检索结果-内蒙古大学图书馆

IEEE Wireless Communications and Networking Conference

作者： Yingqi Lu Pai Zhu Donglin Wang Michel Fattouche Department of Electrical and Computer Engineering University of Calgary Carnegie Mellon University Department of Electrical and Computer Engineering New York Institute of Technology

ISBN: (纸本)9781467398152

We study cooperative spectrum sensing in cognitive radio networks (CRN) using machine learning techniques in this paper. A low-dimensional probability vector is proposed as the feature vector for machine learning based classification, instead of the N-dimensional energy vector in a CRN with a single primary user (PU) and N secondary users (SUs). This proposed method down-converts a high-dimensional feature vector to a constant two-dimensional feature vector for machine learning techniques while keeping the same spectrum sensing performance if not better. Due to its lower dimension, the probability vector based classification is capable of having a smaller training duration and a shorter classification time for testing vectors.

关键词： Cognitive radio network (CRN) Cooperative spectrum sensing Probability vector Energy vector k-means clustering algorithm Support vector machine (SVM)

来源：评论

学校读者我要写书评

暂无评论

Task scheduling scheme based on resource clustering in desktop grids

引用

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2014年第6期27卷 918-930页

作者： Gil, Joon-Min kim, Sungsuk Lee, JongHyuk Catholic Univ Daegu Sch Informat Technol Engn Gyongsan 712702 Gyeongbuk South Korea Seokyeong Univ Dept Comp Sci Seoul 136704 South Korea Samsung Elect Software R&D Ctr Suwon 443742 Gyeonggi South Korea

Desktop grids are platforms for grid computing that incorporate desktop resources into a grid infrastructure. The purpose of this computing paradigm is to process a massive computational tasks by exploiting the donated resources connected over the Internet. In desktop grids, it is important to guarantee fast turnaround time in the presence of dynamic properties, such as volatility and heterogeneity. To achieve this objective, we propose a task scheduling scheme based on resource clustering that can selectively allocate tasks to those resources that are most suitable for the current situation of a desktop grid environment. As a classifier of resources, the k-means clustering algorithm is introduced to classify resources according to their own task execution availability and result-return probability. The experimental results show that our scheduling scheme is more efficient than existing scheduling schemes with respect to reducing both the turnaround time and the quantity of resources consumed. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： task scheduling resource classification k-means clustering algorithm desktop grids

来源：评论

学校读者我要写书评

暂无评论

Electricity Information Big Data Based Load Curve clustering 6

Electricity Information Big Data Based Load Curve Clustering

引用

China International Conference on Electricity Distribution (CICED)

作者： Zheng, Haiyan Jin, Nong Xiong, Zheng Ji, Cong Fang, Chao Zhong, Chunlin Jiangsu Frontier Elect Technol CO LTD Nanjing 211102 Jiangsu Peoples R China Jiangsu Elect Power Co Nanjing 210024 Jiangsu Peoples R China

ISBN: (纸本)9781479941261

clustering analysis of load curves on basis of electricity information big data is an important basis of load characterisitic and electricity consumption habits analysis of large users. In view of the slow speed of traditional k-means clustering algorithm in the background of big data, a parallel k-means clustering algorithm is proposed to speed up the clustering procedure. Firstly, all the load curves are de-noised by wavelet decomposing in order to reduce the influence of small fluctuations. Secondly, a multi-core parallel technology based k-means clustering algorithm is applied to load curve clustering. Thirdly, more than 40,000 load curves are clustered by the multi-core parallel technology based k-means clustering algorithm. Test results show that the proposed parallel k-means clustering algorithm can speed up clustering procedure effectively.

关键词： big data wavelet denoising load curve cluster k-means clustering algorithm multi-core paralleltechnology

来源：评论

学校读者我要写书评

暂无评论

Study on the Path Optimized method Based on an Improved clustering Ant Colony algorithm for CNC Laser Drilling

Study on the Path Optimized method Based on an Improved Clus...

引用

International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Guo, Ente Wu, Ting Zhang, Libing Huang, Fengli Jiaxing Univ Coll Mech & Elect Engn Jiaxing 314001 Peoples R China

ISBN: (纸本)9783038351153

In order to improve the machining efficiency of the CNC laser drilling, this paper presents the path optimized method based on an improved ant colony algorithm of the k-means clustering approach. The mathematical model of the path optimization is constructed, and the path optimized method based on the improved clustering ant colony algorithm is designed and actualized. The optimized path method for CNC laser drilling based on the improved clustering ant colony algorithm is tested, and the simulative and experimental result have shown that the proposed method is better performance, and the machining efficiency is greatly improved.

