• Korean Institute of Interior Design Journal
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• v.19 no.6
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• pp.20-29
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• 2010

Art historians and critics have defined the style as common features appeared in a class of objects. Abstract common features from a set of objects have been used as a bench mark for date and location of original works. Commonalities in shapes are identified by relationships as well as physical properties from shape descriptions. This paper will focus on how the computer and human can recognize common shape properties from a class of shape objects to learn design knowledge. Shape representation using schema theory has been explored and possible inductive generalization from shape descriptions has been investigated. Also learned shape knowledge can be used. for new design process as design concept. Several design process such as parametric design, replacement design, analogy design etc. are used for these design processes. Works of Mario Botta and Louis Kahn are analyzed for explicitly clarifying the process from conceptual ideas to final designs. In this paper, theories of computer science, artificial intelligence, cognitive science and linguistics are employed as important bases.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.137-146
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• 2008

Fault localization plays a significant role in enormous distributed system because it can identify root cause of observed faults automatically, supporting self-managing which remains an open topic in managing and controlling complex distributed systems to improve system reliability. Although many Artificial Intelligent techniques have been introduced in support of fault localization in recent research especially in increasing complex ubiquitous environment, the provided functions such as diagnosis and prediction are limited. In this paper, we propose fault localization for self-managing in performance evaluation in order to improve system reliability via learning and analyzing real-time streams of system performance events. We use probabilistic reasoning functions based on the basic Bayes' rule to provide effective mechanism for managing and evaluating system performance parameters automatically, and hence the system reliability is improved. Moreover, due to large number of considered factors in diverse and complex fault reasoning domains, we develop an efficient method which extracts relevant parameters having high relationships with observing problems and ranks them orderly. The selected node ordering lists will be used in network modeling, and hence improving learning efficiency. Using the approach enables us to diagnose the most probable causal factor with responsibility for the underlying performance problems and predict system situation to avoid potential abnormities via posting treatments or pretreatments respectively. The experimental application of system performance analysis by using the proposed approach and various estimations on efficiency and accuracy show that the availability of the proposed approach in performance evaluation domain is optimistic.