Because of considerable high labor cost in developed countries particularly Japan and Korea, the end of life products required manual or semi-manual processes have been relocated to developing countries. In addition, the relocation creates an immediate financial profit on old asset sale while it is revalued to 30 % of its initial value in the new relocated factory. The relocation is not only production location change but also the experience in operation and maintenance process to its Foreign Direct Investment affiliate. Since the old product and process are both nearly at their end of life, it needs to extend the life as long as the product still exists in the market. In order to immediately launch the operation as well as to retain the knowledge, therefore, the asset management and new maintenance modes are proposed at all product, process and people aspects to improve reliability of the old asset and to minimize maintenance cost. The research in this paper was carried out on a Korean relocated electronic factory in Thailand. The result clearly shows that the reliability, quality, cost and intellectual capital are significantly improved.
Resource management in private clouds is a more challenging task than in public clouds. Because there are only finite resources in private cloud compare to public cloud with vast resources. Often, marginal resources are assigned to the application in the private cloud, which causes to the changes of service level of application executions. A resource management system is responsible for fulfilling SLA requirement so that it assigns resources when the allocated resource is not enough. Compare to conventional distributed environment, it is easier to meet the SLA requirement in virtualized one because there are many helpful mechanisms like consolidation, virtual machine migration, and dynamic scaling. We propose a resource management system for private cloud based on a novel server consolidation algorithm that uses live migration of the virtual machine, with dynamic scaling capabilities. We present our proposed architecture, the consolidation algorithm and the profile scheme to support dynamic scaling. The empirical experiments results show that our proposed approach outperforms other popular approaches in process time and load balancing.
1Supawadee Hiranpongsin and 2Pattarasinee Bhattarakosol 1, First Author Innovative Network and Software Engineering Technologies Laboratory (INSET), Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Thailand, supawadee.h@student.chula.ac.th *2,Corresponding Author Innovative Network and Software Engineering Technologies Laboratory (INSET), Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Thailand, pattarasinee.b@chula.ac.th
In smart environments, a multi-robot system is difficult to achieve a high confidence level of information for reliable decision making.The pieces of sensed information obtained from a multi-robot system have different degrees of uncertainty.Generated contexts about the situation of the environment can be conflicting even if they are acquired by simultaneous operations.In particular, unpredictable temporal changes in sensory information reduce the confidence level of information then lead to wrong decision making.In order to solve this problem, we propose a reasoning method based on Dynamic Evidential Fusion Network (DEFN).First, we reduce conflicting information in multi-sensor networks using Evidential Fusion Network (EFN).Second, we improve the confidence level of information using Temporal Belief Filtering (TBF) and Normalized Weighting technique.We distinguish a sensor reading error from sensed information.Finally, we compare our approach with a fusion process based on Dynamic Bayesian Networks (DBNs) using paired observations so as to show the improvement of our proposed method.
............................................................................................................................ i ACKNOWLEDGEMENTS ................................................................................................... iii TABLE OF CONTENTS ....................................................................................................... iv LIST OF FIGURES .............................................................................................................. vii LIST OF TABLES .................................................................................................................. x 1.1 Background .............................................................................................................. 13 1.2 Motivations .............................................................................................................. 14 1.3 Research Questions .................................................................................................. 14 1.4 Research Contributions ............................................................................................ 15 1.5 Thesis Content .......................................................................................................... 17 2.1 Literature Reviews ................................................................................................... 19 2.1.1 Knowledge Acquisition ..................................................................................... 19 2.1.2 Data Mining and Knowledge Discovery in Database ....................................... 21 2.1.3 The KDD Process Model................................................................................... 21 2.1.4 Comparison of the KDD Process Models ......................................................... 24 2.1.5 Problems of Applying the KDD process for Informal Data .............................. 27 2.2 Improvement of Knowledge Discovery Methodology ............................................. 29 2.3 Related Concepts and Techniques ........................................................................... 31 2.3.1 Ontology Development ..................................................................................... 31 2.3.2 Natural Language Processing ............................................................................ 39 2.3.3 N-gram............................................................................................................... 46 2.3.4 Text Mining ....................................................................................................... 48 2.3.5 Closed-domain question answering ................................................................... 54 2.3.6 Evaluation Measures ......................................................................................... 57 3.1 The Main Concept for the On-KDT Modelling ....................................................... 60 3.2 The Variant Lexicon Ontology Development .......................................................... 62 3.2.1 Background ....................................................................................................... 63 3.2.2 Definition of the VL-ontology........................................................................... 65 3.2.3 The use of the VL-ontology .............................................................................. 67 3.2.4 How to implement the VL-ontology ................................................................. 68 3.2.5 Example of the use of the VL-ontology ............................................................ 72 3.2.6 The Experiments of the VL-ontology ................................................................ 73 3.3 The Elaboration of the ON-KDT methodology ....................................................... 75 3.3.1 Understanding of the application domain and defining the problem ................ 75
Automatic lexicon generation is a useful task in learning text fragment patterns. In our previous work we have focused on text fragment pattern learning through the fuzzy grammar method which inputs include a predefined lexicon and text fragments that represents the expression of the grammar class to be learned. However, the bottleneck of the success of the fuzzy grammar creation and in common with other text learner often lies in the knowledge acquisition phase; due to the labour intensive text annotation which also demands skills and background knowledge of the text. For this reason, a semi-automated technique called automatic Terminal Grammar Recommender (TGR) is devised to identify conceptually related lexicons in the texts and their related to create terminal grammars by mining associations of words contexts. The approach recognizes that there is a degree of local structure within such text and the technique exploits the local structure without the large computational overhead of deeper analysis. Result from the comparison of the associative words detected by TGR with the definition of a content category tool called General Inquirer on the data from European Central Bank data is reported. Our findings show that our proposed method has managed to reduce the manual effort of identifying conceptually similar lexicons to form terminal grammars. The average of matched generated terminal grammar clusters compared to General Inquirer is 54.85% which indicates that at least half the expensive effort to construct conceptually related lexicon is saved. This hint the potential of word context association mining in automated conceptual lexicon generation.
“Ubiquitous” represents a concept which means ―God exists everywhere. At the era of information technology, by using all kinds of information products and networks, the word represents that the ubiquitous information ideal world can be realized. However, introducing new technology could also cause other issues because of the impractical lead-in plan. Therefore, we integrated the ubiquitous computing technology with the business process to improve system efficiencies by means if well-defined BPM. Through case study, we chose National Palace Museum as our target and analyzed the operating process using module standardized tools to evaluate the difference in performance after the implementation of ubiquitous application. We have identified the KPIs for introducing ubiquitous services into museum and verified that ubiquitous service has positive benefits.
