With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

본 연구에서는 4대강 살리기 사업 후 퇴적현상이 지배적으로 발생하는 남한강과 섬강 합류부 구간을 대상으로 2차원 수치모형인 CCHE2D 모형을 이용하여 하천의 흐름 및 하상변동에 대한 해석을 수행하였다. 대상지점 합류부는 남한강 본류의 만곡부에 지류 섬강이 유입되는 특성을 갖는다. CCHE2D 모형은 비평형 유사이송을 해석하며 소류사와 부유사 조정거리가 중요한 입력변수로 대상지점에서는 소류사 조정거리가 하상변동에 가장 큰 영향을 주는 것으로 나타났다. 수치모의 결과 유량비(Qr) 변화가 남한강과 섬강 합류부 지점에서 흐름 및 하상변동에 영향을 미쳤으며, Qr≤ 2.5인 경우에는 합류전 본류의 유속이 증가하여 흐름박리구역을 감소시켰으며 이로 인해 합류부 내측의 퇴적이 감소하였다. Qr>2.5이면 합류부 구간에 퇴적이 증가하여 사주가 형성될 가능성이 높은 것으로 나타났다. 수치모의를 통해 2013년에 발생한 유량비 변화에 의해 합류부에 고정사 주가 형성된 것을 알 수 있었다.