• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.34 no.3
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• pp.50-66
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• 2016

This study attempts to understand and explain how "Directed Attention Restorative Environment (DARE)" is managed and fostered in "Gang-Hak (講學)" and "Yu-Sik (遊息)" spaces both inside and outside of Oksan Seowon Confucian Academy, Gyeongju. Directed Attention is a pivotal element in human information processing so that its restoration is crucial for effective thinking and learning. According to Kaplan & Kaplan's Attention Restoration Theory, an environment, in order to be restorative, should have four elements: 'Being Away,' 'Extent,' 'Fascination,' and 'Compatibility.' We could confirm OkSan Seowon Confucian Academy has an inner logic that integrates two basically different spacial concepts of "Jangsu" and "Yusik" and thus fosters the Attention Restorative Environment. Particularly, the Four Mountains and Five Platforms (四山五臺) surrounding the premises provides an excellent learning environment, and is in itself educational in terms of the Neo-Confucian epistemology with "Attaining Knowledge by way of Positioning Things (格物致知)" as its principle precept, and of its aesthetics with "Connectedness with Nature" as its central tenet. This study attempts to recapture the value of Korea's cultural heritage concerning the Human/Nature relationship; and it may provide useful insights and practical guidelines/grounds in designing today's schools and campuses, where the young people's needs for the Directed Attention- and Attention Restorative- Servicescapes seem to be greater than ever.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.281-286
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• 2003

A large number of features are collected for problem solving in real life, but to utilize ail the features collected would be difficult. It is not so easy to collect of correct data about all features. In case it takes advantage of all collected data to learn, complicated learning model is created and good performance result can't get. Also exist interrelationships or hierarchical relations among the features. We can reduce feature's number analyzing relation among the features using heuristic knowledge or statistical method. Heuristic technique refers to learning through repetitive trial and errors and experience. Experts can approach to relevant problem domain through opinion collection process by experience. These properties can be utilized to reduce the number of feature used in learning. Experts generate a new feature (highly abstract) using raw data. This paper describes machine learning model that reduce the number of features used in learning using heuristic function and use abstracted feature by neural network's input value. We have applied this model to the win/lose prediction in pro-baseball games. The result shows the model mixing two techniques not only reduces the complexity of the neural network model but also significantly improves the classification accuracy than when neural network and heuristic model are used separately.

• Journal of KIISE:Databases
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• v.33 no.6
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• pp.600-619
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• 2006

According to recent technical advances on sensors and mobile devices, processing of data streams generated by the devices is becoming an important research issue. The data stream of real values obtained at continuous time points is called streaming time-series. Due to the unique features of streaming time-series that are different from those of traditional time-series, similarity matching problem on the streaming time-series should be solved in a new way. In this paper, we propose an efficient algorithm for streaming time- series matching problem that supports normalization transform. While the existing algorithms compare streaming time-series without any transform, the algorithm proposed in the paper compares them after they are normalization-transformed. The normalization transform is useful for finding time-series that have similar fluctuation trends even though they consist of distant element values. The major contributions of this paper are as follows. (1) By using a theorem presented in the context of subsequence matching that supports normalization transform[4], we propose a simple algorithm for solving the problem. (2) For improving search performance, we extend the simple algorithm to use $k\;({\geq}\;1)$ indexes. (3) For a given k, for achieving optimal search performance of the extended algorithm, we present an approximation method for choosing k window sizes to construct k indexes. (4) Based on the notion of continuity[8] on streaming time-series, we further extend our algorithm so that it can simultaneously obtain the search results for $m\;({\geq}\;1)$ time points from present $t_0$ to a time point $(t_0+m-1)$ in the near future by retrieving the index only once. (5) Through a series of experiments, we compare search performances of the algorithms proposed in this paper, and show their performance trends according to k and m values. To the best of our knowledge, since there has been no algorithm that solves the same problem presented in this paper, we compare search performances of our algorithms with the sequential scan algorithm. The experiment result showed that our algorithms outperformed the sequential scan algorithm by up to 13.2 times. The performances of our algorithms should be more improved, as k is increased.