关键词： path optimization laser drilling ant colony algorithm k-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Study on Multi-UAV Task clustering and Task Planning in Cooperative Reconnaissance 6

Study on Multi-UAV Task clustering and Task Planning in Coop...

引用

6th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Zhao Junwei Zhao Jianjun Naval Aeronaut & Astronaut Univ Grad Students Brigade Yantai Peoples R China Naval Aeronaut & Astronaut Univ Ordnance Sci & Technol Dept Yantai Peoples R China

ISBN: (纸本)9781479949557

For multi-UAV cooperative reconnaissance to enemy's multi-task points, because of multi-task, reasonable clustering is needed and the task clustering model should be established. In this paper, the task planning model is established according to task clustering of each UAV, and the sequence of task execution is determined. Reasonable task clustering optimization index is put forward. Task allocation is proposed based on improved k-means clustering algorithm of simulated annealing. The shortest path task planning is designed using the simulated annealing algorithm, which makes multi-UAV relatively balanced in the assignments, the task group in the group centralized distribution, inter-group distribution scattered and the total cruise time shortest. Simulation results show that the task clustering is well achieved and the optimum task planning program is obtained. The validity of the model and algorithm is verified and the algorithm has certain theoretical and practical value.

关键词： cooperative reconnaissance task clustering Task Planning Task allocation k-means clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Principal component analysis-based learning for preceding vehicle classification

引用

IET INTELLIGENT TRANSPORT SYSTEMS 2014年第1期8卷 28-35页

作者： Mangai, Muthulingam Alarmel Gounden, Nanjappagounder Ammasai Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli Tamil Nadu India

This study presents a new scheme for cluster generation and classification of preceding vehicles from images. The proposed clustering algorithm models the distribution of vehicle images using vehicle' clusters. Non-vehicle' clusters are generated by modelling the distribution of non-vehicle images. The clusters are created using k-means clustering algorithm. Hierarchically related nested eigenspaces are acquired to reassign the patterns of each cluster. An appropriate classifier is obtained to classify the vehicles based on the distance-from-feature-space' measurement. The eigenspaces of vehicle clusters together with non-vehicle clusters are used for classification. The approach of modelling the distribution of vehicle and non-vehicle images and the choice of the classifier used are investigated through experiments thoroughly. Comparison on the performance of the proposed scheme is made with that of MultiClustered Modified Quadratic Discriminant Function approach of categorising the preceding vehicles. The superior performance of the proposed scheme is clearly illustrated through the classification results.

关键词： image classification learning (artificial intelligence) pattern clustering principal component analysis traffic engineering computing principal component analysis-based learning preceding vehicle classification cluster generation vehicle image distribution nonvehicle clusters k-means clustering algorithm nested eigenspaces classifier distance-from-feature-space measurement multiclustered modified quadratic discriminant function approach

来源：评论

学校读者我要写书评

暂无评论

Application of Neural Networks in Early Detection and Diagnosis of Parkinson's Disease

Application of Neural Networks in Early Detection and Diagno...

引用

International Conference on Cyber and it Service Management (CITSM)

作者： Olanrewaju, Rashidah Funke Sahari, Nur Syarafina Musa, Aibinu A. Hakiem, Nashrul Int Islamic Univ Malaysia Dept Elect & Comp Engn Fac Engn Kuala Lumpur Malaysia Fed Univ Technol Minna Minna Niger State Nigeria UIN Syarif Hidayatullah Jakarta Fac Sci & Technol Dept Informat Engn South Tangerang Indonesia

ISBN: (纸本)9781479979738

Parkinson's disease (PD) is a chronic neurological progressive disorder caused by lack of the chemical dopamine in the brain. Up to today, there is still no cure or prevention for PD, and usually the disease worsens gradually over time. However, this disease can be controlled with some treatment, especially in the early stage. Hence, this study proposes a method in early detection and diagnosis of PD by using the Multilayer Feedforward Neural Network (MLFNN) with Back-propagation (BP) algorithm. This MLFNN with BP algorithm is simulated using MATLAB software. The dataset information used in this study was taken from the Oxford Parkinson's Disease Detection Dataset. The output of the network is classified into healthy or PD by using k-means clustering algorithm. The performance of this classifier was evaluated based on the three parameters;sensitivity, specificity and accuracy. The result shows that network can be used in diagnosis and detection of PD due to the good performance, which is 83.3% for sensitivity, 63.6% for specificity, and 80% for accuracy.

