• Journal of English Language & Literature
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• v.58 no.4
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• pp.723-752
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• 2012

Although his mythic poetry deals with the fall and resurrection of Albion as the origin of humankind, William Blake (1757-1827) simultaneously links it to the professionalization and unification of disciplinary knowledge itself. He particularly takes a great interest in the cross-referential relation of poetry to science. He argues for the communication of poetry and science on equal footing with each other without the former's prioritization over the latter, or vice versa. In his works Vala, or The Four Zoas (1797-1807) and Jerusalem: The Emanation of the Giant Albion (1804-1820), on which I focus in this essay, Blake's primary problematic is to display strong conflicts among different systems of knowledge. I approach this issue in light of the ideological clash of Newtonian thought, Romantic thought, and postmodern thought. In his poetry, Blake thematizes the very clashes of these different thought patterns. From the standpoint of Romantic thought, first of all, Blake problematizes Newtonian Enlightenment. He criticizes abstract universalization both in poetry and science, which Urizen, one of four Zoas, propagates. Protesting against Urizen's Newtonism, Los values "living form." Thus, Blake demonstrates, through this figure, that poetic imagination and scientific organicism are discursively communicative. Blake, however, also questions the network of Romantic science and Romantic poetry so as to suggest what current critics would call postmodern thought. Blakean postmodernism pursues the self-similarity of organic structure in science and poetry. Precisely, Blake sees polypus as a proliferation of organic body; he arranges four Zoas' self-repetitive stories in a non-linear way. Blake aspires for the conflicting coexistence of different thought patterns.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.101-125
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• 2022

Recently, with the development of computing technology and the improvement of the cloud environment, deep learning technology has developed, and attempts to apply deep learning to various fields are increasing. A typical example is anomaly detection, which is a technique for identifying values or patterns that deviate from normal data. Among the representative types of anomaly detection, it is very difficult to detect a contextual anomaly that requires understanding of the overall situation. In general, detection of anomalies in image data is performed using a pre-trained model trained on large data. However, since this pre-trained model was created by focusing on object classification of images, there is a limit to be applied to anomaly detection that needs to understand complex situations created by various objects. Therefore, in this study, we newly propose a two-step pre-trained model for detecting abnormal situation. Our methodology performs additional learning from image captioning to understand not only mere objects but also the complicated situation created by them. Specifically, the proposed methodology transfers knowledge of the pre-trained model that has learned object classification with ImageNet data to the image captioning model, and uses the caption that describes the situation represented by the image. Afterwards, the weight obtained by learning the situational characteristics through images and captions is extracted and fine-tuning is performed to generate an anomaly detection model. To evaluate the performance of the proposed methodology, an anomaly detection experiment was performed on 400 situational images and the experimental results showed that the proposed methodology was superior in terms of anomaly detection accuracy and F1-score compared to the existing traditional pre-trained model.