• The Journal of Korean Philosophical History
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• no.32
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• pp.215-249
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• 2011

This essay reflects on the modernity of Korea by examining the transformation of the traditional knowledge system from a historico-semantic perspective with its focus on the opposition and collision of the old and the new conception occurred in the early period(1890~1910) of the acceptance of the Western modern civilization. With scientific success, trick of reason, Christianity and evolutionary view of history, the Western modernity regarded itself as a peak of civilization and forced the non-Western societies into the world system in which they came to be considered as 'barbarism(野蠻)' or 'half-enlightened(半開).' The East Asian civilization, which had its own history for several centuries, became degraded as kind of delusion and old-fashioned customs from which it ought to free itself. The Western civilization presented itself as exemplary future which East Asian people should achieve, while East Asian past traditions came to be conceived as just unnecessary vestiges which it was better to wipe out. It can be said that East Asian modernization was established through the propagation and acceptance of the modern products of the Western civilization rather than through the preservation of its past experience and pursuit of the new at the same time. Accordingly, it is difficult to apply directly to East Asian societies Koselleck's hypothesis; while mapping out his Basic Concept of History, he assumed that, in the so-called 'age of saddle,' semantic struggle over concepts becomes active between the past experience and the horizon of expectation on the future, and concepts undergoes 'temporalization', 'democratization', 'ideologization', 'politicization.'The struggle over the old and new conceptions in Korea was most noticeable in the opposition of the Neo-Confucian scholars of Hwangseongsinmun and the theorists of civilization of Doknipsinmun. The opposition and struggle demanded the change of understanding in every field, but there was difference of opinion over the conception of the past traditional knowledge system. For the theorists of civilization, 'the old(舊)' was not just 'past' and 'old-fashioned' things, but rather an obstacle to the building of new civilization. On the other hand, it contained the possibility of regeneration(新) for the Neo-Confucian scholars; that is, they suggested finding a guide into tomorrow by taking lessons from the past. The traditional knowledge system lost their holy status of learning(聖學) in the process of its change into a 'new learning(新學),' and religion and religious tradition also weakened. The traditional knowledge system could change itself into modern learning by accepting scientific methodology which pursues objectivity and rationality. This transformation of the traditional knowledge system and 'the formation of the new learning from the old learning' was accompanied by the intersection between the old and new conceptions. It is necessary to pay attention to the role played by the concept of Sil(hak)(實學) or Practical Learning in the intersection of the old and new conceptions. Various modern media published before and after the 20th century show clearly the multi-layered development of the old and new conceptions, and it is noticeable that 'Sil(hak)' as conceptual frame of reference contributed to the transformation of the traditional knowledge system into the new learning. Although Silhak often designated, or was even considered equivalent to, the Western learning, Neo-Confucian scholars reinterpreted the concept of 'Silhak' which the theorists of civilization had monopolized until then, and opened the way to change the traditional knowledge system into the new learning. They re-appropriated the concept of Silhak, and enabled it to be invested with values, which were losing their own status due to the overwhelming scientific technology. With Japanese occupation of Korea by force, the attempt to transform the traditional knowledge system independently was obliged to reach its own limit, but its theory of 'making new learning from old one' can be considered to get over both the contradiction of Dondoseogi(東道西器: principle of preserving Eastern philosophy while accepting Western technology) and the de-subjectivity of the theory of civilization. While developing its own logic, the theory of Dongdoseogi was compelled to bring in the contradiction of considering the indivisible(道and 器) as divisible, though it tried to cope with the reality where the principle of morality and that of competition were opposed each other and the ideologies of 'evolution' and 'progress' prevailed. On the other hand, the theory of civilization was not free from the criticism that it brought about a crack in subjectivity due to its internalization of the West, cutting itself off from the traditional knowledge system.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.