2014 International Conference on Control, Decision and Information Technologies (CoDIT)最新文献_第10页

Experimental second order sliding mode fault tolerant control for moment gyroscope system with sensor fault 传感器故障力矩陀螺仪系统的实验二阶滑模容错控制

2014 International Conference on Control, Decision and Information Technologies (CoDIT) Pub Date : 2014-12-29 DOI: 10.1109/CoDIT.2014.6996928

A. Estaca, M. Boukhnifer

引用次数: 2

A dichotomic algorithm for an operating room scheduling problem 手术室调度问题的二分类算法

2014 International Conference on Control, Decision and Information Technologies (CoDIT) Pub Date : 2014-12-29 DOI: 10.1109/CoDIT.2014.6996882

Mohamed Amine Abdeljaouad, N. E. H. Saadani, Zied Bahroun

引用次数: 4

Frequency adaptive repetitive control of grid-connected inverters 并网逆变器频率自适应重复控制

2014 International Conference on Control, Decision and Information Technologies (CoDIT) Pub Date : 2014-12-29 DOI: 10.1109/CoDIT.2014.6996960

R. Nazir, Keliang Zhou, N. Watson, A. Wood

{"title":"Frequency adaptive repetitive control of grid-connected inverters","authors":"R. Nazir, Keliang Zhou, N. Watson, A. Wood","doi":"10.1109/CoDIT.2014.6996960","DOIUrl":"https://doi.org/10.1109/CoDIT.2014.6996960","url":null,"abstract":"Grid-connected inverters (GCI) are widely used to feed power from renewable energy distributed generators into smarter grids. Repetitive control (RC) enables such inverters to inject high quality fundamental-frequency sinusoidal currents into the grid. However, digital RC which can get approximately zero tracking error of any periodic signal with known integer period in steady-state, cannot exactly track or reject periodic signal of frequency variations. Thus digital RC would lead to a significant power quality degradation of GCIs when grid frequency varies and causes periodic signal with non-integer periods. In this research paper a frequency adaptive repetitive control scheme (FARC) at a predefined sampling rate is proposed to deal with all types of periodic signal of variable frequency. A fractional delay filter which is based on Lagrange interpolation is used to estimate the fractional period terms in RC. This proposed FARC controller offers the fast, during process modification of fractional delay and fast revise of filter parameters, and then provides GCIs with a simple but very accurate real-time frequency adaptive control solution to the injection of high quality sinusoidal current under grid frequency variations. A case study a three-phase GCI is conducted to testify the validity of the proposed strategy.","PeriodicalId":161703,"journal":{"name":"2014 International Conference on Control, Decision and Information Technologies (CoDIT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128651020","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: 11

Multi-variable flatness-based control of a helicopter with two degrees of freedom 基于多变量平面度的二自由度直升机控制

2014 International Conference on Control, Decision and Information Technologies (CoDIT) Pub Date : 2014-12-29 DOI: 10.1109/CoDIT.2014.6996914

S. Butt, R. Prabel, H. Aschemann

引用次数: 9

Effectiveness of combined time-frequency imageand signal-based features for improving the detection and classification of epileptic seizure activities in EEG signals 结合时频图像和信号特征对脑电图信号中癫痫发作活动的检测和分类的有效性

2014 International Conference on Control, Decision and Information Technologies (CoDIT) Pub Date : 2014-12-29 DOI: 10.1109/CoDIT.2014.6996977

