2010 IEEE 9th International Conference on Cyberntic Intelligent Systems最新文献_第5页

A comparison of nonlinear Kalman filtering applied to feed-forward neural networks as learning algorithms 非线性卡尔曼滤波作为前馈神经网络学习算法的比较

2010 IEEE 9th International Conference on Cyberntic Intelligent Systems Pub Date : 2010-09-01 DOI: 10.1109/UKRICIS.2010.5898137

W. Pietruszkiewicz

引用次数: 3

A novel human-machine interface using subdermal magnetic implants 使用皮下磁性植入物的新型人机界面

2010 IEEE 9th International Conference on Cyberntic Intelligent Systems Pub Date : 2010-09-01 DOI: 10.1109/UKRICIS.2010.5898141

J. Hameed, Ian Harrison, M. Gasson, K. Warwick

引用次数: 28

Robust H∞ optimal controller designs for feedback substitution schemes using LSDP and MOGA 基于LSDP和MOGA的反馈替代鲁棒H∞最优控制器设计

2010 IEEE 9th International Conference on Cyberntic Intelligent Systems Pub Date : 2010-09-01 DOI: 10.1109/UKRICIS.2010.5898152

M. Trujillo, S. Hadjiloucas, V. Becerra

引用次数: 0

A new deformable model for cataract eye surgery simulation 一种新的白内障眼手术模拟变形模型

2010 IEEE 9th International Conference on Cyberntic Intelligent Systems Pub Date : 2010-09-01 DOI: 10.1109/UKRICIS.2010.5898132

El-mabruk Abulgasem, S. Nefti

引用次数: 3

Comparison of interactive multiple model particle filter and interactive multiple model unscented particle filter for tracking multiple manoeuvring targets in sensors array 交互式多模型粒子滤波器与交互式多模型无气味粒子滤波器在传感器阵列中多机动目标跟踪中的比较

2010 IEEE 9th International Conference on Cyberntic Intelligent Systems Pub Date : 2010-09-01 DOI: 10.1109/UKRICIS.2010.5898109

Z. Messaoudi, A. Ouldali, M. Oussalah

{"title":"Comparison of interactive multiple model particle filter and interactive multiple model unscented particle filter for tracking multiple manoeuvring targets in sensors array","authors":"Z. Messaoudi, A. Ouldali, M. Oussalah","doi":"10.1109/UKRICIS.2010.5898109","DOIUrl":"https://doi.org/10.1109/UKRICIS.2010.5898109","url":null,"abstract":"Tracking multiple targets in cluttered environment has been acknowledged as a challenging task involving handling of measurement track-to-track uncertainty association in conjunction with nonlinearity and imprecision pervading the target dynamic models. In this paper an approach based on the use of an interacting multiple model particle filter (IMMPF) has been put forward, where the particle filter (PF) allows the system to handle non-linearity of the target cinematic models while the interacting multiple model (IMM) deals with the model switch when a target changes its manoeuvre. On the other hand, Cheap Joint Probabilistic Data Association (CJPDA) was used to tackle the data association problem. Two fusion architectures using the federated and the centralized form of Kalman filter were investigated. Performances and feasibility of the proposal are demonstrated through a set of Monte Carlo simulations involving three crossing targets. Also, a comparison analysis with an alternative approach using the IMM filter in conjunction with the Unscented Particle Filter (IMMUPF) is carried out. The results demonstrate the feasibility of the proposal and satisfactory tracking of the targets.","PeriodicalId":359942,"journal":{"name":"2010 IEEE 9th International Conference on Cyberntic Intelligent Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116520913","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: 4

Prediction of output response probability for sound environment system by introducing stochastic regression and fuzzy inference for simplified standard system model 对简化标准系统模型引入随机回归和模糊推理的声环境系统输出响应概率预测

2010 IEEE 9th International Conference on Cyberntic Intelligent Systems Pub Date : 2010-09-01 DOI: 10.1109/UKRICIS.2010.5898144

