• Convergence Security Journal
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• v.23 no.4
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• pp.122-129
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• 2023

The paradigm of warfare is undergoing a revolutionary transformation due to the advancements in technology brought forth by the Fourth Industrial Revolution. Specifically, the U.S. military has introduced the concept of mosaic warfare as a means of military innovation, aiming to integrate diverse resources and capabilities, including various weapons, platforms, information systems, and artificial intelligence. This integration enhances the ability to conduct agile operations and respond effectively to dynamic situations. The incorporation of mosaic warfare could facilitate efficient and rapid command and control by integrating AI staff with human commanders. Ukrainian military operations have already employed mosaic warfare in response to Russian aggression. This paper focuses on the mosaic war fare concept, which is being proposed as a model for future warfare, and suggests the strategy for introducing the Korean mosaic warfare concept in light of the changing battlefield paradigm.

• Management & Information Systems Review
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• v.36 no.4
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• pp.33-51
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• 2017

With the advent of 'The Forth Industrial Revolution' and the growing demand for quality of life due to economic growth, needs for the quality of medical services are increasing. Artificial intelligence has been introduced in the medical field, but it is rarely used in chronic skin diseases that directly affect the quality of life. Also, atopic dermatitis, a representative disease among chronic skin diseases, has a disadvantage in that it is difficult to make an objective diagnosis of the severity of lesions. The aim of this study is to establish an intelligent severity recognition model of atopic dermatitis for improving the quality of patient's life. For this, the following steps were performed. First, image data of patients with atopic dermatitis were collected from the Catholic University of Korea Seoul Saint Mary's Hospital. Refinement and labeling were performed on the collected image data to obtain training and verification data that suitable for the objective intelligent atopic dermatitis severity recognition model. Second, learning and verification of various CNN algorithms are performed to select an image recognition algorithm that suitable for the objective intelligent atopic dermatitis severity recognition model. Experimental results showed that 'ResNet V1 101' and 'ResNet V2 50' were measured the highest performance with Erythema and Excoriation over 90% accuracy, and 'VGG-NET' was measured 89% accuracy lower than the two lesions due to lack of training data. The proposed methodology demonstrates that the image recognition algorithm has high performance not only in the field of object recognition but also in the medical field requiring expert knowledge. In addition, this study is expected to be highly applicable in the field of atopic dermatitis due to it uses image data of actual atopic dermatitis patients.