본 연구를 통해 병 재배 느타리버섯 ‘춘추2호’의 정밀 재배를 위한 최적 생육모델 개발하기 위하여 느타리 농가를 대상으로 스마트팜 기술을 적용하여 생육환경을 분석한 결과를 보고하고자 한다. 실험 농가의 균상면적은 114 m 2 , 균상형태는 2열 5단, 냉동기는 10마력, 단열은 샌드위치 판넬 100T, 가습기는 초음파 가습기 2대, 난방은 10KW를 사용하였고, 5,500병을 입병하여 재배하고 있었다. 느타리버섯 재배농가에서 생육환경 데이터를 수집하기 위하여 설치한 환경센서부로 부터 버섯의 생육에 직접적으로 영향을 미치는 온도, 습도, 이산화탄소 농도, 조도 등을 수집 분석하였다. 온도는 균 긁기한 후 입상시 19.5 o C에서 시작하여 버섯이 발생되어 병을 뒤집기 후 5 일차까지 거의 21 o C를 유지하고 자실체가 자라서 수확기에 가까워지면 18 o C에서 14 o C를 유지하면서 버섯을 수확 하였다. 습도는 균 긁기한 후 입상시 거의 100%에 가까 웠고, 버섯 발생 및 생육과정 중에도 습도는 거의 95~100%를 유지하였다. 이산화탄소농도는 입상후 5일까 지는 최고 5,500 ppm까지 증가하였고, 6일차부터는 환기를 통해 단계적으로 농도를 낮추어 수확기에는 1,600 ppm 을 유지하였다. 조도는 입상후 6일차까지는 8 lux의 빛을 조사하였고, 그 이후 주기적으로 4 lux의 빛을 조사하면서 생육을 진행하였다. 농가에 재배하고 있는 ‘춘추2호’의 자실체 특성은 갓 직경은 26.5 mm, 갓 두께는 4.9 mm이며, 대 굵기는 8.9 mm, 대 길이는 68.7 mm였다. 대 경도 는 3.9 g/mm, 갓 경도는 0.9 g/mm였고, 대와 갓의 L값은 78.2와 60.5이였다. 자실체 수량은 166.8 g/850 ml였고, 개체중은 12.8 g/10 unit였다.

In this study, we analyzed the factors affecting the introduction of Smart Factory by domestic SMEs through AHP analysis and tried to provide implications for the introduction of Smart Factory. It was confirmed that the manufacturing and introduction group, the non-manufacturing introduction group, and the already introduced group had the highest weight in the cost reduction in the first hierarchy standard. At this time, it can be seen that the weight for cost reduction is relatively high in the manufacturing introduction group and the introduction group, and the weight for the productivity improvement is relatively high in the non-manufacturing introduction group. It can also be seen that the portion of marketing enhancement does not have a significant impact on smart factory choices. It was confirmed that image enhancement is the highest in the manufacturing introduction group and the non-manufacturing introduction group in the first hierarchy standard, and the marketing has the highest weight in the introduction group. In the two - tiered standard, customer - friendly and proper inventory maintenance weights were relatively high in all the introduced groups, except for the high rankings.

This study is to develop a diagnostic model for the effective introduction of smart factories in the manufacturing industry, to diagnose SMEs that have difficulties in building their own smart factory compared to large enterprise, to identify the current level and to present directions for implementation. IT, AT, and OT experts diagnosed 18 SMEs using the "Smart Factory Capacity Diagnosis Tool" developed for smart factory level assessment of companies. They analyzed the results and assessed the level by smart factory diagnosis categories. Companies' smart factory diagnostic mean score is 322 out of 1000 points, between 1 level (check) and 2 level (monitoring). According to diagnosis category, Factory Field Basic, R&D, Production/Logistics/Quality Control, Supply Chain Management and Reference Information Standardization are high but Strategy, Facility Automation, Equipment Control, Data/Information System and Effect Analysis are low. There was little difference in smart factory level depending on whether IT system was built or not. Also, Companies with large sales amount were not necessarily advantageous to smart factories. This study will help SMEs who are interested in smart factory. In order to build smart factory, it is necessary to analyze the market trends, SW/ICT and establish a smart factory strategy suitable for the company considering the characteristics of industry and business environment.

A smart connective control system was invented recently for coupling control of adjacent buildings. Previous studies on this topic focused on development of control algorithm for the smart connective control system and design method of control device. Usually, a smart control devices are applied to building structures after structural design. However, because structural characteristics of building structure with control devices changes, a iterative design is required for optimal design. To defeat this problem, an integrated optimal design method for a smart connective control system and connected buildings was proposed. For this purpose, an artificial seismic load was generated for control performance evaluation of the smart coupling control system. 20-story and 12-story adjacent buildings were used as example structures and an MR (magnetorheological) damper was used as a smart control device to connect adjacent two buildings. NSGA-II was used for multi-objective integrated optimization of structure-smart control device. Numerical simulation results show the integrated optimal design method proposed in this study can provide various optimal designs for smart connective control system and connected buildings presenting good control performance.