• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.41 no.4
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• pp.21-29
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• 2023

The Northern Song Dynasty, the heyday of cultural and artistic achievements, brought significant changes to the history of gardens in China. The developments and contemplations that had evolved during the previous Tang Dynasty became intertwined with literature, painting, and art, leading to garden being perceived as works of art. In particular, the emergence of Su Shi(蘇軾) that permeated literature and art during the Northern Song Dynasty, had an impact beyond individual garden creation, influencing the development of public gardens and the diversification of garden. His long exile periods served as an opportunity to understand and reflect the local culture and characteristics, influencing the development of the garden. This study focuses on the ideology of Su Shi(蘇軾) that managed various gardens, examining the relationship between his exlie life and ideology. To do so, the study examines the form of the literati's gardens managed by Su Shi(蘇軾), with a particular emphasis on the Baiheju(白鶴居) garden in Huizhou, revealing the following characteristics and values. First, Su Shi(蘇軾), who was proficient in the Three Houses: Confucianism, Buddhism, and Taoism, combined his philosophy and unique perspective techniques with the location and composition elements of Baiheju(白鶴居) to enjoy the landscape. Although the ancient residence has a simple form, it possesses expansiveness through the combination of internal and external views. The interior is designed to be perceived as a single space, but it allows overlapping experiences of space and simultaneous appreciation of different sceneries. On the other hand, the spatial layout incorporates a hierarchical order to establish a sense of order. Second, the garden reflects the local characteristics, featuring numerous tropical plants and presenting vibrant and contrasting colors with structures. The planting forms embrace the concept of "huosei seikou" (活色生香) to enhance the color harmoniously. Additionally, the garden incorporates the poet's spiritual world, projecting it onto the garden as a contemplative place for spiritual nourishment and exploration of the ideal realm. For the pursuit of serenity and profound contemplation, the selected plantings are simple yet distinctive, providing rhythm and depth to the garden space. Third, Baiheju(白鶴居) has undergone changes over the years, but fundamentally, the form and elements of the garden shaped by Su Shi(蘇軾)'s descendants persist, confirming its heritage value.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.