关键词： backpropagation diseases medical computing medical disorders multilayer perceptrons neurophysiology pattern clustering BP algorithm MLFNN Matlab software Oxford Parkinson's disease detection dataset PD detection PD diagnosis PD output Parkinson's disease detection Parkinson's disease diagnosis accuracy parameter back-propagation algorithm chemical dopamine chronic neurological progressive disorder healthy output k-means clustering algorithm multilayer feedforward neural network performance evaluation sensitivity parameter specificity parameter Accuracy Classification algorithms clustering algorithms Diseases Neural networks Neurons Sensitivity Multilayer feedforward neural network MATLAB back propagation maladjustment Parkinson Disease clustering algorithms medical computing medical disorders Neural network Multilayer perceptrons Sorting algorithm Neurophysiology Cluster Analysis Performance Evaluation Early Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Exploring Users' Preference on Mobile Based on Customer Features 32

Exploring Users' Preference on Mobile Based on Customer Feat...

引用

32nd International Conference on Conceptual Modeling (ER)

作者： Yu, Gang Wang, Zhiyan Xue, Jiang S China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Guangdong Peoples R China

ISBN: (纸本)9783319141398;9783319141381

Users' mobile model chosen can indicate their telecommunication network demand and consumption habit. The study of users' preference on mobile model has count for much meaning for mobile operators' business strategy and mobile model product sale. This paper looks for customer features as the basis for users' preference judgment from the basic features such as customer attributes, consumption and service usage, that most related to mobile model characteristics, using a genetic algorithm iteration method and k-means clustering algorithm. Our method performs well on how to judge user's preference on mobile model.

关键词： telecommunication customer feature feature selection mobile model characteristic k-means clustering algorithm Adaptive genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Image Segmentation algorithm for Disease Detection of Wheat Leaves

Image Segmentation Algorithm for Disease Detection of Wheat ...

引用

International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Niu, Xiaojing Guo, Shihui Wang, Meili Zhang, Hongming Chen, Xianqiang He, Dongjian Northwest A&F Univ Coll Informat Engn Yangling Shaanxi Peoples R China Bournemouth Univ Media Sch Natl Ctr Comp Animat Bournemouth Dorset England Northwest A&F Univ Mech & Elect Engn Yangling Shaanxi Peoples R China

ISBN: (纸本)9781479963812

Wheat diseases are harmful to wheat production, but there are few segmentation algorithms that can effectively identify common diseases of wheat leaves. This paper proposes an automatic and efficient solution with k-means clustering. Firstly, the colour image is transformed to Lab colour space from RGB. clustering is then done by taking the absolute difference between each pixel and the clustering centre in Lab colour space. Unlike traditional methods, our method does not need manual setting for threshold value and is not affected by the selected channel. Our results show that the segmentation accuracy rates for three common diseases (powdery mildew, leaf rust and stripe rust) is more than 90%, which proves the efficacy of our method.

关键词： wheat disease k-means clustering algorithm segmentation algorithm

来源：评论

学校读者我要写书评

暂无评论

A Study of Spore Image Classification Based on Feature Extraction

A Study of Spore Image Classification Based on Feature Extra...

引用

International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Yue, Lulu Yang, Ming Peng, Li Southwest Univ Sch Comp & Informat Sci Chongqing 400715 Peoples R China

ISBN: (纸本)9783038351153

Fungal diseases are the major diseases of agricultural production, and have brought tremendous impact to it. Identification of spore morphology plays an important role in the identification of fungi. This paper uses the microscopy images of two kinds of fungal spore and utilizes the technology of image analysis and recognition to classify them. We firstly get the underlying feature descriptors of these two kinds of microscopy images by RGB SIFT (Scale Invariant Feature Transform), then create the visual word dictionary using k-means clustering algorithm, at finally we use LDA, kNN and SVM to classify these two kinds of images. The results indicate that the classification of spore image based on feature extraction is feasible. In our future work, we will conduct the classification of related species and highly similar spore images.

关键词： RGB SIFT (Scale Invariant Feature Transform) k-means clustering algorithm Latent Dirichlet Allocation k Nearest Neighbor algorithm Support Vector Machine

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：