A. Ikuta, H. Orimoto

{"title":"Prediction of output response probability for sound environment system by introducing stochastic regression and fuzzy inference for simplified standard system model","authors":"A. Ikuta, H. Orimoto","doi":"10.1109/UKRICIS.2010.5898144","DOIUrl":"https://doi.org/10.1109/UKRICIS.2010.5898144","url":null,"abstract":"The traditional standard stochastic system models, such as the AR (Autoregressive), MA (Moving average) and ARMA (Autoregressive moving average) models, usually assume the Gaussian property for the fluctuation distribution. These models assume also the linear regression function for the time series of system input and output, and the well-known least squares method is applied based only on the linear correlation data. In the actual sound environment system, however, the stochastic process exhibits various non-Gaussian distributions, and there potentially exist various nonlinear correlations in addition to the linear correlation between input and output time series. Consequently, often the system input and output relationship in the actual phenomenon cannot be represented by a simple model such as the AR, MA and ARMA models. In this study, a prediction method of output response probability for sound environment system is derived by introducing a correction method for simplified standard system models. More precisely, a parameter-linear regression model is adopted as a simplified standard system model for the input and output relationship. Furthermore, a correction method for the simplified standard system model is proposed by introducing the stochastic regression and fuzzy inference. The proposed method is applied to the actual data in a sound environmnet system, and the practical usefulness is verified.","PeriodicalId":359942,"journal":{"name":"2010 IEEE 9th International Conference on Cyberntic Intelligent Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127161385","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

An adaptive approach to the characters size for text detection in images 图像文本检测中字符大小的自适应方法

2010 IEEE 9th International Conference on Cyberntic Intelligent Systems Pub Date : 2010-09-01 DOI: 10.1109/UKRICIS.2010.5898128

Mohmmadreza Nazarahari, Vahid Ezzati Chahar Ghaleh

引用次数: 0

Meta-heuristics tunning using CBR for dynamic scheduling 基于CBR的元启发式动态调度

2010 IEEE 9th International Conference on Cyberntic Intelligent Systems Pub Date : 2010-09-01 DOI: 10.1109/UKRICIS.2010.5898093

I. Pereira, A. Madureira

引用次数: 3

Multi-class AdaBoost learning of facial feature selection through Grid Computing 基于网格计算的AdaBoost多类人脸特征选择学习

2010 IEEE 9th International Conference on Cyberntic Intelligent Systems Pub Date : 2010-09-01 DOI: 10.1109/UKRICIS.2010.5898149

Mian Zhou, Hong Wei, I. Bland, A. Worrall, David Spence, Xiangjun Wang, Pengcheng Wen, Feng Liu

{"title":"Multi-class AdaBoost learning of facial feature selection through Grid Computing","authors":"Mian Zhou, Hong Wei, I. Bland, A. Worrall, David Spence, Xiangjun Wang, Pengcheng Wen, Feng Liu","doi":"10.1109/UKRICIS.2010.5898149","DOIUrl":"https://doi.org/10.1109/UKRICIS.2010.5898149","url":null,"abstract":"AdaBoost is an efficient method for producing a highly accurate learning algorithm by assembling multiple classifiers, but it is also widely known for its long duration of off-line learning. Especially, when it is applied for feature selection for object detection, its learning process is to exhaustively evaluate every feature in a large set. With the increasing of image resolution and complexity of feature transformation approaches, the computational time will be extremely long, which makes the large scale AdaBoost learning very difficult. In this paper, we have employed Grid Computing to solve the difficulty. The proposed algorithm is to select the most significant features for face recognition. The selection algorithm is derived from multi-class AdaBoost, which exhaustively evaluate every feature from a large set. The deployed Grid Computing system is actually used for High Throughput Computing specialised on advanced resource management. To utilizing Grid Computing on the feature selection process, we have improved multi-class AdaBoost learning algorithm with parallel structure, so that the task of High Performance Computing is accomplished in the environment of High Throughput Computing. With Grid Computing, selecting 200 features from a large set of 30240 features is finished in 20 days, while without Grid Computing the time would be more than two years. It shows that Grid Computing brings vast advantage to computer vision, machine learning, image processing, and pattern recognition.","PeriodicalId":359942,"journal":{"name":"2010 IEEE 9th International Conference on Cyberntic Intelligent Systems","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130583664","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: 16

3D Cooperative Pythagorean Hodograph path planning and obstacle avoidance for multiple UAVs 多无人机三维协同毕达哥拉斯轨迹规划与避障

2010 IEEE 9th International Conference on Cyberntic Intelligent Systems Pub Date : 2010-09-01 DOI: 10.1109/UKRICIS.2010.5898124

M. A. Shah, N. Aouf

引用次数: 22