http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
정예림(Jung, Ye-Rim),이흔지(Lee, Heun Ji),김주연(Kim, Ju Yeon) 한국실내디자인학회 2024 한국실내디자인학회 학술대회논문집 Vol.26 No.1
This study aims to present branding space designs that expand consumers’ purchasing power in offline spaces amid the increasing trend of online shopping. The research methods are as follows: 1) Theoretical background and related case studies on OMO technology and concepts were analyzed according to the theory. 2) Based on the investigated concepts and case studies, a site was selected, and corresponding images were derived. The images were developed step-by-step using keywords extracted from the concept through a generative AI program. 3) The concept exploration was completed with the derived images, and the program and design direction for the space were developed. This exploration of the presented design is at the conceptual stage of the design process and aims to enhance practical communication between learners and instructors in the initial stages. Additionally, this will assist designers in visually understanding core ideas while progressing specific directions during the conceptual extraction phase.
Word2Vec을 활용한 제품군별 시장규모 추정 방법에 관한 연구
정예림(Ye Lim Jung),김지희(Ji Hui Kim),유형선(Hyoung Sun Yoo) 한국지능정보시스템학회 2020 지능정보연구 Vol.26 No.1
With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies’ business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies’ product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Koreas microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies’ sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearsons correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our gro