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 연구는 한우 번식 기록이 잘 유지되고 있는 4개 농장의 2007년 1월부터 2010년 10월까지의 번식 자료 수집하여 분석하였다. 수태 당 평균 수정 횟수와 평균 공태일은 A농장 1.7 ± 0.1회와 77.4 ± 4.8일, B농장 1.5 ± 0.1회와 150.8 ± 11.2일, C농장 1.5 ± 0.1회와 90.4 ± 4.5일, D농장 1.4 ± 0.1회와 71.4 ± 2.5일이었다 호르몬으로 발정을 유도하는 D농장을 제외한 3개 농장 531두의 번식 기록으로 분만 후 첫 수정 시기에 따른 평균 수정 횟수와 평균 공태일을 분석한 결과, 총 5개의 수정시기에 따른 수정 횟수는 30일 이전 첫 수정이 2.1 ± 0.2회로 31일 이후 첫 수정보다 유의적으로 높았다. 번식 장애우 58두에 2가지 배란 동기화법을 사용하여 수태율을 확인해 본 결과, Ovsynsh 법은 55.2%의 수태율을, CIDR-based TAI 법은 65.5%의 수태율을 나타냈다. 농장의 번식률을 높이기 위해서는 정확한 번식 기록 작성, 발정 관찰, 수정 후 임신 감정, 번식 기관 검진, 번식률을 고려한 첫 수정 시기 수정 등이 필요하다.

The recent development in genetic assisted selection (combining traditional- and genome assisted selection method) and reproduction technologies will allow multiplying elite cow in Hanwoo small farm. This review describes the new context and corresponding needs for genome assisted selection schemes and how reproductive technologies can be incorporated to get more genetic gain for cow genetic improvement in Hanwoo. New improved massive phenotypes and pedigree information are being generated from commercial farm sector and these are allowing to do genetic evaluation using BLUP to get elite cows in Korea. Moreover cattle genome information can now be incorporated into breeding program. In this context, this review will discuss about combining the reproductive techniques (Multiple Ovulation Embryo Transfer; MOET) and genome assisted selection method to get more genetic gain in Hanwoo breeding program. Finally, how these technologies can be used for multiplication of elite cow in small farm was discussed.