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.

대다수의 중·저층 RC 건물은 다양한 수평저항시스템으로 이루어져 있으며, 그 가운데에서도 취성적인 파괴성상을 나타내 는 전단파괴형 부재와 연성능력이 탁월한 휨파괴형 부재가 대표적이며, RC 건축물의 내진성능은 각 파괴모드를 나타내는 부재의 강도 및 변형특성의 조합으로 평가되어야한다. 본 연구에서는 전단 및 휨파괴형 부재가 혼합된 중·저층 RC 건물을 대상으로 반복가력실험을 수행하여 내진성능을 검토하였다.

본 연구에서는 비내진상세를 가지는 중⦁저층 R/C 건물의 1층 골조를 제작하여 무보강 실험체에 대한 구조실험을 실시하였다. 실험결과 실험체는 부재각 1.33%에서 전단파괴를 나타내어, 비내진상세를 가지는 R/C 건물의 내진성능에 관한 중요한 자료를 획득하였다. 본 연구에서는 간주형 좌굴방지 강재 슬릿댐퍼 시스템을 개발하였으며 내진보강효과를 검증하기 위하여 구조실험에 선행하여 상기 국내 비내진상세 RC 골조 실험결과를 기반으로 비선형해석을 실시하였다.

수직거동 마찰댐퍼 시스템의 내진보강공법의 유효성을 판단하기 위해 제진장치의 반복가력실험과 1980년대 비내진 상세를 가지는 R/C건축물을 대상으로 하는 실물 2층 골조를 제작하여 유사동적실험을 실시하였다. 실험결과 수직거동 마찰댐퍼 시스템으로 보강된 실험체는 동일 가속도(200 gal)에서 기준 실험체 대비 최대하중은 약 1.6배 증가하였고, 응답변위는 0.4배로 저감되어 본 연구에서 개발된 수직거동 마찰댐퍼 내진보강공법의 유효성이 검증되었다.