Learning by Seamless Migration – A Kind of Mobile Working Paradigm

2010

Appl Intell

142

Seamless migration is one of pervasive computing applications. The function of seamless mobility is suitable for mobile services such as mobile Web-based learning. In this paper, we propose an approach that supports an attentive mobile learning paradigm. This mobile learning dynamically follows the user from place to place and machine to machine without user's awareness or intervention by active service. This capability can be obtained by componentbased smart system and agent-based migrating mechanism. To demonstrate the approach, the theoretical background of fuzzy-neural network for attentive service will be explained. The proposed fusion decision method is based on fuzzyneural network which can make the input signal data to fuse better. Using online tuning, the fusion processing can be accelerated and the fusion belief degree can be improved. Description of mobile learning task and migrating granularity of the task is suggested. The design of the seamless migration mechanism is introduced. This includes solving several important sub-problems, such as transferring delay, transferring failure, and residual computation dependency. Our implemented system for attentive mobile learning based on

Section: Comparison and Discussionmentioning

confidence: 99%

A new approach and system for attentive mobile learning based on seamless migration

2010

Appl Intell

142

“…In this paper, we selected 150 sample training value and 50 test sample value respectively, carried out pattern recognition test of image to the above-mentioned fuzzy neural network [9]- [13] , and compared with the BP neural network of same structure [14]- [16] , the specific result shown in table1. It can be found from the table that, because the structure of fuzzy neural network is integrated the fuzzy inference theory, the ability to identify classification has been greatly improved, and good network performance has laid a good foundation for the image fusion.…”

Section: Test and Verification Of New Methodsmentioning

confidence: 99%

A new method of image data fusion based on FNN

2010 Sixth International Conference on Natural Computation

Wang

et al. 2010

The image data are obtained by a variety of multimedia, information equipment, which include amount of information, extensive coverage, and redundancy in ubiquitous computing paradigm. In order to make use of these information reasonably and efficiently, it is necessary to fuse such massive data, therefore the multi-sensor image information fusion become a key technology to ubiquitous computing. A new approach of image data fusion based on fuzzy neural network (FNN) is presented in this paper. Here, neural network is a parallel information processing model, there are more adaptive and self-organization, and can accomplish the complexity of real-time computing and mass data retrieval, it demonstrate its unique superiority of image understanding, pattern recognition and the handling of incomplete information. In the new approach, fuzzy theory has the advantages such as: easy-to-understand, flexible and inclusive of non-precise data, as well as using expert knowledge and based on the natural language. The fuzzy neural network system developed by us has used in multi-sensor image fusion, proved that the fusion is fast, effective, good, and can meet the real-time requirements of ubiquitous computing.

“…But in many cases of applications, the relativity between evidence exists absolutely [26,27]. From the relativity degree, we can classify it into two cases: relativity partly, and relativity totally.…”

Section: Methods Of Service-aware Computing With Relativitymentioning

confidence: 99%

A kind of novel method of service-aware computing for uncertain mobile applications

Mathematical and Computer Modelling

Liang

2013

a b s t r a c tService-aware computing is a hot research topic under the banner of web-based uncertain mobile applications. As we know, in the research domain of uncertain mobile service, service-aware evidence with uncertainty is dynamic and changing randomly. In order to ensure the QoS of different mobile application fields based on decision making, we think the method of service-aware computing for uncertain mobile applications is very important. The key insight of this paper is that we modified the computing method of evidence information, which has been considered the reliability, time-efficiency, and relativity of service context. The method has improved the classical computing rule of D-S (Dempster-Shafer) Evidence Theory when being used in uncertain cases. The novel method may be called the extended D-S (EDS) method, which has overcome the drawbacks of classical D-S Evidence Theory. All these new ideas have been successfully used in our service-aware computing field of uncertain mobile applications. By comparing EDS with related methods, such as Bayesian Theory (BT), and Random Set Theory (RST), the advantage of the new service-aware computing method has been proved successfully.