• Journal of KIISE:Software and Applications
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• v.37 no.3
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• pp.194-204
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• 2010

A space, real or virtual, comprises regions as its parts and physical objects residing in them. A coherent and sophisticated representaion scheme for their spatial relations premises the precision and plausibility in its associated agents' inferencing on the spatial relations and the development of events occurring in such a space. The existing spatial models are not suitable for a comprehensive representation of the general spatial relations in that they have limited expressive powers based on the dichotomy between the large and small scales, or support only a small set of topological relations. The representaion model we propose has the following distinctive chracteristics: firstly, our model provides a comprehensive representation scheme to accommodate large and small scale spaces in an integrated fashion; secondly, our model greatly elaborated the spatial relations among the small-scale objects based on their contact relations and the compositional relations among their respective components objects beyond the basic topological relations like disjoint and touch; thirdly, our model further diversifies the types of supported relations by adding the container property besides the soildness together with considering the gravity direction. The resulting integrated spatial knowledge representation scheme considering the gravity allows the diverse spatial relations in the real world to be simulated in a precise manner in relation to the associated spatial events and provides an expression measure for the agents in such a cyber-world to capture the spatial knowledge to be used for recognizing the situations in the spatial aspects.

• Journal of Digital Convergence
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• v.15 no.7
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• pp.147-157
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• 2017

Recently, ICT Retail Convergence(IRC) has been rapidly increasing to improve consumer satisfaction and consumer experience. In this paper, we aim to diagnose IRC from consumers' point of view by reviewing the present status and value of IRC according to consumer purchase decision making process. Based on the previous studies in retail industry, we classified IRC into 4 types: Experience-specific tech(Virtual Reality and Augmented Reality); Information-specific tech(Artificial Intelligence and Big Data); Location-based tech(Radio Frequency Identification and Beacon); Payment-related tech(Fin-tech and Biometrics). Next, we found that there is a difference in value provided to consumers according to the type of technology, analysing the value by consumer purchase decision making process. This study can be useful to introduce IRC for improving consumer satisfaction as well as ICT and Retail. Also, it can be basic data for future technology studies with a consumer perspective.

• Journal of Service Research and Studies
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• v.12 no.2
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• pp.80-93
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• 2022

This study is a study on a methodology that can extract various factors that affect purchase and use of products/services from the consumer's point of view through previous studies, and analyze the types and tendencies of consumers according to age and gender. To this end, we quantify factors in terms of general personal propensity, consumption influence, consumption decision, etc. to check the consistency of data, and based on these studies, we conduct research to suggest and prove data analysis methodologies of consumer types that are meaningful from the perspectives of startups and SMEs. did As a result, it was confirmed through cross-validation that there is a correlation between the three main factors assumed for data analysis from the consumer's point of view, the general tendency, the general consumption tendency, and the factors influencing the consumption decision. verified. This study presented a data analysis methodology and a framework for consumer data analysis from the consumer's point of view. In the current data analysis trend, where digital infrastructure develops exponentially and seeks ways to project individual preferences, this data analysis perspective can be a valid insight.

• Smart Media Journal
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• v.12 no.7
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• pp.43-51
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• 2023

As online transactions increase, the image of clothing has a great influence on consumer purchasing decisions. The importance of image information for clothing materials has been emphasized, and it is important for the fashion industry to analyze clothing images and grasp the materials used. Textile materials used for clothing are difficult to identify with the naked eye, and much time and cost are consumed in sorting. This study aims to classify the materials of textiles from clothing images based on deep learning algorithms. Classifying materials can help reduce clothing production costs, increase the efficiency of the manufacturing process, and contribute to the service of recommending products of specific materials to consumers. We used machine vision-based deep learning algorithms ResNet and Vision Transformer to classify clothing images. A total of 760,949 images were collected and preprocessed to detect abnormal images. Finally, a total of 167,299 clothing images, 19 textile labels and 20 fabric labels were used. We used ResNet and Vision Transformer to classify clothing materials and compared the performance of the algorithms with the Top-k Accuracy Score metric. As a result of comparing the performance, the Vision Transformer algorithm outperforms ResNet.