期刊：
Data in Brief,2016年7(1):546-550 ISSN：2352-3409
通讯作者：
Xiong, H.
作者机构：
[Xiong, Hang] Geary Institute for Public Policy and School of Sociology, University College Dublin, Dublin, Ireland;[Wang, Puqing] College of Economics and Management, Wuhan Polytechnic University, Wuhan, China;[Zhu, Yueji] School of Economics and Management, Hainan University, Haikou, China
通讯机构：
[Xiong, H.] G;Geary Institute for Public Policy and School of Sociology, University College Dublin, Dublin, Ireland
摘要：
For more accurate fault detection and diagnosis, there is an increasing trend to use a large number of sensors and to collect data at high frequency. This inevitably produces large-scale data and causes difficulties in fault classification. Actually, the classification methods are simply intractable when applied to high-dimensional condition monitoring data. In order to solve the problem, engineers have to resort to complicated feature extraction methods to reduce the dimensionality of data. However, the features transformed by the methods cannot be understood by the engineers due to a loss of the original engineering meaning. In this paper, other forms of dimensionality reduction technique(feature selection methods) are employed to identify machinery condition, based only on frequency spectrum data. Feature selection methods are usually divided into three main types: filter, wrapper and embedded methods. Most studies are mainly focused on the first two types, whilst the development and application of the embedded feature selection methods are very limited. This paper attempts to explore a novel embedded method. The method is formed by merging a sequential bidirectional search algorithm into scale parameters tuning within a kernel function in the relevance vector machine. To demonstrate the potential for applying the method to machinery fault diagnosis, the method is implemented to rolling bearing experimental data. The results obtained by using the method are consistent with the theoretical interpretation, proving that this algorithm has important engineering significance in revealing the correlation between the faults and relevant frequency features. The proposed method is a theoretical extension of relevance vector machine, and provides an effective solution to detect the fault-related frequency components with high efficiency.
作者机构：
[Mal, Yong; Liul, Jianuo] Hubei Univ, Tourism Dev Inst, Wuhan 430062, Peoples R China.;[Zhang, Xuexi] Wuhan Polytech Univ, Coll Econ & Management, Wuhan 43023, Peoples R China.
会议名称：
14th Wuhan International Conference on E-Business
会议时间：
JUN 19-21, 2015
会议地点：
Wuhan, PEOPLES R CHINA
会议主办单位：
[Mal, Yong;Liul, Jianuo] Hubei Univ, Tourism Dev Inst, Wuhan 430062, Peoples R China.^[Zhang, Xuexi] Wuhan Polytech Univ, Coll Econ & Management, Wuhan 43023, Peoples R China.
关键词：
Smart tourism;core value;tourist perception
摘要：
With the depth integration of the information technology industry and tourism industry, the intelligent tourism has become the inevitable choice of tourism transformation and upgrading. Represented by cloud computing and internet of things, the intelligent capability which was produced by the information technology industrial integration innovation application mode has become the main power to promote deep change of tourism industry. The article made an empirical analysis of smart tourism and extracted the five core values which are experience value, information value, innovation value, function value and cost value. Thus to put forward the four innovative development countermeasures so that make the smart tourism became the important grasp of promoting science technology value and strategic position of tourism industry.
作者机构：
[Xiang Xizhang; Wu Suchun; Hu Kun] Wuhan Polytech Univ, Sch Econ & Management, Wuhan 430023, Peoples R China.
会议名称：
ICIM2010
会议时间：
20101204-05
会议地点：
Wuhan(CN)
会议主办单位：
Wuhan Univ Technol
会议论文集名称：
Proceedings of the 7th international conference on innovation and management. 1
关键词：
Resource-based;Agro-processing industry;Industry cluster;Innovation model
摘要：
According to the degree of competition and cooperation among enterprises, and knowledge dependence of cluster, this paper classifies agro-processing industry cluster into three types, which are resource-based cluster, chain-type cluster, and cyclic cluster, resource-based cluster is most common. The innovation model varies with the type of cluster, for this reason, the innovation model of resource-based agro-processing industry cluster is constructed. Resource-based agro-processing industry cluster carries out innovative activities through technological innovation platform that is found by the government, which is taken as the core. Furthermore, this paper makes deep research on the main body's status, the function, and the movement way of behaviors in this cluster.
