynomial model showed the least accurate model.","PeriodicalId":309250,"journal":{"name":"2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132881337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Modelling and Simulation of Indoor Reverse RFID Tag Localization Method Based on Mobile Antenna Reader Position 基于移动天线读取器位置的室内反向RFID标签定位方法建模与仿真

2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim) Pub Date : 2017-04-01 DOI: 10.1109/UKSim.2017.33

Y. Madany, D. Mohamed, W. Ali, Reem F. Emara

{"title":"Modelling and Simulation of Indoor Reverse RFID Tag Localization Method Based on Mobile Antenna Reader Position","authors":"Y. Madany, D. Mohamed, W. Ali, Reem F. Emara","doi":"10.1109/UKSim.2017.33","DOIUrl":"https://doi.org/10.1109/UKSim.2017.33","url":null,"abstract":"Radio frequency identification (RFID) is a rapidly growing technology that has the potential to make great impacts on many industries. While RFID is a relatively old technology, more recent advancements in chip manufacturing technology are making RFID practical to affect many different industries and applications. These advancements have the potential to revolutionize supply-chain management, inventory control and logistics. The aim of this paper is to develop an efficient algorithm for RFID tag localization which uses an interesting method for positioning. The modeling and simulation of indoor reverse RFID tag localization based on mobile antenna reader position using enhancement triangulation method with a commercial RFID system are introduced for the positioning. Tag identification detection is done by a commercial RFID system and the specific aim is satisfied through the use of the proposed tag localization algorithm by Matlab program. Based on the proposed tag localization algorithm, the tag localization demonstration system has been designed and fabricated. Different trace scenarios based on practical data have been introduced and discussed to demonstrate the performance of the proposed indoor reverse RFID tag localization method to meet the requirements of different numerous new applications.","PeriodicalId":309250,"journal":{"name":"2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132592675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Low-Complexity, Power-Efficient, Scalable System for Linear Wireless Sensor Networks Used in Water Pipeline Monitoring Applications 用于供水管道监测应用的线性无线传感器网络低复杂度、高能效、可扩展系统

2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim) Pub Date : 2017-04-01 DOI: 10.1109/UKSim.2017.40

Muteb Alsaqhan, Mohammed A Alsuliman, Omar Alharthi, Y. Seddiq, M. A. Enazi

引用次数: 4

Artificial Fish Swarm Algorithm Based-Maximum Power Generation for Grid-Connected PV Panels 基于人工鱼群算法的并网光伏板最大功率发电

2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim) Pub Date : 2017-04-01 DOI: 10.1109/UKSim.2017.13

Mingxuan Mao, Li Zhang, Mark Musembi, B. Chong, Qi-chang Duan

引用次数: 3

An Architecture for Intelligent Data Processing on IoT Edge Devices 一种物联网边缘设备智能数据处理架构

2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim) Pub Date : 2017-04-01 DOI: 10.1109/UKSim.2017.19

R. Young, Sheila Fallon, P. Jacob

引用次数: 12

Analysis of Performance of Instruction Pipeline with Transactional Slice Mechanism in CMP 基于事务切片机制的CMP指令管道性能分析

2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim) Pub Date : 2017-04-01 DOI: 10.1109/UKSim.2017.15

A. Rajper, Shahnawaz Talpur, Noor-U.-Zaman Laghari, N. J. Rajper

{"title":"Analysis of Performance of Instruction Pipeline with Transactional Slice Mechanism in CMP","authors":"A. Rajper, Shahnawaz Talpur, Noor-U.-Zaman Laghari, N. J. Rajper","doi":"10.1109/UKSim.2017.15","DOIUrl":"https://doi.org/10.1109/UKSim.2017.15","url":null,"abstract":"Instruction throughput is one of the most important metric for improving the performance of systems and instruction pipeline play a major role in increasing this factor. But still, satisfactory performance of instruction pipeline is not yet achieved, due to some limitations, particularly branch instructions. These branch instructions restrict the full utilization of instruction pipeline. Therefore, the challenge is to reduce those branch penalties which cause significant degradation in performance of instruction pipelining. One optimal solution of reducing branch penalties is Transactional Slice (TS) mechanism with no branch overhead. The main objective of this research is the accurate size estimation for each transactional slice (TS), which can easily be incorporated into instruction pipeline. In previous research, the size of TS blocks estimated by using only one benchmarks suites has been investigated. However in this research article, the experiments have been conducted with three benchmark workloads to accomplish the accurate size estimation based on results from these workloads. From the results, we can also analyze the frequency of branch occurrence in instruction pipeline. The given mathematical model, examines the timing and pipeline behavior for in-order pipeline.","PeriodicalId":309250,"journal":{"name":"2017 UKSim-AMSS 19th International Conference on Computer Modelling & Simulation (UKSim)","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131949277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0