L. Boubchir, S. Al-Maadeed, A. Bouridane

{"title":"Effectiveness of combined time-frequency imageand signal-based features for improving the detection and classification of epileptic seizure activities in EEG signals","authors":"L. Boubchir, S. Al-Maadeed, A. Bouridane","doi":"10.1109/CoDIT.2014.6996977","DOIUrl":"https://doi.org/10.1109/CoDIT.2014.6996977","url":null,"abstract":"This paper presents new time-frequency (T-F) features to improve the detection and classification of epileptic seizure activities in EEG signals. Most previous methods were based only on signal features derived from the instantaneous frequency and energies of EEG signals generated from different spectral sub-bands. The proposed features are based on T-F image descriptors, which are extracted from the T-F representation of EEG signals, are considered and processed as an image using image processing techniques. The idea of the proposed feature extraction method is based on the application of Otsu's thresholding algorithm on the T-F image in order to detect the regions of interest where the epileptic seizure activity appears. The proposed T-F image related-features are then defined to describe the statistical and geometrical characteristics of the detected regions. The results obtained on real EEG data suggest that the use of T-F image based-features with signal related-features improve significantly the performance of the EEG seizure detection and classification by up to 5% for 120 EEG signals, using a multi-class SVM classifier.","PeriodicalId":161703,"journal":{"name":"2014 International Conference on Control, Decision and Information Technologies (CoDIT)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128349897","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: 5

Survival analysis of pre-operative GBM patients by using quantitative image features 定量图像特征分析术前GBM患者的生存

2014 International Conference on Control, Decision and Information Technologies (CoDIT) Pub Date : 2014-12-23 DOI: 10.1109/CoDIT.2014.6996968

P. Wangaryattawanich, Jixin Wang, Ginu A. Thomas, A. Chaddad, P. Zinn, R. Colen

引用次数: 2

Quantitative texture analysis for Glioblastoma phenotypes discrimination 胶质母细胞瘤表型鉴别的定量结构分析

2014 International Conference on Control, Decision and Information Technologies (CoDIT) Pub Date : 2014-12-23 DOI: 10.1109/CoDIT.2014.6996964

A. Chaddad, P. Zinn, R. Colen

引用次数: 17

Linked data, data mining and external open data for better prediction of at-risk students 关联数据，数据挖掘和外部开放数据，以更好地预测有风险的学生

2014 International Conference on Control, Decision and Information Technologies (CoDIT) Pub Date : 2014-12-16 DOI: 10.1109/CoDIT.2014.6996973

F. Sarker, T. Tiropanis, H. Davis

{"title":"Linked data, data mining and external open data for better prediction of at-risk students","authors":"F. Sarker, T. Tiropanis, H. Davis","doi":"10.1109/CoDIT.2014.6996973","DOIUrl":"https://doi.org/10.1109/CoDIT.2014.6996973","url":null,"abstract":"Research in student retention is traditionally survey-based, where researchers use questionnaires to collect student data to analyse and to develop student predictive model. The major issues with survey-based study are the potentially low response rates, time consuming and costly. Nevertheless, a large number of datasets that could inform the questions that students are explicitly asked in surveys is commonly available in the external open datasets. This paper describes a new student predictive model that uses commonly available external open data instead of traditional questionnaires/surveys to spot `at-risk' students. Considering the promising behavior of neural networks led us to develop student predictive models to predict `at-risk' students. The results of empirical study for undergraduate students in their first year of study shows that this model can perform as well as or even out-perform traditional survey-based ones. The prediction performance of this study was also compared with that of logistic regression approach. The results shows that neural network slightly improved the overall model accuracy however, according to the model sensitivity, it is suggested that logistic regression performs better for identifying `at-risk' students in their programme of study.","PeriodicalId":161703,"journal":{"name":"2014 International Conference on Control, Decision and Information Technologies (CoDIT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121666046","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: 24

Application of electronic nose to beer recognition using supervised artificial neural networks 电子鼻在监督人工神经网络啤酒识别中的应用

2014 International Conference on Control, Decision and Information Technologies (CoDIT) Pub Date : 2014-11-01 DOI: 10.1109/CoDIT.2014.6996971

M. Siadat, E. Losson, M. Ghasemi-Varnamkhasti, S. Mohtasebi

引用次数: 18

A piezo servo hydraulic actuator for use in camless combustion engines and its control with MPC 一种用于无凸轮内燃机的压电伺服液压执行器及其MPC控制

2014 International Conference on Control, Decision and Information Technologies (CoDIT) Pub Date : 2014-11-01 DOI: 10.1109/CoDIT.2014.6996939

Benedikt Haus, Paolo Mercorelli, N. Werner

引用次数: 2